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
McHenryabstract
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 infl 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 infl uence the number of immigrants in the country.
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 infl ow of low- skilled immigrants tends to reduce the schooling resources available for natives—for example, by shifting teachers toward English- profi 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 benefi t of immigration that is largely overlooked in the research literature about costs and benefi 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.
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 infl 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 benefi 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 specifi 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 specifi c school resources and the student body. The NELS:88’s focus on education translates into more accurate measures of diploma receipt and specifi c certifi 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. Specifi 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 infl 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 (Hi,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 infl uenced by the immigration fl ow to i’s origin c (say, city), which is measured by∆Ic. Individual characteristics like sex and race infl uence school decisions and might vary across locations, so I con-trol for them in Xi (which also includes a constant term). In some specifi cations, Xi
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 Wc includes characteristics of the individual’s origin: region, population size, and metropolitan status. To control for lo-cal features that infl 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 specifi 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 specifi cations for school- and district- level measures of educational resources (Zs). One control is the percent of classmates in the respondent’s school who have limited profi 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 Zs 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
McHenry 39
outcome variables in my data pertain to a single cohort. However, the control variables in Xi, Wc, and Zs 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- specifi 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 specifi 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 identifi cation strategy comes from Altonji and Card (1991) and is employed frequently in the economics literature.
The specifi 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. Io,c,t is the
The instrument identifi es variation in immigration fl ows across locations using nation-wide trends in immigration by origin (Io,–c,1980 and Io,–c,1990) and the origins of local immigrants in the previous period (Io,c,1980/Ic,1980 and Io,c,1970/Ic,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
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).
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 identifi 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 coeffi 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 identifi 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
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 certifi 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-cifi 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 (Xi) 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.
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 infl 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 specifi cations, I control for characteristics of the NELS:88 respondent’s eighth grade school (Zs). School administrators of NELS:88 schools were asked how many students in their school’s eighth grade cohort had limited English profi 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 profi 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.
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 infl 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. Ic,t is the number of (low- skilled) immigrants in CZ c in Census year t. The specifi 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 defi 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.
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
nc
k ≡ (⌺pwpc2np−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 nkc ≡(∑pwpc2n
p
−1)−1, where c indexes CZs, w
pc is the share of CZ c population that is in PUMA p, and np is the PUMA p sample size. Appendix 3 derives and provides justifi cations for using nkc, including the fact that nkc
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 nkc 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 (Ic,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 (∆Ic,1990) they experienced. In addition to the in-strument Ic,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) specifi cations below include mother’s educa-tion or school quality measures, so Columns 2 and 3 include these variables. In all three specifi 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 confi dentiality). Sample sizes
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
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***
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 infl 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 specifi 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 refl ect other features of CZs that are incidentally correlated with low- skilled immigration. I control for such potential confounding factors in specifi 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
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 specifi cations and dependent variables (Equa-tion 1). Each cell in the table reports the coeffi 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 specifi cations. The fi rst four columns refl 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 coeffi cient (0.0025) refl ects a baseline OLS specifi cation that controls for local immigration, individual sex and race/ethnic-ity, and eighth grade CZ controls, while the second column’s coeffi cient (0.0281) is from the analogous 2SLS specifi cation that instruments for immigration. These
coef-fi cients are not statistically signifi 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 coeffi 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 specifi 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 specifi 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.
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)
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 confi 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 specifi 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 signifi cant, using alternative controls for the subsample of students with less- educated parents (Columns 5–7). A coeffi 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 specifi cations are analogous to those in Table 4. Overall, low- skilled immigration increases the educational attainment of natives.
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)
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 coeffi cient on immigration though it remains positive. The specifi 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 specifi cations from the fi rst row using a new dependent variable: an indicator for the respondent attending any postsecond-ary schooling. All specifi 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 signifi 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 certifi 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
McHenry 57
effects on attendance only, which is not surprising if immigration induces marginal college- goers to stay in school after getting the high school diploma. It is signifi cant that immigration induces local natives to complete postsecondary educational pro-grams, which yield high returns in the labor market.
The larger effect on postsecondary attendance than on high school completion im-plies that the effect of immigration is strongest on high school graduates on the margin of attending postsecondary education. These are not all four- year college degree pro-grams. Immigration appears to induce native high school graduates to start (and—to a lesser extent—complete) license and certifi cate programs, perhaps to differentiate their labor skills from those of immigrants.
VII. Results About Immigration and Natives’ Early-
Career Jobs
This section describes another way that native- born residents change their human capital investments in response to low- skilled immigration: augmenting their comparative advantage in communication skills. My fi ndings agree with Peri and Sparber’s (2009): Native- born workers in the midst of less- skilled immigration tend to supply relatively more communication tasks. Table 6 assesses the relationship between early immigration exposure (in eighth grade) and job characteristics in the early careers of native workers (at age 26). The dependent variables in the table are indicators for “a lot” being the response to questions about how much the respondent performs each set of tasks at work. Table 6 includes OLS and 2SLS specifi cations that control for respondents’ sex and race/ethnicity and characteristics of the CZ where they live (and perform their work tasks) in 2000. The coeffi cients on immigration exposure in regressions predicting reading and writing tasks (the fi rst two rows of results in Table 6) are uniformly positive. There is not much statistical precision, but the results are consistent with low- skilled immigration inducing natives to invest more in communication skills and use them at work.
The next panel of Table 6 is labeled “Computer and communication tasks.” There is not a consistent effect of immigration on computer use but natives originating in CZs with more immigration tend to use word processing, email, and the Internet more fre-quently. Effects are somewhat noisy for the subsample of natives with less- educated parents (Columns 5–7) but the coeffi cients are somewhat large and statistically signifi -cant for Internet use. The last row of the table investigates the effect of immigration exposure on manual tasks at work. The effect is negative in all specifi cations and both samples. Overall, the results are consistent with Peri and Sparber’s (2009) hypothesis that natives respond to low- skilled immigration by augmenting communication skills at the expense of manual skills.24
Table 6
Immigration and Characteristics of Natives’ Jobs in 2000
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
Communication tasks (general)
Read letters, memos, or reports 0.0308* 0.0235 0.0199 0.0321 0.0461 0.0459 0.0633
(0.0165) (0.0315) (0.0311) (0.0317) (0.0485) (0.0484) (0.0471)
Write letters, memos, or reports 0.0076 0.0224 0.0179 0.0405 0.0623 0.0613 0.0858*
(0.0144) (0.0264) (0.0258) (0.025) (0.0451) (0.0449) (0.0439)
Computer and communication tasks
Use a computer 0.0143 0.0263 0.0161 0.0407 –0.0048 –0.0091 0.0255
(0.0158) (0.0306) (0.0276) (0.0297) (0.0391) (0.0383) (0.0378)
Use word processing 0.0214 0.0777** 0.0677* 0.0994*** 0.0593 0.0576 0.0856*
(0.0198) (0.0371) (0.035) (0.0346) (0.0492) (0.0491) (0.0489)
Use email 0.0218 0.0681** 0.0576* 0.086*** 0.0783 0.0767 0.1068**
(0.0186) (0.0327) (0.0308) (0.0311) (0.0488) (0.0487) (0.0482)
Use Internet 0.0127 0.0813** 0.0713** 0.1024*** 0.1058** 0.1042** 0.1299***
(0.0181) (0.0359) (0.0333) (0.034) (0.0465) (0.0461) (0.0475)
Manual tasks
Measure size or weight of objects –0.0151 –0.0559* –0.0495* –0.0684** –0.0531 –0.0518 –0.0703 (0.0151) (0.0289) (0.0277) (0.0286) (0.0418) (0.0416) (0.0431)
McHenry 59
My fi ndings about immigration and job tasks are related to previous research show-ing that manufacturshow-ing fi rms in cities experiencing large low- skilled immigration waves are less likely to invest in automation machinery (Lewis 2011), and fi rms near higher skill supplies are more likely to adopt personal computers (Beaudry, Doms, and Lewis 2010). In light of this previous research about business fi rms, it would appear that workers overall are less likely to use computers in cities with many low- skilled immigrants, but I fi nd that native- born workers experiencing low- skilled immigration waves early in life use computers more frequently at work later on. The increased early- career computer use I observe is probably due more to individual natives’ human capital investment and occupational choices than to local fi rms’ production decisions. Both workers and fi rms are choosing skill and task mixes in production. I have focused on choices of workers to invest in particular tasks, and I believe this is appropriate given that the behavior I observe is in response to immigration waves early in life. However, the effects I estimate are probably also partially due to changes in fi rms’ productive processes.
VIII. Robustness of the Empirical Results
The results described above are robust to several changes in specifi ca-tion. As already mentioned, local immigration has a positive effect on natives’ human capital investments when controlling for mother’s education or a variety of school quality measures. Estimated effects are also positive when I use the share of less- educated immigrants in the local population to measure immigration instead of growth in the less- educated immigrant population (results available upon request).
Table 7 shows results from several alternative specifi cations. Each row represents a separate dependent variable used in prior tables. While prior results measure immigra-tion at the commuting zone (CZ) level, Column 1 of Table 7 uses the state level. Re-sults are similar to those above: More low- skilled immigration is associated with more human capital investments, educational attainment, and email use on the job. Column 2 shows results using an alternative instrumental variable. I categorized immigrants in the Census samples based on their countries of origin and placed them into 16 groups. For each group, I calculated the share of immigrants with a high school degree or less education. I selected four country groups with consistently high shares of less- educated immigrants: Central America, Southern Europe, Caribbean, and Oceania. The alternative immigration measure is based on the number of all immigrants from those four country groups coming to the local area. Results in Column 2 of Table 7 show that immigration measured in this way is associated with increases in human capital investment decisions.
Columns 3 and 4 of Table 7 show results when the local immigration is matched to NELS:88 respondents where they are last observed in secondary school (12th grade
