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
n
c
k ≡ ∑
p
w
pc 2
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 justifi cations for using
n
c
k , including the fact that
n
c
k 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 n
c
k 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 raceethnicity 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 education. The local education distribution 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
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.
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 48
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
12th grade test score percentile 11,070
54 6,840
53.5 4,230
54.7 1.11
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 raceethnicity 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