My fi ndings about immigration and job tasks are related to previous research show- ing that manufacturing 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 12
th
grade for most of them and at the fi nal followup survey when most were 26 years old. The
estimated effects are similar across immigration timing measures, which is consistent with the strong links between early and later locations the majority of respondents
stay where they are. The estimated effects of immigration measured at later times are relatively weakest in explaining respondents’ secondary schooling efforts. Estimated
effects of immigration measured at later times are relatively strong in explaining
Table 7 Alternative Specifi cations for Immigration and Natives’ Human Capital Investment
1 2
3 4
5 6
State- Level By Origin
Country Immigration
Matched to Last Secondary
School Immigration
Matched to Final
Followup High- Skilled
Immigration High School
High- Skilled Immigration
Bachelor’s Degree Holders
School attendance composite 0.0082
0.0167 0.0088
0.0025 –0.073
–0.029 0.0044
0.0056 0.0035
0.0025 0.0701
0.0246 Took Advanced Placement class
0.1565 0.1736
0.1358 0.1304
–0.0477 0.1973
0.0375 0.057
0.0322 0.0303
0.295 0.1933
Took vocational class –0.1965
–0.1788 –0.157
–0.1281 0.5657
0.2238 0.0612
0.0615 0.0356
0.0271 0.669
0.2819 Grades composite
0.0297 0.0407
0.0274 0.0231
–0.1818 –0.0712
0.0088 0.0104
0.0057 0.005
0.1607 0.056
High school diploma receipt 0.0426
0.0467 0.0489
0.0656 –0.1142
–0.0112 0.0255
0.0368 0.0261
0.0206 0.1926
0.0936 Ever attended postsecondary education
0.1749 0.1297
0.1543 0.161
–0.4806 –0.0115
0.0267 0.0531
0.0331 0.0282
0.4174 0.1238
Postsecondary education credential 0.0975
0.0573 0.1118
0.1087 –0.4289
–0.0309 0.0393
0.0596 0.0381
0.035 0.3956
0.122 Use email
0.0617 0.0881
0.0842 0.1581
0.9848 0.1601
0.0337 0.0463
0.0301 0.0429
2.265 0.1981
Measure size or weight of objects –0.0567
–0.0631 –0.0726
–0.0945 1.52
0.2607 0.0355
0.0439 0.0293
0.0347 2.942
0.2318
Notes: p 0.01 p 0.05 p 0.1. Data from the NELS:88. Each row corresponds to a dependent variable. Table only shows the coeffi cient on 1990 immigration from OLS or 2SLS specifi cations. All models also include a constant, indicators for gender and race ethnicity, 1990 eighth grade CZ characteristics percent adult population with a bachelor’s degree,
percent population without a high school diploma, and indicators for urbanicity fi ve of them and region three of them, and the full set of eighth grade school characteristics listed in the text. Standard errors clustered at eighth grade CZ level.
behavior that takes place later in life, such as schooling attainment and job tasks. This is consistent with contemporary immigration being more salient in making decisions
than earlier experiences with immigration. A potential concern with my interpretation of results is that immigrants tend to
settle in places with relatively high education, and the instrumental variables strategy might fail to solve the problem. In such a case, it might be true that any immigra-
tion high- skilled or low- skilled is associated with higher human capital investment of natives. However, Columns 5 and 6 of Table 7 show that the infl uence of high-
skilled immigration on human capital investment of natives is very different from the estimated effect of low- skilled immigration. Column 5 defi nes high- skilled as
having more than a high school degree, and Column 6 defi nes high- skilled as having a bachelor’s degree. Point estimates for high- skilled immigration tend to be negative,
although confi dence intervals are very large in these specifi cations. The instruments for high- skilled immigration are quite weak, which means that this robustness check is
not a high- power test. Nevertheless, as far as I can tell, the observed positive effect of immigration on natives’ human capital is specifi c to low- skilled immigrants, which is
consistent with my interpretation that natives increase their skills to avoid competing with an abundant local supply of low- skilled labor.
IX. Bias in Substitution Elasticity Estimates
