“The past is never dead. It’s not even past.” William Faulkner,

“…Healthy, Wealthy, and Wise?

Physical, Economic and Cognitive Effects

of Early Life Conditions on Later Life Outcomes in the

U.S., 1915-2005”March 12, 2009

Douglas Almond, COLUMBIA UNIVERSITY Hoyt Bleakley, UNIVERSITY OF CHICAGO Joseph Ferrie, NORTHWESTERN UNIVERSITY Bhashkar Mazumder, FEDERAL RESERVE BANK OF CHICAGO Karen Rolf, UNIVERSITY OF NEBRASKA AT OMAHA Werner Troesken, UNIVERSITY OF PITTSBURGH

“The past is never dead. It’s not even past.”

William Faulkner, Requiem for a Nun, Act I, Scene III (1951)

We examine effects later in life from early lifecircumstances (family, neighborhood, cohort)like the Early Indicators Project & C2S at NU

Why? Two important differences:

1.A national population (incl. females), with rich detail on family & local circumstances very early in life (under age 5)

2. Less info on morbidity: analysis is on longevity, height & weight, and IQ ; eventuallyalso cause of death, disability, LFP, earnings

Introduction

H. Clarence Nixon described the U.S. South in the early 20th century as having “the economy of the Middle Ages without the cathedrals.” (Forty Acres and Steel Mules, 1938)

This project could be said to be “like theEarly Indicators Project without the wool uniformsand strangulated hernias.”

The Sources Used to Assess TheseEffects Are Inadequate

Large, longitudinal epidemiological datasets often lack detailed information on subjects’ early lives

Genealogical datasets have small numbers ofobservations and provide little context

Our Approach

Detailed information on conditions < age 5, in mid- life, at older ages, and at death – the intervals span up to 106 years

Large numbers (2.5 million+)of nationally-representative observations with rich neighborhood and household context

Early-life conditions: birth records, U.S. Census, maps, published info

Mid-life conditions: World War Two enlistment

Later-life conditions: Social Security, Medicare and VA

End-of-life conditions: Social Security, State Death Records

exact street addressfamily structure (incl. presence of parents & birth order)parents’ SES and literacyparents’ unemploymentparents’ asset ownership

Early-life conditions (birth to age 5)

characteristics of neighbors local hazards/assets

local mortality/disease environment

birth weight, mother’s health, gestational age, delivery

[early-life conditions of parents and grandparents…]

occupationmarital statusfamily structureeducational attainment

Mid-life conditions (around age 25, males only)

heightweightIQ

place of residence

Later-life conditions (age 65+)

“income” (inferred from Social Security pension)disability (from Social Security)specific health conditions (Medicare/VA)

End-of-life conditions (at death)longevityspecific cause of death

How? Following individuals (1) from U.S. Censussamples (1900-30) into the Social Security records & State Death Records (SDR); or (2) from State Death Records back into the U.S. Census of Population

and for those who served in World War II, linkageto U.S Army enlistment records (height, weight,marital status, occupation, and residence)

Result: 25,000 U.S.-born males followed from birth to death, with detailedinfo on household & neighborhood;5,837 also linked to enlistment

The Linkage Process

1900-30Census surname given name birth month birth year birth place

1940-43WW Two surname given name

birth year birth place

1965-2005SSDIsurnamegiven namebirth monthbirth year

SSN

Post-1970SDRsurnamegiven namebirth monthbirth yearbirth placeSSN

(1)

(2) Census WW Two SDR SSDI

Today’s results (2): SDR 1920 Census & WWII

We use 1,537,659 Death Certificates of individualswho died in 8 states and were born in the U.S., 1915-19

From these, we randomly drew 96,099 and located28,839 (30%) in the 1920 U.S. Census of Population

Identifying information

Mortality informationLater life outcomes

The 30% linkage rate results from individualsmissed or incorrectly enumerated in the censusor individuals who could be matched to morethan one person in the 1920 Census

But information from the individual’s originalSS-% (Social Security application) on detailedbirthplace and the full names of both parentswill eliminate any ambiguities

Social Security Form SS-5: source for NUMIDENTand Social Security Death Index

The states were chosen on the basis of the easy availability of their computerized death records

But they are also convenient in other respects:

All 8 states are in the DeathRegistration Area by 1911 detailed local mortality info

9 of 20 largest cities in 1920

“A Very Specific Example”

Or

“The Calvin C. Denning Story”

Ohio Death Certificate Name: Calvin C. Denning Birth Date: 7 Apr 1916Birth City: Hamilton Birth County: Butler Birth State: Ohio Gender: Male Race: White

Death Date: 24 Mar 1996SSN: 275-10-7548 Father: Denning Mother: Menzer Marital Status: Married Education: 13 years Armed Forces: Yes, US Army Industry: U.S. Postal Service Occupation: Supervisor

1920 U.S. Census of PopulationButler Co., OH, Hamilton Ward 3Calvin Denning, age

3 years 9 months in 1920 Born April, 1916 in OH

Hamilton, OHWard 3

346 N. 11th Street

factories

church

public school

railroad tracks

346 N. 11th Street

The Vulcan Foundry

Figure 5. Sanborn Map for Part of City of Rockford, Winnebago County, Illinois, 1913.

Figure 6. Plat Map for Part of Otter Creek Township,Jersey County, Illinois, 1916.

U.S. Army Enlistment Records

Name: Calvin C. Denning Birth Year: 1916 Race: White, citizen Nativity State or Country: Ohio Residence State: Ohio County or City: Butler Enlistment Date: 1 Dec 1941 State: Kentucky City: Fort Thomas Newport

Education: 1 year of college Civ. Occup: Multilith Operator Marital Status: Single, no dependents Height: 68 inchesWeight: 131 pounds

14 children born, 10 surviving

1900 U.S. Census of Population


Attended school since Sept. 1, 1929

Able to read and write


Neighborhood characteristics:- Economic and demographic info on all

neighbors (incl. local 1930 unemployment rate),along with exact street addresses in city/town- Location of environmental hazards such asgas stations (source of lead after introductionof leaded gasoline c. 1926), steel mills,lead smelters, and polluted waterways- Proximity to retailers, health care, and schools

We will have more than 500,000 linked 1900-1930 census SSDI SDR by Spring, 2009 w/education, earnings, longevity,and cause of death

Shortcomings of the Data

1.Today’s analysis uses mostly males (linkage uses name & date & place of birth, but women’s name changes at marriage prevent their linkage)

but Social Security is providing informationon women’s names at birth to help, and statedeath records post-1978 provide maiden name

2. Cause of death info (along w/education) will come from 50 sets of state death records (8 now in hand: CA, CT, MA, MI, MO, MN, NC, OH)

Social Security Form SS-5

3. The key ingredient is the SSA NUMIDENTFile, which (1) includes only individuals in the Social Security system, so

- someone whose entire career was in “uncovered employment” is missed- someone whose death occurred before collecting any SS benefits is missed

and (2) is computerized only in the early 1970s,so

- individuals who retired prior to then will lack the full set of info they provided on their SS-5 form (sent back to local office)

Figure 1. Coverage of Deaths in SSA’s Death Master FileBy Age and Year, 1960-2000. Source: Hill & Rosenwaike,2001/2002.

Figure 2. Iowa & Nebraska Males With SSN in 1940By Age & Migration. Source: 1940 IPUMS.

60-70%

Figure 3. Percent in SSA NUMIDENT File WithOriginal Application Information By Birth Year.

4. The Social Security Death Index is available only 1965-2005, so the “window” in which we can observe deaths is only 40 years of calendar time

But 70% of the 1910-1919 birth cohort diedin this window

as a result, for each cohort, we will need tolimit the ranges for age at death within which we examine the correlates of mortality

example: for the sample drawn from the 1920census (males born 1915-1919), we canlook at only those who died between 50 & 85

To examine the correlates of longevity, we willrun regressions of the form:

E(Agedeath | Agedeathmin < Agedeath < Agedeath

max)

= β´Xi+γ´Yi +δ´Zi+εi

where Xi are individual & household characteristics,Yi are neighborhood characteristics and Zi areeconomy-wide effects (e.g. GDP, pandemic, war)

5. State death records are only generally available 1970-2006, so the “window” there is even smaller

California’s records go back to 1940, and alsomake it possible to include women (most statesdo not report birth surnames until 1979):

CA birth records are also computerizedfrom 1905: match on birth date, given name,and birthplace (CA) 25,000 males & femaleswho died in California 1940-2004

6. The World War Two data has some oddities:-individuals are selected on the basis of physical fitness for military service, so their mortality after the war is somewhat better than the general population over 2 the decades after war

-their height and weight reflect the selection criteria in place at enlistment (changes over war) -military provided tobacco, leading to higher than average lung cancer & heart disease at older ages

-data on height/weight only available 7/40-3/43

Data AnalysisLongevity, height, & weight of individuals born 1915-1919 linked from State Death Records to the 1920 U.S. Census & WWII

-shows impact of 1918-19 influenza pandemic

-maximizes the number of links to WWII records and to State Death Records (70% of this cohort dies 1970-2006)

Why 1915-1919 births & 1920 Census?

-shows effect of conditions < age 5

(1) (2) (3) (4)Age at Height at Weight atDeath Enlistment Enlistment BMI at(days) (inches) (pounds) Enlistment

Household Size -11.485 -0.061** 0.175 0.070**(15.817) (0.031) (0.258) (0.034)

Father Absent -172.520** -0.131 -4.675*** -0.588***(80.221) (0.180) (1.481) (0.197)

Mother Absent 14.764 -0.600*** 0.226 0.452**(93.255) (0.206) (1.692) (0.225)

Birth Order 1.044 0.006 -0.391 -0.067*(17.308) (0.035) (0.286) (0.038)

Non-Migrant 64.719* -0.101 1.069* 0.234***(36.909) (0.078) (0.643) (0.086)

White 265.228*** 0.274* -0.054 -0.156(71.885) (0.161) (1.319) (0.175)

Top 50 City -18.655 -0.071 1.606** 0.279***(38.987) (0.081) (0.666) (0.089)

Observations24,993 5,836 5,836 5,836Adjusted R2 0.024 0.030 0.009 0.014Standard errors in parentheses. * p < 0.10 ** p<0.05 *** p<0.01


Household Size -11.485 -0.061** 0.175 0.070**(15.817) (0.031) (0.258) (0.034)

Father Absent -172.520** -0.131 -4.675*** -0.588***(80.221) (0.180) (1.481) (0.197)

Mother Absent 14.764 -0.600*** 0.226 0.452**(93.255) (0.206) (1.692) (0.225)

Birth Order 1.044 0.006 -0.391 -0.067*(17.308) (0.035) (0.286) (0.038)

Non-Migrant 64.719* -0.101 1.069* 0.234***(36.909) (0.078) (0.643) (0.086)

White 265.228*** 0.274* -0.054 -0.156(71.885) (0.161) (1.319) (0.175)

Top 50 City -18.655 -0.071 1.606** 0.279***(38.987) (0.081) (0.666) (0.089)


TABLE 6. OLS Regressions on Age at Death, Males Linked from 1920Census to SSDI or Army Enlistment Records Who Died Age 57-83.TABLE 6. OLS Regressions on Age at Death, Males Linked from 1920Census to SSDI or Army Enlistment Records Who Died Age 57-83.


Household Size -11.485 -0.061** 0.175 0.070**(15.817) (0.031) (0.258) (0.034)

Father Absent -172.520** -0.131 -4.675*** -0.588***(80.221) (0.180) (1.481) (0.197)

Mother Absent 14.764 -0.600*** 0.226 0.452**(93.255) (0.206) (1.692) (0.225)

Birth Order 1.044 0.006 -0.391 -0.067*(17.308) (0.035) (0.286) (0.038)

Non-Migrant 64.719* -0.101 1.069* 0.234***(36.909) (0.078) (0.643) (0.086)

White 265.228*** 0.274* -0.054 -0.156(71.885) (0.161) (1.319) (0.175)

Top 50 City -18.655 -0.071 1.606** 0.279***(38.987) (0.081) (0.666) (0.089)



Household Size -11.485 -0.061** 0.175 0.070**(15.817) (0.031) (0.258) (0.034)

Father Absent -172.520** -0.131 -4.675*** -0.588***(80.221) (0.180) (1.481) (0.197)

Mother Absent 14.764 -0.600*** 0.226 0.452**(93.255) (0.206) (1.692) (0.225)

Birth Order 1.044 0.006 -0.391 -0.067*(17.308) (0.035) (0.286) (0.038)

Non-Migrant 64.719* -0.101 1.069* 0.234***(36.909) (0.078) (0.643) (0.086)

White 265.228*** 0.274* -0.054 -0.156(71.885) (0.161) (1.319) (0.175)

Top 50 City -18.655 -0.071 1.606** 0.279***(38.987) (0.081) (0.666) (0.089)


TABLE 6. OLS Regressions on Age at Death, Males Linked from 1920Census to SSDI or Army Enlistment Records Who Died Age 57-83.TABLE 6. OLS Regressions on Age at Death, Males Linked from 1920Census to SSDI or Army Enlistment Records Who Died Age 57-83.

Maximum: 70.5 in.

(1) (2)Born January-March -0.258 0.984*

(1.19) (1.83)Born April-June -0.623*** -1.540***

(2.81) (2.72)Born July-September -0.398* 0.213

(1.85) (0.37)Household Mortality Rate -0.713 -5.019***

(1.06) (2.77)State Mortality Rate -0.160

(1.20) Constant 80.546*** 83.056***

(236.59) (29.71)Observations 7,209 1,100Adjusted R2 0.00 0.03Absolute value of t-statistics in parentheses. * p < 0.10 **p<0.05 *** p<0.01


(1.19) (1.83)Born April-June -0.623*** -1.540***




(1.20) Constant 80.546*** 83.056***


TABLE 5. OLS Regressions on Age at Death, Males Linkedfrom 1900-10 IPUMS to SSDI Who Died Age 70-95.TABLE 5. OLS Regressions on Age at Death, Males Linkedfrom 1900-10 IPUMS to SSDI Who Died Age 70-95.


(1.19) (1.83)Born April-June -0.623*** -1.540***




(1.20) Constant 80.546*** 83.056***



(1.19) (1.83)Born April-June -0.623*** -1.540***




(1.20) Constant 80.546*** 83.056***


TABLE 5. OLS Regressions on Age at Death, Males Linkedfrom 1900-10 IPUMS to SSDI Who Died Age 70-95.TABLE 5. OLS Regressions on Age at Death, Males Linkedfrom 1900-10 IPUMS to SSDI Who Died Age 70-95.

An additional outcome:

Results of the Army General Certification Testat enlistment in World War II

A standardized IQ-like test used todetermine branch and task assignments

These test results have never been used at the individual level on a large scale

Two examples:1. early conditions → AGCT score2. AGCT score → longevity

Where can we get these test results?: some forensic cliometrics

May, 1943

How can we tell when “weight” is punched andwhen AGCT is punched?

Look at distributions:

150 110

Height & Weight:

AGCT:

March through May 1943:mean “weight” falls to ∼ 100standard deviation rises

The peak shifts from 150 to 110

Particularly for the period March through May 1943, the distribution in the “weight” field mimics the Army’s published AGCT tabulations

What can we do with this information

1. Examine the set of early-life influences alreadyused2. Add the influence of growing up in a placewith lead water pipes3. Examine AGCT as a predictor of later lifeoutcomes

For a city or town with lead pipes hard water → more calcification, less lead → IQ↑↑

acidic water → more corrosion, more lead → IQ↓

Lead & Lead &Baseline No Lead Lead Mover Stayer

(1) (2) (3) (4) (5)Intercept 99.15 101.99 98.16 101.17 95.03

(6.28)***(13.70)*** (7.18)***(10.82)*** (9.94)***Water Hardness 0.01 0.00 0.01 0.01 0.02

(0.01)** (0.01) (0.01)** (0.01) (0.01)**Water Acidity -1.37 -1.90 -1.19 -1.53 -1.25

(0.82)* (1.91) (0.91) (1.40) (1.21)Adjusted R2 0.0922 0.0757 0.0974 0.1099 0.0940Observations 2,009 562 1,447 747 700Standard errors in parentheses. p-values * < 10% ** < 5% *** < 1%


(1) (2) (3) (4) (5)Intercept 99.15 101.99 98.16 101.17 95.03

(6.28)***(13.70)*** (7.18)***(10.82)*** (9.94)***Water Hardness 0.01 0.00 0.01 0.01 0.02

(0.01)** (0.01) (0.01)** (0.01) (0.01)**Water Acidity -1.37 -1.90 -1.19 -1.53 -1.25


TABLE 7. OLS Regressions on AGCT Score (IQ) at Enlistment, MalesBorn 1915-19 Located in the 1920 Census in Cities and Town WithWater Supply Information.



(1) (2) (3) (4) (5)Intercept 99.15 101.99 98.16 101.17 95.03

(6.28)***(13.70)*** (7.18)***(10.82)*** (9.94)***Water Hardness 0.01 0.00 0.01 0.01 0.02

(0.01)** (0.01) (0.01)** (0.01) (0.01)**Water Acidity -1.37 -1.90 -1.19 -1.53 -1.25



(1) (2) (3) (4) (5)Intercept 99.15 101.99 98.16 101.17 95.03

(6.28)***(13.70)*** (7.18)***(10.82)*** (9.94)***Water Hardness 0.01 0.00 0.01 0.01 0.02

(0.01)** (0.01) (0.01)** (0.01) (0.01)**Water Acidity -1.37 -1.90 -1.19 -1.53 -1.25




Hardness ↑→ AGCT ↑ Acidity ↑→ AGCT ↓

How large are these effects?Moving from the 5th to the 95th percentilesin hardness and from the 95th to the 5th percentiles in acidity (i.e. to harder, less acidicwater) and using the coefficients in Column 5:

Reyes (2007) reports that the scientific consensus is that with a fall in atmospheric lead exposure of 15 μg/dL (as actually occurred 1976-1990) → IQ ↑ 7.5 points

AGCT ↑ by 6.0 points

What’s the mechanism?The impact of lead exposure on the brain:

Result: lower IQ, more impulsivityand criminality

Omaha, Nebraska

American Smelting and Refining Co. consolidated several plants in 1899. By 1924, the plant at the corner of 5th and Douglasstreets was the largest lead refinery in the world.

The plant was closed in July, 1997 & cleanup began

IQ and Longevity: very few existing studies

1. Holsinger, Helms, & Plassman (2007): 492 twin pairs from the National Research Council Twins Registry of WWII veterans → no effect once genetic component is removed

2. Whalley & Deary (2008): 2,972 children born in Aberdeen and followed to age 76 → relative survival probability to age 76 was only 0.79 if IQ was 15 pts. ↓

We have 500,000 males w/IQ linkable to death

The California data (births in CA 1915-1919 linkedto deaths in CA 1940-2004, using only given name,exact date of birth, and place of birth/death – CA) makes possible two additional comparisons:

1. males vs. females: does the impact of early life conditions differ by sex?

2. how much of the impact for males is missed because of the 1970-2006 “window” for the State Death Records outside CA?

Sensitivity Analysis

Conclusions and Future Directions

1. Month of birth matters, effect varies by age/sex2. Home environment (presence of parents, household size, birth order) matters3. The effects of early environment are stronger for males than for females (greater ♂ frailty?)4. Absent father shorter life, lower weight & BMI but absent mother shorter stature; mechanism?5. Strongest impact of 1918-19 influenza pandemic is on weight & BMI at enlistment in WWII6. Height ≈ age 25 has a non-linear effect on longevity (optimum=70.5 in.)

7. At least in California, early life conditions (except for month of birth) have a stronger effect on longevity at younger ages the effects we find using data from other states at older ages is probably a lower bound8. The future - Adding more women (outside California) - Disaggregating by cause of death - Adding local controls (weather, local mortality, physical features of the neighborhood) -Adding macroeconomic data (Davis’s IP series) -Earnings, LFP, Disability, Medicare/VA health

Documents

“The past is never dead. It’s not even past.” William Faulkner,