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Economics of Education Review 35 (2013) 24–40

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he relationship between schooling and migration:vidence from compulsory schooling laws

ter McHenry *

partment of Economics, College of William and Mary, P.O. Box 8795, Williamsburg, VA 23187, United States

Introduction

Growing evidence demonstrates that the return tohooling includes more benefits than just higher laborarket earnings (Dickson & Harmon, 2011). For example,ore education tends to improve health outcomes (Eide &owalter, 2011), reduce participation in crime (Buonan-a & Leonida, 2009), and increase voter turnout (Milligan,oretti, & Oreopoulos, 2004). In this paper, I investigatehether additional schooling also increases geographicobility. Improvements in households’ location choices.g., improved ability to acquire better information andalyze it more productively) may be another positiveturn to schooling. More education would improve bothdividual outcomes – as jobs improve when people move healthier local economies – and also economy-widetcomes – as migration matches workers to jobs better

and thereby increases overall productivity. Improvedgeographic mobility would provide another justificationfor public education subsidies.

A very common finding in population research is thatpeople with more education tend to migrate more (e.g.,Greenwood, 1997, chap. 12). Researchers have interpretedthe positive relationship between education and migrationto imply that increased access to schooling is a usefulpolicy for encouraging productive migration among thepoor in a country, in particular toward regions with morejobs available. For example, Lall, Timmins, and Yu (2009)study migration of the poor out of undesirable rural placestoward large cities with better public services in Brazil.They propose encouraging migration among the poor andsuggest increasing education levels as a policy option. Insupport of the idea, they cite Margo (1990), who finds animportant role for increased education of blacks in theGreat Migration, which improved many outcomes forblacks in the U.S.

However, it is possible that the observed correlationbetween schooling and migration reflects mechanismsother than the effect of schooling on the costs or returns to

R T I C L E I N F O

icle history:

ceived 16 May 2012

ceived in revised form 15 March 2013

cepted 18 March 2013

classification:

3

ywords:

gration

ucation

mpulsory schooling law

A B S T R A C T

I estimate the effect of schooling on the propensity to migrate by exploiting variation in

schooling due to compulsory schooling laws (CSLs) in the United States. I obtain negative

estimates of this effect among those with relatively little schooling. In contrast, previous

research estimates positive schooling effects on migration at higher levels of schooling. I

speculate that additional schooling at low levels enhances local labor market contacts and

thereby increases the opportunity cost of migration (leaving those contacts behind).

� 2013 Elsevier Ltd. All rights reserved.

Tel.: þ1 757 221 1796; fax: þ1 757 221 1175.

E-mail address: pmchenry@wm.edu.

URL: http://wmpeople.wm.edu/site/page/pmchenry

Contents lists available at SciVerse ScienceDirect

Economics of Education Review

jo u rn al h om epag e: ww w.els evier .c o m/lo c at e/eco n ed ur ev

72-7757/$ – see front matter � 2013 Elsevier Ltd. All rights reserved.

p://dx.doi.org/10.1016/j.econedurev.2013.03.003

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P. McHenry / Economics of Education Review 35 (2013) 24–40 25

igration. Schooling and migration are both investmenthoices that could be chosen together mostly by peopleith a greater propensity to invest (a lower discount rate

r greater patience) or by people who are more career-riented in their preferences. In Appendix A, I present anconomic model that illustrates the possibility of such aelationship.

The empirical work in this paper estimates the effectf schooling on migration by exploiting variation inchooling caused by compulsory schooling laws (CSLs).tate CSL policy changes over much of the 20th centuryield negative point estimates of the relationshipetween schooling and migration in U.S. Census samplesnd the Panel Study of Income Dynamics (PSID).stimates in some specifications are too imprecise toule out zero or small positive effects of schooling on

igration, but the overall evidence is most consistentith a negative effect. Compulsory schooling lawscrease schooling from quite low levels, and I interprety findings as relevant for schooling effects among

eople with about 10 years of schooling (that is, a localverage treatment effect).1

This paper’s evidence calls into question the previouslyupposed positive effect of schooling on migration.creasing education from low levels probably causes less

igration, not more. Policy-induced increases in educationttainment may not result in higher geographic mobility. Ipeculate that additional education at low levels increases

e strength of local job network ties and thereby providesmployment stability in the local area. This raises thepportunity cost of migration and reduces geographicobility.

. Related literature

The migration literature in economics has consistentlyund a positive relationship between schooling andigration. Greenwood’s (1997, chap. 12) survey docu-ents a monotonic increasing relationship between

chooling level and migration propensity in the U.S. andtates that this relationship holds in other developedountries as well.2 There are plausible reasons to believe

is positive relationship is causal. Education may enhancen individual’s ability to respond to disequilibria, includingigration in response to regional wage differenceschultz, 1975). Also, more education may induce moreigration if labor markets for higher-skilled jobs are

elatively thin at local levels.However, the relationship might be due to unobser-

ables correlated with both schooling and migration. Sincet least Sjaastad (1962), economists have viewed migra-on as an investment decision, much like schooling. Hence, a candidate correlated unobservable is the

willingness to invest, or patience. Another candidate is apreference toward having a career or consuming marketgoods, rather than consuming non-market goods (e.g., timeat leisure or with family). The demands of a career oftenimply higher migration rates and more schooling.

Recognizing similar possible biases, Machin, Salvanes,and Pelkonen (2012) exploit large increases in compulsoryschooling in Norway to identify the relationship betweeneducation and geographic mobility. Following nationallegislation passed in 1959, Norwegian municipalitiesincreased compulsory schooling from 7 to 9 years atdifferent times between 1961 and 1972. Machin et al.(2012) use data from Norwegian administrative registersfor 1986–2002. They take a sample of people with 7, 8, or 9years of schooling and measure migration in two mainways: the number of moves between 1986 and 2002 andresidence in a large town in 2002. Interestingly, OLSspecifications reveal a negative correlation betweenschooling years and migration. Then, they instrumentfor schooling with the compulsory schooling reform.Policy-induced variation in schooling reveals a large andstatistically significant positive relationship between extrayears of schooling at low levels and migration.

Machin et al. (2012) conclude that additional schoolingincreases migration that is beneficial for the national labormarket. They also show positive cross-country correlationsbetween measures of average schooling and migration(roughly speaking, the U.S., U.K., and northern Europeancountries have relatively high schooling and migration,while southern European countries have relatively lowschooling and migration). My results below reveal theopposite relationship using a similar instrumental vari-ables strategy with data from the United States. Hence,increasing schooling levels does not increase mobilityuniversally, as suggested in the literature to date. Inparticular, relatively high compulsory schooling levels inthe U.S. are probably not responsible for its relatively highgeographic mobility.

Malamud and Wozniak (2012) use U.S. data to estimatethe causal effect of education on migration. Instead offocusing on low education levels, they estimate the effectsof attending college and graduating from college on thelikelihood of living outside one’s state of birth inadulthood. They use U.S. Census data and instrument foryears of schooling with risk of being drafted for theVietnam War. Their instrumental variables estimatesimply a positive effect of schooling on migration,somewhat larger than OLS estimates.3 This result shouldbe interpreted as the effect of college-level education onmigration, since draft risk influenced schooling at relative-ly high levels.

It is not clear that the estimated effects of college-leveleducation on migration from Malamud and Wozniak(2012) imply what effects lower schooling levels haveon migration. Indeed, in a working paper version of theirarticle, they estimate negative point estimates in alterna-tive specifications that instrument for schooling with

1 My findings do not conflict with the literature showing a positive

ffect of higher levels of education on migration (e.g., college education in

alamud and Wozniak (2012)).2 Borsch-Supan (1990) finds in the U.S. Panel Study of Income

ynamics that more-educated workers engage in more geographic3

obility (but less job mobility). Schultz (1971) shows that local schooling

positively correlated with rural-to-urban migration in Colombia.

Alternative specifications estimate positive effects of college atten-

dance and graduation on migration.

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P. McHenry / Economics of Education Review 35 (2013) 24–4026

arter of birth, which shifts schooling mostly at lowels.4 There exists a growing body of evidence that

igration behavior is quite different for individuals withffering levels of skill or education. For example, Ham, Li,d Reagan (2006) find negative wage growth effects ofigration for high school dropouts and positive effects forllege graduates. Topel (1986), Bound and Holzer (2000),d Wozniak (2010) demonstrate that less-educatedsidents of a local area are less likely to move away fromegative local labor demand shock. Perhaps labor market

ctors are less important in the migration decisions ofgh school dropouts than for those with high levels ofhooling. Indeed, reasons that Current Population Surveyspondents give for moving are more job-related amonge more-educated respondents: among inter-stateovers, sixty-two percent of bachelor’s degree holderst only forty-three percent of high school dropouts gave

b-related reasons for moving.5

In light of these differences, it should not be surprisingat the effect of schooling on migration is different forgh school dropouts and college goers. There might beme additional mechanism through which school atten-nce increases the payoff to staying in the local area forgh school dropouts. For example, more schooling mayable a high school dropout to have a more stable job and

less likely to migrate for non-job-related reasons. In thisse, the effect of low levels of schooling on migration can

negative, as I demonstrate below.

Empirical strategy

In the empirical work below, I study the effect ofhooling on geographic mobility by exploiting shifts inhooling that are caused by compulsory schooling lawsSLs). That is, I regress an indicator of migration onucation and other controls, and I instrument forucation with state-level CSLs. I assume that peopleake schooling and migration decisions after taking intonsideration the costs and benefits of those actions,hich could be related to one another and also to otherdividual characteristics (like patience and risk-aversion).wever, schooling choices are constrained by CSLs, which

crease schooling for people who would have chosen lessucation absent the legal requirement. In this way, the

CSLs instrument shifts schooling choices of people Iobserve in the data. Appendix A presents an economicmodel of the relationship between education and migra-tion behavior, and how CSL changes allow inference aboutthat relationship.

Identification of the effect of schooling on geographicmobility follows a difference-in-differences approach.States change their CSLs at different levels and differenttimes, and I use them as controls for each other. The mainspecification is

Mi ¼ a0 þ bSi þ GðAgeiÞ þ a1 Femalei þ nt

þ nb þ ns þ ei; (1)

where i indexes individuals, Si is years of schooling and nt,nb, and ns are Census year, year of birth, and state of birtheffects, respectively. Mi is a measure of migration. Eq. (1)controls (with ns) for fixed state characteristics (like landsize) that influence migration. It also controls for year-to-year changes in migration, reflecting both differences inmigration across birth cohorts (nb) and across years atwhich the respondent answers the survey (nt).

6 Theidentification comes from changes in schooling andmigration within states over time. G(Agei) is a flexiblefunction of age that controls for life cycle patterns ofmigration (respondents are more likely to have movedaway from their birth state as they age).7

I instrument for Si with measures of compulsoryschooling laws that I define in the Data section. Thisspecification exploits variation in schooling choices thatcorrespond to contemporaneous changes to a state’s CSLs.If changes to CSLs are orthogonal to the migration error,then this specification will estimate b consistently. MostCSLs in the states affect the lower tail of the schoolingdistribution. Hence, I will interpret estimates of b as theeffect of schooling on migration among people with lowschooling levels (a local average treatment effect asdescribed by Imbens and Angrist (1994)).

Although the Eq. (1) specification controls for manypotentially-confounding factors (e.g., fixed-across-timestate effects), there may still exist unobserved shifters inmigration that are correlated with CSLs, rendering themendogenous and therefore inappropriate. I present resultsfrom specifications that augment Eq. (1) to control forstate-level characteristics that might influence CSLs andalso migration rates. Specifically, I control for each state’spercent employment in manufacturing, unemploymentrate, percent black, and percent of the population with lesseducation than a high school degree. These traits couldplausibly influence CSLs and also migration. For example, ahigher statewide education level makes a higher level ofcompulsory schooling cheaper to enforce and mightthereby increase its probability of legislative success. In

These specifications are similar to those that I estimate below for

ites in the U.S. Census samples. Malamud and Wozniak (2012)

aracterize their alternative instrumental variables specifications as

ustness checks for their main Vietnam Draft instruments. Their

arter of birth specifications are for male respondents to the 1980

nsus who were born from 1942 to 1953. I include more Census years

d birth years, include women, separate white and black respondents,

d use alternative measures of migration. The compulsory schooling

trumental variable I use is distinct from quarter of birth. I also

estigate compulsory schooling and migration in the PSID.

These calculations use the Current Population Surveys, accessed using

MS (Ruggles et al., 2010). I appended responses about reasons for

ving from the March surveys from 2005 through 2009. Job-related

sons include new job or job transfer, to look for work or lost job, for

sier commute, retired, and other job-related reason. Reasons for

ving that are categorized as non-job-related include change in marital

6 Migration rates vary significantly over time. In the U.S., they fell in the

second half of the 19th century, and rose for much of the 20th century

(Rosenbloom & Sundstrom, 2004). Since the 1980s, U.S. migration rates

have fallen steadily (Molloy, Smith, & Wozniak, 2011).7 In the main specifications, G(�) is a quadratic function (age and age

tus, better neighborhood, desire to own a home, better house, attend or

ve college, climate, and health reasons.

squared are controls). Age is separately identified from birth year and

Census year fixed effects, because I use multiple Census years.

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P. McHenry / Economics of Education Review 35 (2013) 24–40 27

ome specifications, I control for state-level variables in940 only.8 In other specifications, I control for time-arying state-level variables measured in the Census year940, 1950, etc.) prior to the year the respondent reachese compulsory schooling age.Stephens and Yang (2012) study the use of CSLs in U.S.

ata and demonstrate when estimating wage returns tochooling that additional controls are necessary for CSLs toe valid instruments. They argue that regional trends are

portant in explaining CSL changes and are alsoorrelated with residuals of wage functions. In specifica-ons below, I augment the Eq. (1) specification to include

egion-times-birth-cohort fixed effects to accommodatee potential for regional trends to influence both CSLs andigration. Separately, I include state-specific linear trends

the specification.Throughout the analysis, I attempt to use conservative

ssumptions in the estimation of standard errors. I reporttandard errors that are robust to heteroskedasticity andrbitrary error correlation within birth states. That is,tandard errors allow clustering at the birth state level (thetate where CSLs are assigned).9

. Data

I collect data from the 1950 to 2000 U.S. decennialensuses (Ruggles et al., 2010).10 The extract includes non-stitutionalized men and women aged 30–64 in the

urvey year who were born and living in the continental.S. Table 1 lists sample sizes by birth cohort in the Census

ample I use. Birth years span from 1900 to 1964, withousands of observations in each.Especially when going back to the 1950 Census and

sking about previous schooling, it is important toecognize that school experiences of blacks and whitesere separate and not equal for a significant portion of the

elevant states and years. The compulsory schooling lawsat applied for Census respondents in my sample go back

s far as 1914, when many schools for black and whitehildren were separate and unequally funded (Margo,990). For example, the academic year in schools for blackhildren in the South was significantly shorter than inearby schools for white children (Margo, 1990, Table 2.6).

In addition, there were major differences in black andwhite migration behavior through the 20th century. Forexample, the Great Migration of blacks out of the Southwas race-specific.11 In response, I analyze behavior ofwhites and blacks separately in the Census data.12

With the Census samples, I measure migration in twoways. The first migration variable is an indicator for livingin a state other than one’s birth state. This should capturelong-term transitions but is unsatisfactory for people whowere barely attached to their birth state, perhaps onlyliving there for a few months or couple of years. The secondmigration variable is an indicator for living in a state otherthan one’s state of residence five years ago, whichmeasures adult mobility more explicitly.

The years of education variable takes values from 0to 17, so responses are consistent across Census years(17 years was the highest response in 1950). In the 1990

Table 1

Sample sizes by birth year, U.S. Census data.

Birth year Sample sizes Birth year Sample sizes

Whites Blacks Whites Blacks

(1) (2) (3) (4) (5) (6)

1900 14,916 1671 1933 46,045 5511

1901 16,866 2188 1934 44,506 5189

1902 14,487 1464 1935 46,338 5507

1903 16,229 1622 1936 59,796 6987

1904 16,591 1564 1937 59,830 6772

1905 17,748 1869 1938 62,089 7158

1906 28,482 2995 1939 63,297 7074

1907 29,738 2826 1940 65,210 7914

1908 32,157 3252 1941 51,088 5930

1909 32,019 2938 1942 53,215 5858

1910 33,958 3719 1943 62,446 6624

1911 34,510 4024 1944 59,849 6532

1912 35,265 3473 1945 58,647 6808

1913 36,626 3730 1946 56,822 6568

1914 37,804 3628 1947 77,516 7962

1915 39,328 3846 1948 77,007 8779

1916 53,044 4915 1949 72,999 9225

1917 53,091 4817 1950 75,646 9634

1918 55,161 5190 1951 49,462 6434

1919 56,596 5388 1952 50,644 6472

1920 57,544 6459 1953 52,702 6503

1921 52,855 5169 1954 52,836 6726

1922 54,114 5085 1955 55,105 7224

1923 53,378 5512 1956 54,851 7346

1924 53,548 5354 1957 55,848 7449

1925 54,844 5626 1958 56,556 7566

1926 68,287 7334 1959 54,830 7241

1927 67,715 6997 1960 57,494 8019

1928 69,328 7361 1961 27,450 3806

1929 65,353 6952 1962 27,144 3754

1930 67,154 7620 1963 26,509 3899

1931 48,378 5089 1964 26,053 3828

1932 45,792 4848

Notes: Data from the 1950–2000 U.S. decennial censuses from IPUMS

(Ruggles et al., 2010). Samples include non-institutionalized men and

women aged 30 to 64 in the survey year who were born and living in the

continental U.S. Samples are split by race and do not include Hispanics.

8 In practice, I subtract from each variable its state-level mean and run

e specification on demeaned variables excluding state-of-birth fixed

ffects. This exploits within-state variation but avoids collinearity

etween 1940 (fixed-over-time) state-level variables and state-of-birth

xed effects.9 This follows Bertrand, Duflo, and Mullainathan (2004). However,

hile they use an estimation sample of state-year averages, I use the

isaggregated data where each observation represents a single person. I

onfirmed that the results were the same when I aggregated the data set

state-year averages of variables after individual characteristics (e.g.,

male) had been partialled out. The alternative two-way clustering of

ameron, Gelbach, and Miller (2011) – by birth state and year – produced

ery similar results.0 I use the 1950 1%, 1960 1%, 1970 1% Form 2 State, 1980 1% metro,

990 unweighted 1%, and 2000 unweighted 1% samples. The 1950 sample

cludes only ‘‘sample line’’ individuals, who answer more questions, and

is is a 1 in 330 sample of the population. To account for the variety of

11 Gill (1979) and Collins (1997) provide economic analyses of the Great

Migration.12

mpling procedures across the censuses, I apply sampling weights when

nning estimation commands.

I focus on non-Hispanic whites, rather than all whites, to keep a

relatively homogenous sample.

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P. McHenry / Economics of Education Review 35 (2013) 24–4028

d 2000 censuses, the schooling variable is categorical. Ipute years of schooling from these categories using

lationships found between both variables in the Currentpulation Survey (CPS) by Park (1994).Some results use labor force variables, including

dicators for being employed, unemployed, and self-ployed during the week prior to taking the survey.

dustry indicators reflect the one-digit industry classifi-tion. I measure labor market earnings with the naturalg of average weekly wage and salary income.13

I also investigate the relationship between schoolingd migration with the Panel Study of Income DynamicsSID), which is a panel data set consisting of a largemple of U.S. individuals and their family units.14 I extractta from the 1968 through 1996 waves. Due to variableailability, I keep only those sample members who areer household heads, their spouses, or their co-habitors. I

keep data for individuals when they are 32 years old. Thisresults in a data set with one observation per individual,but the data come from all survey years. For the PSIDresults, I include only white respondents, since the sampleis too small to analyze whites and blacks separately.

Independent variables in specifications with the PSIDare years of schooling and indicators for female, surveyyear, and state where the respondent grew up. The PSIDeducation variable is years of schooling. When the PSIDgives a range of schooling levels, I impute the midpoint(e.g., I impute 7 years of schooling when the PSID says therespondent attained between 6 and 8 years). As in theCensus data, the maximum years of schooling is 17, whichis the code I use to indicate the respondent attained anadvanced or professional degree past a bachelor’s degree.

Use of the PSID allows me to investigate migration (thedependent variable) defined in four ways. The first is along-term, long-distance measure. The migration origin inthis case is the state where the respondent says he or shegrew up. The first migration variable is an indicator forliving at age 32 in a different state. The origin in this long-term migration definition improves upon the birth stateorigin of the Census, which mis-measures some migrationbehavior. For example, suppose a baby is born while herparents are on vacation in Montana but then lived in

. 1. Compulsory school laws of the United States. Notes: Derived as described in Section 4 from raw data made available by Josh Angrist at http://econ-

w.mit.edu/faculty/angrist/data1/data/aceang00. Original data from various publications. Vertical axis is the years of attendance required before leaving

ool in the state.

More specifically, construction of the earnings variable starts with

-tax wage and salary income in the previous year divided by weeks

rked in the previous year. I adjust to 1999 dollars with the CPI-U. I trim

h (greater than $5000) and low (less than $100) values of weekly

ges.

The PSID oversampled some families (e.g., low-income families), so I

ply sampling weights in estimation procedures.

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P. McHenry / Economics of Education Review 35 (2013) 24–40 29

alifornia from week 2 until responding to surveys at age2. In this case, she has not migrated across state lines. Theensus would identify a migration event (from Montana toalifornia), but the PSID would not, since the respondentving in California would have identified California as thelace where she grew up. The second migration definition

an indicator for living in different states at ages 27 and 32orresponding to the 5-year migration variable in theensus).

The third and fourth PSID migration definitions exploitSID restricted-access geocode data, which include eachespondent’s county of residence.15 I use these data toentify the commuting zone (CZ) of residence. A

ommuting zone is a collection of adjacent counties thathare strong commuting links and approximate a localbor market.16 In urban areas, CZs are similar toetropolitan statistical areas, but the CZ definition also

rovides economically-meaningful agglomerations ofural counties. The third PSID migration measure is an

indicator for living at age 32 in a CZ that is different fromthe CZ where the respondent grew up. The fourth PSIDmigration measure is an indicator for living in different CZsat ages 27 and 32. Changing CZs is probably a betterindication of long-distance migration than changing states.For example, moving from Los Angeles to San Franciscoimplies a change in labor and housing markets and manyother networks. Measuring migration only across stateswould miss this migration event, though. In addition,moving from Alexandria, Virginia to Bethesda, Maryland isnot a long-distance move (e.g., many of the same jobs areavailable), but it does involve migrating across states. Itwould not, however, count as inter-CZ migration.

I follow Acemoglu and Angrist (2000) in the definitionof compulsory schooling laws (CSLs).17 There are two typesof CSL. The first (CA) is the number of schooling yearsrequired before the student is allowed to leave school. CAequals the higher of minimum schooling required beforedropping out and the difference between youngestdropout age and oldest enrollment age. The second

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1920 1940 1960 1980 1920 1940 1960 1980 1920 1940 1960 1980 1920 1940 1960 1980 1920 1940 1960 1980 1920 1940 1960 1980 1920 1940 1960 1980

Alabama Arizona Arkansas California Colorado Connecticut Delaware

District of Columbia Florida Georgia Idaho Illinois Indiana Iowa

Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan

Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire

New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma

Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas

Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

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ig. 2. Work permit laws of the United States. Notes: Derived as described in Section 4 from raw data made available by Josh Angrist at http://econ-

ww.mit.edu/faculty/angrist/data1/data/aceang00. Original data from various publications. Vertical axis is the school years required before obtaining a

ork permit in the state.

5 Reported samples sizes for the PSID are rounded to the nearest ten for

onfidentiality.6

17 I thank Josh Angrist for making CSL data available on his webpage,

Tolbert and Sizer (1996) describe how they define CZs using journey-

-work data from the 1990 Census.

which is where I obtained them: <http://econ-www.mit.edu/faculty/

angrist/data1/data/aceang00>.

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P. McHenry / Economics of Education Review 35 (2013) 24–4030

easure (CL) is the number of schooling years requiredfore a person is allowed to obtain a work permit. CLuals the higher of minimum schooling required beforetaining a work permit and the difference betweenungest allowed work age and oldest required enrollmente. The CSL instruments I use are indicators for levels of

and CL and their interactions. As in Acemoglu andgrist (2000), the indicators are:

6 for CL � 6

7 for CL = 7

8 for CL = 8

9 for CL � 9

8 for CA � 8

9 for CA = 9

10 for CA = 10

11 for CA � 11.

Figs. 1 and 2 describe the history of compulsoryschooling and work permit laws in each state of the U.S.Salient features of the data include a rising trend incompulsory schooling, heterogeneity in the timing of statepolicy changes, and the existence of reductions in the levelof compulsory schooling in some states. The rising trend incompulsory schooling is not surprising. The U.S. increasedits emphasis on public education throughout the 20thcentury.

The evident heterogeneity in timing of CSL changes isreassuring for a researcher hoping to use CSLs asinstruments for schooling. In the extreme case where allstate CSLs followed the same path, identification would beimpaired. Even within regions, CSLs do not changesimultaneously. For example, the paths of Mississippi,Alabama, and Georgia are quite different from one another.

ble 2

e positive correlation between schooling and migration.

(1) (2) (3) (4) (5) (6)

hite men and women in U.S. Census

ears of school 0.024*** 0.026*** 0.026*** 0.027*** 0.025*** 0.025***

(0.002) (0.002) (0.002) (0.002) (0.002) (0.002)

emale �0.000 �0.010*** �0.008*** �0.007***

(0.001) (0.002) (0.002) (0.002)

ge 0.003*** 0.004*** 0.003*** 0.003***

(0.001) (0.001) (0.001) (0.000)

ge2 �0.000 �0.000*** �0.000* �0.000**

(0.000) (0.000) (0.000) (0.000)

mployed �0.015***

(0.005)

nemployed 0.013* 0.023*** 0.023***

(0.007) (0.007) (0.007)

elf-employed �0.040*** �0.031*** �0.029***

(0.011) (0.007) (0.005)

bservations 3,152,736 3,152,736 3,152,736 3,152,736 3,152,736 3,152,7362 0.022 0.063 0.063 0.063 0.067 0.068

t, nb, ns FE NO YES YES YES YES YES

ndustry FE NO NO NO NO YES YES

ndustry � log wage NO NO NO NO NO YES

lack men and women in U.S. Census

ears of school 0.008*** 0.025*** 0.026*** 0.025*** 0.021*** 0.020***

(0.002) (0.001) (0.001) (0.001) (0.001) (0.001)

emale �0.033*** �0.034*** �0.029*** �0.018***

(0.003) (0.003) (0.002) (0.003)

ge 0.006*** 0.006*** 0.006*** 0.005***

(0.001) (0.001) (0.001) (0.001)

ge2 �0.000*** �0.000** �0.000** �0.000

(0.000) (0.000) (0.000) (0.000)

mployed 0.013**

(0.006)

nemployed 0.042*** 0.036*** 0.043***

(0.008) (0.011) (0.011)

elf-employed �0.100*** �0.039*** �0.006

(0.022) (0.010) (0.010)

bservations 356,824 356,824 356,824 356,824 356,824 356,8242 0.004 0.098 0.100 0.101 0.115 0.121

t, nb, ns FE NO YES YES YES YES YES

ndustry FE NO NO NO NO YES YES

ndustry � log wage NO NO NO NO NO YES

ata from the 1950–2000 U.S. decennial censuses at IPUMS (Ruggles et al., 2010). Results from OLS regressions. Dependent variable is an indicator for

gration (between birth state and state of residence at survey response). All specifications include a constant. nt, nb, ns refer to Census year, birth year, and

th state effects, respectively. Standard errors clustered at the birth state level.

p < 0.1.

* p < 0.05.

** p < 0.01.

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P. McHenry / Economics of Education Review 35 (2013) 24–40 31

the states of a region all changed their CSLs at the sameme, it would appear that there is some regional economic

petus for CSL changes, which would cast doubt on theiralidity as instruments.

The reductions in CSLs are perhaps surprising, but theyppear to be accurate rather than mis-measured. Edwards978) notes, for example, that Mississippi, South Carolina,

nd Virginia dropped their compulsory schooling laws inesponse to Federal desegregation efforts of the 1950s. Theata I use indicate that Mississippi and South Carolina dido completely, whereas Virginia dropped the compulsoryears of schooling law but maintained a maximumnrollment age and a minimum dropout age.

I assign to each Census respondent the CSL policy in hisirth state in the year when he is 14 years old. I assign toach PSID respondent the CSL policy in the state where herew up in the year when he is 14 years old. This is a typicalge at which compulsory schooling is binding. The PSIDrigin (state of growing up) should more accurately assignompulsory schooling laws to respondents. The Censusirth state is the best location information available inose surveys.In some specifications, I control explicitly for char-

cteristics of respondents’ states of origin (where theyce compulsory schooling laws). I calculate state

haracteristics using the 1940–1970 U.S. decennialensuses at IPUMS (Ruggles et al., 2010). The share ofanufacturing employment is the share of workers inanufacturing industries (IND1950 code between 306

nd 499), weighted by workers’ annual hours worked. Thehare of black residents refers to the entire populationnot just adults). The state unemployment rate is theroportion of 16–64-year-olds who are unemployedmong those in the labor force. The high school dropouteasure is the share of 24–64-year-olds reporting less

I assign state-level variables to Census and PSIDrespondents in two ways. First, for some specifications Iassign state variables from 1940 to all respondents,regardless of their birth year. Second, in other specifica-tions I assign to each respondent the state variables fromthe Census year prior to the year the respondent turned 14years old (the CSL year). So, a respondent born in Alabamain 1950 faces Alabama’s CSLs in 1964. For this respondent,the time-varying state controls measure characteristics ofAlabama in the 1960 Census.

5. Results from the U.S. Census, 1950–2000

The top panel of Table 2 shows that schooling andmigration are positively correlated among the whitepopulation of the U.S., and the relationship is robust tochanges in control variables. The table shows coefficientsfrom ordinary least squares (OLS) regressions where thedependent variable is an indicator for changing statesbetween birth and the Census year. The coefficient on yearsof schooling does not move much after including a batteryof controls for individual traits and labor market behavior.Column 1 shows the univariate regression coefficient onyears of schooling in the migration equation. Column 2adds controls for Census year, birth year, and birth state.Controls for sex and age in column 3 leave the relationshipessentially unchanged, as do additional controls foremployment status in column 4. Column 5 adds controlsfor one-digit industry, and column 6 adds controls forindustry and their interactions with workers’ log weeklywages18: neither changes the positive relationship

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4 5 6 7 8 9 10 11 12 13 14 15 16 17Years of Schooling

BlackWhite

ig. 3. Predicted migration rates by years of schooling. Notes: Predicted migration rates by schooling level. OLS regressions run separately for black and

hite respondents to the U.S. Census (men and women from 1950 to 2000). Dependent variable is an indicator for migration (between birth state and

ate of residence at survey response). Series shown are coefficients on indicators for individual years of schooling. Other variables in the regressions are

ensus year, birth year, and birth state effects, a quadratic in age, an indicator for female, employment status, one-digit industry effects, and industry

teracted with individual log wages. Dotted lines are two standard errors away from the prediction (robust standard errors clustered at the birth

ate level).

18 The wage variable is set to zero if weekly wages were missing (non-

orker or a trimmed wage). This specification also includes separate

dicators for low and high trimming of wages.

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P. McHenry / Economics of Education Review 35 (2013) 24–4032

tween schooling attained and the likelihood of livingtside of one’s birth state.The bottom panel of Table 2 displays analogous

gressions for the Census sample of black men andomen. It demonstrates that the OLS relationship betweenhooling and migration is positive and robust for blacks,

it is for whites. The size of the relationship increasestween columns 1 and 2. This mainly reflects the addition

controls for year, since the earlier cohorts wereographically very mobile (the Great Migration) butd relatively low schooling levels. Additional controls ine subsequent columns do not explain away the strongsitive correlation between schooling and migrationong blacks. In the column 6 specification with the full

t of controls, the relationship is similar to but a littleeaker than the one for whites in the top panel.

Fig. 3 plots coefficients from migration regressions thatow each year of schooling to have its own effect. Theecification follows column 6 of Table 2 with migrationtween birth state and residence state as the dependent

for each year of attained schooling, female, Census year,birth year, birth state, employment status, and one-digitindustry, plus interactions between individuals’ log wagesand industry.19 I run the regression separately for whiteand black samples and plot the predicted migration ratesfor each schooling year (coefficients on years of schoolingindicators). The solid line represents the white sample, andthe dashed line represents the black sample (the dottedlines are two standard errors above and below theprediction lines). Fig. 3 demonstrates that the positiverelationship between schooling and migration is presentthroughout the schooling years distribution, although it isweak for whites until college attendance (between 12 and13 years). For both the white and black samples, thepositive OLS relationship between schooling and migration

ble 3

st stage: the effect of CSLs on schooling: white men and women in U.S. Census.

(1) (2) (3)

Full sample High school and less Some college and more

L7: S = 7 for work permit 0.162 0.157* �0.010

(0.112) (0.092) (0.021)

L8: S = 8 for work permit 0.001 0.012 �0.050

(0.150) (0.116) (0.041)

L9: S � 9 for work permit 0.879*** 1.040*** 0.002

(0.160) (0.136) (0.025)

A9: S = 9 compulsory 0.160* 0.191** �0.061

(0.095) (0.085) (0.048)

A10: S = 10 compulsory 0.255*** 0.298*** �0.003

(0.065) (0.040) (0.019)

A11: S � 11 compulsory �0.312** �0.419*** �0.002

(0.136) (0.143) (0.022)

L7*CA9 �0.144 �0.144 0.065

(0.141) (0.130) (0.056)

L7*CA10 �0.434*** �0.424*** �0.040

(0.132) (0.105) (0.034)

L7*CA11 0.665*** 0.720*** �0.030

(0.143) (0.158) (0.028)

L8*CA9 0.134 0.144 0.076

(0.159) (0.127) (0.063)

L8*CA10 0.057 0.013 0.006

(0.173) (0.125) (0.050)

L8*CA11 0.683** 0.790*** 0.037

(0.262) (0.237) (0.049)

L9*CA9 �0.442*** �0.656*** 0.073

(0.148) (0.148) (0.051)

L9*CA10 �0.633*** �0.908*** 0.029

(0.177) (0.115) (0.028)

bservations 3,152,736 1,914,960 1,237,7762 0.224 0.203 0.024

test for CSLs 7.760 16.66 2.786

artial R2 0.00179 0.00313 9.09e-05

ata from the 1950–2000 U.S. decennial censuses at IPUMS (Ruggles et al., 2010). Results from OLS regressions. Dependent variable is years of schooling.

efficients for compulsory schooling laws (instrumental variables) are shown. Other controls are Census year effects, birth year effects, birth state effects, a

ale indicator, and a quadratic in age. Standard errors clustered at the birth state level.

p < 0.1.

* p < 0.05.

** p < 0.01.

19 The specification includes workers and non-workers. For non-

workers and workers with missing or trimmed wages, I impute a log

ge of zero. The specification also includes indicators for high and low

mming of wages.

riable and a full set of independent variables: indicatorswa

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P. McHenry / Economics of Education Review 35 (2013) 24–40 33

xists at levels of schooling where the CSLs have effectsround 8–12 years).20

Table 3 shows the relationship between CSLs anddividual schooling levels among whites in the U.S. (the

rst stage in the instrumental variables (IV) method). Theartial R-squared values measuring instrument strengthre quite small,21 and the F-test statistics for the

instruments are not uniformly large. However, theinstrument is stronger for the subset of the populationreceiving 12 or fewer years of schooling (16.66 versus7.76). If CSLs had a large effect on the schooling of peoplewho receive more than a high school education, it wouldraise flags about the validity of the instrument. The lowerF-test values and partial R-squared values among college-goers (column 3) are somewhat encouraging for theempirical strategy. Table 4 shows analogous first-stageresults for the sample of blacks. The CSL instruments in thesample of blacks are more powerful than for whites asmeasured by F-statistics of the joint significance of CSLs.For both white and black samples, CSLs appear to be strongenough instruments for valid inference of schoolingeffects.

The top panel of Table 5 shows results from estimatingthe effect of schooling on migration with samples of whitesin the U.S. Column 1 shows the two-stage least squaresestimates of Eq. (1). The estimated effect of schooling onmigration is negative but too noisy to reject zero. Column 2shows Limited-Information Maximum Likelihood (LIML)estimates, since LIML is known to perform better than 2SLS

able 4

irst stage: the effect of CSLs on schooling: black men and women in U.S. Census.

(1) (2) (3)

Full sample High school and less Some college and more

CL7: S = 7 for work permit �0.182 �0.108 0.062*

(0.161) (0.123) (0.037)

CL8: S = 8 for work permit �0.104 �0.101 0.032

(0.221) (0.178) (0.043)

CL9: S � 9 for work permit 0.787** 0.894*** �0.053

(0.339) (0.299) (0.073)

CA9: S = 9 compulsory �0.222 �0.129 0.069

(0.195) (0.176) (0.118)

CA10: S = 10 compulsory 0.349 0.375 �0.024

(0.366) (0.311) (0.068)

CA11: S � 11 compulsory �0.402* �0.453** 0.007

(0.208) (0.206) (0.039)

CL7*CA9 0.442 0.343 �0.124

(0.274) (0.251) (0.114)

CL7*CA10 �0.553 �0.455 �0.117

(0.401) (0.329) (0.075)

CL7*CA11 0.682 0.647 �0.122*

(0.441) (0.421) (0.064)

CL8*CA9 0.542* 0.516* �0.152

(0.297) (0.266) (0.095)

CL8*CA10 �0.477 �0.369 �0.127

(0.466) (0.422) (0.085)

CL8*CA11 0.457 0.550 �0.050

(0.538) (0.539) (0.055)

CL9*CA9 �0.213 �0.362* �0.072

(0.221) (0.204) (0.127)

CL9*CA10 �0.959** �0.845** 0.058

(0.408) (0.373) (0.087)

Observations 356,824 259,518 97,306

R2 0.387 0.365 0.021

F test for CSLs 54.64 45.45 9.324

Partial R2 0.00207 0.00259 0.000282

Data from the 1950–2000 U.S. decennial censuses at IPUMS (Ruggles et al., 2010). Results from OLS regressions. Dependent variable is years of schooling.

oefficients for compulsory schooling laws (instrumental variables) are shown. Other controls are Census year effects, birth year effects, birth state effects, a

male indicator, and a quadratic in age. Standard errors clustered at the birth state level.

* p < 0.1.

** p < 0.05.

*** p < 0.01.

0 For the black sample, these average relationships mask changes over

me. In the 1950 and 1960 censuses, migration rates of blacks who just

ent to high school were higher than migration rates of college-goers.

owever, later years saw a monotonic increasing relationship between

igration rates and education attainment. The black sample displays a

igher migration rate overall, which reflects very high rates of migration

the early years.1 The partial R-squared is defined to be the R-squared from the

gression:

i � Si ¼ ðzi � ziÞ’G þ hi

here Si is schooling as above, zi is the vector of excluded instrumentsSLs), and a tilde over a variable denotes the fitted values from agression of that variable on the exogenous variables (such as age

nd female). See Cameron and Trivedi (2005, p. 104).

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P. McHenry / Economics of Education Review 35 (2013) 24–4034

ith weak instruments (Staiger & Stock, 1997). The LIMLint estimate on years of school is more negative but stilldistinguishable from zero. Columns 3 and 4 limit themple to respondents with 12 or fewer years of school –ose most likely affected by CSL instruments. Both two-ge least squares and LIML estimates are negative buttistically insignificant.The bottom panel of Table 5 displays IV estimates of the

fect of schooling on migration in the sample of blacknsus respondents. Here as in the white sample, thetimated effect of additional schooling (at low levels) onigration is negative. Point estimates are somewhatisily estimated and statistically indistinguishable fromro. LIML point estimates of the schooling and migrationlationship are very large and negative, although the largendard errors cannot reject more modest negative effects

positive effects.Results in the top panel of Table 6 are for analogous

ecifications but which have as the dependent variable andicator for living in a state other than one’s residencete 5 years ago. All four columns imply that additional

hooling associated with CSLs causes lower migrationtes among white adults. The relationships are strongerr subsamples with less education. In this case, 2SLS and

LIML results are about the same. An additional year ofschooling in this sample of white men and women reducesthe 5-year migration rate by about 1.5 percentage pointsamong those with 12 or fewer years of schooling. This is alarge effect relative to the 6.4 percent 5-year migration ratein the same sample.

Unlike in the sample of whites, the bottom panel ofTable 6 implies that additional years of secondaryschooling did not increase 5-year migration rates of blackadults. Point estimates of the effect are very near zero andestimated somewhat precisely. This is true for the fullsample, those with a high school degree or less, and in LIMLspecifications.

Table 7 shows the results of many alternative specifica-tions to Eq. (1). It includes only LIML estimates for samplesof people with high school or less education. Columns 1and 2 reflect white men and women and the two migrationdefinitions: (1) living in a state other than the birth state,and (2) living in a different state five years ago. Columns 3and 4 show analogous results for blacks. The F-statisticsrefer to the test of joint significance of the CSLs in the firststage equation. The first row of results repeats those inTables 5 and 6: estimates for the coefficient on years ofschooling in Eq. (1).

ble 5

estimates of the effect of schooling on migration: living outside own birth state.

Full sample High school and less

2SLS LIML 2SLS LIML

(1) (2) (3) (4)

hite men and women in U.S. Census

ears of school �0.003 �0.074 �0.033 �0.061

(0.041) (0.104) (0.043) (0.064)

emale �0.003 �0.011 0.025* 0.035

(0.004) (0.011) (0.015) (0.022)

ge 0.003*** 0.002 0.001 0.000

(0.001) (0.002) (0.001) (0.002)

ge2 �0.000 �0.000 �0.000 �0.000

(0.000) (0.000) (0.000) (0.000)

bservations 3,152,736 3,152,736 1,914,960 1,914,960

igration rate 0.381 0.381 0.322 0.322

test for CSLs 7.760 7.760 16.66 16.66

artial R2 0.00179 0.00179 0.00313 0.00313

lack men and women in U.S. Census

ears of school �0.043 �0.158 �0.049 �0.131

(0.040) (0.151) (0.047) (0.113)

emale �0.003 0.048 0.008 0.048

(0.019) (0.070) (0.024) (0.058)

ge 0.003 �0.004 0.001 �0.005

(0.002) (0.008) (0.003) (0.007)

ge2 �0.000*** �0.000* �0.000** �0.000*

(0.000) (0.000) (0.000) (0.000)

bservations 356,824 356,824 259,518 259,518

igration rate 0.428 0.428 0.411 0.411

test for CSLs 54.64 54.64 45.45 45.45

artial R2 0.00207 0.00207 0.00259 0.00259

ata from the 1950–2000 U.S. decennial censuses at IPUMS (Ruggles et al., 2010). Instrumental variables estimates of the effect of schooling on migration.

Ls are instruments for years of school. Dependent variable is an indicator for migration. Other controls are Census year effects, birth year effects, and birth

te effects. Standard errors clustered at the birth state level.

p < 0.1.

* p < 0.05.

** p < 0.01.

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P. McHenry / Economics of Education Review 35 (2013) 24–40 35

The second panel displays results from specificationsat control for 1940 characteristics of respondents’ birth

tates. The purpose is to control explicitly for variables thatre correlated with CSLs and migration rates. Thestimated effect of schooling on migration is negativend now statistically significant for both migrationefinitions among both blacks and whites. Changing theontrol variables does not affect the estimates very much.he third panel of results allows the same state-levelontrols to vary over time but still pre-date the CSLs. Again,

e estimated effect of schooling on migration is negativend statistically significant for whites. Results for blacksre similar, but the estimated effects are not as clearlyegative in that sample.

The fourth panel of Table 7 displays results frompecifications that allow for linear time trends that arepecific to each birth state. The purpose is to capturecation-specific changes over time that are correlatedith CSL changes and also migration (in addition to

he birth year fixed effects already included in Eq. (1)).lthough no longer uniformly statistically significant,

he coefficient estimates are still consistent with negative effect of schooling on migration. Theame result holds in the fifth panel, which captures

location-specific time trends by including region-times-birth-year fixed effects to the specification. The resultsremain consistent with a negative effect of schooling onmigration.

I emphasize again that since the relationship betweenschooling and migration probably changes over theschooling distribution, I am estimating a local averagetreatment effect with CSLs variation. The evidence heresuggests that additional schooling – at low levels ofschooling – reduces migration.

6. Additional results from the PSID

Table 8 shows results from estimates of Eq. (1) withPSID data. The table shows only the coefficients on years ofschool for the various specifications, although eachregression also controls for indicators of female, birthyear, and CSL state (where the respondent grew up). Eachpanel reflects a different migration definition for itsdependent variable. Columns 1 and 3 include OLSspecifications to demonstrate that the correlation be-tween schooling and migration is positive in this PSIDsample, regardless of the migration definition. The OLSestimate of a 3.8 percentage point increase in migration

able 6

estimates of the effect of schooling on migration: different residence state 5 years ago and today.

Full sample High school and less

2SLS LIML 2SLS LIML

(1) (2) (3) (4)

White men and women in U.S. Census

Years of school �0.008** �0.009** �0.016*** �0.016***

(0.004) (0.004) (0.005) (0.005)

Female �0.011*** �0.011*** 0.004** 0.004***

(0.001) (0.001) (0.002) (0.002)

Age �0.018*** �0.018*** �0.013*** �0.013***

(0.001) (0.001) (0.000) (0.000)

Age2 0.000*** 0.000*** 0.000*** 0.000***

(0.000) (0.000) (0.000) (0.000)

Observations 2,754,878 2,754,878 1,631,735 1,631,735

Migration rate 0.0852 0.0852 0.0639 0.0639

F test for CSLs 7.760 7.760 16.66 16.66

Partial R2 0.00179 0.00179 0.00313 0.00313

Black men and women in U.S. Census

Years of school 0.002 0.002 0.001 0.000

(0.006) (0.006) (0.005) (0.005)

Female �0.016*** �0.016*** �0.010*** �0.010***

(0.002) (0.003) (0.002) (0.003)

Age �0.012*** �0.012*** �0.010*** �0.010***

(0.001) (0.001) (0.001) (0.001)

Age2 0.000*** 0.000*** 0.000*** 0.000***

(0.000) (0.000) (0.000) (0.000)

Observations 315,834 315,834 224,429 224,429

Migration rate 0.0597 0.0597 0.0463 0.0463

F test for CSLs 54.64 54.64 45.45 45.45

Partial R2 0.00207 0.00207 0.00259 0.00259

Data from the 1950–2000 U.S. decennial censuses at IPUMS (Ruggles et al., 2010). Instrumental variables estimates of the effect of schooling on migration.

SLs are instruments for years of school. Dependent variable is an indicator for 5 year state-to-state migration. Other controls are Census year effects, birth

ear effects, and birth state effects. Standard errors clustered at the birth state level.

p < 0.1.

** p < 0.05.

*** p < 0.01.

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P. McHenry / Economics of Education Review 35 (2013) 24–4036

opensity from an additional year of schooling is largeran the analogous Census data estimates in Table 2.portant differences in the Census and PSID specifica-ns include the migration definition (starting at birthrsus ‘‘growing up’’), age at the survey (the range 30 to 64rsus 32), and more recent cohorts in the PSID.nsistent with the non-linear schooling-migrationadient in Fig. 3, the OLS relationship between schoolingd migration is weaker in the sample of less-educatedspondents (column 3).However, when I instrument for schooling with CSLs,

ese PSID data do not show a positive relationshiptween schooling and migration. The CSL instruments areong in the PSID data, which first-stage F-statistics

ound 100 in the first two panels. The (LIML) estimatedfect of schooling on the likelihood of moving away frome’s origin state (first panel) is negative, althoughtistically insignificant. The result is the same in the

second panel, which defines migration as changingcommuting zones (CZs). The point estimates are largerelative to the migration rates (mean of the dependentvariables), although imprecisely estimated.

The third and fourth panels of Table 8 investigate 5-yearmigration (between ages 27 and 32), for comparison withCensus results in the previous section. The OLS results incolumns 1 and 3 show a positive relationship betweenschooling and 5-year migration in the PSID. It is difficult toinfer anything from the LIML IV estimates in columns 2 and4, however: the standard errors are very large. The first-stage F-statistics in these specifications are very small.Although the sample sizes fell only a little when using 5-year migration, the loss rendered some of the CSLinstruments collinear with each other, so their overallexplanatory power fell.

Although the PSID data do not demonstrate astatically significant negative effect of schooling on

ble 7

estimates of the effect of schooling on migration: Census respondents with high school or less education, alternative specifications.

igration definition White men and women Black men and women

Birth to residence 5-year Birth to residence 5-year

(1) (2) (3) (4)

q. (1) specification �.0612 �.016*** �.1306 4.8e�04

(.0639) (.0049) (.1135) (.0052)

F = 16.66 F = 14.68 F = 45.45 F = 25.5

nclude controls for 1940 CSL state characteristics (using only post-1940 CSLs)

% manufacturing, % black, �.1111** �.0252*** �.1865*** �.0095*

and unemployment rate (.0546) (.0064) (.0454) (.0055)

F = 8.974 F = 8.802 F = 29.19 F = 42.4

% high school dropouts �.1376** �.0281*** �.186*** �.0089

(.0576) (.0068) (.0468) (.0055)

F = 7.864 F = 7.443 F = 68.75 F = 45.12

% manufacturing, % black, �.1426** �.0285*** �.1859*** �.0093*

unemployment rate, (.0565) (.0068) (.0449) (.0055)

and % high school dropouts F = 8.76 F = 8.316 F = 28.63 F = 41.08

nclude controls for time-varying CSL state characteristics (post-1940 CSLs)

% manufacturing, % black, �.1621* �.0298*** �.3821 .002

and unemployment rate (.0864) (.0085) (.382) (.0071)

F = 5.873 F = 6.907 F = 14.93 F = 19.23

% high school dropouts �.1804*** �.0337*** �.2141*** �.0087

(.061) (.0079) (.0529) (.0066)

F = 15.04 F = 18.11 F = 19.95 F = 16.46

% manufacturing, % black, �.1773** �.0317*** �.4481 .0036

unemployment rate, (.085) (.009) (.4775) (.0089)

and % high school dropouts F = 6.933 F = 8.845 F = 25.68 F = 19.04

nclude state-specific time trends �.0576 �.0252*** �1.222 .0084

(.0443) (.0068) (5.292) (.007)

F = 31.22 F = 21.51 F = 175.6 F = 120.3

nclude region � birth year �.563 �.0222*** �.6233 .0066

fixed effects (.7603) (.0075) (5.582) (.0074)

F = 5.643 F = 7.313 F = 11.43 F = 17.31

ata from the 1950–2000 decennial censuses at IPUMS (Ruggles et al., 2010). Dependent variable is an indicator for interstate migration, either birth-to-

idence or 5-year. Displayed are coefficients on years of school from LIML specifications where CSLs are instruments for years of school. Each row shows a

ferent specification (added controls). Controls included in all specifications are female, age, age squared, Census year effects, birth year effects, and birth

te effects. Standard errors clustered at the birth state level. Specifications with state controls use respondents born in 1927 and later (affected by CSLs in

41 and later).

p < 0.1.

* p < 0.05.

** p < 0.01.

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P. McHenry / Economics of Education Review 35 (2013) 24–40 37

igration, the IV estimates cast doubt on the typically-ssumed positive effects. In this way, they are consistentith results from the larger Census samples. The PSIDelpfully identifies where respondents grew up and alsoigration across local labor markets (e.g., cities) rather

han states.

. Discussion of results and conclusion

The previous sections provide evidence that additionalchooling associated with compulsory schooling lawseduces geographic mobility. This is true in data from

e U.S. Census describing much of the twentieth centurynd also in the PSID (although the PSID evidence isomewhat mixed and weak statistically). It is true withultiple measures of geographic mobility: moves out of a

irth state, out of a state of self-identified origin, andithin the past five years. The negative relationship exists

samples of both black and white Americans. Theegative causal effect of additional schooling (from lowvels) on migration appears to be robust and somewhatidespread.

The causal interpretation depends on the validity of

schooling. Although CSLs appear to be sufficiently strongpredictors of education levels, it is possible that they arecorrelated with local factors that affect migrationdirectly.22 For example, states may increase compulsoryschooling at times when doing so is cheapest, such aswhen the local returns to schooling are increasingrapidly and citizens are wanting to get more schoolinganyway. Such unobserved local factors could alsoindependently cause outmigration rates to fall, if theyalso imply more job opportunities are available in thestate. I provide evidence in Table 7 that my findingshold up to controls for such potentially confoundingfactors.

In addition, Lleras-Muney (2002) finds evidence thatAmerican state compulsory schooling laws from 1915 to1939 are orthogonal to such factors. In particular, shedemonstrates that future CSLs do not predict students’schooling levels. That is, CSLs in a person’s birth statewhen he is 14 years old affect his schooling level, but CSLsin a person’s birth state when he is 20 years old do not

able 8

LS and IV estimates of the effect of schooling on migration: men and women in the PSID sample.

Full sample High school and less

OLS LIML (IV) OLS LIML (IV)

(1) (2) (3) (4)

Dep. var.: Different growing up and age 32 state

Years of school .0384*** �.0628 .0031 �.0166

(.0054) (.0436) (.0094) (.0269)

Sample size 3460 3460 1660 1660

Migration rate .272 .272 .193 .193

First-stage F 127.3 137.6

Dep. var.: Different growing up and age 32 Commuting Zone

Years of school .0558*** �.0628 .0138 �.0318

(.0061) (.0424) (.0099) (.0419)

Sample size 3300 3300 1570 1570

Migration rate .417 .417 .316 .316

First-stage F 121.1 98.64

Dep. var.: Different state 5 years ago

Years of school .0277*** .2182 .0099 .3605

(.0032) (.3603) (.0089) (.3956)

Sample size 3070 3070 1420 1420

Migration rate .147 .147 .096 .096

First-stage F 2.111 14.36

Dep. var.: Different Commuting Zone 5 years ago

Years of school .0362*** �.2897 .0237*** .563

(.0037) (1.237) (.007) (.4638)

Sample size 3070 3070 1410 1410

Migration rate .206 .206 .144 .144

First-stage F 2.064 14.58

PSID data from 1968 through 1996 waves. Respondents observed at age 32. Dependent variable is an indicator for migration: each panel has a different

efinition. Displayed are coefficients on years of school from LIML specifications where CSLs are instruments for years of school. Other controls are female,

irth year effects, and CSL state effects. Standard errors clustered at the growing-up state level.

* p < 0.1.

** p < 0.05.

*** p < 0.01.

22

Another weakness is that they limit inference to schooling only at

elatively low levels.

ompulsory schooling laws (CSLs) as instruments for r

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P. McHenry / Economics of Education Review 35 (2013) 24–4038

fect his schooling level, as would be the case if stategislatures just passed CSLs to reflect already-increasingtainment.23

More recently, Gradstein and Justman (2009) argue thatgher-income societies are more likely to vote for higherLs, so comparing enrollments across societies withfferent CSLs will confuse CSL effects with the effects ofher income-related determinants of enrollments. Insponse to this issue, I investigate discrete changes inLs from one year to the next in the same place, whichould control for most unobservable resident character-ics. In addition, evidence for a negative schooling effect

migration is even stronger in Table 7 specifications thatntrol explicitly for state-level variables that influenceLs. Very flexible specifications that allow for state-ecific times trends or region-times-birth-year fixedfects also yield negative point estimates.

Accepting the validity of the CSLs instrument, whatechanism could underlie the negative effect of schooling

migration? Most previous studies suggest educationcreases migration by opening up opportunities tompete in national labor markets, enhancing informationquisition skills, or increasing income and wealth thatlp finance migration. Additional education at the highhool level probably does not take a worker’s labor market the national level. However, information processing andancial resources probably increase with additional lowels of schooling. Information processing should increase

e return to migration effort and increased wealth may bechannel through which schooling reduces the cost ofigration.24 McHenry (2012) demonstrates that lowmily wealth levels do not discourage long-distanceigration, so the wealth channel is probably not particu-ly important.Evidently, some other mechanism outweighs the likely

fect of those channels, which may not be strong after all.potential mechanism behind the negative effect of

hooling on migration is expanded local networks.aying in school extends contacts with communityembers (students, parents, and teachers) who can shareformation about local jobs. In general, staying in schoolay provide students with increased stability: less risk of

unemployment, shorter unemployment spells, higherearnings. Such changes increase the opportunity cost ofmoving and cause geographic stability as well. I hope theempirical results in this paper lead to future research thatidentifies the specific mechanisms behind the negativeschooling-migration relationship.

Regardless of the specific mechanisms at work, thenegative effect of schooling on migration implies thatpolicy-induced increases in schooling do not encouragelong-distance geographic mobility in the U.S. Althoughschooling improves many outcomes for individuals, long-distance geographic mobility appears not to be one ofthem (at low levels of schooling). However, the results inthis paper do not rule out the possibility that additionalschooling from low levels enhances short-distancemobility, such as the choice of neighborhood withina city.

Acknowledgements

I thank Joe Altonji, Pat Kline, Fabian Lange, PhilOreopoulos, Paul Schultz, seminar participants at YaleUniversity, and several anonymous referees for helpfulcomments. All errors are mine. Some of the data used inthis analysis are derived from Restricted Data Files of thePanel Study of Income Dynamics, obtained under specialcontractual arrangements designed to protect the ano-nymity of respondents. These data are not available fromthe author.

Appendix A. A model of schooling and migrationchoices

This appendix presents an economic model, whose goal isto illustrate how shifters in schooling choices enableinference about the effect of schooling on the costs orreturns to migration. Compulsory schooling laws (CSLs) arethe primary shifters in this analysis. Since CSLs shiftschooling choices for people obtaining somewhat low levelsof schooling, this analysis should be thought of as pertainingmostly to them. The empirical work implies that I need amodel flexible enough to accommodate both a positivecorrelation and either a positive or a negative causal linkbetween schooling and migration.

The model is static. It is similar to the model of schoolchoice in Card (1999, chap. 30). Agents choose variables M

and S to maximize expected utility. M is effort spentsearching for migration opportunities. Below, I address mycurrent interpretation of the migration choice as a continu-ous variable rather than a choice to migrate or not. S is anamount of schooling (say, number of years). The utilityfunction for a person is:

UðM; SÞ ¼ gM þ wS þ bSM

1 þ d;

where d is the time discount rate, g measures the expectedreturn to migration effort, w is a measure of the return toschooling, and b measures the effect of schooling on theexpected return to migration effort. In the model, the

Edwards (1978) addresses the issue of endogeneity of state CSLs from

40 to 1960. She begins by regressing state enrollment rates on a CSL

arately for different subsets of the population (16- and 17-year-old

les and females in 1940 and 1960). She compares these with estimates

this effect from maximum likelihood (ML) estimation of a system

luding a CSL policy equation and an enrollment equation. The

clusion restrictions (explaining the CSL but not enrollment) are all

te-specific variables (e.g., ratio of teachers to adult population,

employment rate, proportion of population living in rural areas). In

estimation of the enrollment equation, fewer subsets have coefficients

CSLs that are distinguishable from zero. Edwards interprets this as

idence that the CSLs were endogenous in this period. However, the

ults more likely follow from the ML estimates being noisier. In fact, the

clusion restrictions appear to be poor predictors of the CSL, with several

expected signs and noisy estimates. The ML point estimates are neither

nsistently nor dramatically smaller than OLS estimates. This exercise

es not appear to be strong evidence against the exogeneity of CSLs.

See Appendix A for an economic model that describes the migration

oice as a function of the return to migration effort and the cost of

gration.

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P. McHenry / Economics of Education Review 35 (2013) 24–40 39

ayoffs to investments in schooling and migration occur in future period and are discounted, to account for theotion that higher discount rates reduce the returns tovestment behavior.25

The interaction term captures the possibility that schoolingas a causal effect on the wage return to migration, which cane positive or negative depending on the sign of b. If schoolingrovides workers with more and higher wage offers inlternative locations, then b > 0. If schooling provides moretable local employment, raising expected wages at the originelative to outside alternatives, then b < 0.

The investment cost function of schooling and migration is

ðM; SÞ ¼ 1

2cmM2 þ 1

2csS

2 þ tSM;

here cm, cs, and t are scalar parameters. The interactionrm captures the possibility that schooling has a causal

ffect on the cost of migration. For example, schooling canecrease the cost of processing information, such as jobpportunities far away. The interaction term does notonstrain the effect to be negative: schooling mightcrease the cost of migration as well.

To maximize utility, the individual chooses M and S suchat their marginal benefits just equal their marginal costs.26

his means

þ bS

þ d¼ cmM þ tS (M)

þ bM

1 þ d¼ csS þ tM: (S)

Optimal migration effort as a function of schooling choice then

¼ 1

cm

g1 þ d

þ b1 þ d

� t� �

S

� �: (2)

he term multiplying S directly is the discounted net effectf schooling on the expected return to migration effort. Forxpositional simplicity, define this net effect as follows:

� b1 þ d

� t:

measures the causal relationship between schooling andigration and is therefore the empirical object of interest.

Solving the system of first-order necessary conditions forptimal M and S yields

¼ csg þ Rw

ð1 þ dÞ½cscm � R2�(3)

¼ cmw þ Rg

ð1 þ dÞ½cscm � R2�: (4)

If parameters are such that M > 0 and S > 0, then thesedemand functions imply that people with higher discountrates choose less schooling and less migration effort.Migration is increasing in the return to migration g anddecreasing in the direct cost of migration cm. Similarly,schooling is increasing in its return w and decreasing in itsdirect cost cs. The effects of the return to schooling onmigration and the return to migration on schooling dependon their causal relationship as measured by R.

A sufficient condition for M > 0 and S > 0 is that R is asmall number. Even if R < 0 (schooling reduces net migrationbenefits), there can still exist a positive sample correlationbetween schooling and migration choices if R is small. Thereason is that the (negative) effect of discount rates on bothschooling and migration will swamp the causal relationshipbetween them. So, a negative causal relationship can beconsistent with the generally observed higher migrationrates among people with more schooling. There will also exista positive sample correlation between schooling and migra-tion if R > 0, which means that schooling directly increasesthe net return to migration.

A.1. Identification of the sign of R with CSLs

Suppose the local government imposes a minimumschooling level Sc (a compulsory schooling law or CSL). LetS* be the optimal schooling choice in the absence of theminimum schooling level. In this model, the policy affectsonly people who would have chosen S*< Sc otherwise. Thesepeople will now have Sc of schooling (their choices areconstrained). Their optimal migration effort choice is

Mc ¼1

cm

g1 þ d

þ RSc

� �:

The change in migration effort induced by the CSL is

DCSLM ¼0 if S � ScR

cmðSc � SÞ if S< Sc

8<:

So, R/cm can be identified by associating changes inmigration induced by the CSL with changes in schoolinginduced by the CSL. Since cm can be assumed to be positive,this allows identification of the sign of R, indicatingwhether schooling raises or lowers the net return tomigration.

A.2. Linking M in the model to migration behavior

The model treats migration as a continuous choice variable,rather than a discrete (say, binary) choice. Each person chooseshow much to search for migration opportunities and obtainsexpected utility from that decision. The binary migrationoutcome is an afterthought as the person simply migrates in alater period based on realized wage outcomes.

In the empirical work, I observe whether a personmigrated or not, rather than search effort. I assume that ifthe empirical propensity for a person to migrate is high, then Ican infer that the person chose a high search effort. The logicis the same as in most discrete-choice economic models of

5 Schooling and migration choices are made at the same time, which is

artly to simplify the model. It also incorporates forward-looking

ehavior in a simple way, since the agent considers future migration

ptions when making schooling choices.6 I assume strictly positive migration effort M and schooling S to

cilitate the model’s exposition.

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P. McHenry / Economics of Education Review 35 (2013) 24–4040

dividual behavior. There is a latent continuous variable (M

re) that determines the relative value of alternative optionsigrate or not), and a discrete choice is made when theent variable passes some threshold value.

ferences

emoglu, D., & Angrist, J. (2000). How large are the social returns toeducation? Evidence from compulsory schooling laws. NBER Macroann-ual, 9–59.

rtrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trustdifferences-in-differences estimates? Quarterly Journal of Economics,119(1), 249–275.

rsch-Supan, A. (1990). Education and its double-edged impact on mobili-ty? Economics of Education Review, 9(1), 39–53.

und, J., & Holzer, H. J. (2000). Demand shifts, population adjustments, andlabor market outcomes during the 1980? Journal of Labor Economics,18(1), 20–54.

onannoa, P., & Leonida, L. (2009). Non-market effects of education oncrime: Evidence from Italian regions. Economics of Education Review, 28,11–17.

meron, A. C., Gelbach, J. B., & Miller, D. L. (2011). Robust inference withmulti-way clustering. Journal of Business and Economic Statistics, 29(2),238–249.

meron, A. C., & Trivedi, P. K. (2005). Microeconometrics: Methods andapplications. Cambridge: Cambridge UP.

rd, D. (1999). The causal effect of education on earnings. In O. Ashenfelter& D. Card (Eds.), Handbook of labor economics. Amsterdam: ElsevierNorth Holland.

llins, W. J. (1997). When the tide turned: Immigration and the delay of theGreat Black Migration? The Journal of Economic History, 57(3), 607–632.

kson, M., & Harmon, C. (2011). Economic returns to education: What weknow, what we don’t know, and where we are going – Some briefpointers. Economics of Education Review, 30, 1118–1122.

wards, L. N. (1978). An empirical analysis of compulsory schooling legis-lation, 1940–1960? Journal of Law and Economics, 21(1), 203–222.

e, E. R., & Showalter, M. H. (2011). Estimating the relation between healthand education: What do we know and what do we need to know?Economics of Education Review, 30, 778–791.

l, F. (1979). Economics and the black exodus. New York: Garland Publishing.adstein, M., & Justman, M. (2009). Understanding compulsory schooling

legislation: A formal model and implications for empirical analysis. IZADiscussion Paper No. 4420 (September).

eenwood, M. J. (1997). Internal migration in developed countries. In M. R.Rosenzweig & O. Stark (Eds.), Handbook of population and family eco-nomics (pp. 648–720). Amsterdam: Elsevier North Holland.

m, J. C., Li, X., & Reagan, P. B. (2006). Propensity score matching, a distance-based measure of migration, and the wages of young men. mimeo.

Imbens, G. W., & Angrist, J. D. (1994). Identification and estimation of localaverage treatment effects? Econometrica, 62(2), 467–475.

Lall, S. V., Timmins, C., & Yu, S. (2009). Connecting lagging and leadingregions: The role of labor mobility. In G. Burtless & J. R. Pack (Eds.),Brookings-Wharton papers on urban affairs (pp. 151–174). Washington,D.C. Brookings Institution Press.

Lleras-Muney, A. (2002). Where compulsory attendance and child labor lawseffective? An analysis from 1915 to 1939. Journal of Law and Economics,45, 401–435.

Machin, S., Salvanes, K. G., & Pelkonen, P. (2012). Education and mobility.Journal of the European Economic Association10(2).

Malamud, O., & Wozniak, A. (2012). The impact of college on migration:Evidence from the Vietnam generation? Journal of Human Resources,47(4), 913–950.

Margo, R. A. (1990). Race and schooling in the south, 1880–1950. Chicago: U ofChicago P.

McHenry, P. (2012). Does low wealth constrain long-distance migration?Evidence from the NLSY79 cohort. College of William and Mary Depart-ment of Economics Working Paper #119.

Milligan, K., Moretti, E., & Oreopoulos, P. (2004). Does education improvecitizenship? Evidence from the United States and the United Kingdom?.Journal of Public Economics, 88(9), 1667–1695.

Molloy, R., Smith, C., & Wozniak, A. K. (2011). Internal migration in theUnited States? Journal of Economic Perspectives, 25(3), 173–196.

Park, J. H. (1994). Estimation of sheepskin effects and returns to schooling:Using the old and the new CPS measures of educational attainment.Princeton University Industrial Relations Section Working Paper #338.

Rosenbloom, J. L., & Sundstrom, W. A. (2004). The decline and rise ofinterstate migration in the United States: Evidence from the IPUMS,1850–1990. In (Series Ed.) & & A. J. Field (Vol. Eds.), Research in economichistory (Vol. 22, pp. 289–325). Amsterdam: Elsevier.

Ruggles, S., Alexander, J. T., Genadek, K., Goeken, R., Schroeder, M. B., & Sobek,M. (2010) Integrated Public Use Microdata Series: Version 5.0 [Machine-readable database]. Minneapolis: University of Minnesota.

Schultz, T. P. (1971). Rural–urban migration in Colombia? The Review ofEconomics and Statistics, 53(2), 157–163.

Schultz, T. W. (1975). The value of the ability to deal with disequilibria?Journal of Economic Literature, 13(3), 827–846.

Sjaastad, L. A. (1962). The costs and returns of human migration? Journal ofPolitical Economy, 70(5), 80–93.

Staiger, D., & Stock, J. H. (1997). Instrumental variables regression with weakinstruments? Econometrica, 65(3), 557–586.

Stephens, M., Jr., & Yang, D. (2012) Schooling laws, school quality, and thereturns to schooling. Working paper.

Tolbert, C. M., & Sizer, M. (1996). U.S. commuting zones and labor marketareas. A 1990 update. Economic Research Service Staff Paper No. 9614.

Topel, R. H. (1986). Local labor markets. Part 2: Hoover Institution LaborConference. Journal of Political Economy, 94(3), S111–S143.

Wozniak, A. (2010). Are college graduates more responsive to distant labormarket opportunities? Journal of Human Resources, 45(4), 944–970.