First Stage Results: Explaining Local Immigration Flows with Prior Immigration Summary Statistics from the NELS:88

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