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
= ␣⌬I
c
+ X
i
+ ⌫W
c
+ ⌳Z
s
+ e
i,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 infl 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 infl 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 specifi 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 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 Z
s
. 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 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.
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 location- 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. 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 than c 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 identifi 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 differences 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
Kategori