Human Capital Development before Age Five$ - Princeton University

CHAPTER1515Human Capital Development beforeAge FiveI

Douglas Almond, Janet CurrieColumbia University

Contents1. Introduction 13162. Conceptual Framework 1322

2.1. Complementarity 13232.2. Fixed investments 1324

2.2.1. Remediation 13252.3. Responsive investments 1326

3. Methods 13283.1. Power 13333.2. Data constraints 1336

3.2.1. Leveraging existing datasets 13363.2.2. Improvements in the production of administrative data 13383.2.3. Additional issues 1339

4. Empirical Literature: Evidence of Long Term Consequences 13404.1. Prenatal environment 1341

4.1.1. Maternal health 13414.1.2. Economic shocks 13674.1.3. Air pollution 1367

4.2. Early childhood environment 13704.2.1. Infections 13714.2.2. Health status 13714.2.3. Home environment 13944.2.4. Toxic exposures 13964.2.5. Summary re: long term effects of fetal and early childhood environment 1396

5. Empirical Literature: Policy Responses 13965.1. Income enhancement 13975.2. Near-cash programs 14055.3. Early intervention programs 1419

5.3.1. Home visiting 14255.3.2. US supplemental feeding program for women, infants, and children (WIC) 14265.3.3. Child care 14335.3.4. Health insurance 1453

I We thank Maya Rossin and David Munroe for excellent research assistance, participants in the Berkeley Handbookof Labor Economics Conference in November 2009 for helpful comments, and Christine Pal and Hongyan Zhao forproofreading the equations.

E-mail addresses: [email protected] (Douglas Almond), [email protected] (Janet Currie).

Handbook of Labor Economics, Volume 4b ISSN 0169-7218, DOI 10.1016/S0169-7218(11)02413-0c© 2010 Elsevier Ltd. All rights reserved. 1315

1316 Douglas Almond and Janet Currie

6. Discussion and Conclusions 1467Appendix A 1468Appendix B 1471Appendix C 1472Appendix D 1475References 1476

AbstractThis chapter seeks to set out what economists have learned about the effects of early childhoodinfluences on later life outcomes, and about ameliorating the effects of negative influences. We beginwith a brief overview of the theory which illustrates that evidence of a causal relationship between ashock in early childhood and a future outcome says little about whether the relationship in question isbiological or immutable. We then survey recent work which shows that events before five years old canhave large long term impacts on adult outcomes. Child and family characteristics measured at schoolentry do as much to explain future outcomes as factors that labor economists have more traditionallyfocused on, such as years of education. Yet while children can be permanently damaged at this age,an important message is that the damage can often be remediated. We provide a brief overview ofevidence regarding the effectiveness of different types of policies to provide remediation.We concludewith a list of some of the many outstanding questions for future research.

JEL classification: I12; I21; J13; J24; Q53

Keywords: Human capital; Early childhood; Health; Fetal origins

1. INTRODUCTION

The last decade has seen a blossoming of research on the long term effects of earlychildhood conditions across a range of disciplines. In economics, the focus is on howhuman capital accumulation responds to the early childhood environment. In 2000,there were no articles on this topic in the Journal of Political Economy, Quarterly Journalof Economics, or the American Economic Review (excluding the Papers and Proceedings),but there have been five or six per year in these journals since 2005. This work hasbeen spurred by a growing realization that early life conditions can have persistentand profound impacts on later life. Table 1 summarizes several longitudinal studieswhich suggest that characteristics that are measured as of age 7 can explain a greatdeal of the variation in educational attainment, earnings as of the early 30s, and theprobability of employment. For example, McLeod and Kaiser (2004) use data fromthe National Longitudinal Surveys and find that children’s test scores and backgroundvariables measured as of ages 6 to 8 predict about 12% of the variation in the probabilityof high school completion and about 11% of the variation in the probability of collegecompletion. Currie and Thomas (1999b) use data from the 1958 British Birth Cohortstudy and find that 4% to 5% of the variation in employment at age 33 can be predicted,and as much as 20% of the variation in wages. Cunha and Heckman (2008) and Cunhaet al. (2010) estimate structural models in which initial endowments and investments feed

Human Capital Development before Age Five 1317

Table1

How

muchof

thediffe

rences

inlatero

utcomes

(testscores,ed

ucationa

lattainm

ent,earnings)can

beexplaine

dby

early

child

hood

factors?

Stud

yan

dda

taInpu

ts/in

term

ediate

variab

les

Outco

mes

Results

Stud

iesus

ingNationa

lLon

gitudina

lSurve

yof

Youth-ch

ildSa

mpleData

Chi

ldho

odem

otio

nala

ndbe

havi

oral

prob

lem

sand

educ

atio

nal

atta

inm

ent

(McL

eod

and

Kai

ser,

2004

)N

LSY-

child

sam

ple

data

onch

ildre

n6-

8in

1986

.Fi

vew

aves

thro

ugh

2000

(whe

nch

ildre

n20

-22)

n=

424.

Em

otio

nala

ndbe

havi

oral

prob

lem

sata

ge6-

8m

easu

red

byB

PI,m

othe

rem

otio

nal

prob

lem

sand

delin

quen

cy,

pove

rty

stat

us,m

othe

r’sA

FQT

scor

e,m

othe

r’sed

ucat

ion,

mot

her’s

mar

itals

tatu

sand

age,

child

’sag

e,se

x,an

dra

ce,

dum

my

for

LBW

.

Dum

my

for

grad

uatin

ghi

ghsc

hool

by20

00,

dum

my

for

enro

lling

inco

llege

by20

00.

R-s

quar

edfo

rpre

dic

ting

HS

gra

duat

ion

:O

nly

child

emot

iona

land

beha

vior

alpr

oble

ms:

0.04

6A

ddch

ildan

dm

othe

rde

mog

raph

ics:

0.11

1A

ddm

othe

r’sem

otio

nalp

robl

emsa

ndde

linqu

ency

:0.

124.

R-s

quar

edfo

rpre

dic

ting

colle

geen

rollm

ent:

Onl

ych

ildem

otio

nala

ndbe

havi

oral

prob

lem

s:0.

017

Add

child

and

mot

her

dem

ogra

phic

s:0.

093

Add

mot

her’s

emot

iona

lpro

blem

sand

delin

quen

cy:0.

112.

(con

tinue

don

next

page

)


Table1(con

tinue

d)Stud

yan

dda

taInpu

ts/in

term

ediate

variab

les

Outco

mes

Results

Form

ulat

ing,

iden

tifyi

ng,an

des

timat

ing

the

tech

nolo

gyof

cogn

itive

and

nonc

ogni

tive

skill

form

atio

n(C

unha

and

Hec

kman

,20

08)N

LSY-

child

sam

ple

data

onw

hite

mal

es.

n=

1053

.

Hom

een

viro

nmen

tmea

sure

dby

HO

ME

scor

e.M

easu

reso

fpa

rent

alin

vest

men

ts:nu

mbe

rof

child

’sbo

oks,

whe

ther

the

child

hasa

mus

ical

inst

rum

ent,

whe

ther

the

fam

ilyre

ceiv

esa

daily

new

spap

er,w

heth

erth

ech

ildre

ceiv

essp

ecia

lles

sons

,ho

wof

ten

the

child

goes

tom

useu

ms,

and

how

ofte

nth

ech

ildgo

esto

the

thea

ter.

Mea

sure

sofc

hild

cogn

itive

skill

s:PI

AT

test

scor

esat

vari

ous

ages

.M

easu

reso

fchi

ld’s

non-

cogn

itive

skill

s:B

PIat

vari

ousa

ges.

Log

earn

ings

and

likel

ihoo

dof

grad

uatio

nfr

omhi

ghsc

hool

.E

stim

ated

ady

nam

icfa

ctor

mod

elth

atex

ploi

tscr

oss-

equa

tion

rest

rict

ions

for

iden

tifica

tion.

Est

imat

edeff

ects

ofco

gniti

vean

dno

n-co

gniti

vesk

illsa

sw

ella

spar

enta

lin

vest

men

tsat

differ

ent

stag

esof

the

child

’slif

ecyc

leon

log

earn

ings

and

likel

ihoo

dof

grad

uatio

nfr

omhi

ghsc

hool

.

A10

%in

crea

sein

pare

ntal

inve

stm

ents

atag

es6-

7in

crea

sese

arni

ngsb

y24

.9%

(12.

5%th

roug

hco

gniti

vesk

ills,

12.4

%th

roug

hno

n-co

gniti

vesk

ills)

,an

din

crea

sesl

ikel

ihoo

dof

grad

uatin

ghi

ghsc

hool

by64

.4%

(54.

8%th

roug

hco

gniti

vesk

ills,

9.6%

thro

ugh

non-

cogn

itive

skill

s).

(con

tinue

don

next

page

)


Table1(con

tinue

d)Stud

yan

dda

taInpu

ts/in

term

ediate

variab

les

Outco

mes

Results

Est

imat

ing

the

tech

nolo

gyof

cogn

itive

and

non-

cogn

itive

skill

form

atio

n(C

unha

etal

.,20

10)

NLS

Y-ch

ildsa

mpl

efirs

t-bo

rnw

hite

child

ren.

N=

2207

.

Exa

mpl

esof

mea

sure

sofc

hild

cogn

itive

skill

s:M

otor

-soc

ial

deve

lopm

enta

tbir

th;PI

AT

read

ing-

com

preh

ensio

nat

ages

5-6;

PIA

Tm

ath

atag

es13

-14.

Exa

mpl

esof

mea

sure

sofc

hild

non-

cogn

itive

skill

s:B

PIat

vari

ousa

ges;

“fri

endl

ines

sat

birt

h”.E

xam

ples

ofm

easu

reso

fpa

rent

alin

vest

men

ts:“H

owof

ten

child

goes

onou

tings

duri

ngye

arof

birt

h”;“H

owof

ten

mom

read

sto

child

duri

ngye

arof

birt

h”;“C

hild

hasa

CD

play

er,ag

es3-

4”;H

owof

ten

child

ispr

aise

d,ag

es13

-14”

.

Mai

nou

tcom

eva

riab

le:

com

plet

edye

arso

fed

ucat

ion

byag

e19

.M

ulti-

stag

epr

oduc

tion

func

tions

estim

ated

inw

hich

the

prod

uctiv

ityof

late

rin

vest

men

tsde

pend

son

earl

yin

vest

men

tsan

don

the

stoc

kof

cogn

itive

and

non-

cogn

itive

skill

sand

inve

stm

ents

are

endo

geno

us.

Iden

tifica

tion

base

don

nonl

inea

rfa

ctor

mod

els

with

endo

geno

usin

puts

.In

pref

erre

dsp

ecifi

catio

n,us

edm

axim

um-l

ikel

ihoo

dm

etho

dsto

estim

ate

the

prod

uctio

nte

chno

logy

.

34%

ofva

riat

ion

ined

ucat

iona

latt

ainm

enti

sex

plai

ned

bym

easu

reso

fcog

nitiv

ean

dno

n-co

gniti

veca

pabi

litie

s.16

%is

due

toad

oles

cent

cogn

itive

capa

bilit

ies;

12%

isdu

eto

adol

esce

ntno

n-co

gniti

veca

pabi

litie

s.Pa

rent

alen

dow

men

ts/i

nves

tmen

tsac

coun

tfor

15%

ofva

riat

ion

ined

ucat

iona

latt

ainm

ent.

(con

tinue

don

next

page

)


Table1(con

tinue

d)Stud

yan

dda

taInpu

ts/in

term

ediate

variab

les

Outco

mes

Results

Stud

iesus

ingNationa

lChild

Dev

elop

men

tStudy

(195

8BritishBirthCo

hort)D

ata

Abi

lity,

fam

ily,

educ

atio

n,an

dea

rnin

gsin

Bri

tain

(Dea

rden

,19

98)

Focu

son

indi

vidu

alsw

hopa

rtic

ipat

edin

wav

es4

and

5of

the

surv

eyin

1981

and

1991

,w

how

ere

empl

oyee

sin

1991

.n=

2597

mal

es,

2362

fem

ales

.

Mat

han

dve

rbal

abili

ty(a

ge7)

,ty

peof

scho

ol,fa

mily

char

acte

rist

ics—

teac

her’s

asse

ssm

ento

fint

eres

tsho

wn

bypa

rent

sin

child

’sed

ucat

ion

at7;

type

ofsc

hool

atte

nded

at16

fam

ily’s

fina

ncia

lsta

tusa

t11

and

16;re

gion

dum

mie

s;fa

ther

’sSE

S;pa

rent

s’ed

ucat

ion

leve

ls.

Year

soff

ull-

time

educ

atio

n;E

arni

ngsa

tag

e33

.

R-s

quar

edfo

red

uca

tion

asoutc

om

e:In

clud

ing

alle

xpla

nato

ryva

riab

les:

0.33

-0.3

4.R

-squar

edfo

rea

rnin

gsas

outc

om

e:B

asel

ine

earn

ings

equa

tion

incl

udin

gon

lyye

ars

educ

atio

n:0.

15(m

ales

),0.

25(f

emal

es)

Add

read

ing

and

mat

hsc

ores

atag

e7,

scho

olty

pean

dre

gion

aldu

mm

ies:

0.26

(mal

es),

0.31

(fem

ales

)In

clud

ing

alle

xpla

nato

ryva

riab

les:.

029

(mal

es),

0.41

for

fem

ales

.

Ear

lyte

stsc

ores

,so

cioe

cono

mic

stat

us,an

dfu

ture

outc

omes

(Cur

rie

and

Tho

mas

,19

99b)

Rea

ding

and

mat

hte

stsc

ores

atag

e7,

mot

her

and

fath

er’s

SES

and

educ

atio

n,bi

rth

wei

ght,

othe

rch

ildba

ckgr

ound

vari

able

sat

age

7.

Num

ber

ofO

-lev

elpa

sses

ofex

amsb

yag

e16

;em

ploy

edat

age

23,

33;lo

gw

age

atag

e23

,33

.

R-s

quar

edfo

rpre

dic

ting

age

16ex

ampas

ses:

Rea

ding

and

mat

hsc

ores

only

:0.

21-0

.22

Add

othe

rba

ckgr

ound

vari

able

s:0.

31-0

.32.

(con

tinue

don

next

page

)


Table1(con

tinue

d)Stud

yan

dda

taInpu

ts/in

term

ediate

variab

les

Outco

mes

Results

Full

sam

ple

size

(bas

edon

resp

onse

sat

7):n=

14,02

2.

R-s

quar

edfo

rpre

dic

ting

emplo

ymen

tat

33:

Rea

ding

and

mat

hte

stsc

ores

only

:0.

01A

ddot

her

back

grou

ndva

riab

les:

0.04

-0.0

5.R

-squar

edfo

rpre

dic

ting

log

wag

eat

33:

Rea

ding

and

mat

hte

stsc

ores

only

:0.

08-0

.09

Add

othe

rba

ckgr

ound

vari

able

s:0.

18-0

.20.

The

last

ing

impa

ctof

child

hood

heal

than

dci

rcum

stan

ce(C

ase

etal

.,20

05)

n=

14,3

25(7

016

men

,70

39w

omen

)

Mot

her’s

and

fath

er’s

educ

atio

nan

dSE

S,LB

W,in

dica

tors

for

mod

erat

e,he

avy,

and

vari

edm

ater

nals

mok

ing

duri

ngpr

egna

ncy,

num

ber

ofch

roni

cco

nditi

onsa

tage

7an

d16

.

Num

ber

ofO

-lev

elpa

sses

ofex

amsb

yag

e16

;ad

ulth

ealth

stat

usat

age

42;pa

rt-t

ime

orfu

llem

ploy

men

tata

ge42

.

R-s

quar

edfo

rpre

dic

ting

age

16ex

ampas

ses/

adult

hea

lth/e

mplo

ymen

tat

42.

Mot

her’s

educ

atio

nan

dSE

S:0.

062/

0.08

2/0.

076

Fath

er’s

educ

atio

nan

dSE

S:0.

241/

0.18

9/0.

173

LBW

and

mat

erna

lsm

okin

g:0.

024/

0.08

6/0.

052.

Exp

lain

ing

inte

rgen

erat

iona

lin

com

epe

rsist

ence

:no

n-co

gniti

vesk

ills,

abili

ty,an

ded

ucat

ion

(Bla

nden

etal

.,20

06)

A.N

CD

S:19

58co

hort

Focu

son

2163

mal

es.

B.B

ritis

hC

ohor

tSt

udy:

1970

coho

rtFo

cuso

n33

40m

ales

.

A.Fa

mily

inco

me

atag

e16

read

ing

and

mat

hte

stsc

ores

atag

e11

,sc

ores

for

“beh

avio

ral

synd

rom

es”

atag

e11

,O

-lev

elex

amsc

ores

atag

e16

.B

.Ye

arso

fpre

scho

oled

ucat

ion,

birt

hw

eigh

t,he

ight

at5

and

10,

emot

iona

l/be

havi

oral

scor

esat

ages

5,10

,&

16,fa

mily

inco

me

atag

es10

and

16,re

adin

gan

dm

ath

test

scor

esat

age

10,IQ

atag

e10

,du

mm

yfo

rH

Sde

gree

,ex

amsc

ores

atag

e16

.

A.E

arni

ngsa

tage

33.

B.E

arni

ngsa

tage

30.

R-s

quar

edM

odel

A.B

irth

wei

ght,

child

hood

heal

th,an

dag

e11

test

scor

eson

ly:0.

116

Incl

udin

g“b

ehav

iora

lsyn

drom

es”

atag

e11

:0.

151

All

vari

able

s:0.

263.

R-s

quar

edM

odel

B.B

irth

wei

ght,

child

hood

heal

th,an

dag

e10

test

scor

eson

ly:0.

075.

Incl

udin

gem

otio

nal/

beha

vior

alch

arac

teri

stic

sat

age

10:0.

087.

All

vari

able

s:0.

222.


through to later outcomes; they arrive at estimates that are of a similar order of magnitudefor education and wages. To put these results in context, labor economists generally feelthat they are doing well if they can explain 30% of the variation in wages in a humancapital earnings function.

This chapter seeks to set out what economists have learned about the importanceof early childhood influences on later life outcomes, and about ameliorating the effectsof negative influences. We begin with a brief overview of the theory which illustratesthat evidence of a causal relationship between a shock in early childhood and a futureoutcome says little about whether the relationship in question is biological or immutable.Parental and social responses are likely to be extremely important in either magnifying ormitigating the effects of a shock. Given that this is the case, it can sometimes be difficult tointerpret the wealth of empirical evidence that is accumulating in terms of an underlyingstructural framework.

The theoretical framework is laid out in Section 2 and followed by a brief discussionof methods in Section 3. We do not attempt to cover issues such as identification andinstrumental variables methods, which are covered in some depth elsewhere (cf Angristand Pischke (2009)). Instead, we focus on several issues that come up frequently in theearly influences literature, including estimation using small samples and the potentiallyhigh return to better data.

Section 4 discusses the evidence for long-term effects of early life influences in greaterdetail, while Section 5 focuses on the evidence regarding remediation programs. Thediscussion of early life influences is divided into two subsections corresponding to in uteroinfluences and after birth influences. The discussion of remediation programs starts fromthe most general sort of program, income transfers, and goes on to discuss interventionsthat are increasingly targeted at specific domains. In surveying the evidence we haveattempted to focus on recent papers, and especially those that propose a plausible strategyfor identifying causal effects. We have focused on papers that emphasize early childhood,but in instances in which only evidence regarding effects on older children is available,we have sometimes strayed from this rule. A summary of most of the papers discussed inthese sections is presented in Tables 4 through 13. A list of acronyms used in the tablesappears in Appendix A. We conclude with a summary and a discussion of outstandingquestions for future research in Section 6.

2. CONCEPTUAL FRAMEWORKGrossman (1972) models health as a stock variable that varies over time in response toinvestments and depreciation. Because some positive portion of the previous period’shealth stock vanishes in each period (e.g., age in years), the effect of the health stock andhealth investments further removed in time from the current period tends to fade out. Asindividuals age, the early childhood health stock and the prior health investments that itembodies become progressively less important.


In contrast, the “early influences” literature asks whether health and investments inearly childhood have sustained effects on adult outcomes. The magnitude of these effectsmay persist or even increase as individuals age because childhood development occurs indistinct stages that are more or less influential of adult outcomes.

Defining h as health or human capital at the completion of childhood, we can retainthe linearity of h in investments and the prior health stock as in Grossman (1972), butleave open whether there is indeed “fade out” (i.e. depreciation). For simplicity, we willconsider a simple two-period childhood.1 We can consider production of h:

h = A[γ I1 + (1− γ )I2], (1)

where: {I1 ∼= investments during childhood through age 5I2 ∼= investments during childhood after age 5.

For a given level of total investments I1 + I2, the allocation of investments betweenperiod 1 and 2 will affect h for γ 6= 0.5. If γ > 0.5, then health at the end of period1 is more important to h than investments in the second period, and if γ A > 1, h mayrespond more than one-for-one with I1. Thus, (1) admits the possibility that certainchildhood periods may exert a disproportionate effect on adult outcomes that does notnecessarily decline monotonically with age. This functional form says more than “earlylife” matters; it suggests that early-childhood events may be more influential than laterchildhood events.

2.1. ComplementarityThe assumption that inputs at different stages of childhood have linear effects is commonin economics. While it opens the door to “early origins,” perfect substitutability betweenfirst and second period investments in (1) is a strong assumption. The absence ofcomplementarity implies that all investments should be concentrated in one period(up to a discount factor) and no investments should be made during the low-returnperiod. In addition, with basic preference assumptions, perfect substitutability “hard-wires” the optimal investment response to early-life shocks to be compensatory, as seenin Section 2.3.

As suggested by Heckman (2007), a more flexible “developmental” technology is theconstant elasticity of substitution (CES) function:

h = A[γ Iφ1 + (1− γ )I

φ

2

]1/φ, (2)

1 See Zweifel et al. (2009) for a two period version of the Grossman (1972) model.


For a given total investment level I1+ I2, how the allocation between period 1 and 2 willalso affect h depends on the elasticity of substitution, 1/(1−φ),and the share parameter,γ . For φ = 1 (perfect substitutability of investments), (2) reduces to (1).

Heckman (2007) highlights two features of “capacity formation” beyond thosecaptured in (2). First, there may be “dynamic complementarities” which imply thatinvestments in period t are more productive when there is a high level of capability inperiod t − 1. For example, if the factor productivity term A in (2) were an increasingfunction of h0, the health endowment immediately prior to period 1, this would raisethe return to investments during childhood. Second, there may be “self-productivity”which implies that higher levels of capacity in one period create higher levels of capacityin future periods. This feature is especially noteworthy when h is multidimensional, asit would imply that “cross-effects” are positive, e.g. health in period 1 leads to highercognitive ability in period 2. “Self-productivity” is more trivial in the unidimensionalcase like Grossman (1972)—even though the effect of earlier health stocks tends to fadeout as the time passes, there is still memory as long as depreciation in each period is lessthan total (i.e., when δ < 1 in Zweifel et al. (2009)).

Here, we will use the basic framework in (2) to consider the effect of exogenousshocks µg to health investments that occur during the first childhood period.2 Webegin with the simplest case, where investments do not respond to µg (and denote theseinvestments I1 and I2). Net investments in the first period are:

I1 + µg.

We assume thatµg is independent of I1. Whileµg can be positive or negative, we assumeI1 + µg > 0. We will then relax the assumption of fixed investments, and considerendogenous responses to investments in the second period, i.e. δ I ∗2 /δµg, and how thisinvestment response may mediate the observed effect on h.

2.2. Fixed investmentsConceptually, we can trace out the effect of µg while holding other inputs fixed, i.e., weassume no investment response to this shock in either period. Albeit implicitly, mostbiomedical and epidemiological studies in the “early origins” literature aim to inform usabout this ceteris paribus, “biological” relationship.

In this two-period CES production function adopted from Heckman (2007), theimpact of an early-life shock on adult outcomes is:

δh

δµg= γ A

[γ ( I1 + µg)

φ+ (1− γ ) Iφ2

](1−φ)/φ( I1 + µg)

φ−1. (3)

2 We include the subscript here because environmental influences at some aggregated geographic level g may provideexogenous variation in early childhood investments.


The simplest production technology is the perfect substitutability case where φ = 1.In this case:

δh

δµg= γ A.

Damage to adult human capital is proportional to the share parameter on period 1investments, and is unrelated to the investment level I1.

For less than perfect substitutability between periods, there is diminishing marginalproductivity of the investment inputs. Thus, shocks experienced at different baselineinvestment levels have heterogenous effects on h. Other things equal, those with higherbaseline levels of investment will experience more muted effects in h than those wherebaseline investment is low. A recurring empirical finding is that long-term damage dueto shocks is more likely among poorer families (Currie and Hyson, 1999). This is in partdue to the fact that children in poorer families are subject to more or larger early-lifeshocks (Case et al., 2002; Currie and Stabile, 2003). However, it is also possible that thesame shock will have a greater impact among children in poorer families if these childrenhave lower period t investment levels to begin with. This occurs because they are on asteeper portion of the production function. Ceteris paribus, this would tend to accentuatethe effect of an equivalent-sized µg shock on h among poor families.3,4

2.2.1. RemediationIs it possible to alter “bad” early trajectories? In other words, what is the effect of a shockµ′g > 0 experienced during the second period on h? Remediation is of interest to theextent that (3) is substantially less than zero. However, large damage to h from µg per sesays little about the potential effectiveness of remediation in the second period, as bothinitial damage and remediation are distinct functions of the three parameters A, γ , and φ.

The effectiveness of remediation relative to initial damage is:

δh/δµ′gδh/δµg

=1− γγ

(I1 + µg

I2 + µ′

g

)1−φ

. (4)

Thus, for I1 > I2 and a given value of γ , a unit of remediation will be more effectiveat low elasticities of substitution—the lack of I2 was the more critical shortfall priorto the shock. If I1 < I2 high elasticities of substitution increase the effectiveness ofremediation—adding to the existing abundance of I2 remains effective.

3 I.e. δ2h/δµgδ I1 < 0. On the other hand, δ2h/δµgδ I2 > 0 so lower period two investments would tend to reducedamage to h from µg . The ratio of the former effect to the latter is proportional to γ /(1 − γ ) (Chiang, 1984). Thus,damage from a period 1 shock is more likely to be concentrated among poor families when the period-1 share parameter(γ ) is high.

4 The cross-effect δ2h/δµgδ I2 is similar to dynamic complementarity, but Heckman (2007) reserves this term for thecross-partial between the stock and flow, i.e. δ2h/δh0δ It for t = 1, 2 in the example of Section 2.1.


Fortunately, it is not necessary to observe investments and estimate all threeparameters in order to assess the scope for remediation. In some cases, we merely need toobserve how a shock in the second period, µ′g, affects h. Furthermore, this does notnecessarily require a distinct shock in addition to µg. In an overlapping generationsframework, the same shock, µg = µ′g could affect one cohort in the first childhoodperiod (but not the second) and an older cohort in the second period (but not the first).For a small, “double-barreled” shock, we would have reduced form estimates of both thedamage in (3) and the potential to alter trajectories in (4).5 For example, in addition toobserving how income during the prenatal period affects newborn health (Kehrer andWolin, 1979), we might also be able to see how parental income affects the health of pre-school age children to gain a sense of what opportunities there are to remediate negativeincome shocks experienced during pregnancy.

2.3. Responsive investmentsMost analyses of “early origins” focus on estimating the reduced form effect, δh/δµg.Whether this empirical relationship represents a purely biological effect or also includesthe effect of responsive investments is an open question. In general, to the extent that“early origins” are important, so too will be any response of childhood investments toµg.For expositional purposes, we will consider µg < 0 and responses that either magnify orattenuate initial damage.

Unless the investment response is costless, damage estimates that monetize δh/δµg

alone will tend to understate total damage. In the extreme, investment responses couldfully offset the effect of early-life shocks on h, but this would not mean that such shockswere costless (Deschnes and Greenstone, 2007). More generally, the damage from early-life shocks will be understated if we focus only on long-term effects and there arecompensatory investments (i.e. investments that are negatively correlated with the early-life shock (δ I ∗2 /δµg < 0)). The cost of investments that help remediate damage shouldbe included. But even when the response is reinforcing (δ I ∗2 /δµg > 0), total costs canstill be understated by focussing on the reduced form damage to h alone (see below).

To consider correlated investment responses more formally, we assume parentsobserve µg at the end of the first period. The direction of the investment response—whether reinforcing or compensatory—will be shaped by how substitutable period 2investments are for those in period 1. If substitutability is high, the optimal response willtend to be compensatory, and thereby help offset damage to h.

A compensatory response is readily seen in the case of perfect substitutability. Cunhaand Heckman (2007) observed that economic models commonly assume that productionat different stages of childhood are perfect substitutes. When φ = 1, (2) reduces to:

h = A[γ (I1 + µg)+ (1− γ )I2

]. (5)

5 How parameters of the production function might be recovered is discussed in Appendix D.


This linear production technology is akin to that used in a previous Handbookchapter on intergenerational mobility (Solon, 1999), which likewise considered parentalinvestments in children’s human capital. Further, Solon (1999) assumed parent’s utilitytrades off own consumption against the child’s human capital:

Up = U (C, h), (6)

where p denotes parents and C their consumption. The budget constraint is:

Yp = C + I1 + I2/(1+ r). (7)

With standard preferences, changes to h through µg will “unbalance” the marginalutilities in h versus C .6 If µg is negative, the marginal utility of h becomes too highrelative to that in consumption. The technology in (5) permits parents to convert someconsumption into h at a constant rate. This will cause I ∗2 to increase, which attenuates theeffect of the µg damage. This attenuation comes at the cost of reduced parental utility.Similarly, if µg is positive parents will “spend the bounty” (at least in part), reduce I ∗2 andincrease consumption. Again, this will temper effects on h, leading to an understatementof biological effects in analyses that ignore investments (or parental utility). In either case,perfect substitutability hard-wires the response to be compensatory.

The polar opposite technology is perfect complementarity between childhood stages,i.e., a Leontieff production function. Here, a compensatory strategy would be completelyineffective in mitigating changes to h. As h is determined by the minimum of period1 and period 2 investments, optimal period 2 investments should reinforce µg. Ifµg is negative, parents would seek to reduce I2 and consume more. Despite higherconsumption, parents’ utility is reduced on net due to the shock (or this bundle of lowerh and higher C would have been selected absent µg). Again, the full-cost of a negativeµg shock is understated when parental utility is ignored.

The crossover between reinforcing and compensating responses of I ∗2 will occurat an intermediate parameter value of substitutability. (The fixed investments case ofSection 2.2 can be seen to reflect an optimized response at this point of balance betweenreinforcing and compensating responses). The value of φ at this point of balance willdepend on the functional form of parental preferences in (6), as shown for CES utility inAppendix B.

To take a familiar example, assume a Cobb-Douglas utility function of the form:

Up = (1− α)logC + αlog h. (8)

6 Obviously, the marginal utilities themselves will not be the same but equal subject to discount factor, preferenceparameters, and prices of C versus I , which have been ignored.


If the production technology is also Cobb-Douglas (φ = 0), then no change to I ∗2 iswarranted. If instead substitution between period 1 and period 2 is relatively easy (φ > 0),compensating for the shock is optimal. If substitution is relatively difficult (φ < 0), thenparents should “go with the flow” and reinforce. For this reason, whether conventionalreduced form analyses under or over-state “biological” effects (effects with I2 held fixed)depends on how easy it is to substitute the timing of investments across childhood. Ifthe elasticity of substitution across periods is low, then it may be optimal for parents toreinforce the effect of a shock.

Tension between preferences and the production technology may also be relevantfor within-family investment decisions. For example, Behrman et al. (1982) consideredparental preferences that parameterize varying degrees of “inequality aversion” among(multiple) children. Depending on the strength of parents’ inequality aversionrelative to the production technology (as reflected by φ), parents may reinforceor compensate exogenous within-family differences in early-life health and humancapital. If substitutability between periods of childhood is sufficiently difficult (lowφ), reinforcement of sibling differences will be optimal. This reinforcement may beoptimal even when the parents place a higher weight in their utility function on theaccumulation of human capital by the less able sibling (see Appendix C). Thus, empiricalevidence that some parents reinforce early-life shocks could reveal less about “humannature” than it would reveal about the developmental nature of the childhood productiontechnology.

3. METHODSAs discussed above, we confine our discussion to methodological issues that seemparticularly germane to the early influences literature. One of these is the question ofwhen sibling fixed effects (or maternal fixed effects) estimation is appropriate. Fixedeffects can be a powerful way to eliminate confounding from shared family backgroundcharacteristics, even when these are not fully observed. This approach is particularlyeffective when the direction of unobserved sibling-specific confounders can be signed.For example, Currie and Thomas (1995) find that in the cross-section, children whowere in Head Start do worse than children who were not. However, compared to theirown siblings, Head Start children do better. Since there is little evidence that Head Startchildren are “favored” by parents or otherwise (on the contrary, in families where onechild attends and the other does not, children who attended were more likely to havespent their preschool years in poverty), these contrasting results suggest that unobservedfamily characteristics are correlated both with Head Start attendance and poor childoutcomes. When the effect of such characteristics is accounted for, the positive effectsof Head Start are apparent.

However, fixed effects can not control for sibling-specific factors. The theorydiscussed above suggests that it may be optimal for parents to either reinforce or


compensate for the effects of early shocks by altering their own investment behaviors.Whether parents do or do not reinforce/compensate obviously has implications for theinterpretation of models estimated using family fixed effects. If on average, familiescompensate, then fixed effects estimates will understate the total effect of the shock(when the compensation behavior is unobserved or otherwise not accounted for). Insome circumstances, such a bias might be benign in the sense that any significantcoefficient could then be interpreted as a lower bound on the total effect. It is likelyto be more problematic if parents systematically reinforce shocks, because then any effectthat is observed results from a combination of underlying effects and parental reactionsrather than the shock itself. In the extreme, if parents seized on a characteristic that wasunrelated to ability and systematically favored children who had that characteristic, thenresearchers might wrongly conclude that the characteristic was in fact linked to successeven in the absence of parental responses.

The issue of how parents allocate resources between siblings has received a good dealof attention in economics, starting with Becker and Tomes (1976) and Behrman et al.(1982). Some empirical studies from developing countries find evidence of reinforcingbehavior (see Rosenzweig and Paul Schultz (1982), Rosenzweig and Wolpin (1988) andPitt et al. (1990)). Empirical tests of these theories in developed countries such as theUnited States and Britain generally use adult outcomes such as completed education as aproxy for parental investments (see for example, Griliches (1979), Behrman et al. (1994)and Ashenfelter and Rouse (1998)).

Several recent studies have used birth weight as a measure of the child’s endowmentand asked whether explicit measures of parental investments during early childhood arerelated to birth weight. For example, Datar et al. (2010) use data from the NationalLongitudinal Survey of Youth-Child and show that low birth weight children are lesslikely to be breastfed, have fewer well-baby visits, are less likely to be immunized, andare less likely to attend preschool than normal birth weight siblings. However, all of thesedifferences could be due to poorer health among the low birth weight children. Forexample, if a child is receiving many visits for sick care, they may receive fewer visits forwell care and this will not say anything about parental investment behaviors. Hence, Dataret al. (2010) also look at how the presence of low birth weight siblings in the householdaffects the investments received by normal birth weight children. They find no effect ofhaving a low birth weight sibling on breastfeeding, immunizations, or preschool. Theonly statistically significant interaction is for well-baby care. This could however, be dueto transactions costs. It may be the case that if the low birth weight sibling is getting a lotof medical care, it is less costly to bring the normal birth weight child in for care as well,for example.

Del Bono et al. (2008) estimate a model that allows endowments of other children toaffect parental investments in the index child. They find, however, that the results fromthis dynamic model are remarkably similar to those of mother fixed effects models in most


cases. Moreover, although they find a positive effect of birth weight on breastfeeding, theeffect is very small in magnitude.

We conducted our own investigation of this issue using data on twins from theEarly Childhood Longitudinal Study-Birth Cohort (ECLS-B), using twin differencesto control for potential confounders. At the same time, twins routinely have largedifferences in endowment in the form of birth weight. Table 2 presents estimates forall twins (with and without controls for gender), same sex twins, and identical twins.Overall, there are very few significant differences in the treatment of these twins: Parentsseemed to be more concerned about whether the low birth weight twin was ready forschool, and to delay introducing solid food (but this is only significant in the identicaltwin pairs). We see no evidence that parents are more likely to praise, caress, spank orotherwise treat children differently, and despite their worries about school readiness,parents have similar expectations regarding college for both twins. This table largelyreplicates the basic finding of Royer (2009), who also considered parental investmentsand birth weight differences in the ECLS-B data. In particular, Royer (2009) focussedon investments soon after birth, finding that breastfeeding, NICU admission, and othermeasures of neonatal medical care did not vary with within twin pair birth weightdifferences.

The parental investment response has also been explored in the context of naturalexperiments. Kelly (2009) asked whether observed parental investments (e.g., time spentreading to child) were related to flu-induced damages to test scores in the 1958 Britishbirth cohort study but did not detect an investment response.

In an interesting contribution to this literature, Hsin (2009) looks at the relationshipbetween children’s endowments, measured using birth weight, and maternal time useusing data from the Child Supplement of the Panel Study of Income Dynamics. Shefinds that overall, there is little relationship between low birth weight and maternaltime investments. However, she argues that this masks important differences by maternalsocioeconomic status. In particular, she finds that in models with maternal fixed effects,less educated women spend less time with their low birth weight children, while moreeducated women spend more time. This finding is based on only 65 sibling pairs whohad differences in the incidence of low birth weight, and so requires some corroboration.

Still, one interpretation of this result in the context of the Section 2 framework is that theelasticity of substitution between C and h varies by socioeconomic status. In particular,if ϕpoor > ϕrich, low income parents tend to view their consumption and children’s has relatively good substitutes. This would lead low-income parents to be more likely toreinforce a negative shock than high-income parents (assuming that the developmentaltechnology, captured by γ and φ, does not vary by socioeconomic status). A secondpossible interpretation of the finding is that parents’ responses may reflect their budgetconstraint more than their preferences. If parents would like to invest in both children,

but have only enough resources to invest adequately in one, then they may be forced


Table 2 Estimated effect of birth weight on parental investments within twin pairs, estimates fromthe early childhood longitudinal study.Outcome All twins Same sex

twinsIdenticaltwins

9month survey

1 if child was ever breastfed 0.0183 0.0187 0.0031[0.0238] [0.0277] [0.0355]1550 1000 350

1 if child is now being breastfed 0.0038 −0.0039 −0.0007[0.0126] [0.0152] [0.001]1550 1000 350

How long child was breastfed in months, −0.0753 −0.2165 −0.343given breastfed [0.1752] [0.204] [0.3182]

800 500 150Age solid food was introduced in months, −0.1802 −0.2478 −0.6660*given introduced [0.1523] [0.1906] [0.2914]

1550 1000 350Number of well-baby visits 0.283 0.3803 0.5797

[0.1883] [0.2414] [0.5253]1550 1000 350

Number of well-baby visits 0.1956 0.2329 0.2668only children in excellent or very good health [0.1624] [0.1944] [0.3799]

1500 950 3001 if caregiver praises child −0.0015 −0.051 0.096

[0.0941] [0.1189] [0.2089]1250 800 250

1 if caregiver avoids negative comments −0.0051 −0.0077 0[0.0055] [0.0084] [.]1250 800 250

1 if somewhat difficult or difficult to raise −0.0181 −0.0772 −0.0946(caregiver report) [0.0583] [0.0712] [0.1395]

1550 1000 3501 if not at all difficult or not very difficult to raise 0.1065 0.153 0.2237(caregiver report) [0.0707] [0.0812] [0.1195]

1550 1000 350

2-year survey

1 if caress/kiss/hug child 0.0228 0.0055 0.0021[0.0266] [0.0254] [0.0049]1350 850 300

(continued on next page)


Table 2 (continued)

Outcome All twins Same sextwins

Identicaltwins

1 if spank/slap child −0.0195 −0.0095 −0.0048[0.0249] [0.0192] [0.0316]1350 850 300

1 if time spent calming child > 1 hr usually 0.0317 −0.024 0.0719[0.0646] [0.0759] [0.093]1450 950 300

1 if somewhat difficult or difficult to raise −0.0432 −0.0901 −0.1412(caregiver report) [0.0555] [0.0621] [0.086]

1450 950 3001 if not at all difficult or not very difficult to raise(caregiver report)

−0.0031[0.0757]

0.068[0.0869]

0.0527[0.1258]

1450 950 300Age when stopped feeding formula in months −0.1903 −0.4504 −0.5903

[0.255] [0.3204] [0.7844]1150 750 250

Age when stopped breastfeeding in months −0.1492 −0.0267 −0.0422[0.5981] [0.044] [0.069]100 50 50

Preschool survey

1 if parent expects child to enter kindergarten early −0.0082[0.012]

−0.0071[0.0102]

0[0]

1300 800 2501 if parent concerned about −0.1435** −0.1299* −0.1099child’s kindergarten readiness [0.0554] [0.0636] [0.1253]

1300 850 2501 if expect child to get ≥ 4 yrs of college −0.0073 0.0069 0.0228

[0.0272] [0.0327] [0.0264]1350 850 300

Number of servings of milk in the past 7 days −0.0598 −0.0577 0.0819[0.2074] [0.2278] [0.2489]1350 850 300

Number of servings of vegetables past 7 days 0.0632 0.2131 0.0871[0.2634] [0.3027] [0.4091]1350 850 300

Standard errors clustered on the mother are shown in brackets with sample sizes below. Twin pairs in which a child hada congenital anomaly are omitted. Birth weight measured in kilograms. Each entry is from a separate regression of thedependent variable on birth weight and a mother fixed effect. Models in column 1 also control for child gender. Samplesizes are rounded to the nearest multiple of 50.Significance levels: *p < 0.10, **p < 0.05, ***p < 0.001.


to choose the more well endowed child.7 Interventions that relaxed resource constraintswould have quite different effects in this case than in the case in which parents preferredto maximize the welfare of a favored child. More empirical work on this question seemswarranted. For example, the PSID-CDS in 1997 and 2002 has time diary data for severalthousand sibling pairs which have not been analyzed for this purpose.

Parent’s choices are determined in part by the technologies they face, and thesetechnologies may change over time, with implications for the potential biases in fixedeffects estimates.8 For example, Currie and Hyson (1999) asked whether the long termeffects of low birth weight differed by various measures of parental socioeconomic statusin the 1958 British birth cohort. They found little evidence that they did (except thatlow birth weight women from higher SES backgrounds were less likely to suffer frompoor health as adults). But it is possible that this is because there were few effectiveinterventions for low birth weight infants in 1958. In contrast, Currie and Moretti (2007)looked at Californian mothers born in the late 1960s and 70s and find that women bornin low income zip codes were less educated and more likely to live in a low incomezipcode than sisters born in better circumstances. Moreover, women who were low birthweight were more likely to transmit low birth weight to their own children if they wereborn in low income zip codes, suggesting that early disadvantage compounded the initialeffects of low birth weight.

To the extent that behavioral responses to early-life shocks are important empirically,they will affect estimates of long-term effects whether family fixed effects are employedor not. Our conclusion is that users of fixed effects designs should consider any evidencethat may be available about individual child-level characteristics and whether parentsare reinforcing or compensating for the particular early childhood event at issue. Thisinformation will inform the appropriate interpretation of the estimates. There is littleevidence at present that parents in developed countries systematically reinforce orcompensate for early childhood events, but more research is needed on this question.

3.1. PowerGiven that there are relatively few data sets with information about early childhoodinfluences and future outcomes, economists may be tempted to make use of relativelysmall data sets that happen to have the requisite variables. Power calculations can behelpful in determining ex ante whether analysis of a particular data set is likely to yieldany interesting findings. Table 3 provides two sample calculations. The first half of thetable considers the relationship between birth weight and future educational attainment

7 In the siblings model of Appendix C, this can be seen in the case of a Leontief production technology, where the secondperiod investments generate increases in h only up to the level of first period investments. If parental income Y fallsbelow the cost of maintaining the initial investment level 2 I +µg in the second period, it may be optimal to invest fullyin child b (i.e. I∗2b = I ), but not in child a (i.e., I∗2a < I + µg ), who experiences the negative first-period shock.

8 For example, the effectiveness of remedial investments would change over time if γ varied with the birth cohort.Remediation would be more effective for later cohorts if γt > γt+1 in Eq. (4).


Table3

Samplepo

wer

calculations.

Given

atrue

popu

lation

effects

ize,wha

tisthepo

wer

ofasize

alph

a=0.05

test

agains

tthe

nullhy

pothesisthat

thereisno

effect

ford

ifferen

tsam

plesizes?

Basisstud

yAssum

ptions

Samplesize

Power

Bla

cket

al.(2

007)

Key

resu

lt:a

1%in

crea

sein

birt

hw

eigh

tin

crea

sest

hepr

obab

ility

ofhi

ghsc

hool

com

plet

ion

by0.

09pe

rcen

tage

poin

ts.

Bir

thw

eigh

tsam

ple

sum

mar

yst

ats(

twin

s):

mea

n=

2598

g,SD=

612

g.Pr

obab

ility

ofH

Sgr

adsa

mpl

esu

mm

ary

stat

s:m

ean=

0.73

,SD=

0.44

.

Tru

em

odel

:Pr

ob(H

SGR

AD

)=0.

7+

0.1∗

ln(b

irth

wei

ght)+

erro

rC

alcu

latio

nof

erro

rva

rian

cean

dSD

:Le

ty

=Pr

ob(H

SGR

AD

),x

=ln

(bir

thw

eigh

t),

e=

erro

rVa

r(y)

=0.

44ˆ2

=0.

19Va

r(x)

=0.

26ˆ2

=0.

07(w

here

SD(x)=

0.26

,ac

cord

ing

toth

edi

stri

butio

nof

ln(b

irth

wei

ght)

).If

y=

0.7+

0.1x+

e,an

dx

and

ear

ein

depe

nden

t,Va

r(e)

=Va

r(y)−

(0.1

ˆ2)∗

Var(

x)=

0.19−

(0.1

ˆ2)∗

(0.0

7)=

0.19

.So

,SD(e)=

sqrt(V

ar(e))=

0.44

The

refo

re,as

sum

e:bi

rthw

eigh

t∼N

(259

8,61

2),

and

take

the

natu

rall

ogof

birt

hwei

ght.

erro

r∼

N(0,0.

44).

100

300

500

600

700

800

900

1000

1250

1500

1620

2000

2200

2500

3000

3500

4000

4500

5000

5500

6000

6500

7000

0.09

70.

167

0.26

30.

298

0.35

10.

376

0.40

90.

446

0.53

10.

617

0.66

00.

744

0.75

00.

825

0.89

20.

928

0.96

20.

975

0.98

20.

993

0.99

40.

996

0.99

9

(con

tinue

don

next

page

)


Table3(con

tinue

d)

Given

asamplesize,h

owlargewou

ldthetrue

effects

izeha

veto

bein

orde

rtobe

able

tode

tect

itwithrelia

blepo

wer

usingatest

ofsize

alph

a=0.05

?

Basisstud

yAssum

ptions

True

B1Po

wer

Con

ley

etal

.(2

007)

Sibl

ing

sam

ple

from

PSID

(n=

1360

)M

odel

:y=

B0+

B1∗

x+

erro

rA

ssum

e:z∼

N(2

598,

612)

,x=

ln(z)

erro

r∼

N(0,0.

44)

sam

ple

size=

1500

0.00

50.

010.

020.

030.

040.

050.

060.

070.

080.

090.

10.

120.

150.

170.

2

0.04

60.

047

0.07

70.

092

0.14

60.

198

0.27

40.

354

0.46

10.

525

0.63

10.

769

0.92

60.

975

0.99

Pow

erca

lcul

atio

nsar

eba

sed

onM

onte

Car

losim

ulat

ions

with

1000

repl

icat

ions

.


as in Black et al. (2007). Their key result was that a 1% increase in birth weight increasedhigh school completion by 0.09 percentage points. The example shows that underreasonable assumptions about the distribution of birth weight and schooling attainment,it requires a sample of about 4000 children to be able to detect this effect in an OLSregression. We can also turn the question around and ask, given a sample of a certain size,

how large would an effect have to be before we could be reasonably certain of findingit in our data? The second half of the table shows that if we were looking for an effectof birth weight on a particular outcome in a sample of 1300 children, the coefficient on(the log of) birth weight would have to be at least 0.15 before we could detect it withreasonable confidence. If we have reason to believe that the effect is smaller, then it is notlikely to be useful to estimate the model without more data.

3.2. Data constraintsThe lack of large-scale longitudinal data (i.e. data that follows the same persons over time)has been a frequent obstacle to evaluating the long-term impacts of early life influences.Nevertheless, the answer may not always be to undertake collection of new longitudinaldata. Drawbacks include the high costs of data collection; the fact that long termoutcomes cannot be assessed for some time; and the fact that limiting sample attritionis particularly costly. Unchecked, attrition in longitudinal data can pose challenges forinference.

3.2.1. Leveraging existing datasetsIn many cases, existing cross-sectional microdata can serve as a platform for constructinglongitudinal datasets. First, it may be possible to add retrospective questions to ongoingdata collections. Second, it may be possible to merge new group-level informationto existing data sets. Third, it may be possible to merge administrative data sets byindividual in order to address previously unanswerable questions. The primary obstacleto implementing each of these data strategies is frequently data security. Depending onthe approach adopted, there are different demands on data security, as described below.

Smith (2009) and Garces et al. (2002) are examples of adding retrospective questionsto existing data collections. Smith had retrospective questions about health in childhoodadded to the Panel Study of Income Dynamics (PSID). The PSID began in the 1960swith a representative national sample, and has followed the original respondents and theirfamily members every since. Using these data, Smith (2009) is able to show that adultrespondents who were in poor health during childhood have lower earnings than theirown siblings who were not in poor health. Such comparisons are possible because thePSID has data on large numbers of sibling pairs. Garces et al. (2002) added retrospectivequestions about Head Start participation to the PSID, and were able to show that youngadults who had attended Head Start had higher educational attainment, and were lesslikely to have been booked or charged with a crime than siblings who had not attended.


While these approaches may enable analyses of long-term impacts even in the absenceof suitable “off the shelf ” longitudinal data, they have their drawbacks. First, retrospectivedata may be reported with error, although it may be possible to assess the extent ofreporting error using data from other sources. Second, only outcomes that are already inthe data can be assessed, so the need for serendipity remains. Still, the method is promisingenough to suggest that on-going, government funded data collections should build-inmechanisms whereby researchers can propose the addition of questions to subsequentwaves of the survey.

A second way to address long-term questions is to merge new information at thegroup level to existing data sets. The merge generally requires the use of geocoded data.For some purposes, such as exploring variations in policies across states, only a stateidentifier is required. For other purposes, such as examining the effects of traffic patternson asthma, ideally the researcher would have access to exact latitude and longitude.There are many examples in which this approach has been successfully employed. Forexample, Ludwig and Miller (2007) study the long term effects of Head Start, whichexploits the fact that the Office of Economic Opportunity initially offered the 300poorest counties in the country assistance in applying for Head Start. They show, usingdata from the National Educational Longitudinal Surveys, that children who were incounties just poor enough to be eligible for assistance were much more likely to haveattended Head Start than children in counties that were just ineligible. They go on toshow that child mortality rates in the relevant age ranges were lower in counties whoseHead Start enrollments were higher due to the OEO assistance. Using Census datathey find that education is higher for people living in areas with higher former HeadStart enrollment rates. Unfortunately, however, neither the decennial Census nor theAmerican Community Survey collect county of birth, so they cannot identify peoplewho were born in these counties (substantial measurement error is obviously introducedby using county of residence or county where someone went to school as a proxy forcounty of birth). An exciting crop of new research would be enabled by the additionof Census survey questions on county of birth, as well as county of residence at keydevelopmental ages (e.g., ages 5 and 14).9

In addition to the observational approaches described above, an intriguing possibilityis that participants in a completed randomized trial could be followed up. Forinstance, Rush et al. (1980) conducted a randomized intervention of a prenatal nutritionprogram in Harlem during the early 1970s. Following these children over time would

9 In another example, Currie and Gruber (1996a) were able to examine the effects of the Medicaid expansions on theutilization of care among children by merging state-level information on Medicaid policy to data from the NationalHealth Interview Survey (NHIS). At the time, this was only possible because one of the authors had access to the NHISstate codes through his work at the Treasury Department. It has since become easier to access geocoded health dataeither by traveling to Washington to work with the data, or by using it in one of the secure data centers that Censusand the National Center for Health Statistics (NCHS) support. However, it remains a source of frustration to healthresearchers that NCHS does not make state codes and/or codes for large counties available on its public use data sets.


allow researchers to evaluate cognitive outcomes in secondary school, and it might bepossible to collect retrospective data on parental investments during childhood, and toevaluate whether parental investments were affected by the randomization.

A third approach to leveraging existing data merges administrative records frommultiple sources at the individual level, which obviously requires personal identifiers suchas names and birth dates or social security numbers. Access to such identifiers is especiallysensitive. Nevertheless, it constitutes a powerful way to address many questions of interest.Several important studies have successfully exploited this approach outside of the US. Forexample, Black et al. (2005) and Black et al. (2007) use Norwegian data on all twins bornover 30 years to look at long-term effects of birth weight, birth order, and family sizeon educational attainment. Currie et al. (2010) use Canadian data on siblings to examinethe effects of health shocks in childhood on future educational attainment and welfareuse. Almond et al. (2009) use Swedish data to look at the long term effects of low-levelradiation exposure from the Chernobyl disaster on children’s educational attainment.

In the US, Doyle (2008) uses administrative data from child protective services andthe criminal justice system in Illinois to examine the effects of foster care. He shows firstthat there is considerable variation between foster care case workers in whether or nota child will be sent to foster care. Moreover, whether a child is assigned to a particularworker is random, depending on who is on duty at the time a call is received. Using thisvariation, Doyle shows that the marginal child assigned to foster care is significantly morelikely to be incarcerated in future. These examples exploit large sample sizes, objectiveindicators of outcomes, sibling or cohort comparisons, as well as a long follow up period.

Some limitations of using existing data include the fact that administrative data sets oftencontain relatively little background information, and that outcomes are limited to thosethat are collected in the data bases. Finally, the application process to obtain individual-matched data is often protracted.

Looking forward, the major challenge to research that involves either merging newinformation to existing data sets, or merging administrative data sets to each other, is thatprivacy concerns are making it increasingly difficult to obtain data just as it is becomingmore feasible to link them. In some cases, access to public use data has deteriorated.

For example, for many years, individual level Vital Statistics Natality data from birthcertificates included the state of birth, and the county (for counties with over 100,000population). Since 2005, however, these data elements have been suppressed and it isnow necessary to get special permission to obtain US Vital Statistics data with geocodes.

3.2.2. Improvements in the production of administrative dataThere are several “first best” potential solutions to these problems. First, creators oflarge data sets need to be sensitive to the fact that their data may well be useful foraddressing questions that they have not envisaged. In order to preserve the ability to usedata to answer future questions, it is essential to retain information that can be used for


linkage. At a minimum, this should include geographic identifiers at the smallest levelof disaggregation that is feasible (for example a Census tract). Ideally, personal identifierswould also be preserved.

Second, more effort needs to be expended in order to make sensitive data available toresearchers. A range of mechanisms exist that protect privacy while enabling research:

1. Suppress small cells or merge small cells in public use data files. For example, NCHSdata sets such as NHIS could be released with state identifiers for large states, and withidentifiers for groups of smaller states.

2. Add small amounts of “noise” to public use data sets, or do data swapping in orderto prevent identification of outliers. For example, Cornell University is coordinatingthe NSF-Census Bureau Synthetic Data Project which seeks to develop public-use“analytically valid synthetic data” from micro datasets customarily accessed at secureCensus Research Data Centers.

3. Create model servers. In this approach, users login to estimate models using the truedata, but get back output that does not allow individuals to be identified.

4. Data use agreements. The National Longitudinal Survey of Youth and the NationalEducational Longitudinal Survey have successfully employed data use agreementswith qualified users for many years, and without any documented instances of datadisclosure.

5. Creation of de-identified merged files. For example, Currie et al. (2009) asked thestate of New Jersey to merge birth records with information about the location ofpollution sources, and create a de-identified file. This allows them to study the effectof air pollution on infant health.

6. Secure data facilities. The Census Research Data Centers have facilitated access tomuch confidential data, although researchers who are not located close to the facilitiesmay still face large costs of accessing them.

These approaches to data dissemination have been explored in the statistics literaturefor more than 20 years (see Dalenius and Reiss (1982)), and have been much discussed atCensus (see for example, Reznek (2007)).

3.2.3. Additional issuesWe conclude with two new and relatively unexplored data issues. First, how caneconomists make effective use of the burgeoning literature on biomarkers? Thesemeasures have recently been added to existing health surveys, such as the NationalLongitudinal Study of Adolescent Health data. Biomarkers include not only informationabout genetic variations but also hormones such as cortisol (which is often interpretedas a measure of stress). It is tempting to think of these markers as potential instrumentalvariables (Fletcher and Lehrer, 2009). For instance, if it was known that a particular genewas linked to alcoholism, then one might think of using the gene as an instrument for


alcoholism. The potential pitfall in this approach is clear if we consider using somethinglike skin color as an instrument in a human capital earnings function–clearly, skin colormay predict educational attainment, but it may also have a direct effect on earnings. Justbecause a variable is “biological” does not mean that it satisfies the criteria for a validinstrument.

A second issue is the evolving nature of what constitutes a “birth cohort.”Improvements in neonatal medicine have meant that stillbirths and fetal deaths thatwould previously have been excluded from the Census of live births may be increasinglyimportant, e.g. MacDorman et al. (2005). Such a compositional effect on live births mayhave first-order implications for program evaluation and the long-term effects literature.Indeed, both the right and left tails of the birth weight distribution have elongated overtime—in 1970 there were many fewer live births with birthweight either less than 1500g or over 4000 g. To date there has been little research exploring the implications of thesecompositional changes.

In summary, there are many secrets currently locked in existing data that researchersdo not have access to. Economists have been skillful in navigating the many datachallenges inherent in the analysis of long-term (and sometimes latent) effects.Nevertheless, we need to explore ways to make more of these data available, and to moreresearchers. In many cases, this will be a more cost effective and timely way to answerimportant questions than carrying out new data collections.

4. EMPIRICAL LITERATURE: EVIDENCE OF LONG TERM CONSEQUENCES

What is of importance is the year of birth of the generation or group of individuals underconsideration. Each generation after the age of 5 years seems to carry along with it thesame relative mortality throughout adult life, and even into extreme old age.

Kermack et al. (1934) in The Lancet (emphasis added).

In this section, we summarize recent empirical research findings that experiencesbefore five have persistent effects, shaping human capital in particular. A hallmark of thiswork is the attention paid to identification strategies that seek to isolate causal effectsof the early childhood environment. An intriguing sub-current is the possibility thatsome of these effects may remain latent during childhood (at least from the researcher’sperspective) until manifested in either adolescence or adulthood. Recently, economistshave begun to ask how parents or other investors in human capital (e.g. school districts)respond to early-life shocks, as suggested by the conceptual framework in Section 2.3.

As the excerpt from Kermack et al. (1934) indicates, the idea that early childhoodexperiences may have important, persistent effects did not originate recently, nor didit first appear in economics. An extensive epidemiological literature has focussed onthe early childhood environment, nutrition in particular, and its relationship to health


outcomes in adulthood. For a recent survey, see Gluckman and Hanson (2006). Thisliterature has been criticized within epidemiology for credulous empirical comparisons(see, e.g. Rasmussen (2001) or editorial in The Lancet [2001]). Absent clearly-articulatedidentification strategies, health determinants that are difficult to observe and are thereforeomitted from the analysis (e.g., parental concern) are presumably correlated with thetreatment and can thereby generate the semblance of “fetal origins” linkages, even whensuch effects do not exist.

4.1. Prenatal environmentIn the 1990s, David J. Barker popularized and developed the argument that disruptionsto the prenatal environment presage chronic health conditions in adulthood, includingheart disease and diabetes (Barker, 1992). Growth is most rapid prenatally and in earlychildhood. When growth is rapid, disruptions to development caused by the adverseenvironmental conditions may exert life-long health effects. Barker’s “fetal origins”perspective contrasted with the view that pregnant mothers functioned as an effectivebuffer for the fetus against environmental insults.10

In Table 4, we categorize prenatal environmental exposures into three groups.Specifically, we differentiate among factors affecting maternal and thereby fetalhealth (e.g. nutrition and infection), economic shocks (e.g. recessions), and pollution(e.g. ambient lead).

4.1.1. Maternal healthCurrie and Hyson (1999) broke ground in economics by exploring whether “fetalorigins” (FO) effects were confined to chronic health conditions in adulthood, or mightextend to human capital measures. Using the British National Child DevelopmentSurvey, low birth weight children were more than 25% less likely to pass English andmath O-level tests, and were also less likely to be employed. The finding that testscores were substantially affected was surprising, as epidemiologists routinely posited fetal“brain sparing” mechanisms, whereby adverse in utero conditions were parried through aplacental triage that prioritized neural development over the body, see, e.g., Scherjonet al. (1996). Furthermore, Stein et al. (1975)’s influential study found no effect ofprenatal exposure to the Dutch Hunger Winter on IQ.

Currie and Hyson (1999) were followed by a series of papers that exploited differencesin birthweight among siblings and explored their relationship to sibling differences incompleted schooling. In relatively small samples (approximately 800 families), Conleyand Bennett (2001) found negative but imprecise effects of low birth weight oneducational attainment. Statistically significant effects of low birth weight on educationalattainment were found when birth weight was interacted with being poor, but in general

10 For example, it has been argued that nausea and vomiting in early pregnancy (morning sickness) is an adaptive responseto prevent maternal ingestion of foods that might be noxious to the fetus.


Table4

Pren

ataleff

ectson

laterchild

andad

ulto

utcomes.

Stud

yan

dda

taStud

yde

sign

Results

Effects

ofmaterna

lhea

lth

Isth

eim

pact

ofhe

alth

shoc

kscu

shio

ned

byec

onom

icst

atus

?T

heca

seof

low

birt

hw

eigh

t.C

urri

ean

dH

yson

(199

9)N

CD

S19

58co

hort

.N=

11,6

09at

age

20,10

,267

atag

e23

,94

02at

age

33.

Mul

tivar

iate

regr

essio

nw

ithnu

mer

ousb

ackg

roun

dan

dde

mog

raph

icco

ntro

ls(in

clud

ing

mat

erna

lgra

ndfa

ther

’sSE

S,bi

rth

orde

r,an

dm

ater

nals

mok

ing

duri

ngpr

egna

ncy)

.K

eyex

plan

ator

yva

riab

lesa

rein

dica

tors

for

LBW

,SE

S,an

dth

ein

tera

ctio

ns.

Out

com

esm

easu

red

are

the

num

ber

ofO

-lev

elpa

sses

atag

e16

(tra

nscr

ipts

colle

cted

atag

e20

),em

ploy

men

t,w

ages

and

heal

thst

atus

atag

es23

and

33.SE

Sas

signe

dus

ing

fath

er’s

soci

alcl

assi

n19

58(o

rm

othe

r’sSE

Sif

fath

eris

miss

ing)

.

LBW

child

ren

are

38-4

4%le

sslik

ely

topa

ssM

ath

O-l

evel

.LB

Wfe

mal

esar

e25

%le

sslik

ely

topa

ssE

nglis

hO

-lev

elte

sts.

LBW

fem

ales

are

16%

less

likel

yto

beem

ploy

edfu

ll-tim

eat

age

23,LB

Wm

ales

are

9%le

sslik

ely

tobe

empl

oyed

full-

time

atag

e33

.LB

Wfe

mal

esar

e54

%m

ore

likel

yto

have

fair

/poo

rhe

alth

atag

e23

,LB

Wm

ales

are

43%

mor

elik

ely

toha

vefa

ir/p

oor

heal

that

age

33.

Few

signi

fica

ntdi

ffer

ence

sby

SES.

Ret

urns

tobi

rthw

eigh

t(B

ehrm

anan

dR

osen

zwei

g,20

04).

Mon

ozyg

otic

fem

ale

twin

sbor

n19

36-1

955

from

the

Min

neso

taTw

insR

egist

ryfo

r19

94an

dth

ebi

rth

wei

ghts

ofth

eir

child

ren.

N=

804

twin

s,an

d60

8tw

in-m

othe

rpa

irs.

Twin

fixe

deff

ects

estim

ates

com

pare

dto

OLS

.To

expl

ore

gene

raliz

abili

tyof

find

ings

from

twin

ssam

ple,

wei

ghte

dth

esa

mpl

eus

ing

the

US

singl

eton

dist

ribu

tion

offe

talg

row

thra

tes.

Res

ultsfr

om

twin

ssa

mple

:1

oz.pe

rw

eek

ofpr

egna

ncy

incr

ease

infe

talg

row

thle

adst

oin

crea

seso

f5%

,in

scho

olin

gat

tain

men

t;2%

inhe

ight

;8%

hour

lyw

ages

.R

esultsfr

om

twin

ssa

mple

wei

ghte

dusi

ng

singl

eton

dis

trib

ution

:1

oz.pe

rw

eek

ofpr

egna

ncy

incr

ease

infe

talg

row

thle

adst

oin

crea

seof

5%in

scho

olin

gat

tain

men

t;1.

7%in

heig

ht;no

signi

fica

nteff

ecto

nw

ages

.O

LSun

dere

stim

ates

effec

tsof

birt

hw

eigh

tby

50%

.

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

The

cost

sofl

owbi

rth

wei

ght

(Alm

ond

etal

.,20

05).

Link

edbi

rth

and

infa

ntde

ath

file

sfor

US

for

1983

-85,

1989

-91

and

1995

-97.

Hos

pita

lcos

tsfr

omhe

alth

care

cost

and

utili

zatio

npr

ojec

tsta

tein

patie

ntda

taba

sefo

r19

95-2

000

inN

ewYo

rkan

dN

ewJe

rsey

.N

CH

SN=

189,

036

twin

s,49

7,13

9sin

glet

ons.

HC

UP

N=

44,4

10.

Twin

fixe

deff

ects

toes

timat

eeff

ect

oflo

wbi

rth

wei

ght(

LBW

)on

hosp

italc

osts

,he

alth

atbi

rth,

and

infa

ntm

orta

lity.

Also

estim

ated

impa

ctof

mat

erna

lsm

okin

gdu

ring

preg

nanc

yon

heal

tham

ong

singl

eton

birt

hs,co

ntro

lling

for

num

erou

sbac

kgro

und

and

dem

ogra

phic

char

acte

rist

icsu

sing

OLS

and

prop

ensit

ysc

ore

mat

chin

g.

Res

ultsusi

ng

twin

fixe

deff

ects

:1

SDin

crea

sein

birt

hw

eigh

tlea

dsto

:0.

08SD

decr

ease

inho

spita

lcos

tsfo

rde

liver

yan

din

itial

care

0.03

SDde

crea

sein

infa

ntm

orta

lity

rate

s0.

03SD

incr

ease

inA

pgar

scor

es0.

01SD

decr

ease

inus

eof

assis

ted

vent

ilato

raf

ter

birt

h(O

LSes

timat

esw

/out

twin

fixe

deff

ects

are

0.51

SD,0.

41SD

,0.

51SD

,0.

25SD

).R

esultsfr

om

OLS

on

effec

tsofm

ater

nal

smoki

ng:

Mat

erna

lsm

okin

gre

duce

sbir

thw

eigh

tby

200

g(6

%);

incr

ease

slik

elih

ood

that

infa

ntis

LBW

(<25

00g)

bym

ore

than

100%

(mea

n=

0.06

1).

No

stat

istic

ally

signi

fica

nteff

ects

onA

pgar

scor

e,in

fant

mor

talit

yra

tes,

orus

eof

assis

ted

vent

ilato

rat

birt

h.

The

1918

influe

nza

pand

emic

and

subs

eque

nthe

alth

outc

omes

(Alm

ond

and

Maz

umde

r,20

05).

Dat

afr

omSI

PPfo

r19

84-1

996.

N=

25,1

69.

Com

pare

coho

rtsi

nut

ero

befo

re,

duri

ngan

daf

ter

Oct

.19

18flu

pand

emic

.R

egre

ssio

nses

timat

eco

hort

effec

tsin

clud

ing

surv

eyye

ardu

mm

iesa

ndqu

adra

ticin

age

inte

ract

edw

ithsu

rvey

year

.

Indi

vidu

alsb

orn

in19

19ar

e10

%m

ore

likel

yto

bein

fair

orpo

orhe

alth

,al

soin

crea

seso

f19%

,35

%,13

%,17

%in

trou

ble

hear

ing,

spea

king

,lif

ting

and

wal

king

.

Est

imat

ing

the

impa

ctof

larg

eci

gare

tte

tax

hike

s:th

eca

seof

mat

erna

lsm

okin

gan

din

fant

birt

hw

eigh

t(Li

enan

dE

vans

,20

05).

1990

-199

7U

SN

atal

ityfile

s.D

ata

onci

gare

tte

taxe

sfro

mth

eta

xbu

rden

onto

bacc

o,va

riou

syea

rs.

IVus

ing

tax

hike

sin

four

stat

esas

inst

rum

ents

for

mat

erna

lsm

okin

gdu

ring

preg

nanc

y.C

ontr

olle

dfo

rst

ate

and

mon

thof

conc

eptio

nan

dba

ckgr

ound

char

acte

rist

ics.

Mat

erna

lsm

okin

gdu

ring

preg

nanc

yre

duce

sbir

thw

eigh

tby

5.4%

and

incr

ease

slik

elih

ood

oflo

wbi

rth

wei

ghtb

y∼

100%

.

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Long

term

effec

tsof

inut

ero

expo

sure

toth

e19

18in

flue

nza

pand

emic

inth

epo

st-1

940

US

popu

latio

n(A

lmon

d,20

06).

1960

-198

0U

SC

ensu

sdat

a.19

17-1

919

Vita

lSta

tistic

sdat

aon

mor

talit

y.Fo

r19

60n=

114,

031,

for

1970

n=

308,

785,

for

1980

n=

471,

803.

Est

imat

edeff

ects

ofin

uter

oin

flue

nza

expo

sure

byco

mpa

ring

coho

rtsb

orn

imm

edia

tely

befo

re,

duri

ng,an

daf

ter

the

1918

pand

emic

and

byem

ploy

ing

the

idio

sync

ratic

geog

raph

icva

riat

ion

inin

tens

ityof

expo

sure

toco

nduc

tw

ithin

-coh

orta

naly

sis.E

xpos

edco

hort=

thos

ebo

rnin

1919

.Su

rrou

ndin

gco

hort

s=th

ose

born

in19

18an

d19

20.U

sed

mul

tivar

iate

regr

essio

nw

ithdu

mm

yfo

rbi

rth

coho

rt=

1919

,an

da

quad

ratic

coho

rttr

end

tom

easu

rede

part

ures

inth

e19

19bi

rth

coho

rtou

tcom

esfr

omth

etr

end.

For

geog

raph

icco

mpa

riso

n,us

edda

taon

viru

sstr

engt

hby

wee

kas

wel

lasd

ata

onep

idem

ictim

ing

byC

ensu

sdiv

ision

toyi

eld

am

easu

reof

aver

age

pand

emic

viru

lenc

eby

divi

sion.

The

nes

timat

edm

ultiv

aria

tere

gres

sion

incl

udin

gth

evi

rule

nce

mea

sure

,st

ate

and

year

ofbi

rth

fixe

deff

ects

,th

ein

fant

mor

talit

yra

tein

stat

ean

dye

arof

birt

h,an

dth

eat

triti

onof

birt

hco

hort

inth

eC

ensu

sdat

a.

Est

imat

ion

resu

ltsco

mpar

ing

bir

thco

hort

s:T

he19

19bi

rth

coho

rt,co

mpa

red

with

the

coho

rttr

end:

was

4%-5

%(1

3%-1

5%am

ong

trea

ted)

less

likel

yto

com

plet

ehi

ghsc

hool

rece

ived

0.6%

-1.6

%fe

wer

year

sofe

duca

tion

had

1%-3

%le

ssto

tali

ncom

e(f

orm

ales

only

,20

05do

llars

)ha

d1%

-2%

low

erso

cioe

cono

mic

stat

usin

dex

(Dun

can

inde

x)w

as1%

-2%

mor

elik

ely

toha

vea

disa

bilit

yth

atlim

itsw

ork

(for

mal

eson

ly)

had

12%

high

erav

erag

ew

elfa

repa

ymen

t(fo

rw

omen

).E

stim

ates

are

sligh

tlyla

rger

for

nonw

hite

subg

roup

.Est

imat

ion

resu

ltsusi

ng

geogra

phic

vari

atio

nan

dst

ate

fixe

deff

ects

:(F

orth

e19

19bi

rth

coho

rt,us

edav

erag

em

ater

nali

nfec

tion

rate=

1/3)

Mat

erna

linf

ectio

n:re

duce

ssch

oolin

gby

2.2%

redu

cesp

roba

bilit

yof

high

scho

olgr

adua

tion

by0.

05%

decr

ease

sann

uali

ncom

eby

6%re

duce

ssoc

ioec

onom

icst

atus

inde

xby

2%-3

%.

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Exp

lain

ing

siblin

gdi

ffer

ence

sin

achi

evem

enta

ndbe

havi

oral

outc

omes

:th

eim

port

ance

ofw

ithin

-an

dbe

twee

n-fa

mily

fact

ors(

Con

ley

etal

.,20

07)

Dat

afr

omch

ildde

velo

pmen

tsu

pple

men

tofP

SID

.N=

1360

.

Sibl

ing

fixe

deff

ects

toex

amin

eeff

ects

ofbi

rth

wei

ght,

birt

hor

der,

and

gend

eron

late

rou

tcom

es.

Cog

nitiv

eou

tcom

esm

easu

red

byth

eW

oodc

ock-

John

son

revi

sed

test

sofa

chie

vem

ent.

Beh

avio

ral

outc

omes

mea

sure

dby

the

beha

vior

alpr

oble

msi

ndex

(BPI

).C

ontr

olfo

rfa

mily

-an

dch

ild-s

peci

fic

char

acte

rist

ics.

No

stat

istic

ally

signi

fica

nteff

ects

ofbi

rth

wei

ghto

nB

PIor

onco

gniti

veas

sess

men

tsfo

rw

hole

sam

ple.

For

blac

ks,po

sitiv

eeff

ecto

fbir

thw

eigh

ton

cogn

itive

asse

ssm

ents

.

Twin

differ

ence

sin

birt

hw

eigh

t:th

eeff

ects

ofge

noty

pean

dpr

enat

alen

viro

nmen

ton

neon

atal

and

post

nata

lmor

talit

y(C

onle

yet

al.,

2006

)D

ata

ontw

inbi

rths

from

the

1995

-97

mat

ched

mul

tiple

birt

hda

taba

se.

N=

258,

823.

Twin

fixe

deff

ects

mod

elso

feffec

tsof

birt

hw

eigh

ton

mor

talit

yfo

rsa

me-

sex

and

mix

ed-s

expa

irs.

1lb

incr

ease

inbi

rth

wei

ghtl

eads

to:

9%(1

0%)r

educ

tion

inin

fant

mor

talit

yfo

rm

ixed

-sex

(sam

e-se

x)7%

(8%

)red

uctio

nin

neon

atal

mor

talit

yfo

rm

ixed

-sex

(sam

e-se

x)2%

redu

ctio

nin

post

-neo

nata

lmor

talit

yfo

rm

ixed

-sex

and

sam

e-se

x.Fo

rfu

ll-te

rmtw

ins,

mix

ed-s

exeff

ects

muc

hla

rger

than

sam

e-se

xeff

ects

.

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Bio

logy

asde

stin

y?Sh

ort-

and

long

-run

dete

rmin

ants

ofin

terg

ener

atio

nalt

rans

miss

ion

ofbi

rth

wei

ght(

Cur

rie

and

Mor

etti,

2007

).C

alifo

rnia

nata

lity

data

for

child

ren

born

betw

een

1989

and

2001

and

thei

rm

othe

rs(if

born

inC

A)

born

betw

een

1970

and

1974

.M

othe

rsw

hoar

esis

ters

are

mat

ched

usin

ggr

andm

othe

r’sna

me.

n=

638,

497

birt

hs.

Exa

min

eeff

ecto

fmot

her’s

birt

hw

eigh

ton

child

’sbi

rth

wei

ghti

nm

odel

swith

gran

dmot

her

fixe

deff

ects

.E

xam

ine

inte

ract

ions

ofm

othe

r’sbi

rth

wei

ghta

ndgr

andm

othe

r’sSE

Sat

time

ofm

othe

r’sbi

rth

aspr

oxie

dby

inco

me

inzi

pco

deof

mot

her’s

birt

h.E

xam

ine

effec

tofm

ater

nall

owbi

rth

wei

ghto

nm

othe

r’sSE

Sat

time

she

give

sbir

th.

Mot

her’s

low

birt

hw

eigh

tinc

reas

eslik

elih

ood

that

child

islo

wbi

rth

wei

ghtb

yab

out5

0%.T

hein

cide

nce

ofch

ildlo

wbi

rth

wei

ghti

s7%

high

erif

mot

her

was

born

into

high

pove

rty

zip

code

than

into

low

pove

rty

zip

code

.C

hild

ren

born

into

poor

hous

ehol

dsar

e0.

7%m

ore

likel

yto

belo

wbi

rth

wei

ghti

fthe

irm

othe

rsw

ere

low

birt

hw

eigh

t;ch

ildre

nbo

rnin

tono

n-po

orho

useh

olds

are

0.4%

mor

elik

ely

tobe

low

birt

hw

eigh

tift

heir

mot

hers

wer

elo

wbi

rth

wei

ght(

so,po

vert

yra

isest

hepr

obab

ility

oftr

ansm

issio

nof

low

birt

hw

eigh

tby

88%

).B

eing

low

birt

hw

eigh

tisa

ssoc

iate

dw

itha

loss

of$1

10in

futu

rein

com

e,on

aver

age,

ona

base

line

inco

me

of$1

0,09

6(in

1970

dolla

rs).

Bei

nglo

wbi

rth

wei

ghti

ncre

ases

the

prob

abili

tyof

livin

gin

ahi

gh-p

over

tyne

ighb

orho

odby

3%re

lativ

eto

the

base

line.

Bei

nglo

wbi

rth

wei

ghtr

educ

esfu

ture

educ

atio

nala

ttai

nmen

tby

0.1

year

s.

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

From

the

crad

leto

the

labo

rm

arke

t:th

eeff

ecto

fbir

thw

eigh

ton

adul

tout

com

es(B

lack

etal

.,20

07).

Bir

thre

cord

sfro

mN

orw

ayfo

r19

67-1

997.

Dro

pped

cong

enita

ldef

ects

.M

atch

edto

regi

stry

data

oned

ucat

ion

and

labo

rm

arke

tou

tcom

esan

dto

mili

tary

reco

rds.

n=

33,3

66tw

inpa

irs.

Twin

fixe

deff

ects

,co

ntro

lling

for

mot

her

and

birt

h-sp

ecifi

cva

riab

les.

Log(

birt

hw

eigh

t)is

prim

ary

inde

pend

entv

aria

ble.

10%

incr

ease

inbi

rth

wei

ght:

redu

ces1

-yea

rm

orta

lity

by13

%in

crea

ses5

min

APG

AR

scor

eby

0.3%

incr

ease

spro

babi

lity

ofhi

ghsc

hool

com

plet

ion

by1.

2%in

crea

sesf

ull-

time

earn

ings

by1%

.M

ale

outc

om

esat

age

18-2

0:10

%in

crea

sein

birt

hw

eigh

t:in

crea

sesh

eigh

tby

0.3%

incr

ease

sBM

Iby

0.5%

incr

ease

sIQ

by1.

1%(s

cale

of1-

9).

Effec

tsofm

oth

er’s

bir

thw

eigh

ton

child’s

bir

thw

eight:

10%

incr

ease

inm

othe

r’sbi

rth

wei

ght:

incr

ease

schi

ld’s

birt

hw

eigh

tby

1.5%

.

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

The

influe

nce

ofea

rly-

life

even

tson

hum

anca

pita

l,he

alth

stat

usan

dla

bor

mar

keto

utco

mes

over

the

life

cour

se(J

ohns

onan

dSc

hoen

i,20

07)P

SID

.A

dult

sam

ple

born

betw

een

1951

and

1975

.N=

5160

peop

lean

d16

55fa

mili

es.C

hild

sam

ple

0-12

year

sold

in19

97.

N=

1127

mot

hers

and

2239

child

ren.

Sibl

ing

fixe

deff

ects

toes

timat

eim

pact

ofm

ater

nals

mok

ing

duri

ngpr

egna

ncy

onbi

rth

outc

omes

.A

lsoes

timat

edm

odel

usin

gO

LS,

cont

rolli

ngfo

rgr

andp

aren

tsm

okin

gin

adol

esce

nce,

mat

erna

lbi

rth

wei

ght,

and

pate

rnal

smok

ing,

amon

got

her

back

grou

ndch

arac

teri

stic

s.

Not

e:m

eans

are

notr

epor

ted,

sore

lativ

eeff

ects

cann

otbe

calc

ulat

ed.

Effec

tsofin

com

ean

dm

oth

er’s

bir

thw

eigh

ton

child’s

bir

thw

eigh

t:A

nin

crea

sein

inco

me

of$1

0,00

0ra

isesc

hild

’sbi

rth

wei

ght

by0.

12lb

sifm

othe

rw

aslo

wbi

rth

wei

ght(

by0.

02lb

sif

mot

her

was

notl

owbi

rth

wei

ght)

for

afa

mily

with

inco

me

of$7

500

(199

7do

llars

).H

avin

gno

heal

thin

sura

nce

incr

ease

sthe

prob

abili

tyof

low

birt

hw

eigh

tby

10pp

.E

ffec

tson

child

hea

lth

outc

om

es:

(hea

lthin

dex

1-10

0)Lo

wbi

rth

wei

ghts

iblin

gsha

vea

1.67

poin

tlow

erhe

alth

inde

x.Pr

ivat

ehe

alth

insu

ranc

ein

crea

sesh

ealth

inde

xby

1.02

poin

ts.

A$1

0,00

0in

crea

sein

inco

me

for

fam

ilies

with

$15,

000-

50,0

00in

com

ein

crea

sesh

ealth

inde

xby

0.53

pp.

Effec

tson

adult

outc

om

es:

Low

birt

hw

eigh

tsib

lings

:4.

7pp

mor

elik

ely

tobe

high

scho

oldr

opou

ts,ha

vea

3.7

poin

tlow

erhe

alth

inde

x(1

-100

scal

e).

Low

birt

hw

eigh

tbro

ther

s(sa

mpl

eof

mal

eson

ly):

are

4.3

ppm

ore

likel

yto

have

nopo

sitiv

eea

rnin

gs(s

igat

10%

leve

l)ha

ve$2

966

less

annu

alea

rnin

gs(s

igat

10%

leve

l).

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Mat

erna

lsm

okin

gdu

ring

preg

nanc

yan

dea

rly

child

outc

omes

(Tom

iney

,20

07)

Chi

ldre

nof

NC

DS

mot

hers

born

betw

een

1973

and

2000

.n=

2799

mot

hers

and

6291

siblin

gch

ildre

n.

Sibl

ing

fixe

deff

ects

toes

timat

eim

pact

ofm

ater

nals

mok

ing

duri

ngpr

egna

ncy

onbi

rth

outc

omes

.A

lsoes

timat

edm

odel

usin

gO

LS,

cont

rolli

ngfo

rgr

andp

aren

tsm

okin

gin

adol

esce

nce,

mat

erna

lbi

rth

wei

ght,

and

pate

rnal

smok

ing,

amon

got

her

back

grou

ndch

arac

teri

stic

s.

Not

e:on

lyre

port

ing

resu

ltsfr

omsib

ling

fixe

deff

ects

regr

essio

nhe

re.

Mat

erna

lsm

okin

gdu

ring

preg

nanc

yre

duce

sbir

thw

eigh

tby

1.7%

.N

ost

atist

ical

lysig

nifica

nteff

ecto

fmat

erna

lsm

okin

gdu

ring

preg

nanc

yon

prob

abili

tyof

havi

nga

low

birt

hw

eigh

tchi

ld,

pre-

term

gest

atio

n,or

wee

ksof

gest

atio

n.N

ost

atist

ical

lysig

nifica

nteff

ects

ofm

ater

nals

mok

ing

amon

gm

othe

rsw

hoqu

itby

mon

th5

ofpr

egna

ncy.

Larg

ereff

ects

ofm

ater

nal

smok

ing

onbi

rth

wei

ghta

mon

glo

wed

ucat

edw

omen

.

Can

api

ntpe

rda

yaff

ecty

our

child

’spa

y?T

heeff

ecto

fpre

nata

lal

coho

lexp

osur

eon

adul

tou

tcom

es(N

ilsso

n,20

08).

Dat

afr

omSw

edish

LOU

ISE

data

base

onfirs

t-bo

rnin

divi

dual

sbor

nbe

twee

n19

64an

d19

72.

n=

353,

742.

Swed

ishna

tura

lexp

erim

enti

nw

hich

alco

hola

vaila

bilit

yin

2tr

eatm

entr

egio

nsin

crea

sed

shar

ply

asre

gula

rgr

ocer

yst

ores

wer

eal

low

edto

mar

kets

tron

gbe

erfo

r6

mon

thsd

urin

g19

67w

ithth

em

inim

umag

efo

rpu

rcha

sebe

ing

16(in

stea

dof

21).

Diff

eren

ce-i

n-di

ffer

ence

-in-

differ

ence

com

pari

ngun

der-

21m

othe

rsw

ithol

der

mot

hers

intr

eatm

enta

ndco

ntro

lre

gion

spre

-,du

ring

,an

dpo

st-

expe

rim

ent.

Bas

elin

ees

timat

ions

focu

sed

onch

ildre

nco

ncei

ved

prio

rto

the

expe

rim

ent,

but

expo

sed

inut

ero

toth

eex

peri

men

t.C

ontr

olle

dfo

rqu

arte

ran

dco

unty

ofbi

rth

fixe

deff

ects

.

DD

Dre

sults:

Year

sofs

choo

ling:

decr

ease

dby

0.27

(2.1

%)y

ears

for

who

lesa

mpl

e;by

0.47

year

sfor

mal

es;no

stat

istic

ally

signi

fica

nteff

ectf

orfe

mal

es.

HS

grad

uatio

n:de

crea

sed

by0.

4%fo

rw

hole

sam

ple;

by10

%fo

rm

ales

;no

stat

.sig

nifica

nteff

ectf

orfe

mal

es.

Gra

duat

ion

from

high

ered

ucat

ion:

decr

ease

dby

16%

for

who

lesa

mpl

e;by

35%

for

mal

es;no

stat

.sig

nifica

nteff

ectf

orfe

mal

es.

Ear

ning

sata

ge32

:de

crea

sed

by24

.1%

for

who

lesa

mpl

e;by

22.8

%fo

rm

ales

;by

17.7

%fo

rfe

mal

es.

Prob

abili

tyno

inco

me

atag

e32

:in

crea

sed

by74

%w

hole

sam

ple.

Prop

ortio

non

wel

fare

:in

crea

sed

by90

%fo

rw

hole

sam

ple;

by5.

1pp

for

mal

es.

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Shor

t-,m

ediu

m-

and

long

-ter

mco

nseq

uenc

esof

poor

infa

nthe

alth

(Ore

opou

lose

tal.,

2008

).D

ata

from

the

Man

itoba

Cen

ter

for

Hea

lthPo

licy

mat

chin

gpr

ovin

cial

heal

thin

sura

nce

clai

msw

ithbi

rth

reco

rds,

educ

atio

nalr

ecor

ds,an

dso

cial

assis

tanc

ere

cord

s.In

clud

esal

lch

ildre

nbo

rnin

Man

itoba

from

1979

to19

85.n=

54,1

23sib

lings

and

1742

twin

s.

Sibl

ing

and

twin

fixe

deff

ects

.U

sed

thre

em

easu

reso

finf

anth

ealth

:bi

rth

wei

ght,

5-m

inA

pgar

scor

e,an

dge

stat

iona

llen

gth

inw

eeks

;us

eddu

mm

iesf

ordi

ffer

ent

cate

gori

esof

the

vari

able

sto

estim

ate

nonl

inea

reff

ects

.A

lsoes

timat

edm

odel

susin

gO

LSfo

rth

ew

hole

sam

ple,

for

the

siblin

gssa

mpl

e,an

dfo

rth

etw

inss

ampl

ew

ithou

tfam

ilyfixe

deff

ects

.

Not

e:on

lyre

port

ing

resu

ltsfr

omre

gres

sions

that

incl

uded

fam

ilyfixe

deff

ects

here

.B

W=

birt

hw

eigh

t.R

elat

ive

toA

pgar

scor

e=

10,

BW>

3500

g,ge

stat

ion

40-4

1w

eeks

low

erva

lues

.E

ffec

tson

infa

ntm

ort

ality:

incr

ease

infa

ntm

oral

ity.

E.g

.in

siblin

gsa

mpl

e(in

fant

mor

talit

y=

0.01

1).

Apg

arsc

ore<

6in

crea

sesp

roba

bilit

yof

infa

ntm

orta

lity

by31

.9pp

BW<

1000

gin

crea

sesp

roba

bilit

yof

infa

ntm

orta

lity

by87

.2pp

Ges

tatio

n<

36w

eeks

incr

ease

spro

babi

lity

ofin

fant

mor

talit

yby

11.9

pp.

Effec

ton

langu

age

arts

score

(tak

enin

gra

de

12):

Apg

arsc

ore<

6de

crea

sest

ests

core

by0.

1of

SD(s

ig.at

10%

leve

l)B

W25

01-3

000

gde

crea

sest

ests

core

by0.

04of

SDE

ffec

ton

pro

bab

ility

ofre

achin

ggra

de

12by

age

17:

Apg

arsc

ore<

6de

crea

sesg

rade

12pr

obab

ility

by4.

1pp

(sig

.at

10%

)B

W10

01-1

500

gde

crea

sesg

rade

12pr

obab

ility

by14

.1pp

Ges

tatio

n<

36w

eeks

decr

ease

sgra

de12

prob

abili

tyby

4.0

pp.

Effec

ton

soci

alas

sist

ance

take

-up

duri

ng

ages

18-2

1.25

:B

W<

1000

decr

ease

spro

babi

lity

ofta

ke-u

pby

21.5

pp.

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Bir

thco

hort

and

the

blac

k-w

hite

achi

evem

entg

ap:th

ero

leof

heal

thso

onaf

ter

birt

h(C

hay

etal

.,20

09).

Dat

afr

omth

ena

tiona

lass

essm

ento

fed

ucat

iona

lpro

gres

slon

g-te

rmtr

ends

for

1971

-200

4.A

FQT

data

from

US

mili

tary

for

1976

-200

1fo

rm

ale

appl

ican

ts17

-20.

n=

2,64

9,57

3w

hite

mal

esan

dn=

1,10

3,74

8bl

ack

mal

es.H

ospi

tald

ischa

rge

rate

sfr

omN

atio

nalH

ealth

Inte

rvie

wSu

rvey

.

Reg

ress

ion

ofte

stsc

ores

onye

ar,

age,

and

subj

ectfi

xed

effec

ts(s

epar

atel

yfo

rbl

acks

and

whi

tes)

usin

gN

AE

P-LT

Tan

dA

FQT

test

scor

eda

ta.In

AFQ

Tte

stsc

ore

data

,co

rrec

tfor

sele

ctio

nbi

asus

ing

inve

rse

prob

abili

tyw

eigh

ting

from

Nat

ality

and

Cen

susd

ata.

Est

imat

edre

gres

sions

sepa

rate

lyfo

rbl

acks

and

whi

tesa

ndfo

rth

eN

orth

,th

eSo

uth,

the

Rus

tbel

t,th

ede

epSo

uth,

and

indi

vidu

alst

ates

with

inth

eSo

uth

and

Nor

th.A

lsoes

timat

eddi

ffer

ence

-in-

differ

ence

-in-

differ

ence

(DD

D)

mod

els,

com

pari

ngbl

ack

and

whi

tete

stsc

ores

betw

een

coho

rtsb

orn

in19

60-6

2an

d19

70-7

2in

the

Sout

hre

lativ

eto

the

Rus

tbel

t,co

ntro

lling

for

regi

on-s

peci

fic,

race

-spe

cific

age-

by-t

ime

effec

tsan

dra

ce-b

y-re

gion

-by-

time

effec

ts.

Use

dpo

stne

onat

alm

orta

lity

rate

(PN

MR

)asa

prox

yfo

rin

fant

heal

then

viro

nmen

tto

asse

ssim

pact

ofin

fant

heal

thon

the

test

scor

ega

p.

NA

EP

-LT

Tdat

a:T

hebl

ack-

whi

tete

stsc

ore

gap

decl

ined

from

1SD

to0.

6of

SDbe

twee

n19

71an

d20

04.T

heco

nver

genc

ew

aspr

imar

ilydu

eto

larg

ein

crea

sesi

nbl

ack

test

scor

esin

the

1980

s.R

egre

ssio

nre

sults

indi

cate

that

the

conv

erge

nce

was

due

toco

hort

effec

ts,ra

ther

than

time

effec

ts.

AFQ

Tdat

a:T

hebl

ack-

whi

tega

pin

AFQ

Tte

stsc

ores

decl

ined

byab

out

19%

betw

een

the

1962

-63

and

1972

birt

hco

hort

s.T

hede

clin

ein

the

gap

isab

out0

.3SD

sgre

ater

inth

eSo

uth

rela

tive

toth

eR

ustb

eltb

etw

een

1960

-62

and

1970

-72

coho

rts.

Con

verg

ence

inPN

MR

expl

ains

52%

ofth

eva

riat

ion

acro

ssst

ates

inA

FQT

conv

erge

nce.

Effec

tsofac

cess

tohosp

ital

s:A

30pp

incr

ease

inbl

ack

hosp

italb

irth

rate

sfro

m19

62-6

4to

1968

-70

incr

ease

scoh

ortA

FQT

scor

esby

7.5

perc

entil

epo

ints

.A

blac

kch

ildw

hoga

ined

adm

issio

nto

aho

spita

lbef

ore

age

4ha

da

0.7-

1SD

gain

inA

FQT

scor

eat

age

17-1

8re

lativ

eto

abl

ack

child

who

did

not.

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Sepa

rate

dat

birt

h:U

Stw

ines

timat

esof

the

effec

tsof

birt

hw

eigh

t(R

oyer

,20

09)

Cal

iforn

iabi

rth

reco

rds.

n=

3028

sam

e-se

xfe

mal

etw

inbi

rths

1960

-198

2.D

ata

from

earl

ych

ildho

odlo

ngitu

dina

lst

udy-

birt

hco

hort

.n=

1496

twin

birt

hs.

Twin

fixe

deff

ects

,al

low

ing

non-

linea

reff

ects

ofbi

rth

wei

ght.

Tw

infixe

d-e

ffec

tre

sults:

250

gin

crea

sein

birt

hw

eigh

tlea

dsto

:8.

3%de

crea

sein

prob

abili

tyof

infa

ntm

orta

lity

0.82

day

decr

ease

inst

ayin

hosp

italp

ost-

birt

h0.

02-0

.04

ofSD

incr

ease

inm

enta

l/m

otor

test

scor

e0.

2%in

crea

sein

educ

.at

tain

men

tofm

othe

rat

child

birt

h0.

5%in

crea

sein

child

’sbi

rth

wei

ght

11%

decr

ease

inpr

egna

ncy

com

plic

atio

ns.

No

stat

istic

ally

signi

fica

nteff

ects

ofbi

rth

wei

ghto

nad

ult

heal

thou

tcom

essu

chas

hype

rten

sion,

anem

ia,an

ddi

abet

es.

No

stat

istic

ally

signi

fica

nteff

ects

ofbi

rth

wei

ghto

nin

com

e-re

late

dm

easu

res.

200

gin

crea

sein

birt

hw

eigh

tlea

dsto

a0.

5pp

incr

ease

inpr

obab

ility

ofbe

ing

obse

rved

inad

ulth

ood—

smal

lsel

ectio

nbi

as.Fo

und

nost

atist

ical

lysig

nifica

ntev

iden

ceof

com

pens

atin

gor

rein

forc

ing

pare

ntal

inve

stm

ents

whe

nco

nsid

erin

gea

rly

med

ical

care

and

brea

stfe

edin

g.

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

The

long

-ter

mec

onom

icim

pact

ofin

uter

oan

dpo

stna

tale

xpos

ure

tom

alar

ia(B

arre

ca,20

10).

Mal

aria

mor

talit

yfr

omU

Sm

orta

lity

stat

istic

s190

0-19

36.

n=

1147

stat

e-ye

arob

serv

atio

ns.C

limat

eda

tan=

1813

obs.

Adu

ltou

tcom

esfr

om19

60C

ensu

s.A

llda

tam

erge

dat

stat

e/ye

arof

birt

hle

vel.

IVus

ing

frac

tion

ofda

ysin

“mal

aria

-ide

al”

tem

pera

ture

rang

eas

IVfo

rm

alar

iade

aths

inst

ate

and

year

.

Not

e:O

nly

resu

ltsfr

omth

eIV

regr

essio

nar

ere

port

edhe

re.

Exp

osur

eto

10ad

ditio

nalm

alar

iade

aths

per

100,

000

inha

bita

ntsc

ause

s3.4

%le

ssye

arso

fsch

oolin

gE

xpos

ure

tom

alar

iaca

nac

coun

tfor

appr

oxim

atel

y25

%of

the

differ

ence

inye

arso

fsch

oolin

gbe

twee

nco

hort

sbor

nin

high

and

low

mal

aria

stat

es.

Long

-run

long

evity

effec

tsof

anu

triti

onsh

ock

inea

rly

life:

The

Dut

chFa

min

eof

1846

-47

(Van

Den

Ber

get

al.,

inpr

ess)

.H

istor

ical

sam

ple

ofth

eN

ethe

rlan

ds.E

xpos

edco

hort

born

9/1/

1846

-6/1/

1848

.N

on-e

xpos

edbo

rn9/

1/18

48-9/1/

1855

and

9/1/

1837

-9/1/

1944

.

Key

inde

pend

entv

aria

ble

isex

posu

reto

fam

ine

atbi

rth.

Inst

rum

enta

cces

sto

food

with

vari

atio

nsin

year

lyav

erag

ere

alm

arke

tpri

ceso

frye

and

pota

toes

for

thre

edi

ffer

entr

egio

ns.

Con

trol

led

for

mac

roec

onom

icco

nditi

ons,

infa

ntm

orta

lity

rate

s,an

din

divi

dual

dem

ogra

phic

and

soci

o-ec

onom

icch

arac

teri

stic

s.

Res

ultsfr

om

nonpar

amet

ric

regre

ssio

n:

Res

idua

llife

expe

ctan

cyat

age

50is

3.1

year

ssho

rter

for

expo

sed

men

than

for

men

born

afte

rth

efa

min

e;1.

4ye

ars

shor

ter

than

for

men

born

befo

refa

min

e.K

olm

ogor

ov-S

mir

nov

test

sugg

ests

that

the

surv

ival

curv

esaf

ter

age

50di

ffer

signi

fica

ntly

for

expo

sed

and

cont

rolm

en.

Max

differ

ence

indi

stri

butio

n=

0.15

atag

e56

.R

esultsfr

om

par

amet

ric

surv

ival

model

s:E

xpos

ure

tofa

min

eat

birt

hfo

rm

enre

duce

sres

idua

llife

expe

ctan

cyat

age

50by

4.2

year

s.N

ost

atist

ical

lysig

nifica

ntre

sults

for

wom

en.

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Mat

erna

lstr

essa

ndch

ildw

ell-

bein

g:ev

iden

cefr

omsib

lings

(Aiz

eret

al.,

2009

).N

atio

nal

Col

labo

rativ

ePe

rina

talP

roje

ct.

Bir

ths1

959-

65in

Prov

iden

cean

dB

osto

n.N=

1103

birt

hsto

915

wom

en.16

3sib

lings

insa

mpl

e.

Sibl

ing

fixe

deff

ects

.E

stim

ated

effec

tofd

evia

tions

from

mea

nco

rtiso

llev

els(

cort

isolm

easu

res

mat

erna

lstr

ess)

duri

ngpr

egna

ncy

onou

tcom

es.

No

signi

fica

nteff

ects

onbi

rth

wei

ghto

rm

ater

nalp

ostn

atal

inve

stm

ents

.E

xpos

ure

toto

pqu

artil

eof

cort

isoll

evel

dist

ribu

tion

(rel

ativ

eto

the

mid

dle)

lead

sto

47%

ofSD

decr

ease

inve

rbal

IQat

age

7(s

ign.

at10

%le

vel).

1SD

devi

atio

nin

cort

isoll

evel

slea

dsto

26%

ofSD

decr

ease

ined

ucat

iona

latt

ainm

ent.

Exp

osur

eto

top

quar

tile

ofco

rtiso

llev

eldi

stri

butio

n(r

elat

ive

toth

em

iddl

e)le

adst

o51

%of

SDde

crea

sein

educ

atio

nal

atta

inm

ent.

The

scou

rge

ofA

sian

flu:

the

phys

ical

and

cogn

itive

deve

lopm

ento

faco

hort

ofB

ritis

hch

ildre

nin

uter

odu

ring

the

Asia

nIn

flue

nza

Pand

emic

of19

57,fr

ombi

rth

until

age

11(K

elly

,20

09).

NC

DS

1958

coho

rt.

N=

16,7

65at

birt

h,14

,358

at7,

14,0

69at

11.

Effec

toffl

uon

each

coho

rtm

embe

rid

entifi

edus

ing

vari

atio

nin

inci

denc

eof

epid

emic

bylo

cal

auth

ority

ofbi

rth.

Epi

dem

icpe

aked

whe

nco

hort

mem

bers

wer

e17

-23

wee

ksge

stat

ion.

1/3

ofw

omen

ofch

ild-b

eari

ngag

ew

ere

infe

cted

,he

nce

true

trea

tmen

teff

ectc

anbe

estim

ated

bym

ultip

lyin

gre

sults

by3.

No

stat

istic

ally

signi

fica

ntev

iden

ceof

rein

forc

ing

orco

mpe

nsat

ing

pare

ntal

inve

stm

ents

.1

SDin

crea

sein

epid

emic

inte

nsity

decr

ease

sbir

thw

eigh

tby

0.03

-0.3

63SD

;de

crea

sest

ests

core

sby

0.06

7of

SDat

age

7,by

0.04

3of

SDat

age

11;

incr

ease

sdet

rim

enta

leffec

tofm

othe

rpr

eecl

amps

iafr

om−

0.07

5to−

0.11

SDon

birt

hw

eigh

t;in

crea

sesd

etri

men

tale

ffec

tofm

othe

rsm

okin

gby

0.03

ofSD

onbi

rth

wei

ght;

incr

ease

sdet

rim

enta

leffec

tofm

othe

run

der

8st

one

wei

ght

from−

0.54

to−

0.61

ofSD

onbi

rth

wei

ght

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Do

low

erbi

rth

wei

ghtb

abie

sha

velo

wer

grad

es?

Twin

fixe

deff

ecta

ndin

stru

men

talv

aria

bles

evid

ence

from

Tai

wan

(Lin

and

Liu,

2009

).B

irth

Cer

tifica

teda

tafo

rT

aiw

an.H

igh

scho

olen

tran

ceex

amre

sults

from

Com

mitt

eeof

Bas

icC

ompe

tenc

eTe

st.

N=

118,

658.

Twin

sam

ple

n=

7772

.

Twin

fixe

deff

ects

and

IVus

ing

the

publ

iche

alth

budg

etan

dnu

mbe

rof

doct

orsi

nco

unty

whe

rech

ildw

asbo

rnas

inst

rum

ents

for

child

’sbi

rth

wei

ght.

Tw

inFE/I

Vfo

rm

oth

ersw

/<9

yrsed

&<

25yr

sold

Incr

ease

inbi

rth

wei

ghto

f100

gle

adst

o:In

crea

seC

hine

sesc

ore:

0.5%

/8.2

%In

crea

seE

nglis

hsc

ore:

0.3%

/8.1

%*

Incr

ease

Mat

hsc

ore:

0.7%

/12.

6%In

crea

seN

atur

alSc

ienc

esc

ore:

0.8%

/11.

8%In

crea

seSo

cial

Scie

nce

scor

e:0.

4%/4

.0%

**

mea

nssig

nifica

ntat

10%

leve

lIV

resu

ltsfo

rot

her

subg

roup

ssho

wsm

alle

r/no

effec

ts.

Poor

,hu

ngry

and

stup

id:

num

erac

yan

dth

eim

pact

ofhi

ghfo

odpr

ices

inin

dust

rial

izin

gB

rita

in,17

50-1

850

(Bat

enet

al.,

2007

).D

ata

onag

ehe

apin

gfr

om19

51to

1881

Bri

tish

Cen

sus.

Info

rmat

ion

onpo

orre

lieff

rom

Boy

er(1

990)

.

Age

heap

ing

isro

undi

ngag

eto

near

est5

or10

.W

hipp

lein

dex

(WI)=

num

ber

ofag

esth

atar

em

ultip

lesr

elat

ive

toex

pect

ednu

mbe

rgi

ven

unifo

rmag

edi

stri

butio

n.R

egre

ssio

nsus

eW

Ias

depe

nden

tvar

iabl

e.W

heat

pric

esan

dpo

orre

liefm

easu

resa

rein

depe

nden

tvar

iabl

es.

Dur

ing

the

Rev

olut

iona

ryan

dN

apol

eoni

cW

ars,

the

pric

eof

whe

atal

mos

tdou

bled

—an

dth

enu

mbe

rof

erro

neou

slyre

port

edag

esat

mul

tiple

sof5

doub

led

from

4%to

8%.M

enan

dw

omen

born

inde

cade

swith

high

erW

Iso

rted

into

jobs

that

had

low

erin

telli

genc

ere

quir

emen

ts.1

SDin

crea

sein

the

Whi

pple

inde

xas

soci

ated

with

a2.

8%(r

elat

ive

tom

edia

nea

rnin

gs)d

ecre

ase

inea

rnin

gs.

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Impa

ctsof

econ

omicsh

ocks

Bir

thw

eigh

tand

inco

me:

inte

ract

ions

acro

ssge

nera

tions

(Con

ley

and

Ben

nett

,20

01)

Dat

afr

omPS

ID.Tw

osa

mpl

es:

(1)c

hild

ren

born

1986

-199

2,n=

1654

;(2

)rea

ched

19by

1992

,n=

1388

indi

vidu

alsi

n76

6fa

mili

es.

Sibl

ing

fixe

deff

ects

.Lo

wbi

rth

wei

ghtc

hild

who

spen

t6ye

arsa

tpov

erty

line

isle

sslik

ely

togr

adua

tehi

ghsc

hool

than

norm

albi

rth

wei

ght

child

who

spen

t6ye

arsa

tpov

erty

line.

Low

birt

hw

eigh

tch

ildw

hosp

ent6

year

swith

inco

me

5tim

esth

epo

vert

ylin

eis

aslik

ely

togr

adua

tehi

ghsc

hool

asno

rmal

birt

hw

eigh

tch

ildof

sam

ein

com

e(s

igni

fica

ntat

10%

leve

l).

Eco

nom

icco

nditi

onse

arly

inlif

ean

din

divi

dual

mor

talit

y(V

anD

enB

erg

etal

.,in

pres

s).D

ata

from

hist

oric

alsa

mpl

eof

the

Net

herl

ands

onin

divi

dual

sbor

n18

12-1

903

with

date

ofde

ath

obse

rved

by20

00.n=

9276

.M

erge

dw

ithhi

stor

ical

mac

rotim

ese

ries

data

.

Com

pare

din

divi

dual

sbor

nin

boom

swith

thos

ebo

rnin

the

subs

eque

ntre

cess

ions

.C

ontr

olle

dfo

rw

arsa

ndep

idem

ics.

Com

pari

ngth

ose

born

inbo

omof

1872

-187

6w

ithth

ose

born

inre

cess

ion

of18

77-1

881:

Boo

mT|T>

2=

66.0

year

s,R

eces

sion

T|T>

2=

62.5

year

sB

oom

T|T>

5=

70.8

year

s,R

eces

sion

T|T>

5=

67.5

year

sR

egre

ssio

nre

sults:

Boo

mT|T>

2is

1.58

year

sgre

ater

than

Rec

essio

nT|T>

2N

otat

ion:

T|T>

2=

aver

age

lifet

ime

give

nsu

rviv

alpa

stag

e2

T|T>

5=

aver

age

lifet

ime

give

nsu

rviv

alpa

stag

e5

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Evi

denc

eon

earl

y-lif

ein

com

ean

dla

te-l

ifehe

alth

from

Am

eric

a’sD

ustb

owle

ra(C

utle

ret

al.,

2007

).T

heH

ealth

and

Ret

irem

entS

tudy

.N=

8739

peop

lebo

rnbe

twee

n19

29an

d19

41.A

gric

ultu

rald

ata

from

Nat

iona

lAgr

icul

tura

lSta

tistic

sSe

rvic

e.In

com

eda

tafr

omB

urea

uof

Eco

nom

icA

naly

sis.

Mea

sure

dec

onom

icco

nditi

onsi

nut

ero

usin

gin

com

ean

dyi

eld

from

the

sam

eca

lend

arye

arfo

rth

ose

born

in3r

dor

4th

quar

ter

ofth

eye

aran

dfr

omth

epr

evio

usca

lend

arye

arfo

rth

ose

born

in1s

tor

2nd

quar

ter.

Reg

ress

ions

incl

ude

the

inut

ero

econ

omic

cond

ition

mea

sure

(log

inco

me,

log

yiel

d),du

mm

yfo

rw

heth

erre

spon

dent

’sfa

ther

was

afa

rmer

and

inte

ract

ion

ofth

edu

mm

yw

ithth

eec

onom

icco

nditi

ons.

Con

trol

led

for

regi

onan

dye

arof

birt

hfixe

deff

ects

,re

gion

-spe

cific

linea

rtim

etr

ends

,an

dre

gion

-yea

rin

fant

deat

han

dbi

rth

rate

s,an

dot

her

dem

ogra

phic

char

acte

rist

ics.

No

stat

istic

ally

signi

fica

ntre

latio

nshi

pbe

twee

npo

orea

rly-

life

econ

omic

cond

ition

sdur

ing

the

Dus

tbow

land

late

-life

heal

thou

tcom

essu

chas

hear

tcon

ditio

ns,st

roke

,di

abet

es,hy

pert

ensio

n,ar

thri

tis,ps

ychi

atri

cco

nditi

ons,

etc.

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Long

-run

heal

thim

pact

sof

inco

me

shoc

ks:w

ine

and

Phyl

loxe

rain

19th

cent

ury

Fran

ce(B

aner

jee

etal

.,fo

rthc

omin

g).Se

epa

per

refe

renc

esfo

rso

urce

ofda

taon

win

epr

oduc

tion,

num

ber

ofbi

rths

,an

din

fant

mor

talit

y.D

epar

tmen

tlev

elda

taon

heig

hts

from

mili

tary

reco

rds1

872-

1912

.n=

3485

year

-dep

artm

ents

.

Reg

iona

lvar

iatio

nin

ala

rge

nega

tive

inco

me

shoc

kca

used

byPh

yllo

xera

atta

ckso

nFr

ench

vine

yard

sbet

wee

n18

63an

d18

90.

Shoc

kdu

mm

yeq

ualt

o0

pre

Phyl

loxe

raan

daf

ter

1890

(whe

ngr

aftin

gso

lutio

nfo

und)

.D

umm

yeq

ual1

whe

nw

ine

prod

uctio

n<

80%

ofpr

e-le

vel.

Diff

eren

ce-i

n-di

ffer

ence

com

pari

ngch

ildre

nbo

rnin

affec

ted

and

unaff

ecte

dar

easb

efor

ean

daf

ter.

Dec

reas

ein

win

epr

oduc

tion

was

notc

ompe

nsat

edby

anin

crea

sein

othe

rag

ricu

ltura

lpro

duct

ion

(e.g

.w

heat

),su

gges

ting

that

this

was

trul

ya

larg

ene

gativ

ein

com

esh

ock.

Mai

noutc

om

es:

3-5%

decl

ine

inhe

ight

atag

e20

for

thos

ebo

rnin

win

e-gr

owin

gfa

mili

esdu

ring

the

year

that

thei

rre

gion

was

affec

ted

byPh

yllo

xera

.T

hose

born

inPh

yllo

xera

-affec

ted

year

are

0.35

-0.3

8pp

mor

elik

ely

tobe

shor

ter

than

1.56

cm.

No

stat

istic

ally

signi

fica

nteff

ects

onot

her

mea

sure

sofh

ealth

orlif

eex

pect

ancy

.

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Impa

ctsof

environm

entalsho

cks

Life

span

depe

ndso

nth

em

onth

ofbi

rth

(Dob

lham

mer

and

Vaup

el,20

01)

Dat

aon

popu

latio

nsof

Den

mar

k,A

ustr

ia,an

dA

ustr

alia

.D

enm

ark:

Long

itudi

nald

ata

base

don

popu

latio

nre

gist

ryfo

r19

68-2

000

n=

1,37

1,00

3.A

ust

ria:

Dat

afr

omde

ath

cert

ifica

tesf

or19

88-1

996

n=

681,

677

Aust

ralia:

Dat

afr

omde

ath

cert

ifica

tesf

rom

1993

-199

7n=

219,

820

Also

used

data

onin

divi

dual

sbo

rnin

Bri

tain

who

died

inA

ustr

alia

:n=

43,0

74D

ata

onin

fant

mor

talit

yfo

rD

enm

ark

Use

dtt

ests

tope

rfor

mpa

irw

iseco

mpa

riso

nsbe

twee

nm

ean

age

atde

ath

byqu

arte

rof

birt

h.To

test

whe

ther

the

seas

onal

differ

ence

inth

eri

skof

deat

hac

coun

tsfo

rdi

ffer

ence

sin

adul

tlife

span

bym

onth

ofbi

rth,

calc

ulat

edm

onth

lyde

viat

ions

from

annu

alde

ath

rate

san

dus

edw

eigh

ted

leas

tsqu

ares

regr

essio

nw

ithdu

mm

iesf

orm

onth

ofbi

rth,

curr

entm

onth

,ag

esin

cela

stbi

rthd

ayin

mon

ths,

sex,

and

birt

hco

hort

.To

test

whe

ther

sele

ctiv

esu

rviv

alor

debi

litat

ion

duri

ngth

e1s

tyea

rof

life

expl

ains

differ

ence

sin

life

expe

ctan

cyat

age

50,ca

lcul

ated

mon

thly

deat

hra

tes

duri

ng1s

tyea

rof

life

and

mon

thly

devi

atio

nsfr

oman

nual

deat

hra

tes

duri

ng1s

tyea

rof

life;

then

used

am

ultiv

aria

tere

gres

sion

with

out

cont

rols

for

sex

and

birt

hco

hort

.

Not

e:A

utum

n-Sp

ring=

aver

age

differ

ence

inag

eat

deat

hbe

twee

npe

ople

born

inA

utum

n(O

ct-D

ec)a

ndin

the

Spri

ng(A

pr-J

un).

Den

mar

k:M

ean

rem

aini

nglif

eex

pect

ancy

atag

e50=

27.5

2ye

ars

0.19

year

ssho

rter

lifes

pans

for

thos

ebo

rnin

2nd

quar

ter

0.12

year

slon

ger

lifes

pans

for

thos

ebo

rnin

4th

quar

ter

Cor

rela

tion

betw

een

infa

ntm

orta

lity

attim

eof

birt

han

dad

ultm

orta

lity

afte

rag

e50=

0.87

.A

ust

ria:

Ave

rage

age

atde

ath=

77.7

0ye

ars

0.28

year

ssho

rter

lifes

pans

for

thos

ebo

rnin

2nd

quar

ter

Aust

ria:

aver

age

age

atde

ath=

77.7

0ye

ars

0.28

year

ssho

rter

lifes

pans

for

thos

ebo

rnin

2nd

quar

ter

0.32

year

slon

ger

lifes

pans

for

thos

ebo

rnin

4th

quar

ter

Add

ition

alre

sults

show

nfo

rsp

ecifi

cca

uses

ofde

ath.

Aust

ralia:

Mea

nag

eof

deat

h=

78.0

0ye

arsf

orth

ose

born

in2n

dqu

arte

r;m

ean

age

ofde

ath=

77.6

5ye

arsf

orth

ose

born

in4t

hqu

arte

rB

ritis

him

mig

rant

sbor

nN

ov.-

Jan.

have

age

ofde

ath

0.36

year

shig

her

than

nativ

es.T

hose

imm

igra

ntsb

orn

Mar

-May

have

age

ofde

ath

0.26

year

slow

erth

anA

ustr

alia

nna

tives

.

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Air

qual

ity,in

fant

mor

talit

y,an

dth

eC

lean

Air

Act

of19

70(C

hay

and

Gre

enst

one,

2003

a,b)

Cou

nty-

leve

lmor

talit

yan

dna

talit

yda

ta19

69-1

974.

Ann

ual

mon

itor

leve

ldat

aon

tota

lsu

spen

ded

part

icle

s(T

SP)f

rom

EPA

.N=

501

coun

ty-y

ears

.

Cle

anA

irA

ctim

pose

dre

gula

tions

onpo

llute

rsin

coun

tiesw

ithT

SPco

ncen

trat

ions

exce

edin

gfe

dera

lce

iling

s.U

sed

nona

ttai

nmen

tsta

tus

asan

inst

rum

entf

orch

ange

sin

TSP

.A

lsous

edre

gres

sion

disc

ontin

uity

met

hods

toex

amin

eeff

ecto

fTSP

onin

fant

mor

talit

y.

1%de

clin

ein

TSP

pollu

tion

resu

ltsin

0.5%

decl

ine

inin

fant

mor

talit

yra

te.M

oste

ffec

tsdr

iven

byre

duct

ion

ofde

aths

occu

rrin

gw

ithin

1m

onth

ofbi

rth.

Air

pollu

tion

and

infa

nthe

alth

:w

hatc

anw

ele

arn

from

Cal

iforn

ia’s

rece

ntex

peri

ence

?(C

urri

ean

dN

eide

ll,20

05)

Dat

aon

pollu

tion

from

Cal

iforn

iaE

PA.In

divi

dual

-lev

elin

fant

mor

talit

yda

tafr

omC

alifo

rnia

vita

lsta

tistic

s19

89-2

000.

n=

206,

353.

Est

imat

edlin

ear

mod

elst

hat

appr

oxim

ate

haza

rdm

odel

s,w

here

the

risk

ofde

ath

isde

fine

dov

erw

eeks

oflif

e,an

dle

ngth

oflif

eis

cont

rolle

dfo

rw

itha

flex

ible

nonp

aram

etri

csp

line.

Con

trol

led

for

pren

atal

and

post

nata

lpol

lutio

nex

posu

re,w

eath

er,ch

ild’s

age,

and

num

erou

soth

erch

ildan

dfa

mily

char

acte

rist

ics,

asw

ella

smon

th,

year

,an

dzi

pco

defixe

deff

ects

.E

xam

ined

effec

tsof

ozon

e(O

3),

carb

onm

onox

ide

(CO

),an

dpa

rtic

ulat

em

atte

r(P

M10

)on

infa

ntm

orta

lity.

1.1

unit

redu

ctio

nin

post

nata

lexp

osur

eto

CO

(the

actu

alre

duct

ion

that

occu

rred

inC

Ain

the

1990

s)sa

ved

991

infa

ntliv

es,−

4.6%

decr

ease

inin

fant

mor

talit

yra

te.N

ost

atist

ical

lysig

nifica

nteff

ects

ofpr

enat

alex

posu

reto

any

ofth

epo

lluta

nts.

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Pren

atal

expo

sure

tora

dioa

ctiv

efa

llout

(fro

mC

hern

obyl

)and

scho

olou

tcom

esin

Swed

en(A

lmon

det

al.,

2009

).A

llSw

edes

born

1983

-198

8,n=

562,

637.

Dat

aon

radi

atio

nfr

omSw

edish

Geo

logi

calS

urve

yat

pari

shle

vel.

Thr

eeem

piri

cals

trat

egie

s:(1

)C

ohor

tcom

pari

sons

—C

ompa

red

coho

rtsi

nut

ero

befo

re,du

ring

,an

daf

ter

Che

rnob

ylw

ithpa

rtic

ular

focu

son

coho

rt8-

25w

eeks

post

conc

eptio

n.(2

)With

in-c

ohor

tcom

pari

sons

-U

sege

ogra

phic

vari

atio

nin

leve

lsof

expo

sure

.D

efine

4re

gion

s,R

0(le

aste

xpos

ure)

,R

1,R

2,R

3.(3

)Diff

-in-

diff

siblin

gco

mpa

riso

n—C

ompa

red

thos

eex

pose

dto

radi

atio

nin

uter

o8-

25to

siblin

gsw

how

ere

not.

Res

ultsfr

om

(1):

Prob

abili

tyof

qual

ifyin

gfo

rhi

ghsc

hool

redu

ced

by0.

2%fo

rco

hort

inut

ero

duri

ngra

diat

ion;

by0.

6%fo

rco

hort

inut

ero

8-25

.G

rade

sred

uced

by0.

4%fo

rco

hort

inut

ero

duri

ngra

diat

ion;

by0.

6%fo

rco

hort

inut

ero

8-25

.R

esultsfr

om

(2):

(R3

rela

tive

toR

0)Pr

obab

ility

ofqu

alify

ing

for

high

scho

olre

duce

dby

3.6%

for

coho

rtin

uter

odu

ring

radi

atio

nin

R3;

by4%

for

coho

rtin

uter

o8-

25in

R3.

Gra

desr

educ

edby

3%fo

rco

hort

inut

ero

duri

ngra

diat

ion

inR

3;by

5.2%

for

coho

rtin

uter

o8-

25in

R3.

Res

ultsfr

om

(3):

(R3

rela

tive

toR

0)D

iffer

ence

inpr

obab

ility

ofqu

alify

ing

for

high

scho

olbe

twee

nsib

lings

incr

ease

dby

6%in

R3.

Diff

eren

cein

grad

esbe

twee

nsib

lings

incr

ease

dby

8%in

R3.

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Air

pollu

tion

and

infa

nthe

alth

:le

sson

sfro

mN

ewJe

rsey

(Cur

rie

etal

.,20

09).

Pollu

tion

data

from

EPA

.In

divi

dual

-lev

elda

taon

infa

ntbi

rths

and

deat

hsfr

omth

eN

ewJe

rsey

Dep

artm

ento

fH

ealth

1989

-200

3.n=

283,

393

for

mot

her

fixe

deff

ects

mod

els.

Mot

her

fixe

deff

ects

toes

timat

eim

pact

ofex

posu

reto

pollu

tion

(dur

ing

and

afte

rbi

rth)

onin

fant

heal

thou

tcom

es.A

irpo

llutio

nm

easu

red

from

air

qual

itym

onito

rsan

das

signe

dto

each

child

base

don

hom

ead

dres

sofm

othe

r.A

lsoin

clud

edin

tera

ctio

nsb/

nva

riab

lefo

rpo

llutio

nex

posu

rean

dm

ater

nals

mok

ing

asw

ella

soth

erm

ater

nalc

hara

cter

istic

s.C

ontr

olle

dfo

rw

eath

er,po

llutio

nm

onito

rlo

catio

ns,tim

etr

ends

,se

ason

aleff

ects

,an

dot

her

back

grou

ndch

arac

teri

stic

s.

1un

itch

ange

inm

ean

CO

expo

sure

duri

ngla

sttr

imes

ter

ofpr

egna

ncy

decr

ease

save

rage

birt

hw

eigh

tby

0.5%

,in

crea

ses

likel

ihoo

dof

low

birt

hw

eigh

tby

8%,an

dde

crea

sesg

esta

tion

by0.

2%.

1un

itch

ange

inm

ean

CO

expo

sure

infirs

t2w

eeks

afte

rbi

rth

incr

ease

slik

elih

ood

ofin

fant

mor

talit

yby

2.5%

.E

stim

ated

that

a1

unit

decr

ease

inm

ean

CO

expo

sure

infirs

t2

wee

ksaf

ter

birt

hw

ould

save

17.6

per

100,

000

lives

.E

ffec

tsof

CO

expo

sure

onin

fant

heal

that

birt

har

e2-

6tim

esla

rger

for

smok

ersa

ndm

othe

rsw

hoar

eov

erag

e35

.E

ffec

tsof

PM10

(par

ticul

ate

mat

ter)

and

ozon

ear

eno

tcon

siste

ntly

signi

fica

ntac

ross

the

spec

ifica

tions

.

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Feta

lexp

osur

esto

toxi

cre

leas

esan

din

fant

heal

th(C

urri

eet

al.,

2009

)To

xic

Rel

ease

Inve

ntor

yda

taat

coun

ty-y

ear

leve

lmat

ched

toco

unty

-yea

rle

veln

atal

ityan

din

fant

mor

talit

yda

ta.

N=

5279

.

Mul

tivar

iate

regr

essio

nof

infa

nthe

alth

outc

omes

onam

ount

ofto

xic

rele

ases

inea

chco

unty

and

year

,co

ntro

lling

for

dem

ogra

phic

and

soci

o-ec

onom

icch

arac

teri

stic

s,m

othe

rdr

inki

ngor

smok

ing

duri

ngpr

egna

ncy,

coun

tyem

ploy

men

t,an

dco

unty

and

year

fixe

deff

ects

.C

ompa

red

effec

tsof

deve

lopm

enta

ltox

inst

oot

her

toxi

ns,an

d“f

ugiti

ve”

air

rele

ases

to“s

tack

”ai

rre

leas

es(s

ince

emiss

ions

that

goup

asm

oke

stac

kar

em

ore

likel

yto

betr

eate

din

som

ew

ay,

and

henc

ew

illaff

ectt

hose

inth

evi

cini

tyle

ss).

An

addi

tiona

ltho

usan

dpo

unds

per

squa

rem

ileof

allt

oxic

rele

ases

lead

sto:

0.02

%de

crea

sein

leng

thof

gest

atio

n0.

04%

decr

ease

inbi

rth

wei

ght

0.1%

incr

ease

inpr

obab

ility

oflo

wbi

rth

wei

ght

0.8%

incr

ease

inpr

obab

ility

ofve

rylo

wbi

rth

wei

ght

1.3%

incr

ease

inin

fant

mor

talit

yLa

rger

effec

tsfo

rde

velo

pmen

talt

oxin

sand

for

fugi

tive

air

rele

ases

than

for

othe

rto

xins

orfo

rst

ack

air

rele

ases

.A

2-SD

incr

ease

inle

adre

leas

esde

crea

sesg

esta

tion

by0.

02%

and

decr

ease

sbir

thw

eigh

tby

0.05

%.A

2-SD

incr

ease

inca

dmiu

mre

leas

esde

crea

sesg

esta

tion

by0.

03%

,de

crea

ses

birt

hw

eigh

tby

0.07

%,in

crea

sesp

roba

bilit

yof

low

birt

hw

eigh

tby

1.2%

,in

crea

sesp

roba

bilit

yof

very

low

birt

hw

eigh

tby

1.4%

,an

din

crea

sesi

nfan

tmor

talit

yby

5%.Si

mila

rre

sults

for

tolu

ene,

epic

hlor

ohyd

rin.

Red

uctio

nsin

rele

ases

over

1988

-199

9ca

nac

coun

tfor

3.9%

ofth

ere

duct

ion

inin

fant

mor

talit

yov

erth

esa

me

time

peri

od.

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Tra

ffic

cong

estio

nan

din

fant

heal

th:ev

iden

cefr

omE

-ZPa

ss(C

urri

ean

dW

alke

r,20

09)

Bir

thre

cord

sfor

Penn

sylv

ania

and

New

Jers

ey,19

94-2

003.

Dat

aon

hous

ing

pric

esfo

rN

ewJe

rsey

.N=

727,

954

for

diff

-in-

diff

.N=

232,

399

for

mot

her

fixe

deff

ects

.

Iden

tifica

tion

due

toin

trod

uctio

nof

elec

tron

icto

llco

llect

ion

(E-Z

Pass

),w

hich

redu

ced

traffi

cco

nges

tion

and

mot

orve

hicl

eem

issio

nsin

the

vici

nity

ofhi

ghw

ayto

llpl

azas

.D

iffer

ence

-in-

differ

ence

,co

mpa

ring

infa

ntsb

orn

tom

othe

rsliv

ing

near

toll

plaz

asto

infa

nts

born

tom

othe

rsliv

ing

near

busy

road

way

s(bu

taw

ayfr

omto

llpl

azas

),be

fore

and

afte

rin

trod

uctio

nof

E-Z

Pass

.A

lsoes

timat

edim

pact

sofe

xpos

ure

toE

-ZPa

sson

infa

nthe

alth

usin

gm

othe

rfixe

deff

ects

met

hods

.C

ontr

olle

dfo

rva

riou

sbac

kgro

und

char

acte

rist

ics,

year

and

mon

thfixe

deff

ects

,an

dpl

aza-

spec

ific

time

tren

ds.A

lsoes

timat

edim

pact

ofE

-ZPa

ssin

trod

uctio

non

hous

ing

pric

esne

arth

eto

llpl

aza.

Res

ultsfr

om

Diff

eren

ce-i

n-D

iffer

ence

Met

hod

:R

educ

tions

intr

affic

cong

estio

nge

nera

ted

byin

trod

uctio

nof

E-Z

Pass

redu

ced

inci

denc

eof

prem

atur

ebi

rth

by10

.8%

and

low

birt

hw

eigh

tby

11.8

%fo

rch

ildre

nof

mot

hers

livin

gw

/in

2km

ofa

toll

plaz

a.Fo

rth

ose

livin

gw

/in

3km

ofa

toll

plaz

a,eff

ects

are

7.3%

for

prem

atur

ebi

rths

and

8.4%

for

low

birt

hw

eigh

t.Si

mila

rre

sults

usin

gm

othe

rfixe

deff

ects

.N

oeff

ects

ofE

-ZPa

ssin

trod

uctio

non

hous

ing

pric

esor

dem

ogra

phic

com

posit

ion

ofm

othe

rsliv

ing

near

toll

plaz

as.

(con

tinue

don

next

page

)


Table4(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Cau

tion,

Dri

vers

!Chi

ldre

nPr

esen

t.T

raffi

c,Po

llutio

n,an

dIn

fant

Hea

lth(K

nitt

elet

al.,

2009

).T

raffi

cda

tafr

omth

efr

eew

aype

rfor

man

cem

easu

rem

ents

yste

m.Po

llutio

nda

tafr

omE

PA.B

irth

data

from

Cal

iforn

iaD

ept.

ofPu

blic

Hea

lth20

02-2

006.

N=

373,

800.

IVus

ing

traffi

csh

ocks

(due

toac

cide

ntso

rro

adcl

osur

es)t

oin

stru

men

tfor

air

pollu

tion.

Pref

erre

dsp

ecifi

catio

nsin

clud

etr

affic

flow

,de

lays

,an

din

tera

ctio

nsbe

twee

ntr

affic

and

wea

ther

.

Not

e:on

lyre

sults

from

the

IVre

gres

sion

are

repo

rted

here

.1

unit

decr

ease

inex

posu

reto

part

icul

ate

mat

ter

(PM

10)l

eads

to5%

decr

ease

inin

fant

mor

talit

yra

te(s

aves

14liv

espe

r10

0,00

0bi

rths

).

Unl

esso

ther

wise

note

d,on

lyre

sults

signi

fica

ntat

5%le

vela

rere

port

ed.


sample size prevented detection of all but the largest effects (see Section 3.1). Usinga comparable sample size, Behrman and Rosenzweig (2004) found the schooling ofidentical female twins was nearly one-third of a year longer for a pound increase in birthweight (454 grams), with relatively imprecise effects on adult BMI or wages.

In light of the above power concerns, Currie and Moretti (2007) matched mothersto their sisters in half a million birth records from California. Here, low birth weightwas found to have statistically significant negative impacts on educational attainmentand the likelihood of living in a wealthy neighborhood. However, the estimatedmagnitudes of the main effects were more modest: low birth weight increased thelikelihood of living in a poor neighborhood by 3% and reduced educational attainmentapproximately one month on average. Like Conley and Bennett (2001), the relationshipwas substantially stronger for the interaction between low birth weight and being bornin poor neighborhoods.

In a sample of Norwegian twins, Black et al. (2007) also found long-term effects ofbirth weight, but did not detect any heterogeneity in the strength of this relationshipby parental socioeconomic status.11 Oreopoulos et al. (2008) find similar results forCanada and Lin and Liu (2009) find positive long term effects of birth weight inTaiwan. Royer (2009) found long-term health and educational effects within Californiatwin pairs, with a weaker effect of birth weight than several other studies, esp. Blacket al. (2007). Responsive investments could account for this discrepancy if they differedbetween California and elsewhere (within twin pairs). Alternatively, there may be morehomogeneity with respect to socioeconomic status in Scandinavia than in California. Asdescribed in Section 3, Royer (2009) analyzed investment measures directly with theECLS-B data, concluding that there was no evidence of compensatory or reinforcinginvestments (see Section 2.2).

Following a literature in demography on seasonal health effects, Doblhammer andVaupel (2001) and Costa and Lahey (2005) focused on the potential long-term healtheffects of birth season. A common finding is that in the northern hemisphere, peopleborn in the last quarter of the year have longer life expectancies than those born inthe second quarter. Both the availability of nutrients can vary seasonally (particularlyhistorically), as does the likelihood of common infections (e.g., pneumonia). Therefore,either nutrition or infection could drive this observed pattern. Almond (2006) focused onprenatal exposure to the 1918 Influenza Pandemic, estimating that children of infectedmothers were 15% less likely to graduate high school and wages were between 5 and9% lower. Kelly (2009) found negative effects of prenatal exposure to 1957 “Asianflu” in Britain on test scores, though the estimated magnitudes were relatively modest.Interestingly, while birth weight was reduced by flu exposure, this effect appears to beindependent of the test score effect. Finally, Field et al. (2009) found that prenatal iodine

11 Royer (2009) notes that Black et al. (2007) find a “negligible effect of birth weight on high school completion for the1967-1976 birth cohort, but for individuals born between 1977 and 1986, the estimate is nearly six times as large”.


supplementation raised educational attainment in Tanzania by half a year of schooling,with larger impacts for girls.

4.1.2. Economic shocksA second set of papers considers economic shocks around the time of birth. Here, healthin adulthood tends to be the focus (not human capital), and findings are perhaps lessconsistent than in the studies of nutrition and infection described above. Van Den Berget al. (2006)’s basic result is that adult survival in the Netherlands is reduced for thoseborn during economic downturns. In contrast, Cutler et al. (2007) detected no long termmorbidity effects in the Health and Retirement Survey data for cohorts born during theDustbowl era of 1930s. Banerjee et al. (forthcoming) found that shocks to the productivecapacity of French vineyards did not have detectable effects on life expectancy or healthoutcomes, but did reduce height in adulthood. Baten et al. (2007) related variations ingrain prices in the decade of birth to numeracy using an ingenious measure based on“age heaping” in the British Censuses between 1851 and 1881. Persons who are morenumerate are less likely to round their ages to multiples of 5 or 10. They find that childrenborn in decades with high grain prices were less numerate by this index.

4.1.3. Air pollutionThe third strand of the literature examines the effect of pollution on fetal health.Epidemiological studies have demonstrated links between very severe pollution episodesand mortality: one of the most famous focused on a “killer fog” in London, Englandand found dramatic increases in cardiopulmonary mortality (Logan and Glasg, 1953).Previous epidemiological research on the effects of moderate pollution levels on prenatalhealth suggests negative effects but have produced inconsistent results. Cross-sectionaldifferences in ambient pollution are usually correlated with other determinants of fetalhealth, perhaps more systematically than with nutritional or disease exposures consideredabove. Many of the pollution studies have minimal (if any) controls for these potentialconfounders. Banzhaf and Walsh (2008) found that high-income families move out ofpolluted areas, while poor people in-migrate. These two groups are also likely to providediffering levels of (non-pollution) investments in their children, so that fetuses and infantsexposed to lower levels of pollution may tend to receive, e.g., better quality prenatalcare. If these factors are unaccounted for, this would lead to an upward bias in estimates.Alternatively, certain pollution emissions tend to be concentrated in urban areas, andindividuals in urban areas may be more educated and have better access to health care,factors that may improve health. Omitting these factors would lead to a downward bias,suggesting the overall direction of bias from confounding is unclear.

Two studies by Chay and Greenstone (2003a,b) address the problem of omittedconfounders by focusing on “natural experiments” provided by the implementation ofthe Clean Air Act of 1970 and the recession of the early 1980s. Both the Clean Air Actand the recession induced sharper reductions in particulates in some counties than in


others, and they use this exogenous variation in levels of pollution at the county-yearlevel to identify its effects. They estimate that a one unit decline in particulates caused bythe implementation of the Clean Air Act (recession) led to between five and eight (fourand seven) fewer infant deaths per 100,000 live births. They also find some evidence thatthe decline in Total Suspended Particles (TSPs) led to reductions in the incidence of lowbirth weight. However, only TSPs were measured at that time, so that they could notstudy the effects of other pollutants. And the levels of particulates studied by Chay andGreenstone are much higher than those prevalent today; for example, PM10 (particulatematter of 10 microns or less) levels have fallen by nearly 50% from 1980 to 2000.

Several recent studies consider natural experiments at more recently-encounteredpollution levels. For example, Currie et al. (2009) use data from birth certificates inNew Jersey in which they know the exact location of the mothers residence, and birthsto the same mother can be linked. They focus on a sample of mothers who live nearpollution monitors and show that variations in pollution from carbon monoxide (whichcomes largely from vehicle exhaust) reduces birth weight and gestation. Currie andWalker (2009) exploit a natural experiment having to do with introduction of electronictoll collection devices (E-ZPass) in New Jersey and Pennsylvania. Since much of thepollution produced by automobiles occurs when idling or accelerating back to highwayspeed, electronic toll collection greatly reduces auto emissions in the vicinity of a tollplaza. Currie and Walker (2009) compare mothers near toll plazas to those who live nearbusy roadways but further from toll plazas and find that E-ZPass increased birth weightand gestation. They show that they obtain similar estimates following mothers over timeand estimating mother fixed effects models. These papers are notable in part because ithas proven more difficult to demonstrate effects of pollution on fetal health than on infanthealth, as discussed further below. Hence, it appears that being in utero may be protectiveagainst at some forms of toxic exposure (such as particulates) but not others.

This literature on the effects of air pollution is closely related to that on smoking.

Smoking is, after all, the most important source of indoor air pollution. Medicalresearch has shown that nicotine constricts the oxygen supply to the fetus, so thereis an obvious mechanism for smoking to affect infant health. Indeed, there is nearunanimity in the medical literature that smoking is the most important preventable causeof low birth weight. Economists have focused on ways to address heterogeneity in otherdeterminants of birth outcomes that are likely associated with smoking. Tominey (2007)found that relative to a conventional multivariate control specification, roughly one-

third of the harm from smoking to birth weight is explained by unobservable traits ofthe mother. Moreover, the reduction in birth weight from smoking was substantiallylarger for low-SES mothers. In a much larger sample, Currie et al. (2009) showedthat smoking significantly reduced birth weight, even when comparisons are restrictedto within-sibling differences. Moreover, Currie et al. (2009) document a significantinteraction effect between exposure to carbon monoxide exposure and infant health


in the production of low birth weight, which may help explain the heterogeneity inbirth weight effects reported by Tominey (2007). Aizer and Stroud (2009) note thatimpacts of smoking on birth weight are generally much smaller in sibling comparisonsthan in OLS and matching-based estimates. Positing that attenuation bias is accentuatedin the sibling comparisons, Aizer and Stroud (2009) use serum cotinine levels as aninstrument for measurement error in smoking and find that sibling comparisons yieldsimilar birth weight impacts (around 150 g). Lien and Evans (2005) use increases instate excise taxes as an instrument for smoking and find large effects of smoking onbirth weight (182 g) as a result. Using propensity score matching, Almond et al. (2005)document a large decrease in birth weight from prenatal smoking (203 g), but argue thatthis weight decrease is weakly associated with alternative measures of infant health, suchas prematurity, APGAR score, ventilator use, and infant mortality.

Some recently-released data will enable new research on smoking’s short and long-term effects. In 2005, twelve states began using the new US Standard Certificate ofLive Birth (2003 revision). Along with other new data elements (e.g., on surfactantreplacement therapy), smoking behavior is reported by trimester. It will be useful toconsider whether smoking’s impact on birth weight varies by trimester, and also whethersmoking is more closely tied to other measures of newborn health if it occurs early versuslate in pregnancy. Second, there is relatively little research by economists on the long-term effects of prenatal exposure to smoking. Between 1990 and 2003, there were 113increases in state excise taxes on cigarettes (Lien and Evans, 2005).12 Since 2005, theAmerican Community Survey records both state and quarter of birth, permitting linkageof these data to the changes in state excise taxes during pregnancy.

Almond et al. (2009) examine the effect of pollution from the Chernobyl disasteron the Swedish cohort that was in utero at the time of the disaster. Since the pathof the radiation was very well measured, they can compare affected children to thosewho were not affected as well as to those born in the affected areas just prior to thedisaster. They find that in the affected cohort those who suffered the greatest radiationexposure were 3% less likely to qualify for high school, and had 6% lower math grades(the measure closest to IQ). The estimated effects were much larger within families.A possible interpretation is that cognitive damage from Chernobyl was reinforced byparents. An alternative hypothesis is that there were fixed characteristics of families thatwere correlated both with poor outcomes and with exposure. It might be the case, forexample, that pregnant mothers of higher socioeconomic status were more likely to stayindoors or move temporarily to reduce radiation exposure.13

To summarize, the recent “fetal origins” literature in economics finds substantialeffects of prenatal health on subsequent human capital and health. As we discuss in12 Some states enacted earlier excise taxes: the “average state tax rate increased from 5.7 cents in 1964 to 15.5 cents in

1984” (Farrelly et al., 2003); high 1970s inflation can be an additional potential source of identification as excise taxeswere set nominally.

13 That said, the radiation dosages estimated in Sweden were not believed harmful at the time of the Chernobyl accident.


Section 5, this suggests a positive role for policies that improve human capital by affectingthe birth endowment. That is, despite being congenital (i.e. present from birth), thisresearch indicates that the birth endowment is malleable in ways that shape human capital.This finding has potentially radical implications for public policy since it suggests that oneof the more effective ways to improve children’s long term outcomes might be to targetwomen of child bearing age in addition to focusing on children after birth.

4.2. Early childhood environmentIt would be surprising to find that a very severe shock in early childhood (e.g., a headinjury, or emotional trauma) had no effect on an individual. Therefore, a more interestingquestion from the point of view of research is how developmental linkages operatingat the individual level affect human capital formation in the aggregate. To answer thisquestion, we need to know how many children are affected by negative early childhoodexperiences that could plausibly exert persistent effects? How big and long-lasting arethe effects of less severe early childhood shocks relative to more severe shocks? Takentogether, how much of the differences in adult attainments might be accounted for bythings that happen to children between birth and age five? Furthermore, how are theselinkages between shocks and outcomes mediated or moderated by third factors? Forexample, is the effect of childhood lead exposure on subsequent test scores stronger forfamilies of lower socioeconomic status (i.e. is the interaction with SES an important one)and if so why? Alternatively, is the effect of injury mediated by health status, or is thecausal pathway a direct one to cognition?

We might also wish to know how parents respond to early childhood shocks. Todate, there has been less focus on this question in the early childhood period than inthe prenatal period, perhaps because it seems less plausible to hope to uncover a “pure”biological effect of a childhood shock given that children are embedded in families and insociety. However, this embeddedness opens the possibility that a richer set of behavioralresponses—of the kind considered by economists—might be at play. Furthermore, earlychildhood admits a wider set of environmental influences than the prenatal period. Forexample, abuse in early childhood can be distinguished from malnutrition, a distinctionmore difficult for the in utero period, and these may have quite different effects.

We define early childhood as starting at birth and ending at age five. From anempirical standpoint, early childhood so defined offers advantages and disadvantagesover analyses that focus on the prenatal period. Mortality is substantially lower duringearly childhood than in utero, which reduces the scope for selective attrition caused byenvironmental shocks to affect the composition of survivors. On the other hand, it isunlikely that environmental sensitivity during early childhood tapers discontinuously atany precise age (including age five). From a refutability perspective, we cannot makesharp temporal comparisons of a cohort “just exposed” to a shock during early childhoodto a neighboring cohort “just unexposed” by virtue of its being too old to be sensitive.


Moreover, it will often be difficult to know a priori whether prenatal or postnatal exposureis more influential.14 Thus, studies of early childhood exposures tend to emphasize cross-sectional sources of variation, including that at the geographic and individual level. Thestudies reviewed in this section focus on tracing out the relationships between events inearly childhood and future outcomes, and are summarized in Table 5.

4.2.1. InfectionsInsofar as specific health shocks are considered, infections are the most commonlystudied. In epidemiology, long-term health effects of infections—and the inflammationresponse they trigger—has been explored extensively, e.g. Crimmins and Finch (2006).Outcomes analyzed by economists include height, health status, educational attainment,test scores, and labor market outcomes. The estimated impacts tend to be large. Usinggeographic differences in hookworm infection rates across the US South, Bleakley (2007)found that eradication after 1910 increased literacy rates but did not increase the amountof completed schooling, except for Black children. The literacy improvement was muchlarger among Blacks than Whites, and stronger among women then men. The returnto education increased substantially, and Bleakley (2007) estimated that hookworminfection throughout childhood reduced wages in adulthood by as much as 40%. Caseand Paxson (2009) focussed on reductions in US childhood mortality from typhoid,malaria, measles, influenza, and diarrhea during the first half of the 20th Century. Theyfound that improvements in the disease environment in one’s state of birth were mirroredby improved cognitive performance at older ages, but like Bleakley (2007), this effectdid not seem to operate through increased years of schooling. However, the estimatedcognitive impacts in Case and Paxson (2009) were not robust to the inclusion of state-specific time trends in their models.

Chay et al. (2009) found that reduced exposure to pneumonia and diarrhea in earlychildhood among Blacks during the late 1960s raised subsequent AFQT and NAEPscores towards those of Whites. Changes in postneonatal mortality rates (dominated byinfections) explained between 50% and 80% of the (large) reduction in the Black-WhiteAFQT gap. Finally, Bozzoli et al. (forthcoming) highlight that in developing countries,high average mortality rates cause the selection effect of early childhood mortality tooverwhelm the “scarring” effect. Thus, the positive relationship between early childhoodhealth and subsequent human capital may be absent in analyses that do not account forselective attrition in high mortality settings.

4.2.2. Health statusMany of the studies reviewed in Table 5 investigate the link between health in childhoodand future cognitive or labor market outcomes. These studies can be viewed as a subset of

14 For example, early postnatal exposure to Pandemic influenza apparently had a larger impact on hearing than didprenatal flu exposure (Heider, 1934).


Table5

Impa

ctsof

early

child

hood

shocks

onlatero

utcomes.

Stud

yan

dda

taStud

yde

sign

Results

Child

hood

physical

andmen

talh

ealth

Men

talh

ealth

inch

ildho

odan

dhu

man

capi

tal(

Cur

rie

and

Stab

ile,

2006

).D

ata

from

NLS

CY

and

NLS

Y.C

anad

ian

NLS

CY

:da

taon

child

ren

aged

4-11

in19

94.

Men

talh

ealth

scre

enin

gin

1994

;ou

tcom

esm

easu

red

in20

02.

n=

5604

.U

SN

LSY

:da

taon

child

ren

aged

4-11

in19

94.

Men

talh

ealth

scor

eav

erag

edov

er19

90-1

994.

n=

3758

.

Sibl

ing

fixe

deff

ects

.H

yper

activ

ityan

dag

gres

sion

scor

esba

sed

on“s

cree

ner”

ques

tions

aske

dof

all

child

ren.

Est

imat

edeff

ecto

fhy

pera

ctiv

ityon

grad

ere

petit

ion,

read

ing

and

mat

hsc

ores

,sp

ecia

led

ucat

ion,

and

delin

quen

cy.

Con

trol

led

for

indi

vidu

alba

ckgr

ound

char

acte

rist

ics.

Est

imat

edsa

me

mod

elom

ittin

gch

ildre

nw

ithot

her

lear

ning

disa

bilit

iesb

esid

esth

ose

inth

em

ain

expl

anat

ory

vari

able

.

1unit

chan

gein

hyper

activi

tysc

ore

:in

crea

sesp

roba

bilit

yof

grad

ere

tent

ion

by10

-12%

inbo

thU

San

dC

anad

ade

crea

sesm

ath

scor

esby

0.04

-0.0

7SD

inbo

thU

San

dC

anad

ain

crea

sesp

roba

bilit

yof

bein

gin

spec

iale

dby

11%

inU

Sde

crea

sesr

eadi

ngsc

ores

by0.

05SD

inU

S1

unit

chan

gein

conduct

dis

ord

ersc

ore

(in

the

US

only

):in

crea

sesp

roba

bilit

yof

grad

ere

tent

ion

by10

%de

crea

sesm

ath

scor

esby

0.02

SDde

crea

sesr

eadi

ngsc

ores

by0.

03SD

1unit

chan

gein

aggre

ssio

nsc

ore

(in

Can

ada

only

):de

crea

sesp

roba

bilit

yth

ata

yout

hag

ed16

-19

isin

scho

olby

4% high

depr

essio

nsc

ores

incr

ease

prob

abili

tyof

grad

ere

tent

ion

by10

%in

both

US

and

Can

ada

nosig

nifica

nteff

ects

ofin

tera

ctio

nbe

twee

nm

enta

lhea

lthsc

ores

and

inco

me

orm

ater

nale

duca

tion. (con

tinue

don

next

page

)


Table5(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Dise

ase

and

deve

lopm

ent:

Evi

denc

efr

omho

okw

orm

erad

icat

ion

inth

eA

mer

ican

Sout

h(B

leak

ley,

2007

).D

ata

onho

okw

orm

infe

ctio

nra

tesf

rom

the

Roc

kefe

ller

Sani

tary

Com

miss

ion

surv

eysf

or19

10-1

914.

Cen

susd

ata

from

IPU

MS

for

1880

-199

0.n=

115

stat

e-ye

ars.

Iden

tifica

tion

due

todi

ffer

ent

pre-

erad

icat

ion

hook

wor

min

fect

ion

rate

sin

differ

ents

tate

s.C

ompa

rein

divi

dual

sbor

nbe

fore

and

afte

rer

adic

atio

nca

mpa

igns

fund

edby

the

Roc

kefe

ller

Sani

tary

Com

miss

ion

(RSC

).A

ctiv

eye

arso

fRSC

wer

e19

10-1

915.

Con

sider

edco

ntem

pora

neou

seffec

tson

child

ren

asw

ella

slon

g-te

rmeff

ects

onad

ultw

ages

and

educ

atio

nala

ttai

nmen

t.C

ontr

olle

dfo

rge

ogra

phic

and

year

fixe

deff

ects

,as

wel

lass

ome

indi

vidu

alch

arac

teri

stic

s.In

regr

essio

nsfo

rco

ntem

pora

neou

seff

ects

,ge

ogra

phic

units

are

stat

eec

onom

icar

eas.

Inre

gres

sions

for

long

-ter

meff

ects

,ge

ogra

phic

units

are

stat

esof

birt

h.

Conte

mpora

neo

useff

ects

ofin

fect

ionson

childre

n:

1SD

incr

ease

inla

gged

hook

wor

min

fect

ion

asso

ciat

edw

ith:

0.18

-0.2

5SD

decr

ease

insc

hool

enro

llmen

t0.

21-0

.28

SDde

crea

sein

full-

time

scho

olen

rollm

ent

0.1

SDde

crea

sein

liter

acy

Res

ults

robu

stto

incl

usio

nof

stat

e-ye

arfixe

deff

ects

,co

ntro

lling

for

mea

n-re

vers

ion

insc

hool

ing,

and

usin

gst

ate-

leve

linf

ectio

nra

tes.

Larg

ereff

ects

for

blac

ksth

anfo

rw

hite

s.N

oco

ntem

pora

neou

seffec

tson

adul

ts.

Long

-ter

meff

ects

ofin

fect

ionsin

childhood

on

adults:

Bei

ngin

fect

edw

ithho

okw

orm

inon

e’sc

hild

hood

lead

sto

are

duct

ion

inw

ages

of43

%an

da

decr

ease

inre

turn

sto

scho

olin

gby

5%.80

%re

duct

ion

inw

ages

due

toho

okw

orm

infe

ctio

nsex

plai

ned

byre

duce

dre

turn

sto

scho

olin

g.B

eing

infe

cted

with

hook

wor

min

one’

schi

ldho

odle

adst

oa

redu

ctio

nin

occu

patio

nali

ncom

esc

ore

by23

%an

da

decr

ease

inD

unca

n’sS

ocio

-Eco

nom

icIn

dex

by42

%.

(con

tinue

don

next

page

)


Table5(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Adu

lthe

alth

and

child

hood

dise

ase

(Boz

zoli

etal

.,fo

rthc

omin

g)D

ata

onhe

ight

from

the

Eur

opea

nC

omm

unity

Hou

seho

ldPa

nel,

the

Hea

lthSu

rvey

ofE

ngla

nd,an

dth

eN

atio

nalH

ealth

Inte

rvie

wSu

rvey

inth

eU

Sfo

rin

divi

dual

sbo

rnin

1950

-198

0.D

ata

onpo

st-n

eona

talm

orta

lity

from

the

Wor

ldH

ealth

Org

aniz

atio

n.n=

316

coun

try-

year

s.A

lsous

edda

taon

wom

en’s

heig

hts

from

the

inte

rnat

iona

lsys

tem

ofD

emog

raph

ican

dH

ealth

Surv

eys

onw

omen

aged

15-4

9in

mor

eth

an40

coun

trie

sin

the

late

1990

s-20

00s.

Dat

aon

infa

ntm

orta

lity

from

the

Uni

ted

Nat

ions

popu

latio

ndi

visio

n.n=

1514

coun

try-

year

s.

Ana

lyze

dre

latio

nshi

pb/

npo

st-n

eona

talm

orta

lity

(PN

M,

deat

haf

ter

28da

ysan

dbe

fore

firs

tbi

rthd

ay)a

ndad

ulth

eigh

t.PN

Mis

am

easu

reof

child

hood

heal

then

viro

nmen

t.O

LSre

gres

sion

with

adul

thei

ghta

sout

com

eva

riab

le,co

ntro

lling

for

coun

try

and

year

fixe

deff

ects

and

atim

etr

end.

Also

cont

rolle

dfo

rne

onat

alm

orta

lity

rate

sand

GD

Pin

year

and

coun

try

ofbi

rth.

Con

sider

edm

orta

lity

from

pneu

mon

ia,

inte

stin

aldi

seas

e,co

ngen

ital

anom

alie

s,an

dot

her

caus

esse

para

tely

.

Surv

ivor

sare

expe

cted

tobe

posit

ivel

yse

lect

edre

lativ

eto

thos

ew

hodi

ed,bu

tmay

still

best

unte

dby

illne

ss.In

poor

coun

trie

s,th

ese

lect

ion

effec

tdom

inat

es,w

here

asin

rich

coun

trie

s(w

ithlo

wm

orta

lity

rate

s)th

est

untin

geff

ect

dom

inat

es.O

vera

llco

rrel

atio

nb/

nPN

Man

dav

erag

ead

ult

heig

htof

the

sam

eco

hort

ina

give

nco

untr

y=−

0.79

.In

the

US,

PNM

was

3xla

rger

than

inSw

eden

in19

70.T

hisd

iffac

coun

tsfo

r20

-30%

ofth

e2-

cmdi

ffin

aver

age

heig

htb/

n30

-yr-

old

Am

eric

ansa

ndSw

edes

in20

00.A

fter

cont

rolli

ngfo

rPN

M,no

rela

tions

hip

b/n

adul

thei

ghta

ndG

DP

inth

eye

aran

dco

untr

yof

birt

h.B

igge

stde

term

inan

tofd

iffer

ence

sin

PNM

rate

sacr

ossc

ount

ries

ism

orta

lity

from

inte

stin

aldi

seas

e,fo

llow

edby

mor

talit

yfr

ompn

eum

onia

.O

utof

the

4de

term

inan

tsof

PNM

,on

lym

orta

lity

from

pneu

mon

iaha

sasig

nifica

ntne

gativ

eeff

ecto

nad

ulth

eigh

t. (con

tinue

don

next

page

)


Table5(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Stat

ure

and

stat

us:he

ight

,ab

ility

and

labo

rm

arke

tout

com

es(C

ase

and

Paxs

on,20

08a)

.D

ata

from

NC

DS

(on

coho

rtof

indi

vidu

als

born

inw

eek

of3/

3/19

58in

Bri

tain

),B

CS,

NLS

Y(o

nsib

lings

inth

eU

S),an

dFr

agile

Fam

ilies

and

Chi

ldW

ell-

Bei

ngSt

udy

(on

youn

gch

ildre

nin

the

US)

.N

CD

S:n=

9155

;B

CS:

n=

9003

;N

LSY

:n=

13,8

84(t

otal

,no

tsib

lings

);Fr

agile

Fam

ilies

:n=

2150

.

Ana

lyze

dre

latio

nshi

pb/

nad

ult

heig

htan

dco

gniti

veab

ility

asa

pote

ntia

lexp

lana

tion

for

the

signi

fica

ntan

dpo

sitiv

ere

latio

nshi

pb/

nad

ulth

eigh

tand

earn

ings

that

isob

serv

ed.A

dult

heig

htis

apr

oxy

for

earl

ych

ildho

odhe

alth

cond

ition

s.O

LSre

gres

sion

ofco

gniti

vete

stsc

ores

onhe

ight

-for

-age

(HFA

)z-

scor

es,c

ontr

ollin

gfo

rba

ckgr

ound

and

dem

ogra

phic

vari

able

s(us

ing

NC

DS,

BC

San

dfr

agile

fam

ilies

data

).Sa

me

regr

essio

nw

ithm

othe

rfixe

deff

ects

usin

gN

LSY

79da

ta.O

LSre

gres

sion

oflo

gho

urly

earn

ings

onad

ulth

eigh

t,co

ntro

lling

for

cogn

itive

test

scor

esat

ages

7,10

,an

d11

,an

dot

her

back

grou

ndan

dde

mog

raph

icva

riab

les.

Child’s

hei

ghtan

dco

gnitiv

ete

stsc

ore

sat

age

3:1

SDin

crea

sein

HFA

z-sc

ore

linke

dto

a0.

05-0

.1SD

incr

ease

inPP

VT

scor

e.C

hild’s

hei

ghtan

dco

gnitiv

ete

stsc

ore

sat

ages

5-10

:(r

epor

ting

only

mot

her

fixe

deff

ects

resu

lts)

1SD

incr

ease

inH

FAz-

scor

elin

ked

toin

crea

sed

PIA

Tm

ath

scor

e,PI

AT

read

ing

reco

gniti

onsc

ore,

and

PIA

Tre

adin

gco

mpr

ehen

sion

scor

eby

0.03

SD.H

eigh

tdoe

snot

expl

ain

differ

ence

sin

test

scor

esac

ross

raci

algr

oups

.A

dult

hei

ght,

earn

ings

,an

dco

gnitiv

ete

stsc

ore

s:In

clus

ion

ofco

gniti

vete

stsc

ores

atag

es7,

10,an

d11

mak

esth

eco

effici

ento

nad

ulth

eigh

tins

igni

fica

ntfo

rpr

edic

ting

hour

lyw

ages

.H

eigh

tdiff

eren

ceb/

nm

enan

dw

omen

does

nota

ccou

ntfo

rth

edi

ffer

ence

inea

rnin

gs.

(con

tinue

don

next

page

)


Table5(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Hei

ght,

heal

than

dco

gniti

vefu

nctio

nat

olde

rag

es(C

ase

and

Paxs

on,20

08b)

.D

ata

from

HR

Son

men

and

wom

enag

ed50

and

olde

rfr

om19

96to

2004

.n=

72,2

58.

OLS

regr

essio

nof

heal

than

dco

gniti

onm

easu

reso

nad

ult

heig

ht,co

ntro

lling

for

age,

surv

eyw

ave,

race

and

sex.

Adu

lthe

ight

isa

prox

yfo

rea

rly

child

hood

heal

than

dnu

triti

on.A

lsoes

timat

edm

odel

sinc

ludi

ngco

ntro

lsfo

rch

ildho

odhe

alth

(sel

f-re

port

ed),

com

plet

eded

ucat

ion,

and

adu

mm

yfo

rem

ploy

men

tin

aw

hite

-col

lar

occu

patio

n.

1in

incr

ease

inhe

ight

asso

ciat

edw

ith:

0.9%

incr

ease

inde

laye

dw

ord

reca

llsc

ore

0.3%

incr

ease

inpr

obab

ility

ofbe

ing

able

toco

untb

ackw

ards

0.3%

incr

ease

inpr

obab

ility

ofkn

owin

gth

eda

te1.

4%de

crea

sein

depr

essio

nsc

ore

0.4%

decr

ease

inhe

alth

stat

ussc

ale

(1=

exce

llent

,5=

poor

)C

hild

hood

heal

th,co

mpl

eted

educ

atio

n,an

dem

ploy

men

tin

aw

hite

-col

lar

occu

patio

nal

lpos

itive

lyre

late

dto

adul

tco

gniti

vefu

nctio

nan

dhe

alth

.In

clus

ion

ofth

ese

cont

rols

mak

esth

eco

effici

ents

onad

ulth

eigh

tins

igni

fica

ntex

cept

for

the

case

sofd

elay

edw

ord

reca

llan

dde

pres

sion.

The

role

ofch

ildho

odhe

alth

for

the

inte

rgen

erat

iona

ltra

nsm

issio

nof

hum

anca

pita

l:ev

iden

cefr

omad

min

istra

tive

data

(Sal

man

dSc

hunk

,20

08).

Adm

inist

rativ

eda

tafr

omth

ede

part

men

tof

heal

thse

rvic

esin

the

city

ofO

snab

ruec

k,G

erm

any,

colle

cted

duri

ngoffi

cial

scho

olen

tran

cem

edic

alex

amin

atio

nson

child

ren

aged

6be

twee

n20

02-2

005.

Sibl

ing

sam

ple:

n=

947.

321

child

ren

had

atle

asto

nepa

rent

w/

colle

gede

gree

.

Sibl

ing

fixe

deff

ects

toes

timat

eim

pact

ofhe

alth

cond

ition

son

cogn

itive

and

verb

alab

ility

atsc

hool

entr

ance

.C

ontr

olle

dfo

rin

divi

dual

back

grou

ndch

arac

teri

stic

s.U

sed

the

Oax

aca

(197

3)de

com

posit

ion

and

ano

npar

amet

ric

deco

mpo

sitio

nto

estim

ate

how

muc

hof

the

gap

b/n

child

ren

ofco

llege

-edu

cate

dpa

rent

sand

child

ren

ofle

ssed

ucat

edpa

rent

scan

beex

plai

ned

bych

roni

cch

ildho

odhe

alth

cond

ition

s.

Men

talh

ealth

cond

ition

sred

uce

cogn

itive

abili

tyby

10%

for

the

who

lesa

mpl

e,by

9%fo

rco

llege

-edu

cate

dsa

mpl

e,by

11%

for

less

-edu

cate

dsa

mpl

e.A

sthm

are

duce

scog

nitiv

eab

ility

by8%

for

less

-edu

cate

dsa

mpl

eon

ly.M

enta

lhea

lthco

nditi

onsr

educ

eve

rbal

abili

tyby

11%

for

the

who

lesa

mpl

e,an

dby

13%

for

the

less

-edu

cate

dsa

mpl

e.H

ealth

cond

ition

sex

plai

n18

%of

the

gap

inco

gniti

veab

ility

and

65%

ofth

ega

pin

lang

uage

abili

tyb/

nch

ildre

nof

colle

ge-e

duca

ted

and

less

-edu

cate

dpa

rent

s.

(con

tinue

don

next

page

)


Table5(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Long

-ter

mec

onom

icco

stso

fps

ycho

logi

calp

robl

emsd

urin

gch

ildho

od(S

mith

and

Smith

,20

08)

Dat

afr

omPS

IDon

siblin

gs(w

itha

spec

ials

uppl

emen

tdes

igne

dby

the

auth

orsi

nth

e20

07w

ave,

aski

ngre

tros

pect

ive

ques

tions

abou

tchi

ldho

odhe

alth

).Sa

mpl

eco

nsist

sofs

iblin

gch

ildre

nof

the

orig

inal

part

icip

ants

who

wer

eat

leas

t16

in19

68.Si

blin

gch

ildre

nw

ere

atle

ast2

5in

2005

.

Sibl

ing

fixe

deff

ects

toes

timat

eth

eim

pact

ofre

port

ing

havi

ngha

dch

ildho

odps

ycho

logi

calp

robl

ems

(mea

sure

dby

depr

essio

n,dr

ugor

alco

hola

buse

orot

her

psyc

holo

gica

lpro

blem

sbef

ore

age

17)o

nla

ter

life

soci

o-ec

onom

icou

tcom

es.C

ontr

olle

dfo

rin

divi

dual

child

hood

phys

ical

illne

sses

(ast

hma,

diab

etes

,al

lerg

icco

nditi

ons,

and

man

yot

hers

)as

wel

lasf

amily

back

grou

ndch

arac

teri

stic

s.

Hav

ing

had

psyc

holo

gica

lpro

blem

sdur

ing

child

hood

lead

sto

:20

%re

duct

ion

inad

ulte

arni

ngs(

$10,

400

less

per

year

;$1

7,53

4le

ssfa

mily

asse

ts)

redu

ctio

nin

num

bero

fwee

ksw

orke

dby

5.76

wee

kspe

ryea

r11

ppre

duct

ion

inlik

elih

ood

ofge

ttin

gm

arri

ed33

.5pp

redu

ctio

nin

educ

atio

nala

ttai

nmen

t(m

eans

not

repo

rted

,so

can’

tcal

cula

tere

lativ

eeff

ects

izes

)C

ost

sofch

ildhood

psy

cholo

gic

alpro

ble

ms:

Life

time

cost

:$3

00,0

00lo

ssin

fam

ilyin

com

e;$3

.2tr

illio

nco

stfo

ral

ltho

seaff

ecte

d

Ear

lylif

ehe

alth

and

cogn

itive

func

tion

inol

dag

e(C

ase

and

Paxs

on,20

09)

Reg

ion-

leve

lhist

oric

alda

taon

mor

talit

yfr

omin

fect

ious

dise

ases

,as

wel

last

otal

infa

ntm

orta

lity.

Dat

afo

r19

00-1

936

from

Gra

ntM

iller

’sda

taar

chiv

eon

NB

ER

web

site.

Dat

afo

r19

37-1

950

from

vita

lsta

tistic

sdoc

umen

ts.D

ata

onla

ter

life

outc

omes

from

Hea

lthan

dR

etir

emen

tStu

dyfo

r19

96-2

004

onm

enan

dw

omen

aged

50-9

0.n=

60,0

00.

Iden

tifica

tion

due

tova

riat

ion

inm

orta

lity

rate

sacr

osst

ime

and

regi

ons.

OLS

regr

essio

nof

late

rlif

eou

tcom

eson

log

mor

talit

yra

tesf

rom

vari

ousi

nfec

tious

dise

ases

inye

arof

birt

han

din

2nd

year

oflif

ein

regi

onof

birt

h.C

ontr

olle

dfo

rag

e,se

x,ra

ce,an

dcu

rren

tCen

susr

egio

nof

resid

ence

.

Dec

reas

ein

infa

ntm

orta

lity

byha

lfdu

ring

2nd

year

oflif

eas

soci

ated

w/

anin

crea

sein

dela

yed

wor

dre

call

scor

eby

0.1

SD.Si

gnifi

cant

nega

tive

impa

ctso

ftyp

hoid

,in

flue

nza,

and

diar

rhea

mor

talit

yin

2nd

year

oflif

eon

dela

yed

wor

dre

call

scor

e.(M

eans

notr

epor

ted

soca

n’tc

alcu

late

rela

tive

effec

tsiz

es).

Wea

ker

asso

ciat

ions

b/n

dise

ase

mor

talit

yan

dab

ility

toco

untb

ackw

ards

.N

osig

nifica

ntim

pact

sofd

iseas

em

orta

lity

and

over

allm

orta

lity

duri

ngye

arof

birt

h,on

cem

orta

lity

in2n

dye

arof

life

isin

clud

ed,on

eith

erof

the

late

rlif

eou

tcom

es.R

esul

tsno

trob

ustt

oad

ding

Cen

sus

regi

on-s

peci

fic

time

tren

ds.

(con

tinue

don

next

page

)


Table5(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Chi

ldhe

alth

and

youn

gad

ult

outc

omes

(Cur

rie

etal

.,20

10)

Adm

inist

rativ

eda

taon

publ

iche

alth

insu

ranc

ere

cord

sfro

mth

eC

anad

ian

prov

ince

ofM

anito

baon

child

ren

born

b/n

1979

and

1987

,fo

llow

edun

til20

06.

n=

50,0

00.

Sibl

ing

fixe

deff

ects

ofth

elo

ngte

rmeff

ects

ofhe

alth

prob

lem

sat

vari

ousc

hild

ages

cont

rolli

ngfo

rhe

alth

atbi

rth

(with

birt

hw

eigh

tan

dco

ngen

itala

nom

alie

s).K

eyex

plan

ator

yva

riab

lese

xam

ined

are:

asth

ma,

maj

orin

juri

es,

AD

HD

,co

nduc

tdiso

rder

s,an

dot

her

maj

orhe

alth

prob

lem

sat

ages

0-3,

4-8,

9-13

,an

d14

-18.

Key

outc

ome

vari

able

s:ac

hiev

emen

ton

stan

dard

ized

test

onla

ngua

gear

tsin

grad

e12

,w

heth

erch

ildto

okco

llege

-pre

para

tory

mat

hco

urse

sin

high

scho

ol,w

heth

erch

ildis

ingr

ade

12by

age

17,an

dw

elfa

repa

rtic

ipat

ion

afte

rbe

com

ing

elig

ible

at18

.

Res

ultsre

port

edbel

owar

eonly

those

that

contr

olf

or

hea

lth

pro

ble

msat

alla

gegro

ups:

An

addi

tiona

lmaj

orhe

alth

cond

ition

atag

es0-

3/4-

8/14

-18

incr

ease

sthe

prob

abili

tyof

bein

gon

wel

fare

by10

%/9

%/3

1%.

Effec

tsof

maj

orhe

alth

cond

ition

saty

oung

erag

eson

educ

atio

nalo

utco

mes

nots

igni

fica

ntw

hen

cont

rolli

ngfo

rhe

alth

atag

es14

-18.

AD

HD

/conduct

dis

ord

ersdia

gnosi

s:at

ages

0-3/

4-8/

9-13/14

-18

decr

ease

spro

babi

lity

ofbe

ing

ingr

ade

12by

age

17by

4%/1

0%/1

7%/1

9%;at

ages

4-8/

9-13/14

-18

incr

ease

spro

babi

lity

ofbe

ing

onw

elfa

reby

38%

/44%

/109

%;

atag

es4-

8/9-

13/14

-18

decr

ease

spro

babi

lity

ofta

king

aco

llege

prep

mat

hcl

assb

y11

%/2

5%/3

5%;at

ages

4-8/

9-13/14

-18

decr

ease

slite

racy

scor

eby

0.15

SD/0

.23

SD/0

.27

SD.

Effec

tsof

asth

ma

atyo

unge

rag

esno

tsta

tistic

ally

signi

fica

nton

cehe

alth

atag

es14

-18

isco

ntro

lled

for.

Maj

orin

jury

:at

ages

0-3/

14-1

8in

crea

sesp

roba

bilit

yof

bein

gon

wel

fare

by7%

/9%

;at

ages

9-13/14

-18

decr

ease

spr

obab

ility

ofbe

ing

ingr

ade

12by

age

17by

2%/2

%;at

ages

9-13/14

-18

decr

ease

spro

babi

lity

ofta

king

aco

llege

prep

mat

hcl

assb

y6%

/8%

;at

ages

9-13/14

-18

decr

ease

slite

racy

scor

eby

0.03

SD/0

.03

SD.C

hild

ren

who

have

am

ajor

phys

ical

heal

thco

nditi

onan

dth

enre

cove

rdo

noth

ave

signi

fica

ntad

vers

eou

tcom

es.C

hild

ren

with

men

talh

ealth

cond

ition

s,ch

ildre

nw

ithm

ajor

heal

thco

nditi

onsa

tage

s14

-18,

and

thos

ew

ithco

nditi

onst

hatp

ersis

tfor

mul

tiple

age

peri

odss

uffer

wor

seou

tcom

es.

(con

tinue

don

next

page

)


Table5(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

The

impa

ctof

child

hood

heal

thon

adul

tlab

orm

arke

tout

com

es(S

mith

,20

09)

Dat

aon

siblin

gsfr

omPS

ID.

Chi

ldho

odhe

alth

mea

sure

das

ase

lf-re

port

edre

tros

pect

ive

heal

thin

dex

rega

rdin

ghe

alth

atag

esyo

unge

rth

an17

.A

dult

outc

omes

mea

sure

din

1999

.n=

2248

.

Sibl

ing

fixe

deff

ects

toes

timat

eim

pact

ofse

lf-re

port

edre

tros

pect

ive

child

hood

heal

thst

atus

(on

a5-

poin

tsca

le)b

efor

eag

e16

onad

ulte

arni

ngs,

empl

oym

ent,

educ

atio

n,m

arita

lst

atus

,et

c.,co

ntro

lling

for

dem

ogra

phic

sand

fam

ilyba

ckgr

ound

.

Bet

ter

heal

thdu

ring

child

hood

incr

ease

sadu

ltfa

mily

inco

me

by24

%,in

crea

sesa

dult

fam

ilyw

ealth

by20

0%(r

elat

ive

tom

ean=

$200

0),an

din

crea

sesa

dult

earn

ings

by25

%.

Bet

ter

heal

thdu

ring

child

hood

incr

ease

spro

babi

lity

ofha

ving

wor

ked

the

year

befo

reby

5.4

pp(m

ean

notr

epor

ted)

.A

bout

2/3

ofov

eral

lim

pact

ofpo

orch

ildho

odhe

alth

onad

ultf

amily

inco

me

ispr

esen

tata

ge25

;th

ere

mai

ning

1/3

isdu

eto

aslo

wer

grow

thpa

thaf

ter

age

25du

eto

poor

child

hood

heal

th.A

bout

1/2

ofov

eral

lim

pact

ofpo

orch

ildho

odhe

alth

onin

divi

dual

earn

ings

ispr

esen

tata

ge25

;th

ere

mai

ning

1/2

isdu

eto

aslo

wer

grow

thpa

thaf

ter

age

25du

eto

poor

child

hood

heal

th.N

ost

atist

ical

lysig

nifica

ntim

pact

sofc

hild

hood

heal

thon

educ

atio

nala

ttai

nmen

t.

The

effec

tofc

hild

hood

cond

uct

diso

rder

onhu

man

capi

tal(

Vuj

icet

al.,

2008

)D

ata

from

the

Aus

tral

ian

Twin

Reg

ister

ontw

insb

orn

betw

een

1964

and

1971

.D

ata

colle

cted

in19

89-1

990

and

1996

-200

0n=

5322

twin

s;22

50id

entic

altw

ins.

Twin

fixe

deff

ects

toes

timat

eim

pact

ofch

ildho

odco

nduc

tdi

sord

er(m

easu

red

byva

riou

sin

dica

tors

)bas

edon

diag

nost

iccr

iteri

afr

omps

ychi

atry

oned

ucat

iona

latt

ainm

enta

ndcr

imin

albe

havi

orin

adul

thoo

d.C

ontr

olle

dfo

rbi

rth

wei

ght,

timin

gof

the

onse

tofc

ondu

ctdi

sord

ers,

and

othe

rfa

mily

and

indi

vidu

alba

ckgr

ound

char

acte

rist

ics.

Con

duct

edse

para

tean

alys

esfo

ral

ltw

insa

ndid

entic

altw

ins.

Chi

ldho

odco

nduc

tdiso

rder

slea

dto

:5-

16%

decr

ease

inlik

elih

ood

ofhi

ghsc

hool

grad

uatio

n10

0-22

8%in

crea

sein

likel

ihoo

dof

bein

gar

rest

ed(m

ean=

0.07

)6-

68%

incr

ease

inlik

elih

ood

ofgr

ade

rete

ntio

n20

-60%

incr

ease

inlik

elih

ood

ofha

ving

3+jo

bqu

its(n

otsig

nifica

ntin

iden

tical

twin

sam

ple)

50-3

25%

incr

ease

inlik

elih

ood

ofte

lling

lies(

mea

n=

0.04

)37

-526

%in

crea

sein

likel

ihoo

dof

goin

gto

jail

(mea

n=

0.01

9)E

arlie

roc

curr

ence

ofco

nduc

tdiso

rder

hasl

arge

rne

gativ

eeff

ects

than

late

roc

curr

ence

.

(con

tinue

don

next

page

)


Table5(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Cau

sesa

ndco

nseq

uenc

esof

earl

ylif

ehe

alth

(Cas

ean

dPa

xson

,20

10a)

Dat

afr

omN

CD

S,B

CS,

PSID

,W

hite

hall

IISt

udy

(long

itudi

nal

stud

yof

Bri

tish

civi

lser

vant

sb/n

ages

34an

d71

,co

llect

edfr

om19

85to

2001

),H

RS,

and

NLS

Y79

.Se

eC

ase

and

Paxs

on(2

008a

,b)f

orm

ore

info

onth

eda

tase

ts.n=

11,6

48(N

CD

S),

11,1

81(B

CS)

,63

,995

(PSI

D),

29,7

74(W

hite

hall

II),

66,2

69(H

RS)

,n=

3200

-46,

000

(NLS

Y79

).

Mul

tivar

iate

regr

essio

nof

educ

atio

nala

ttai

nmen

t,em

ploy

men

t,lo

gea

rnin

gs,an

dse

lf-re

port

edhe

alth

stat

usan

dco

gniti

vefu

nctio

non

adul

thei

ght

inin

ches

(pro

xyfo

rea

rly

child

hood

heal

th)u

sing

5lo

ngitu

dina

ldat

ase

ts.C

ontr

olle

dfo

rag

e,et

hnic

ity,se

x,an

dsu

rvey

wav

e.U

sed

siblin

gfixe

deff

ects

inN

LSY

79to

unde

rsta

ndw

hat

aspe

ctso

fear

lylif

ehe

alth

adul

the

ight

capt

ures

—re

gres

sed

child

ren’

stes

tsco

res,

grad

ele

vel,

self-

perc

eptio

nin

scho

ol,an

dch

ildho

odhe

alth

outc

omes

onch

ildre

n’sH

AZ

.

Res

ults

from

NC

DS,

BC

S,PS

ID,W

hite

hall

II,an

dH

RS:

One

inch

ofhe

ight

isas

soci

ated

with

:0.

05-0

.16

mor

eye

ars

ofsc

hool

ing;

0.2-

0.6

ppin

crea

sein

likel

ihoo

dof

empl

oym

ent;

0.01

2-0.

028

incr

ease

inav

erag

eho

urly

earn

ings

for

men

;0.

007-

0.02

7in

crea

sein

aver

age

hour

lyea

rnin

gsfo

rw

omen

.A

4-in

incr

ease

inhe

ight

lead

sto:

8%de

crea

sein

prob

abili

tyof

long

-sta

ndin

gill

ness

;40

%de

crea

sein

prob

abili

tyof

disa

bilit

y;4%

ofSD

decr

ease

inde

pres

sion

scor

e.R

esul

tsfr

omN

LSY

79(r

epor

ting

siblin

gfixe

deff

ects

only

)in

dica

teth

at1

pt.in

crea

sein

HA

Zle

adst

o:0.

3%in

crea

sein

PIA

Tm

ath

scor

e;0.

1%in

crea

sein

PIA

Tre

adin

gre

cogn

ition

scor

e;0.

2%in

crea

sein

PIA

Tre

adin

gco

mpr

ehen

sion

scor

e;0.

9%in

crea

sein

digi

tals

pan

test

scor

e;0.

4%in

crea

sein

PPV

Tsc

ore.

2%de

crea

sein

likel

ihoo

dth

atch

ildis

inap

prop

riat

egr

ade

leve

lfor

age

(age

s6-1

4).1%

incr

ease

inch

ilddo

ing

scho

olw

ork

quic

kly;

1%in

crea

sein

child

rem

embe

ring

thin

gsea

sily;

0.9%

incr

ease

into

tals

chol

astic

com

pete

nce

scor

e.8%

decr

ease

inlik

elih

ood

that

child

has

limiti

ngem

otio

nal/

neur

olog

ical

cond

ition

;0.

105

incr

ease

inbi

rth

wei

ghtz

-sco

re(m

ean

notr

epor

ted)

.

(con

tinue

don

next

page

)


Table5(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

The

long

reac

hof

child

hood

heal

than

dci

rcum

stan

ce:ev

iden

cefr

omth

eW

hite

hall

IISt

udy

(Cas

ean

dPa

xson

,20

10b)

Dat

afr

omth

eW

hite

hall

IISt

udy

(see

abov

efo

rde

scri

ptio

n).

n=

10,3

08.

Sinc

em

ostW

hite

hall

IIst

udy

part

icip

ants

belo

ngto

the

high

est

occu

patio

nalc

lass

es,es

timat

edth

ese

lect

ion

effec

tusin

gda

tafr

omN

CD

San

dB

CS

for

com

pari

son.

OLS

regr

essio

nsof

initi

alpl

acem

enta

ndpr

omot

ion

inW

hite

hall

onhe

ight

,fa

mily

back

grou

ndch

arac

teri

stic

s,ed

ucat

iona

latt

ainm

ent,

and

othe

rin

divi

dual

char

acte

rist

ics.

Also

estim

ated

indi

vidu

alfixe

deff

ects

and

firs

t-di

ffer

ence

dre

gres

sions

offu

ture

prom

otio

nson

self-

repo

rted

heal

th,an

dof

futu

rehe

alth

ongr

ade

inW

hite

hall.

Sele

ctiv

ityof

Whi

teha

llII

stud

yat

tenu

ates

the

impa

ctof

fath

er’s

soci

alcl

asso

nva

riou

sout

com

esre

lativ

ees

timat

esth

atw

ould

beob

tain

edin

the

full

popu

latio

n.O

LSre

gres

sions

indi

cate

signi

fica

ntco

rrel

atio

nsbe

twee

nfa

mily

back

grou

nd(f

athe

r’sso

cial

clas

s),in

itial

plac

emen

tand

likel

ihoo

dof

prom

otio

n.Si

gnifi

cant

rela

tions

hip

betw

een

initi

alpl

acem

ent

and

prom

otio

n,he

ight

,an

dse

lf-re

port

edhe

alth

.A

lthou

ghso

me

ofth

eeff

ects

ofhe

alth

onpl

acem

enta

ndpr

omot

ion

are

med

iate

dby

educ

atio

nala

ttai

nmen

t,th

ere

isan

inde

pend

ent

effec

tofh

ealth

.In

divi

dual

fixe

deff

ects

estim

ates

indi

cate

that

coho

rtm

embe

rsw

hore

port

“exc

elle

nt”

or“v

ery

good

”he

alth

are

13%

mor

elik

ely

tobe

prom

oted

(rel

ativ

eto

low

est

grad

e).N

oeff

ecto

fgra

dein

Whi

teha

llon

futu

rehe

alth

.

(con

tinue

don

next

page

)


Table5(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Hom

een

vironm

ent

The

impa

ctof

mat

erna

lalc

ohol

and

illic

itdr

ugus

eon

child

ren’

sbe

havi

orpr

oble

ms:

evid

ence

from

the

child

ren

ofth

eN

atio

nal

Long

itudi

nalS

urve

yof

Yout

h(C

hatt

erji

and

Mar

kow

itz,20

00)

Dat

afr

omth

e19

88,19

92,an

d19

94w

aves

ofN

LSY-

Chi

ldsu

rvey

son

child

ren

who

wer

e4-

14yr

sold

duri

ngth

esu

rvey

year

s.n=

6194

child

ren

into

tal

sam

ple;

n=

2498

mot

hers

who

have

siblin

gsin

sam

ple;

n=

7546

obsf

orch

ildre

nw

hoha

ve2

or3

obse

rvat

ions

insa

mpl

e.

Est

imat

edim

pact

ofm

ater

nal

subs

tanc

eab

use

onch

ildre

n’s

beha

vior

prob

lem

susin

gO

LS,IV

,ch

ild-s

peci

fic

&m

ater

nalf

amily

fixe

deff

ects

mod

els.

InIV

,us

edal

coho

land

illic

itdr

ugpr

ices

asin

stru

men

tsfo

rm

ater

nal

subs

tanc

eab

use.

Con

trol

led

for

num

erou

sdem

ogra

phic

and

back

grou

ndva

riab

les.

Beh

avio

rpr

oble

msm

easu

red

byth

eB

ehav

ior

Prob

lem

sInd

ex(h

ighe

rB

PIm

eans

mor

epr

oble

ms)

.

No

stat

istic

ally

signi

fica

ntre

latio

nshi

pbe

twee

nm

ater

nal

subs

tanc

eab

use

and

child

beha

vior

inIV

regr

essio

ns,bu

t1st

stag

ere

latio

nshi

pis

wea

k.Fi

xed

effec

tssu

gges

ttha

t1m

ore

drin

kco

nsum

edby

mot

her

inpa

stm

onth

incr

ease

schi

ldB

PIby

1%.

Mat

erna

lmar

ijuan

aus

ein

past

mon

thin

crea

sesc

hild

BPI

by7-

8%.

Mat

erna

lcoc

aine

use

inpa

stm

onth

incr

ease

schi

ldB

PIby

19%

.

(con

tinue

don

next

page

)


Table5(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Pare

ntal

empl

oym

enta

ndch

ildco

gniti

vede

velo

pmen

t(R

uhm

,20

04).

Dat

afr

omth

eC

hild

ren

ofN

LSY

79fo

r19

86-1

996

(mot

hers

aged

29-3

8at

the

end

of19

95).

n=

3042

.

OLS

toes

timat

eth

eim

pact

ofm

ater

nalw

ork

inth

eye

arpr

ior

toch

ild’s

birt

h,an

din

the

firs

t4

year

sofc

hild

’slif

eon

child

cogn

itive

test

scor

esat

ages

3-4

and

5-6.

Use

da

rich

seto

fco

ntro

lsfo

rm

ater

nalb

ackg

roun

d,de

mog

raph

ic,an

dso

cio-

econ

omic

char

acte

rist

ics,

asw

ella

sass

essm

enty

ear

fixe

deff

ects

.M

ater

nalw

ork

mea

sure

dbo

thby

hour

sand

wee

ksw

orke

ddu

ring

the

year

.C

ontr

olle

dfo

rfa

mily

inco

me.

20m

ore

hrs

.w

ork

edea

chw

eek

bym

oth

erduri

ng

child’s

1stye

ar:

Dec

reas

ePP

VT

scor

eby

0.06

-0.1

0SD

(age

s3-4

)20

more

hrs

.w

ork

edea

chw

eek

bym

oth

erduri

ng

child’s

age

2-3:

Dec

reas

ePI

AT-

Rea

ding

scor

eby

0.06

-0.0

8SD

(age

s5-6

)D

ecre

ase

PIA

T-M

ath

scor

eby

0.05

-0.0

6SD

(age

s5-6

)R

esul

tsro

bust

toal

tern

ativ

esp

ecifi

catio

ns(m

easu

ring

empl

oym

entb

yw

eeks

wor

ked

orpa

rt-t

ime

vs.no

empl

oym

ent)

.M

ore

nega

tive

effec

tson

PPV

Tan

dPI

AT-

Mat

hsc

ores

for

boys

than

girl

s.M

ore

nega

tive

effec

tson

PIA

T-R

eadi

ngsc

ores

for

girl

stha

nbo

ys.M

ore

nega

tive

effec

tson

PPV

Tan

PIA

T-R

eadi

ngsc

ores

for

whi

test

han

blac

ks.M

ore

nega

tive

effec

tson

PIA

T-M

ath

scor

esfo

rbl

acks

than

whi

tes.

Mat

erni

tyle

ave,

earl

ym

ater

nal

empl

oym

enta

ndch

ildhe

alth

and

deve

lopm

enti

nth

eU

S(B

erge

ret

al.,

2005

)D

ata

from

the

Chi

ldre

nof

NLS

Y79

onch

ildre

nbo

rnb/

n19

88an

d19

96.n=

1678

.

OLS

and

prop

ensit

ysc

ore

mat

chin

gto

estim

ate

impa

ctof

mat

erna

lret

urn

tow

ork

with

in12

wee

ksof

birt

hon

child

heal

than

dde

velo

pmen

talo

utco

mes

.M

odel

sco

ntro

lfor

num

erou

sbac

kgro

und

char

acte

rist

icsa

swel

lass

tate

and

year

ofbi

rth

fixe

deff

ects

.Sa

mpl

elim

ited

tom

othe

rsw

how

orke

dpr

e-bi

rth.

Moth

erre

turn

ing

tow

ork

within

12w

eeks

ofgiv

ing

bir

thle

adsto

:2.

5%de

crea

sein

likel

ihoo

dof

wel

l-ba

byvi

sit12

.8%

decr

ease

inlik

elih

ood

ofan

ybr

east

feed

ing

40.4

%de

crea

sein

num

ber

ofw

eeks

brea

stfe

d4.

3%de

crea

sein

child

gett

ing

allD

PT/P

olio

imm

uniz

atio

nsR

esul

tsfr

omO

LSco

nsist

entw

ithre

sults

from

prop

ensit

ysc

ore

mat

chin

gm

etho

ds.La

rger

nega

tive

effec

tsof

retu

rnin

gto

wor

kfu

ll-tim

ew

ithin

12w

eeks

ofgi

ving

birt

hco

mpa

red

toan

yw

ork

atal

l.

(con

tinue

don

next

page

)


Table5(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Evi

denc

efr

omm

ater

nity

leav

eex

pans

ions

ofth

eim

pact

ofm

ater

nalc

are

onea

rly

child

deve

lopm

ent(

Bak

eran

dM

illig

an,

2010

)D

ata

from

the

NLS

CY

inC

anad

aon

child

ren

upto

age

29m

onth

san

dth

eir

mot

hers

in19

98-2

003.

Iden

tifica

tion

due

tola

rge

expa

nsio

nsin

pare

ntal

leav

epo

licie

sin

Can

ada

onD

ecem

ber

31,20

00(f

rom

25to

50w

eeks

ofle

ave)

.O

LSre

gres

sion.

Key

expl

anat

ory

vari

able

isa

dum

my

for

birt

haf

ter

Dec

embe

r31

,200

0.O

utco

mes

incl

ude

child

deve

lopm

enti

ndic

ator

s,m

ater

nal

empl

oym

ent,

and

use

ofch

ildca

re.C

ontr

olle

dfo

rva

riou

sin

divi

dual

and

fam

ilyba

ckgr

ound

char

acte

rist

icsa

swel

lasl

abor

mar

ketv

aria

bles

.O

bser

vatio

nsfr

omQ

uebe

com

itted

due

toch

ange

sin

child

care

polic

ieso

ver

the

sam

etim

epe

riod

.O

bser

vatio

nsfr

omsin

gle-

pare

ntfa

mili

esom

itted

b/c

ofex

pans

ions

inbe

nefits

toth

ose

fam

ilies

.Su

bsam

ple

anal

ysis

for

wom

enw

hore

turn

edto

wor

kw

ithin

1ye

arof

child

’sbi

rth.

Exp

ansio

nsin

pare

ntal

leav

epo

licie

sled

toa

48-5

8%in

crea

sein

mon

thso

fmat

erna

lcar

edu

ring

the

1sty

ear

oflif

ean

da

25-2

9pp

decr

ease

inno

n-pa

rent

alca

re.N

ost

atist

ical

lysig

nifica

nteff

ects

ofpa

rent

alle

ave

expa

nsio

nson

child

deve

lopm

enti

ndic

ator

s(m

otor

/soc

iald

evel

opm

ent,

beha

vior

,ph

ysic

alab

ility

,co

gniti

vede

velo

pmen

t).

(con

tinue

don

next

page

)


Table5(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

The

effec

tofe

xpan

sions

inm

ater

nity

leav

eco

vera

geon

child

ren’

slon

g-te

rmou

tcom

es(D

ustm

ann

and

Scho

nber

g,20

09)

Adm

inist

rativ

eda

taon

stud

ents

inG

erm

any

atte

ndin

gpu

blic

scho

ols

inH

esse

,B

avar

ia,an

dSc

hles

wig

-Hol

stei

n(3

stat

esin

Ger

man

y)fo

r20

02-0

3to

2005

-06.

n=

101,

257.

Adm

inist

rativ

eda

taon

soci

alse

curi

tyre

cord

sfor

Ger

man

indi

vidu

alsb

orn

b/n

July

1977

and

June

1980

.n=

140,

387

Iden

tifica

tion

due

to3

polic

yre

form

sin

Ger

man

yth

atex

pand

edun

paid

and

paid

mat

erni

tyle

ave

toes

timat

eim

pact

onad

ultw

ages

,em

ploy

men

t,an

ded

ucat

iona

lout

com

es.Fi

rst

refo

rmin

1979

incr

ease

dpa

idle

ave

from

2to

6m

onth

s.Se

cond

refo

rmin

1986

incr

ease

dpa

idle

ave

from

6to

10m

onth

s.Thi

rdre

form

in19

92in

crea

sed

unpa

idle

ave

from

18to

36m

onth

s.O

LSre

gres

sion,

com

pari

ngch

ildre

nbo

rnon

em

onth

befo

rean

daf

ter

each

refo

rm,c

ontr

ollin

gfo

rba

ckgr

ound

vari

able

sand

stat

efixe

deff

ects

.A

lsodi

ffer

ence

-in-

differ

ence

,co

mpa

ring

child

ren

born

befo

rean

daf

ter

leav

eex

pans

ions

with

child

ren

born

inth

esa

me

mon

ths

the

year

befo

re(h

ence

nota

ffec

ted

byex

pans

ions

).

Des

pite

signi

fica

ntde

lays

inm

ater

nalr

etur

nto

wor

kdu

eto

polic

yre

form

s,th

ere

are

nost

atist

ical

lysig

nifica

ntim

pact

sof

expa

nsio

nsin

mat

erni

tyle

ave

polic

ies(

paid

orun

paid

)on

any

long

-run

outc

omes

(wag

es,em

ploy

men

t,se

lect

ive

high

scho

olat

tend

ance

,gr

ade

rete

ntio

nan

dgr

ade

atte

ndan

ce).

(con

tinue

don

next

page

)


Table5(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Chi

ldpr

otec

tion

and

adul

tcri

me:

usin

gin

vest

igat

oras

signm

entt

oes

timat

eca

usal

effec

tsof

fost

erca

re(D

oyle

,20

08)

Dat

afr

omco

mpu

teri

zed

crim

inal

hist

ory

syst

emfr

omth

eIl

linoi

sSt

ate

Polic

eon

arre

stsa

ndim

priso

nmen

tup

toag

e31

for

2000

-200

5,lin

ked

with

child

abus

ein

vest

igat

ion

data

onin

divi

dual

swho

are

18-3

5in

2005

.n=

23,2

54.

IVm

odel

soft

heeff

ecto

ffos

ter

care

plac

emen

ton

mea

sure

sof

crim

inal

activ

ity.Id

entifi

catio

ndu

eto

the

fact

that

child

prot

ectio

nca

sesa

rera

ndom

ized

toin

vest

igat

ors,

and

inve

stig

ator

sin

flue

nce

whe

ther

ach

ildis

plac

edin

fost

erca

reor

rem

ains

atho

me.

Mod

elal

low

sfor

trea

tmen

teffec

the

tero

gene

ity—

used

rand

omco

effici

entm

odel

.IV

whe

reth

ein

stru

men

tist

hein

vest

igat

or’s

prob

abili

tyof

usin

gfo

ster

plac

emen

trel

ativ

eto

the

othe

rin

vest

igat

ors.

Chi

ldre

non

the

mar

gin

for

plac

emen

tint

ofo

ster

care

are

1.5

times

mor

elik

ely

tobe

arre

sted

,2.

68tim

esm

ore

likel

yto

have

ase

nten

ceof

guilt

y/w

ithhe

ld,3.

41tim

esm

ore

likel

yto

bese

nten

ced

topr

ison

ifth

eyar

epl

aced

into

fost

erca

re.

Chi

ldre

non

the

mar

gin

are

likel

yto

incl

ude

Afr

ican

-Am

eric

ans,

girl

s,an

dyo

ung

adol

esce

nts.

(con

tinue

don

next

page

)


Table5(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Long

-ter

mco

nseq

uenc

esof

child

abus

ean

dne

glec

ton

adul

tec

onom

icw

ell-

bein

g(C

urri

ean

dW

idom

,20

09).

Sam

ple

ofab

used

/neg

lect

edch

ildre

nba

sed

onco

urt-

subs

tant

iate

dca

seso

fch

ildho

odph

ysic

alan

dse

xual

abus

ein

1967

-197

1in

one

Mid

wes

tern

met

ropo

litan

coun

ty.

Mal

trea

ted

child

ren

mat

ched

toco

ntro

lson

the

basis

ofse

x,ra

ce,

and

elem

enta

rysc

hool

clas

s/ho

spita

lofb

irth

.C

ases

rest

rict

edto

child

ren

11yr

sor

youn

ger.

Adu

ltou

tcom

esm

easu

red

atm

ean

age

41.U

sed

info

colle

cted

in19

89-9

5an

d20

03-0

4in

terv

iew

s.n=

1195

in19

89-9

5;n=

807

in20

03-0

4.M

atch

edsa

mpl

e(b

oth

mem

bers

ofm

atch

edpa

irin

terv

iew

eddu

ring

the

2w

aves

):n=

358.

Mul

tivar

iate

regr

essio

nw

ithke

yex

plan

ator

yva

riab

lebe

ing

adu

mm

yfo

rha

ving

been

mal

trea

ted.

Con

trol

led

for

dem

ogra

phic

and

fam

ilyba

ckgr

ound

char

acte

rist

ics,

asw

ell

asqu

arte

rof

year

attim

eof

inte

rvie

w.A

lsoes

timat

edm

odel

sse

para

tely

for

mal

esan

dfe

mal

esan

dfo

rth

esu

bsam

ple

ofpa

rtic

ipan

tsw

hose

fam

ilies

rece

ived

food

stam

psor

wel

fare

whe

nth

eyw

ere

child

ren.

Indiv

idual

sw

ho

wer

em

altr

eate

das

childre

n:

com

plet

e4.

3%le

ssye

arso

fsch

oolin

g(1

989-

95)

scor

e5.

3%lo

wer

onIQ

test

(198

9-95

)ha

ve24

%lo

wer

impu

ted

earn

ings

(200

3-04

)ar

e0.

52tim

esas

likel

yto

have

ask

illed

job

(198

9-95

)ar

e0.

46tim

esas

likel

yto

beem

ploy

ed(2

003-

04)

are

0.58

times

aslik

ely

toow

na

vehi

cle

(200

3-04

)Fo

rm

ales

,th

eon

lysig

nifica

nteff

ects

ofm

altr

eatm

enta

refo

rye

arso

fsch

oolin

gan

dha

ving

ask

illed

job.

For

fem

ales

,sig

nifica

ntne

gativ

eeff

ects

ofm

altr

eatm

ento

nye

arso

fsc

hool

ing,

IQte

stsc

ores

,im

pute

dea

rnin

gs,be

ing

empl

oyed

,ow

ning

aba

nkac

coun

t,ow

ning

ast

ock,

owni

nga

vehi

cle,

and

owni

nga

hom

e.E

ffec

tsla

rger

for

fem

ales

than

for

mal

es.

(con

tinue

don

next

page

)


Table5(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Chi

ldm

altr

eatm

enta

ndcr

ime

(Cur

rie

and

Teki

n,20

06)

Dat

afr

omth

eN

atio

nal

Long

itudi

nalS

tudy

ofA

dole

scen

tH

ealth

(Add

Hea

lth).

Firs

twav

e:19

94-9

5;la

stw

ave:

2001

-200

2.n=

13,5

09(f

ulls

ampl

e),n=

928

(tw

inss

ampl

e).

OLS

,pr

open

sity

scor

em

atch

ing,

and

twin

fixe

deff

ects

met

hods

.E

stim

ate

the

impa

ctof

mal

trea

tmen

ton

crim

inal

activ

ity.

Con

trol

led

for

num

erou

sde

mog

raph

ican

dba

ckgr

ound

char

acte

rist

icsa

swel

lass

tate

fixe

deff

ects

.A

lsoco

ntro

lled

for

pare

ntal

repo

rtso

fchi

ldbe

ing

bad-

tem

pere

dor

havi

nga

lear

ning

disa

bilit

y.

Onl

yre

sults

that

are

signi

fica

ntin

all3

mai

nsp

ecifi

catio

ns(O

LS,pr

open

sity

scor

es,an

dtw

infixe

deff

ects

)are

repo

rted

.C

hildre

nw

ho

exper

ience

dan

ym

altr

eatm

entar

e:99

-134

%m

ore

likel

yto

com

mit

any

non-

drug

rela

ted

crim

e(m

ean=

0.10

9)28

8-48

9%m

ore

likel

yto

com

mit

abu

rgla

ry(m

ean=

0.00

9)11

3-18

1%m

ore

likel

yto

dam

age

prop

erty

(mea

n=

0.05

2)10

6-13

1%m

ore

likel

yto

com

mit

anas

saul

t(m

ean=

0.04

9)18

3-22

2%m

ore

likel

yto

com

mit

ath

eft>

$50

(mea

n=

0.01

8)76

-101

%m

ore

likel

yto

com

mit

any

hard

-dru

gre

late

dcr

ime

(mea

n=

0.08

5)96

-103

%m

ore

likel

yto

bea

crim

evi

ctim

(mea

n=

0.07

7)Pr

obab

ility

ofcr

ime

incr

ease

sifa

pers

onsu

ffer

smul

tiple

form

sofm

altr

eatm

ent.

Bei

nga

vict

imof

mal

trea

tmen

tdo

uble

sthe

prob

abili

tyth

atan

indi

vidu

alis

conv

icte

das

aju

veni

le.

Cost

-Ben

efit:

Est

imat

edco

stso

fcri

me

indu

ced

byab

use=

$8.8

-68.

6bi

llion

/yea

rE

stim

ated

cost

sofh

ome

visit

ing

prog

ram

s(th

atha

vebe

ensh

own

tore

duce

case

sofm

altr

eatm

entb

y50

%)=

$14

billi

on/y

ear.

(con

tinue

don

next

page

)


Table5(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

The

effec

tofm

ater

nald

epre

ssio

nan

dsu

bsta

nce

abus

eon

child

hum

anca

pita

ldev

elop

men

t(F

rank

and

Mea

ra,20

09)

Dat

afr

omch

ildre

nof

NLS

Y79

onch

ildre

nag

ed1-

5in

1987

.n=

1587

.

OLS

regr

essio

nof

the

impa

ctof

mat

erna

ldep

ress

ion

and

subs

tanc

eab

use

onm

ater

nali

nput

sand

child

ren’

sout

com

esin

grad

es1-

5an

d6-

9.C

ontr

olle

dfo

ra

rich

set

ofde

mog

raph

ican

dba

ckgr

ound

vari

able

sinc

ludi

ngfa

mily

mea

sure

sofp

aren

tand

siblin

gbe

havi

oran

dhe

alth

.Fo

rro

bust

ness

,es

timat

edm

odel

susin

gm

othe

rfixe

deff

ects

and

prop

ensit

ysc

ores

.

Effec

tson

mat

ernal

inputs

:M

ater

nald

epre

ssio

nle

adst

oa

0.2

SDde

crea

sein

emot

iona

lst

imul

atio

nsu

b-co

mpo

nent

ofth

eH

OM

Esc

ore

(age

s7-1

0,11

-14)

.M

ater

nals

ubst

ance

abus

ele

adst

oa

0.23

SDde

crea

sein

emot

iona

lstim

ulat

ion

sub-

com

pone

ntof

the

HO

ME

scor

e(a

ges1

1-14

).E

ffec

tson

child

outc

om

es:

Mat

erna

ldep

ress

ion

lead

sto

a0.

46SD

incr

ease

inbe

havi

oral

prob

lem

sind

ex(a

ges7

-10,

11-1

4).

Mat

erna

lalc

ohol

abus

ele

adst

oa

0.31

SDde

crea

sein

child

’sPI

AT

mat

hsc

ore

(age

s11-

14);

0.29

SDin

crea

sein

beha

vior

alpr

oble

msi

ndex

(age

s7-1

0);37

7%de

crea

sein

likel

ihoo

dof

ever

bein

gsu

spen

ded

orex

pelle

dat

any

age

(mea

n=

0.22

).

(con

tinue

don

next

page

)


Table5(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Environm

entalsho

cks

Env

ironm

enta

lpol

icy

asso

cial

polic

y?T

heim

pact

ofch

ildho

odle

adex

posu

reon

crim

e(R

eyes

,20

07).

Dat

aon

lead

inga

solin

efo

r19

50-1

990

ona

stat

e-by

-sta

tele

velf

rom

Year

lyR

epor

tof

Gas

olin

eSa

lesb

ySt

ate,

Petr

oleu

mM

arke

ting

Ann

ual,

and

Petr

oleu

mPr

oduc

tsSu

rvey

.D

ata

onai

rle

adex

posu

refr

omth

eE

PA’s

Aer

omet

ric

Info

rmat

ion

Ret

riev

alSy

stem

.D

ata

onpe

rca

pita

crim

era

tesf

rom

the

Uni

form

Cri

me

Rep

orts

com

pile

dby

the

Fede

ral

Bur

eau

ofIn

vest

igat

ion

for

1985

-200

2.D

ata

onin

divi

dual

bloo

dle

adle

vels

from

NH

AN

ES

for

1976

-198

0on

ly.

Use

dst

ate-

by-y

ear

vari

atio

nin

the

decl

ine

ofle

adex

posu

refr

omga

solin

ebe

twee

n19

75an

d19

85du

eto

enfo

rcem

ento

fthe

Cle

anA

irA

ctto

iden

tify

the

impa

ctso

fch

ildho

odle

adex

posu

reon

viol

entc

rim

ein

youn

gad

ulth

ood.

Also

estim

ated

the

impa

ctof

lead

expo

sure

onle

vels

ofle

adin

bloo

ddu

ring

child

hood

for

robu

stne

ss.

Prim

ary

mea

sure

ofle

adex

posu

reis

the

gram

sofl

ead

per

gallo

nof

gaso

line

inau

tom

obile

sour

ces.

Cal

cula

ted

air

lead

expo

sure

(mea

sure

ofav

erag

eco

ncen

trat

ion

ofle

adin

the

air

inea

chst

ate

and

year

).C

alcu

late

deff

ectiv

ele

adex

posu

rere

lativ

eto

each

crim

ein

each

stat

ean

dye

aras

the

wei

ghte

dav

erag

eof

lead

expo

sure

b/n

ages

0an

d3

for

allc

ohor

tsof

arre

stee

s.R

egre

ssio

nof

per

capi

tacr

ime

rate

oneff

ectiv

ele

adex

posu

re,

cont

rolli

ngfo

rst

ate

and

year

fixe

deff

ects

and

othe

rst

ate-

year

char

acte

rist

icst

hatc

ould

affec

tcr

ime

rate

s.

Ela

stic

ityof

viol

entc

rim

ew

ithre

spec

tto

lead

expo

sure=

0.8.

Cha

nges

inch

ildho

odle

adex

posu

rein

the

1970

sare

resp

onsib

lefo

ra

56%

decr

ease

invi

olen

tcri

me

inth

e19

90s(

whi

leab

ortio

nis

resp

onsib

lefo

ra

29%

drop

invi

olen

tcri

me)

.W

eak

evid

ence

for

link

b/n

lead

expo

sure

and

mur

der

orpr

oper

tycr

imes

.C

ost

-Ben

efitA

nal

ysis

:C

umul

ativ

eso

cial

cost

ofsw

itchi

ngto

unle

aded

gaso

line:

$15-

65bi

llion

.C

umul

ativ

eso

cial

bene

fito

fred

uced

crim

efr

omre

duce

dle

adex

posu

re:$1

.2tr

illio

n.N

ote,

due

toda

talim

itatio

ns,it

isno

tpos

sible

tom

ake

adi

rect

conn

ectio

nbe

twee

nbl

ood

lead

leve

ls,le

adin

gaso

line,

and

crim

inal

activ

ityat

the

indi

vidu

alle

vel.

(con

tinue

don

next

page

)


Table5(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

The

long

-ter

meff

ects

ofea

rly

child

hood

lead

expo

sure

:evi

denc

efr

omsh

arp

chan

gesi

nlo

cala

irle

adle

vels

indu

ced

byth

eph

ase-

outo

fle

aded

gaso

line

(Nils

son,

2009

)D

ata

onai

rle

adex

posu

refr

omSw

edish

Env

ironm

enta

lPr

otec

tion

Age

ncy

that

uses

ana

tionw

ide

grid

ofm

osss

ampl

esco

llect

edin

1000

loca

tions

even

lysp

read

acro

ssSw

eden

for

aver

age

lead

expo

sure

leve

lsfo

r19

72-7

4,19

77-7

9,an

d19

82-8

4.In

divi

dual

outc

ome

data

from

the

Edu

catio

nald

atab

ase

atth

eIn

stitu

tefo

rLa

bor

Mar

ketP

olic

yE

valu

atio

nin

Upp

sala

,Sw

eden

onth

ose

born

in19

72-7

4,19

77-7

9,an

d19

82-8

4.n=

797,

889

Iden

tifica

tion

due

toge

ogra

phic

alva

riat

ion

inch

ildho

odle

adex

posu

redu

eto

apo

licy

inSw

eden

that

indu

ced

aph

ase-

out

ofle

aded

gaso

line

betw

een

1973

and

1981

.D

iffer

ence

-in-

differ

ence

com

pari

ngch

ildre

nbo

rnin

mun

icip

aliti

esth

atex

peri

ence

dla

rge

redu

ctio

nsin

lead

expo

sure

toth

ose

born

inm

unic

ipal

ities

with

rela

tivel

yno

chan

ge,be

fore

and

afte

rpo

licy

wen

tint

oeff

ect,

toes

timat

eim

pact

ofch

ildho

odle

adex

posu

reon

late

rlif

eou

tcom

es.A

lsous

edm

othe

rfixe

deff

ectm

etho

ds,co

mpa

ring

siblin

gsw

hoex

peri

ence

ddi

ffer

ent

leve

lsof

lead

expo

sure

inch

ildho

od.C

ontr

olle

dfo

rva

riou

sba

ckgr

ound

char

acte

rist

icsa

swel

las

year

ofbi

rth

and

mun

icip

ality

ofbi

rth

fixe

deff

ects

.

Ade

crea

sein

bloo

dle

adle

vels

by30

mg/

kgle

adst

o:3%

decr

ease

inlik

elih

ood

ofbe

ing

inth

elo

wer

end

ofth

eG

PAdi

stri

butio

n0.

6pp

incr

ease

inav

erag

eIQ

for

mal

es0.

05yr

incr

ease

insc

hool

ing

atta

inm

ent

0.7

ppin

crea

sein

likel

ihoo

dof

grad

uatin

ghi

ghsc

hool

0.5

ppde

crea

sein

likel

ihoo

dof

rece

ivin

gw

elfa

rein

adul

thoo

dE

ffec

tsof

lead

are

stro

nger

atlo

wer

end

ofab

ility

/ski

llsdi

stri

butio

nan

dfo

rch

ildre

nfr

ompo

orer

hous

ehol

ds.E

ffec

tsof

lead

are

stro

nger

whe

nex

posu

reis

atag

es0-

2th

anw

hen

expo

sure

isat

ages

5-7.

No

stat

istic

ally

signi

fica

ntdi

ffer

ence

ineff

ects

byge

nder

.N

onlin

ear

effec

ts—

signi

fica

ntan

dne

gativ

eeff

ects

ofle

adex

posu

reab

ove

75th

perc

entil

e(>

50m

g/kg

).N

ost

atist

ical

lysig

nifica

nteff

ects

ofex

posu

reto

lead

belo

w75

thpe

rcen

tile

leve

ls.E

stim

ated

that

ther

eex

istsa

bloo

dle

adth

resh

old

of60

mg/

Lbe

low

whi

chre

duct

ions

inle

adex

posu

reha

veno

effec

ton

outc

omes

.Si

mila

rre

sults

usin

gm

othe

rfixe

deff

ects

.N

ote:

mea

nsno

trep

orte

d,so

rela

tive

effec

tsiz

esca

n’tb

eca

lcul

ated

.

Unl

esso

ther

wise

note

d,on

lyre

sults

signi

fica

ntat

the

5%le

vela

rere

port

ed.Pe

rcen

tcha

nges

repo

rted

rela

tive

toth

em

ean.


a broader literature asking whether income affects health, and how health affects income?For example, using cross-sectional US data, Case et al. (2002) find a striking relationshipbetween family income and a child’s reported health status, which becomes stronger aschildren age. Their motivation for looking at children is that the child’s health is unlikelyto have a large direct effect on family income, so that the direction of causality is relativelyclear. Currie and Stabile (2003) investigate this relationship using Canadian panel dataand argue that one reason the relationship between income and child health increasesover time is that poorer children are subject to many more negative health shocks. Infact, in Canada, this is the dominant mechanism driving the relationship (which is notsurprising given that all Canadian children have public health insurance so that gaps intreatment rates are small).15

The question we focus on here is how much poor health in childhood, in turn, affectsfuture outcomes. One of the chief ambiguities in answering this question is what wemean by health in childhood. While it has become conventional to measure fetal healthusing birth weight (though there may be better measures, see Almond, Chay and Lee,2005) there are a wide variety of different possible measures of child health, ranging frommaternal reports about the child’s general health status through questions about diagnosesof specific chronic conditions, to the occurrence of “adverse events”.

Case and Paxson (2008a,b, 2010a,b) do not have a direct measure of child health, butargue that adult height is a good proxy for early child health. This is a useful observationgiven that most surveys of adults have no direct information on child health. Heightat age 5 is affected by a range of early health shocks including fetal conditions, poornutrition, and illness. In turn, height at age 5 is strongly predictive of adult height. Andlike birthweight, it is predictive of many shorter and longer range outcomes.

A second problem is that it is often unclear whether the ill health dates from aparticular period (e.g. an injury) or whether it might reflect a continuing, perhaps acongenital, condition. For example, Smith (2009) uses data from the Panel Study ofIncome Dynamics which asked young adults a retrospective question about their healthstatus before age 16. In models with sibling fixed effects, he finds that the sib withthe worse health had significantly lower earnings, although educational attainment wasnot significantly affected. He also finds using data from the Health and RetirementSurvey that reports of general poor health in childhood do tend to be correlated in theexpected way with the presence of specific health conditions. However, it is not possibleto ascertain that the negative effects are due to poor health at any particular “critical”window. Salm and Schunk (2008) attempt to deal with this problem using detailed health

15 Condliffe and Link (2008) argue that in the US, differential access to care also plays a role in the steepening of therelationship between income and child health with age. A number of studies have investigated this relationship, dubbed“the gradient”, in other countries (cf Currie et al. (2007), Case et al. (2008), Doyle et al. (2005), Khanam et al. (2009)).Propper et al. (2007) and Khanam et al. (2009) are particularly interesting because they find that when maternal mentalhealth is controlled, the relationship between family income and child health disappears, suggesting that it is mediatedlargely by factors that affect maternal mental health.


information from a medical examination of young German children entering school. Inmodels with sibling fixed effects, they find a significant relationship between poor mentalhealth and asthma on the one hand, and measures of cognitive functioning on the other.They control for the child’s birth weight in an effort to distinguish between the effectsof health at birth and health after birth (though to the extent that birth weight is animperfect measure of health at birth, it is possible that the other health measures partlycapture congenital conditions).

Currie et al. (2010) use administrative data from the Canadian province of Manitoba’spublic health insurance system to follow children from birth through young adulthood.

Using information about all contacts with medical providers, they construct measuresof whether children suffered injuries, asthma, mental health problems or other healthproblems at ages 0 to 3, 4 to 8, 9 to 13, and 14 to 18. It is interesting that even ina large sample, there were relatively few children with specific health problems otherthan injuries, asthma, or mental health problems, so that it was necessary to groupthe remaining problems together. They then look at the relationship between healthat various ages, educational attainment, and use of social assistance as a young adult insibling fixed effects models that also control flexibly for birth weight and for the presenceof congenital anomalies. The results are perhaps surprising in view of the conceptualframework developed in Section 3. When entered by themselves, early childhood healthconditions (at age 0-3 and at age 4-8) are predictive of future outcomes, conditionalon health at birth. However, when early physical health conditions are entered alongwith later ones, generally only the later ones matter. This result suggests that physicalhealth in early childhood affects future outcomes largely because it affects future health(i.e., subsequent health mediates the relationship), and not because there is a direct linkbetween early physical health status and cognition. In contrast, mental health conditionsat early ages seem to have significant negative effects on future outcomes even if there areno intermediate report of a mental health condition. This result suggests that commonmental health problems such as Attention Deficit Hyperactivity Disorder (ADHD, alsocalled Attention Deficit Disorder or ADD) or Conduct Disorders (i.e. disorders usuallyinvolving abnormal aggression and anti-social behavior) may impair the process of humancapital accumulation even if they do not lead to diagnoses of mental health disorders inadulthood.

Several recent papers focus specifically on measures of mental health conditions.Currie and Stabile (2006) use questions similar to those on mental health “screeners”which were administered to large samples of children in the US and Canada in twonational surveys. They find that children whose scores indicated mental health problemsin 1994 had worse outcomes as of 2002-4 than siblings without such problems. Theycontrolled for birth weight (among other variables) and estimated models with andwithout including children with diagnosed learning disabilities. In all specifications, theyfound negative effects of high ADHD scores on test scores on schooling attainment.


Smith and Smith (2008) report similar results using data from the PSID which includesretrospective questions about mental health problems before age 16. Like Smith (2009)and Currie et al. (2010) they estimate models with sibling fixed effects, and findsignificant long term effects of mental health conditions which are much larger thanthose of physical health conditions. Vujic et al. (2008) focus on conduct disorders usinga panel of Australian twins and find that conduct disorder before age 18 has strongnegative effects on the probability of high school graduation as well as positive effectson the probability of criminal activity. None of these three papers focus specifically onmeasures of mental health conditions before age 5 but “externalizing” mental healthconditions such as ADHD and Conduct Disorder typically manifest themselves at earlyages. Finally, although they are conceptually distinct, many survey measures of mentalhealth resemble measures of “non-cognitive skills”. Hence, one might interpret evidencethat non-cognitive skills in childhood are important determinants of future outcomes asfurther evidence of the importance of early mental health conditions (Blanden et al.,2006; Heckman and Rubinstein, 2001).

4.2.3. Home environmentThe home is one of the most important environments affecting a young child and thereis a vast literature in related disciplines investigating the relationship between differentaspects of the home environment and child outcomes. We do not attempt to summarizethis literature here, but pick three aspects that may be most salient: Maternal mental healthand/or substance abuse, maternal employment, and child abuse/foster care (which maybe considered to be an extreme result of bad parenting). Given the importance of childmental health and non-cognitive skills, it is interesting to ask how maternal mental healthaffects child outcomes? Frank and Meara (2009) examine this question using data fromthe National Longitudinal Survey of Youth. They include a rich set of control variables(mother’s cognitive test score, grandparent’s substance abuse, permanent income) andestimate models with mother fixed effects and models with propensity scores. Theirestimates suggest large effects (relative to the effects of income) of contemporaneousmaternal depression on the quality of the home environment and on children’s behavioralproblems, but little effect on math and reading scores. Estimates of the effects of maternalsubstance abuse are mixed, which echoes the findings of Chatterji and Markowitz (2000)using the same data. Unfortunately, the authors are not able to look at the long termeffects of maternal depression experienced by children aged 0 to 5 because the depressionquestions in the NLSY have been added only recently. As these panel data are extendedin time, further investigation of this issue is warranted.

There is also a large literature, including some papers by economists, examining theeffect of maternal employment at early ages on child outcomes. Much of this literaturesuffers from the lack of an appropriate conceptual framework. If we think of childoutcomes being produced via some combination of inputs, then the important questionis how maternal employment affects the inputs chosen? This will evidently depend on


how much her employment income relaxes the household budget constraint, and theprice and quality of the child care alternatives and other inputs that are available. Some ofthe literature on maternal employment seems to implicitly assume that the mother’s timeis such an important and unique input that no purchased input can adequately replace it.This may possibly be the case but is a strong assumption. If the mother’s time is replaceableat some price, then one might expect maternal employment to have quite different effectson women with different levels of household income (moreover, mother’s time may notall be of equal quality, so that it is easier to replace some mother’s time than others withthe market). This argument suggests that it is extremely important to consider explicitlythe quality of the mother’s time inputs and the availability of potential substitute inputsin models of maternal employment, something that is difficult to do in most availabledata sets. Studies that rely on regression methods and propensity score matching (seeBerger et al. (2005) and Ruhm (2004) often find small negative effects of maternalemployment (especially in the first year) on children’s cognitive development. However,two recent studies using variation in maternity leave provisions find that while moregenerous maternity leave policies are associated with increased maternal employment,there is little effect on children’s outcomes (Baker and Milligan, 2010; Dustmann andSchonberg, 2009). Dustmann and Schonberg (2009) have data that permit cohortsaffected by expansions in German maternity leave laws to be followed for many years.They see no effect of maternal employment on educational attainment or wages.

Finally, there are a few papers examining the effects of child abuse/foster care on childoutcomes. This is a difficult area to investigate because it is hard to imagine that abuse (orneglect) can be divorced from other characteristics of the household. Currie and Widom(2009) use data from a prospective longitudinal study in which abused children (thetreatments) were matched to controls. After following these children until their mid 40s,they found that the abused children were less likely to be employed, had lower earnings,and fewer assets, and that these patterns were particularly pronounced among women. Itis possible that these results are driven by unobserved differences between the treatmentsand controls, although focusing on various subsets of the data (e.g. children whosemothers were on welfare; children of single mothers) produced similar results. Currieand Tekin (2006) use data from the National Longitudinal Study of Adolescent Healthto examine the effect of having been abused before age 7 on the propensity to commitcrime. They find strong effects which are quite similar in OLS, sibling, and twin fixedeffects models. It is possible that these results reflect a characteristic of an individual child(such as difficult temperament) which makes it both more likely that they will be abusedand more likely that they will commit crime. However, controlling directly for measuresof temperament and genetic endowments does not alter the results. The Doyle (2008)study of the effects of foster care on the marginal child is also summarized in Table 5.


4.2.4. Toxic exposuresEpidemiological studies of postnatal pollution exposure and infant mortality have yieldedmixed results and many are likely to suffer from omitted variables bias. Currie and Neidell(2005) examine the effect of more recent (lower) levels of pollution on infant health,along with the role of specific pollutants in addition to particulates (only TSPs weremeasured during the time periods analyzed by Chay and Greenstone (2003a,b)). Usingwithin-zip code variation in pollution levels, they find that a one unit reduction in carbonmonoxide over the 1990s in California saved 18 infant lives per 100,000 live births.However, unlike Currie et al. (2009) they were unable to find any consistent evidence ofpollution effects on health at birth, probably because of the crudeness of their measure ofmaternal location.

Reyes (2007) found large effects of banning leaded gasoline on crime in the US, butresults were not robust to state-specific time trends despite a relatively long panel of state-level lead measurements. Nilsson (2009) considered reductions in ambient lead levelsin Sweden following the banning of lead in gasoline and measures possible exposuresusing the concentrations of lead in 1000 moss (bryophyte) collection sites that havebeen maintained by the Swedish environmental protection agency since the early 1970s.Nilsson (2009) found that early childhood exposure reduced human capital, as reflectedby both grades and graduation rates. These effects persisted when comparisons wererestricted within siblings, and were substantially larger for low-income families.

4.2.5. Summary re: long term effects of fetal and early childhood environmentThe last 10 years have seen an upsurge of empirical work on the long-term effects ofearly childhood. As a result, much has been learned. We can state fairly definitivelythat at least some things that happen before age five have long-term consequences forhealth and human capital. Moreover, these effects are sufficiently large and general toshape outcomes at the population level. On balance, effects of fetal exposure tend to besomewhat larger than postnatal effects, but there are important exceptions. Mental healthis a prime example. Mental health conditions and non-cognitive skills seem to have large,persistent effects independent of those captured by measures of child health at birth.

5. EMPIRICAL LITERATURE: POLICY RESPONSES

The evidence discussed above indicates that prenatal and early childhood often have acritical influence on later life outcomes. However, by itself this evidence says little aboutthe effectiveness of remediation. Hence, this section discusses evidence about whetherremediation in the zero to five period can be effective in shaping future outcomes. Inso doing, we take a step away from explicit consideration of an early-childhood shockug as in Section 2. Instead, we focus on the specific public policies that may be able toalter developmental trajectories, often in disadvantaged sub-populations. We begin withprograms that raise income, and then move on to programs that target specific domains.


5.1. Income enhancementIn the model sketched above, there are many ways for poverty to affect child outcomes.Even with identical preferences, poorer parents will make different investment choicesthan richer ones. In particular, poor families will optimize at lower investment (andconsumption) levels and thereby have children with lower health and human capital,other things equal. Further, poor parents may face different input prices for certain goods,or have access to different production technologies. Providing cash transfers addresses thebudgetary problems without necessarily changing the production technology. Hence, it isof interest to see whether cash transfers, in and of themselves, can improve outcomes. It ishowever, remarkably difficult to find examples of policies that increase incomes withoutpotentially having a direct effect on outcomes. For example, many studies of cash welfareprograms have demonstrated that children who are or have been on welfare remain worseoff on average than other children. This does not necessarily mean, however, that welfarehas failed them. Without welfare, their situation might have been even worse. Bergeret al. (2009) explore the relationship between family income, home environments, childmental health outcomes, and cognitive test scores using data from the Fragile Familiesand Child Well-being Study which follows a cohort of five thousand children born inseveral large US cities between 1998 and 2000. They show that all of the measures of thehome environment they examine (which include measures of parenting skills as well asphysical aspects of the home) are highly related to income and that controlling for thesemeasures reduces the effects of income on outcomes considerably.

Levine and Zimmerman (2000) showed that children who spent time on welfarescored lower than other children on a range of tests, but that this difference disappearedwhen the test scores of their mothers were controlled for, suggesting that welfare hadlittle effect either positive or negative. Similarly, Levine and Zimmerman (2000) arguethat children of welfare mothers were more likely to grow up to be welfare mothers,mainly because of other characteristics of the household they grew up in.

Currie and Cole (1993) compare siblings in families where the mother receivedwelfare while one child was in utero, but not while the other child was in utero, and find nodifference in the birth weight of the siblings. Given that research has shown little evidenceof positive effects of cash welfare on children, it is not surprising that the literatureevaluating welfare reform in the United States has produced similarly null findings. TheNational Research Council (Smolensky and Gootman, 2003) concluded that “no strongtrends have emerged, either negative or positive, in indicators of parent well-being orchild development across the years just preceding and following the implementationof [welfare reform]”. However, US welfare reform was a complex intervention thatchanged many parameters of daily life by, for example, imposing work requirements onrecipients.

Conditional tax credits represent an alternative approach to providing income to poorfamilies, and hence to poor children. The early years of the Clinton administration in the


United States saw a huge expansion of the Earned Income Tax Credit (EITC), whilein the UK, the Working Families Tax Credit approximately doubled in 1999. Theseare tax credits available to poor working families. Their essential feature is that theyare “refundable”—in other words, a family whose credit exceeds its taxes receives thedifference in cash. The tax credits are like welfare in that they give cash payments topoor families. But like welfare reform, the tax credits are a complex intervention inthat recipients need to work and file tax returns in order to be eligible, and a great dealof research has shown that such tax credits affect maternal labor supply and marriagepatterns (Eissa and Liebman, 1996; Meyer and Rosenbaum, 2001; Blundell, 2006). Thisis because the size of the payment increases with earnings up to a maximum level beforebeing phased out, so that it creates an incentive to work among the poorest householdsbut a work disincentive for households in the phase-out range. In the US, the numberof recipients grew from 12.5 million families in 1990 to 19.8 million in 2003, and themaximum credit grew from $953 to $4204. The rapid expansion of this formerly obscureprogram run through the tax system has resulted in cash transfers to low-income familiesthat were much larger than those that were available under welfare. Gundersen and Ziliak(2004) estimate that the EITC accounted for half of the reduction in after-tax poverty thatoccurred over the 1990s (the other half being mainly accounted for by strong economicgrowth).

Table 6 provides an overview of some of the research on the effects of income onchildren. Dahl and Lochner (2005) use variation in the amount of the EITC householdsare eligible for over time and household type to identify the effects of household incomeand find that each $1000 of income improves childrens’ test scores by 2% to 4% of astandard deviation. An attractive feature of the changes in the EITC is that householdsmay well have regarded them as permanent, so this experiment may approximate theeffects of changes in permanent rather than transitory income. Their result implies,though, that it would take on the order of a $10,000 transfer to having an educationallymeaningful effect on test scores.

Milligan and Stabile (2008) take advantage of a natural experiment resulting fromchanges in Canadian child benefits. These benefits vary across provinces and werereformed at different times. An advantage of their research is that the changes in incomewere not tied to other changes in family behavior, in contrast to programs like the EITC.

They find that an extra $1000 of child benefits leads to an increase of about 0.07 ofa standard deviation in the math scores and in the Peabody Picture Vocabulary Test, astandardized test of language ability for four to six year old children. If we think of achange of a third or a half a standard deviation in test scores as a meaningful educationaleffect, then these results suggest that an increase of as little as $5000 in family incomehas a meaningful effect. Milligan and Stabile (2008) go beyond Dahl and Lochnerby examining effects on other indicators. They find that higher child benefits loweraggression in children and decrease depression scores for mothers. They do not find much


Table6

Effectsof

incomeon

birthan

dearly

child

hood

outcom

es:evide

ncefrom

theUSan

darou

ndtheworld.

Stud

yan

dda

taStud

yde

sign

Results

The

link

betw

een

AFD

Cpa

rtic

ipat

ion

and

birt

hwei

ght(

Cur

rie

and

Col

e,19

93).

Dat

afr

omN

LSY

mer

ged

with

stat

ean

dco

unty

leve

lda

ta.D

ata

onch

ildre

nbo

rnbe

twee

n19

79-1

988(n=

5000).

IV:us

ing

para

met

erso

fAFD

C,FS

Pan

dM

edic

aid

asin

stru

men

ts,co

ntro

lling

for

child

char

acte

rist

icsa

ndre

gion

fixe

deff

ects

.Se

para

tere

gres

sions

for

poor

blac

kan

dpo

orw

hite

wom

en.Si

blin

gFE

:co

mpa

ring

sibsw

here

mot

her

onA

FDC

duri

ngpr

egna

ncy

toot

hers

.

OLS

resu

ltssu

gges

ttha

tAFD

Cha

sneg

ativ

eeff

ects

.Si

blin

gfixe

deff

ects

indi

cate

nosig

nifica

nteff

ects

.IV

resu

ltssu

gges

ttha

tAFD

Cdu

ring

preg

nanc

yca

uses

larg

ein

crea

sesi

nm

ean

birt

hw

eigh

tam

ong

poor

whi

teso

nly.

Hen

ce,

evid

ence

sugg

ests

that

AFD

Cha

sneu

tral

orpo

sitiv

era

ther

than

nega

tive

effec

ts.N

egat

ive

estim

ates

driv

enby

sele

ctio

n.

Doe

smon

eyre

ally

mat

ter?

estim

atin

gim

pact

soff

amily

inco

me

onch

ildre

n’sa

chie

vem

entw

ithda

tafr

omra

ndom

-ass

ignm

ente

xper

imen

ts(M

orri

seta

l.,20

04).

Dat

afr

omfo

urst

udie

stha

teva

luat

ed8

wel

fare

and

anti-

pove

rty

prog

ram

swith

rand

omiz

edde

signs

:C

onne

ctic

ut’s

Jobs

Firs

t,th

eLo

sAng

eles

Jobs

Firs

tG

AIN

,th

eN

ewB

runs

wic

kan

dB

ritis

hC

olum

bia

sites

ofth

eC

anad

ian

Self-

Suffi

cien

cyPr

ojec

t,an

dth

eA

tlant

a,G

A,G

rand

Rap

ids,

MI,

and

Riv

ersid

e,C

Asit

esof

the

Nat

iona

lEva

luat

ion

ofW

elfa

reto

Wor

kSt

rate

gies

.n=

18,47

1ch

ildre

nag

ed2-

15at

the

time

ofra

ndom

assig

nmen

t.

IVm

odel

suse

rand

omas

signm

enta

sin

stru

men

tsfo

rin

com

e,w

elfa

rere

ceip

t,an

dem

ploy

men

tto

estim

ate

impa

ctof

inco

me

onch

ildre

n’sc

ogni

tive

achi

evem

ent.

Incl

uded

dum

mie

sfor

sites

inbo

thst

ages

ofth

ere

gres

sion

anal

ysis.

Cog

nitiv

eac

hiev

emen

tmea

sure

dw

ithte

stsc

ores

and

pare

nt/t

each

erre

port

s.C

ontr

olle

dfo

rva

riou

sbas

elin

eba

ckgr

ound

fam

ilych

arac

teri

stic

s.

A$1

000

incr

ease

inan

nual

inco

me

raise

sco

gniti

veac

hiev

emen

tby

0.06

ofSD

for

child

ren

aged

2-5.

No

stat

istic

ally

signi

fica

nteff

ects

ofin

com

eon

child

ren

aged

6-9

or10

-15.

A3-

SDin

crea

sein

the

prop

ortio

nof

quar

ters

that

wel

fare

isre

ceiv

edle

adst

oa

1.5

SDde

crea

sein

cogn

itive

achi

evem

enta

mon

g10

-15

year

-old

s.

(con

tinue

don

next

page

)


Table6(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Who

bene

fits

from

child

bene

fit?

(Blo

wet

al.,

2005

).D

ata

from

UK

Fam

ilyE

xpen

ditu

reSu

rvey

1980

-200

0.n=

9811

two-

pare

ntho

useh

olds

;n=

2920

one-

pare

ntho

useh

olds

.

Iden

tifica

tion

from

vari

atio

nin

real

valu

eof

Chi

ldB

enefi

t(C

B)1

980-

2000

due

toin

flat

ion

and

polic

ych

ange

s.C

alcu

late

dan

ticip

ated

and

unan

ticip

ated

chan

gesi

nC

Bpa

ymen

ts.E

xam

ine

the

prop

ensit

yto

spen

dC

Bon

child

good

s(ch

ildre

n’s

clot

hing

)and

adul

tgoo

ds(a

lcoh

ol,

toba

cco

and

adul

tclo

thin

g).

Spen

din

goutofunan

tici

pat

edvs

.an

tici

pat

edch

ange

inC

B:

15x

grea

ter

for

alco

holi

n2-

pare

ntho

useh

olds

;10

xgr

eate

rfo

rad

ultc

loth

ing

inlo

ne-p

aren

tho

useh

olds

.R

esul

tssu

gges

ttha

tpar

ents

fully

insu

rech

ildre

nag

ains

tinc

ome

shoc

ksso

that

unan

ticip

ated

chan

geso

nly

affec

tspe

ndin

gon

adul

tgoo

ds.

Effec

tsof

EIT

Con

birt

han

din

fant

heal

thou

tcom

es.(B

aker

,20

08).

US

Vita

lSta

tistic

sNat

ality

data

file

1989

-96.

(n=

781,

535)

.E

xclu

deob

serv

atio

nsfr

om19

94.U

sed

data

from

1997

Mar

chC

PSto

com

pute

prop

ortio

nof

trea

tmen

tgro

upth

atis

elig

ible

for

EIT

C.

Diff

eren

ce-i

n-D

iffer

ence

-in-

Diff

eren

ces

(DD

D).

Exp

loit

the

larg

eex

pans

ion

ofE

ITC

in19

93(p

hase

din

1994

-96)

that

incr

ease

dbe

nefits

tofa

mili

esw

ith2

orm

ore

child

ren.

“Tre

atm

ent”

grou

p=

mot

hers

givi

ngbi

rth

to3r

dor

high

erch

ild.C

ontr

ol1

=m

othe

rsgi

ving

birt

hto

1sto

r2n

dch

ild.C

ontr

ol2

=m

othe

rsgi

ving

birt

hto

1stc

hild

.U

sed

mot

hers

with

less

than

Hig

hsc

hool

asa

prox

yfo

rE

ITC

elig

ibili

ty.E

ffec

tof

inte

rest

isin

tera

ctio

nbe

twee

n<

HS*

trea

tmen

t*af

ter

EIT

Cex

pans

ion.

Bir

thw

eigh

t:In

crea

sed

by0.

4%fo

ral

lwom

enIn

cide

nce

oflo

wbi

rth

wei

ght:

Dec

reas

edby

3.7%

for

all

No

stat

istic

ally

signi

fica

nteff

ects

on#

pren

atal

visit

sor

mat

erna

lsm

okin

gdu

ring

preg

nanc

y.

(con

tinue

don

next

page

)


Table6(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

The

impa

ctof

fam

ilyin

com

eon

child

achi

evem

ent:

evid

ence

from

the

earn

edin

com

eta

xcr

edit

(Dah

land

Loch

ner,

2008

).D

ata

from

NLS

Yon

child

ren

and

thei

rm

othe

rsfo

r19

88-2

000.

n=

4720

child

ren

(252

7m

othe

rs).

Iden

tifica

tion

base

don

EIT

Cex

pans

ions

in19

87-1

993

and

1993

-199

7th

atin

crea

sed

bene

fits

for

low

-an

dm

iddl

e-in

com

e.U

sed

simul

ated

inst

rum

enta

lva

riab

les(

IV):

IVis

pred

icte

dch

ange

inE

ITC

inco

me.

Con

trol

led

for

fam

ilyba

ckgr

ound

and

dem

ogra

phic

vari

able

san

dfo

rtim

e-va

ryin

gst

ate-

spec

ific

polic

ies

that

mig

htaff

ectc

hild

outc

omes

.E

xam

ine

cont

empo

rane

ousa

ndla

gged

inco

me.

Also

estim

ate

OLS

and

child

fixe

deff

ects

mod

els.

A$1

000

incr

ease

inin

com

era

isesc

ombi

ned

mat

han

dre

adin

gsc

ores

by0.

06SD

.La

rger

gain

sfro

mco

ntem

pora

neou

sinc

ome

for

child

ren

aged

5to

10th

anfo

rth

ose

aged

11-1

5.La

rger

gain

sfor

child

ren

from

disa

dvan

tage

dfa

mili

es.

Do

child

tax

bene

fits

affec

tthe

wel

l-be

ing

ofch

ildre

n?E

vide

nce

from

Can

adia

nch

ildbe

nefit

expa

nsio

ns(M

illig

anan

dSt

abile

,20

08).

Dat

afr

omsu

rvey

ofla

bor

and

inco

me

dyna

mic

son

fam

ilies

with

child

ren

for

1999

-200

4fo

rbe

nefit

simul

atio

n.D

ata

from

Can

adia

nN

atio

nalL

ongi

tudi

nalS

tudy

ofC

hild

ren

&Yo

uth

for

1994

-200

5fo

rch

ildre

n10

and

unde

r(n=

56,0

00).

Inst

rum

enta

lvar

iabl

es.U

sed

vari

atio

nin

child

bene

fits

acro

sstim

e,pr

ovin

ces,

and

num

ber

ofch

ildre

npe

rfa

mily

tode

velo

pa

mea

sure

ofbe

nefiti

ncom

eas

IVfo

rch

ildbe

nefits

inre

gres

sions

ofth

eeff

ecto

fchi

ldbe

nefits

onch

ildou

tcom

es.C

ontr

olle

dfo

rpr

ovin

ce-y

ear

fixe

deff

ects

.A

lsore

duce

dfo

rmsf

orsim

ulat

edbe

nefits

’effec

ton

child

outc

omes

.

An

incr

ease

in$

1000

insim

ulat

edbe

nefits

lead

sto

:1.

5%re

duct

ion

inlik

elih

ood

ofle

arni

ngdi

sabi

lity

(ifm

om<

HS)

;3.

6%de

clin

eco

nduc

tdiso

rder

/agg

ress

ion

scor

e4-

10;4.

3%of

SDde

clin

em

ater

nald

epre

ssio

n;11

.6%

SDde

clin

ein

mat

erna

ldep

ress

ion

(ifm

om<

HS)

;1.

1%de

clin

ein

child

ever

expe

rien

cing

hung

er(if

mom

<H

S)La

rger

posit

ive

impa

ctso

ned

ucat

ion

and

phys

ical

heal

thfo

rbo

ysth

angi

rls;

larg

erpo

sitiv

eim

pact

son

men

talh

ealth

for

girl

s.E

ffec

tson

mat

han

dvo

cabu

lary

scor

es,be

havi

oral

outc

omes

,m

ater

nald

epre

ssio

nan

dlik

elih

ood

ofev

erex

peri

enci

nghu

nger

pers

istat

leas

t4ye

ars.

(con

tinue

don

next

page

)


Table6(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Sout

hA

fric

anC

hild

Supp

ortG

rant

(CSG

):U

ncon

ditio

nalc

ash

tran

sfer

prog

ram

with

paym

ents

mad

eto

child

’spr

imar

yca

regi

ver.

Inte

nded

toco

ver

the

coun

try’

spoo

rest

30%

ofch

ildre

n.St

udy

ofeff

ects

onch

ildnu

triti

on(A

guer

oet

al.,

2009

).D

ata

from

Kw

aZul

u-N

atal

Inco

me

Dyn

amic

sStu

dy.M

ain

outc

ome

mea

sure

:H

eigh

t-fo

r-A

gez-

scor

e(H

AZ

).T=

245,

C=

886.

Exp

loit

vary

ing

leng

thso

fexp

osur

eto

CSG

amon

gch

ildre

nag

ed0-

3du

eto

the

timin

gof

the

rollo

utof

CSG

.Fi

taqu

adra

ticO

LSm

odel

toap

plic

atio

nde

lay

(num

ber

ofda

ysbe

twee

npr

ogra

mcr

eatio

nan

dch

ild’s

enro

llmen

t)us

ing

data

for

child

ren

born≤

2ye

arsp

rior

tosu

rvey

date

.C

alcu

late

dan

expe

cted

appl

icat

ion

dela

yva

riab

lefo

rea

chch

ildco

nditi

onal

onbi

rth

date

,lo

catio

nan

dfa

mily

char

acte

rist

ics,

define

das

%de

viat

ion

inap

plic

atio

nde

lay

from

expe

cted

dela

y.C

ondi

tiona

lon

fam

ilych

arac

teri

stic

s,de

viat

ion

inde

lay,

and

obse

rvab

les,

the

exte

ntof

CSG

trea

tmen

tsho

uld

bera

ndom

.Use

gene

raliz

edpr

open

sity

scor

es,

MLE

.

No

gain

sin

HA

Zfo

rtr

eatm

ents

cove

ring

≤20

%of

the

0-3

win

dow

.H

AZ

0.20

high

erfo

rtr

eatm

entc

over

ing

2/3

ofw

indo

wth

anfo

ra

child

rece

ivin

gtr

eatm

entc

over

ing

1%of

win

dow

.B

enefi

t-co

stra

tio

ofC

SG

:B

etw

een

1.06

and

1.48

(est

imat

ing

lifet

ime

earn

ings

gain

sfro

mga

insi

nH

AZ

,us

ing

annu

al5%

disc

ount

rate

,an

das

sum

ing

unem

ploy

ed33

%of

time)

.R

esul

tsro

bust

toch

ecki

ngfo

rag

eco

hort

effec

tsan

dlo

catio

n/sp

acia

leffec

ts.

(con

tinue

don

next

page

)


Table6(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Cond

itiona

lCashTran

sfer

Prog

rams(CCT

)

Mex

ican

“Pro

gres

a”pr

ogra

m(n

ow“O

port

unid

ades

”):C

ondi

tiona

lcas

htr

ansf

erso

f20-

30%

ofho

useh

old

inco

me

ever

y2

mon

ths.

Fam

ilies

mus

ttak

eyo

ung

child

ren

tohe

alth

clin

ics,

imm

uniz

atio

ns,ge

tade

quat

epr

enat

alca

re,&

rece

ive

nutr

ition

supp

lem

ent.

(Fam

ilies

also

toke

epol

der

child

ren

insc

hool

)(G

ertle

r,20

04).

Dat

afr

omsu

rvey

colle

cted

from

expe

rim

enta

lvill

ages

.Sa

mpl

esiz

es:77

03ch

ildre

nun

der

age

3at

base

line;

1501

new

born

s;H

eigh

tan

alys

is:T=

1049

,C=

503;

Ane

mia

anal

ysis:

T=

1404

,C=

608.

Prog

ram

phas

edin

to32

0tr

eatm

ent

villa

gesa

nd18

5co

ntro

lvill

ages

rand

omly

sele

cted

.C

ontr

olvi

llage

srec

eive

dbe

nefits

2ye

arsa

fter

star

tofp

rogr

am.T

hey

did

not

know

this

wou

ldbe

the

case

.In

vest

igat

e0/

1tr

eatm

entd

umm

yas

wel

lasd

iffer

ent

leng

thso

fpro

gram

expo

sure

.

Tre

atm

enteff

ects

:N

ewbo

rns2

5.3%

less

likel

yto

beill

inpa

stm

onth

.C

hild

ren

0-3

22.3

%le

sslik

ely

tobe

illin

past

mon

th.

Hei

ght(

12-3

6m

os.)

1.2%

high

er.

Prob

abili

tyof

bein

gan

emic

(12-

48m

os.)

25.5

%le

ss.

(con

tinue

don

next

page

)


Table6(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

“Ate

ncio

na

Cri

sis”

inru

ral

Nic

arag

ua.C

ondi

tiona

lcas

htr

ansf

er∼

15%

aver

age

per

capi

taex

pend

iture

s.W

omen

rece

ive

paym

ents

ever

y2

mon

thsi

fpre

scho

olch

ildre

nta

ken

for

regu

lar

visit

sto

heal

thcl

inic

s.(O

lder

child

ren

mus

tal

sobe

enro

lled

insc

hool

)(M

acou

rset

al.,

2008

).Sa

mpl

esiz

es:T=

3002

,C=

1019

.A

dditi

onal

data

from

the

2001

Nic

arag

uaLi

ving

Stan

dard

sM

easu

rem

entS

tudy

.

Ran

dom

ized

expe

rim

enta

ldes

ign.

4gr

oups

:C

CT,

CC

T+

voca

tiona

ltra

inin

g,C

CT+

prod

uctiv

ein

vest

men

tgra

nt,

cont

rol.

Subg

roup

anal

ysis

byge

nder

and

age.

Ana

lyze

dva

riou

stra

nsm

issio

nm

echa

nism

sby

estim

atin

gex

pend

iture

son

differ

entf

oods

(to

mea

sure

nutr

ient

inta

ke),

and

differ

ence

sin

indi

cato

rsfo

rea

rly

child

hood

deve

lopm

ent.

Tre

atm

enteff

ects

:D

evel

opm

enta

lscr

eene

r(D

DST

soci

al-p

erso

nal):

incr

ease

by0.

13SD

s.D

DST

lang

uage

:in

crea

seby

0.17

SDs.

Rec

eptiv

evo

cabu

lary

(TV

IP):

incr

ease

by0.

22SD

s.(C

hild

ren

mad

eup

appr

ox.1.

5m

onth

sof

dela

yon

TV

IP.)

Effec

tsby

age:

DD

STla

ngua

ge:up

0.06

SDs

0-35

mos

.,up

0.20

SDs6

0-83

mos

.T

VIP

:up

0.05

SDs3

6-59

mos

.,up

0.36

SDs

60-8

3m

os.(O

ldes

tchi

ldre

nm

ade

up2.

4m

onth

sofd

elay

onT

VIP

)N

ost

atist

ical

lysig

nifica

ntdi

ffer

ence

sby

gend

er.

Tra

nsm

issio

nm

echa

nism

s:B

ette

rdi

et;m

ore

likel

yto

have

book

s,pa

per,

penc

ils;m

ore

likel

yto

bere

adto

;m

ore

likel

yto

have

chec

kup,

vita

min

s,de

-wor

min

gdr

ugs;

mor

elik

ely

tobe

enro

lled;

mor

elik

ely

tose

edo

ctor

whe

nill

.

Unl

esso

ther

wise

note

d,on

lyre

sults

signi

fica

ntat

5%le

vela

rere

port

ed.

Perc

entc

hang

es(d

enot

edby

%in

stea

dof

“pp”

)are

repo

rted

rela

tive

toth

em

ean,

ifm

eans

wer

ere

port

edin

the

pape

r.“T

”=

Tre

atm

entg

roup

,“C

”=

Con

trol

grou

p.O

utco

mes

wri

tten

as“T>

C”

mea

nth

attr

eatm

ento

utco

me

grea

ter

than

cont

rolo

utco

me

at5%

signi

fica

nce

leve

l.O

utco

mes

wri

tten

as“T=

C”

mea

nth

atth

ere

isno

stat

istic

ally

signi

fica

ntdi

ffer

ence

inou

tcom

esbe

twee

n2

grou

psat

5%le

vel.


impact on physical health measures, though they do find a decrease in families reportingthat their children went hungry. There is some evidence of gender differences, with girlsshowing greater responsiveness to income on the mental health and behavioral scoreswhile boys show greater responsiveness on test scores.

These findings are extremely intriguing, but raise several questions. First, do theeffects of income vary depending on the child’s age? Morris et al. (2004) argue thatincome is more important at younger ages, though persistent poverty is worst of all.Second, are there really gender effects in the impact of income, and if so why? Third,the effects that Milligan and Stabile find are roughly twice those found by Dahl andLochner. Is this because the former study a pure income transfer while the latter studya tied transfer? Fourth, will the effects last, or will they be subject to “fade out” as thechildren grow older?

Table 6 also includes examples from a growing literature analyzing “conditional cashtransfer programs” (CCTs). These are programs that tie transfers to specific behavior onthe part of the family. For example, the parents may be required to make sure that thechildren attend school or get medical care in return for the transfer. These programs havebecome increasingly popular in developing countries, and have also been implementedto a limited extent in rich countries (for example, there is a program in New York Citywhich is being evaluated by Manpower Development Research Corporation). By theirnature, CCTs are complex programs that cannot tell us about the pure impacts of income.Still, these programs have attracted attention because randomized controlled trials haveshown at least short-term results. It is difficult however to compare across programs, giventhat they all tend to focus on different outcomes.

Given this positive evidence about the effects of income, it is a puzzle why so much aidto poor families is transferred in kind. Currie and Gahvari (2008) survey the many reasonsfor this phenomena that have been offered in the economics literature and concludethat the most likely reasons aid is offered in kind are agency problems, paternalism,and politics. In a nutshell, policy makers and the voters they represent may be moreconcerned with ensuring that children have medical care than with maximizing theirparent’s utility, even if the parent’s utility is assumed to be affected by the children’s accessto health care. Politics come in because coherent lobby groups (such as doctors, teachers,or farmers) may have incentives to advocate for various types of in kind programs. Inany case, in kind programs are an important feature of aid policies in all Organizationfor Economic Cooperation and Development states, accounting for over 10% of GDP ifhealth care and educational programs are included. In what follows, we first discuss “nearcash programs” and then programs whose benefits are less fungible with cash.

5.2. Near-cash programsPrograms such as the US Food Stamp Program (FSP, now renamed the SupplementalNutrition Assistance Program, or SNAP) and housing assistance are often referred to


as “near cash” programs because they typically offer households benefits that are worthless than what the household would have spent on food or housing in any case. Hence,

canonical microeconomic theory suggests that households should think of them asequivalent to cash and that they should have the same impact as the equivalent cashtransfer would have. In the case of food stamps, it has proven difficult to test thisprediction because the program parameters are set largely at the national level, so thatthere is only time series variation.

Currie (2003) provides an overview of the program, and the research on its effectsthat had been conducted up to that point. Schanzenbach (forthcoming) uses data from afood stamp cash out experiment to examine the effect on food spending. She finds that aminority of households actually received more in food stamps than they would otherwisespend on food. In these constrained households, families did spend more on food thanthey would have otherwise, while in other households, food stamps had the same effectas cash. Unfortunately, there is little evidence that constrained households bought foodsthat were likely to have beneficial effects; they seem, for example, to have spent some ofthe “extra” food money on products such as soda.

Hoynes and Schanzenbach (2009) use variation from the introduction of the FSP toidentify its effects on food spending. The FSP began as a small pilot program in 1961, andgradually expanded over the next 13 years: In 1971, national eligibility standards wereestablished, and all states were required to inform eligible households about the program.

In 1974, states were required to extend the program statewide if any areas of the stateparticipated. Using data from the PSID, the introduction of the FSP was associated withan 18% increase in food expenditures in the full sample, with somewhat larger effects inthe most disadvantaged households. They find that the marginal propensity to consume(MPC) food out of food stamp income was 0.16 compared to 0.09 for cash income. Thus,it does seem that many households were constrained to spend more on food than theyotherwise would have (or alternatively, that the person receiving the food stamps had astronger preference for food than the person controlling cash income in the household).From a policy maker’s point of view, this means that the FSP has a bigger impact on foodspending than an equivalent cash transfer. Still, it is a leaky bucket if only 16 cents ofevery dollar transferred goes to food.

Bingley and Walker (2007) conduct an investigation of the Welfare Milk Program inthe UK. They identify the effect of the program on household milk expenditures usinga large change in eligibility for the program that had differential effects by householdtype. They find that about 80% of a transfer of free milk is crowded out by reductionsin milk purchases by the household. This estimate is quite similar to that of Hoynes andSchanzenbach, though it still suggests that the in kind transfer is having some effect onthe composition of spending. Details of these two studies are shown in Table 7.

Given that these programs appear to have some effect on food expenditures, it isreasonable to ask what effect they have on child outcomes. There is a substantial older


Table7

Impa

ctsof

Food

Stam

pson

birthan

dearly

child

hood

outcom

es.

Stud

yStud

yde

sign

Results

Con

sum

ptio

nre

spon

sest

oin

-kin

dtr

ansf

ers:

evid

ence

from

the

intr

oduc

tion

ofth

eFo

odSt

amp

prog

ram

(Hoy

nesa

ndSc

hanz

enba

ch,20

09).

Dat

afr

omPS

IDfo

r19

68-7

8an

dfr

omth

e19

60,19

70,an

d19

80de

cenn

ial

cens

uses

.n=

39,6

23pe

rson

-yea

rob

s.

Diff

eren

ce-i

n-di

ffer

ence

usin

gva

riat

ion

inco

unty

-lev

elim

plem

enta

tion

ofFS

Pto

estim

ate

impa

ctof

FSP

onfo

odco

nsum

ptio

nan

dla

bor

supp

ly.C

ontr

olle

dfo

rco

unty

and

year

fixe

deff

ects

asw

ella

sst

ate

linea

rtim

etr

ends

.In

clud

edtr

ends

inte

ract

edw

/pr

e-tr

eatm

entc

hara

cter

istic

san

dth

ree

mea

sure

sofa

nnua

lper

capi

taco

unty

tran

sfer

paym

ents

.

Intr

oduc

tion

ofFS

Pis

asso

ciat

edw

ith:18

%in

crea

sein

tota

lfoo

dex

pend

iture

s(w

hole

sam

ple)

;26

-28%

incr

ease

into

talf

ood

expe

nditu

resf

orfe

mal

e-he

aded

HH

s;6-

13%

incr

ease

into

talf

ood

exp.

for

non-

whi

tefe

mal

e-he

aded

HH

s.N

osig

nifica

nteff

ecto

nm

eals

outa

ndca

shex

pend

iture

son

food

atho

me.

Ela

stic

ityof

food

spen

ding

with

resp

ectt

oin

com

e=

0.30

.M

PCfo

rfo

odou

tofc

ash

inco

me=

0.09

(for

who

lesa

mpl

e);

MPC

for

food

outo

fcas

hin

com

e=

0.11

1(<

$25,

000

inco

me)

;M

PCfo

rfo

odou

tofF

SPin

com

e=

0.16

(for

who

lesa

mpl

e);

MPC

for

food

outo

fFSP

inco

me=

0.23

8(<

$25,

000

inco

me)

.D

ecre

ase

inw

heth

erth

eH

Hhe

adre

port

sany

wor

kby

21%

.

The

re’s

nosu

chth

ing

asa

free

lunc

h:A

ltrui

stic

pare

ntsa

ndth

ere

spon

seof

hous

ehol

dfo

odex

pend

iture

sto

nutr

ition

prog

ram

refo

rms(

Bin

gley

and

Wal

ker,

2007

).D

ata

from

UK

Fam

ilyE

xpen

ditu

reSu

rvey

sfor

1981

-199

2.n=

29,22

2.

Ana

lyze

d3

nutr

ition

prog

ram

sin

the

UK

:fr

eesc

hool

lunc

hfo

rch

ildre

nfr

ompo

orH

Hs,

free

milk

topo

orH

Hsw

/pr

e-sc

hool

child

ren,

and

free

milk

atda

yca

refo

rpr

e-sc

hool

ersi

nat

tend

ance

rega

rdle

ssof

inco

me.

For

iden

tifica

tion,

expl

oite

d19

88re

form

that

ende

del

igib

ility

for

poor

HH

sw

ithw

orki

ngpa

rent

s.D

iffer

ence

-in-

differ

ence

(DD

).A

lsodi

dD

Dus

ing

the

fact

that

free

scho

ollu

nche

sav

aila

ble

only

duri

ngte

rmtim

e,an

dsu

mm

erho

liday

sbeg

inea

rlie

rin

Scot

land

.

Free

scho

ollu

nch

redu

cesf

ood

expe

nditu

reby

15%

ofth

epu

rcha

sepr

ice

ofth

elu

nch.

Free

pint

ofm

ilkre

duce

smilk

expe

nditu

reby

80%

.

(con

tinue

don

next

page

)


Table7(con

tinue

d)Stud

yStud

yde

sign

Results

Impa

ctof

Food

Stam

pPr

ogra

m(F

SP)o

nbi

rthw

eigh

t,ne

onat

alm

orta

lity,

and

fert

ility

(Alm

ond

etal

.,20

09).

Bir

than

dde

ath

mic

roda

tafr

omth

eN

atio

nal

Cen

ter

for

Hea

lthSt

atist

ics

mer

ged

with

coun

ty-l

evel

data

for

1968

-77.

FSP

data

from

USD

A.

Cou

nty

char

acte

rist

icsf

rom

1960

City

and

Cou

nty

Dat

aB

ook.

Dat

aon

gove

rnm

entt

rans

fers

and

per-

capi

tain

com

efr

omR

EIS

.Pa

rtic

ipat

ion

rate

scal

cula

ted

usin

gC

PS.n=

97,78

5w

hite

s;27

,274

blac

ks.

Diff

eren

ce-i

n-di

ffer

ence

,us

ing

the

fact

that

FSP

was

intr

oduc

edto

differ

entc

ount

iesa

tdi

ffer

entt

imes

due

toav

aila

ble

fund

ing

and

polic

ych

ange

s.K

eypo

licy/

trea

tmen

tva

riab

leis

the

mon

than

dye

arth

atea

chco

unty

impl

emen

ted

FSP.

Est

imat

edth

eim

pact

ofFS

Pon

coun

ty-l

evel

birt

hou

tcom

es,us

ing

coun

tyan

dtim

efixe

deff

ects

.M

ain

outc

omes

conc

erne

dw

ithav

aila

bilit

yof

FSP

duri

ng3r

dtr

imes

ter

ofpr

egna

ncy.

Intr

oduc

tion

ofFS

Pin

3rd

trim

este

rle

dto

:0.

06-0

.08%

(0.1

-0.2

%)i

ncre

ase

inbi

rth

wei

ghtf

orw

hite

s(bl

acks

);a

1%(0

.7-1

.5%

)dec

reas

ein

frac

tion

oflo

wbi

rth

wei

ghtf

orw

hite

s(bl

acks

).In

signi

fica

ntim

pact

sofe

xpos

ure

toFS

Pdu

ring

earl

ier

trim

este

rs.

Res

ults

robu

stto

addi

ngco

unty

&tim

efixe

d-eff

ecta

ndot

her

cont

rols,

asw

ella

svar

ious

time

tren

dsto

the

anal

ysis.

Res

ults

robu

stto

cond

uctin

gan

even

tstu

dyan

alys

is.

Effec

tsof

FSP

bene

fits

onw

eigh

tga

ined

byex

pect

antm

othe

rs(B

aum

,20

08).

Dat

afr

omth

eN

LSY.

Lim

ited

tolo

w-i

ncom

ebl

ack

and

Hisp

anic

wom

enw

/pr

egna

ncy

info

rmat

ion

inth

esu

rvey

s.n=

1477

preg

nanc

y-le

velo

bs.

Ran

dom

effec

tsm

odel

susin

gH

eckm

anan

dSi

nger

met

hod

tom

odel

unob

serv

edhe

tero

gene

ity.D

epen

dent

vari

able

isw

heth

erw

omen

gain

corr

ecta

mou

ntof

wei

ghtd

urin

gpr

egna

ncy

base

don

pre-

preg

nanc

yB

MI.

Ass

ume

stat

eva

riat

ion

inFS

Pel

igib

ility

rule

s,an

dpr

ogra

mad

min

istra

tion

affec

tsFS

Pta

keup

butn

otw

eigh

tgai

n.C

ontr

olfo

rge

stat

ion,

pre-

preg

nanc

yFS

P,W

IC.

Incr

easin

gav

erag

em

onth

lyFS

Pbe

nefits

from

$0to

$100

decr

ease

spro

babi

lity

ofga

inin

gto

olit

tlew

eigh

tby

11.8

-13.

7%.N

oeff

ecto

npr

obab

ility

ofga

inin

gto

om

uch

wei

ght.

No

stat

istic

ally

signi

fica

ntdi

ffer

ence

ineff

ects

ofFS

Pon

wei

ght

gain

betw

een

firs

t-tim

ean

dno

n-firs

t-tim

em

othe

rs.

(con

tinue

don

next

page

)


Table7(con

tinue

d)Stud

yStud

yde

sign

Results

Impa

ctof

FSP

onbi

rth

outc

omes

inC

alifo

rnia

(Cur

rie

and

Mor

etti,

2008

).D

ata

onFS

Pfr

omst

ate

reco

rdsa

ndR

EIS

.D

ata

from

birt

hre

cord

sin

CA

for

1960

-74.

Agg

rega

ted

data

into

cells

define

dus

ing

coun

ty,ra

ce,ye

arof

birt

h,m

ater

nala

gegr

oup,

pari

ty,a

ndth

eth

ird

ofth

eye

ar.n=

38,4

75ce

lls.

Diff

eren

ce-i

n-di

ffer

ence

usin

gco

unty

-lev

elva

riat

ion

intim

ing

ofFS

Pin

trod

uctio

n.FS

Pm

easu

red

usin

gdu

mm

y(=

1if

FSP

intr

oduc

ed),

log

expe

nditu

res,

orlo

gpa

rtic

ipat

ion.

FSP

dum

my

refe

rsto

9m

onth

spri

orto

birt

h.C

ount

yfixe

deff

ects

and

coun

ty-s

peci

fic

time

tren

dsin

clud

ed.

Exa

min

edte

enag

em

othe

rsan

dLA

coun

tyse

para

tely

.

FSP

intr

oduc

tion

led

toa

10%

incr

ease

innu

mbe

rof

firs

tbir

thst

ow

hite

teen

mot

hers

(onl

yin

Los

Ang

eles

);a

24%

incr

ease

innu

mbe

rof

firs

tbir

ths

tobl

ack

teen

mot

hers

;a

12%

incr

ease

innu

mbe

rof

firs

tbir

thst

oal

lbla

cks;

a0.

1%in

crea

sein

prob

abili

tyin

fant

1500

-200

0g

surv

ives

for

whi

tes;

a4%

decr

ease

inpr

obab

ility

infa

nt<

3000

gsu

rviv

esfo

rbl

acks

;a

4%in

crea

sein

prob

abili

tyof

low

birt

hw

eigh

tam

ong

whi

tete

ens.

Unl

esso

ther

wise

note

d,al

lrep

orte

dre

sults

are

stat

istic

ally

signi

fica

ntat

5%le

vel.

Perc

entc

hang

es(d

enot

edby

%in

stea

dof

“pp”

)are

repo

rted

rela

tive

toth

em

ean.


literature examining this question (see Currie (2003) for a summary). The modal studycompares eligible participants to eligible non-participants using a multiple regressionmodel. The main problem with drawing inferences about the efficacy of the FSPfrom this exercise is that participants are likely to differ from eligible non-participantsin ways that are not observed by the researcher. Thus, for example, Basiotis et al.(1998) and Butler and Raymond (1996) both find that participation in the FSP reducesconsumption of some important nutrients. Since it is hard to imagine how giving peoplefood coupons could do this, one suspects that these results are driven by negative selectioninto the FSP program.

Several recent papers examining the effects of the FSP on young children aresummarized in Table 7. Currie and Moretti (2008) were the first to try to use variationin the timing of the introduction of the Food Stamp program to look at effects onbirth outcomes. Using Vital Statistics Natality data from California, they find that theintroduction of the FSP increased the number of births, particularly in Los AngelesCounty. They also find some evidence that the FSP increased the probability of fetalsurvival among the lightest white infants, but the effect is very small, and only detectablein Los Angeles. Notably, the FSP increased (rather than decreased) the probability of lowbirth weight but the estimated effect is small, and concentrated among teenagers givingbirth for the first time. Thus, it appears that in California, the FSP increased fertility andinfant survival (in some groups) with overall zero or negative effects on the distributionof birth weight.

Almond et al. (forthcoming) examine the same question using national data, and focuson receipt of the FSP during the third trimester, when the fetus typically puts on most ofthe weight the baby will have at birth. In contrast to Currie and Moretti, they find thatthe introduction of the FSP increased birth weights for whites and had even larger effectson blacks. The percentage reductions in the incidence of low birth weight were greaterthan the percentage increases in mean birth weight, suggesting that the FSP had its largesteffects at the bottom of the birth weight distribution. Almond et al. find no effect of FoodStamp receipt in the first trimester of pregnancy and much weaker evidence for effects ofreceipt in the second. This suggests that one reason for the contrast between their resultsand those of Currie and Moretti is that the latter did not focus narrowly enough on therelevant part of pregnancy. Moreover, Almond et al. find larger effects in the South thanin other regions, raising the possibility that overall effects were smaller in California thanin other regions. Finally, it is possible that the effects in California are obscured by thesubstantial in-migration that the state experienced over this period.

Baum (2008) examines the effects of the FSP on weight gain among pregnant women,

with particular attention to whether women gained either less than the recommendedamount or greater than the recommended amount given their pre-pregnancy bodymass index. He estimates a simultaneous equations model in which weight gain andFSP participation are jointly determined. FSP participation is assumed to be affected by


various state-level rules about eligibility, outreach and so on. One difficulty is that theserules may be affected by other characteristics of states (such as overall generosity of socialprograms) which have direct effects on weight gain (e.g. through superior access to healthcare during pregnancy). Baum finds that FSP participation reduces the probability thatwomen experienced inadequate weight gain during pregnancy, but has no effect on theprobability that they gained too much weight. Since inadequate maternal weight gain isan important risk factor for low birth weight, it is likely that FSP had a positive effect onbirth weights among affected mothers.

As discussed above, the other large category of “near cash” offer subsidized housing.

Many OECD countries have large housing assistance programs, but their effects onfamilies are understudied. In fact, we were able to find only one paper that examinedthe effects of housing programs on the outcomes of children less than five, and onlya handful that examined effects on children at all. These studies are summarized inTable 8.

Since by design, families receiving housing assistance are among the poorest of thepoor, it is clearly important to address the endogeneity of program receipt. Currie andYelowitz (2000) look at the effects of living in a public housing project in families withtwo children. They combine information from the Census and from the Survey ofIncome and Program Participation in a two-sample instrumental variables frameworkwhere the instrument for receipt of housing assistance is the sex composition of thesiblings (families with a boy and a girl are entitled to larger apartments, and so are morelikely to take up housing benefits). They find that families living in projects are less likelyto be subject to overcrowding and that the children are much less likely to have been heldback in school. The latter effect is three times bigger for boys (who are more likely to beheld back in any case) than for girls. Since most “holding back” occurs at younger ages(Kindergarten and grade 1), this suggests that this type of assistance is in fact beneficialfor young children.

Goux and Maurin (2005) focus on the effect of overcrowding in France using asimilar instrumental variables strategy: They argue that children in families in which thetwo eldest children are the same sex are more likely to live in crowded conditions inchildhood. They also propose an alternative strategy in which crowding is instrumentedwith whether or not the parent was born in an urban area—parents who are from urbanareas are more likely to live in crowded conditions. They find evidence consistent withCurrie and Yelowitz in that crowding has a large and significant effect on the probabilitythat a child falls behind in school and eventually drops out.

Fertig and Reingold (2007) examine the effect of receipt of public housing assistanceusing data from the Fragile Families Study and three instruments: the gender compositionof children in the household, the supply of public housing in each location, and thelength of waiting lists in each location. They find mixed estimates of effects on maternalhealth and little evidence of an effect on child health, though their samples are quite


Table8

Effectsof

housingan

dne

ighb

orho

odson

child

outcom

es.

Stud

yan

dda

taStud

yde

sign

Results

Hou

sing

andch

ildou

tcom

es

Are

publ

icho

usin

gpr

ojec

tsgo

odfo

rki

ds?

(Cur

rie

and

Yelo

witz

,20

00).

Dat

afr

omSI

PPfo

r19

92-1

993,

Mar

chC

PSfo

r19

90-1

995,

and

US

Cen

susf

or19

90on

fam

ilies

w/

2ch

ildre

n(u

nder

18)a

ndin

com

e<

$50,

000.

n=

279,

129.

Two-

sam

ple

inst

rum

enta

lvar

iabl

e(T

SIV

)oft

heeff

ects

ofliv

ing

inpu

blic

hous

ing

proj

ects

onch

ild’s

educ

atio

n,ho

usin

gco

nditi

ons.

Out

com

eva

riab

lesa

rein

SIPP

and

Cen

sus.

End

ogen

ous

regr

esso

ris

inC

PS.In

stru

men

tisa

dum

my

for

siblin

gsbe

ing

ofdi

ffer

ents

exes

since

fam

ilies

with

differ

ents

exch

ildre

nge

tlar

ger

apar

tmen

tsin

publ

icho

usin

gth

anfa

mili

esw

ithsa

me

sex

child

ren.

Con

trol

led

for

per-

capi

taav

aila

bilit

yof

proj

ects

,vo

uche

rs,Se

ctio

n8

subs

idie

s,as

wel

las

othe

rne

ighb

orho

odan

dfa

mily

back

grou

ndch

arac

teri

stic

s.

Firs

tsta

gere

sults

:H

avin

gsib

lings

ofdi

ffer

ent

sex

incr

ease

slik

elih

ood

offa

mily

livin

gin

proj

ectb

y24

%.

Seco

ndst

age

resu

lts:Fa

mili

esw

holiv

ein

proj

ects

are

16pp

(mea

n=

0.04

)les

slik

ely

tobe

over

crow

ded;

and

12pp

(mea

n=

0.02

)les

slik

ely

toliv

ein

high

-den

sity

hous

ing.

Chi

ldre

nw

holiv

ein

proj

ects

are

111%

less

likel

yto

have

been

held

back

insc

hool

(larg

ereff

ects

for

boys

than

for

girl

s).B

lack

child

ren

who

live

inpr

ojec

tsar

e19

%le

sslik

ely

toha

vebe

enhe

ldba

ckin

scho

ol.N

ost

atist

ical

lysig

nifica

nteff

ectf

orw

hite

child

ren.

(con

tinue

don

next

page

)


Table8(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

The

long

-ter

meff

ects

ofpu

blic

hous

ing

onse

lf-su

ffici

ency

(New

man

and

Har

knes

s,20

02)

Dat

afr

omPS

ID—

Ass

isted

hous

ing

data

base

onco

hort

sbor

nb/

n19

57an

d19

67.Sa

mpl

elim

ited

toin

divi

dual

swho

sefa

mili

esw

ere

elig

ible

for

publ

icho

usin

gb/

nag

es10

and

16.

n=

1183

.A

dult

outc

omes

mea

sure

dat

ages

20-2

7.

Am

emiy

age

nera

lized

leas

tsqu

ares

regr

essio

nw

here

the

inst

rum

entw

asth

eve

ctor

ofre

sidua

lsfr

oma

regr

essio

nof

the

num

ber

ofpu

blic

hous

ing

units

per

elig

ible

fam

ilyin

the

area

onde

mog

raph

icch

arac

teri

stic

soft

hear

ea.

Inst

rum

ente

dfo

rw

heth

erch

ildliv

edin

publ

icho

usin

gto

estim

ate

impa

cton

vari

ousa

dult

outc

omes

.C

ontr

olle

dfo

rnu

mer

ousb

ackg

roun

dch

arac

teri

stic

sand

stat

ean

dye

arfixe

deff

ects

.

Livi

ngin

publ

icho

usin

gas

ach

ildle

adst

oan

incr

ease

inth

epr

obab

ility

ofan

yem

ploy

men

tb/

nag

es25

and

27of

7.8%

;an

incr

ease

inan

nual

earn

ings

b/n

ages

20an

d27

of14

.3%

;an

incr

ease

inth

enu

mbe

rof

year

snot

onw

elfa

reb/

nag

es20

and

27of

11.3

%.N

ost

atist

ical

lysig

nifica

nteff

ecto

nho

useh

old

earn

ings

rela

tive

toth

epo

vert

ylin

e.N

ote:

abov

ere

sults

signi

fica

ntat

6%le

vel.

Publ

icH

ousin

g,H

ousin

gVo

uche

rs,an

dSt

uden

tA

chie

vem

ent:

Evi

denc

efr

omPu

blic

Hou

sing

Dem

oliti

onsi

nC

hica

go(J

acob

,20

04)

Adm

inist

rativ

eda

tafr

omth

eC

hica

goH

ousin

gA

utho

rity

and

Chi

cago

Publ

icSc

hool

sfor

1991

-200

2on

stud

ents

who

lived

inhi

gh-r

isepu

blic

hous

ing

for

atle

asto

nese

mes

ter.

n=

10,5

56.

Inst

rum

enta

lvar

iabl

esre

gres

sion

ofth

eeff

ecto

fliv

ing

ina

publ

icho

usin

gpr

ojec

ton

stud

ent

outc

omes

.Id

entifi

catio

nfr

omva

riat

ion

intim

ing

ofho

usin

gde

mol

ition

sin

Chi

cago

inth

e19

90s.

Inst

rum

ente

dfo

rliv

ing

inpu

blic

hous

ing

with

adu

mm

yfo

rw

heth

erth

est

uden

tliv

edin

aun

itsc

hedu

led

for

dem

oliti

onat

the

time

ofth

ecl

osur

ean

noun

cem

ent.

Con

trol

sfor

back

grou

ndch

arac

teri

stic

sand

proj

ecta

ndye

arfixe

deff

ects

.

No

stat

istic

ally

signi

fica

nteff

ecto

fliv

ing

inhi

gh-r

isepr

ojec

tson

stud

ento

utco

mes

.Li

ving

ina

build

ing

that

was

dem

olish

edle

ads

toan

8.2%

incr

ease

inpr

obab

ility

that

stud

ents>

14yr

sdro

pou

tofs

choo

lwith

in3

year

srel

ativ

eto

thos

eth

atliv

edin

build

ings

sche

dule

dfo

rde

mol

ition

that

had

noty

etbe

ende

mol

ished

(12%

mor

elik

ely

for

girl

s;4%

mor

elik

ely

for

boys

). (con

tinue

don

next

page

)


Table8(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Publ

icho

usin

g,he

alth

and

heal

thbe

havi

ors:

isth

ere

aco

nnec

tion?

(Fer

tigan

dR

eing

old,

2007

).D

ata

from

the

frag

ilefa

mili

esan

dch

ildw

ell-

bein

gst

udy

onch

ildre

nbo

rnin

20U

Sci

tiesb

etw

een

1998

and

2000

.Sa

mpl

e1:

T=

422,

C=

2,05

5;Sa

mpl

e2:

T=

323,

C=

1,99

9;Sa

mpl

e3:

T=

150,

C=

919.

Inst

rum

enta

lvar

iabl

esre

gres

sion.

Thr

eein

stru

men

ts:ge

nder

com

posit

ion

ofho

useh

old,

the

supp

lyof

publ

icho

usin

gin

each

city

,an

dle

ngth

ofw

aitin

glis

t.T

hree

subs

ampl

es.

“Con

trol

”gr

oup

ism

othe

rsw

hoha

vebe

low

80%

ofm

edia

nin

com

ein

thei

rar

eabu

tare

noti

npu

blic

hous

ing.

“Tre

atm

ent1

”is

mot

hers

who

live

inpu

blic

hous

ing

attim

eof

firs

tint

ervi

ewim

med

iate

lyaf

ter

child

birt

h.“T

reat

men

t2”

ism

othe

rsw

hoha

vem

oved

into

publ

icho

usin

gbe

twee

nch

ildbi

rth

and

seco

ndin

terv

iew

atch

ild’s

age

1(“

mov

e-in

”su

bsam

ple)

.“T

reat

men

t3”

isth

e“‘

mov

e-in

”su

bsam

ple

limite

dto

mot

hers

with

two

orth

ree

child

ren

atse

cond

inte

rvie

w.C

ontr

olle

dfo

rfa

mily

,ba

ckgr

ound

,ne

ighb

orho

od,an

dot

her

dem

ogra

phic

and

soci

o-ec

onom

icch

arac

teri

stic

s.

Not

e:on

lyre

port

ing

IVes

timat

esw

ithco

mpl

ete

com

bina

tion

ofin

stru

men

tshe

re.

Effec

tsofm

ovin

gin

topublic

housi

ng

bet

wee

nch

ildbir

than

done-

year

inte

rvie

w:

1.63

poin

tinc

reas

ein

mot

her’s

heal

thin

dex

(5po

ints

cale

,m

ean

notr

epor

ted,

can’

tca

lcul

ate

effec

tsiz

e)55

ppde

crea

sein

likel

ihoo

dth

atm

othe

rha

slim

iting

heal

thco

nditi

on(m

ean=

0.08

)41

ppde

crea

sein

likel

ihoo

dth

atm

othe

ris

unde

rwei

ght(

mea

n=

0.07

)18

%in

crea

sein

mot

her’s

BM

ID

ecre

ase

indo

mes

ticvi

olen

ce(im

prec

iseco

effici

ente

stim

ate)

No

stat

istic

ally

signi

fica

nteff

ects

onch

ild’s

birt

hw

eigh

tor

PPV

Tsc

ores

.

Neigh

borhoo

dch

aracteristicsan

dch

ildou

tcom

es

The

long

-run

cons

eque

nces

ofliv

ing

ina

poor

neig

hbor

hood

(Ore

opou

los,

2003

)D

ata

from

the

inte

rgen

erat

iona

lin

com

eda

taba

sefo

r19

78-1

999

(tak

enfr

omin

com

eta

xfile

s)on

indi

vidu

alsl

ivin

gin

Toro

nto,

born

b/n

1963

and

1970

.n=

4060

.

Am

ong

fam

ilies

who

appl

yfo

rho

usin

gpr

ojec

ts,

assig

nmen

tto

apa

rtic

ular

proj

ecti

sapp

roxi

mat

ely

rand

om—

base

don

1sta

vaila

ble

unit.

Com

pare

dho

usin

g-pr

ojec

tmea

nsof

vari

ousa

dult

outc

omes

acro

ssne

ighb

orho

odqu

ality

(mea

sure

dby

dens

ityof

hous

ing,

tota

lsiz

eof

proj

ect,

prop

ortio

nof

the

Cen

sust

ract

belo

wth

elo

w-i

ncom

ecu

toff

,an

dw

heth

erth

epr

ojec

tisa

llhi

gh-r

ises)

.

The

qual

ityof

hous

ing

proj

ecto

rne

ighb

orho

odha

sno

stat

istic

ally

signi

fica

ntim

pact

onto

tali

ncom

e,an

nual

earn

ings

,or

num

ber

ofye

arso

nw

elfa

rein

adul

thoo

d.Fa

mily

char

acte

rist

icsa

ccou

ntfo

rup

to30

%of

tota

lvar

iatio

nin

adul

tout

com

es.

(con

tinue

don

next

page

)


Table8(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Nei

ghbo

rhoo

dE

ffec

tson

Cri

me

for

Fem

ale

and

Mal

eYo

uth:

Evi

denc

efr

oma

Ran

dom

ized

Hou

sing

Vouc

her

Exp

erim

ent

(Klin

get

al.,

2005

)D

ata

from

Mov

ing

toO

ppor

tuni

ties(

MT

O)

expe

rim

ent.

Fam

ilies

livin

gin

publ

icho

usin

gpr

ojec

tsin

Bos

ton,

LosA

ngel

es,N

ewYo

rkC

ity,

Bal

timor

e,an

dC

hica

gow

ere

rand

omly

chos

ento

stay

inth

eir

curr

enth

ome,

tore

ceiv

ea

Sect

ion

8ho

usin

gvo

uche

r,or

tore

ceiv

ea

Sect

ion

8ho

usin

gvo

uche

rre

stri

cted

tous

ein

neig

hbor

hood

sw

/po

vert

yra

tele

ssth

an10

%.

Surv

eysc

ondu

cted

in20

02.D

ata

onyo

uths

aged

15-2

5.D

ata

onar

rest

reco

rdsf

orM

TO

stat

es.

n=

4475

.

OLS

regr

essio

nof

crim

eou

tcom

eson

dum

my

for

whe

ther

fam

ilyus

eda

vouc

her

(inst

rum

ente

dby

whe

ther

fam

ilyw

asas

signe

dto

trea

tmen

tgro

up)

oron

vouc

her

type

,an

don

anag

e-tr

eatm

ent

inte

ract

ion

vari

able

.In

stru

men

tnec

essa

rysin

ceno

tall

fam

ilies

who

wer

eas

signe

da

vouc

her

chos

eto

use

it-

som

ere

mai

ned

inth

eir

initi

alho

usin

g.

Ass

ignm

entt

oa

rest

rict

edvo

uche

rle

adst

oa

31%

decr

ease

invi

olen

tcri

me

arre

stsf

orfe

mal

eyo

uths

.U

seof

rest

rict

edvo

uche

rle

ads

toa

76%

redu

ctio

nin

viol

entc

rim

ear

rest

sam

ong

fem

ale

yout

hs.N

ost

atist

ical

lysig

nifica

nteff

ects

onm

ales

for

viol

entc

rim

ear

rest

s.A

ssig

nmen

tto

are

stri

cted

vouc

her

lead

sto

a33

%de

crea

sein

prop

erty

crim

ear

rest

sfor

fem

ale

yout

hs.U

seof

rest

rict

edvo

uche

rle

adst

oa

85%

decr

ease

inpr

oper

tycr

ime

arre

stsf

orfe

mal

eyo

uths

.Ass

ignm

entt

oa

rest

rict

edvo

uche

rle

adst

oa

32%

incr

ease

inpr

oper

tycr

ime

arre

stsf

orm

ale

yout

hs.U

seof

rest

rict

edvo

uche

rle

adst

oa

77%

incr

ease

inpr

oper

tycr

ime

arre

stsf

orm

ale

yout

hs.

(con

tinue

don

next

page

)


Table8(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Nei

ghbo

rhoo

dsan

dA

cade

mic

Ach

ieve

men

t(Sa

nbon

mat

suet

al.,

2006

)D

ata

from

MT

Oex

peri

men

tsu

rvey

sfor

2002

(see

abov

e)on

yout

hsag

ed6-

20.n=

5074

.

Sam

ere

gres

sion

asK

ling

etal

.(2

005)

for

educ

atio

nalo

utco

mes

.C

hild

ren

infa

mili

esas

signe

dto

are

stri

cted

vouc

her

atte

ndbe

tter

qual

itysc

hool

s:th

em

ean

rank

ofsc

hool

son

stat

eex

amsi

s30%

grea

ter

than

the

cont

rolg

roup

mea

n,an

dth

em

ean

prop

ortio

nof

scho

ol-l

unch

-elig

ible

child

ren

is8%

low

er.N

ost

atist

ical

lysig

nifica

nteff

ects

ofei

ther

vouc

her

onch

ilded

ucat

iona

lout

com

es(s

choo

latt

enda

nce,

whe

ther

child

does

hom

ewor

k,ch

ild’s

beha

vior

incl

ass,

whe

ther

child

atte

nded

clas

sfo

rgi

fted

stud

ents

orcl

assf

orsp

ecia

lhel

p,an

dW

oodc

ock-

John

son

Rev

ised

read

ing

and

mat

hte

stsc

ores

).

Clo

seN

eigh

borh

oods

Mat

ter:

Nei

ghbo

rhoo

dE

ffec

tson

Ear

lyPe

rfor

man

ceat

Scho

ol(G

oux

and

Mau

rin,

2005

).D

ata

from

the

Fren

chLa

bor

Forc

eSu

rvey

for

1991

-200

2on

16-y

ear-

old

yout

hs.n=

1311

6.

Aut

hors

assu

me

that

ach

ild’s

birt

hdat

ew

illim

pact

his/

her

own

educ

atio

nalo

utco

mes

,bu

tnot

the

outc

omes

ofhi

s/he

rne

ighb

ors.

(Pro

port

ion

of15

-yr-

olds

held

back

agr

ade

is15

pphi

gher

amon

gth

ose

born

atth

een

dof

the

year

rela

tive

toth

ose

born

atth

ebe

ginn

ing

ofth

eye

ar).

IVre

gres

sion,

whe

refirs

tsta

gesa

re:re

gres

sion

ofbe

ing

held

back

atag

e15

ontim

ing

ofbi

rthd

ayan

dre

gres

sion

ofpr

opor

tion

ofne

ighb

orho

odyo

uths

held

back

atag

e15

onth

eir

birt

hday

s.Se

cond

stag

eis

regr

essio

nfo

rbe

ing

held

back

atag

e16

onha

ving

been

held

back

atag

e15

and

prop

ortio

nof

neig

hbor

hood

yout

hshe

ldba

ckat

age

15.C

ontr

olle

dfo

rye

arfixe

deff

ects

,ge

nder

,an

dna

tiona

lity.

1SD

incr

ease

inth

epr

opor

tion

ofne

ighb

orin

gyo

uth

that

wer

ehe

ldba

ckin

scho

olat

age

15in

crea

sest

hepr

obab

ility

ofbe

ing

held

back

atag

e16

by0.

2SD

.

(con

tinue

don

next

page

)


Table8(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Exp

erim

enta

lana

lysis

ofne

ighb

orho

odeff

ects

(Klin

get

al.,

2007

).D

ata

from

MT

Oex

peri

men

tsu

rvey

sfor

2002

(see

abov

e)on

yout

hsag

ed15

-20.

n=

1807

.

Sam

ere

gres

sion

as(K

ling

etal

.,20

05)f

orhe

alth

and

beha

vior

outc

omes

.A

ssig

nmen

tto

anun

rest

rict

edvo

uche

rle

ads

toa

decr

ease

inlik

elih

ood

ofex

peri

enci

ngan

xiet

ysy

mpt

omsb

y62

%/9

1%am

ong

fem

ales

/mal

es;a

decr

ease

inm

ariju

ana

use

by44

%am

ong

fem

ales

;an

incr

ease

inth

elik

elih

ood

ofno

n-sp

orts

inju

ryby

130%

amon

gm

ales

;an

incr

ease

inin

cide

nce

ofsm

okin

gby

121%

amon

gm

ales

.A

ssig

nmen

tto

rest

rict

edvo

uche

rle

adst

oa

decr

ease

inlik

elih

ood

ofex

peri

enci

ngps

ycho

logi

cal

dist

ress

by10

0%am

ong

fem

ales

;a

decr

ease

inlik

elih

ood

ofex

peri

enci

ngan

xiet

yby

57%

amon

gfe

mal

es;a

decr

ease

inm

ariju

ana

use

by50

%am

ong

fem

ales

;an

incr

ease

inlik

elih

ood

ofno

n-sp

orts

inju

ryby

140%

amon

gm

ales

;an

incr

ease

inin

cide

nce

ofsm

okin

gby

82%

amon

gm

ales

.

Unl

esso

ther

wise

note

d,on

lyre

sults

signi

fica

ntat

5%le

vela

rere

port

edhe

re.


small. Newman and Harkness (2002) use data from the PSID to examine the effectof living in public housing as a child on future earnings and employment. Living inpublic housing is instrumented using the residual from a regression of local housingsupply on the demographic characteristics of the area. They find that public housingis associated with increases in the probability of any employment (from 88% to about95%) and increases in annual earnings (by $1861 from a mean of $11,210). While allof these instrumental variables strategies are subject to caveats (is gender compositionreally uncorrelated with sibling’s outcomes? Are characteristics of local housing marketsassociated with unobserved factors such as the quality of schools that might also affectchild outcomes?) they certainly all point in a similar direction.

An important question is whether public housing assistance benefits children morethan the equivalent cash transfer. It is difficult to answer this question given the availabledata. However, it is possible to eliminate some possible channels through which publichousing programs might have different effects. One is that public housing programsmay constrain the recipient’s choice of neighborhoods, with either positive or negativeeffects. Jacob (2004) studies students displaced by demolitions of the most notoriousChicago high-rise projects. The US Congress passed a law in 1996 that required localhousing authorities to destroy units if the cost of renovating and maintaining them wasgreater than the cost of providing a voucher for 20 years. Jacobs argues that the orderin which doomed buildings were destroyed was approximately random. For example, inJanuary 1999, the pipes froze in some buildings in the Robert Taylor Homes, whichmeant that those buildings were demolished before others in the same complex. Bycomparing children who stayed in buildings scheduled to be demolished to otherswho had already been displaced by demolitions, he obtains a measure of the effect ofliving in high-rise public housing. Despite the fact that the high rises in Jacob’s studywere among the most notorious public housing projects in the country, he finds verylittle effect of relocation on children’s educational outcomes. However, this may bebecause for the most part, children stayed in the same neighborhoods and in the sameschools.

The most exhaustive examination of the effects of giving vouchers to project residentsis an ongoing experiment called “Moving to Opportunity” (MTO). MTO was inspiredby the Gautreaux program in Chicago, which resulted from a consent decree designedto desegregate Chicago’s public housing by relocating some black inner-city residentsto white suburbs. MTO is a large-scale social experiment that is being conducted inChicago, New York, Los Angeles, Boston and Baltimore (see Orr et al. (2003), Klinget al. (2005) and Sanbonmatsu et al. (2006)). Between 1994 and 1998, volunteers frompublic housing projects were assigned by lottery to one of three groups. The first groupreceived a voucher that could only be used to rent housing in a low-poverty area (aCensus tract with a poverty rate less than 10%). This group also received help locating asuitable apartment (referred to here as the “MTO group”). The second group received


a voucher which they could use to rent an apartment in any neighborhood. The thirdgroup was the control and received no vouchers or assistance although they were eligibleto remain in their project apartment. Families in the first group did move to lower povertyneighborhoods and the new neighborhoods of the MTO group were considerably safer.The move to new neighborhoods had positive effects on the mental health and schoolingattainment of girls, and negative effects on the probability that they were ever arrested.But surprisingly, MTO either had no effect, or negative effects, on boys. Boys in theexperimental group were 13% more likely than controls to have ever been arrested. Thisincrease was due largely to increases in property crimes. These boys also report morerisky behaviors such as drug and alcohol use. And boys in the MTO and voucher groupswere more likely to suffer injuries. These differences between boys and girls are apparenteven within families (Orr et al., 2003).

It remains to be seen how the long-term outcomes of the MTO children willdiffer from controls. Oreopoulos (2003) uses data from Canadian income tax recordsto examine the earnings of adults who lived in public housing projects in Toronto aschildren. There are large differences between projects in Toronto, both in terms of thedensity of the projects, and in terms of the poverty of the neighborhoods. Oreopoulosargues that the type of project a family lives in is approximately randomly assigned becausethe family is offered whatever happens to be available when they get to the top of thewaiting list. Oreopoulos finds that once the characteristics of the family are controlled,the neighborhood has no effect on future earnings or on the likelihood that someoneworks.

The findings on near cash programs can be summarized as follows. There is credibleevidence that the FSP may improve birth weight. More work remains to be done todetermine whether it has positive effects on the nutrition of children after birth, whethersimilar programs in other countries have positive effects, and whether this particular typeof in kind program has effects that are different than cash subsidies to poor households.The evidence regarding housing programs also suggests that they can be beneficial tofamilies, but offers little guidance about the important question of whether housingprograms matter primarily because they subsidize family incomes or operate throughsome other mechanism. It seems doubtful, given the available evidence, that housingprograms benefit child outcomes primarily by improving their neighborhoods (especiallysince many housing projects are located in less desirable neighborhoods).

5.3. Early intervention programsMany programs specifically seek to intervene in the lives of poor children in order toimprove their outcomes. Three interventions that have been shown to be effective arenurse home visiting programs, nutritional supplementation for pregnant women, andquality early childhood education programs. Table 9 summarizes some recent evidenceabout home visiting programs.


Table9

Rand

omized

trialsof

homevisitin

gprog

rams.

Stud

y/prog

ram

name

Data,prog

ram

description,

andstud

yde

sign

Results

The

Com

preh

ensiv

eC

hild

Dev

elop

men

tPr

ogra

m(C

CD

P)(S

t.Pi

erre

and

Layz

er,19

99)

Biw

eekl

yvi

sitss

tart

ing

0-1,

endi

ngat

5ye

ars.

Popu

latio

nse

rved

:43

%A

fric

an-A

mer

ican

,26

%H

ispan

ic;al

lbel

owpo

vert

y.B

ackg

roun

dof

Hom

eV

isito

rs:pa

rapr

ofes

siona

lsSa

mpl

esiz

es:T=

2,21

3,C=

2,19

7E

valu

atio

nSi

tes:

21sit

esth

roug

hout

the

US.

Age

ofch

ildre

nat

last

follo

w-u

p:5

year

sold

Dev

elop

men

talC

heck

list:

T=

57.9

3,C=

57.5

1Fo

und

nosig

nifica

nteff

ects

onw

ide

rang

eof

outc

omes

incl

udin

g:D

evel

opm

enta

ndB

ehav

ior

scor

es,m

edic

alca

re,

mor

talit

y,H

OM

Esc

ores

,m

ater

nald

epre

ssio

n,w

elfa

reus

e,m

ater

nali

ncom

e,ed

ucat

ion

orem

ploy

men

t,m

ater

nal

subs

tanc

eus

e.To

talc

ostp

erpa

rtic

ipan

t:$

37,4

88.To

tal

bene

fitp

erpa

rtic

ipan

t:$9

1.N

etpr

esen

tval

ue=−

$37,

397.

Hea

lthy

star

t(D

ugga

net

al.,

1999

,20

04;H

ardi

nget

al.,

2007

;D

uMon

teta

l.,20

06)

Wee

kly

visit

s,fa

ding

toqu

arte

rly

age

ofpa

rtic

ipat

ion:

birt

hto

5ye

ars.

popu

latio

nse

rved

:lo

w-i

ncom

e,at

-risk

fam

ilies

ofne

wbo

rnsr

ecru

ited

thro

ugh

anH

SPsc

reen

ing

and

asse

ssm

entp

roto

col.

All

Eng

lish-

spea

king

.B

ackg

roun

dof

hom

evi

sitor

s:pa

rapr

ofes

siona

ls.Sa

mpl

esiz

es:A

lask

a:T=

179,

C=

185;

Haw

aii:

T=

373,

C=

270,

6sit

es;

Vir

gini

a:T=

422,

C=

197,

2sit

es.

19ad

ditio

nals

itesd

iscus

sed

in(H

ardi

nget

al.,

2007

).A

geof

child

ren

atla

stfo

llow

-up:

2ye

ars

old

(3in

San

Die

go)

Som

epo

sitiv

eeff

ects

onpa

rent

ing

prac

tices

and

nega

tive

effec

tson

dom

estic

viol

ence

inso

me

sites

.E

.g.H

awai

ipar

tner

viol

ence

:T=

16%

,C=

24%

.Le

ssco

rpor

al/v

erba

lpun

ishm

entT

<C

(odd

srat

io0.

59).

Hea

ltheff

ects

inso

me

sites

butn

otot

hers

,e.

g.V

A:bi

rth

com

plic

atio

nsT=

0.2,

C=

0.48

;N

ewYo

rk:lo

wbi

rth

wei

ghtT=

3.3%

,C=

8.3%

;m

ater

nald

epre

ssio

nT=

23%

,C=

38%

.In

crea

sesi

nch

ildB

ayle

ySc

ale

for

Infa

ntD

evel

opm

enti

nSa

nD

iego

,A

rkan

sas.

Incr

ease

sin

mat

erna

ledu

catio

nan

dde

crea

sesi

nse

riou

schi

ldab

use

only

inN

ewYo

rk(T

12.5

%le

ssth

anC

).A

llsit

este

sted

for

aw

ide

rang

eof

poss

ible

effec

ts,w

ithge

nera

llyin

signi

fica

nteff

ects

onot

her

mea

sure

sofc

hild

wel

lbei

ngan

dch

ildab

use.

(con

tinue

don

next

page

)


Table9(con

tinue

d)Stud

y/prog

ram

name

Data,prog

ram

description,

andstud

yde

sign

Results

The

nurs

e-fa

mily

part

ners

hip

prog

ram

(Old

seta

l.,19

99)

Wee

kly

visit

s,fa

ding

tom

onth

ly,pr

enat

alto

2yr

s.Po

pula

tion

serv

ed:di

sadv

anta

ged

firs

t-tim

em

othe

rsle

ssth

an30

wee

kspr

egna

nt(6

2%un

mar

ried

,47

%te

enag

e,23

%po

or,

unm

arri

edan

dte

enag

e).

Bac

kgro

und

ofho

me

visit

ors:

Nur

ses.

Sam

ple

sizes

:C

1=

90,C

2=

94,T

3=

100

T4=

116

(see

belo

wfo

rde

scri

ptio

nof

grou

ps).

Eva

luat

ion

site:

Elm

ira,

New

York

.C

1=

scre

enin

g;C

2=

scre

enin

g&

tran

spor

tatio

n;T

3=

scre

enin

g,tr

ansp

orta

tion,

&pr

enat

alho

me

visit

s;T

4=

scre

enin

g,tr

ansp

orta

tion,

pren

atal

and

post

nata

lhom

evi

sits.

Pren

atal

anal

ysis:

T=

T3+

T4,

C=

C1+

C2;

Post

nata

lana

lysis

:T=

T4,

C=

C1+

C2.

Age

ofch

ildre

nat

last

follo

w-u

p:15

year

sold

.

Pre-

term

birt

hsfo

rw

omen

who

smok

edm

ore

than

4ci

gare

ttes

per

day:

T=

2.08

%,C=

9.81

%(m

othe

rsal

sole

sslik

ely

tosm

oke

duri

ngpr

egna

ncy,

bett

ernu

triti

ondu

ring

preg

nanc

y,m

ore

likel

yto

use

WIC

).Fo

rch

ildre

n:fe

wer

emer

genc

yro

omvi

sitsa

t0-1

2,12

-24

mon

ths.

AT

15-y

rfo

llow

-up

Mot

her’s

num

ber

ofm

onth

srec

eivi

ngA

FDC

:T

4=

60.4

,C=

90.3

;M

othe

r’ssu

bsta

nce

use

impa

irm

ents

:T

4=

0.41

,C=

0.73

;M

othe

r’sar

rest

s:T

4=

0.18

,C=

0.58

;C

onvi

ctio

ns:

T4=

0.06

,C=

0.28

;Su

bsta

ntia

ted

repo

rtso

fchi

ldab

use

and

negl

ect,

0to

15yr

s:T

4=

0.29

,C=

0.54

;C

hild

’sin

cide

nce

ofar

rest

s:T

4=

0.20

,C=

0.45

;C

hild

’sco

nvic

tions

and

prob

atio

nvi

olat

ions:

T4=

0.09

,C=

0.47

;C

hild

’snu

mbe

rof

sex

part

ners

:T

4=

0.92

,C=

2.48

;C

hild

’snu

mbe

rof

days

dran

kal

coho

l:T

4=

1.09

,C=

2.49

.C

ost

-ben

efitan

alys

is:

Tota

lcos

tper

child

:$1

0,30

0;To

talb

enefi

tper

child

:$

30,0

00.N

etpr

esen

tval

ue=+

$19,

700.

Mos

tben

efits

due

tore

duce

dcr

ime

onpa

rtof

the

child

and

redu

ctio

nsin

child

abus

eon

part

ofpa

rent

.

(con

tinue

don

next

page

)


Table9(con

tinue

d)Stud

y/prog

ram

name

Data,prog

ram

description,

andstud

yde

sign

Results

The

nurs

e-fa

mily

part

ners

hip

prog

ram

(Old

seta

l.,20

07)

Wee

kly

visit

s,fa

ding

tom

onth

ly,pr

enat

alto

2yr

s.Po

pula

tion

serv

ed:

firs

t-tim

em

othe

rsle

ssth

an29

wee

kspr

egna

ntan

dat

leas

t2of

the

follo

win

g:un

mar

ried

,le

ssth

an12

yrso

fed

ucat

ion,

orun

empl

oyed

;92

%A

fric

an-A

mer

ican

,98

%un

mar

ried

,64

%18

yrso

fage

oryo

unge

r,85

%at

orbe

low

pove

rty

leve

l.B

ackg

roun

dof

hom

evi

sitor

s:nu

rses

.Sa

mpl

esiz

es:C

1=

166,

C2=

515,

T3=

230,

T4=

228

(see

belo

wfo

rde

scri

ptio

nof

grou

ps).

Eva

luat

ion

sites

:M

emph

is,Te

nnes

see.

C1=

tran

spor

tatio

n;C

2=

scre

enin

g&

tran

spor

tatio

n;T

3=

scre

enin

g,tr

ansp

orta

tion,

pren

atal

hom

evi

sits,

one

visit

post

part

umin

hosp

ital,

one

post

part

umvi

sitat

hom

e;T

4=

T3

plus

hom

evi

sitst

hrou

ghch

ild’s

2nd

birt

hday

.Pr

enat

alan

alys

is:T=

T3+

T4,

C=

C1+

C2;

Post

nata

lana

lysis

:T=

T4,

C=

C2.

Age

ofch

ildre

nat

last

follo

w-u

p:9

year

sold

.

Dur

ing

firs

t2ye

arso

fchi

ld’s

life:

Num

ber

ofhe

alth

enco

unte

rsfo

rin

juri

es/i

nges

tions

:T=

0.43

,C=

0.56

Num

ber

ofou

tpat

ient

visit

sfor

inju

ries

/ing

estio

ns:

T=

0.11

,C=

0.20

Num

ber

ofda

ysho

spita

lized

for

inju

ries

/ing

estio

ns:

T=

0.04

,C=

0.18

Mot

her

atte

mpt

edbr

east

-fee

ding

:T=

26%

,C=

16%

Subs

eque

ntliv

ebi

rths

:T=

22%

,C=

31%

AT

9-yr

follo

w-u

pC

hild

GPA

(low

-res

ourc

eon

ly):

T=

2.68

,C=

2.44

Rea

ding

and

mat

hac

hiev

emen

t(lo

w-r

esou

rce

only

):T=

44.8

9,C=

35.7

2M

othe

r’s#

mon

thsw

ithcu

rren

tpar

tner

:T=

51.8

9,C=

44.4

8N

umbe

rof

mon

thso

nA

FDC

/TA

NF

per

year

:T=

5.21,C=

5.92

Num

ber

ofm

onth

son

food

stam

pspe

rye

ar:T=

6.98

,C=

7.80

Mat

erna

lmas

tery

:T=

101.

03,C=

99.5

0N

o.of

mon

thsw

ithem

ploy

edpa

rtne

r:T=

46.0

4,C=

48.4

3N

osig

nifica

nteff

ects

onE

Rvi

sitsf

orin

juri

esin

firs

t2

year

s,he

alth

atbi

rth,

use

ofm

edic

alca

re,m

ater

nalh

ealth

.

(con

tinue

don

next

page

)


Table9(con

tinue

d)Stud

y/prog

ram

name

Data,prog

ram

description,

andstud

yde

sign

Results

Ear

lyst

art(

Ferg

usso

net

al.,

2006

)W

eekl

yvi

sitsf

or1s

tmon

th,th

enva

ryin

gag

eof

part

icip

atio

n:pr

enat

alto

3ye

ars.

Popu

latio

nSe

rved

:fa

mili

esre

crui

ted

thro

ugh

the

sam

esc

reen

ing

proc

essa

sin

Haw

aiiH

ealth

ySt

art.

Bac

kgro

und

ofho

me

visit

ors:

“fam

ilysu

ppor

tw

orke

rs”

with

nurs

ing

orso

cial

wor

kqu

alifi

catio

ns.

Sam

ple

sizes

:T=

220,

C=

223.

1sit

ein

New

Zea

land

.A

geof

child

ren

atla

stfo

llow

-up:

3ye

arso

ld.

Ave

rage

num

ber

ofdo

ctor

’svi

sits0

-36

mo:

T>

Cby

0.24

SD.

Perc

enta

geof

up-t

o-da

tew

ell-

child

chec

ks0-

36m

o:T>

Cby

0.25

SD.

Perc

enta

geen

rolle

dw

/de

ntist

0-36

mo:

T>

Cby

0.20

SD.

Perc

enta

geat

tend

edho

spita

lfor

acci

dent

/inj

ury

orac

cide

ntal

poiso

ning

0-36

mo:

T<

Cby

0.22

SD.

Mea

ndu

ratio

nof

earl

ych

ildho

oded

ucat

ion:

T>

Cby

0.22

SD.

Mea

nnu

mbe

rof

com

mun

ityse

rvic

eco

ntac

ts:T>

Cby

0.31

SD.

Posit

ive

pare

ntin

gat

titud

essc

ore:

T>

Cby

0.26

SD.

Non

-pun

itive

pare

ntin

gat

titud

essc

ore:

T>

Cby

0.22

SD.

Ove

rall

pare

ntin

gsc

ore:

T>

Cby

0.27

SD.

Perc

enta

geof

pare

ntal

repo

rtof

seve

reph

ysic

alas

saul

t:T<

Cby

0.26

SD.

Chi

ldin

tern

aliz

ing

(neg

ativ

e)be

havi

orsc

ore*

:T<

Cby

0.26

SD.

Chi

ldto

taln

egat

ive

beha

vior

scor

e*:T<

Cby

0.24

SD.

(con

tinue

don

next

page

)


Table9(con

tinue

d)Stud

y/prog

ram

name

Data,prog

ram

description,

andstud

yde

sign

Results

Effec

tiven

esso

fhom

evi

sitat

ion

bypu

blic

heal

thnu

rses

inpr

even

tion

ofth

ere

curr

ence

ofch

ildph

ysic

alab

use

and

negl

ect:

ara

ndom

ized

cont

rolle

dtr

ial

(Mac

Mill

anet

al.,

2005

)

Wee

kly

visit

sfor

firs

t6m

onth

s,th

enbi

wee

kly

Age

ofpa

rtic

ipat

ion:

Ent

ry:0-

13yr

sold

,pr

ogra

mla

sted

3ye

ars.

Popu

latio

nse

rved

:pa

rent

swho

have

are

port

edep

isode

ofph

ysic

alab

use

orne

glec

tin

the

3m

onth

spri

orto

join

ing

prog

ram

.B

ackg

roun

dof

hom

evi

sitor

s:pu

blic

heal

thnu

rses

.Sa

mpl

esiz

es:T=

73,C=

66.1

site

inH

amilt

on,C

anad

a.N

ote:

Con

trol

grou

pre

ceiv

edst

anda

rdse

rvic

esfr

omth

ech

ildpr

otec

tion

agen

cyC

PA).

Tre

atm

entg

roup

also

rece

ived

the

stan

dard

serv

ices

inad

ditio

nto

the

hom

evi

sitin

g.A

geof

child

ren

atla

stfo

llow

-up:

Vari

ed(3

-yea

rfo

llow

-up)

.

Rec

urre

nce

ofph

ysic

alab

use

orne

glec

tbas

edon

hosp

ital

reco

rds:

T=

24%

,C=

11%

Also

test

ed,bu

tfou

ndno

stat

istic

ally

signi

fica

nteff

ects

on:

Rec

urre

nce

ofab

use

orne

glec

tbas

edon

CPA

reco

rds;

HO

ME

scor

e;ch

ild’s

beha

vior

al,an

xiet

y,at

tent

ion

prob

lem

s,ag

gres

sion

orco

nduc

tdiso

rder

scor

es;pa

rent

ing

beha

vior

scor

esor

CA

Psc

ore.

Eco

nom

icev

alua

tion

ofan

inte

nsiv

eho

me

visit

ing

prog

ram

me

for

vuln

erab

lefa

mili

es:A

cost

-eff

ectiv

enes

sana

lysis

ofa

publ

iche

alth

inte

rven

tion

(McI

ntos

het

al.,

2009

)

Wee

kly

visit

sA

geof

Part

icip

atio

n:pr

enat

alto

18m

onth

sold

.Po

pula

tion

Serv

ed:Pr

egna

ntw

omen

iden

tified

byco

mm

unity

mid

wiv

esas

bein

gat

risk

for

child

abus

eor

negl

ect.

Bac

kgro

und

ofho

me

visit

ors:

para

prof

essio

nals

who

rece

ived

prog

ram

trai

ning

.Sa

mpl

eSi

zes:

T=

67,C=

64.

1sit

ein

the

UK

.A

geof

Chi

ldre

nat

Last

Follo

w-u

p:1

year

old

Mat

erna

lsen

sitiv

ity(C

AR

EIn

dex)

:T>

Cby

13%

.In

fant

coop

erat

iven

ess(

CA

RE

Inde

x):T>

Cby

18%

.Li

kelih

ood

infa

ntpl

aced

onch

ildpr

otec

tion

regi

ster

:T>

C(1

.35

times

mor

elik

ely)

.Pr

opor

tion

ofch

ildre

nre

mov

edfr

omho

me:

T=

6%,

C=

0%.

Som

eco

st-b

enefi

tana

lysis

from

Aos

etal

.(2

004)

,Te

chni

calA

ppen

dix.

“T”

refe

rsto

trea

tmen

tgro

up,“C

”;re

fers

toco

ntro

lgro

up.“T=

C”

mea

nsno

disc

erna

ble

differ

ence

betw

een

grou

psat

5%sig

nifica

nce

leve

l.


5.3.1. Home visitingUnlike many social programs, home visiting has been subject to numerous evaluationsusing randomized control trials. A recent survey appears in Howard and Brooks-Gunn(2009). David Olds and collaborators have developed a particular model for home visitingand conducted randomized controlled trials in a number of settings (Olds et al., 1999,

2007) to evaluate it. Olds’ programs focus on families that are at risk because the motheris young, poor, uneducated and/or unmarried, and involve home visits by trained publichealth nurses from the prenatal period up to two years postpartum. The evaluationshave shown many positive effects on maternal behavior, and on child outcomes. As oftwo years of age, children in the Elmira New York were much less likely to have beenseen in a hospital emergency room for unintentional injuries or ingestion of poisonoussubstances, although this finding was not replicated at other study sites. As of age 15,

children of visited mothers were less likely to have been arrested or to have run away fromhome, had fewer sexual partners, and smoked and drank less. The children were also lesslikely to have been involved in verified incidents of child maltreatment. This finding isimportant given the high incidence of maltreatment among US children (and especiallyamong poor children), and the negative outcomes of maltreated children discussed above.

There was little evidence of effects on cognition at four years of age (except amongchildren of initially heavy smokers), though one might expect the documented reductionin delinquent behavior among the teens to be associated with improvements in eventualschooling attainment.

In Olds’ model, using nurses as home visitors is viewed as key to getting goodresults. This may be because nurse home visitors are more acceptable to parents thansocial workers or community workers because families may want medical services. Arandomized trial of nurses versus trained paraprofessionals (Olds et al., 2002) suggests thatthe effects that can be obtained by paraprofessionals are smaller. Also, the Olds programsare strongly targeted at families considered to be at risk and so they do not shed lighton the cost-effectiveness of the universal home visiting programs for pregnant womenand/or newborns that exist in many countries.

Olds’ positive results do not imply that all home visiting programs are equallyeffective. In fact, Table 9 suggests that the average home visiting program has relativelysmall effects. They often improve parenting in subtle ways and may result in someimprovements in specific health outcomes. However, these may not be sufficient tojustify the cost of a large scale program (Aos et al. (2004) offers a cost benefit analysisof several programs). Home visiting programs can be viewed as a type of parentingprogram—presumably the reason why Olds’ home visitors improved outcomes is becausethey taught mothers to be better parents. Since parents are so important to children,

programs that seek to improve parenting practices are perennially popular. Yet studies ofthese programs suggest that it is remarkably difficult to change parents’ behavior and thatmany attempted interventions are unsuccessful. The most successful parenting programs


are those that combine parent education with some other intervention that parents want,such as visits by nurses (as in Olds case) or child care (Brooks-Gunn and Markham, 2005).

5.3.2. US supplemental feeding program for women, infants, and children (WIC)A second type of early intervention program that has been extensively studied is theUS Supplemental Feeding Program for Women, Infants, and Children (WIC). As itsname implies, WIC is a program targeted at pregnant and lactating women, infants, andchildren up to age 5. Participants receive vouchers that can be redeemed for particulartypes of food at participating retailers. Participants must generally go to the WIC officeto receive the vouchers, and generally receive nutrition education services at that time.Many WIC offices are run out of clinics and may also facilitate access to medicalcare. Dozens of studies (many of them reviewed in Currie (2003)) have shown thatparticipation in WIC during pregnancy is associated with longer gestations, higher birthweights, and generally healthier infants, and that the effects tend to be largest for childrenborn to the most disadvantaged mothers. Economists have critiqued these studies, onthe grounds that there may be unobservable variables that are correlated with WICparticipation among eligibles and also with better birth outcomes. Moreover, it may beimplausible to expect WIC to have an effect on pre-term birth. A recent Institute ofMedicine report on the subject reviewed the evidence and concluded that randomizedtrials of many different interventions with women at risk of pre-term birth had failedto find effects (Behrman and Butler, 2007). So it might be surprising to find an effectfor WIC, when more specific and intensive interventions aimed at preventing pre-termbirth have generally failed.

A number of new studies have attempted to deal with various aspects of this critique,as shown in Table 10. Bitler and Currie (2005) look at data from the Pregnancy RiskMonitoring System, which contains very detailed data from new mothers obtainedby combining data from birth records and survey data taken from women before andafter pregnancy. They directly address the question of selection bias by examining thepopulation of mothers eligible for Medicaid (all of whom are adjunctively eligiblefor WIC) and asking how participants differ from non-participants along observabledimensions. They find that the WIC women are more disadvantaged than the non-participants along all observables. This finding does not prove that WIC women are alsonegatively selected in terms of unobservable variables, but it does mean that women whowere very negatively selected in terms of education, health, family relationships and soon would have to have other attributes that were systematically correlated with positiveoutcomes. Like previous studies, Bitler and Currie also find that WIC participation isassociated with higher maternal weight gain, longer gestation, and higher birth weight,particularly among women on public assistance, high school dropouts, teen mothers, andsingle mothers.

Joyce et al. (2004) adopt a similar strategy with regard to selection, and focus on asample of first births to women who initiated prenatal care in the first four months of


Table10

Selected

stud

iesof

specialsup

plem

entalfee

ding

prog

ram

forw

omen

,infan

ts,and

child

ren(W

IC).

Stud

yStud

yde

sign

Results

Impa

cton

birthou

tcom

es

Bitl

eran

dC

urri

e(2

005)

.D

ata

from

Preg

nanc

yR

iskA

sses

smen

tM

onito

ring

Syst

em(P

RA

MS)

,(n=

60,73

1).

Com

pare

dW

ICpa

rtic

ipan

tsan

dno

n-pa

rtic

ipan

tsin

the

sam

ple

ofw

omen

who

sede

liver

iesw

ere

paid

byM

edic

aid.

Add

ress

edse

lect

ion

bias

byco

mpa

ring

abr

oad

rang

eof

obse

rvab

lech

arac

teri

stic

sbet

wee

nel

igib

leW

ICpa

rtic

ipan

tsan

dno

n-pa

rtic

ipan

ts.

WIC

mot

hers

are

nega

tivel

yse

lect

edin

toth

epr

ogra

mre

lativ

eto

allM

edic

aid

reci

pien

ts.W

ICpa

rtic

ipan

tsar

e1.

4-1.

5tim

esm

ore

likel

yto

have

had

pren

atal

care

in1s

ttri

mes

ter;

0.7

times

aslik

ely

togi

vebi

rth

tolo

wbi

rth

wei

ghti

nfan

t;0.

9tim

esas

likel

yto

give

birt

hto

infa

nts

who

are

belo

wth

e25

thpe

rcen

tile

ofbi

rth

wei

ghtg

iven

gest

atio

nala

ge;0.

9tim

esas

likel

yto

have

thei

rin

fant

adm

itted

toth

eIn

tens

ive

Car

eU

nit.

WIC

asso

ciat

edw

ithin

crea

sesi

nm

ater

nalw

eigh

tgai

n,ge

stat

ion,

and

birt

hw

eigh

t.La

rger

impa

ctfo

rm

ore

disa

dvan

tage

dw

omen

(suc

has

thos

ew

hore

ceiv

edpu

blic

assis

tanc

e,hi

ghsc

hool

drop

-out

s,te

enm

othe

rs,sin

gle

mot

hers

).

(con

tinue

don

next

page

)


Table10

(con

tinue

d)Stud

yStud

yde

sign

Results

Figl

ioet

al.(2

009)

.M

atch

edda

taon

birt

hsin

Flor

ida

duri

ng19

97-2

001

with

scho

olre

cord

sofo

lder

siblin

gsof

the

infa

ntst

oid

entif

yw

heth

erth

eol

der

child

rece

ives

free

lunc

hor

redu

ced-

pric

elu

nch

thou

ghth

eN

SLP

(tho

seon

NSL

Par

eW

ICel

igib

le).

Mar

gina

llyel

igib

le:

n=

2530

;M

argi

nally

inel

igib

le:

n=

1744

.

Inst

rum

enta

lvar

iabl

esco

mpa

ring

mar

gina

llyel

igib

lean

din

elig

ible

WIC

wom

en.M

argi

nally

inel

igib

lefo

rW

IC=

fam

ilies

notp

artic

ipat

ing

inN

LSP

duri

ngpr

egna

ncy,

butp

artic

ipat

ing

duri

ngei

ther

the

year

befo

reor

afte

r.M

argi

nally

elig

ible=

fam

ilies

that

rece

ived

NSL

Pdu

ring

the

preg

nanc

y,bu

tdid

notr

ecei

veth

elu

nche

satl

east

one

adja

cent

year

.A

fede

ralp

olic

ych

ange

that

incr

ease

din

com

ere

port

ing

requ

irem

ents

for

WIC

elig

ibili

tyin

Sept

embe

r19

99,m

ade

itm

ore

diffi

cult

for

elig

ible

sto

obta

inW

IC.T

hein

stru

men

tfor

WIC

part

icip

atio

nis

anin

tera

ctio

nbe

twee

nan

indi

cato

rfo

rth

epo

licy

chan

gean

del

igib

ility

.

WIC

part

icip

atio

nre

duce

sthe

likel

ihoo

dof

low

birt

hw

eigh

tby

12.9

pp(im

prec

isees

timat

e).N

ost

atist

ical

lysig

nifica

nteff

ecto

fWIC

onge

stat

iona

lage

orlik

elih

ood

ofpr

emat

urity

.

Gue

orgu

ieva

etal

.(2

009)

.D

ata

onm

othe

r-in

fant

pair

sfro

mbi

rth

file

son

alls

ingl

eton

birt

hsin

Flor

ida

hosp

itals

betw

een

1996

and

2004

.M

erge

dw

ithM

edic

aid

elig

ibili

tyan

dW

ICpa

rtic

ipat

ion

data

from

the

Flor

ida

Dep

artm

ento

fHea

lth.

(n=

369,

535)

.

Adj

uste

dfo

rse

lect

ion

usin

gpr

open

sity

scor

es.

“Tre

atm

ent”

ispe

rcen

tday

son

WIC

duri

ngpr

egna

ncy.

Mai

nou

tcom

eis

SGA

(“sm

allf

orge

stat

iona

lage

”).S

epar

ate

anal

yses

for

full-

term

,lat

epr

e-te

rm,ve

rypr

e-te

rm,an

dex

trem

ely

pre-

term

birt

hs.

A10

%in

crea

sein

perc

ento

ftim

edu

ring

preg

nanc

yin

WIC

asso

ciat

edw

itha

2.5%

decr

ease

inpr

obab

ility

ofa

full-

term

and

SGA

infa

nt;2.

0%de

crea

sein

prob

abili

tyof

ala

tepr

e-te

rman

dSG

Ain

fant

;3.

7%de

crea

sein

prob

abili

tyof

ave

rypr

e-te

rman

dSG

Ain

fant

.

(con

tinue

don

next

page

)


Table10

(con

tinue

d)Stud

yStud

yde

sign

Results

(Joy

ceet

al.,

2004

).D

ata

from

birt

hce

rtifi

cate

file

sin

New

York

City

betw

een

1988

and

2001

onw

omen

who

wer

eon

Med

icai

dan

d/or

WIC

duri

ngpr

egna

ncy.

(n=

35,4

15in

1988

-199

0;n=

50,6

59in

1994

-199

6;n=

52,6

08in

1999

-200

1).

Mul

tivar

iate

regr

essio

nw

ithdu

mm

iesf

orW

ICpa

rtic

ipat

ion,

inte

ract

edw

ithye

arof

birt

h.To

addr

esss

elec

tion

bias

,lim

ited

anal

ysis

tow

omen

with

firs

tbir

thsw

hoin

itiat

edpr

enat

alca

rein

firs

t4m

onth

sofp

regn

ancy

.E

stim

ated

sepa

rate

mod

elsb

yra

ce,et

hnic

ity,na

tivity

,an

dpa

rity

.E

stim

ated

sam

em

odel

com

pari

ngtw

inbi

rths

.

Am

ong

US-

born

blac

ks,W

ICpa

rtic

ipan

tsar

e2.

4pp

less

likel

yto

expe

rien

cea

low

birt

hw

eigh

tbir

thth

anno

n-W

ICpa

rtic

ipan

tsin

1988

-199

1.N

ost

atist

ical

lysig

nifica

nteff

ects

ofW

ICpa

rtic

ipat

ion

for

fore

ign-

born

Hisp

anic

wom

en.In

twin

anal

ysis,

for

US-

born

blac

ks,W

ICpa

rtic

ipat

ion

isas

soci

ated

with

a55

gin

crea

sein

birt

hw

eigh

tad

just

edfo

rge

stat

ion,

and

a3.

9pp

decr

ease

inSG

Ara

tes.

Effec

tsbi

gges

tfor

US-

born

blac

kw

omen

unde

rag

e25

.(N

ote,

mea

nsfo

rsu

bgro

upsn

otre

port

ed,so

effec

tsiz

esca

n’tb

eca

lcul

ated

).

Joyc

eet

al.(2

007)

.Pr

egna

ncy

Nut

ritio

nSu

rvei

llanc

eSy

stem

(PN

SS)d

ata

(n=

2,87

0,03

1).

Incl

uded

allw

omen

who

wer

een

rolle

din

WIC

duri

ngpr

egna

ncy

and

re-e

nrol

led

post

part

um.

Com

pari

son

grou

pis

wom

enw

hoen

rolle

din

WIC

afte

rde

liver

ybu

tw

ere

note

xpos

edto

WIC

duri

ngpr

egna

ncy.

Mul

tivar

iate

regr

essio

nw

ithdu

mm

iesf

orW

ICin

each

trim

este

r.To

deal

with

sele

ctio

nbi

as,es

timat

edse

para

tem

odel

sby

race

/eth

nici

ty.A

lsoan

alyz

edsu

bgro

upsw

hose

pre-

preg

nanc

ych

arac

teri

stic

sput

wom

enat

high

risk

for

anem

ia,lo

ww

eigh

tgai

n,an

din

trau

teri

negr

owth

reta

rdat

ion.

Fina

lly,

anal

yzed

subg

roup

offirs

t-bi

rths

.

Con

ditio

nalo

nge

stat

iona

lage

,m

ean

birt

hw

eigh

tis4

0g

grea

ter

amon

gpr

enat

alen

rolle

esth

anpo

stpa

rtum

enro

llees

.R

ates

ofSG

Aar

e1.

7pp

(14%

)le

ss,an

dra

teso

fter

mlo

wbi

rth

wei

ght

are

0.7

pp(3

0%)l

ess.

Diff

eren

cebe

twee

n1s

tand

3rd

trim

este

ren

rolle

esin

mea

nbi

rth

wei

ghti

s13.

5g.

(con

tinue

don

next

page

)


Table10

(con

tinue

d)Stud

yStud

yde

sign

Results

Kow

ales

ki-J

ones

and

Dun

can

(200

2).

NLS

YM

othe

r-C

hild

data

.20

00ch

ildre

n19

90-9

6.10

4sib

ling

pair

s,71

pair

sin

whi

chon

ech

ildpa

rtic

ipat

edan

don

edi

dn’t.

Sibl

ing

fixe

deff

ects

.In

crea

seof

7ou

nces

inm

ean

birt

hwei

ght.

Posit

ive

effec

ton

tem

pera

men

tsco

re.N

oeff

ecto

nso

cial

orm

otor

skill

stes

tsco

res.

(Hoy

nese

tal.,

2009

).D

ata

onco

unty

-lev

elW

ICav

aila

bilit

yin

1971

-197

5an

d19

78-1

982

from

lists

oflo

cala

genc

iest

hatp

rovi

ded

WIC

serv

ices

.D

ata

onbi

rth

wei

ghtf

rom

vita

lsta

tistic

srec

ords

.D

ata

onpo

pula

tion

ofw

omen

byco

unty

-yea

rfr

omth

eC

AN

CE

R-S

EE

Rpo

pula

tion

data

.D

ata

onva

riou

scon

trol

vari

able

sfr

om19

70IP

UM

S.(n=

18,5

17co

unty

-yea

rce

lls)

Diff

eren

ce-i

n-di

ffer

ence

,us

ing

vari

atio

nin

the

timin

gof

WIC

impl

emen

tatio

nac

ross

differ

ent

coun

tiesi

n19

72-1

979,

toes

timat

eim

pact

ofW

ICav

aila

bilit

yon

birt

hw

eigh

t.C

ontr

olle

dfo

rth

ree

mea

sure

sofp

erca

pita

gove

rnm

entt

rans

fers

,an

indi

cato

rfo

rFo

odSt

amp

prog

ram

avai

labi

lity,

othe

rde

mog

raph

ics,

and

coun

tyan

dye

arfixe

deff

ects

,an

dst

ate-

year

fixe

deff

ects

.Sc

aled

estim

ates

byW

ICpa

rtic

ipat

ion

rate

s.A

lsoco

nduc

ted

sub-

grou

pan

alys

isin

coun

ty-y

ear-

mat

erna

ledu

catio

nle

vel

cells

.

IfW

ICis

avai

labl

ein

aco

unty

byth

eth

ird

trim

este

r,av

erag

ebi

rth

wei

ght

incr

ease

sby

0.1%

(est

imat

eno

tsca

led

bypa

rtic

ipat

ion

rate

).A

mon

gw

omen

with

low

leve

lsof

educ

atio

n,W

ICin

crea

ses

aver

age

birt

hw

eigh

tby

10%

(est

imat

esc

aled

bypa

rtic

ipat

ion

rate

)and

redu

ces

the

frac

tion

ofbi

rths

clas

sified

aslo

wbi

rth

wei

ghtb

y11

%(e

stim

ate

scal

edby

part

icip

atio

nra

te).

No

effec

tsof

WIC

onfe

rtili

ty—

sore

sults

notd

rive

nby

sele

ctio

nin

tobi

rth.

Impa

cton

brea

stfeed

ingan

dinfant

feed

ingpractices

Cha

tter

jiet

al.(2

002)

.N

LSY

Mot

her-

Chi

ldfile

.12

82ch

ildre

nbo

rn19

91-9

5.97

0sib

lings

born

1989

-95.

IVw

ithW

ICst

ate

prog

ram

char

acte

rist

icsa

sin

stru

men

ts.Si

blin

gfixe

deff

ects

.O

LSan

dIV

indi

cate

WIC

redu

ces

brea

stfe

edin

gin

itiat

ion,

butn

oeff

ecto

ndu

ratio

n.Fi

xed

effec

tsug

gest

sre

duct

ions

inle

ngth

brea

stfe

edin

g.

(con

tinue

don

next

page

)


Table10

(con

tinue

d)Stud

yStud

yde

sign

Results

Impa

cton

nutritionan

dhe

alth

outcom

esof

child

ren

Bla

cket

al.(2

004)

Dat

afr

omsu

rvey

sad

min

ister

edby

The

Chi

ldre

n’s

Sent

inel

Nut

ritio

nalA

sses

smen

tPr

ogra

min

am

ulti-

site

stud

yat

urba

nm

edic

alce

nter

sin

5st

ates

and

Was

hing

ton,

DC

in19

98-2

001

(n=

5923

:53

95re

ceiv

edW

ICas

sista

nce,

528

did

not)

.

Com

pare

dW

IC-e

ligib

lefa

mili

esw

hopa

rtic

ipat

edin

WIC

with

thos

ew

hodi

dno

tdue

tose

lf-re

port

edac

cess

prob

lem

s.M

ultiv

aria

tere

gres

sion

incl

udin

ga

part

icip

atio

ndu

mm

y,co

ntro

lling

for

rele

vant

back

grou

ndch

arac

teri

stic

s.

Com

pare

dto

infa

ntsw

hore

ceiv

edW

ICas

sista

nce,

thos

ew

hodi

dno

trec

eive

WIC

assis

tanc

ew

ere

mor

elik

ely

tobe

unde

rwei

ght(

wei

ght-

for-

age

z-sc

ore=−

0.23

vs.0.

009)

,sh

ort

(leng

th-f

or-a

gez-

scor

e=−

0.23

vs.

−0.

02),

and

perc

eive

das

havi

ngfa

iror

poor

heal

th(a

djus

ted

odds

ratio=

1.92

).N

ost

atist

ical

lysig

nifica

ntdi

ffer

ence

sin

rate

soff

ood

inse

curi

ty.

Lee

and

Mac

key-

Bila

ver

(200

7).

Dat

afr

omIL

Inte

grat

edD

atab

ase

onC

hild

ren’

sSer

vice

s.In

clud

esin

foon

Med

icai

d,FS

P,W

ICen

rollm

ent.

All

ILch

ildre

nbo

rnbe

twee

n19

90an

d19

96w

hoen

tere

dM

edic

aid

with

infirs

tmon

th.T

rack

edto

2001

.To

tal

sam

ple=

252,

246.

Sibl

ing

FEsa

mpl

e=

36,2

77.

Mul

tivar

iate

regr

essio

nw

ithsib

ling

fixe

d-eff

ects

,us

ing

part

icip

atio

ndu

mm

iesf

orFS

P,W

IC,an

dFS

P-W

ICjo

intly

.(N

ote,

siblin

gFE

only

mak

ese

nse

for

WIC

,eff

ects

ofFS

Pes

timat

edus

ing

OLS

.A

lso,

mos

tfam

ilies

onW

ICal

sore

ceiv

eFS

P).

Effec

tofW

IC:A

buse

orne

glec

trat

esde

crea

seby

84%

(mea

n=

0.10

).In

cide

nce

ofan

emia

decr

ease

by41

%(m

ean=

0.19

5).Fa

ilure

toth

rive

decr

ease

by78

%(m

ean=

0.12

8).

Nut

ritio

nald

efici

ency

decr

ease

by11

5%(m

ean=

0.03

8).

Unl

esso

ther

wise

note

d,on

lyre

sults

signi

fica

ntat

5%le

vela

rere

port

edhe

re.

Perc

entc

hang

es(d

enot

edby

%in

stea

dof

“pp”

)are

repo

rted

rela

tive

toth

em

ean.


pregnancy in order to ensure that participants and non-participants were more likely tobe similar in terms of unobservables. In their sample of women giving birth in NewYork City, they find positive effects of WIC among US born black women, but notin other groups. Joyce et al. (2007) use a national sample of women, compare womenwho enrolled in WIC pre and post delivery, and focus on whether infants are smallfor gestational age (SGA). If one does not believe that WIC can affect gestation, thenfocusing on SGA is appropriate because it is not affected by gestational age. They findthat the incidence of SGA is lower for the prenatal enrollees than for the postpartumenrollees. Gueorguieva et al. (2009) use a large sample of births from Florida and tryto deal with potential selection using propensity score matching. They side step theissue of whether WIC affects gestation by presenting separate analyses for pregnanciesof different length, and focusing on SGA. They find that longer participation in WIC isassociated with reductions in the incidence of SGA. Kowaleski-Jones and Duncan (2002)examine sibling pairs from the NLSY and find that WIC participation is associated withan increase of seven ounces in birth weight. However, the number of pairs in which onechild participated and one did not is quite small, so it would be useful to try to replicatethis finding in a larger sample of siblings.

Figlio et al. (2009) present an innovative instrumental variables strategy using alarge sample of births from Florida that have been merged to school records of oldersiblings. While the characteristics of WIC programs vary across states, they do notshow a lot of variation over time, and previous analyses have demonstrated that thesecharacteristics are weak instruments (Bitler and Currie, 2005). Figlio et al. (2009) firsttry to select participant and non-participant groups who are very similar. They do this bydefining “marginally ineligible” families as those who participated in the National SchoolLunch Program (NLSP) in the year before or after the birth, but did not participatein the birth year. Thus, the study focuses on families whose incomes hover around theeligibility threshold for NSLP, which is the same as the eligibility threshold for WIC. Theinstrument is a change in income reporting requirements for WIC in Sept. 1999 whichmade it more difficult for eligible families to receive benefits. Figlio et al. (2009) find thatWIC participation reduces the probability of low birth weight, but find no significanteffect on gestational age or prematurity.

There has been much less study of the effects of WIC on other outcomes, orother groups of participants. A couple of studies that make some attempt to deal withthe selection issue are summarized in Table 10. One problem with WIC is that itsubsidizes baby formula, which is likely to discourage breast-feeding. Chatterji andBrooks-Gunn (2004) use the NLSY Mother-Child file and estimate both sibling fixedeffects models and instrumental variables models using characteristics of state programsas instruments. They find that WIC reduces breast feeding initiation and the length ofbreastfeeding. However, these results are subject to the caveats above (i.e. small samplesand possibly weak instruments). Turning to the effects of WIC on older children, Black


et al. (2004) compare WIC eligible participants and those who did not participate dueto “access problems”. These problems were assessed based on the families own reportsabout why they were not participating. They found that infants who received WIC wereless likely to be underweight, short, or perceived by their parents to be in fair or poorhealth. Lee and Mackey-Bilaver (2007) use a large data base from Illinois that integratesadministrative data from several sources. Using sibling fixed effects models, they find thatsiblings who received WIC were less likely to be anemic, to have exhibited failure-to-thrive, or other nutritional deficiencies, and that the infants were less likely to be abusedor neglected. As discussed above, one issue in the interpretation of these findings is whyone infant would receive WIC while the other did not?

In one of the most interesting recent studies, Hoynes et al. (2009) use the initialroll-out of the WIC program in the 1970s to identify its effects. They find that theimplementation of WIC increased average birth weight by 10% and decreased thefraction of low birth weight births. They did not find any evidence of changes in fertility.

In summary, the latest group of studies of WIC during pregnancy largely support thefindings of earlier studies which consistently found beneficial effects on infant health. Thefinding is remarkable because WIC benefits are relatively modest (often amounting toabout $40 per month) and Americans are generally well fed (if not overfed at least in termsof total calories). Research that attempted to peer into the “black box” and shed light onwhy the program is effective would be extremely interesting. Another question that criesout for future research is whether WIC benefits infants and children (i.e. children whoparticipate after birth)? While a few studies suggest that it does, the effects of WIC in thispopulation has been subject to much less scrutiny than the effects on newborns.

5.3.3. Child careThere have been many evaluations of early intervention programs delivered throughthe provision of child care. One reason for focusing on early intervention through theprovision of quality child care is that the majority of young children are likely to beplaced in some form of care. In 2008, 64% of women with children under 3 workedfor pay (US Bureau of Labor Statistics, 2009). While the US may be an outlier in thisrespect, labor force participation among women with children is high and rising inmany other economies. Blau and Currie (2006) provide an overview of the literature onearly intervention through child care. Many studies concern experimental evaluationsof model programs that serve relatively small numbers of children and involve intensiveservices delivered by well-trained and well-supervised staff. These studies generallyfind that early intervention has long-lasting effects on schooling attainment and otheroutcomes such as teen pregnancy and crime, even if it does not result in any lastingincrease in cognitive test scores. These results point to the tremendous importanceof “non-cognitive skills” (cf Heckman and Rubinstein (2001)) or alternatively, tothe importance of mental as well as physical health in the production of good childoutcomes (Currie and Stabile, 2006).


A few of the most notable model programs are summarized in Table 11. Two studies of“model” early intervention child care programs stand out because they randomly assignedchildren to treatment and control group, had low dropout rates, and followed childrenover many years. They are the Carolina Abecedarian Project and the Perry PreschoolProject. Both found positive effects on schooling. A recent cost-benefit analysis of theAbcedarian data through age 21 found that each dollar spent on Abecedarian saved taxpayers four dollars. And by focusing only on cost savings, this calculation does not eveninclude the value of higher achievement to the individual children and society (Masse andBarnett, 2002). Each dollar spent on Perry Preschool has been estimated to have saved upto seven dollars in social costs (Karoly et al., 1998), although this high benefit-cost ratiois driven largely by the effect of the intervention on crime, which in turn depends on ahandful of individuals.

Anderson (2008) conducts a re-analysis of the Perry Preschool and Abcedarian data(and a third intervention called the Early Training Project) and finds that like theMTO public housing experiment, the significant effects of the intervention were largelyconcentrated among girls. In addition to analyzing the data by gender, Anderson payscareful attention to the idea that there may be a reporting bias in the published studies ofearly intervention experiments; that is, researchers who found largely null effects of theexperiment might still be able to publish results focusing on one or two positive outcomesout of many outcomes investigated. Conversely, if all effects tended in the same direction,

but there was insufficient power to detect significant effects on each outcome, it mightbe possible to detect a significant effect on an index of the outcomes. Anderson findspositive effects (for girls) on a summary index of effects, and the effects are quite large atabout a half a standard deviation. This study highlights an interesting question, which iswhether it is generally easier to intervene with girls than with boys, and why that mightbe the case?

The fact that special interventions like Perry Preschool or Abcedarian had an effecton at least some target children does not prove that the types of programs typicallyavailable to poor inner-city children will do so. Head Start is a preschool program fordisadvantaged 3, 4, and 5 year olds which currently serves about 800,000 children eachyear. It is funded as a federal-local matching grant program and over time, federal fundinghas increased from $96 million when the program began in 1965 to about $7 billion in2009 (plus additional “stimulus” funds). Head Start is not of the same quality as the modelinterventions, and the quality varies from center to center. But Head Start centers havehistorically been of higher average quality than other preschool programs available to lowincome people. This is because, in contrast to the private child care market, there are fewvery low-quality Head Start programs (see Blau and Currie (2006) for an overview ofpreschool quality issues).

An experimental evaluation of Head Start has recently been conducted (Puma et al.,2010). The evaluation compares Head Start children to peers who may or may not be in


Table11

Selected

recent

evalua

tions

ofearly

child

hood

prog

ramswith

rand

omized

design

s.Stud

y/prog

ram

namea

Data,prog

ram

description,

andstud

yde

sign

Results

Car

olin

aA

bece

dari

anfo

llow

-up

and

cost

-ben

efit

anal

ysis

at21

year

sofa

ge(B

arne

ttan

dM

asse

,20

07)

Pres

choo

lers

:fu

ll-da

ych

ildca

reSc

hool

age:

pare

ntpr

ogra

mSa

mpl

esiz

es:

Initi

al:T=

57,C=

54A

ge8:

T=

48,C=

42A

ge15

:T=

48,C=

44A

ge21

:T=

53,C=

51A

geof

part

icip

atio

nin

prog

ram

:E

ntry

:6

wee

ksto

3m

onth

sold

Exi

t:5

to8

year

s

Follo

w-u

pat

21ye

arsofag

e:G

rade

rete

ntio

n:T=

34%

,C=

65%

,ag

e21

Spec

iale

duca

tion:

T=

31%

,C=

49%

,ag

e21

Hig

hsc

hool

drop

out:

T=

33%

,C=

49%

,ag

e21

Col

lege

atte

ndan

ce:T=

36%

,C=

13%

,ag

e21

Cri

me

rate

:T=

C,ag

e21

Em

ploy

men

tsta

tus:

T=

Cat

age

21A

vera

geag

efirs

tchi

ldbo

rn:T>

Cat

age

21C

ost

-Ben

efitA

nal

ysis

:(u

sing

5%di

scou

ntra

te,$2

002)

Net

cost

per

child=

$34,

599

Net

bene

fito

fpro

gram=

$72,

591

per

part

icip

ant

Infa

ntH

ealth

and

Dev

elop

men

tPro

ject

Follo

w-u

pat

18ye

arso

fag

e(M

cCor

mic

ket

al.,

2006

)

Hom

evi

sits,

full-

day

child

care

Sam

ple

sizes

:In

itial

:T=

377,

C=

608

Follo

wup

atag

e8:

T=

336,

C=

538

Follo

wup

atag

e18

:T=

254,

C=

381

(div

ided

in2

grou

ps:lig

hter

low

birt

hw

eigh

t(LL

BW

)and

heav

ier

low

birt

hw

eigh

t(H

LBW

))A

geof

part

icip

atio

nin

prog

ram

:E

ntry

:bi

rth

(hom

evi

sits)

,1

year

(car

e).

Exi

t:3

year

s

Mat

hac

hiev

emen

t:T>

Cby

6.8%

,ag

e18

HLB

WR

eadi

ngac

hiev

emen

t:T>

Cby

5.6%

,ag

e18

HLB

WR

isky

beha

vior

s:T>

Cby

23.3

%,ag

e18

HLB

WIQ

:T=

C,ag

e18

HLB

WN

ote:

For

allo

utco

mes

:T=

C,ag

e18

LLB

W

(con

tinue

don

next

page

)


Table11

(con

tinue

d)Stud

y/prog

ram

namea

Data,prog

ram

description,

andstud

yde

sign

Results

Are

eval

uatio

nof

earl

ych

ildho

odin

terv

entio

n—A

bece

dari

an,Pe

rry

Pres

choo

land

Ear

lyT

rain

ing

Proj

ect—

with

emph

asis

onge

nder

differ

ence

sand

mul

tiple

infe

renc

e(A

nder

son,

2008

)

Abe

ceda

rian

:T=

57,C=

54Pe

rry:

T=

58,C=

65E

TP:

T=

44,C=

21A

geso

fent

ry:

Abe

ceda

rian

/Per

ry/E

TP:

4.4

mo.

/3yr

s./3-

4yr

s

Out

com

esin

clud

e:IQ

,gr

ade

repe

titio

n,sp

ecia

led.

,hi

ghsc

hool

,co

llege

atte

ndan

ce,em

ploy

men

t,ea

rnin

gs,re

ceip

ttr

ansf

ers,

arre

sts,

conv

ictio

ns,dr

ugus

e,te

enpr

egna

ncy,

mar

riag

e.Su

mm

ary

inde

xpo

olsm

ultip

leou

tcom

esfo

ra

singl

ete

st.Se

para

tete

stsb

yge

nder

.E

ffec

tson

sum

mar

yin

dex

for

girl

s5-1

2:A

BC

/Per

ry:in

crea

seby

0.45/0.

54SD

s.E

ffec

tson

sum

mar

yin

dex

for

girl

s13-

19:

AB

C/P

erry

/ET

P:in

crea

seby

0.42

/0.6

1/0.

46SD

s.E

ffec

tson

sum

mar

yin

dex

for

wom

enov

er21

-40:

AB

C/P

erry

:in

crea

seby

0.45/0.

36SD

s.N

ost

atist

ical

lysig

nifica

nteff

ects

onm

ales

ofan

yag

e.

Hig

h/Sc

ope

Perr

yPr

esch

oolp

roje

ctfo

llow

-up

and

cost

-ben

efit

anal

ysis

at40

year

sofa

ge(B

arne

ttet

al.,

2006

)

Hom

evi

sits,

Pres

choo

lpro

gram

Sam

ple

sizes

:In

itial

:T=

58,C=

65A

ge40

:T=

56,C=

63A

geof

part

icip

atio

nin

prog

ram

:E

ntry

:3

to4

year

s,E

xit:

5ye

ars

Arr

ests

:T=

32%

,C=

48%

;In

jail:

T=

6%,C=

17%

;R

epor

tofs

topp

ing

wor

kfo

rhe

alth

reas

ons:

T=

43%

,C=

55%

;H

ard

drug

use:

T=

22%

,C=

29%

;A

bort

ions

:T=

17%

,C=

32%

Cost

-ben

efitan

alys

is:

Mai

nre

sult:

Ben

efito

f$12

.90

for

each

$1co

stM

ostb

enefi

tsdu

eto

redu

ced

crim

era

tesf

orm

ales

Cos

t:$1

5,82

7($

2000

)per

stud

ent

Tota

lNet

Priv

ate

Ben

efit=

$17,

730

per

part

icip

ant

Tota

lNet

Publ

icB

enefi

t=$1

80,4

55pe

rpa

rtic

ipan

t

(con

tinue

don

next

page

)


Table11

(con

tinue

d)Stud

y/prog

ram

namea

Data,prog

ram

description,

andstud

yde

sign

Results

Nat

iona

leva

luat

ion

ofE

arly

Hea

dSt

art

(Adm

inist

ratio

non

Chi

ldre

n,Yo

uth

and

Fam

ilies

,20

02&

Love

etal

.,20

05)C

ost-

Ben

efit

(Aos

etal

.,20

04).

17E

arly

Hea

dSt

arts

itess

elec

ted

tore

flec

tE

HS

prog

ram

sfun

ded

in19

95-9

6.R

ando

mas

signm

entw

ithin

site

(pos

sible

give

nw

aitl

ists)

.Sa

mpl

e:T

=15

13,C

=14

88.

Age

ofpa

rtic

ipat

ion

inpr

ogra

m:E

ntry

at0-

1ye

ar,ex

it3

year

s

Men

talD

evel

opm

entI

ndex

(MD

I):T>

Cby

0.12

SDPP

VT-

III

Voca

bula

rysc

ore:

T>

Cby

0.13

SDPe

rcen

tage

with

PPV

Tsc

ore<

85pt

s:T<

Cby

0.12

SDA

ggre

ssiv

ebe

havi

or:T<

Cby

0.11

SDSu

ppor

tiven

essd

urin

gpa

rent

-chi

ldpl

ay:T>

Cby

0.15

SDH

OM

Esc

ore:

T>

Cby

0.11

SDIn

dex

ofse

veri

tyof

disc

iplin

e:T<

Cby

0.11

SDN

ost

atist

ical

lysig

nifica

nteff

ects

onpa

rent

alm

enta

lor

phys

ical

heal

thor

onm

easu

reso

ffam

ilyfu

nctio

ning

.C

ost

-ben

efitan

alys

is:

Tota

lCos

tper

child

:$2

0,97

2To

talB

enefi

tper

child

:$4

768,

NPV

:−

$16,

203

Hea

dSt

arti

mpa

ctst

udy

(US

Dep

artm

ento

fH

ealth

and

Hum

anSe

rvic

es,20

10)

Con

gres

siona

lly-m

anda

ted

stud

yof

Hea

dSt

art.

Chi

ldre

nfr

omw

aitl

istsr

ando

mly

assig

ned

toon

eof

383

rand

omly

sele

cted

Hea

dSt

artc

ente

rsac

ross

23di

ffer

ents

tate

s.B

asel

ine

data

colle

cted

infa

ll20

02;an

nual

spri

ngfo

llow

-ups

thro

ugh

spri

ng20

06.

Sam

ple

Size

s:T=

2783

,C=

1884

.E

ntry

:3-

4ye

arso

ld;E

xit:

4-5

year

sold

Sum

mar

yof

effec

tsfo

r4-

year

-old

entr

yco

hort

aten

dof

1stg

rade

:PP

VT

:T>

Cby

0.09

SD;W

ithdr

awn

beha

vior

:T<

Cby

0.13

SD;Sh

y/so

cial

lyre

ticen

t:T>

Cby

0.19

SD;Pr

oble

msw

ithte

ache

rin

tera

ctio

n:T>

Cby

0.13

SD.

No

stat

istic

ally

signi

fica

ntim

pact

sata

ge4,

kind

erga

rten

,or

1stg

rade

on:m

ath

scor

es,Sp

anish

lang

uage

test

s,or

alco

mpr

ehen

sion,

and

seve

ralp

aren

t-an

dte

ache

r-re

port

edm

easu

reso

fem

otio

nala

ndbe

havi

oral

outc

omes

.N

ost

atist

ical

lysig

nifica

ntim

pact

satk

inde

rgar

ten

or1s

tgra

deon

:sc

hool

acco

mpl

ishm

ents

,pr

omot

ion,

lang

uage

and

liter

acy

abili

ty,m

ath

abili

ty,an

dso

cial

stud

iesa

ndsc

ienc

eab

ility

.Su

mm

ary

ofeff

ects

for

3-ye

ar-o

lden

try

coho

rtas

of1s

tgra

de(s

elec

ted

resu

lts):

Ora

lcom

preh

ensio

n:T>

Cby

0.08

SD.N

osig

nifica

nteff

ecto

not

her

outc

omes

.

(con

tinue

don

next

page

)


Table11

(con

tinue

d)Stud

y/prog

ram

namea

Data,prog

ram

description,

andstud

yde

sign

Results

The

rate

sofr

etur

nto

the

Hig

h/Sc

ope

Perr

yPr

esch

oolP

rogr

am(H

eckm

anet

al.,

2009

)

See

Bar

nett

etal

.(2

006)

entr

yfo

rsa

mpl

esiz

esan

din

form

atio

nab

outt

hePe

rry

Pres

choo

lPro

gram

.R

ando

miz

atio

nw

asco

mpr

omise

dbe

caus

eof

reas

signm

enta

fter

initi

alra

ndom

izat

ion.

Stan

dard

erro

rson

the

rate

ofre

turn

estim

ates

adju

sted

for

failu

reof

rand

omiz

atio

nus

ing

boot

stra

ppin

gan

dM

onte

Car

lom

etho

ds.

Also

,adj

uste

dfo

rthe

dead

wei

ghtl

ossd

ueto

taxa

tion

(ass

umin

g0%

,50

%,an

d10

0%de

adw

eigh

tlos

ses)

.U

sed

aw

ide

vari

ety

ofm

etho

dsfo

rw

ithin

-sam

ple

impu

tatio

nof

miss

ing

earn

ings

.U

sed

loca

ldat

aon

cost

sof

educ

atio

n,w

elfa

repa

rtic

ipat

ion,

and

crim

ein

stea

dof

natio

nald

ata,

whe

reve

rpo

ssib

le.

Use

dse

vera

lmet

hods

toex

trap

olat

ebe

nefits

and

cost

sbey

ond

age

40(a

fter

last

follo

w-u

p).U

sed

seve

ralm

easu

reso

fthe

stat

istic

alco

stof

life

toes

timat

eco

stso

fm

urde

r.

Est

imat

edso

cial

rate

sofr

etur

nto

the

Perr

yPr

esch

ool

Prog

ram

are

7%-1

0%.E

stim

ated

bene

fit-

cost

ratio

=2.

2to

31.5

(dep

ends

ondi

scou

ntra

teus

ed,an

dth

em

easu

reof

cost

ofm

urde

r).

Thr

ough

outt

heta

ble,

“T”

refe

rsto

trea

tmen

tgro

upan

d“C

”re

fers

toco

ntro

lor

com

pari

son

grou

p.O

utco

mes

liste

das

T>

Cor

C>

Tw

ere

stat

istic

ally

signi

fica

ntat

the

5%le

vel.

aPr

ogra

msa

regr

oupe

dsu

chth

atth

ose

enro

lling

child

ren

youn

ger

than

thre

eye

arso

ldap

pear

firs

t,fo

llow

edby

thos

een

rolli

ngch

ildre

naf

ter

age

thre

e.


some other form of preschool (including state-funded preschools modeled in Head Start).In fact, the majority of children who did not attend Head Start did end up attending someother preschool program. Even relative to this baseline, initial results show that Head Startchildren make gains, particularly in terms of language ability. But children are followedonly into the first grade, and so this evaluation did not address the important issue ofwhether Head Start has longer term effects. This example illustrates one of the limitationsof experiments for the study of longer-term effects, which is that one may have to waita long time for evidence to accumulate. There has also been a federal evaluation of EarlyHead Start (EHS), a version of the program geared to infants and toddlers under threeyears old. As Table 11 shows, EHS has small positive effects on cognitive test scores andsome measures of behavior though Aos et al. (2004) conclude that it does not pass acost-benefit test.

Table 12 summarizes notable non-experimental evaluations of Head Start and otherpublic preschool programs. In a series of studies Currie and Thomas use nationalpublicly-available survey data to try to measure the effect of Head Start. In most ofthese studies, they compare the outcomes of children who attended Head Start tosiblings who did not attend. As discussed above, sibling fixed effects control for manyshared characteristics of children, but are not a panacea. However, careful examination ofdifferences between participant and non-participant children within families suggestedthat the Head Start sibling typically attends when the family is relatively disadvantaged.

For example, a young single mother might have her first child attend Head Start.If she then marries, her next child will enjoy higher income and be ineligible forHead Start. Currie and Thomas found no within-family differences in birth weight orother individual characteristics of the children. They also investigated spillover effects,which as discussed above, can bias the estimated effect of Head Start. They foundsome evidence (Garces et al., 2002) that having an older sibling attend Head Start hadpositive effects on younger siblings. In all, it seems likely that sibling fixed effects modelsunderstate the true effect of Head Start.

Nevertheless, they found significant positive effects of Head Start on educationalattainments among white youths, and reductions in the probabilities of being bookedor charged with a crime among black youths (Garces et al., 2002). Test score gainsfor blacks and whites were initially the same, but these gains tended to fade out morequickly for black than white students, perhaps because black former Head Start studentstypically attend worse schools than other students (Currie and Thomas, 1995). Effectswere especially large for Hispanic children (Garces et al., 2002).

More recently, Deming (2009) replicates the results of Currie and Thomas (1995)using the same cohort of NLSY children observed at older ages. Like Anderson, hefocuses on an index of outcomes (although he also reports results for separate outcomes)and finds that Head Start results in an increase of 0.23 standard deviations, which isequivalent to about 1/3 of the gap between Head Start and other children. He notes


Table12

Selected

stud

iesof

large-scalepu

blicearly

child

hood

prog

rams.

Stud

y/prog

ram

namean

dda

taStud

yde

sign

Results

Evalua

tion

sof

Hea

dStart

Doe

sHea

dSt

artm

ake

adi

ffer

ence

?D

oesH

ead

Star

the

lpH

ispan

icch

ildre

n?(C

urri

ean

dT

hom

as,19

95,

1999

a).N

LSC

M.

Sam

ple

size:

T=

896,

C=

911

Hisp

anic

stud

y:T=

182,

C=

568

Ent

ry:3

to5

year

s;E

xit:

5to

6ye

ars.

Est

imat

esib

ling

fixe

deff

ects

mod

els

ofeff

ects

ofH

ead

Star

tand

othe

rpr

esch

oola

tten

danc

eon

vari

ous

outc

omes

.E

xam

ine

differ

ence

sbe

twee

nsib

lings

that

mig

htpo

tent

ially

expl

ain

differ

entia

lat

tend

ance

bysib

lings

.

Ach

ieve

men

tte

sts:

T>

C(1/3

SDw

hite

sand

Hisp

anic

sonl

y)G

rade

rete

ntio

n:T<

C(∼

50%

whi

tesa

ndH

ispan

icso

nly)

Imm

uniz

atio

nra

tes:

T>

C(8

%-1

1%)

Chi

ldhe

ight

-for

-age

:T=

C

Long

term

effec

tsof

Hea

dSt

art(

Gar

cese

tal.,

2002

).PS

ID,Sa

mpl

esiz

e:T=

583,

C=

3502

.E

ntry

:3

to4

year

s;E

xit5

to6

year

s.

Com

pare

dH

ead

Star

tpar

ticip

ants

toth

eir

own

siblin

gsw

hodi

dno

tpa

rtic

ipat

e.O

utco

mes

mea

sure

dbe

twee

nag

es18

and

31.

Ret

rosp

ectiv

ere

port

son

Hea

dSt

art

part

icip

atio

n.

Hig

hsc

hool

grad

uatio

n:T>

C(∼

25%

for

whi

teso

nly)

Arr

ests

T<

C(∼

50%

for

Afr

ican

-Am

eric

anso

nly)

Col

lege

T>

C(∼

25%

for

Whi

tes)

Teen

preg

nanc

yT=

CW

elfa

reT=

C

Effec

tofH

ead

Star

ton

heal

than

dsc

hool

ing

(Lud

wig

and

Mill

er,20

05).

Vita

lSta

tistic

sCom

pres

sed

Mor

talit

yFi

les1

973-

83;

Indi

vidu

alda

tafr

omN

ELS

,w

here

T=

649,

C=

674.

Reg

ress

ion

disc

ontin

uity

arou

ndcu

toff

atw

hich

coun

tiesw

ere

elig

ible

for

assis

tanc

ein

appl

ying

for

Hea

dSt

arti

n19

65.T=

300

poor

est

coun

tiesi

n19

65,C=

301-

600t

hpo

ores

tcou

ntie

s.80

%of

trea

tmen

tco

untie

srec

eive

dfu

ndin

gvs

.43

%of

allc

ount

iesn

atio

nwid

e.

Effec

tsof

part

icip

atio

nin

Hea

dSt

art:

Mor

talit

y,ag

e5-

9:T<

Cby

35%

-79%

Hig

hsc

hool

com

plet

ion

rate

s:T>

Cby

5.2%

-8.5

%So

me

colle

ge+

:16

.2%

-22.

4%fo

rol

dest

coho

rton

ly

(con

tinue

don

next

page

)


Table12

(con

tinue

d)Stud

y/prog

ram

namean

dda

taStud

yde

sign

Results

Hea

dSt

artP

artic

ipat

ion

and

Chi

ldho

odO

besit

y(F

risv

old,

2006

).PS

IDC

hild

Dev

elop

men

tSup

plem

ent.

Sam

ple

size=

1332

.

Est

imat

edth

eeff

ecto

fHea

dSt

arto

nlik

elih

ood

ofa

child

bein

gov

erw

eigh

tor

obes

e.A

ssum

eth

at#

ofsp

aces

avai

labl

ein

aco

mm

unity

isa

valid

inst

rum

entf

orH

ead

Star

tpa

rtic

ipat

ion.

Hea

dSt

artr

educ

espr

obab

ility

ofob

esity

atag

es5-

10am

ong

blac

ks.N

oeff

ecti

nfu

llsa

mpl

eof

child

ren

orin

child

ren

over

10.E

stim

ates

are

larg

ere

lativ

eto

sam

ple

mea

nsim

plyi

ng∼

100%

redu

ctio

nsin

over

wei

ght/

obes

ity.

Evi

denc

efr

omH

ead

Star

tO

nLi

fecy

cle

Skill

Dev

elop

men

t(D

emin

g,20

09).

Dat

afr

omN

LSY

Chi

ldre

nfo

rco

hort

enro

lled

inH

ead

Star

tbet

wee

n19

84an

d19

90.C

hild

ren

inst

udy

atle

ast5

year

sold

in19

90.

Sam

ple

size:

3415

tota

l.

Sibl

ing

fixe

deff

ects

estim

ates

ofbe

nefits

ofH

ead

Star

t.Te

stsc

ores

:T>

Cby

0.14

5SD

ages

5-6,

by0.

133

SDag

es7-

10,by

0.05

5SD

ages

11-1

4.N

onco

gniti

vesc

hool

-age

outc

omes

inde

x:T>

Cby

0.26

5SD

.Lo

ng-t

erm

effec

ton

inde

x*of

youn

gad

ulto

utco

mes

:T>

Cby

0.22

8SD

.La

rge

fade

-out

inte

stsc

ores

ofA

fric

anA

mer

ican

s,no

nefo

rw

hite

sor

Hisp

anic

s.N

oeff

ects

oncr

imin

alac

tivity

.Sum

mar

y:H

ead

Star

tin

crea

sesi

ndex

oflo

ngte

rmou

tcom

esby

0.23

SD(∼

1/3

ofga

pat

tend

eesa

ndot

hers

).Pr

ojec

ting

wag

esim

plie

stha

tben

efits

(∼$1

500

ingr

eate

rea

rnin

gspe

rye

ar)e

xcee

dpr

ogra

mco

stso

f∼

$600

0.*

inde

xin

clud

es:gr

adua

tehi

ghsc

hool

,co

mpl

ete

1yr

colle

geid

le(n

ojo

b,no

tin

scho

ol),

poor

heal

th.

(con

tinue

don

next

page

)


Table12

(con

tinue

d)Stud

y/prog

ram

namean

dda

taStud

yde

sign

Results

Prev

entin

gbe

havi

orpr

oble

msi

nch

ildho

odan

dad

oles

cenc

e:E

vide

nce

from

Hea

dSt

art(

Car

neiro

and

Gin

ja,20

08).

NLS

YC

hild

ren

data

.Sa

mpl

esiz

e=

1786

mal

es.

Beh

avio

rpr

oble

ms,

grad

ere

p.an

dob

esity

at12

-13.

Dep

ress

ion,

crim

e,an

dob

esity

at16

-17.

Old

esti

nda

ta—

born

in19

74;yo

unge

stin

data

—bo

rnin

1992

.

Hea

dSt

arte

ligib

ility

rule

scre

ate

disc

ontin

uitie

sin

inco

me

elig

ibili

ty.

Com

pare

fam

ilies

abov

ean

dbe

low

the

cuto

ff.Id

entifi

catio

nst

rate

gyre

quir

esth

atfa

mili

esar

eno

tabl

eto

stra

tegi

cally

loca

teab

ove

orbe

low

cuto

ff.

USIN

GR

ED

UC

ED

FO

RM

EST

IMAT

ION

Hea

dSta

rtpar

tici

pat

ion

impac

tson

12-1

3ye

ar-o

ldm

ales

:be

havi

oral

prob

lem

sind

exde

crea

sesb

y38

%pr

obab

ility

ofgr

ade

rete

ntio

nde

crea

sesb

y33

.3%

prob

abili

tyof

obes

ityde

crea

sesb

y∼

100%

for

blac

kson

lyH

ead

Sta

rtpar

tici

pat

ion

impac

tson

16-1

7ye

ar-o

ldm

ales

:D

epre

ssio

n(C

ESD

)dec

reas

esby

23.4

%pr

obab

ility

ofob

esity

decr

ease

sby

57.9

%pr

obab

ility

ofbe

ing

sent

ence

dde

crea

sesb

y>

100%

for

blac

kson

lyU

SIN

GST

RU

CT

UR

AL

EQ

UAT

ION

SH

ead

Sta

rtpar

tici

pat

ion

impac

tson

12-1

3ye

ar-o

ldm

ales

:pr

obab

ility

ofgr

ade

rete

ntio

nde

crea

sesb

y>

100%

Hea

dSta

rtpar

tici

pat

ion

impac

tson

16-1

7ye

ar-o

ldm

ales

:pr

obab

ility

ofbe

ing

sent

ence

dde

crea

sesb

y>

100%

prob

abili

tyof

obes

ityde

crea

sesb

y>

100%

prob

abili

tyof

bein

gse

nten

ced

decr

ease

sby>

100%

for

blac

kson

lypr

obab

ility

ofob

esity

decr

ease

sby>

100%

for

blac

kson

lyN

ote:

base

line

mea

nsar

efo

rsa

mpl

eof

child

ren

with

inco

mes

betw

een

5%an

d19

5%of

Hea

dSt

arte

ligib

ility

cut-

off.

(con

tinue

don

next

page

)


Table12

(con

tinue

d)Stud

y/prog

ram

namean

dda

taStud

yde

sign

Results

Inve

stin

gin

heal

th:th

elo

ng-t

erm

impa

ctof

Hea

dSt

arto

nsm

okin

g(A

nder

son

etal

.,20

09).

Dat

afr

omth

ePS

ID.U

sed

smok

ing

data

from

1999

and

2003

onin

divi

dual

sage

d21

-36

in19

99.n=

922

in19

99;

n=

1005

in20

03.

Com

pare

dsm

okin

gof

siblin

gsw

hodi

dan

ddi

dno

tatt

end

Hea

dSt

arto

ran

ypr

esch

oolu

sing

siblin

gfixe

deff

ects

.C

ontr

olle

dfo

rfa

mily

back

grou

ndch

arac

teri

stic

sspe

cific

toth

eag

ech

ildre

nw

ere

elig

ible

for

Hea

dSt

art.

Exa

min

edsib

ling

differ

ence

stha

tmig

htbe

pred

ictiv

eof

Hea

dSt

arta

tten

danc

e.E

xam

ined

spill

over

effec

tsby

incl

udin

gin

tera

ctio

nsbe

twee

nH

ead

Star

tand

birt

hor

der.

Res

ultsfr

om

1999

dat

a:H

ead

Star

tpar

ticip

ants

are

58%

less

likel

yto

smok

eth

ansib

lings

.R

esultsfr

om

2003

dat

a:H

ead

Star

tpar

ticip

ants

are

65%

less

likel

yto

smok

eth

ansib

lings

.In

clud

ing

cont

rolf

ored

ucat

iona

latt

ainm

entm

akes

resu

ltsst

atist

ical

lyin

signi

fica

nt.

Cost

-Ben

efit:

PVre

duct

ion

insm

okin

gis

$996

7pe

rpa

rtic

ipan

t(us

ing

3%di

scou

ntra

te,ac

coun

ting

for

med

ical

expe

nses

and

prod

uctiv

itylo

sses

)Ave

rage

cost

per

Hea

dSt

art

part

icip

anti

n20

03is

$709

2.D

epen

ding

ondi

scou

ntra

teus

ed,

the

valu

eof

redu

ctio

nin

smok

ing

isas

soci

ated

with

36-1

41%

ofpr

ogra

mco

sts.

Exp

andi

ngex

posu

re:ca

nin

crea

sing

the

daily

expo

sure

toH

ead

Star

tred

uce

child

hood

obes

ity?

(Fri

svol

dan

dLu

men

g,20

09)

Adm

inist

rativ

eda

tafr

oma

Mic

higa

nH

ead

Star

tfor

2002

-200

6.n=

1833

obs.

(fro

m15

32ch

ildre

n,sin

ceso

me

atte

ndfo

rm

ultip

leye

ars)

Full-

day

clas

ssa

mpl

e=

424

obs.

Hal

f-da

ycl

asss

ampl

e=

1409

obs.

Est

imat

edth

eeff

ecto

fful

l-da

yvs

.ha

lf-da

yH

ead

Star

ton

obes

ityat

end

ofsc

hool

year

.Id

entifi

catio

nvi

ael

imin

atio

nof

agr

antw

hich

led

toa

decr

ease

inth

e#

offu

ll-da

ycl

asse

sfr

om16

clas

sroo

msi

n20

02to

4cl

assr

oom

sin

2003

.(I

V=

%fu

ll-da

yfu

nded

slots

).C

ontr

olsf

orob

serv

able

fam

ilych

arac

teri

stic

s.

Fir

stSta

geR

esults:

10pp

incr

ease

inpe

rcen

tage

offu

ll-da

yslo

tsin

crea

sesl

ikel

ihoo

dof

full-

day

atte

ndan

ceby

85%

(rel

ativ

eto

base

line=

11%

enro

llmen

tin

full-

day

slots

in20

03).

Sec

ond

Sta

geR

esults:

Full-

day

enro

llmen

tin

Hea

dSt

art

decr

ease

slik

elih

ood

ofob

esity

by14

3%.T

hisi

mpl

iest

hat

child

ren

who

atte

nded

full-

day

clas

sesw

ould

have

been

alm

ost3

lbsh

eavi

erha

dth

eyat

tend

edha

lf-da

ycl

asse

s.Si

mul

atio

nre

sults

sugg

estt

hatt

he14

3%ch

ange

inth

elik

elih

ood

ofob

esity

can

beex

plai

ned

bya

chan

gein

calo

ric

inta

keof

75ca

lori

espe

rda

yw

ithno

chan

gein

phys

ical

activ

ity.

(con

tinue

don

next

page

)


Table12

(con

tinue

d)Stud

y/prog

ram

namean

dda

taStud

yde

sign

Results

Stud

iesof

publicpre-K/Kan

dch

ildcare

prog

rams

Impa

ctof

earl

ych

ildho

odca

rean

ded

ucat

ion

onch

ildre

n’sp

resc

hool

cogn

itive

deve

lopm

ent:

Can

adia

nre

sults

from

ala

rge

quas

i-ex

peri

men

t(Le

febv

reet

al.,

2006

)D

ata

from

Can

ada’s

NLS

CY

on4-

and

5-yr

old

child

ren

from

5co

nsec

utiv

ecy

cles

.n=

15,54

6

Iden

tifica

tion

from

chan

gesi

nQ

uebe

c’sc

hild

care

subs

idie

s.O

nSe

p.1,

1997

,ch

ildca

refa

cilit

ies

offer

ed$5

-per

-ful

l-da

yse

rvic

esto

child

ren

who

wer

e4

yrso

ldby

Sep.

30th

.In

the

follo

win

gye

ars,

age

cuto

ffsd

ecre

ased

and

the

num

ber

ofsp

aces

incr

ease

d.N

osim

ilar

polic

ies

inot

her

prov

ince

sofC

anad

ain

1994

-200

3.D

iffer

ence

-in-

differ

ence

(DD

)and

Diff

eren

ce-i

n-di

ffer

ence

-in

-diff

eren

ce(D

DD

)des

igns

,co

mpa

ring

Que

bec’

spre

scho

olch

ildre

nw

ithch

ildre

nof

simila

rag

esfr

omot

her

prov

ince

susin

gth

efa

ctth

atdi

ffer

entc

ohor

tsof

child

ren

wer

eex

pose

dto

differ

entn

umbe

rsof

trea

tmen

tyea

rs.

Subs

idie

sinc

reas

eth

enu

mbe

rof

hour

sin

child

care

by5.

5-7.

4ho

ursp

erw

eek

for

child

ren

aged

4-5.

Effec

tlar

ger

for

mot

hers

inhi

ghes

tedu

catio

nalg

roup

.N

oeff

ects

on4-

year

-old

child

ren’

sPPV

Tsc

ores

.Dec

reas

ein

PPV

Tsc

ores

of0.

33SD

for

5-ye

ar-o

ldch

ildre

n.

(con

tinue

don

next

page

)


Table12

(con

tinue

d)Stud

y/prog

ram

namean

dda

taStud

yde

sign

Results

Prom

otin

gsc

hool

read

ines

sin

Okl

ahom

a:A

nev

alua

tion

ofTu

lsa’s

Pre-

KPr

ogra

m(G

orm

ley

and

Gay

er,20

06)a .

Dat

afr

omTu

lsaPu

blic

Scho

ols(

TPS

)on

test

scor

esof

Pre-

Kan

dK

inde

rgar

ten

child

ren

from

test

adm

inist

ered

inA

ug.20

01.

T=

1112

,C=

1284

(T=

child

ren

who

just

com

plet

edPr

e-K

,C=

child

ren

who

are

abou

tto

begi

nPr

e-K

).E

ntry

:4

year

sold

;E

xit:

5ye

arso

ld.

Reg

ress

ion

disc

ontin

uity

desig

nar

ising

from

cuto

ffof

Sept

.1

toen

roll

inPr

e-K

ina

give

nye

ar.

Com

pare

kind

erga

rten

child

ren

who

just

com

plet

edPr

e-K

with

sligh

tlyyo

unge

rchi

ldre

nw

how

ere

inel

igib

leto

atte

nd.U

sed

quad

ratic

poly

nom

ial

tofitu

nder

lyin

gag

e/te

stsc

ore

rela

tions

hip.

Cog

nitiv

e/kn

owle

dge

scor

e:T>

Cby

0.39

SDM

otor

skill

ssco

re:T>

Cby

0.24

SDLa

ngua

gesc

ore:

T>

Cby

0.38

SDLa

rges

tim

pact

sfor

Hisp

anic

s,fo

llow

edby

blac

ks,lit

tleim

pact

for

whi

tes.

Chi

ldre

nw

hoqu

alify

for

free

scho

ollu

nch

have

larg

erim

pact

stha

not

her

child

ren.

Doe

sPre

kind

erga

rten

Impr

ove

Scho

olPr

epar

atio

nan

dPe

rfor

man

ce?

(Mag

nuso

net

al.,

2007

)D

ata

from

EC

LS-K

.n=

10,22

4.

Prim

ary

met

hod

isa

mul

tivar

iate

regr

essio

nto

estim

ate

the

impa

ctof

Pre-

Kat

tend

ance

onva

riou

sou

tcom

es.R

obus

tnes

sche

cksu

sing

teac

her

fixe

deff

ects

,pr

open

sity

scor

em

atch

ing,

and

inst

rum

enta

lvar

iabl

es(I

V).

IVis

differ

entm

easu

reso

fac

cess

topr

e-K

ina

give

nst

ate.

Dep

ende

ntva

riab

lesa

rem

easu

red

inth

efa

llof

kind

erga

rten

and

inth

esp

ring

offirs

tgra

deto

asse

ssan

yla

stin

gim

pact

sofP

re-K

.

Pre-

Kat

tend

ance

:in

crea

sesr

eadi

ngsc

ores

atsc

hool

entr

yby

0.86

SD(I

V);

incr

ease

sagg

ress

ion

atsc

hool

entr

yby

0.69

SD(I

V).

No

effec

ton

mat

hsc

ores

orse

lfco

ntro

lin

IV.E

ffec

tsiz

esfo

ral

lout

com

esar

ela

rger

for

Pre-

Kth

anfo

rot

her

form

sof

child

care

,bu

tPre

-Kch

ildre

nha

vedi

ffer

entc

hara

cter

istic

stha

not

her

child

ren.

Am

ong

child

ren

atte

ndin

gPr

e-K

inth

esa

me

publ

icsc

hool

asth

eir

kind

erga

rten

,hi

gher

read

ing

scor

esar

eno

tacc

ompa

nied

byin

crea

sed

beha

vior

alpr

oble

ms.

For

disa

dvan

tage

dch

ildre

n,co

gniti

vega

insa

rem

ore

last

ing

than

inth

ew

hole

sam

ple.

Effec

tsiz

esfo

rco

gniti

veou

tcom

esm

uch

low

erin

spri

ngof

1stg

rade

than

atsc

hool

entr

y.E

ffec

tsiz

esfo

rbe

havi

oral

outc

omes

are

the

sam

e.

(con

tinue

don

next

page

)


Table12

(con

tinue

d)Stud

y/prog

ram

namean

dda

taStud

yde

sign

Results

The

Effec

tsof

Okl

ahom

a’sPr

e-K

Prog

ram

onH

ispan

icC

hild

ren

(Gor

mle

y,20

08)

Test

sadm

inist

ered

byTu

lsapu

blic

scho

olsi

nA

ug.20

06.

T=

194,

C=

295.

(T=

child

ren

who

just

com

plet

edPr

e-K

,C=

child

ren

who

are

abou

tto

begi

nPr

e-K

).E

ntry

:4

year

sold

;E

xit:

5ye

arso

ld.

See

Gor

mle

yan

dG

ayer

(200

6)en

try.

Lett

er-W

ord

Iden

tifica

tion

Test

scor

e:T>

Cby

0.84

6SD

Spel

ling

scor

e:T>

Cby

0.52

SDA

pplie

dPr

oble

mss

core

:T>

Cby

0.38

SDSi

gnifi

cant

effec

tson

lyfo

rH

ispan

icst

uden

tsw

hose

prim

ary

lang

uage

atho

me

isSp

anish

.

Uni

vers

alC

hild

Car

e,M

ater

nalL

abor

Supp

ly,an

dFa

mily

Wel

l-B

eing

(Bak

eret

al.,

2008

).C

anad

ian

NLS

CY

(199

4-20

03)

incl

udes

only

mar

ried

wom

enan

dth

eir

child

ren.

Ave

rage

of20

00ch

ildre

nat

each

age

per

yr.Pr

imar

ysa

mpl

eag

es0-

4,ro

bust

ness

chec

ksag

es8-

11.

Com

pare

outc

omes

inQ

uebe

c,w

hich

bega

n$5

per

day

dayc

are

for

4ye

arol

dsin

1997

,ex

tend

edpr

ogra

mto

3ye

arol

dsin

1998

,2

year

olds

in19

99,an

dal

lchi

ldre

n<

2in

2000

,to

the

rest

ofC

anad

a.D

iffer

ence

indi

ffer

ence

s.

Incr

ease

inus

eof

any

child

care

/ins

titut

iona

lcar

e/m

othe

rsw

orki

ngby

35%

/>10

0%/1

4.5%

.C

row

ding

outo

foth

erin

form

alch

ildca

re.

Incr

ease

inem

otio

nald

isord

eran

xiet

ysc

ore

(phy

sical

aggr

essio

nan

dop

posit

ion)

by12

%(9

%)f

or2-

3yr

olds

.D

ecre

ase

inst

anda

rdiz

edm

otor

and

soci

alde

velo

pmen

tsco

reby

1.7%

.In

crea

sein

mot

her

depr

essio

nsc

ore

by10

%.

40%

ofth

eco

stof

the

child

care

subs

idy

isoff

setb

yin

crea

sed

taxe

son

extr

ala

bor

supp

ly.

(con

tinue

don

next

page

)


Table12

(con

tinue

d)Stud

y/prog

ram

namean

dda

taStud

yde

sign

Results

Impa

ctso

fNew

Mex

ico

Pre-

Kon

Chi

ldre

n’sS

choo

lR

eadi

ness

atK

inde

rgar

ten

Ent

ry:R

esul

tsfr

omth

eSe

cond

Year

ofa

Gro

win

gIn

itiat

ive

(Hus

tedt

etal

.,20

08).

Dat

aon

child

ren

who

part

icip

ated

inth

e2n

dye

arof

the

Pre-

Kpr

ogra

mdu

ring

2006

-200

7an

den

tere

dki

nder

gart

enin

Fall

2007

.T=

405,

C=

519

(T=

child

ren

who

just

com

plet

edPr

e-K

,C=

child

ren

who

are

abou

tto

begi

nPr

e-K

).E

ntry

:4

year

sold

;E

xit:

5ye

arso

ld.

Reg

ress

ion

disc

ontin

uity

desig

ndu

eto

abi

rthd

ayel

igib

ility

cut-

offof

Aug

.31

stto

enro

llin

Pre-

Kin

agi

ven

year

.C

ompa

red

“you

ng”

kind

erga

rten

child

ren

who

just

com

plet

edPr

e-K

with

sligh

tlyyo

unge

rch

ildre

nw

hoar

eab

outt

obe

ing

Pre-

K.U

sed

linea

rm

odel

for

voca

bula

rysc

ore

asde

pend

ent

vari

able

,qu

adra

ticm

odel

for

earl

ylit

erac

ysc

ore

asde

pend

entv

aria

ble,

cubi

cm

odel

for

mat

hsc

ore

asde

pend

entv

aria

ble.

Voca

bula

ry(P

PVT

)sco

re:T>

Cby

0.25

SDM

ath

scor

e:T>

Cby

0.50

SDE

arly

liter

acy

scor

e:T>

Cby

0.59

SDN

ost

atist

ical

lysig

nifica

ntdi

ffer

ence

ineff

ects

betw

een

Pre-

Kpr

ogra

msf

unde

dby

the

Publ

icE

duca

tion

Dep

artm

enta

ndth

ose

fund

edby

the

Chi

ldre

n,Yo

uth

and

Fam

ilies

Dep

artm

ent.

(con

tinue

don

next

page

)


Table12

(con

tinue

d)Stud

y/prog

ram

namean

dda

taStud

yde

sign

Results

An

Effec

tiven

ess-

Bas

edE

valu

atio

nof

Five

Stat

ePr

e-K

inde

rgar

ten

Prog

ram

s(W

ong

etal

.,20

08).

Dat

aon

test

scor

esfr

omfa

ll20

04fr

omM

ichi

gan,

New

Jers

ey,

Okl

ahom

a,So

uth

Car

olin

aan

dW

estV

irgi

nia.

Sam

ple

sizes

:T=

485,

C=

386

(MI)

;T=

1177

,C=

895

(NJ)

;T=

431,

C=

407

(OK

);T=

353,

C=

424

(SC

);T=

379,

C=

341

(WV

).(T=

child

ren

who

just

com

plet

edPr

e-K

,C=

child

ren

who

are

abou

tto

begi

nPr

e-K

).E

ntry

:4

year

sold

;E

xit:

5ye

arso

ld.

Reg

ress

ion

disc

ontin

uity

desig

ndu

eto

ast

rict

birt

hday

elig

ibili

tycu

t-off

.Lo

oked

ateff

ects

ize

differ

ence

sdue

topr

ogra

mm

atic

vari

atio

nbe

twee

nst

ates

.U

sed

apo

lyno

mia

lap

prox

imat

ion

toth

eco

ntin

uous

func

tion

onth

eas

signm

entv

aria

ble

inth

ere

gres

sions

.

Inte

nt-

to-T

reat

Res

ults:

MI:

T>

Cby

0.53

SDfo

rm

ath;

1.09

SDfo

rPr

intA

war

enes

s.N

J:T>

Cby

0.36

SDfo

rPP

VT,

0.23

SDfo

rm

ath,

0.32

SDfo

rPr

intA

war

enes

s.O

K:T>

Cby

0.28

SDfo

rPPV

T,0.

78SD

forP

rint

Aw

aren

ess.

WV

:T>

Cby

0.92

SDfo

rPr

intA

war

enes

s.Tre

atm

ent-

on

-Tre

ated

Res

ults:

MI:

T>

Cby

0.47

SDfo

rm

ath,

0.96

SDfo

rPr

intA

war

enes

s.N

J:T>

Cby

0.36

SDfo

rPP

VT,

0.23

SDfo

rm

ath,

0.50

SDfo

rPr

intA

war

enes

s.O

K:T>

Cby

0.29

SDfo

rPP

VT.

SC:T>

Cby

0.79

SDfo

rPr

intA

war

enes

s.N

ost

atist

ical

lysig

nifica

ntre

sults

for

WV.

No

clea

rre

latio

nshi

pbe

twee

nst

ate

fund

ing

for

Pre-

Kpr

ogra

msa

ndeff

ects

izes

.St

ate

Pre-

Kpr

ogra

msh

ave

larg

ereff

ects

izes

than

Hea

dSt

art.

(con

tinue

don

next

page

)


Table12

(con

tinue

d)Stud

y/prog

ram

namean

dda

taStud

yde

sign

Results

Do

Inve

stm

ents

inU

nive

rsal

Ear

lyE

duca

tion

Pay

Off?

Long

-ter

mE

ffec

tsof

Intr

oduc

ing

Kin

derg

arte

nsin

toPu

blic

Scho

ols(

Cas

cio,

2009

).D

ata

from

4D

ecen

nial

cens

uses

for

1970

,19

80,an

d20

00fr

omth

ePu

blic

Use

Mic

roda

taSa

mpl

es.n=

840

whi

tes,

425

blac

ks.D

ata

from

PSID

onH

ead

Star

tenr

ollm

ent:

n=

174

whi

tes,

126

blac

ks.

Ana

lyze

deff

ecto

fexp

ansio

nof

publ

icki

nder

gart

enon

long

-ter

mou

tcom

es.Id

entifi

catio

nfr

omth

eva

riat

ion

inth

etim

ing

offu

ndin

gin

itiat

ives

amon

gtr

eate

dst

ates

.E

vent

stud

ym

odel

,co

mpa

ring

indi

vidu

als

aged

5be

fore

and

afte

rth

ein

itiat

ives

wer

eim

plem

ente

d.In

clud

eddu

mm

iesf

orco

hort

sint

erac

ted

with

dum

mie

sfor

3di

ffer

entg

roup

sof

trea

ted

stat

esde

fine

don

the

basis

ofav

erag

eed

ucat

ion

expe

nditu

repe

rpu

pili

nth

eea

rly

1960

s.A

lsoco

ntro

lled

for

coho

rt-b

y-re

gion

-of-

birt

hfixe

deff

ects

and

stat

efixe

deff

ects

.U

nits

ofob

serv

atio

nar

eco

hort

-sta

tece

lls.

Whi

tech

ildre

nag

ed5

afte

rth

ety

pica

lsta

tere

form

are

2.5%

less

likel

yto

behi

ghsc

hool

drop

-out

sand

22%

less

likel

yto

bein

stitu

tiona

lized

asad

ults

.N

osig

nifica

nteff

ects

ongr

ade

rete

ntio

n,co

llege

atte

ndan

ce,em

ploy

men

t,or

earn

ings

.N

osig

nifica

nteff

ects

for

blac

ks,de

spite

com

para

ble

incr

ease

sin

enro

llmen

tin

publ

icki

nder

gart

ensp

ostr

efor

m.Po

tent

ial

expl

anat

ion

isth

atst

ate

fund

ing

for

publ

icki

nder

gart

ens

redu

ced

the

likel

ihoo

dth

ata

blac

k5-

year

-old

atte

nded

Hea

dSt

artb

y∼

100%

.R

educ

tion

inH

ead

Star

tatt

enda

nce

may

acco

untf

or16

%of

the

1.13

ppin

crea

sein

the

blac

k-w

hite

gap

inhi

ghsc

hool

drop

-out

rate

s.D

iffer

ence

ineff

ects

oned

ucat

iona

latt

ainm

entb

etw

een

whi

tesa

ndbl

acks

are

driv

enby

fem

ales

.

(con

tinue

don

next

page

)


Table12

(con

tinue

d)Stud

y/prog

ram

namean

dda

taStud

yde

sign

Results

No

Chi

ldLe

ftB

ehin

d:U

nive

rsal

Chi

ldC

are

and

Chi

ldre

n’sL

ong-

Run

Out

com

es(H

avne

sand

Mog

stad

,20

09).

Dat

afr

omSt

atist

icsN

orw

ayon

indi

vidu

alsf

rom

1967

to20

06.H

ouse

hold

info

rmat

ion

from

the

Cen

tral

Popu

latio

nR

egist

er.

Adm

inist

rativ

eda

taon

child

care

inst

itutio

nsan

dth

eir

loca

tions

for

1972

-199

6.R

estr

icte

dsa

mpl

eto

indi

vidu

alsw

hose

mot

hers

wer

em

arri

edat

the

end

of19

75.n=

499,

026

child

ren;

318,

367

fam

ilies

.A

dult

outc

omes

mea

sure

dbe

twee

nag

es30

and

33.

Diff

eren

ce-i

n-di

ffer

ence

s.E

xplo

ited

ach

ildca

rere

form

in19

75in

Nor

way

,w

hich

assig

ned

resp

onsib

ility

for

child

care

tolo

cal

gove

rnm

ents

,an

dth

usre

sulte

din

grea

tvar

iatio

nin

child

care

cove

rage

for

child

ren

aged

3-6

both

betw

een

coho

rtsa

ndac

ross

mun

icip

aliti

es.

T=

mun

icip

aliti

esw

here

child

care

expa

nded

alo

t;C=

mun

icip

aliti

esw

here

child

care

did

notc

hang

em

uch.

Com

pare

dch

ange

sin

outc

omes

for

trea

tmen

tand

cont

rol

adul

tsw

how

ere

3-6

year

sold

befo

rean

daf

ter

the

refo

rm.A

lsoin

vest

igat

edhe

tero

gene

ityof

effec

ts.

Con

trol

led

for

vari

ousc

hild

and

fam

ily-s

peci

fic

char

acte

rist

ics,

asw

ell

asm

unic

ipal

ityfixe

deff

ects

.R

obus

tnes

sche

cks:

incl

uded

atim

etr

end,

chec

ked

for

plac

ebo

effec

tby

com

pari

ngth

etw

ogr

oups

befo

reth

ere

form

,an

dus

edsib

ling

fixe

deff

ects

com

pari

ngsib

lings

expo

sed

toth

ere

form

toth

ose

who

wer

eno

t.

TT

Effec

ts(I

TT

effec

tsin

par

enth

eses

)O

nem

ore

child

care

plac

e:in

crea

sese

duca

tiona

latt

ainm

entb

y2.

7%(0

.4%

)de

crea

sesp

roba

bilit

yof

drop

ping

outo

fHS

by22

.8%

(3.8

%)

incr

ease

spro

babi

lity

ofco

llege

enro

llmen

tby

17%

(2.5

%)

decr

ease

spro

babi

lity

ofha

ving

little

orno

earn

ings

by23

.3%

(3.9

%)

incr

ease

spro

babi

lity

ofha

ving

aver

age

earn

ings

by7.

4%(1

.3%

)de

crea

sesp

roba

bilit

yof

bein

gon

wel

fare

by31

.9%

(5.6

%)

decr

ease

spro

babi

lity

ofpa

rent

hood

by10

.4%

(1.8

%)

incr

ease

spro

babi

lity

ofbe

ing

singl

ew

ithno

child

ren

by23

.3%

(4%

)A

lmos

tall

ofth

ere

duct

ion

inpr

obab

ility

ofbe

ing

alo

wea

rner

isdr

iven

byfe

mal

es.W

omen

mor

elik

ely

tode

lay

child

bear

ing

and

fam

ilyfo

rmat

ion

than

men

asa

resu

ltof

incr

ease

dch

ildca

re.M

ostb

enefi

tsof

univ

ersa

lchi

ldca

rear

efo

rch

ildre

nof

low

-edu

cate

dm

othe

rs.

Subs

idiz

edfo

rmal

child

care

crow

dsou

tinf

orm

alch

ildca

rew

ithal

mos

tno

neti

ncre

ase

into

talc

hild

care

use

orm

ater

nal

empl

oym

ent.

No

impa

ctof

child

care

refo

rmon

mat

erna

led

ucat

ion.

Cost

-Ben

efitA

nal

ysis

:C

ost:

Ann

ualb

udge

tary

cost

per

child

care

plac

e=

$540

0B

enefi

t:In

crea

sein

0.35

yrso

fedu

catio

nim

plie

san

incr

ease

in$2

7,00

0in

lifet

ime

earn

ings

.

See

Kar

oly

etal

.(2

005)

for

mor

ein

form

atio

nab

outs

ome

ofth

est

udie

sdes

crib

edin

this

tabl

e.U

nles

soth

erw

iseno

ted,

none

ofth

ese

eval

uatio

nsw

asra

ndom

ized

.“T

”re

fers

toth

etr

eatm

ent,

and

“C”

refe

rsto

the

cont

rolo

rco

mpa

riso

ngr

oup.

T>

Cm

eans

that

the

differ

ence

was

signi

fica

ntat

the

5%le

vel.

aA

very

simila

rst

udy

byG

orm

ley

etal

.(2

005)

eval

uate

sthe

effec

tsof

Okl

ahom

a’sU

nive

rsal

Pre-

Kpr

ogra

mon

scho

olre

adin

essu

sing

the

sam

ere

gres

sion

disc

ontin

uity

desig

n,bu

tmea

suri

ngou

tcom

esw

ithth

eW

oodc

ock-

John

son

Ach

ieve

men

tTes

t.T

hey

find

a0.

79SD

incr

ease

inth

eLe

tter

-Wor

dId

entifi

catio

nsc

ore,

a0.

64SD

incr

ease

inth

eSp

ellin

gsc

ore,

and

a0.

38SD

incr

ease

inth

eA

pplie

dPr

oble

mss

core

.


that projected gains in earnings are enough to offset the cost of the program, so that thereis a positive cost/benefit ratio. Carneiro and Ginja (2008) use the same data but a differentidentification strategy: they focus on families around the cutoff for income eligibility forthe program and compare families who are just below (and therefore eligible) to thosewho are just above (and therefore ineligible). A potential problem with this strategy isthat it implicitly assumes that families cannot game the system by reducing their incomesin order to become eligible for the program. Consistent with other studies, they findpositive effects of Head Start attendance on adolescents including reductions in behaviorproblems, grade repetition, depression, and obesity.

Since its inception, Head Start has aimed to improve a broad range of child outcomes(not only test scores). When the program was launched in 1965, the Office of EconomicOpportunity assisted the 300 poorest counties in applying for Head Start funds, andthese counties were significantly more likely than other counties to receive funds. Usinga regression discontinuity design, Ludwig and Miller (2007) show that mortality fromcauses likely to be affected by Head Start fell among children 5 to 9 in the assisted countiesrelative to the others. Mortality did not fall in slightly older cohorts who would not havebeen affected by the introduction of the program.

Frisvold (2006) and Frisvold and Lumeng (2009) also focus on health effects byexamining the effect of Head Start on obesity. The former instruments Head Startattendance using the number of Head Start places available in the community, whilethe later takes advantage of a cut in a Michigan Head Start program which resulted in theconversion of a number of full-day Head Start places to half day places. Both studies findlarge and significant effects of Head Start on the incidence of obesity. In defense of theirestimates, which some might find implausibly large, Frisvold and Lumeng point out thata reduction of only 75 calories per day (i.e. less than a slice of bread or an apple) wouldbe sufficient to yield their results. In small children, even small changes in diet may havelarge cumulative effects. Anderson et al. (2009) follow Garces et al. and use sibling fixedeffects and data from the PSID to estimate the effect of Head Start on smoking as anadult. Again, they find large effects.

Head Start has served as a model for state preschools targeted to low-income childrenin states such as California, and also for new (non-compulsory) universal preschoolprograms in Georgia, and Oklahoma. The best available evaluations of universalpreschool programs highlight the importance of providing a high quality program thatis utilized by the neediest children. Baker et al. (2008) examine the introduction of auniversal, $5 per day (later $7), preschool program in the Canadian province of Quebec.

The authors find a strong response to the subsidy in terms of maternal labor supply andthe likelihood of using care, but they find negative effects on children for a range ofoutcomes. Lefebvre et al. (2006) focus on the same natural experiment and examine theeffects on children’s vocabulary scores, which have been shown to be a good predictorof schooling attainment in early grades. They find strong evidence of negative effects.


In interpreting this study, it is important to consider who was affected by the program.

Because poor children were already eligible for child care subsidies, the marginal childaffected by this program was a child who probably would have stayed home with his orher middle-class, married, mother, and instead was put into child care. Moreover, themarginal child care slot made available by the program was of low quality—the suddeninflux of children into care caused the province to place more emphasis on makingslots available than on regulating their quality. Hence, the study should be viewed as theconsequence of moving middle class children from home care to relatively poor qualitycare. It is not possible to draw any conclusion from this study about the effect of drawingpoor children into care of good quality, which is what model preschool programs andHead Start aim to do.

Gormley and Gayer (2006) examine the effects of Oklahoma’s universal pre-Kprogram, which is run through the public schools and is thought to be of high quality.They take advantage of strict age cutoffs for the program and compare children who hadjust attended for a year to similar children who were ineligible to attend because theywere slightly younger. They find a 52% gain in pre-reading skills, a 27% gain in pre-

writing skills, and a 21% gain in pre-math skills. These results suggest that a high qualityuniversal pre-K program might well have positive effects, though one would have to trackchildren longer to determine whether these initial gains translate into longer term gainsin schooling attainment. Several other recent studies use a similar regression discontinuitydesign, including Hustedt et al. (2008) and Wong et al. (2008) who examine state pre-Kprograms in five states. These studies find uniformly positive effects. It has been arguedin fact, that the effects of quality state preschool programs are larger than those of HeadStart. However, it is difficult to control for pre-existing differences between the HeadStart children and children who attend other preschools. For example in Magnusonet al. (2007), the preschool children had systematically higher incomes than those whoattended Head Start.

A handful of studies examine the long-term effects of public pre-school orkindergarten programs. Cascio (2009) uses data from four decennial censuses to analyzethe impact of introducing kindergarten into public schools in the US, where kindergartenwas phased in on a state-by-state basis. Using a cohort-based design, she finds that whitechildren born in adopting states after the reform were less likely to dropout of high schooland less likely to be institutionalized as adults. However, she finds no significant effect forblacks, which may be due to significant crowd out of blacks from other programs, suchas Head Start. Like Anderson, she finds that the effects were larger for girls. Havnes andMogstad (2009) study a 1975 policy change in Norway which increased the availability ofregulated child care in some areas but not in others. They find that children “exposed” tomore child care received more education and were more likely to have earnings as adults.Once again, much of the benefit was concentrated among females, and children of lesseducated mothers were particularly likely to benefit. In terms of mechanisms, they find


that the increase in formal care largely displaced informal care, without much net effecton the mother’s labor force participation.

Finally, it is worth mentioning the “Sure Start” program in England and Wales. Thisprogram aimed to provide early intervention services in disadvantaged neighborhoodsbut allowed a wide variety of program models, which obviously complicates anassessment of the program. An evaluation was conducted by comparing communities thatwere early adopters to those that adopted later. A second evaluation compared Sure Startchildren to children from similar neighborhoods who were drawn from the MillenniumCohort study. This second study used propensity scores to balance the samples. Thefirst evaluation found that the most disadvantaged households were actually doing morepoorly in intervention areas than in other areas (NESS, 2005), while the second foundsome evidence of positive effects (NESS, 2008). Following the first evaluation, there hasbeen a move to standardize the intervention and most communities are now offering SureStart Children’s Centers. This latest incarnation of the program remains to be evaluated.

This discussion shows the value of using a framework for the production of childquality as a lens for the interpretation of the program evaluation literature. As discussedabove, child human capital is produced using inputs that may come from either the familyor from other sources. A program that augments the resources available to the child islikely to have positive effects (subject of course to diminishing returns), while a programthat reduces the resources available to the child is likely to have negative effects. Hence, aprogram that causes poor quality group time to be substituted for relatively high qualitymaternal time can have a negative effect and vice versa. The important point is that it ispossible to intervene effectively and to improve the trajectories of young children.

5.3.4. Health insuranceHealth insurance is not an intervention program in the sense of the programs describedabove. Yet, there is a good deal of evidence that access to health insurance improveschildren’s health at birth and afterwards. Much of the evidence comes from studies ofthe introduction, or expansion, of health insurance benefits. Some of this literature issummarized in Table 13. For example, Hanratty (1996) examined the introduction ofpublic health insurance in Canada, which was phased in on a province-by-provincebasis. Using county-level panel data, she finds that the introduction of health insurancewas associated with a decline of four percent in the infant mortality rate, and that theincidence of low birth weight also decreased by 1.3% for all parents and by 8.9% forsingle parents. Currie and Gruber (1996) conduct a similar exercise for the US, focusingon an expansion of public health insurance to pregnant women and infants. They findthat the effects vary depending on whether the expansion covered the poorest women, orwomen somewhat higher in the income distribution. Narrowly targeted expansions thatincreased the fraction of the poorest women eligible by 30%, reduced low birth weightby 7.8%, and reduced infant mortality by 11.5%. Expansions of eligibility of a similarmagnitude to women of higher incomes had very small effects on the incidence of low


Table13

Effectsof

Med

icaidan

dothe

rpub

liche

alth

insuranceon

birthan

dearly

child

hood

outcom

es.

Stud

yan

dda

taStud

yde

sign

Results

Effects

onbirthweigh

tand

health

atbirth

The

effica

cyan

dco

stof

chan

gesi

nth

eM

edic

aid

elig

ibili

tyof

preg

nant

wom

en(C

urri

ean

dG

rube

r,19

96).

Not

e:A

utho

rsal

soco

nduc

tan

anal

ysis

ofM

edic

aid

take

-up,

whi

chis

noti

nclu

ded

inth

ere

sults

here

.D

ata

from

CPS

and

Vita

lSt

atist

ics.

Dat

aon

Med

icai

dex

pend

iture

sfr

omth

eH

ealth

Car

eFi

nanc

ing

Adm

inist

ratio

n.D

ata

onth

eus

eof

med

ical

serv

ices

bypr

egna

ntw

omen

from

the

NLS

Y.Si

mul

ated

mod

elfo

rta

rget

edch

ange

sest

imat

edfo

r19

79-1

992.

Sim

ulat

edm

odel

for

broa

dch

ange

ses

timat

edfo

r19

87-1

992.

(n=

600)

.

Exp

loite

dva

riat

ion

betw

een

stat

esin

the

timin

gof

expa

nsio

nsof

Med

icai

del

igib

ility

.U

sea

fixe

dsa

mpl

eto

simul

ate

the

frac

tion

elig

ible

unde

rdi

ffer

ents

tate

rule

s.D

istin

guish

“tar

gete

d”ch

ange

saff

ectin

gve

rylo

win

com

ew

omen

from

“bro

ad”

chan

gest

ow

omen

furt

her

upth

ein

com

edi

stri

butio

n.In

stru

men

tthe

actu

alfr

actio

nof

wom

enel

igib

lein

each

stat

ean

dye

arw

ithth

esim

ulat

edel

igib

ility

mea

sure

.C

ontr

olle

dfo

rst

ate

fixe

deff

ects

and

time

vary

ing

stat

ech

arac

teri

stic

s.

The

perc

enta

geof

15-4

4yr

old

wom

enel

igib

lefo

rM

edic

aid

(had

they

beco

me

preg

nant

)ros

efr

om12

.4%

to43

.3%

b/n

1979

and

1991

.A

30%

incr

ease

inel

igib

ility

lead

sto

a1.

9%de

crea

sein

inci

denc

eof

low

birt

hw

eigh

t(sig

.at

10%

leve

l)an

da

8.5%

decr

ease

inin

fant

mor

talit

yra

te.Fo

rta

rget

edpr

ogra

mch

ange

s,a

30%

incr

ease

inel

igib

ility

decr

ease

slow

birt

hw

eigh

t(in

fant

mor

talit

y)by

7.8%

(11.

5%).

For

broa

dpr

ogra

mch

ange

sa30

%in

crea

sein

elig

ibili

tyde

crea

sesl

owbi

rth

wei

ght(

infa

ntm

orta

lity)

by0.

2%(2

.9%

).

(con

tinue

don

next

page

)


Table13

(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Can

adia

nN

atio

nalH

ealth

Insu

ranc

ean

dIn

fant

Hea

lth(H

anra

tty,

1996

).C

ount

y-le

velp

anel

data

onin

fant

mor

talit

yfr

om10

prov

ince

sin

1960

-197

5fr

omth

eC

ensu

sofC

anad

aan

dfr

omC

anad

a’sD

ivisi

onof

Vita

lSta

tistic

s.(n=

204

coun

ties)

.D

ata

onbi

rth

wei

ghtf

rom

asa

mpl

eof

all

birt

hre

cord

sin

Can

ada

from

1960

to19

74.

Use

dva

riat

ion

intim

ing

ofim

plem

enta

tion

ofna

tiona

lhea

lthin

sura

nce

acro

sspr

ovin

cesi

nC

anad

aov

er19

62-7

2.Lo

gito

fout

com

eson

adu

mm

yfo

rha

ving

natio

nalh

ealth

insu

ranc

ein

apa

rtic

ular

coun

ty-y

ear,

cont

rolli

ngfo

rde

mog

raph

ican

dso

cio-

econ

omic

fact

ors,

atim

etr

end,

and

year

fixe

deff

ects

.

Intr

oduc

tion

ofna

tiona

lhea

lthin

sura

nce

lead

sto

decl

ines

of4%

inin

fant

mor

talit

yra

tes;

1.3%

inlo

wbi

rth

wei

ght

(who

lesa

mpl

e);8.

9%in

low

birt

hw

eigh

t(sin

gle

pare

nts)

.N

oim

pact

onbi

rth

wei

ghta

mon

gm

arri

edw

omen

.

Cha

nges

inpr

enat

alca

retim

ing

and

low

birt

hw

eigh

tby

race

and

soci

oeco

nom

icst

atus

:Im

plic

atio

nsfo

rth

eM

edic

aid

expa

nsio

nsfo

rpr

egna

ntw

omen

(Dub

ayet

al.,

2001

).D

ata

onbi

rths

from

the

1980

,19

86,an

d19

93N

atal

ityFi

les.

n=

8,10

0,00

0bi

rths

.

Diff

eren

ce-i

n-di

ffer

ence

,su

btra

ctin

gdi

ffer

ence

inob

stet

rica

lout

com

es(r

ates

ofla

tein

itiat

ion

ofpr

enat

alca

rean

dra

teso

flo

wbi

rth

wei

ght)

b/n

1980

and

1986

from

differ

ence

inou

tcom

esb/

n19

86an

d19

90,w

ithin

soci

oeco

nom

ic(S

ES)

grou

ps.

Also

com

pare

dch

ange

sin

obst

etri

cal

outc

omes

in19

86-9

3b/

nw

omen

oflo

wan

dhi

ghSE

S(s

ince

high

SES

wom

enw

ere

nota

ffec

ted

byM

edic

aid

expa

nsio

ns).

SES

define

dby

mar

itals

tatu

sand

year

sof

scho

olin

g.M

edic

aid

expa

nsio

nsoc

curr

edin

1986

-93.

Con

trol

led

for

year

,ag

eof

mot

her,

pari

ty,an

dag

e-pa

rity

inte

ract

ions

.

Res

ultsfr

om

diff

-in

-diff

within

SES

:M

edic

aid

expa

nsio

nsas

soci

ated

with

decr

ease

sof:

12%

-21%

pren

atal

care

initi

atio

naf

ter

1stt

rim

este

rfo

rw

hite

wom

en;3%

-5%

inlo

wbi

rth

wei

ghta

mon

gw

hite

wom

enw

ith<

12ye

arse

duca

tion;

10%

-13%

inpr

enat

alca

rein

itiat

ion

afte

r1s

ttri

mes

ter

for

blac

kw

omen

with<

12ye

arse

duca

tion;

13%

-27%

inpr

enat

alca

rein

itiat

ion

afte

r1s

ttri

mes

ter

for

blac

kw

omen

with

12-1

5ye

arso

fedu

catio

n;12

%-3

5%in

pren

atal

care

initi

atio

naf

ter

1stt

rim

este

rfo

rbl

ack

wom

enw

ith>

15ye

arso

fedu

catio

n.A

ssoc

iatio

nw

itha

3%in

crea

sein

likel

ihoo

dof

low

birt

hw

eigh

tfor

unm

arri

edbl

ack

wom

enw

ith<

12ye

ars

educ

atio

n.Si

mila

rre

sults

usin

gdi

ff-i

n-di

ffac

ross

SES

for

1986

-93.

(con

tinue

don

next

page

)


Table13

(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Effec

tsof

Med

icai

dex

pans

ions

and

wel

fare

cont

ract

ions

onpr

enat

alca

rean

din

fant

heal

th(C

urri

ean

dG

rogg

er,

2002

).D

ata

onbi

rth

outc

omes

from

VSD

Nfile

s19

90-1

996.

Dat

aon

feta

lde

aths

from

vita

lsta

tistic

sfe

tald

eath

sdet

aile

dre

cord

s.D

ata

onM

edic

aid

adm

inist

rativ

ere

form

sfr

omN

atio

nalG

over

nor’s

Ass

ocia

tion

Mat

erna

land

Chi

ldH

ealth

new

slett

ers.

Dat

aon

wel

fare

case

load

sfr

omth

eU

SD

epar

tmen

tof

Hea

lthan

dH

uman

Serv

ices

.n=

3,98

5,96

8w

hite

s;4,

014,

935

blac

ks.

Logi

treg

ress

ion

that

incl

udes

dum

mie

sfor

stat

e-le

velM

edic

aid

adm

inist

rativ

ere

form

s;in

com

eel

igib

ility

cuto

ffs;

rate

sof

wel

fare

part

icip

atio

n;un

empl

oym

entr

ates

;an

dm

ater

nalo

bser

vabl

ech

arac

teri

stic

s.A

lsoes

timat

edau

xilia

ryre

gres

sions

that

exam

ine

the

effec

tofp

olic

yva

riab

leso

nag

greg

ate

Med

icai

dca

selo

ads.

Med

icai

dca

selo

adin

crea

sesb

y0.

233

fore

ach

1%in

crea

sein

wel

fare

rate

.M

edic

aid

case

load

incr

ease

sby

0.66

4fo

rea

ch10

0%in

crea

sein

inco

me

cut-

off(f

orth

ose

notr

ecei

ving

cash

bene

fits

).In

crea

sein

inco

me

cuto

fffr

om10

0%to

200%

ofpo

vert

ylin

ein

crea

sesp

roba

bilit

yof

adeq

uate

pren

atal

care

by0.

4%fo

rw

hite

s.2

ppin

crea

sein

wel

fare

rate

asso

ciat

edw

/1.

1%in

crea

sein

prob

abili

tyth

atpr

enat

alca

rew

asin

itiat

edin

1stt

rim

este

r;0.

7%in

crea

sein

prob

abili

tyof

adeq

uate

care

for

whi

tes;

2%in

crea

sefo

rbo

thfo

rbl

acks

.In

crea

sein

inco

me

cuto

fffr

om10

0%to

200%

ofpo

vert

ylin

eas

soci

ated

w/

ade

crea

seof

1720

feta

ldea

thsp

erye

aram

ong

blac

ks.2%

incr

ease

inw

elfa

reas

soci

ated

w/

10%

decr

ease

infe

tald

eath

sper

year

amon

gbl

acks

.M

ost

adm

inist

rativ

ere

form

shav

eno

effec

t.B

utus

ing

mai

l-in

form

s(in

stea

dof

in-p

erso

nin

terv

iew

s)in

crea

sesp

roba

bilit

yth

atpr

enat

alca

rew

asin

itiat

edin

1stt

rim

este

rby

0.7%

for

blac

ksan

dsh

orte

rfo

rmsi

ncre

ase

prob

abili

tyth

atpr

enat

alca

rew

asin

itiat

edin

1stt

rim

este

rby

3%fo

rw

hite

s.U

sing

mai

l-in

form

sdec

reas

espr

obab

ility

oflo

wbi

rth

wei

ghtb

y26

%;of

very

low

birt

hw

eigh

tby

38%

for

whi

tes.

(con

tinue

don

next

page

)


Table13

(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Usin

gdi

scon

tinuo

usel

igib

ility

rule

sto

iden

tify

the

effec

tsof

the

fede

ral

Med

icai

dex

pans

ions

onlo

w-i

ncom

ech

ildre

n(C

ard

and

Shor

e-Sh

eppa

rd,

2004

).D

ata

from

SIPP

for

1990

-93,

Mar

chC

PSfo

r19

90-9

6,an

dH

ealth

Inte

rvie

wSu

rvey

for

1992

-199

6on

child

ren

unde

r18

year

sold

.n=

10,2

68to

16,1

96ac

ross

the

differ

enty

ears

inSI

PP.

Two

sour

ceso

fide

ntifi

catio

n:“T

he13

3%ex

pans

ion”

(chi

ldre

nun

der

age

6liv

ing

infa

mili

esw

ithin

com

esbe

low

133%

ofth

epo

vert

ylin

ebe

cam

eco

vere

din

1989

)and

“the

100%

expa

nsio

n”(c

hild

ren

born

afte

rSe

ptem

ber

30,19

83in

fam

ilies

with

inco

mes

belo

wth

epo

vert

ylin

ebe

cam

eco

vere

d).D

iffer

ence

-in-

differ

ence

desig

nco

mpa

ring

age-

6an

dag

e-5

child

ren

infa

mili

esw

ithin

com

esbe

twee

n10

0%an

d13

3%of

the

pove

rty

line

for

the

133%

expa

nsio

n,an

dco

mpa

ring

child

ren

born

befo

rean

daf

ter

Sep.

30,19

83fo

rth

e“1

00%

expa

nsio

n”.R

egre

ssio

nof

Med

icai

den

rollm

ento

ndu

mm

yfo

rbe

ing

belo

wpo

vert

yle

vel,

dum

my

for

bein

gbo

rnaf

ter

9/30

/198

3,th

eir

inte

ract

ion,

dum

my

for

age<

6ye

arso

ld,in

tera

ctio

nbe

twee

ndu

mm

yfo

rag

e<

6ye

arso

ldan

ddu

mm

yfo

rbe

ing

betw

een

100%

and

133%

ofth

epo

vert

ylin

e,a

flex

ible

func

tion

ofag

ean

dfa

mily

inco

me,

and

othe

rba

ckgr

ound

char

acte

rist

icsa

swel

las

year

fixe

deff

ects

.

The

100%

expa

nsio

nle

dto

7%-1

1%ta

ke-u

pra

tes,

whi

leth

e13

3%ex

pans

ion

had<

5%ta

ke-u

pra

tes.

No

evid

ence

for

othe

rin

sura

nce

crow

d-ou

tin

SIPP

data

.R

esul

tsfr

omC

PSda

tasu

gges

ttha

tthe

133%

expa

nsio

nle

dto

decl

ine

inot

her

heal

thin

sura

nce

cove

rage

byap

prox

imat

ely

the

sam

eam

ount

asth

eta

ke-u

pin

Med

icai

d.Si

mila

rre

sults

usin

gH

ealth

Inte

rvie

wSu

rvey

data

.

(con

tinue

don

next

page

)


Table13

(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Effec

tsof

Med

icai

dm

anag

edca

reon

pren

atal

care

and

birt

hou

tcom

es(A

izer

etal

.,20

07).

Dat

aon

birt

hou

tcom

esfr

omth

eC

alifo

rnia

Bir

thSt

atist

ical

Mas

ter

File

1990

-200

0an

dB

irth

Coh

ortfi

lesf

orth

esa

me

time

peri

od.H

ospi

tal-

leve

lda

tafr

omV

italS

tatis

tics

reco

rds.

n=

55,0

00bi

rths

.

Exp

loite

dth

eco

unty

-by-

coun

tyva

riat

ion

inim

plem

enta

tion

ofM

edic

aid

man

aged

care

,w

hich

resu

lted

from

aph

ase-

inpo

licy

inC

alifo

rnia

that

requ

ired

wom

enen

rolle

din

Med

icai

dto

switc

hto

man

aged

care

plan

s.So

me

coun

tiess

witc

hed

toC

OH

Spl

an,ot

hers

switc

hed

to2

orm

ore

plan

syst

em.M

ultiv

aria

tere

gres

sion

with

coun

tyfixe

deff

ects

,an

dm

othe

rfixe

deff

ects

,as

wel

laso

ther

obse

rvab

lech

arac

teri

stic

s.R

obus

tnes

sche

cks:

Est

imat

ing

sam

em

odel

for

mar

ried

wom

enon

ly(u

nlik

ely

tobe

onw

elfa

re,

henc

eun

likel

yto

besu

bjec

tto

MM

C);

cont

rolli

ngfo

rm

obili

tyb/

nco

untie

s;re

gres

sion

disc

ontin

uity

desig

nto

elim

inat

etim

etr

end

effec

ts;ad

ding

inte

ract

ion

term

sb/n

time

tren

dan

dse

vera

ldum

mie

s;“i

nten

t-to

-tre

at”

mod

els

toco

ntro

lfor

the

MM

Cad

optio

nbe

ing

note

xoge

nous

toco

untie

s.

Prob

abili

tyof

star

ting

pren

atal

care

in1s

ttri

mes

ter:

decr

ease

dby

9%-1

0%in

both

CO

HS

and

2-pl

anco

untie

s.U

seof

indu

ctio

n/st

imul

atio

nof

labo

r:in

crea

sed

by43

.8%

inC

OH

Sco

untie

s.U

seof

feta

lmon

itors

:in

crea

sed

by25

.9%

inC

OH

Sco

untie

s.In

cide

nce

oflo

wbi

rth

wei

ght:

incr

ease

dby

15%

inbo

thC

OH

San

d2-

plan

coun

ties.

Inci

denc

eof

shor

tges

tatio

n:in

crea

sed

by15

%in

both

CO

HS

and

2-pl

anco

untie

s.In

cide

nce

ofne

onat

alde

ath:

incr

ease

dby

50%

in2-

plan

coun

ties.

(con

tinue

don

next

page

)


Table13

(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Effects

onlaterc

hild

outcom

es

Med

icai

del

igib

ility

and

the

inci

denc

eof

ambu

lato

ryca

rese

nsiti

veho

spita

lizat

ions

forc

hild

ren

(Kae

stne

ret

al.,

2001

).D

ata

from

the

Nat

ionw

ide

Inpa

tient

Sam

ple

ofth

eH

ealth

care

Cos

tand

Util

izat

ion

Proj

ectf

or19

88an

d19

92.n=

36,0

00.

Diff

eren

ce-i

n-di

ffer

ence

,co

mpa

ring

the

chan

gein

ambu

lato

ryca

rese

nsiti

ve(A

CS)

hosp

italiz

atio

nsbe

fore

and

afte

rM

edic

aid

expa

nsio

nsb/

npo

oran

dno

n-po

orch

ildre

n.Po

vert

yst

atus

dete

rmin

edby

med

ian

fam

ilyin

com

ein

child

’szi

pco

deof

birt

h.Tw

otr

eatm

entg

roup

s:in

com

e<

$25,

000

and

$25,

000<

inco

me<

$35,

000.

Con

trol

grou

p:in

com

e>

$35,

000.

Sepa

rate

estim

ates

for

child

ren

aged

2-6,

7-9.

Con

trol

led

for

hosp

ital-

spec

ific,

year

,an

din

divi

dual

fact

ors.

Inci

denc

eof

AC

Sho

spita

lizat

ions

calc

ulat

edus

ing

both

non-

AC

Sho

spita

lizat

ions

and

tota

lbir

ths

inth

ede

nom

inat

or.

For

child

ren

aged

2-6

infa

mili

esw

ith<

$25,

000

inco

me,

inci

denc

eof

AC

Sho

spita

lizat

ions

due

tode

hydr

atio

n,co

nvul

sions

and

non-

asth

ma

illne

sses

decl

ined

by10

%-2

0%.

Est

imat

edeff

ects

izes

of40

%-8

0%fo

rth

ose

affec

ted

byM

edic

aid

expa

nsio

ns.

For

child

ren

aged

2-6

infa

mili

esw

ith$2

5,00

0<

inco

me<

$35,

000,

inci

denc

eof

AC

Sho

spita

lizat

ions

due

tono

n-as

thm

aill

ness

esan

dpn

eum

onia

decl

ined

by10

%-1

4%(o

nly

whe

nde

nom

inat

oris

tota

lbi

rths

).Fo

rch

ildre

nag

ed7-

9in

fam

ilies

with

$25,

000<

inco

me<

$35,

000,

inci

denc

eof

AC

Sho

spita

lizat

ions

due

toas

thm

ade

clin

edby

22%

-30%

;ho

spita

lizat

ions

due

toey

e,no

sean

dth

roat

illne

sses

decl

ined

by>

100%

.N

osig

nifica

nteff

ects

onch

ildre

nag

ed2-

6in

$25,

000<

inco

me<

$35,

000

grou

por

onch

ildre

nag

ed7-

9in<

$25

,000

inco

me

grou

p.

(con

tinue

don

next

page

)


Table13

(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Low

take

-up

inM

edic

aid:

does

outr

each

mat

ter

and

for

who

m?

(Aiz

er,20

03).

Dat

aon

mon

thly

Med

icai

den

rollm

entf

orch

ildre

nag

ed0-

15in

CA

from

1996

to20

00lin

ked

with

info

rmat

ion

onth

enu

mbe

rof

com

mun

ity-b

ased

appl

icat

ion

assis

tant

sin

each

zip

code

inth

em

onth

ofap

plic

atio

nby

age

and

race

.n=

324,

331

for

anal

ysis

ofta

ke-u

p.n

=12

1,80

6fo

ran

alys

isof

hosp

italiz

atio

ns.

Exa

min

edeff

ects

ofC

Aou

trea

chca

mpa

ign

laun

ched

inJu

ne19

98,w

hich

cons

isted

ofco

mm

unity

-bas

edap

plic

atio

nas

sista

ntsa

nda

med

iaca

mpa

ign

tora

iseaw

aren

essa

bout

Med

icai

d,on

Med

icai

den

rollm

ent.

Mul

tivar

iate

regr

essio

nw

ithke

yex

plan

ator

yva

riab

lebe

ing

the

num

ber

ofco

mm

unity

-bas

edap

plic

atio

nas

sista

nts

inea

chzi

pco

dein

the

mon

thof

appl

icat

ion

byag

ean

dra

ce.In

clud

edzi

pco

dean

dm

onth

fixe

deff

ects

asw

ella

sco

ntro

lsfo

rch

ange

sin

busin

essc

ycle

and

the

dem

ogra

phic

com

posit

ions

ofth

est

ate.

Also

exam

ined

effec

tofe

arly

Med

icai

den

rollm

ento

nA

CS

hosp

italiz

atio

nsby

inst

rum

entin

gM

edic

aid

enro

llmen

twith

outr

each

mea

sure

s.

Acc

esst

obi

lingu

alap

plic

atio

nas

sista

ntsi

ncre

ases

new

mon

thly

Med

icai

den

rollm

entb

y4.

6%am

ong

Hisp

anic

child

ren,

and

by6%

amon

gA

sian

child

ren

(rel

ativ

eto

othe

rch

ildre

nin

sam

ene

ighb

orho

od).

A10

00-c

hild

incr

ease

inM

edic

aid

enro

llmen

tsde

crea

sesA

CS

hosp

italiz

atio

nsby

3.26

hosp

italiz

atio

ns(m

ean

notr

epor

ted,

soca

n’tc

alcu

late

rela

tive

effec

tsiz

e).

(con

tinue

don

next

page

)


Table13

(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Publ

icin

sura

nce

and

child

hosp

italiz

atio

ns:A

cces

sand

effici

ency

effec

ts(D

afny

and

Gru

ber,

2005

).D

ata

from

NH

DS

onch

ilddi

scha

rges

for

1983

to19

96.C

ells

define

dfo

r4

age

cate

gori

es(<

1,1-

5,6-

10,11

-15)

for

each

stat

ean

dye

ar.n=

2308

cells

.U

sed

age-

stat

e-ye

arpo

pula

tions

from

the

Cen

susB

urea

uto

calc

ulat

eho

spita

lizat

ion

rate

sfor

each

cell.

Inve

stig

ated

impa

ctof

Med

icai

dex

pans

ions

onho

spita

lizat

ions

usin

gth

eva

riat

ion

inM

edic

aid

elig

ibili

tyb/

nst

ates

,ov

ertim

e,an

dby

age.

Key

expl

anat

ory

vari

able

isth

eel

igib

ility

rate

mea

sure

dby

the

frac

tion

ofch

ildre

nel

igib

lefo

rM

edic

aid

inea

chag

e-st

ate-

year

cell.

Con

trol

led

for

age,

stat

e,an

dye

arfixe

deff

ects

asw

ella

ssta

te-y

ear

inte

ract

ions

.U

sed

the

frac

tion

elig

ible

calc

ulat

edus

ing

afixe

dsa

mpl

eto

inst

rum

entf

orac

tual

elig

ibili

tyin

each

stat

e,ye

ar,an

dag

egr

oup.

Also

exam

ined

effec

tofM

edic

aid

elig

ibili

tyon

leng

thof

stay

inho

spita

land

the

num

ber

ofpr

oced

ures

perf

orm

ed.

A10

ppin

crea

sein

Med

icai

del

igib

ility

lead

sto

a8.

4%in

crea

sein

tota

lhos

pita

lizat

ions

,an

8.1%

incr

ease

inun

avoi

dabl

eho

spita

lizat

ions

,an

dno

stat

istic

ally

signi

fica

ntim

pact

onav

oida

ble

hosp

italiz

atio

ns.A

ssum

ing

acce

ssto

hosp

italc

are

incr

ease

sthe

likel

ihoo

dof

allk

inds

ofho

spita

lizat

ions

equa

lly,th

ese

resu

ltsim

ply

that

incr

ease

dus

eof

prim

ary

care

enge

nder

edby

Med

icai

dex

pans

ions

miti

gate

dth

ein

crea

sein

tota

lhos

pita

lizat

ions

byre

duci

ngth

ein

crea

sein

avoi

dabl

eho

spita

lizat

ions

that

wou

ldha

veot

herw

iseoc

curr

ed.A

10pp

incr

ease

inel

igib

ility

lead

sto

a3.

1%de

crea

sein

the

leng

thof

hosp

itals

tay,

a5%

incr

ease

inth

e#

ofpr

oced

ures

perf

orm

ed,an

da

6.6%

incr

ease

inth

elik

elih

ood

ofha

ving

any

proc

edur

epe

rfor

med

,i.e

.le

adst

om

ore

aggr

essiv

eca

re.

Publ

iche

alth

insu

ranc

e,pr

ogra

mta

ke-u

pan

dch

ildhe

alth

(Aiz

er,20

07).

See

Aiz

er(2

003)

entr

yfo

rde

scri

ptio

nof

data

.

See

Aiz

er(2

003)

entr

y.A

dditi

onal

anal

ysis:

Exa

min

edhe

tero

gene

ityin

effec

tson

take

-up

byag

e.E

xam

ined

nonl

inea

reff

ects

onta

ke-u

p.E

xam

ined

effec

tsof

Eng

lish

and

Span

ishla

ngua

gead

vert

isem

ents

onta

ke-u

p.

Prox

imity

toan

addi

tiona

lbili

ngua

lapp

licat

ion

assis

tant

incr

ease

snew

mon

thly

Med

icai

den

rollm

entb

y7%

-9%

amon

gH

ispan

icsa

ndby

27%

-36%

amon

gA

sians

.Sm

alle

steff

ects

for

infa

ntsw

how

ere

alre

ady

larg

ely

elig

ible

;la

rges

teff

ects

for

ages

6-15

.E

ffec

tisl

inea

rfo

rH

ispan

ics;

sligh

tlyco

ncav

efo

rAsia

ns.E

nglis

hla

ngua

gead

vert

isem

ents

incr

ease

Med

icai

den

rollm

enti

nth

efo

llow

ing

mon

thby

4.7%

amon

gal

lchi

ldre

n,an

dSp

anish

lang

uage

adve

rtise

men

tsin

crea

seM

edic

aid

enro

llmen

tin

the

follo

win

gm

onth

byan

addi

tiona

l2.5

%am

ong

Hisp

anic

child

ren.

Incr

easin

gth

e#

ofch

ildre

nw

/M

edic

aid

by10

%re

sults

ina

2%-3

%de

clin

ein

avoi

dabl

eho

spita

lizat

ions

.

(con

tinue

don

next

page

)


Table13

(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Has

publ

iche

alth

insu

ranc

efo

rol

der

child

ren

redu

ced

disp

ariti

esin

acce

ssto

care

and

heal

thou

tcom

es?

(Cur

rie

etal

.,20

08).

Dat

afr

omth

ena

tiona

lhe

alth

inte

rvie

wsu

rvey

sfo

r19

86-2

005.

n=

474,

164

child

ren<

18yr

sold

.In

stru

men

tdat

afr

omth

eC

PS.

Iden

tifica

tion

due

toth

efa

ctth

atex

pans

ions

inM

edic

aid/

SCH

IPel

igib

ility

for

olde

rch

ildre

nre

lativ

eto

youn

ger

child

ren

happ

ened

atdi

ffer

entt

imes

indi

ffer

ents

tate

s.In

stru

men

ted

for

indi

vidu

alM

edic

aid/

SCH

IPel

igib

ility

usin

gan

inde

xof

gene

rosit

yof

the

stat

e’s

publ

iche

alth

insu

ranc

epr

ogra

ms

calc

ulat

edus

ing

afixe

dgr

oup

ofch

ildre

n.T

hen,

estim

ated

impa

ctof

publ

iche

alth

insu

ranc

eel

igib

ility

onth

ere

latio

nshi

pb/

nfa

mily

inco

me

and

child

heal

thst

atus

and

onth

ere

latio

nshi

pb/

nfa

mily

inco

me

and

doct

orvi

sitsi

nth

epr

evio

usye

ar.A

lsote

sted

whe

ther

the

rela

tions

hip

b/n

inco

me

and

outc

omes

chan

ged

over

time

with

inag

egr

oups

byru

nnin

gse

para

tere

gres

sions

for

4ag

egr

oups

:0-

3,4-

8,9-

12,13

-17.

Fina

lly,es

timat

edre

latio

nshi

pb/

nhe

alth

outc

omes

and

the

frac

tion

ofch

ildre

nel

igib

lein

child

’sbi

rth

coho

rtin

the

child

’scu

rren

tsta

teof

resid

ence

for

child

ren

aged

9-17

.C

ontr

olle

dfo

rfa

mily

back

grou

ndva

riab

lesa

ndye

aran

dst

ate

fixe

deff

ects

.

For

child

ren

aged

9-12

,th

eco

effici

ento

nin

com

ein

am

odel

ofhe

alth

stat

usde

clin

edby

20%

over

2000

-200

5.Fo

rch

ildre

nag

ed13

-17,

itde

clin

edby

18%

over

1996

-200

0an

dby

25%

over

2000

-05.

For

child

ren

0-3/

4-8/

9-12

the

coeffi

cien

ton

inco

me

ina

mod

elof

doct

orvi

sitsd

eclin

edby

64%

/62%

/50%

betw

een

2000

-200

5.Fo

rch

ildre

nag

ed13

-17

ther

ew

asno

signi

fica

ntch

ange

inth

ein

com

eco

effici

ent.

Sign

ifica

ntde

clin

esin

the

inco

me

coeffi

cien

tove

r19

91-1

995

and

1996

-200

0fo

rch

ildre

nag

ed0-

3an

d4-

8as

wel

l.N

ost

atist

ical

lysig

nifica

ntim

pact

ofco

ntem

pora

neou

she

alth

insu

ranc

eel

igib

ility

onch

ildhe

alth

stat

us.

A10

0pp

incr

ease

inth

efr

actio

nel

igib

leat

age

3w

ould

redu

ceth

epr

obab

ility

that

the

aver

age

child

aged

9-17

isin

less

than

exce

llent

heal

thby

11%

.A

100

ppin

crea

sein

the

frac

tion

elig

ible

atag

es1

and

2w

ould

redu

ceth

epr

obab

ility

that

the

aver

age

child

aged

9-17

had

nodo

ctor

visit

inth

epa

stye

arby

41%

.

(con

tinue

don

next

page

)


Table13

(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

The

impa

ctof

child

ren’

spu

blic

heal

thin

sura

nce

expa

nsio

nson

educ

atio

nal

outc

omes

(Lev

ine

and

Scha

nzen

bach

,20

09).

Dat

aon

stat

e-le

vela

vera

gesc

aled

test

scor

esfr

omth

eN

atio

nalA

sses

smen

tof

Edu

catio

nalP

rogr

essf

or19

90to

2003

.(n=

431)

.D

ata

for

simul

ated

inst

rum

ents

from

Mar

chC

PS.D

ata

onch

ildhe

alth

from

Vita

lSta

tistic

sfor

1984

-200

3(n=

1020

).

Exa

min

eth

eim

pact

ofpu

blic

heal

thin

sura

nce

atbi

rth

on4t

han

d8t

hgr

ade

read

ing

and

mat

hte

stsc

ores

.D

iffer

ence

-in-

differ

ence

-in-

differ

ence

usin

gcr

oss-

stat

eva

riat

ion

over

time

and

acro

ssag

esin

child

ren’

shea

lthin

sura

nce

elig

ibili

tydu

eto

Med

icai

dan

dSC

HIP

expa

nsio

ns.In

stru

men

ted

for

publ

iche

alth

insu

ranc

eel

igib

ility

usin

gth

esim

ulat

edfr

actio

nel

igib

le(a

sin

Cur

rie

and

Gru

ber

(199

6)se

eab

ove)

.To

test

whe

ther

chan

ges

ined

ucat

iona

lout

com

esar

edu

eto

impr

ovem

ents

inhe

alth

stat

usor

toad

ditio

nalh

ouse

hold

inco

me

gene

rate

dif

the

avai

labi

lity

ofpu

blic

heal

thin

sura

nce

crow

dsou

tpri

vate

heal

thin

sura

nce,

they

estim

ated

the

dire

ctim

pact

ofhe

alth

atbi

rth

oned

ucat

iona

lout

com

es.C

ontr

olle

dfo

rst

ate

and

year

fixe

deff

ects

and

stat

e-sp

ecifi

ctim

etr

ends

.

A50

ppin

crea

sein

elig

ibili

tyat

birt

hin

crea

sed

read

ing

test

scor

esby

0.09

SDre

lativ

eto

4th

and

8th

grad

eco

mbi

ned

mea

nsc

aled

scor

e.N

oeff

ecto

nm

ath

test

scor

es.

Exp

ansio

nsto

publ

iche

alth

insu

ranc

eel

igib

ility

atbi

rth

asso

ciat

edw

/1.

6%re

duct

ion

inlo

wbi

rth

wei

ghtr

ate

for

who

lesa

mpl

ean

d6.

7%re

duct

ion

inin

fant

mor

talit

yra

tes

amon

gw

omen

with

atle

asta

high

scho

olde

gree

.A

50%

incr

ease

inlo

wbi

rth

wei

ght(

infa

ntm

orta

lity)

rate

wou

ldde

crea

sere

adin

gte

stsc

ores

by0.

12SD

(.07S

D).

(con

tinue

don

next

page

)


Table13

(con

tinue

d)Stud

yan

dda

taStud

yde

sign

Results

Publ

icin

sura

nce,

crow

d-ou

tand

heal

thca

reut

iliza

tion

(Koc

h,20

09).

Dat

afr

omth

em

edic

alex

pend

iture

pane

lsur

vey.

Focu

son

subs

ampl

eof

child

ren<

18yr

swho

sefa

mili

esha

vein

com

esb/

n50

%an

d40

0%of

pove

rty

line.

(n=

32,6

09).

Dat

aon

publ

icin

sura

nce

reim

burs

emen

trat

esfr

omth

eA

mer

ican

Aca

dem

yof

Pedi

atri

csfo

r19

98-2

001.

Reg

ress

ion

disc

ontin

uity

desig

nus

ing

fam

ilyin

com

ecu

t-off

stha

tdet

erm

ine

elig

ibili

tyfo

rM

edic

aid

and

SCH

IP.To

dist

ingu

ishb/

nch

ildre

nw

hoar

eel

igib

lefo

rpu

blic

insu

ranc

ew

/out

acce

ssto

priv

ate

insu

ranc

ean

dth

ose

who

sepr

ivat

ein

sura

nce

may

becr

owde

d-ou

tdue

toex

pans

ions

inpu

blic

heal

thin

sura

nce,

cons

truc

ted

am

easu

reof

priv

ate-

insu

ranc

e“o

ffer

”(=

1if

any

ofth

efo

llow

ing

hold

s:ch

ildis

priv

atel

yin

sure

dat

the

time

ofth

eel

igib

ility

mea

sure

men

tor

afa

mily

mem

ber

ispr

ivat

ely

empl

oyed

atth

etim

ean

dei

ther

hasi

nsur

ance

thro

ugh

the

job

ortu

rnsd

own

the

insu

ranc

eat

the

job)

.E

stim

ated

impa

ctso

fpub

liche

alth

insu

ranc

eel

igib

ility

onm

easu

reso

fcar

eut

iliza

tion

(doc

tor’s

visit

s,et

c.)a

swel

laso

npr

ivat

ein

sura

nce

take

-up.

Also

estim

ated

the

differ

entia

leffec

tofe

ligib

ility

onth

ose

w/

and

w/o

utpr

ivat

ehe

alth

insu

ranc

eby

inte

ract

ing

the

elig

ibili

tyva

riab

lew

ithth

e“o

ffer

”va

riab

le.C

ontr

olle

dfo

rba

ckgr

ound

char

acte

rist

icsa

swel

lass

tate

and

year

fixe

deff

ects

.A

lsolo

oked

atim

pact

sofd

iffer

entia

lrei

mbu

rsem

entr

ates

acro

ssst

ates

and

year

son

acce

ssto

care

for

the

publ

icly

insu

red.

Publ

icin

sura

nce

elig

ibili

tyin

crea

sesp

ublic

insu

ranc

eco

vera

geby

21%

and

decr

ease

spri

vate

insu

ranc

eco

vera

geby

24%

.So

over

alld

ecre

ase

inin

sura

nce

cove

rage

ofab

out

4.8%

.E

ligib

ility

decr

ease

sthe

num

ber

ofoffi

cevi

sits/

self-

repo

rted

heal

thst

atus

/tot

alex

pend

iture

sby

12%

/87%

/23%

.In

crea

sest

henu

mbe

rof

ER

visit

sby

18%

.E

ligib

ility

decr

ease

sthe

num

ber

ofoffi

cevi

sitsb

y18

%fo

rth

ose

with

outs

ide

priv

ate

heal

thin

sura

nce.

No

stat

istic

ally

signi

fica

nteff

ects

onE

Rvi

sits,

pres

crip

tions

,or

refills

.C

hild

ren

just

elig

ible

for

publ

iche

alth

insu

ranc

ear

e4%

less

likel

yto

have

aus

uals

ourc

eof

care

and

4%m

ore

likel

yto

gow

ithou

tcar

e.N

ost

atist

ical

lysig

nifica

nteff

ects

onho

spita

lor

ER

visit

s.E

ligib

ility

incr

ease

schi

ldre

n’sB

MI

by2%

.Pa

rent

sofj

ust

elig

ible

child

ren

are

5%le

sslik

ely

tobe

give

nad

vice

abou

tth

eir

child

’sea

ting

heal

thfu

lly.N

osig

nifica

nteff

ects

onas

thm

adi

agno

sis.C

ondi

tiona

lon

taki

ngm

edic

atio

nfo

ras

thm

a,el

igib

ility

incr

ease

spro

babi

lity

ofta

king

inha

led

ster

oids

(gol

dst

anda

rdtr

eatm

ent)

by32

%.A

$4in

crea

sein

reim

burs

emen

tfor

office

visit

slea

dsto

a5%

incr

ease

inth

enu

mbe

rof

office

visit

s.

Perc

entc

hang

es(d

enot

edby

%in

stea

dof

“pp”

)are

repo

rted

rela

tive

toth

em

ean.


birth weight, but reduced infant mortality. This result suggests that among women ofsomewhat higher income levels, the expansions did not improve health at birth, but mayhave increased access to life-saving technologies after birth. Currie and Grogger (2002)focus on bureaucratic obstacles to obtaining health insurance by looking at contractionsof welfare (women cut from the rolls lost automatic eligibility for Medicaid) as well asoutreach measures undertaken by different states. They find that changes that reducedbarriers to enrollment increased use of prenatal care and had positive effects on infanthealth outcomes.

Baldwin et al. (1998) use individual-level data and compare expansions inWashington, which included enhanced prenatal care services, to expansions in Coloradowhich did not, in a difference-in-differences design. They find reductions in lowbirth weight among medically high risk infants in Washington. Dubay et al. (2001)conduct a difference-in-difference investigation comparing the outcomes of high andlow socioeconomic status women in the 1980-1986 period and in the 1986-1993 period.

They find overall improvements in the use of prenatal care for low SES women, but findimprovements in birth weight only for some groups of white women. However, thisdesign does not really focus on health insurance per se, since the estimates will be affectedby any other changes in health care markets between the two periods that had differentialeffects by SES.

Studies of the effects of health insurance expansions on children often examinepreventable hospitalizations (also called ambulatory care sensitive hospitalizations).The idea is that certain conditions, such as childhood asthma, should not result inhospitalizations if they are properly treated on an outpatient basis. Hence, hospitalizationsfor these conditions are inefficient and indicate that children are receiving inadequatepreventive care. Kaestner et al. (2001) use a difference-in-differences design comparinglow income and other children before and after Medicaid expansions. They findreductions in preventable hospitalizations of 10% to 20%.

Aizer (2003) examines a California outreach program that increased child enrollmentsinto Medicaid. She finds that an increase in enrollments of 1000 reduces hospitalizationsby 3.26. Dafny and Gruber (2005) use a design similar to Currie and Gruber in whichactual individual eligibility is instrumented using a “simulated eligibility measure” whichis an index of the generosity of the Medicaid program in the state. The reason foradopting instrumental variables estimation is that eligibility for Medicaid is determinedby endogenous variables such as parental labor supply. They find that Medicaid eligibilityincreased hospitalizations overall. However, there was no statistically significant increasein avoidable hospitalizations, suggesting that the increase was mainly due to childrenwith unavoidable conditions gaining greater access to care. They also found increasesin the probability of receiving a procedure and reductions in length of stay, suggestingthat children who were hospitalized were receiving more aggressive care and that it mayhave improved their outcomes.


One difficulty with studying child health is that health today is affected by pastinvestments, including health insurance at younger ages. Currie et al. (2008) thereforecompare the health effects of contemporaneous eligibility for health insurance amongolder children to the effect of having been eligible since birth. They find thatcontemporaneous health insurance coverage has little effect on health status but thateligibility from birth is protective. Levine and Schanzenbach (2009) link health insuranceeligibility at birth to 4th and 8th grade scores on the National Assessment of EducationalProgress. They find that a 50% point increase in eligibility at birth is associated with asmall but significant gain on reading scores at both grades, though there is no effect onmath scores. A difficulty with both studies is that neither income at birth nor state ofbirth are directly observed in the cross sectional data sets that they use, so they must beimputed using current income and state of birth.

Another area of research focuses on the quality of care provided by public healthinsurance programs. Analysis of this issue is complicated by the possibility that expansionsof public insurance cause people to lose private health insurance coverage, a phenomenadubbed “crowdout” (Cutler and Gruber 1996; Dubay and Kenney, 1997; Card andShore-Shepard, 2004; Gruber and Simon, 2008). If the private insurance that is lost (ordropped) in response to expansions of public insurance is of superior quality to the privateinsurance, then people’s health may suffer. Koch (2009) concludes that recent expansionsof public health insurance to children at higher income levels have reduced access todoctor’s office visits and increased reliance on emergency rooms. He also shows someevidence consistent with the idea that this is because children are being crowded out ofsuperior (but obviously more expensive) coverage from private heath insurance plans. Infact, it is quite possible that crowding out has increased over time as the public has becomefamiliar with public health insurance plans for children and private health insurance costshave continued to escalate.

Medicaid managed care has also been shown (in at least some cases) to reduce thequality of care. Sloan et al. (2001) conduct a difference-in-difference analysis of Tennesseeand North Carolina before and after Tennessee switched its Medicaid patients to managedcare. They find that use of prenatal care and birth outcomes deteriorated in Tennesseeafter the switch. Aizer et al. (2007) examine data from California, where Medicaidmanaged care was adopted on a county-by-county basis. They also find that compulsorymanaged care had a negative impact on use of prenatal care and birth outcomes. Thismay be because the California Medicaid managed care program “carved out” care forsick newborns–that is, they were covered by a state fund rather than by the managed carecompanies so that companies had little incentive to take actions to improve newbornhealth.

In summary, health insurance matters for children’s outcomes. But quality of care alsomatters. And it is important to remember that for most children threats to health and


well being come from sources such as injuries, poor nutrition, and toxins rather thanonly from lack of access to medical care.

6. DISCUSSION AND CONCLUSIONS

There has been an explosion of research into the early determinants of human capitaldevelopment over the past 10 years. The work surveyed in this chapter conclusively showsthat events before five years old can have large long term impacts on adult outcomes. Itis striking that child and family characteristics measured at school entry do as much toexplain future outcomes as factors that labor economists have more traditionally focusedon, such as years of education. Yet evidence for long term effects of early insults shouldnot be a cause of pessimism. While children can be permanently damaged at this age, thedamage can be remediated. The picture that emerges is one of vulnerability but also ofresilience.

Since early childhood is a new area of research for economists, there remain manyunanswered questions. One major question implicit in the structure of this chapteris whether it will ever be possible to estimate human capital production functions.The opening sections of this chapter showed that the production function paradigmprovides an extremely useful way of thinking about the problem, and in particular,that it highlights the importance of actions taken by parents and others in exacerbatingor mitigating the effects of random shocks. However, to actually estimate the impliedproduction functions would place huge demands on the data, demands that are unlikelyto be met in practice.16 Hence, the evidence we have surveyed is largely reduced form.

A second major question is whether shocks at certain key ages matter more thanothers? Much has been written about “critical periods”. The idea is that certainfunctionalities must be acquired at a particular point in life, and if they are not acquiredat that point, they will either not be acquired at all, or will not be acquired properly.There is to date little hard evidence of critical periods in humans. However, the evidencediscussed above certainly suggests that the period while children are in utero is one ofthe most important to their later development. This has important implications for thetiming of social interventions designed to mitigate harms—it may be that interventionsshould be targeted at pregnant women and/or women of child bearing age in addition toyoung children. But there is insufficient evidence at present to be able to say that insults orinterventions at for example zero to one are likely to be more effective than interventionsat age three or four. Moreover, the cohort designs used to establish the importance of thefetal period can tell us more about the comparison between the fetal period and the earlypost natal period than they do about the comparison between the fetal period and sayexposures at age five.

16 See however Appendix D for one thought about how a production function might be estimated.


A related question is whether some types of shocks matter more than others. Thischapter surveyed many different types of shocks including exposure to disease, inadequatenutrition, exposure to pollution, injuries, maternal mental health problems, maternalsmoking, and maltreatment. However, given that studies of the effects of these shocksrely on different populations, time periods, and methods, it is difficult to get any sense ofwhether one type of shock is more of a threat to human capital development than anyother. Similarly, while it is clear that shocks to health have long-term effects on domainssuch as education and earnings, it is not clear whether health shocks have direct effectson cognition or learning, or whether they act mainly by affecting future health.

Several studies we reviewed suggested that both shocks and interventions can havedifferent long-term effects on males and females. But these findings are too new forus to be able to predict when this difference will occur, and we have virtually noevidence about why it occurs. One possibility is that gender differences are biological.For example, boys may be less robust than girls so that the same health shock can “cull”boys while girls survive (e.g., see Kraemer (2000); Almond and Mazumder (2008)). Inthis case, average health of male survivors might be better than that of female survivors.Alternatively, gender differences could reflect differential parental or societal responses toshocks inspired by son preferences or by beliefs about biological gender differences.17

Finally, given all of this evidence of long-term effects of early life outcomes,what is the least costly way to intervene to improve outcomes? This is still an openquestion and our knowledge of the types of programs that are effective (and why)is evolving rapidly. For example, until recently, there was little evidence that incometransfers had much effect, so it was easy to surmise that in-kind programs were a moreeffective way to improve child outcomes. Recent evidence that cash transfers are indeedeffective should cause a re-evaluation of the received wisdom on this point, given theinefficiencies involved in providing transfers in-kind. Similarly, the large literature aboutnegative effects of maternal employment in the early years is thrown into question byrecent studies showing that large changes in maternity leave policies affected maternalemployment without having any detectable impact on long-term child outcomes. Thisrapid development in our knowledge makes the study of human capital developmentbefore five an exciting frontier for research in labor economics.

APPENDIX AThe following acronyms are used in this chapter:

AFDC = Aid to Families with Dependent Children

BCS = British Birth Cohort Study of 1970.

17 For example, advances in ultrasound technology could have changed the average human capital endowments of boysand girls by allowing parents who prefer sons to invest differentially prenatally (and not only by allowing them to abortfemale fetuses). See Lhila and Simon (2008) for recent work on this topic.


BPI = Behavioral Problems Index

BW = birth weight

CESD = Center for Epidemiological Depression scale

CCT = Conditional Cash Transfer

COHS = County Organized Health System

CPS = Current Population Survey

DDST = Denver Developmental Screening Test

ECLS-B = Early Childhood Longitudinal Study—Birth Cohort

ECLS-K = Early Childhood Longitudinal Study—Kindergarten Class of 1998-1999

EITC = Earned Income Tax Credit

EPA =US Environmental Protection Agency

FSP = Food Stamp Program

HAZ =Height for age z-score

HOME =Home Observation for Measurement of the Environment Score

IHDP = Infant Health and Development Project

IPUMS = Integrated Public-Use Microdata Samples of the US Census

IV = instrumental variables

LBW = Low Birth Weight (birth weight less than 2500 g)

MMC =Medicaid Managed Care

NBER =National Bureau of Economic Research

NCDS =National Child Development Survey (1958 British Birth Cohort)

NELS =National Education Longitudinal Study

NHANES =National Health and Nutrition Examination Survey

NHDS =National Hospital Discharge Survey

NLSY =National Longitudinal Survey of Youth, 1979 cohort

NLSY-Child = Children of the NLSY 1979 cohort


NLSCY =National Longitudinal Survey of Children and Youth (Canadian)

PIAT = Peabody Individual Achievement Test

pp = percentage points

PPVT = Peabody Picture Vocabulary Test

NSLP =National School Lunch Program

OLS =Ordinary Least Squares

PNM = Post Neonatal Mortality (death after 28 days and before 1 year)

PSID = Panel Study of Income Dynamics

RDA =Recommended Dietary Allowance

REIS =Regional Economic Information System

SCHIP = State Child Health Insurance Program

SD = Standard Deviation

SES = Socio-economic Status

SGA = Small for Gestational Age

SIPP = Survey of Income and Program Participation

SNAP = Supplemental Nutrition Assistance Program (formerly, Food Stamps)

TSIV = Two Sample Instrumental Variables

TVIP = Spanish-speaking version of the Peabody Picture Vocabulary Test

TSP = Total Suspended Particles

USDA =US Department of Agriculture

VSDN = Vital Statistics Detailed Natality files (birth certificate data for US)

WIC = Special Supplemental Nutrition Program for Women, Infants, and Children

WPPSI =Wechsler Preschool and Primary Scale of Intelligence


APPENDIX BHuman capital of a child is produced with a CES technology:

h = A[γ ( I1 + µg)

φ+ (1− γ )Iφ2

]1/φ, (9)

where µg is an exogenous shock to (predetermined) period 1 investments. Parents valuetheir consumption and the human capital of their child:

Up = U (C, h) = B[θ(C)ϕ + (1− θ)hϕ

]1/ϕ, (10)

and have the budget constraint:

I1 + I2 + C = y.

Absent discounting, the marginal utility from consuming equals the marginal utilityfrom investing:

δU

δC∗=δU

δh

δh

δ I ∗2.

θCϕ−1= (1− θ)hϕ−1 A[· · ·]

1φ−1(1− γ )I ∗φ−1

2 (11)

θ(y − I1 − I ∗2 )ϕ−1= (1− θ)Aϕ−1

[· · ·]ϕ−1φ A[· · ·]

1φ−1(1− γ )I ∗φ−1

2 (12)

θ(y − I1 − I ∗2 )ϕ−1= (1− θ)(1− γ )Aϕ [· · ·]

ϕ−φφ I ∗φ−1

2 (13)

G(ug, I ∗2 ) ≡ θ(y − I1 − I ∗2 )ϕ−1− (1− θ)(1− γ )Aϕ [· · ·]


2 = 0. (14)

δ I ∗2δµg= −

δGδµg

δGδ I ∗2

=

a(I ∗2 )φ−1[· · ·]

ϕ−2φφ

(ϕ−φφ

)γφ( I1 + µg)

φ−1

−(ϕ − 1)θ(y − I1 − I ∗2 )ϕ−2− a

[[· · ·]

ϕ−φφ (φ − 1)I ∗φ−2

2 +ϕ−φφ[· · ·]

ϕ−2φφ φ(1− γ )I ∗φ−1

2 I ∗φ−12

] ,(15)

using the implicit function theorem and defining a to be (1− θ)(1− γ )Aϕ ≥ 0.

=(ϕ − φ)a(I ∗2 )

φ−1[· · ·]

ϕ−2φφ γ ( I1 + µg)

φ−1

(1− ϕ)θ(y − I1 − I ∗2 )ϕ−2+ a[· · ·]


2

[(1− φ)+ (ϕ − φ)(1− γ )I ∗φ2 /[· · ·]

] . (16)

For ϕ > φ, (16) is positive, so negative shocks in the first period should be reinforced.Accommodation through preferences (i.e., more consumption and less investment,which lowers h in addition to that caused by µg) is optimal.


APPENDIX C

Sibling a has human capital ha , which is affected by a period 1 investment shock of µg:

ha = A[γ ( I1a + µg)

φ+ (1− γ )Iφ2a

]1/φ. (17)

Sibling b does not experience a shock to first period investments:

hb = B[γ Iφ1b + (1− γ )I

φ

2b

]1/φ. (18)

Assume further that first period investments do not distinguish between the twosiblings (absent the shock experienced by sibling a):

I1a = I1b = I1.

Parents have Cobb-Douglas utility that cares only about the human capital of theirtwo children:

Up = U (ha, hb) = (1− α)log ha + αlog hb. (19)

The parents exhaust their budget on investments in their children:

y = 2 I1 + I2a + I2b.

Denoting Y = y − 2 I1 as the budget for second period investments, I2b = Y − I2a.

To maximize utility, the marginal utilities from investing in siblings a and b should beequal:

δUp

δha

δha

δ I2a=δUp

δhb

δhb

δ I2b(1− α

ha

)A

φ

[γ ( I1 + µg)

φ+ (1− γ )Iφ2a

](1/φ)−1φ(1− γ )Iφ−1

2a

=

(α

hb

)B

φ

[γ ( I1)

φ+ (1− γ )Iφ2b

](1/φ)−1φ(1− γ )Iφ−1

2b (20)

(1− α)[γ ( I1 + µg)

φ+ (1− γ )Iφ2a

]−1Iφ−12a

= α[γ ( I1)

φ+ (1− γ )Iφ2b

]−1Iφ−12b (21)

G(µg, I2a) ≡ (1− α)[γ ( I1 + µg)

φ+ (1− γ )Iφ2a

]−1Iφ−12a

−α[γ ( I1)

φ+ (1− γ )(Y − I2a)

φ]−1

(Y − I2a)φ−1= 0 (22)


using budget the constraint: I2b = Y − I2a . By the implicit function theorem:

δ I ∗2a

δµg=

−δGδµg

δGδ I ∗2a

(23)

δG

δµg= −(1− α)Iφ−1

2a

[γ ( I1 + µg)

φ+ (1− γ )Iφ2a

]−2φγ ( I1 + µg)

φ−1 (24)

⇒ signum

[−δG

δµg

]= signum[φ].

δG

δ I ∗2a= (1− α)

[γ ( I1 + µg)

φ+ (1− γ )Iφ2a

]−1(φ − 1)Iφ−2

2a

+ (−1)(1− α)[γ ( I1 + µg)

φ+ (1− γ )Iφ2a

]−2φ(1− γ )Iφ−1

2a Iφ−12a

−

[α[γ ( I1)

φ+ (1− γ )(Y − I2a)

φ]−1

(φ − 1)(Y − I2a)φ−2(−1)

]−

[(−1)α

[γ ( I1)

φ+ (1− γ )(Y − I2a)

φ]−2

× φ(1− γ )(Y − I2a)φ−1(−1)(Y − I2a)

φ−1]

(25)

= (1− α)[γ ( I1 + µg)

φ+ (1− γ )Iφ2a

]−1(φ − 1)Iφ−2

2a

− (1− α)[γ ( I1 + µg)

φ+ (1− γ )Iφ2a

]−2φ(1− γ )I 2φ−2

2a

+

[α[γ ( I1)

φ+ (1− γ )(Y − I2a)

φ]−1

(φ − 1)(Y − I2a)φ−2

]−

[α[γ ( I1)

φ+ (1− γ )(Y − I2a)

φ]−2

φ(1− γ )(Y − I2a)2φ−2

](26)

= (1− α)[γ ( I1 + µg)

φ+ (1− γ )Iφ2a

]−1

× Iφ−22a

[(φ − 1)−

φ(1− γ )Iφ2a

γ ( I1 + µg)φ + (1− γ )Iφ

2a

]+α

[γ ( I1)

φ+ (1− γ )(Y − I2a)

φ]−1

(Y − I2a)φ−2

×

[(φ − 1)−

φ(1− γ )(Y − I2a)φ

γ ( I1)φ + (1− γ )(Y − I2a)φ

](27)

= (1− α)[γ ( I1 + µg)

φ+ (1− γ )Iφ2a

]−1Iφ−22a

×

[φ

(1−

(1− γ )Iφ2a

γ ( I1 + µg)φ + (1− γ )Iφ

2a

)− 1

]


+α[γ ( I1)

φ+ (1− γ )(Y − I2a)

φ]−1

(Y − I2a)φ−2

×

[φ

(1−

(1− γ )(Y − I2a)φ

γ ( I1)φ + (1− γ )(Y − I2a)φ

)− 1

]. (28)

Because φ ≤ 1 and:

(1− γ )Iφ2a

γ ( I1 + µg)φ + (1− γ )Iφ

2a

< 1 and(1− γ )(Y − I2a)

φ

γ ( I1)φ + (1− γ )(Y − I2a)φ< 1,

Eq. (28) is always negative. Therefore:

signum

[δ I ∗2a

δµg

]= − signum[φ].

We consider three cases for the substitutability of period 1 and period 2 investments(as captured by φ):1. Good Substitutability Between Periods 1 and 2 When φ > 0, the optimal I2a

moves in the opposite direction from µg and parents should compensate a negativeshock to child a by reducing second period investments in child b. Intuitively, itis easier to substitute though the production function for human capital than it isthrough the Cobb-Douglas utility function.

2. Cobb-Douglas Substitutability Between Periods 1 and 2 For φ = 0, theelasticity of substitution between periods is the same as the elasticity of substitutionin preferences between the children (both Cobb-Douglas). Here, there is no winning

investment response to the shock to child a, i.e.δ I ∗2aδµg= 0, so period 2 investments

should be left unchanged.

3. Poor Substitutability Between Periods 1 and 2 For φ < 0, it is difficult to repairdamage from a negative µg shock in the second period, so the return to period 2investments in sibling a is below that for sibling b. Therefore, parents should reinforcethe first period shock by allocating second period investments away from sibling a.

Importantly, the direction of these investment responses did not depend on α, therelative weight parents place in their utility function on the human capital of child aversus child b. Favoring the human capital formation of a particular child—even thechild that experiences the negative endowment shock—does not affect the directionof the optimal investment response. Nor do differences in the “overall” productivityof the child, i.e. efficiency parameter A 6= B in Eqs (17) and (18), alter the directionof the optimal investment response to µg. Thus, empirical evidence suggesting eitherreinforcing or compensating investments within the family does not reveal information


on parental preferences absent additional information on the production function forhuman capital.

APPENDIX DIn general, we need to observe the baseline investments I1 and I2 to estimate parametersof the production function φ and γ . However, nearly all datasets with measures ofhuman capital h and an observable investment shock µg lack measures of human capitalinvestments I1 and I2. We can still make progress in estimating parameters of theproduction function despite not observing I1 and I2, so long as we expect baselineinvestment levels to be similar: I1 ∼= I2. For µg = µ

′g,18 Eq. (4) reduces to:

1− γγ

. (29)

That is, we can observe damage to h from the shock µ′g in second period investmentsrelative to the damage from the first period shock µg, which isolates γ (while remainingsilent on the magnitude of φ).

With an estimate of γ in hand, we can then estimate φ by using the total derivativein investment shocks, i.e. how human capital changes as we change both first and secondperiod investments (by an equal amount). In an overlapping generations framework, thiswould require a shock lasting two childhood periods (or longer), and “half-exposed”cohorts on either end of the shock. Damage to the fully exposed cohort relative to thecohort exposed in period 1 alone is:

=1γ

[γ ( I1 + µg)

φ+ (1− γ )( I1 + µg)

φ

γ ( I1 + µg)φ + (1− γ ) Iφ

1

] 1−φφ

, (30)

using the assumption that I1 = I2 and µg = µ′g,

=1γ

[( I1 + µg)

φ

γ ( I1 + µg)φ + (1− γ ) Iφ

1

] 1−φφ

, (31)

=1γ

1

γ + (1− γ ) I1I1+µg

φ

1−φφ

, (32)

=1γ

[γ + (1− γ )

(1+

µg

I1

)−φ] φ−1φ

. (33)

18 The assumption that µg = µ′g simplifies the algebra, but φ and γ can still be estimated for µg 6= µ

′g so long as µg

and µ′g are observed. We thank Christine Pal for pointing this out.


Similarly, damage to the “doubly exposed” cohort relative to that experiencing ashock in just the second childhood period is:

=1

1− γ

[(1− γ )+ γ

(1+

µg

I1

)−φ] φ−1φ

, (34)

Eqs (33) and (34) constitute two equations in the two unknowns I1 and φ, with γknown from Eq. (29).

REFERENCES2001. An overstretched hypothesis? The Lancet 357 (9254), 405.Aguero, Jorge M., Carter, Michael R., Woolard, Ingrd, 2009. The impact of unconditional cash transfers on

nutrition: the South African child support grant. Manuscript, February.Aizer, Anna, 2003. Low take-up in Medicaid: does outreach matter and for whom? American Economic

Review Papers and Proceedings 93 (2), 238–241.Aizer, Anna, 2007. Public health insurance, program take-up and child health. The Review of Economics

and Statistics 89 (3), 400–415.Aizer, Anna, Currie, Janet, Moretti, Enrico, 2007. Does managed care hurt health? Evidence from Medicaid

mothers. The Review of Economics and Statistics 89 (3), 385–399.Aizer, Anna, Stroud, Laura, 2009. Education, medical knowledge and the evolution of disparities in health.

manuscript, Brown University, September.Aizer, Anna, Stroud, Laura, Buka, Stephen, 2009. Maternal stress and child well-being: evidence from

siblings. manuscript. Brown University, February.Almond, Douglas, 2006. Is the 1918 Influenza Pandemic over? Long-term effects of in utero influenza

exposure in the post-1940 US population. Journal of Political Economy 114 (4), 672–712.Almond, Douglas, Chay, Kenneth Y., Lee, David S., 2005. The costs of low birth weight. The Quarterly

Journal of Economics 120 (3), 1031–1084.Almond, Douglas, Edlund, Lena, Palme, Marten, 2009. Chernobyl’s subclinical legacy: prenatal exposure to

radioactive fallout and school outcomes in sweden. Quarterly Journal of Economics 124 (4), 1729–1772.URL http://www.mitpressjournals.org/doi/abs/10.1162/qjec.2009.124.4.1729.

Almond, Douglas, Hoynes, Hilary W., Schanzenbach, Diane Whitmore, 2010. Inside the war on poverty:the impact of food stamps on birth outcomes. The Review of Economics and Statistics (forthcoming).

Almond, Douglas, Mazumder, Bhashkar, 2005. The 1918 influenza pandemic and subsequent healthoutcomes: an analysis of SIPP data. American Ecnomic Review, Papers and Proceedings 95 (2).

Almond, Douglas, Mazumder, Bhashkar, 2008. Health capital and the prenatal environment: the effect ofmaternal fasting during pregnancy. Working Paper 14428, National Bureau of Economic Research,October, URL http://www.nber.org/papers/w14428.

Anderson, Kathryn H., Foster, James E., Frisvold, David E., 2009. Investing in health: the long-term impactof Head Start on smoking. Working Paper 1465-7295, Western Economic Association International.

Anderson, Michael, 2008. Multiple inference and gender differences in the effects of early intervention: areevaluation of the Abecedarian, Perry Preschool, and Early Training projects. Journal of the AmericanStatistical Association 103 (484), 1481–1495.

Angrist, Joshua D., Pischke, Jorn-Steffen, 2009. Mostly Harmless Econometrics: an Empiricist’sCompanion. Princeton University Press, Princeton, New Jersey.

Aos, Steve, Lieb, Roxanne, Mayfield, Jim, Miller, Marna, Pennucci, Annie, 2004. Benefits and costs ofprevention and early intervention programs for youth. Technical appendix, Washington State Institutefor Public Policy, September.


Ashenfelter, Orley, Rouse, Cecilia, 1998. Income, schooling, and ability: evidence from a new sample ofidentical twins. Quarterly Journal of Economics 113 (1), 253–284.URL http://www.mitpressjournals.org/doi/abs/10.1162/003355398555577.

Baker, Kevin, 2008. Do cash transfer programs improve infant health: evidence from the 1993 expansion ofthe earned income tax credit. Manuscript, University of Notre Dame.

Baker, Michael, Gruber, Jonathan, Milligan, Kevin, 2008. Universal child care, maternal labor supply, andfamily well-being. Journal of Political Economy 116 (4), 709–745.URL http://www.journals.uchicago.edu/doi/abs/10.1086/591908.

Baker, Michael, Milligan, Kevin, 2010. Evidence from maternity leave expansions of the impact of maternalcare on early child development. Journal of Human Resources 45 (1), 1–32.

Baldwin, Laura-Mae, Larson, Eric H., Connell, Frederick A., Nordlund, Daniel, Cain, Kevin C., Cawthon,Mary L., Byrns, Patricia, Rosenblatt, Roger A., 1998. The effect of expanding Medicaid prenatal serviceson birth outcomes. American Journal of Public Health 88 (11), 1623–1629.

Banerjee, Abhijit, Duflo, Esther, Postel-Vinay, Gilles, Watts, Timothy, 2010. Long run health impacts ofincome shocks: wine and phylloxera in 19th century France. The Review of Economics and Statistics(forthcoming).

Banzhaf, Spencer, Walsh, Randall P., 2008. Do people vote with their feet? an empirical test of tiebout.American Economic Review 98 (3), 843–863.

Barker, D.J.P. (Ed.), 1992. Fetal and infant origins of adult disease. British Medical Journal.Barnett, W.S., Masse, Leonard N., 2007. Comparative cost-benefit analysis of the Abecedarian program and

its policy implications. Economics of Education Review 26, 113–125.Barnett, Steven, Belfield, Clive R., Nores, Milagros, Schweinhart, Lawrence, 2006. The High/Scope Perry

Preschool program: cost-benefit analysis using data from the age-40 follow-up. The Journal of HumanResources 41 (1), 162–190.

Barreca, Alan, 2010. The long-term economic impact of in utero and postnatal exposure to malaria. Journalof Human Resources 45 (4), 865–892.

Basiotis, Peter, Kramer-LeBlanc, Carol S., Kennedy, Eileen T., 1998. Maintaining nutrition security anddiet quality: the role of the Food Stamp program and WIC. Family Economics 11 (1-2), 4–16.

Baten, Jorg, Crayen, Dorothee, Voth, Joachim, 2007. Poor, hungry and stupid: numeracy and the impact ofhigh food prices in industrializing Britain, 1780-1850. Economics Working Papers 1120, Department ofEconomics and Business, Universitat Pompeu Fabra, October.URL http://ideas.repec.org/p/upf/upfgen/1120.html.

Baum, Charles L., 2008. The effects of food stamp benefits on weight gained by expectant mothers.Discussion Paper 1343-08, Institute for Research on Poverty, September.

Becker, Gary S., Tomes, Nigel, 1976. Child endowments and the quantity and quality of children. TheJournal of Political Economy 84 (4), S143–S162.

Behrman, Richard E., Butler, Adrienne S. (Eds.), 2007. Preterm Birth: Causes, Consequences, andPrevention. Institute of Medicine Report. National Academies Press.

Behrman, Jere R., Pollak, Robert A., Taubman, Paul, 1982. Parental preferences and provision for progeny.Journal of Political Economy 90 (1), 52–73.

Behrman, Jere R., Rosenzweig, Mark R., 2004. Returns to birthweight. The Review of Economics andStatistics 86 (2), 586–601.

Behrman, Jere R., Rosenzweig, Mark R., Taubman, Paul, 1994. Endowments and the allocation ofschooling in the family and in the marriage market: the twins experiment. Journal of Political Economy102 (6), 1131–1174.

Van Den Berg, Gerard J., Lindeboom, Maarten, Portrait, France, 2006. Economic conditions early in lifeand individual mortality. American Economic Review 96 (1), 290–302.

Van Den Berg, Gerard J., Lindeboom, Maarten, Portrait, France, 2010. Long-run longevity effects of anutritional shock early in life: the Dutch potato famine of 1846-1847. Journal of Health Economics (inpress).

Berger, Lawrence M., Hill, Jennifer, Waldfogel, Jane, 2005. Maternity leave, early maternal employment,and child health and development in the us. The Economic Journal 115, F29–F47.

Berger, Lawrence M., Paxson, Christina, Waldfogel, Jane, 2009. Income and child development. Childrenand Youth Services Review 31 (9), 978–989.


Bingley, Paul, Walker, Ian, 2007. There’s no such thing as a free lunch: altruistic parents and the responseof household food expenditures to nutrition program reforms. Discussion Paper 2007-19, UniversityCollege Dublin Geary Institute, June.

Bitler, Marianne, Currie, Janet, 2005. Does WIC work? the effect of WIC on pregnancy and birthoutcomes. Journal of Policy Analysis and Management 24 (1), 73–91.

Black, Maureen M., Cutts, Diana B., Frank, Deborah A., Geppert, Joni, Skalicky, Anne, Levenson, Suzette,Casey, Patrick H., Berkowitz, Carol, Zaldivar, Nievves, Cook, John T., Meyers, Alan F., Herren, Tim,2004. Special supplemental nutrition program for women, infants, and children participation and infants’growth and health: a multi-site surveillance study. Pediatrics 114 (1), 169–176.

Black, Sandra E., Devereux, Paul J., Salvanes, Kjell G., 2005. The more the merrier? The effect of familysize and birth order on children’s education. Quarterly Journal of Economics 120 (2), 669–700.

Black, Sandra E., Devereux, Paul J., Salvanes, Kjell G., 2007. From the cradle to the labor market? The effectof birth weight on adult outcomes. Quarterly Journal of Economics 122 (1), 409–439.

Blanden, Jo, Gregg, Paul, MacMillan, Lindsey, 2006. Explaining intergenerational income persistence: non-cognitive skills, ability and education. CMPO Working Paper Series 06/146, University of Bristol, TheCentre for Market and Public Organisation, April. URL www.bris.ac.uk/Depts/CMPO/.

Blau, David, Currie, Janet, 2006. Pre-school, day care, and after-school care: Who’s minding the kids? 2:1163–1278.

Bleakley, Hoyt, 2007. Disease amd development: evidence from hookworm eradication in the south. TheQuarterly Journal of Economics 122 (1), 73.

Blow, Laura, Walker, Ian, Zhu, Yu, 2005. Who benefits from child benefit. manuscript, February.Blundell, Richard, 2006. Earned income tax credit policies: impact and optimality: the Adam Smith

lecture, 2005. Labour Economics (ISSN: 0927-5371) 13 (4), 423–443. European Association of LabourEconomists, 2nd World Conference SOLE/EALE, Fairmont Hotel San Francisco, USA, 2-5 June 2005.

Boyer, George R., 1990. An Economic History of the English Poor Law. CUP, Cambridge, 1750–1850.Bozzoli, Carlos, Deaton, Angus S., Quintana-Domeque, Climent, 2010. Child mortality, income and adult

height. NBER Working Paper No. 12966, March. Demography (forthcoming).Brooks-Gunn, Jeanne, Markham, Lisa B., 2005. The contribution of parenting to ethnic and racial gaps in

school readiness. The Future of Children 15 (1), 139–168.Butler, J.S., Raymond, Jennie E., 1996. The effect of the Food Stamp program on nutrient intake. Economic

Inquiry 34, 781–798.Card, David, Shore-Sheppard, Laura, 2004. Using discontinuous eligibility rules to identify the effects of

the federal Medicaid expansions on low-income children. Review of Economics and Statistics 86 (3),752–766.

Carneiro, Pedro, Ginja, Rita, 2008. Preventing behavior problems in childhood and adolescence: evidencefrom Head Start. manuscript, March.

Cascio, Elizabeth U., 2009. Do investments in universal early education pay off? long-term effects ofintroducing kindergartens into public schools. Working Paper 14951, National Bureau of EconomicResearch, May.

Case, Anne, Fertig, Angela, Paxson, Christina, 2005. The lasting impact of childhood health andcircumstance. Journal of Health Economics 24, 365–389.

Case, Anne, Lee, Diana, Paxson, Christina, 2008. The income gradient in children’s health: a comment onCurrie, Shields, and Wheatley price. Journal of Health Economics 27 (3), 801–807.

Case, Anne, Lubotsky, Darren, Paxson, Christina, 2002. Economic status and health in childhood: theorigins of the gradient. American Economic Review 92 (5), 1308–1334.

Case, Anne, Paxson, Christina, 2008a. Height, health, and cognitive function in old age. AmericanEconomic Review: Papers & Proceedings 98 (2), 463–467.

Case, Anne, Paxson, Christina, 2008b. Stature and status: height, ability, and labor market outcomes. Journalof Political Economy 116 (3), 499–532.

Case, Anne, Paxson, Christina, 2009. Early life health and cognitive function in old age. American EconomicReview Papers and Proceedings 99 (2), 104–109.

Case, Anne, Paxson, Christina, 2010a. Causes and consequences of early life health. Working Paper 15637,National Bureau of Economic Research, January. URL http://www.nber.org/papers/w15637.


Case, Anne, Paxson, Christina, 2010b. The long reach of childhood health and circumstance: evidence fromthe Whitehall II study. Working Paper 15640, National Bureau of Economic Research, January. URLhttp://www.nber.org/papers/w15640.

Chatterji, Pinka, Brooks-Gunn, Jeanne, 2004. Wic participation, breastfeeding practices, and well-child careamong unmarried, low-income mothers. American Journal of Public Health 94 (8), 1324–1327.

Chatterji, Pinka, Markowitz, Sarah, 2000. The impact of maternal alcohol and illicit drug use on children’sbehavior problems: evidence from the children of the national longitudinal survey of youth. WorkingPaper 7692, National Bureau of Economic Research.

Chatterji, Pinka, Bonuck, Karen, Dhawan, Simi, Deb, Nandini, 2002. WIC participation and the initiationand duration of breastfeeding. Discussion Paper No. 1246-02. Madison, WI: Institute for Research onPoverty.

Chay, Kenneth Y., Greenstone, Michael, 2003a. Air quality, infant mortality, and the Clean Air Act of 1970.Working Paper 10053, National Bureau of Economic Research, October.URL http://www.nber.org/papers/w10053.

Chay, Kenneth Y., Greenstone, Michael, 2003b. The impact of air pollution on infant mortality: evidencefrom the geographic variation in pollution shocks induced by a recession. Quarterly Journal of Economics118 (3), 1121–1167.

Chay, Kenneth Y., Guryan, Jonathan, Mazumder, Bhashkar, 2009. Birth cohort and the black-whiteachievement gap: the roles of access and health soon after birth. Working Paper 15078, National Bureauof Economic Research, June. URL http://www.nber.org/papers/w15078.

Chiang, Alpha C., 1984. Fundamental Methods of Mathematical Economics, third edition, McGraw-Hill,New York.

Condliffe, Simon, Link, Charles R., 2008. The relationship between economic status and child health:evidence from the United States. American Economic Review 98 (4), 1605–1618.

Conley, Dalton, Bennett, Neil G., 2001. Birth weight and income: interactions across generations. Journalof Health and Social Behavior (ISSN: 00221465) 42 (4), 450–465.URL http://www.jstor.org/stable/3090189.

Conley, Dalton, Pfeiffer, Katherine M., Velez, Melissa, 2007. Explaining sibling differences in achievementand behavioral outcomes: the importance of within- and between-family factors. Social Science Research36 (3), 1087–1104.

Conley, Dalton, Strully, Kate W., Bennett, Neil G., 2006. Twin differences in birth weight: the effectsof genotype and prenatal environment on neonatal and post-natal mortality. Economics and HumanBiology 4 (2), 151–183.

Costa, Dora L., Lahey, Joanna N., 2005. Predicting older age mortality trends. Journal of the EuropeanEconomic Association 3 (2–3), 487–493.

Crimmins, Eileen M., Finch, Caleb E., 2006. Infection, inflammation, height, and longevity. Proceedingsof the National Academy of Sciences 103 (2), 498–503.

Cunha, Flavio, Heckman, James, 2007. The technolgy of skill formation. American Economic Review 97(2), 31–47.

Cunha, Flavio, Heckman, James J., 2008. Formulating, identifying and estimating the technology ofcognitive and noncognitive skill formation. Journal of Human Resources 43 (4), 738–782. URLhttp://jhr.uwpress.org/cgi/content/abstract/43/4/738.

Cunha, Flavio, Heckman, James, Schennach, Susanne, 2010. Estimating the technology of cognitive andnoncognitive skill formation. Econometrica 78 (3), 883–931.

Currie, Alison, Shields, Michael, Price, Stephen W., 2007. Is the child health/family income gradientuniversal? Evidence from England. Journal of Health Economics 26 (2), 213–232.

Currie, Janet, 2003. US food and nutrition programs. In: Moffitt, Robert (Ed.), Means-tested TransferPrograms in the United States. University of Chicago Press for NBER, Chicago, IL, pp. 199–290.

Currie, Janet, Cole, Nancy, 1993. Welfare and child health: the link between AFDC participation and birthweight. The American Economic Review 83 (4), 971–985.

Currie, Janet, Gahvari, Firouz, 2008. Transfers in cash and in-kind; theory meets the data. Journal ofEconomic Literature 46 (2), 333–383.

Currie, Janet, Grogger, Jeffrey, 2002. Medicaid expansions and welfare contractions: offsetting effects onprenatal care and infant health. Journal of Health Economics 21 (2), 313–335.


Currie, Janet, Gruber, Jonathan, 1996a. Health insurance eligibility, utilization of medical care, and childhealth. The Quarterly Journal of Economics 111 (2), 431–466.

Currie, Janet, Gruber, Jonathan, 1996. Saving babies: the efficacy and cost of recent changes inthe Medicaid eligibility of pregnant women. Journal of Political Economy 104 (6), 1263. URLhttp://www.journals.uchicago.edu/doi/abs/10.1086/262059.

Currie, Janet, Hyson, Rosemary, 1999. Is the impact of shocks cusioned by socioeconomic status? The caseof low birth weight. American Economic Review 89 (2), 245–250.

Currie, Janet, Moretti, Enrico, 2007. Biology as destiny? short- and long-run determinants ofintergenerational transmission of birth weight. Journal of Labor Economics 25 (2), 231–264. URLhttp://www.journals.uchicago.edu/doi/abs/10.1086/511377.

Currie, Janet, Moretti, Enrico, 2008. Short and long-run effects of the introduction of food stamps on birthoutcomes in California. In: Schoeni, R.F., House, J., Kaplan, G., Pollack, H. (Eds.), Making AmericansHealthier: Social and Economic Policy as Health Policy. Russell Sage, New York.

Currie, Janet, Neidell, Matthew, 2005. Air pollution and infant health: what can we learn fromCalifornia’s recent experience? Quarterly Journal of Economics 120 (3), 1003–1030. URLhttp://www.mitpressjournals.org/doi/abs/10.1162/003355305774268219.

Currie, Janet, Stabile, Mark, 2003. Socioeconomic status and child health: why is the relationship strongerfor older children? American Economic Review 93 (5), 1813–1823.

Currie, Janet, Stabile, Mark, 2006. Child mental health and human capital accumulation: the case of ADHD.Journal of Health Economics 25 (6), 1094–1118.

Currie, Janet, Tekin, Erdal, 2006. Does child abuse cause crime? Working Paper 12171, National Bureau ofEconomic Research, April. URL http://www.nber.org/papers/w12171.

Currie, Janet, Thomas, Duncan, 1995. Does Head Start make a difference? American Economic Review 85(3), 341–364.

Currie, Janet, Thomas, Duncan, 1999a. Does Head Start help Hispanic children? Journal of PublicEconomics (ISSN: 0047-2727) 74 (2), 235–262.

Currie, Janet, Thomas, Duncan, 1999b. Early test scores, socioeconomic status and future outcomes.Working Paper 6943, National Bureau of Economic Research, February.URL http://www.nber.org/papers/w6943.

Currie, Janet, Walker, Reed, 2009. Traffic congestion and infant health: evidence from E-Zpass. WorkingPaper 15413, National Bureau of Economic Research, October.URL http://www.nber.org/papers/w15413.

Currie, Janet, Widom, Cathy S., 2009. Long-term consequences of child abuse and neglect on adulteconomic well-being. Manuscript, September.

Currie, Janet, Yelowitz, Aaron, 2000. Are public housing projects good for kids? Journal of Public Economics(ISSN: 0047-2727) 75 (1), 99–124.

Currie, Janet, Decker, Sandra, Lin, Wanchuan, 2008. Has public health insurance for older children reduceddisparities in access to care and health outcomes? Journal of Health Economics 27 (6), 1567–1581.

Currie, Janet, Neidell, Matthew, Schmieder, Johannes F., 2009. Air pollution and infant health: lessons fromNew Jersey. Journal of Health Economics 28 (3), 688–703.

Currie, Janet, Stabile, Mark, Manivong, Phongsack, Roos, Leslie L., 2010. Child health and young adultoutcomes. The Journal of Human Resources 45 (3), 517–548.

Cutler, David M., Miller, Grant, Norton, Douglas M., 2007. Evidence on early-life income and late-life health from America’s Dust Bowl era. Proceedings of the National Academy of Sciences 104 (33),13244–13249. URL http://www.pnas.org/content/104/33/13244.abstract.

Dafny, Leemore, Gruber, Jonathan, 2005. Public insurance and child hospitalizations: access and efficiencyeffects. Journal of Public Economics 89 (1), 109–129.URL http://ideas.repec.org/a/eee/pubeco/v89y2005i1p109-129.html.

Dahl, Gordon, Lochner, Lance, 2008. The impact of family income on child achievement: evidence from theearned income tax credit. Working Paper 14599. National Bureau of Economic Research, December.URL http://www.nber.org/papers/w14599.

Dahl, Gordon B., Lochner, Lance, 2005. The impact of family income on child achievement. Working Paper11279, National Bureau of Economic Research, April. URL http://www.nber.org/papers/w11279.


Dalenius, Tore, Reiss, Steven P., 1982. Data swapping: a technique for disclosure control. Journal of StatisticalPlanning and Inference 6 (1), 73–85.

Datar, Ashlesha, Kilburn, Rebecca, Loughran, David S., 2010. Endowments and parental investments ininfancy and early childhood. Demography 47 (1), 145–162.

Dearden, Lorraine, 1998. Ability, family, education, and earnings in Britain. Working Paper 98/114,Institute for Fiscal Studies, July.

Del Bono, Emilia, Ermisch, John, Francesconi, Marco, 2008. Intrafamily resource allocations: a dynamicmodel of birth weight. IZA Discussion Papers 3704, Institute for the Study of Labor (IZA), September.URL http://ideas.repec.org/p/iza/izadps/dp3704.html.

Deming, David, 2009. Early childhood intervention and life-cycle skill development. American EconomicJournal: Applied Economics 1 (3), 111–134.

Deschnes, Olivier, Greenstone, Michael, 2007. Climate change, mortality, and adaptation: evidence fromannual fluctuations in weather in the US. NBER Working Paper No. 13178, June.

Doblhammer, Gabriele, Vaupel, James W., 2001. Lifespan depends on month of birth. Proceedings of theNational Academy of Sciences 98 (5), 2934–2939.

Doyle Jr., Joseph J., 2008. Child protection and adult crime: using investigator assignment to estimate causaleffects of foster care. Journal of Political Economy 116 (4), 746–770.URL http://www.journals.uchicago.edu/doi/abs/10.1086/590216.

Doyle, Orla, Walker, Ian, Harmon, Colm P., 2005. The impact of parental income and education on thehealth of their children. Discussion Paper 5359, Centre for Economic Policy Research, December.

Dubay, Lisa, Joyce, Ted, Kaestner, Robert, Kenney, Genevieve M., 2001. Changes in prenatal care timingand low birth weight by race and socioeconomic status: implications for the medicaid expansions forpregnant women. Health Services Research 36 (2), 373–398.

Duggan, Anne K., McFarlane, Elizabeth, Fuddy, Loretta, Burrell, Lori, Higman, Susan M., Windham, Amy,Sia, Calvin, 2004. Randomized trial of a statewide home visiting program: impact on preventing childabuse and neglect. Child Abuse and Neglect 28 (6), 597–622.

Duggan, A.K., McFarlane, E.C., Windham, A.M., Rohde, C., Salkever, D.S., Fuddy, L., Rosenberg,L.A., Buchbinder, S.B., Sia, C.C.J., 1999. Evaluation of Hawaii’s Healthy Start Program. The Futureof Children 9 (1), 66–90.

DuMont, Kimberly, Mitchell-Herzfeld, Susan, Greene, Rose, Lee, Eunju, Lowenfels, Ann, Rodriguez,Monica, 2006. Healthy families New York (hfny) randomized trial: impacts on parenting after the firsttwo years. Working Paper Series: evaluating Healthy Families New York 1, New York State Office ofChildren & Family Services, June.

Dustmann, Christian, Schonberg, Uta, 2009. The effect of expansions in maternity leave coverage onchildren’s long-term outcomes. SSRN eLibrary.

Eissa, Nada, Liebman, Jeffrey B., 1996. Labor supply response to the earned income tax credit. The QuarterlyJournal of Economics 113 (3), 653–691.

Farrelly, Matthew C., Nimsch, Christian T., James, Joshua, 2003. State cigarette excise taxes: Implicationsfor revenue and tax evasion. Report 08742.000, RTI International, Research Triangle Park, NC, May.

Fergusson, David M., Grant, Hildegard, Horwood, L. John, Ridder, Elizabeth M., 2006. Randomized trialof Early Start program of home visitation. Pediatrics 117 (3), 803–809.

Fertig, Angela R., Reingold, David A., 2007. Public housing, health, and health behaviors: Is there aconnection? Journal of Policy Analysis and Management 26 (4), 831–859.

Field, Erica, Robles, Omar, Torero, Maximo, 2009. Iodine deficiency and schooling attainment in Tanzania.American Economic Journal: Applied Economics 1 (4), 140–169.

Figlio, David, Hamersma, Sarah, Roth, Jeffrey, 2009. Does prenatal WIC participation improve birthoutcomes? new evidence from Florida. Journal of Public Economics 93 (1–2), 235–245.

Fletcher, Jason M., Lehrer, Steven F., 2009. Using genetic lotteries within families to examine the causalimpact of poor health on academic achievement. Working Paper 15148, National Bureau of EconomicResearch, July. URL http://www.nber.org/papers/w15148.

Frank, Richard G., Meara, Ellen, 2009. The effect of maternal depression and substance abuse on childhuman capital development. Working Paper 15314, National Bureau of Economic Research, September.

Frisvold, David E., 2006. Head Start participation and childhood obesity. Working Paper 06-WG01,Vanderbilt University, February.


Frisvold, David E., Lumeng, Julie C., 2009. Expanding exposure: can increasing the daily duration of HeadStart reduce childhood obesity? manuscript, April.

Garces, Eliana, Thomas, Duncan, Currie, Janet, 2002. Longer-term effects of Head Start. AmericanEconomic Review 92 (4), 999–1012.

Gertler, Paul J., 2004. Do conditional cash transfers improve child health? Evidence from Progresa’s controlrandomized experiment. Health, Health Care, and Economic Development 94 (2), 336–341.

Gluckman, Peter D., Hanson, Mark A., 2006. Adult disease: echoes of the past. European Journal ofEndocrinology 155 (Suppl. 1), S47–S50.

Gormley Jr., William, 2008. The effects of Oklahoma’s pre-K program on Hispanic children. Social ScienceQuarterly 89 (4), 916–936.

Gormley Jr., William, Gayer, Ted, 2006. Promoting school readiness in Oklahoma: an evaluation of Tulsa’spre-K program. The Journal of Human Resources 40 (3), 533–558.

Gormley Jr., William, Gayer, Ted, Phillips, Deborah, Dawson, Brittany, 2005. The effects of universal pre-Kon cognitive development. Developmental Psychology 41 (6), 872–884.

Goux, Dominique, Maurin, Eric, 2005. The effects of overcrowded housing on children’s performance atschool. Journal of Public Economics 89 (5-6), 797–819.

Griliches, Zvi, 1979. Sibling models and data in economics: beginnings of a survey. Journal of PoliticalEconomy 87 (s5), S37. URL http://www.journals.uchicago.edu/doi/abs/10.1086/260822.

Grossman, Michael, 1972. On the concept of health capital and the demand for health. Journal of PoliticalEconomy 223–255.

Gueorguieva, Ralitza, Morse, Steven B., Roth, Jeffrey, 2009. Length of prenatal participation in WIC andrisk of delivering a small for gestational age infant: Flordida, 1996-2004. Journal of Maternal and ChildHealth 13 (4), 479–488.

Gundersen, Craig, Ziliak, James, 2004. Poverty and macroeconomic performance across space, race, andfamily structure. Demography 41 (1), 61–86.

Hanratty, Maria J., 1996. Canadian national health insurance and infant health. American Economic Review(ISSN: 00028282) 86 (1), 276–284. URL http://www.jstor.org/stable/2118267.

Harding, Kathryn, Galano, Joseph, Martin, Joanne, Huntington, Lee, Schellenbach, Cynthia J., 2007.Healthy Families America effectiveness: a comprehensive review of outcomes. Journal of Prevention andIntervention in the Community 34 (1-2), 149–179.

Havnes, Tarjei, Mogstad, Magne, 2009. No child left behind: universal child care and children’s long-runoutcomes. Discussion Paper 582, Statistics Norway, Research Department, May.

Heckman, James, 2007. The economics, technology, and neuroscience of human capability formation.PNAS 104 (33), 13250–13255.

Heckman, James, Moon, Seong H., Pinto, Rodrigo, Savelyev, Peter A., Yavitz, Adam, 2009. The rateof return to the High/Scope Perry preschool program. Working Paper 15471, National Bureau ofEconomic Research, November.

Heckman, James J., Rubinstein, Yona, 2001. The importance of noncognitive skills: lessons from the GEDtesting program. American Economic Review: Papers & Proceedings 91 (2), 145–149.

Heider, Fritz, 1934. The influence of the epidemic of 1918 on deafness: a study of birth dates of pupilsregistered in schools for the deaf. American Journal of Epidemiology 19, 756–765.

Howard, Kimberly S., Brooks-Gunn, Jeanne, 2009. The role of home visiting programs in preventing childabuse and neglect. The Future of Children 19 (2), 119–146.

Hoynes, Hilary W., Page, Marianne, Stevens, Ann H., 2009. Is a WIC start a better start? evaluatingWIC’s impact on infant health using program introduction. Working Paper 15589, National Bureau ofEconomic Research, December.

Hoynes, Hilary W., Schanzenbach, Diane, 2009. Consumption responses to in-kind transfers: evidence fromthe introduction of the Food Stamp program. American Economic Journal: Applied Economics 1 (4),109–139.

Hsin, Amy, 2009. Is biology destiny? Birth weight and differential parental treatment. Manuscript, PopulationStudies Center, University of Michigan.

Hustedt, Jason T., Barnett, W. Steven, Jung, Kwanghee, Figueras, Alexandra, 2008. Impacts of new Mexicopre-K on children’s school readiness at kindergarten entry: Results from the second year of a growinginitiative. Research report, National Institute for Early Education, Rutgers University, June.


Jacob, Brian, 2004. Public housing, housing vouchers, and student achievement: evidence from publichousing demolitions in Chicago. American Economic Review 94 (1), 233–258.

Johnson, Rucker C., Schoeni, Robert F., 2007. The influence of early-life events on human capital, healthstatus, and labor market outcomes over the life course. Working Paper 07-05, National Poverty Center,February.

Joyce, Theodore, Gibson, Diane, Colman, Sylvie, 2004. The changing association between prenatalparticipation in WIC and birth outcomes in New York city. Working Paper 10796, National Bureauof Economic Research, September.

Joyce, Theodore, Racine, Andrew D., Yunzal-Butler, Cristina, 2007. Reassessing the WIC effect: evidencefrom the pregnancy nutrition surveillance system. Journal of Policy Analysis and Management 87 (2),277–303.

Kaestner, Robert, Joyce, Ted, Racine, Andrew, 2001. Medicaid eligibility and the incidence of ambulatorycare sensitive hospitalizations for children. Social Science and Medicine 52 (2), 305–313.

Karoly, Lynn A., Kilburn, Rebecca M., Cannon, Jill S., 2005. Early Childhood Interventions: ProvenResults, Future Promise. RAND Corporation.

Karoly, Lynn A., Greenwood, Peter W., Everingham, Susan S., Hoube, Jill, Kilburn, M. Rebecca, Rydell, C.Peter, Sanders, Matthew, Chiesa, James, 1998. Investing in our children: what we know and don’t knowabout the costs and benefits of early childhood interventions. Consensus report, Rand, July.

Kehrer, Barbara H., Wolin, Charles M., 1979. Impact of income maintenance on low birth weight: evidencefrom the Gary experiment. The Journal of Human Resources 14 (4), 434–462. autumn.

Kelly, Elaine, 2009. The scourge of Asian flu: in utero exposure to pandemic influenza and the developmentof a cohort of British children. Institute for Fiscal Studies, Working Paper 09/17, September. UniversityCollege London.

Kermack, W.O., McKendrick, A.G, McKinlay, P.L., 1934. Death-Rates in Great Britain and Sweden. TheLancet 698–703.

Khanam, Rasheda, Nghiem, Hong S., Connelly, Luke B., 2009. Child health and the income gradient:evidence from Australia. Journal of Health Economics 28 (4), 805–817.

Kling, Jeffrey R., Liebman, Jeffrey B., Katz, Lawrence F., 2007. Experimental analysis of neighborhoodeffects. Econometrica 75 (1), 83–119.

Kling, Jeffrey R., Ludwig, Jeffrey, Katz, Lawrence F., 2005. Neighborhood effects on crime for femaleand male youth: evidence from a randomized housing voucher experiment. The Quarterly Journal ofEconomics 120 (1), 87–130.

Knittel, Christopher R., Miller, Douglas L., Sanders, Nicholas J., 2009. Caution drivers! children present:Traffic, pollution and infant health. Manuscript, UC Davis.

Koch, Thomas G., 2009. Public insurance, crowd-out, and health care utilization. Manuscript, August.Kowaleski-Jones, Lori, Duncan, Greg J., 2002. Effects of participation in the WIC program on birthweight:

evidence from the National Longitudinal Survey of Youth. Am J Public Health 92 (5), 799–804. URLhttp://ajph.aphapublications.org/cgi/content/abstract/92/5/799.

Kraemer, Sebastian, 2000. The fragile male. BMJ 321 (7276), 1609–1612. URL http://www.bmj.com.Lee, Bong J., Mackey-Bilaver, Lee, 2007. Effects of WIC and Food Stamp program participation on child

outcomes. Children and Youth Services Review 29 (4), 501–517.Lefebvre, Pierre, Merrigan, Philip, Verstraete, Matthieu, 2006. Impact of early childhood care and education

on children’s preschool cognitive development: Canadian results from a large quasi-experiment. WorkingPaper 06-36, Centre interuniversitaire sur le risque, les politiques conomiques et lemploi (CIRPEE),October.

Levine, Philip B., Schanzenbach, Diane W., 2009. The impact of children’s public health insuranceexpansions on educational outcomes. Forum for Health Economics and Policy 12 (1), Article 1.

Levine, Phillip B., Zimmerman, David J., 2000. Children’s welfare exposure and subsequent development.Working Paper 7522, National Bureau of Economic Research, February.URL http://www.nber.org/papers/w7522.

Lhila, Aparna, Simon, Kosali I., 2008. Prenatal health investment decisions: does the childs sex matter?Demography 45 (4), 885–905.

Lien, Diana S., Evans, William N., 2005. Estimating the Impact of large cigarette tax hikes: the case ofmaternal smoking and infant birth weight. J. Human Resources XL (2), 373–392.URL http://jhr.uwpress.org/cgi/content/abstract/XL/2/373.


Lin, Ming-Jen, Liu, Jin-Tan, 2009. Do lower birth weight babies have lower grades? twin fixed effect andinstrumental variable evidence from Taiwan. Social Science & Medicine 68 (10), 1780–1787.

Logan, W.P.D., Glasg, M.D., 1953. Mortality in London fog incident, 1952. Lancet 1 (1), 336–338.Love, John M., Kisker, Ellen E., Ross, Christine, Constantine, Jill, Boller, Kimberly, Chazan-Cohen,

Rachel, Brady-Smith, Christy, Fuligni, Allison S., Raikes, Helen, Brooks-Gunn, Jeanne, Tarullo, LouisaB., Schochet, Peter Z., Paulsell, Diane, Vogel, Cheri, 2005. The effectiveness of Early Head Start for3-year-old children and their parents: lessons for policy and programs. Developmental Psychology 41 (6),885–901.

Ludwig, Jens, Miller, Douglas L., 2005. Does Head Start improve children’s life chances? evidence from aregression discontinuity design. Discussion Paper 1311-05, Institute for Research on Poverty, November.

Ludwig, Jens, Miller, Douglas L., 2007. Does Head Start improve children’s life chances? evidence from aregression discontinuity design. Quarterly Journal of Economics 488 (1), 159–208.

MacDorman, Marian F., Martin, Joyce A., Mathews, T.J., Hoyert, Donna L., Ventura, Stephanie J., 2005.Explaining the 2001-2002 infant mortality increase in the United States: data from the linked birth/infantdeath data set. International Journal of Health Services 35 (3), 415–442.

MacMillan, Harriet L., Thomas, B. Helen, Jamieson, Ellen, Walsh, Christine A., Boyle, Michael H.,Shannon, Harry S., Gafni, Amiram, 2005. Effectiveness of home visitation by public health nurses inprevention of the recurrence of child physical abuse: a randomized controlled trial. Lancet 365 (9473),1786–1793.

Macours, Karen, Schady, Norbert, Vakis, Renos, 2008. Cash transfers, behavioral changes, and cognitivedevelopment in early childhood: evidence from a randomized experiment. Working Paper 4759, TheWorld Bank: Development Research Group, Human Development and Public Services Team, October.

Magnuson, Katherine A., Ruhm, Christopher, Waldfogel, Jane, 2007. Does pre-kindergarten improveschool preparation and performance? Economics of Education Review 26 (1), 33–51.

Masse, Leonard, Barnett, W. Steven, 2002. A Benefit-cost Analysis of the Abecedarian Early ChildhoodIntervention. National Institute for Early Education Research, Rutgers, NJ.

McCormick, Marie C., Brooks-Gunn, Jeanne, Buka, Stephen L., Goldman, Julie, Yu, Jennifer, Salganik,Mikhail, Scott, David T., Bennett, Forrest C., Kay, Libby L., Bernbaum, Judy C., Bauer, Charles R.,Martin, Camilia, Woods, Elizabeth R., Martin, Anne, Casey, Patrick H., 2006. Early intervention in lowbirth weight premature infants: results at 18 years of age for the infant health and development. Pediatrics117 (3), 771–780.

McIntosh, Emma, Barlow, Jane, Davis, Hilton, Stewart-Brown, Sarah, 2009. Economic evaluation of anintensive home visiting programme for vulnerable families: a cost-effectiveness analysis of public healthintervention. Journal of Public Health 31 (3), 423–433.

McLeod, Jane, Kaiser, Karen, 2004. Childhood emotional and behavioral problems and educationalattainment. American Sociological Review 69 (5), 636–658.

Meyer, Bruce D., Rosenbaum, Dan T., 2001. Welfare, the earned income tax credit, and the labor supply ofsingle mothers. Quarterly Journal of Economics 116 (3), 1063–1114.URL http://www.mitpressjournals.org/doi/abs/10.1162/00335530152466313.

Milligan, Kevin, Stabile, Mark, 2008. Do child tax benefits affect the wellbeing of children? evidence fromCanadian child benefit expansions. Working Paper 14624, National Bureau of Economic Research,December. URL http://www.nber.org/papers/w14624.

Morris, Pamela, Duncan, Greg J., Rodrigues, Christopher, 2004. Does money really matter? estimatingimpacts of family income on children’s achievement with data from random-assignment experiments.Manuscript.

Newman, Sandra J., Harkness, Joseph M., 2002. The long-term effects of public housing on self-sufficiency.Journal of Policy Analysis and Management 21 (1), 21–43.

Nilsson, Peter, 2008. Does a pint a day affect your child’s pay? the effect of prenatal alcohol exposure onadult outcomes. Centre for Microdata Methods and Practice Working Paper CWP22/08, The Institutefor Fiscal Studies, August.

Nilsson, Peter, 2009. e long-term effects of early childhood lead expsoure: evidence from sharp changesin local air lead levels induced by the phase-out of leaded gasoline. manuscript, Uppsala University,September.


Oaxaca, Ronald L., 1973. Male-female wage differentials in urban labor markets. International EconomicReview 14, 693–709.

Olds, David L., Henderson, Charles R., Kitzman, Harriet J., Eckenrode, John J., Cole, Robert E.,Tatelbaum, Robert C., 1999. Prenatal and infancy home visitation by nurses: recent findings. The Futureof Children 9 (1), 44–65. Spring-Summer.

Olds, David L., Kitzman, Harriet, Hanks, Carole, Cole, Robert, Anson, Elizabeth, Sidora-Arcoleo,Kimberly, Luckey, Dennis W., Henderson Jr., Charles R., Holmberg, John, Tutt, Robin A., Stevenson,Amanda J., Bondy, Jessica, 2007. Effects of nurse home visiting on maternal and child functioning: age-9follow-up of a randomized trial. Pediatrics 120 (4), 832–845.

Olds, David L., Robinson, JoAnn, O’Brien, Ruth, Luckey, Dennis W., Pettitt, Lisa M., Henderson Jr.,Charles R., Ng, Rosanna K., Sheff, Karen L., Korfmacher, Jon, Hiatt, Susan, Talmi, Ayelet, 2002. Homevisiting by paraprofessionals and by nurses: a randomized controlled trial. Pediatrics 110 (3), 486–496.URL http://pediatrics.aappublications.org/cgi/content/abstract/110/3/486.

Oreopoulos, Phil, Stabile, Mark, Walld, Randy, Roos, Leslie, 2008. Short, medium, and long-termconsequences of poor infant health: an anlysis using siblings and twins. Journal of Human Resources43 (1), Winter.

Oreopoulos, Philip, 2003. The long-run consequences of living in a poor neighborhood. Quarterly Journalof Economics 118 (4), 1533–1575.URL http://www.mitpressjournals.org/doi/abs/10.1162/003355303322552865.

Orr, Larry, Feins, Judith D., Jacob, Robin, Beecroft, Erik, Sanbonmatsu, Lisa, Katz, Lawrence F., Liebman,Jeffrey B., Kling, Jeffrey R., 2003. Moving to opportunity: interim impacts evaluation. Final report, USDepartment of Housing and Urban Development, Office of Policy Development and Research, June.

Pitt, Mark, Rosenzweig, Mark, Hassan, Nazmul, 1990. Productivity, health, and inequality in theintrahousehold distribution of food in low-income countries. American Economic Review 80 (5),1139–1156.

Propper, Carol, Rigg, John, Burgess, Simon, 2007. Child health: evidence on the roles of family income andmaternal mental health from a UK birth cohort. Health Economics 16 (11), 1245–1269.

Puma, Michael, Bell, Stephen, Cook, Ronna, Heid, Camilla, Shapiro, Gary, Broene, Pam, Jenkins,Frank, Fletcher, Philip, Quinn, Liz, Friedman, Janet, Ciarico, Janet, Rohacek, Monica, Adams, Gina,Spier, Elizabeth, 2010. Head start impact study: Final report; executive summary. Technical report,Administration for Children and Families. US Department of Health and Human Services, January.

Rasmussen, Kathleen Maher, 2001. The ”fetal origins” hypothesis: challenges and opportunities for maternaland child nutrition. Annual Review of Nutrition 21, 73–95.

Reyes, Jessica Wolpaw, 2007. Environmental policy as social policy? the impact of childhood leadexposure on crime. Working Paper 13097, National Bureau of Economic Research, May. URLhttp://www.nber.org/papers/w13097.

Reznek, Arnold, 2007. Methods of confidential data protection. VirtualRDC powerpoint presentation,Cornell University, April. URL http://www.vrdc.cornell.edu/news/.

Rosenzweig, Mark R., Paul Schultz, T., 1982. Market opportunities, genetic endowments, and intrafamilyresource distribution: child survival in rural India. American Economic Review 72 (4), 803–815.

Rosenzweig, Mark R., Wolpin, Kenneth I., 1988. Heterogeneity, intrafamily distribution, and child health.Journal of Human Resources 23 (4), 437–461.

Royer, Heather, 2009. Separated at girth: US twin estimates of the effects of birth weight. AmericanEconomic Journal: Applied Economics 1 (1), 49–85.

Ruhm, Christopher J., 2004. Parental employment and child cognitive development. The Journal of HumanResources 39 (1), 155–192.

Rush, David, Stein, Zena, Susser, Mervyn, 1980. Diet in Pregnancy: a Randomized Controlled Trial ofNutritional Supplements. Alan R. Liss, Inc., New York.

Salm, Martin, Schunk, Daniel, 2008. The role of childhood health for the intergenerational transmission ofhuman capital: evidence from administrative data. Discussion Paper 3646, The Institute for the Study ofLabor (IZA), August.

Sanbonmatsu, Lisa, Kling, Jeffrey R., Duncan, Greg J., Brooks-Gunn, Jeanne, 2006. Neighborhoods andacademic achievement. Journal of Human Resources 41 (4), 649–690.


Schanzenbach, Diane W., What are food stamps worth. American Economic Journal - Economic Policy(forthcoming).

Scherjon, Sicco A., Oosting, Hans, Ongerboer de Visser, Bram W., deWilded, Ton, Zondervan, Hans A.,Kok, Joke H., 1996. Fetal brain sparing is associated with accelerated shortening of visual evoked potentiallatencies during early infancy. American Journal of Obstetrics and Gynecology 175 (6), 1569–1575.

Sloan, Frank A., Conover, Christopher J., Rankin, Peter J., 2001. Effects of Tennessee Medicaid managedcare on obstetrical care and birth outcomes. Journal of Health Politics, Policy, and Law 26 (6), 1291–1324.

Smith, James P., 2009. The impact of childhood health on adult labor market outcomes. The Review ofEconomics and Statistics 91 (3), 478–489.

Smith, James P., Smith, Gillian C., 2008. Long-term economic costs of psychological problems duringchildhood. Manuscript, October.

Smolensky, E., Gootman, J. Appleton, 2003. Working Families and Growing Kids: Caring for Children andAdolescents. The National Academies Press, Washington, DC.

Solon, Gary, 1999. Chapter Intergenerational mobility and the labor market. In: Ashenfelter, Orley,Card, David (Eds.), Handbook of Labor Economics, vol. 3. Elsevier, pp. 1761–1800.

St. Pierre, Robert G., Layzer, Jean I., 1999. Using home visits for multiple purposes: the comprehensivechild development program. The Future of Children 9 (1), 134–151.

Stein, Zena, Susser, Mervyn, Saenger, Gerhart, Marolla, Francis, 1975. Famine and Human Development:the Dutch Hunger Winter of 1944-1945. Oxford University Press, New York.

Tominey, Emma, 2007. Maternal smoking during pregnancy and early child outcomes. CEP DiscussionPapers dp0828, Centre for Economic Performance, LSE, October.URL http://ideas.repec.org/p/cep/cepdps/dp0828.html.

Vujic, Suncica, Konig, Pierre, Webbinnk, Dinand, Martin, Nick, 2008. The effect of childhood conductdisorder on human capital. Discussion Paper 3646, CPB Netherlands Bureau for Economic PolicyAnalysis, November.

Wong, Vivian C., Cook, Thomas D., Barnett, W. Steven, Jung, Kwanghee, 2008. An effectiveness-basedevaluation of five state pre-kindergarten programs. Journal of Policy Analysis and Management 27 (1),122–154.

Zweifel, Peter, Breyer, Friedrich H.J., Kifmann, Mathias, 2009. Health Economics, second edition, OxfordUniversity Press.

Documents

Human Capital Development before Age Five$ - Princeton University