Manajemen | Fakultas Ekonomi Universitas Maritim Raja Ali Haji 34.full

Peter McHenry is an assistant professor of economics and public policy at the College of William and Mary. He thanks Daifeng He, Melanie Khamis, Fabian Lange, Melissa McInerney, John Parman, Kaj Thomsson, seminar participants at the College of William and Mary and the Society of Labor Economists 2013 meeting, and three anonymous reviewers for helpful comments. He is grateful to the Thomas Jefferson Program in Public Policy at the College of William and Mary for research support. Some of the analysis uses restricted- access data (NELS:88) that are available from the United States Department of Education to researchers with institutional affi liations. The author would be happy to help interested researchers ac-cess the data. For assistance, please contact Peter McHenry, College of William and Mary, P.O. Box 8795, Williamsburg, VA 23187, pmchenry@wm.edu.

[Submitted June 2013; accepted January 2014]

ISSN 0022- 166X E- ISSN 1548- 8004 © 2015 by the Board of Regents of the University of Wisconsin System T H E J O U R N A L O F H U M A N R E S O U R C E S • 50 • 1

of Natives

Peter McHenry

McHenry

abstract

Large low- skilled immigration fl ows infl uence both the distribution of local school resources and also local relative wages, which exert counterbalancing pressures on the local return to schooling. I use the National Education Longitudinal Study (NELS:88) and U.S. Census data to show that low- skilled immigration to an area induces local natives to improve their performance in school, attain more years of schooling, and take jobs that involve communication- intensive tasks for which they (native English speakers) have a comparative advantage. These results point out mechanisms that mitigate the potentially negative effect of immigration on natives’ wages.

I. Introduction

Immigration is a very important feature of many local labor markets in the United States. In 12 of the largest 25 cities in 2009, the foreign- born share in total populations was greater than 20 percent (U.S. Census Bureau 2012). Immigrants potentially in_fl uence the lives of the native- born population in many ways, including the likelihood of getting a job, wage offers, local prices, migration incentives, and schooling environments. Such relationships are important for public policy because they are potentially large and also because government policies like visa granting directly in_fl uence the number of immigrants in the country.

This paper investigates the impact of immigration on the human capital investment decisions of native- born youth. I focus on the effect of immigrants with relatively low

McHenry 35

education, a particularly important group in the United States.1 _{Betts (1998) provides}

a useful framework for thinking about the effect of immigration on natives’ educa-tion through two channels. The _fi rst is through the quality of schooling. A large local in_fl ow of low- skilled immigrants tends to reduce the schooling resources available for natives—for example, by shifting teachers toward English- pro_fi ciency classes. Diminished school resources reduce the value of education to natives and induce them to get less of it.2

On the other hand, recent immigration has increased the market supply of low- skilled workers and should in theory put downward pressure on wages and employ-ment probabilities for low- skilled residents in many areas. To the extent that low- skilled workers complement the productivity of high- skilled workers, wages in jobs requiring more education may rise. Both mechanisms increase the return to education, and native- born youth in the area with more immigration may have a strong incentive to acquire more schooling.

This paper demonstrates empirically that low- skilled immigration induces born youth to increase their investments in human capital. I study behavior of native- born children in the National Education Longitudinal Study (NELS:88) responding to immigration _fl ows that I measure in the U.S. Census. I _fi nd that low- skilled im-migration _fl ows induce local native youth to increase their high school attendance, grades, test scores, and the academic rigor of their curricula. The results use plausibly exogenous variation in local immigration (based on pre- existing ethnic enclaves) and are robust to controls including mother’s education and also characteristics of the student’s school. In addition, I _fi nd that low- skilled immigration induces native- born students to attain more secondary and postsecondary schooling.

Finally, I study NELS:88 respondents’ early- career jobs to test a recent hypothesis (Peri and Sparber 2009): low- skilled immigrants, who have relatively low English- language skills, induce low- skilled natives to invest in communication- oriented job skills rather than manual skills. Peri and Sparber (2009) describes the relationship in an equilibrium model of behavior and _fi nds support for it in U.S. Census data. I

fi nd that native- born respondents to the NELS:88 with more early immigration expo-sure choose jobs where they use more word processing and email and perform fewer manual tasks. From changes in natives’ job tasks, I infer that natives invest in com-munication skills as a way to differentiate themselves from low- skilled immigrants in the local labor market. This _fi nding complements Peri and Sparber (2009) and points to another way that native workers change their behavior to mitigate wage losses due to immigration.

The empirical results in this paper emphasize a potential bene_fi t of immigration that is largely overlooked in the research literature about costs and bene_fi ts of immigration. In particular, public policies that let in more immigrants will yield increased natives’ skills. Such increased human capital investment is socially desirable if native- born youth underinvest in their own schooling or if education generates positive

exter-1. Card (2005) and others have documented that immigrants to the United States since the late 1960s are much less educated than natives on average. Reasons include global population shifts and the 1965 Immigra-tion Act, which widened the naImmigra-tional origins of immigrants to the United States.

2. The literature on the effects of immigrant students on native students’ school performance yields mixed results. See Diette and Oyelere (2012), Jensen and Rasmussen (2011), and Neymotin (2009).

nalities.3 _{In addition, by augmenting their human capital in response to immigration,}

native- born workers mitigate the effect of immigration on their wages.

My empirical _fi ndings also relate to the estimation of immigration’s effect on na-tives’ wages, a literature with mixed results. One major strand of the literature (for example, Card 2005) demonstrates that native- born workers in high- immigration ar-eas do not earn substantially lower wages than similar workers in low- immigration areas.4 _{A competing strand of the literature (for example, Borjas 2003) estimates large}

elasticities of substitution between immigrant and native- born workers, which imply large wage effects. After showing my empirical results, I note that native children’s increased investments in human capital in response to immigration imply that pre-vious studies have mismeasured labor inputs when estimating substitution elasticities. The result is an upward bias: Researchers infer too much substitutability and thereby overly large effects of immigration on natives’ wages. So my _fi ndings about natives’ human capital investment responses to immigration also highlight a downward bias in estimates of the effect of immigration on natives’ wages (among studies that estimate substitution elasticities). If this bias were corrected, then the literature’s most- negative estimates of immigration on natives’ wages would move toward zero.

II. Prior Literature on Immigration and

Natives’ Schooling

Most of the previous literature on immigration and natives’ education in the United States uses Census data to measure effects on natives.5 _{Betts (1998)}

documents a negative relationship between state- level immigration and the probability that native- born black and Hispanic students complete high school in the 1980 and 1990 Censuses.6 _{Betts (1998) compares schooling of native- born adults (aged 19–25}

and 24–30, respectively) with immigration in the state where those natives live at the time of the Census. But schooling decisions should be more in_fl uenced by immigra-tion when natives are children, and selective migraimmigra-tion may confound the relaimmigra-tionship between immigration and education levels of adults.

3. Among many points of disagreement in a symposium on human capital policy, Carneiro and Heckman (2003) and Krueger (2003) agree that there are many U.S. residents who would be better off if they invested more in human capital. Lange and Topel (2006) and Moretti (2004) describe some evidence of local positive spillovers from education although they both note that the empirical evidence is mixed.

4. Identification often comes from changes over time as well. Typical studies accommodate unobserved area- specific factors that affect local wages and immigration by instrumenting for immigration (typically with pre- existing ethnic enclaves). Critics have suggested that natives migrate out of local areas with large local immigration flows, which would attenuate estimates of wage effects even if competition for jobs and true (nationwide) earnings effects are large (Borjas 2003). However, empirical work on natives’ migration re-sponses is also controversial, including findings of essentially zero and also substantial rere-sponses (Card and DiNardo 2000; Peri and Sparber 2011; Borjas 2006).

5. An exception is Llull (2010). It estimates a dynamic structural model of human capital attainment, occu-pational choice (blue- or white- collar), and wages with the National Longitudinal Survey of Youth 1979 and the Current Population Survey (CPS). It finds that immigration reduces wages even though natives increase their education in response. Eberhard (2012) uses the CPS to calibrate a general equilibrium model of im-migration, human capital accumulation, and wages. In counterfactual exercises, it finds that natives increase their human capital accumulation in response to nationwide immigration shocks.

McHenry 37

Hunt (2012) uses 1940 to 2000 decennial Census samples to expand upon the work of Betts (1998) and Betts and Lofstrom (2000): It assigns immigration _fl ows to natives at younger ages based on birth states, distinguishes among more- and less- educated immigrants, measures natives’ education consistently over time, and instruments for state- level immigration _fl ows (with lagged immigrant origins). It _fi nds that the pres-ence of immigrants in a state raises the probability that natives attain 12 years of schooling, with particularly large effects in the black native- born population. Also with U.S. decennial Census data, Jackson (2009) shows that college enrollment rates among the native- born increase with the entry of more low- skilled immigrants to the state’s labor market.7

Such analysis with Census data clearly bene_fi ts from very large samples and the ability to measure changes over time (for example, comparing differential changes in California and Oregon across the 1980, 1990, and 2000 Censuses). Large samples al-low separate estimation for different racial and ethnic groups (for example, to discern whether immigration differentially affects black and white natives). On the other hand, Census data do not provide ideal outcome measures, and the public- use data do not allow very precise location of individuals.

In this paper, I exploit features of the NELS:88 data set that improve upon analyses of Census data in several ways. While prior studies focus on schooling attainment, the NELS:88 allows me to use information about students’ attitudes, expectations, and speci_fi c experiences in school to study the mechanisms behind immigration’s effects on schooling attainment (for example, curriculum choices, study effort). In addition, the NELS:88 collects information from respondents and their parents so I can control for parents’ education when investigating other determinants of schooling choices. The NELS:88 surveys principals and teachers in respondents’ schools so I can control for speci_fi c school resources and the student body. The NELS:88’s focus on education translates into more accurate measures of diploma receipt and speci_fi c certi_fi cation in comparison to less- informative Census information about years of schooling. In particular, the NELS:88 distinguishes between a high school diploma and the General Educational Development (GED) credential and also collects information about stu-dents’ curricula (for example, Advanced Placement and vocational classes).

Another weakness of Census- based analyses is that they match respondents with local immigrant _fl ows that may not be relevant to them. Studies tend to associate with respondents the state- level immigration _fl ows where they were born or where they live in their 20s. There are two problems with this. First, young people are geographically mobile, and their birthplace or residence in their 20s might be quite different from their residence when making educational choices. Second, some states are very large and contain locations with differing immigration histories. The more precise timing and location information in the NELS:88 allow me to make a more informative match between immigration waves and natives. Speci_fi cally, I measure immigration _fl ows facing natives in local labor markets rather than states, and I match these immigration

fl ows to the place where NELS:88 respondents attend the eighth grade.8

7. Smith (2012) estimates immigration effects on youth outcomes, including whether the teenager is in school. It uses decennial Census and annual American Community Survey data. It finds small positive effects of local immigration flows among white girls and smaller positive effects for white boys. It also obtains somewhat noisy estimates of immigration on a cohort’s future earnings.

III. Empirical Strategy

The empirical goal in this paper is to estimate the effect of low- skilled immigration on human capital investments among young native- born residents nearby. In practice, I regress measures of human capital investment or attainment on local im-migration _fl ows and control variables that might in_fl uence schooling decisions and could be incidentally correlated with immigration. The basic regression equation ex-plaining individual human capital (H) investment is:

(1) H

i,s,c = ␣⌬Ic+␤Xi+⌫Wc+⌳Zs+ei,s,c.

I investigate human capital investment decisions (H_i,s,c) in three categories: in- school investments (attendance, curriculum choices, grades), educational attainment (for ex-ample, graduating from high school), and job tasks. Native- born youth have a compara-tive advantage in English- based communication tasks, and immigrants have a com-parative advantage in manual tasks (Peri and Sparber 2009). I infer investment in job skills from the task- intensity (communications and manual) of native- born workers.

Human capital investment (H) of individual i is in_fl uenced by the immigration _fl ow to i’s origin c (say, city), which is measured by∆I_c. Individual characteristics like sex and race in_fl uence school decisions and might vary across locations, so I con-trol for them in X_i (which also includes a constant term). In some speci_fi cations, X_i

also includes mother’s education, which is a strong predictor of schooling and might also be related to local immigration (say, if highly educated mothers leave locations with high low- skilled immigrant _fl ows). The vector W_c includes characteristics of the individual’s origin: region, population size, and metropolitan status. To control for lo-cal features that in_fl uence schooling decisions (other than recent immigration), I also control for the educational distribution of adults living in the origin.

Betts (1998) notes that immigration may decrease educational attainment of natives, as immigrant children use up resources at the school or school district level. Such an effect would reduce the quality of school and thereby its return to natives. Some speci_fi cations in Betts (1998) control for state- level school resources (pupil- teacher ratio), but it suggests that school- level controls would be preferable in testing the ef-fect of immigration on natives’ educational attainment. I control in some speci_fi cations for school- and district- level measures of educational resources (Z_s). One control is the percent of classmates in the respondent’s school who have limited pro_fi ciency in English, so the regression compares students in schools facing similar resource needs from immigrants. This should help account for the potential that parents in high- immigration areas choose their children’s schools to reduce exposure to immigrants.9

Additional controls in Z_s are indicators for the school being Catholic or private and non- Catholic, school enrollment, school student- teacher ratio, percent of the school’s teachers with post- bachelor’s degrees, average salary at the school for a starting teacher with a bachelor’s degree (adjusted for local cost of living), school- year term length (in hours), and school district expenditures per student.

Studies using Census data tend to take decade- long differences to wipe out all long- term characteristics of states or MSAs. This strategy is not available to me because the

9. Betts and Fairlie (2003) reports evidence that parents in higher- immigration areas are more likely to send their children to private school.

McHenry 39

outcome variables in my data pertain to a single cohort. However, the control variables in X_i, W_c, and Z_s should capture many of the potential schooling shifters that might also be correlated with local immigration _fl ows. Indeed, some of the variables included are not available with Census data (for example, school characteristics, mother’s educa-tion for adult respondents). Still, there may be unobserved locaeduca-tion- speci_fi c features that shift both immigration and natives’ schooling decisions.

In area- based studies of the effects of immigration on wages, there are always con-cerns about omitted variables bias. In particular, local labor demand shifters likely increase immigration and wages and may be unobserved in a regression. Similar bias may be present when associating educational attainment and local immigration, al-though the endogeneity story is less compelling than with wages. Nevertheless, there could be unobserved local traits that affect both immigration and human capital in-vestment of local natives. For example, current wage growth may be unmeasured (or mismeasured), but it could yield both higher immigration and less educational attain-ment among natives by raising the opportunity cost of time in school. With such endo-geneity in mind, I estimate speci_fi cations that instrument for recent immigration _fl ows with origins of earlier local immigrants and nationwide immigration by origin. Bartel (1989) demonstrates that the strongest predictor of where U.S. immigrants choose to live is the prior presence of members of the same ethnic group. This is most true of less- educated immigrants, the focus of my study. The idea of using such behavior in an identi_fi cation strategy comes from Altonji and Card (1991) and is employed frequently in the economics literature.

The speci_fi c instrument I use for immigration _fl ows follows Smith (2012). Let c

index locations of residence and o denote an immigrant’s region of origin. I_o,c,t is the number of immigrants from origin region o living in location c in Census year t. I_c,t is the total number of immigrants living in location c (all origins) at time t, and I_o,–c,t is the total number of immigrants from region o in locations other thanc at time t. The instrument is:

(2) I

c,1990 =ln

o

∑

I

o,c,1980 I

c,1980 I

o,−c,1990

⎛ ⎝⎜

⎞

⎠⎟ −ln

∑

o

I

o,c,1970 I

c,1970 I

o,−c,1980

⎛ ⎝⎜

⎞ ⎠⎟.

The instrument identi_fi es variation in immigration _fl ows across locations using nation-wide trends in immigration by origin (I_o,–c,1980 and I_o,–c,1990) and the origins of local immigrants in the previous period (I_o,c,1980/I_c,1980 and I_o,c,1970/I_c,1970).10 _{A location would}

have a high predicted immigration _fl ow (I

c,1990) if it has a relatively large pre- existing share of immigrants from recent sending countries. Such variation is plausibly unre-lated to contemporary (1990) economic conditions that motivate immigrants to settle locally and also motivate young native residents to invest in education.

Note that location differences in the instrument do not arise from pre- existing dif-ferences in local immigration levels or growth. Rather, they arise from difdif-ferences in the origins of prior immigration _fl ows. The instrument predicts higher immigration

fl ows among locations with relatively large shares of their immigrant populations from regions that subsequently sent many immigrants. Using region shares in the previous

10. There are 16 origin regions. Table A1 lists them. I assign people to origins based on their countries of birth in the Census.

immigrant population normalizes by prior immigration levels and growth. For ex-ample, a location with very low immigration a decade ago may have a large predicted immigrant _fl ow if a large share of its (small) earlier- period immigrant population was from a region that sent many immigrants later.

IV. Data

A. NELS:88

I use the National Education Longitudinal Study (NELS:88) to measure human capital investments of U.S. native youth. The NELS:88 was administered by the U.S. Depart-ment of Education. It began with a representative sample of eighth graders in U.S. schools in 1988. Followup surveys were fi elded in 1990, 1992, 1994, and 2000, and include responses from students, parents, teachers, and school administrators. Using school and residence zip codes in the restricted- access version of the NELS:88, I as-sign to each sample member the immigration _fl ow in the local labor market where she attended the eighth grade (when respondents were about 14 years old).

The NELS:88 provides quite a large sample for a longitudinal survey. The base- year survey reached almost 25,000 students. The next two waves of the survey are similarly large. I use these large samples when measuring respondents’ behaviors and attitudes during secondary school. The fi nal followup survey (in 2000) includes a subsample of previous respondents (about 12,140 of them), and I use this sample when measuring

fi nal educational attainment and early- career job characteristics.11

My focus is on the reaction of local native youth to immigration _fl ows. Some of the NELS:88 sample members are immigrants themselves. I select only those NELS:88 respondents who were born in the United States (or Puerto Rico) and whose parents were born in the United States (or Puerto Rico). Hence, the sample excludes fi rst and second generation immigrants. I do not restrict the sample by race or ethnicity.12 _{I also}

keep respondents who move across local labor markets at any time during the sample, so educational investments and work experiences may not occur where the initial im-migration fl ow was experienced.

B. Measures of School Efforts and Success

The NELS:88 includes a variety of questions about specifi c attitudes and behaviors while students are in secondary school. Local immigration might change attitudes to-ward school and work, especially if local native youth believe they will need to com-pete with immigrants for future jobs. To measure such attitudes, I use responses to three questions in the tenth grade survey. The fi rst is an indicator that the student strongly agrees that education is important to get a job later, which might be more likely among

11. For details about the sampling procedure, see Curtin et al. (2002).

12. Results reported below are similar in NELS:88 samples that include children of immigrants. I experi-mented with analyses on samples of only black respondents or only Hispanic respondents. The results were mostly consistent with, but somewhat less precisely estimated than, those reported below. Separating by race and ethnic category is a productive exercise, though. See Betts (1998) and Hunt (2012) with larger Census samples.

McHenry 41

natives who anticipate future competition in labor markets for high school dropouts. The other two expectations variables are indicators for students being sure they will graduate from high school and sure they will continue education after high school.

Native students may attempt to differentiate themselves from abundant immigrant labor by trying harder in school. To measure such behavior, I collect information about school attendance and hours spent on homework outside of school. The eighth grade survey asks respondents how many days they were absent from school and how fre-quently they skipped classes. Using the _fi nal followup wave of NELS:88 (age 26), I regress an indicator for graduating from high school on indicator variables for absence and skipping frequencies. For each respondent in the full sample, I predict a gradua-tion probability using their responses about absences and skipping class. I use this as a school attendance composite variable in the analysis. For an additional measure of school effort, I also use students’ self- reported hours of out- of- school homework per week in the tenth grade.

Students may also choose their school curricula conditional on their expected work environment. The NELS:88 identi_fi es students who took Advanced Placement (AP) classes, which are academically rigorous and useful in transitioning to college. The surveys also ask students whether they have taken a vocational class. I use indicators for taking any AP classes and any vocational classes to identify students’ curriculum choices. I view taking an AP class as a proxy for effort in academics and the intention of continuing education past high school. I infer the opposite intention for students taking vocational classes.

To measure success in school—a partial indicator of human capital investment—I collect information about grades and test scores. NELS:88 students reported their grades in math, English, history, and science classes during eighth grade. Their responses for each subject were “mostly As,” “half A and half B,” “mostly Bs,” etc. and also “not tak-ing subject.” With the _fi nal followup sample of 26- year- olds, I regress an indicator for graduating from high school on indicators for each response about grades and use the re-sulting coef_fi cient estimates to predict graduation conditional on grades for each student. This is a grades composite to measure academic success in high school. Finally, I use measures of test scores in eighth and 12th _{grades to infer schooling investments. In each}

grade, the NELS:88 reported standardized test scores in reading and math. I calculate the percentile of each student’s scores in the sample’s test score distribution (separately for reading and math tests in eighth and 12th _{grades). The test score measures are the}

aver-ages of a student’s reading and math percentile scores in eighth and 12th _grades. C. Measures of Educational Attainment

The _fi rst measure of education attainment that I use is receipt of a high school diploma. The NELS:88 separately identi_fi es students who graduated from high school and those who obtained a GED credential, and I treat GED holders as high school dropouts.13

Respondents could have received their high school diploma at any time before the

fi nal NELS:88 interview in 2000 (when respondents were mostly 26 years old). I

13. Evidence in Heckman, Humphries, and Mader (2010) that GED recipients do not earn a positive return in the labor market from their GED credential motivates me to categorize them as high school dropouts. How-ever, I also ran specifications that treat GED recipients as high school completers and obtained similar results.

also investigate a second measure of educational attainment: school attendance after high school. The NELS:88 asked respondents whether they had attended any “college, university, or vocational, technical or trade school for academic credit.” Many of the postsecondary attenders did not _fi nish their program. For a third education measure, I create an indicator for the receipt of any postsecondary certi_fi cate, license, or degree (associate’s, bachelor’s, or graduate degrees).

D. Measures of Job Tasks

Peri and Sparber (2009) studies “communication” and “manual” tasks of less- educated workers. The NELS:88 allows me to provide complementary evidence. Instead of inferring tasks from workers’ occupations as in Peri and Sparber (2009), I observe direct responses about tasks that workers perform on the job. The NELS:88 asked respondents about job conditions where they work at the time of the 2000 survey or their most recent job if not currently working. Workers responded “never,” “occasion-ally,” or “a lot” to multiple prompts about tasks they did at work. I generate indicators for workers doing “a lot” of “read letters, memos, or reports,” “write letters, memos, or reports,” “use computer,” “word processing,” “send and receive email,” “search the Internet,” and “measure or estimate the size or weight of objects.” I interpret reading and writing and email, word processing, and Internet computer use as communication tasks. I interpret the estimation of sizes and weights of objects as manual tasks (like spreading mulch on a landscaping job).

To get a sense for what responses about job tasks imply, Table A2 categorizes spe-ci_fi c occupations by task responses. The _fi rst column shows the percent of NELS:88 respondents in each occupation that read letters and memos “a lot” at work, and the second column shows the percent in each occupation that estimate the size and weight of objects “a lot.” The third column takes the ratio of the _fi rst two columns, a measure of relative communication- oriented tasks in each occupation. Legal professionals are clearly intensive employers of communication tasks. The occupations near the bot-tom of the table include manual jobs (for example, cooks, laborers, farm laborers), consistent with my interpretation of the “estimate size and weight” responses as im-plying manual tasks. These occupations are also common among immigrant workers. A native- born worker wanting to avoid labor market competition with low- skilled immigrants would prepare for other jobs, where he would probably “estimate size and weight” less and “read letters and memos” more.

E. Control Variables

The individual controls (X_i) include an indicator for female. Race/ethnicity categories are black, Hispanic, Asian, and American Indian (non- Hispanic white is the omitted category). I measure parental education as years of schooling attained by the dent’s mother. I focus on mothers because I am more likely to have a NELS:88 respon-dent’s mother’s education than father’s education. Mother’s and father’s education are highly positively correlated.

The vector W_c includes characteristics of the individual’s eighth grade local area: region, population size, and metropolitan status. I control for region with indicators of the four Census regions: Northeast, Midwest, West, and South. To control _fl exibly for

McHenry 43

the eighth grade location’s size, I use indicators for six categories: small town rural, small nonmetro, larger nonmetro, small metro, medium metro, and major metro.14 _To

control for local features that in_fl uence schooling decisions (other than recent immi-gration), I also control for the percent of adults in the 1990 Census with less than high school education and the percent of adults with a bachelor’s degree or more education.

In some speci_fi cations, I control for characteristics of the NELS:88 respondent’s eighth grade school (Z_s). School administrators of NELS:88 schools were asked how many students in their school’s eighth grade cohort had limited English pro_fi ciency, which I interpret as a proxy for immigrants’ needs at the school level. In practice, I use indicator variables for 11–20 percent, 21–30 percent, and 31 and higher percent eighth graders with limited English pro_fi ciency. The omitted category is 10 percent or less. Also from the school administrator survey, I create indicators for the school being Catholic or private and non- Catholic, school enrollment (number of students), school student- teacher ratio, and percent of the school’s teachers with post- bachelor’s degrees. The school administrator reported the average salary at the school for a start-ing teacher with a bachelor’s degree, and I divide this value by an index I created to measure housing costs in the area relative to the rest of the country.15 _{I measure the}

school year’s term length in hours by multiplying days and hours per day. I collect the school district’s total expenditures per student from the Common Core of Data (Na-tional Center for Education Statistics 1990).16

F. Location Defi nition and Local Immigration Measure

In this paper, local areas are commuting zones (CZs), which are collections of adja-cent U.S. counties.17 _{Studies of immigration in the United States commonly analyze}

states or metropolitan statistical areas (MSAs). In contrast to states, commuting zones are good approximations of self- contained local labor markets. Their boundaries are very similar to metropolitan statistical areas (MSAs) in cities but CZs describe local markets in rural areas as well. Similarly skilled people living in the same CZ (for ex-ample, immigrants and natives) apply for and work in roughly the same jobs. Outside the CZ, jobs are mostly out of commuting distance and would require a relatively long- distance move to accept.

For the purpose of assessing immigration’s effect on native human capital invest-ment, the immigration measure could be either a stock or a _fl ow. The presence of

14. Locations are sets of adjacent counties called commuting zones (CZs), which I describe in the next subsec-tion. Tolbert and Sizer (1996) categorize CZs into six size categories based on the largest population center in each CZ. Small towns have fewer than 5,000 residents, small nonmetro areas have between 5,000 and 20,000, and larger nonmetro areas have at least 20,000 but no MSAs in the CZ. The remaining three categories are CZs with at least one MSA in their territory. They are classified according to the size of the largest MSA, where small metro centers have fewer than 250,000 residents, medium metro centers have between 250,000 and 1 million, and major metro centers have more than 1 million. These population figures refer to 1990.

15. I assign households in the 1990 U.S. Census to commuting zones and calculate average monthly rental prices for two- and three- bedroom dwellings that are not group quarters. I then divide each CZ’s rent average by the average across CZs to form the local cost of living index.

16. I use expenditures data for the 1989–90 school year as the year prior was not available on the NCES web page. For students enrolled in private schools, I impute the school district expenditures where they live. 17. See Tolbert and Sizer (1996) for a description of how CZs were identified. It uses journey- to- work data from the 1990 Census to identify counties with strong labor market links. There are 741 CZs in the United States.

many immigrants in the local labor market may induce native youth to invest more in education. On the other hand, recent growth in immigration may be more salient than current stocks in in_fl uencing natives’ decisions. I focus here on immigration _fl ows.18

That is, I associate local natives’ skill investment decisions with recent increases in the local foreign- born population. One reason is that I expect large increases to be more salient (noticed by local natives) than large stocks. In addition, large increases probably imply more about future conditions than large current stocks imply. Suppose there are two cities: A and B. City A’s population is 5 percent foreign- born in 1980 and 10 percent foreign- born in 1990. City B’s population is 10 percent foreign- born in both 1980 and 1990. It seems reasonable to expect, based on trends, that in later years, City A will have the larger share of immigrants. Hence, students attending high school in City A in 1990 will have a greater incentive to invest in schooling because they reasonably expect greater competition with immigrant laborers than do students in City B. The alternative stock- based immigration measure would treat City A and City B identically and miss the important dynamic incentives that students face when investing in skills with future payoffs.

I use U.S. Census data in the Integrated Public Use Microdata Series (IPUMS) (Ruggles et al. 2010) to count immigrants by commuting zone (CZ). Let c index CZs of residence. I_c,t is the number of (low- skilled) immigrants in CZ c in Census year t. The speci_fi c measure of a local immigration _fl ow is:

(3) ⌬I

c,1990 =lnIc,1990−lnIc,1980.

I collect data from the 1970, 1980, and 1990 Censuses.19 _{I identify the commuting}

zone (CZ) where each respondent lives using the county group of residence variables in IPUMS.20 _{Immigrants are respondents who were born outside of U.S. territories and}

either a naturalized U.S. citizen or not a citizen.21 _{To focus on relatively low- skilled}

immigration, I select only immigrants with a high school education or less. Evidence that high school dropouts and high school graduates are close- to- perfect substitutes with each other but imperfect substitutes with college- educated workers motivates this working de_fi nition of “low- skilled” (Card 2009).

Table 1 shows summary statistics describing immigration in the 741 CZs. The _fi rst row displays the distribution of 1990 CZ immigrant shares. Not surprisingly, there is a large variance: Some CZs have almost no immigrants while in some CZs im-migrants account for more than 20 percent of the population. The second row de-scribes the percent less- educated immigrants (with a high school degree or less) in

18. Alternative analyses that measure immigration as a stock (that is, percent of local residents who are low- skilled immigrants) yield findings similar to those described below.

19. The data sets are the 1970 form 1 metro and form 2 metro samples, the 1980 5 percent sample, and the 1990 5 percent sample. The citizenship variable is not available in the 1970 form 2 metro sample. For that sample, I impute citizenship status based on the likelihood of citizenship in the 1970 form 1 metro sample conditional on respondents’ birthplace, age, and education.

20. The data identify the county group where each respondent lives [called “county groups” in 1970 and 1980 and called “public- use microdata areas” (PUMAs) in 1990]. Most county groups by these definitions are completely enclosed in a CZ so the identity of the respondent’s CZ is clear. Sometimes county groups intersect with more than one CZ; in these cases, I assign Census respondents to CZs based on the proportion overlap between county group and CZ populations.

21. People born in any of the 50 states, Washington D.C., or outlying areas and territories (American Samoa, Guam, Puerto Rico, U.S. Virgin Islands) are “natives” in the analysis.

45 Table 1

Summary Statistics of Immigration to Commuting Zones

1 2 3 4 5 6

Mean [Standard Deviation]

1st Percentile

10th

Percentile Median

90th Percentile

99th Percentile

Percent immigrants in CZ population, 1990 2.75 0.212 0.547 1.4 5.73 22.1

[3.8]

Percent low- education immigrants in CZ population, 1990 1.92 0.107 0.302 0.88 4.71 18.6

[3]

Percent low- education in CZ’s immigrant population, 1990 64 31.1 47.7 63 84 94.1

[13.9]

Percent change from 1980 to 1990 in CZ’s low- education immigrants –9.64 –74.7 –56.6 –23.6 60.8 199 [52.3]

CZ sample size in 1970 11,852 4,943 5,593 9,039 16,823 65,530

[14,942]

CZ sample size in 1980 20,222 5,022 5,661 10,917 32,730 175,222

[40,162]

CZ sample size in 1990 22,324 5,244 6,779 12,998 36,298 194,109

[40,898]

Notes: Data from 1980 and 1990 U.S. Census (IPUMS: Ruggles et al. 2010). CZ means commuting zone, a group of counties that make up an integrated local labor market. Summary statistics describe the distribution across the 741 CZs in the United States. Immigrants are those born outside U.S. states or territories. Low- education means high school or less. Popu-lation counts refl ect population weights and include children (young children all have low education). Census samples do not always identify CZ. Estimate of CZ c sample size is

n_c

k ≡ (⌺_pw_pc2n_p−1)−1 _where

total CZ population. Again, the variance across CZs is large. The third row shows that less- educated immigrants make up a large share of immigrants in all CZs and the majority in most CZs. The fourth row of Table 1 documents _fl ows of low- skilled immigrants between 1980 and 1990, which is the main independent variable in the analysis below. Most CZs in the 1980s actually experienced reductions in the number of low- skilled immigrants, but there were some CZs with very large increases. The very large percent increases included CZs with both large and small populations so the variety across CZs is not just a consequence of tiny immigrant populations doubling, for example.

The rest of the table describes measures of the sample sizes used to measure local immigration. I cannot always report sample sizes for CZ means because they are weighted averages of PUMA means. Instead, I report the distribution across CZs of a sample size approximation that I call nk_c ≡(∑_pw_pc2_n

p

−1₎−1_{, where c indexes CZs, w}

pc is the share of CZ c population that is in PUMA p, and n_p is the PUMA p sample size. Appendix 3 derives and provides justi_fi cations for using nk_c, including the fact that nk_c

is the same as the CZ c sample size when it can be observed. The bottom three rows of Table 1 describe the distribution of nk_c across CZs in 1970, 1980, and 1990. The sample sizes used to calculate CZ averages are in the thousands, which I interpret as large enough for acceptable precision. Sample sizes are somewhat large even for very small CZs because, in such cases, I assign the average of a relatively large PUMA to several small CZs that it contains. As long as such very small adjacent CZs are similar to one another, I expect their immigration measures to be reliable.

G. First Stage Results: Explaining Local Immigration Flows with Prior Immigration

Table 2 shows that the prior immigration instrument (I_c,1990) is a strong predictor of immigration _fl ows. The observations are NELS:88 respondents. The dependent vari-able is the actual immigration _fl ow (∆I_c,1990) they experienced. In addition to the in-strument I_c,1990, all specifi cations include sex and race/ethnicity variables and

charac-teristics of the respondent’s CZ: indicators for urbanicity and region, percent of the 1990 adult population with a bachelor’s degree, and percent of the 1990 adult popula-tion with less than high school educapopula-tion. The local educapopula-tion distribupopula-tion is meant to capture potential local traits other than immigration _fl ows that shift human capital in-vestment of locals. Some (second- stage) speci_fi cations below include mother’s educa-tion or school quality measures, so Columns 2 and 3 include these variables. In all three speci_fi cations of Table 2, predicted immigration _fl ows are strongly associated with actual _fl ows between 1980 and 1990. The F- statistics for the instrument’s

coef-fi cients equaling zero are above 100: This is a strong instrument.22

H. Summary Statistics from the NELS:88

Table 3 displays average characteristics of NELS:88 sample members. Column 1 shows each variable’s sample size (rounded to the nearest ten for con_fi dentiality). Sample sizes

22. Table 2 describes the fourth followup NELS:88 subsample. In the larger sample of high schoolers (prior to subsampling), the first stage is similarly strong.

McHenry 47

Table 2

First Stage: Predicted and Actual CZ Immigration Flows

1 2 3

Predicted immigration (IV) 0.6214*** 0.6216*** 0.6232***

(0.0594) (0.0593) (0.0587)

Female –0.0024 –0.0025 –0.002

(0.0061) (0.0061) ( 0.006)

Black –0.0522** –0.0524** –0.0697***

(0.0246) (0.0245) (0.0242)

Hispanic –0.003 –0.0036 –0.0053

(0.0298) (0.0302) (0.0278)

Asian –0.0617 –0.0617 –0.0703*

(0.0421) (0.0421) (0.0394)

American Indian –0.033 –0.0335 –0.0169

(0.0357) (0.0359) (0.03)

Mother’s education (years) –4.9e–04

(0.0022)

11–20 percent limited English 0.0771

(0.0668)

21–30 percent limited English 0.019

(0.0459)

31+ percent limited English –0.0964*

(0.0561)

Catholic school –0.1016*

(0.054)

Other private school –0.0041

(0.0406)

School enrollment –2.8e–08

(3.1e–05)

Student- teacher ratio 0.0048*

(0.0026)

School percent post- BA teachers 5.0e–04

(5.4e–04)

Teacher salary ($1000s) –5.1e–04

(0.0076)

School year length (100 hours) 0.0037

(0.0113)

District expenditures per student ($1000s) 0.0361***

(0.0098)

Observations 8,820 8,820 8,820

R- squared 0.6148 0.6148 0.6292

First stage F 109.5 109.7 112.6

Notes: ***p < 0.01 **p < 0.05 *p < 0.1. Data from the NELS:88. Dependent variable is a 1990 measure of immigration to the respondent’s eighth grade commuting zone (CZ). All models include a constant and characteristics of eighth grade CZ: percent adult population with a BA, percent population without a high school diploma, and indicators for urbanicity (fi ve of them) and region (three of them). Percent limited English refers to the percent of students in the respondent’s eighth grade school cohort who have limited English profi ciency. Standard errors clustered at eighth grade CZ level. Sample sizes rounded to the nearest ten for confi dentiality restrictions.

The Journal of Human Resources Table 3

Summary Statistics for NELS:88 Respondents, by Eighth Grade Immigration

1 2 3 4 5 6 7

All Respondents

Eighth Grade Immigration Growth < 0

Eighth Grade Immigration Growth ≥ 0

Column 6 Minus Column 4

N Mean N Mean N Mean Difference

Personal characteristics

Female 19,660 0.503 11,410 0.503 8,250 0.504 9.1e–04

Black 19,660 0.133 11,410 0.129 8,250 0.139 1.0e–02**

Hispanic 19,660 0.086 11,410 0.034 8,250 0.157 0.124***

Asian 19,660 0.016 11,410 0.014 8,250 0.019 5.8e–03***

American Indian 19,660 0.012 11,410 0.013 8,250 0.012 –1.2e–03

Mother’s education (years) 19,660 13.3 11,410 13.2 8,250 13.4 0.211***

Attitudes in school

1990: Education important for career 13,190 0.62 7,940 0.617 5,250 0.626 9.3e–03

1990: Sure to graduate from high school 14,100 0.878 8,360 0.883 5,740 0.871 –0.012**

1990: Sure to continue education after high school 14,060 0.636 8,340 0.618 5,720 0.661 0.043*** Behaviors in school

School attendance composite 19,660 0.921 11,410 0.923 8,250 0.919 –4.0e–03***

Homework hours (out of school) 13,780 4.36 8,190 4.16 5,590 4.65 0.488***

Took Advanced Placement class 12,700 0.383 7,580 0.349 5,120 0.433 0.084***

Took vocational class 12,680 0.146 7,570 0.164 5,110 0.119 –0.045***

Grades composite 19,660 0.917 11,410 0.915 8,250 0.921 6.5e–03***

Eighth grade test score percentile 18,750 52.5 10,930 52.5 7,820 52.4 –8.2e–03

McHenry

49

Educational attainment by age 26

High school diploma receipt 9,730 0.88 5,900 0.884 3,830 0.874 –0.01

Ever attended postsecondary education 9,740 0.79 5,900 0.771 3,840 0.818 0.047***

Postsecondary education credential 9,740 0.483 5,900 0.477 3,840 0.493 0.016

Bachelor’s degree or more education 9,740 0.33 5,900 0.32 3,840 0.345 0.025**

Early career job: communication tasks (general)

Read letters, memos, or reports 8,320 0.475 5,070 0.464 3,260 0.493 0.029***

Write letters, memos, or reports 8,330 0.314 5,070 0.31 3,260 0.319 8.7e–03

Early career job: computer and communication tasks

Use a computer 8,330 0.675 5,070 0.659 3,260 0.7 0.04***

Use word processing 6,720 0.471 4,000 0.451 2,730 0.5 0.049***

Use email 6,720 0.521 4,000 0.501 2,730 0.549 0.048***

Use Internet 6,720 0.346 4,000 0.32 2,720 0.384 0.064***

Early career job: manual tasks

Measure size or weight of objects 8,330 0.292 5,070 0.306 3,260 0.271 –0.036***

Out- migration

Moved between eighth grade and age 26 9,690 0.348 5,870 0.361 3,820 0.329 –0.032***

Notes: ***p < 0.01 **p < 0.05 *p < 0.1. Data from the NELS:88. Sample sizes rounded to the nearest 10 for confi dentiality restrictions. Educational attainment and work variables include only respondents in the fi nal 2000 followup survey. Columns 1 and 2 describe the full sample. Columns 3 and 4 describe only respondents whose eighth grade commuting zones experienced reductions in their low- skilled immigrant populations between 1980 and 1990. Columns 5 and 6 describe only respondents whose eighth grade commuting zones experienced increases in their low- skilled immigrant populations between 1980 and 1990.

differ because of missing data for some variables and because some variables (for ex-ample, educational attainment) are measured in the smaller (subsampled) _fi nal followup survey. Column 2 in the _fi rst panel of the table shows that half of the sample is women and most respondents are white and not Hispanic (the omitted race/ethnicity category). The average respondent’s mother had a little more than 13 years of school. Columns 4 and 6 break down NELS:88 respondents’ characteristics by the 1980–90 low- skilled immigrant growth rates in their eighth grade CZs. The differences (in Column 7) are somewhat small, except that the share of Hispanic respondents in high- immigration CZs is much higher than in low- immigration CZs. Because much of the contemporary im-migration was from Central America, and ethnic groups tend to cluster near each other, this is not surprising. Mother’s education is also higher in places with more immigration. I control for all of these demographic and background variables when assessing the relationship between local immigration and natives’ schooling levels.

The second and third panels of Table 3 describe NELS:88 respondents’ attitudes and behaviors in school. Students in high- immigration CZs think they are more likely to go to college, do more homework, take more AP classes, take fewer vocational classes, and get higher grades. This is consistent with the hypothesis that local natives distinguish themselves from low- skilled immigrants by attaining more education. Of course, Table 3 also shows native- born youth in higher- immigration areas attending less school. In addition, the simple differences in means mask potential confounding factors and other explanations. The mean differences do not control for student demo-graphics and backgrounds that surely in_fl uence schooling expectations and efforts. In addition, they do not account for potential local factors that both induce low- skilled im-migration and raise the return to schooling of local natives, like a local positive shock to labor demand. Empirical speci_fi cations in the next section address both of those issues.

The fourth panel of Table 3 describes highest schooling attainment of NELS:88 respondents. The difference in Column 7 shows that native- born eighth graders in high- immigration CZs stay in school longer than those in lower- immigration CZs. The difference is statistically indistinguishable from 0 for high school graduation, but the likelihood of getting postsecondary schooling increases as local low- skilled immigration increases. From the lower panels of Table 3, native- born workers from high- immigration origins tend to read somewhat more on the job, use computers more frequently, and use fewer manual tasks. These mean differences are consistent with na-tives differentiating their skills from local immigrants, but they could also re_fl ect other features of CZs that are incidentally correlated with low- skilled immigration. I control for such potential confounding factors in speci_fi cations below. The _fi nal row shows that the majority of respondents live in the same CZ in eighth grade and when they are 26 years old and that those in higher- immigration areas are less likely to move away.

V. Results About Immigration and Natives’ Efforts and

Success at School

This section describes the relationship between local immigration

fl ows and students’ efforts in secondary school. I exploit the rich information about students’ experiences in the NELS:88 to illuminate mechanisms behind immigration’s

McHenry 51

role in natives’ education attainment. Overall, native- born students in relatively high- immigration CZs appear to invest more in academics.

Table 4 displays results from ordinary least squares (OLS) and two- stage least squares (2SLS) regressions with various speci_fi cations and dependent variables (Equa-tion 1). Each cell in the table reports the coef_fi cient (and standard error) on the eighth grade CZ low- skilled immigration _fl ow. Each row describes a different dependent vari-able, and columns contain different speci_fi cations. The _fi rst four columns re_fl ect the sample of all respondents with nonmissing data for these variables measured in second-ary school. The sample sizes are quite large (above 10,000) because these variables are measured prior to subsampling for the fourth followup sample. Column 1 of Table 3 illustrates the range of sample sizes for these regressions (for example, larger when measuring attendance and smaller for the 12th _{grade test score). Columns 5 through 7}

of Table 4 select only NELS:88 respondents whose mothers had no more than 12 years of schooling. Children of less- educated parents are likely to compete in labor markets with less- educated immigrants, so their behaviors are of particular interest.

The _fi rst panel of Table 4 reports the effects of local immigration _fl ows on attitudes and expectations of native- born tenth graders. The _fi rst row includes little evidence that immigration increases the extent to which tenth grade natives think education is important for their careers. The _fi rst column’s coef_fi cient (0.0025) re_fl ects a baseline OLS speci_fi cation that controls for local immigration, individual sex and race/ethnic-ity, and eighth grade CZ controls, while the second column’s coef_fi cient (0.0281) is from the analogous 2SLS speci_fi cation that instruments for immigration. These

coef-fi cients are not statistically signi_fi cantly different from zero.

One standard deviation of the low- skilled immigration _fl ow distribution across CZs is about 0.43 (53.7 percent). It is informative to multiply the immigration regression coef_fi cient by 0.43, which yields the predicted human capital investment change given a one standard deviation increase in low- skilled immigration _fl ow. In this case, the result from Column 2 (2SLS) is a 1.2 percentage point increase in the likelihood of claiming that education is important for a career. The third column adds a control for mother’s education, and the fourth column instead adds controls for resources at the respondent’s eighth grade school. Columns 5 through 7 refer to the subsample of respondents whose mothers have 12 or fewer years of schooling, where the effect of immigration on natives’ attitudes toward education and careers is very small.

The second results row in Table 4 shows in similar speci_fi cations that increased local immigration does not induce tenth graders to increase their expectations of com-pleting high school. However, the third row implies that students experiencing high lo-cal immigration are more likely to expect to continue their education after high school. This is true with different speci_fi cations that control for family background and early school environment, and in both the full sample and those with less- educated parents. This expectations result is consistent with actual postsecondary attainment increases reported in the next section.

The fourth results row of Table 4 shows that low- skilled immigration tends to in-crease school attendance among native- born students. If this is the sum of both the (negative) effect of fellow immigrant students—working through school resources— and the (positive) effect of immigrants in the labor market, then it is particularly strong evidence that natives increase their human capital investments in the face of competi-tion with immigrants. Indeed, this interpretacompeti-tion is consistent with the stronger result

Table 4

Immigration and Natives’ Efforts at School

1 2 3 4 5 6 7

All Respondents Mother Had High School or Less

OLS 2SLS 2SLS

Control for Mother’s Education (Years)

Control for Grade

8 School Quality

Control for Mother’s Education (Years)

Control for Grade

8 School Quality Attitudes in school

1990: Education important for career

0.0025 0.0281 0.0179 0.0405 –0.0092 –0.0096 0.0069

(0.0141) (0.0246) (0.025) (0.026) (0.0393) (0.0394) (0.0396)

1990: Sure to graduate from high school

0.0072 –0.0104 –0.0215 –0.0038 –0.0234 –0.0259 –0.0122

(0.0094) (0.0162) (0.0166) (0.0164) (0.0287) (0.0285) (0.0283)

1990: Sure to continue education after high school

0.0226 0.097*** 0.0633** 0.1121*** 0.0786* 0.0732* 0.0802*

(0.0179) (0.0333) (0.0267) (0.0321) (0.0432) (0.0421) (0.0438)

Behaviors in school

School attendance composite 7.9e–04 0.0064* 0.0049 0.0103*** 0.0107*** 0.0108*** 0.0158***

(0.0017) (0.0033) (0.0031) (0.0036) (0.0041) (0.004) (0.0043)

Homework hours (out of school) 0.1572 0.3287 0.116 0.5792** 0.4549 0.444 0.5304

53

Took Advanced Placement class 0.0446** 0.1161*** 0.0884*** 0.144*** 0.138*** 0.1372*** 0.1624***

(0.0187) (0.0358) (0.0307) (0.0336) (0.0405) (0.0404) (0.0421)

Took vocational class –0.0652*** –0.1601*** –0.1482*** –0.1664*** –0.2489*** –0.248*** –0.2443***

(0.0192) (0.0391) (0.0372) (0.0366) (0.0547) (0.0547) (0.0542)

Grades composite 0.0112*** 0.0248*** 0.0203*** 0.029*** 0.0245*** 0.0246*** 0.0285***

(0.0032) (0.0055) (0.0051) (0.0058) (0.0071) (0.0068) (0.0072)

Eighth grade test score percentile 1.805* 5.16** 3.128* 7.466*** 4.206** 4.343** 5.135**

(1.02) (2.291) (1.647) (2.25) (2.026) (1.875) (2.017)

12th grade test score percentile 2.772** 8.713*** 6.265*** 10.02*** 6.673** 6.244** 7.401**

(1.128) (2.814) (2.09) (2.772) (3.067) (2.793) (3.06)

Notes: ***p < 0.01 **p < 0.05 *p < 0.1. Data from the NELS:88. Each row corresponds to a dependent variable. Table only shows the coeffi cient on 1990 immigration from OLS or 2SLS specifi cations (instrument for contemporary immigration is a prediction of low- skilled immigration to the CZ using previous immigrant populations). All models also include a constant, indicators for gender and race / ethnicity, and eighth grade CZ characteristics: percent adult population with a bachelor’s degree, percent population without a high school diploma, and indicators for urbanicity (fi ve of them) and region (three of them). See text for list of school quality controls. Standard errors clustered at eighth grade CZ level.

in Column 4 that controls for school resources. The _fi nding in Columns 5 through 7 that the effect is somewhat stronger among natives with less- educated parents lends further weight to this interpretation. The next row shows results about homework hours, providing some weak evidence of positive immigration effects.

I hypothesize that relatively large local _fl ows of immigrants with little formal schooling would raise the labor market return to AP classes and lower the labor mar-ket return to vocational classes. The sixth and seventh rows in Table 4 con_fi rm this hypothesis. Native- born students in higher- immigration CZs are more likely to take AP classes and less likely to take vocational classes. The effects are stronger among students with less- educated parents (Columns 5–7).

The eighth row in Table 4 shows a positive effect of immigration on native- born students’ grades, and the effect is very consistent across 2SLS speci_fi cations. The _fi nal two rows of results in Table 4 display the effect of low- skilled immigration to the CZ on the test scores of native- born students. The effects are uniformly positive and statistically signi_fi cant, using alternative controls for the subsample of students with less- educated parents (Columns 5–7). A coef_fi cient of 5 (as in Column 2 for eighth grade tests) implies that a one standard deviation (across CZs) increase in low- skilled immigration _fl ow (53.7 percent) causes natives’ test scores to increase by 2.15 percen-tiles. The test score increases are somewhat larger in 12th _{grade, which is consistent}

with cumulative effects.

The results about test scores are related to a previous literature that mostly em-phasizes how native- born students are affected by immigrants in their own school. The focus is on the school quality effect of immigration rather than the effect of labor market expectations. For example, Diette and Oyelere (2012) shows that immigration

fl ows to North Carolina affected test scores of natives. Interestingly, low- ability natives increased their scores while high- ability natives decreased theirs, which is consistent with the dual mechanisms affecting natives differently by pre- existing ability. Perhaps native students who are likely to drop out of high school (“low- ability” in Diette and Oyelere 2012) are more likely to increase their motivation and performance in the midst of expected labor market competition. On the other hand, higher- ability natives appear more affected by a reallocation of schooling resources and see their test scores fall.

VI. Results About Immigration and Natives’

School Attainment

Table 5 illustrates the relationship between local low- skilled immigra-tion and educaimmigra-tional attainment of local native- born youth. The three rows of results describe different levels of education: receiving a high school diploma, attending postsecondary school, and earning a postsecondary credential. The regressions

re-fl ect smaller—though still sizable—samples than those used in the previous section because this section’s samples include only respondents kept after subsampling for the _fi nal followup survey. Table 5’s speci_fi cations are analogous to those in Table 4. Overall, low- skilled immigration increases the educational attainment of natives.

The dependent variable in the _fi rst row is an indicator for obtaining a high school diploma (not a GED credential). Column 2’s 2SLS coef_fi cient of 0.0501 implies that

McHenry

55

Table 5

Immigration and Educational Attainment of Natives

1 2 3 4 5 6 7

All Respondents Mother Had High School or Less

OLS 2SLS 2SLS

Control for Mother’s Education (Years)

Control for Grade 8 School Quality

Control for Mother’s Education (Years)

Control for Grade 8 School Quality Dependent variable: received high school diploma (no GEDs)

0.0167 0.0501* 0.0302 0.0601** 0.0405 0.0309 0.0608

(0.016) (0.0256) (0.0223) (0.0259) (0.0385) (0.0369) (0.0397)

Dependent variable: ever attended a postsecondary education program

0.0337* 0.152*** 0.1152*** 0.1707*** 0.1974*** 0.1847*** 0.2213***

(0.02) (0.0348) (0.0272) (0.0343) (0.0503) (0.0452) (0.0517)

Dependent variable: earned a postsecondary education credential

0.0316 0.1126** 0.0714** 0.1256*** 0.1087** 0.0996** 0.1175**

(0.0241) (0.0472) (0.0357) (0.0418) (0.0516) (0.047) (0.0523)

Notes: ***p < 0.01 **p < 0.05 *p < 0.1. Data from the NELS:88. Dependent variables are indicators for educational attainment. Table only shows the coeffi cient on 1990 immigration from OLS or 2SLS specifi cations (instrument for contemporary immigration is a prediction of low- skilled immigration to the CZ using previous immigrant populations). All models also include a constant, indicators for gender and race / ethnicity, and 1990 eighth grade CZ characteristics: percent adult population with a bachelor’s degree, percent population without a high school diploma, and indicators for urbanicity (fi ve of them) and region (three of them). See text for list of school quality controls. Full sample size is approximately 8,820, and sample with less- educated mothers has approximately 3,880 observations (rounded to the nearest ten for confi dentiality restric-tions). Standard errors clustered at eighth grade CZ level.

if the local immigration _fl ow increases by one standard deviation of its distribution across CZs (0.43 or 53.7 percent), the high school graduation rate among natives will increase by about two percentage points. This is a large effect. The high school gradu-ation rate is an important social metric that has been stubbornly low in the United States.

Column 3 includes a control for mother’s years of schooling. Although the

speci-fi cations already control for the educational distribution of adults in the CZ, it is still possible that something other than immigration about high- immigration CZs induces children to get more education. For example, growing labor markets may quickly attract highly educated workers23 _{whose children tend to get plenty of}

edu-cation as well. The control for mother’s eduedu-cation should capture such an effect directly. The control predicts higher schooling among respondents and reduces the coef_fi cient on immigration though it remains positive. The speci_fi cation in Column 4 of Table 5 controls for school resources and generates a larger, positive effect. Columns 5 through 7 show the same results for the subsample of the NELS:88 stu-dent population whose mothers did not pursue education past high school. Because mother’s education predicts own education, children of less- educated mothers are probably more likely to compete with low- skilled immigrants in the labor market. However, the _fi rst row of results in Table 5 implies that children of more- and less- educated mothers react about the same to low- skilled immigration. The standard errors in Columns 5 through 7 are higher because of the smaller sample size, but the point estimates are similar.

The second row of results in Table 5 repeats speci_fi cations from the _fi rst row using a new dependent variable: an indicator for the respondent attending any postsecond-ary schooling. All speci_fi cations reveal strong positive effects. Column 2 implies that a one standard deviation (across CZs) increase in local immigration _fl ow raises the college- going rate among natives by 6.5 percentage points (100×0.43×0.152). The control in Column 3 for mother’s education lowers the effect somewhat but it remains statistically and economically signi_fi cant. Inclusion of school quality con-trols in Column 4 strengthens the result. The effect on postsecondary attendance is larger than the effect on high school completion, implying that native- born high school graduates (not just dropouts) are particularly affected by local immigration. Columns 5 through 7 show that immigration’s effect on postsecondary attendance is even larger in the subsample of youth with less- educated mothers. There exists evidence of a positive return to postsecondary credits, even for students who do not earn a degree (Kane and Rouse 1995). So, immigration’s positive effect on natives’ postsecondary attendance probably increases their future earnings, holding other things _fi xed.

The third results row in Table 5 documents the relationship between local immigra-tion in eighth grade and the likelihood that a native- born student acquires a postsec-ondary credential. The dependent variable is an indicator for earning (by age 26) any postsecondary degree, including certi_fi cates, licenses, and associate’s and bachelor’s degrees. The results here imply that immigration induces local natives not just to start postsecondary school but also to _fi nish. The effects are somewhat smaller than the

23. Wozniak (2010) shows that highly educated workers are more likely to move in response to local labor market conditions than less- educated workers.

66

Table A2

Occupation Titles for NELS:88 Respondents, By Reported Job Task Frequency

1

2

3

Percent Read Letters and

Memos a Lot (Communication)

Percent Estimate Size and

Weight a Lot (Manual)

Communication Divided

by Manual or (1) / (2)

Legal professionals

86

0

.

Legal support

62

3

23

Human services professionals

54

4

13.29

Financial services professionals

66

6

10.68

Editors, writers, reporters

49

5

9.2

Business /

fi

nancial support services

65

10

6.72

Secretaries and receptionists

64

11

5.9

Computer systems / related professionals

55

10

5.74

Protective services, criminal justice

72

15

4.86

Clerks, data entry

52

11

4.86

Educators- instructors other than K–12

37

10

3.89

Educators- K–12 teachers

64

17

3.83

Computer programmers

37

10

3.57

Customer service

58

17

3.5

Military 68

21

3.22

Sales / purchasing

58

25

2.34

Clerical other

48

23

2.06

Managers- supervisory, of

fi

ce, other

administra-tors

59

30

1.97

Managers- executive

67

38

1.76

Managers- midlevel

61

36

1.69

Health / recreation services

39

24

1.64

67

Medical licensed professionals

41

35

1.17

Medical services

43

38

1.14

Cashiers, tellers, sales clerks

29

26

1.13

Performers / artists

28

27

1.07

Mechanic, repairer, service technicians

36

45

0.81

Personal services

18

22

0.8

Scientist, statistician professionals

57

71

0.8

Medical practice professionals

50

67

0.75

Research assistants / lab technicians

33

52

0.63

Farmers, foresters, farm laborers

30

50

0.61

Transport operatives (not pilots)

36

60

0.6

Laborers (other than farm)

25

48

0.53

Cooks, chefs, bakers, cake decorators

26

55

0.47

Skilled operatives

26

61

0.43

Craftsmen

23

72

0.32

Notes: Data from the NELS:88 fi nal (2000) followup survey. It asked respondents currently working for pay how often they “read letters, memos, or reports” in a typical week at work. Column 1 shows the percent of respondents answering “a lot” (rather than “never” or “occasionally”) in each occupation. Working respondents were asked how often they “measure or estimate the size or weight of objects” on the job. Column 2 shows the percent of respondents answering “a lot” in each occupation. Column 3 divides Column 1 by Column 2 to identify occupations where communication tasks are employed much more frequently than manual tasks.

The Journal of Human Resources

68

Appendix 2

Derivation of Marginal Products

Here I derive the ratio of marginal products of foreign- born and

native- born workers in the CES production function of Section IX. To simplify

no-tation, I suppress

jkt

subscripts. Let total output produced from immigrant (

F

) and

native- born (

D

) labor inputs be

Y

. Then,

ln_{Y =} 1 ␭ln[␺(L

F₎␭

+(1−␺)(LD₎␭_]

_.

So,

∂lnY ∂LF =

␭␺(LF₎␭−1 ␭[␺(LF₎␭

+(1−␺)(LD₎␭_]

and since

∂lnY /∂LF

=Y−1∂_{Y /}∂_LF

_{, the marginal product of foreign- born workers is}

∂Y

∂LF =[␺(L F₎␭

+(1−␺)(LD₎␭_]1/␭ ␭␺(LF)␭−1 ␭[␺(LF₎␭

+(1−␺)(LD₎␭_] =[␺(LF₎␭

+(1−␺)(LD₎␭_]1/␭−1_␺(LF₎␭−1_.

Similarly, the marginal product of native- born workers’ labor is

∂Y

∂LD =[␺(L F₎␭

+(1−␺)(LD₎␭_]1/␭ ␭(1−␺)(LD)␭−1 ␭[␺(LF₎␭

+(1−␺)(LD₎␭_] =[␺(LF₎␭

+(1−␺)(LD₎␭_]1/␭−1₍₁−_␺)(LD₎␭−1_.

These imply that the ratio of marginal products of foreign- born and native labor is:

∂Y /∂LF

∂Y /∂LD =

␺(LF₎␭−1 (1−␺)(LD₎␭−1

.

Equilibrium implies that wages equal marginal products of labor:

wF wD =

␺(LF₎␭−1 (1−␺)(LD₎␭−1

.

Taking logs yields Equation 4.

Appendix 3

Derivation of a Sample Size Measure for Estimates of

Commuting Zone Characteristics

This appendix describes asymptotic results that justify a measure of the

sample size used to calculate commuting zone (CZ) average characteristics from U.S.

Census data (for example, average education level). The calculation of CZ averages is

complicated because the Census location de

_fi

nition is not the CZ but rather the county

group or public- use microdata area (PUMA). Both CZs and PUMAs are collections of

counties but some PUMAs overlap multiple CZs. Consequently, it is not

straightfor-ward to assign some respondents to CZs and thereby assess CZ- speci

_fi

c sample sizes.

This appendix compares the sample size in a simple calculation of a mean (where

we get our intuition about what is a “large” or “small” sample) to the corresponding

expression in the more complicated calculation of a CZ mean from PUMA data.

Let CZs be indexed by

c

, PUMAs be indexed by

p

, and people be indexed by

i

.

x

is

the variable of interest (for calculating averages).

n

denotes a sample size.

w

_pc

is the

share of CZ

c

population that is in PUMA

p

. The CZ

c

average of

x

is calculated as:

xc =

∑

_pwpc

(

n−1p

∑

_ixip

)

.

Assume that Var(

x

_ip

) =

σ

2

_{for each}

_i

_,

_p

_{. The asymptotic variance of the CZ mean}

esti-mate is:

Var(xc)= Var w_pc p

∑

n_p−1 x_ip i

∑

(

)

(

)

= w_pc2_n

p

−2

p

∑

Var(x_ip)

i

∑

= w_pc2_n

p

−1_␴2

p

∑

.

So the standard error of the estimate is

SE(xc)=␴ ∑_pw_pc2n−1_p

.

Suppose I knew each person

i

’s CZ. Then, the calculation would be much simpler.

The asymptotic variance of the CZ mean would be

Var(xc)= Var(n_c−1∑_ix_ic)= n_c−1␴2

and the standard error of the estimate would be

SE(xc)= ␴ nc−1

. This hypothetical

simple standard error is the true

σ

multiplied by

n_c−1

. What I actually see in the data

with PUMAs—but not CZs—identi

_fi

ed is the true

σ

multiplied by

∑_pw_pc2_n

p

−1

_{. So the}

intuition we have for sample size

n

_c

in the simple case should apply correspondingly

to the following expression:

nk_c= w2_pcn

p

−1

p

∑

(

)

−1

.

This is the sample size measure that I calculate for each CZ to describe the “sample

size” used in calculating CZ means and to gauge how much con

_fi

dence to have in them.

This sample size measure

nk_c

is the same as the CZ

c

sample size when it can be

observed. If there is a single PUMA

p

= 1 with the same boundaries as CZ

c

, then

w

_1c

= 1 and

nk_c =n_p

. More generally,

nk_c

is the same as the obvious sample size measure

when a CZ consists of multiple PUMAs that are each entirely contained in the CZ. Let

the total sample size across those PUMAs be

N ≡ ∑_pn_p

. Assume that

w_pc= n_p/ N

for each

p

, which is a suf

_fi

cient condition for the PUMAs under consideration to be

wholly contained in the CZ (the share of each PUMA’s population in the CZ’s

popula-tion is the same as the share of that PUMA’s populapopula-tion in the set of PUMAs). With

this assumption, the sample size measure becomes

n_c

k = np

N

⎛ ⎝⎜ ⎞⎠⎟

p

∑

2n_p−1

⎛ ⎝⎜

⎞ ⎠⎟ −1

= np

N2 p

∑

⎛ ⎝⎜ ⎞⎠⎟ −1 = N N2 ⎛ ⎝⎜ ⎞⎠⎟ −1

= N

.

For example, suppose CZ

c

consists of three PUMAs with

n

₁

= 200,

n

₂

= 400, and

n

₃

=

400. Also, none of the PUMAs includes any population outside of CZ

c

. If the

indi-vidual PUMA sample sizes are proportional to their shares in the CZ population, then

the assumption that

w_pc= n_p/ N

for each

p

is met. In this example,

nk_c

= 1,000, which

is just the sum of sample sizes across contributing PUMAs. In the simple case where

The Journal of Human Resources

70

PUMAs do not cross CZ boundaries, this is the obvious way to assess the CZ sample

size. The added value of

n

c

k

is that it provides a sample size measure for other cases,

where PUMAs cross CZ boundaries.

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