Immigration and wages: new evidence from the African American Great Migration

2.1 Data and summary statistics

The analysis in this section uses data drawn from Integrated Public Use Microdata Series (IPUMS) extracts of the 1940 and 1950 US Censuses (Ruggles et al. 2015). These data contain detailed information on wage income and residential location, as well as a host of demographic and economic variables. To my knowledge, they are the earliest data that contain rich wage and geographic information for the Great Migration period. I restrict the sample to US-born black and white men of working age and living in the North at the time of enumeration. I use the Eichenlaub et al. (2010) definition of the North and obtain similar results when the foreign born are included in the sample. I define local labor markets as Metropolitan Statistical Areas (MSAs). Further details regarding the construction of the sample and the variables used in the empirical analysis are available in Appendix 1.

Figure 1 plots the immigrant share of the black labor force against the share of the white labor force for Northern metropolitan areas. As the figure shows, there was substantial overlap between the destinations of black and white immigrants from the South. Table 2 compares the education distribution for natives and immigrants to the North. Southern immigrants are slightly overrepresented at lower levels of education and slightly underrepresented at higher levels, particularly among blacks. However, the overall distributions are similar for both races and all years.⁴ For this reason, I focus my analysis on race-specific average, rather than within-skill-group, effects.

		Black		White
Year	Education group	Northern	Southern	Northern	Southern
1940	Less than 5th grade	0.10	0.16	0.02	0.05
	5th–8th grade	0.48	0.56	0.39	0.44
	Some high school	0.23	0.15	0.23	0.21
	High school degree	0.12	0.08	0.22	0.17
	Greater than high school	0.07	0.05	0.13	0.13
	Total	1.00	1.00	1.00	1.00
1950	Less than 5th grade	0.05	0.12	0.02	0.04
	5th–8th grade	0.34	0.45	0.27	0.35
	Some high school	0.28	0.24	0.24	0.24
	High school degree	0.21	0.13	0.29	0.21
	Greater than high school	0.11	0.05	0.18	0.15
	Total	1.00	1.00	1.00	1.00
1960	Less than 5th grade	0.03	0.07	0.01	0.04
	5th–8th grade	0.25	0.36	0.18	0.28
	Some high school	0.33	0.28	0.25	0.25
	High school degree	0.27	0.19	0.32	0.25
	Greater than high school	0.13	0.09	0.24	0.19
	Total	1.00	1.00	1.00	1.00

Notes: Standard errors for the estimated proportions, each of which is less than.01, suppressed

2.2 Empirical strategy

To apply the local labor markets approach to the Great Migration, I estimate a series of linear models that relate individuals’ log wages to the immigrant share of the labor force in the metropolitan area where they live. A threat to the validity of this approach is that Southern immigrants were not randomly assigned to labor markets in the North. It is possible, for example, that immigrants selected into areas where wages were higher, perhaps due to productivity or demand shocks, in which case my estimates of the wage impacts of immigration would be attenuated. The standard solution to this problem, first used by Altonji and Card (1991), is to predict immigration according to historical immigrant settlement patterns, then use these predictions to instrument for observed immigration. This strategy draws on work by Bartel (1989), who shows that successive waves of immigrants tend to move to the same areas, a phenomenon which has also been documented among the Great Migrants (Tolnay 2003). Altonji and Card (1991) argue that this enclaving behavior provides a source of variation in immigration that is uncorrelated with the contemporary economic conditions of local labor markets.⁵

To exploit this variation, I predict the immigrant fraction p _jt of the labor force in area j and decade t using \(\hat {p}_{jt}=\mu _{j20}M_{t}/N_{jt}\), where μ _j20 is the fraction of all Southern immigrants who resided in area j in 1920, M _t is the total number of Southerners living in the North in decade t, and N _jt is the total population of area j in decade t.⁶ I then use the predicted immigrant shares \(\hat {p}_{jt}\) as instruments for the observed shares p _jt . In some of the specifications that I estimate, I replace the overall immigrant fraction of the labor force with the fractions comprised of black and, separately, white Southerners, in which case the instruments are constructed by replacing the components of the \(\hat {p}_{jt}\) with their race-specific counterparts. Figure 2 plots, separately by decade and race, the immigrant share of the labor force against predictions of that share based on 1920 settlement patterns. The figure shows that immigration predicted from historical settlement patterns is strongly correlated with actual immigration. The formal first-stage regressions, presented in Appendix 3: Table 19, support this conclusion, showing that predicted immigration has substantial power to explain the overall and race-specific immigrant shares of Northern labor markets in both the black and white regression samples.

Although the 1920 settlement patterns of Southern immigrants are necessarily uncorrelated with economic conditions that were idiosyncratic to the 1930s and 1940s in the North, instrumental variable estimates based on predicted migration patterns may still be inconsistent if metro-average wages are serially correlated and immigrants were attracted to areas that paid high wages in 1920. I take several steps to account for this possibility. To control for the local determinants of wages, I estimate specifications that include a full set of indicators for age and education, as well as the metro-level shares of the black and white populations employed in manufacturing, the shares living on farms, and metro-average education among blacks and whites. In addition, for the 1950 regression samples, I am able to include metro-average wages in 1940 as a covariate (this is not possible for the 1940 samples since the Census did not ask wage questions prior to 1940). If local wages follow a lagged dependent variable structure, including lagged average wages completely accounts for any residual correlation between wages and 1920 settlement patterns. In Appendix 1, I show that including these lags can also mitigate the inconsistency that would arise if wages follow a fixed-effects, rather than a lagged dependent variable, structure, although I also present some evidence that a lagged model fits the data better. Lagged average wages may also proxy for time-varying unobserved heterogeneity and control for any direct and lasting effect that 1920 immigration had on local wages. The models that include lagged metro-average wages should be interpreted as identifying a combination of the short- to medium-run effects of immigrant inflows and lasting effects of existing stocks of immigrants.⁷ Because each specification models wages as a function of the overall immigrant share of the local labor force, they identify the total effect of Southern immigration on each racial group’s wages.⁸

2.3 Wage impact results

Note that, while the wage variable used in these regressions varies at the individual level, the immigrant share varies only across metropolitan areas.⁹ While the clustering of blacks into a small number of metropolitan areas in the North is advantageous from the standpoint of concern about spatial arbitrage, it implies that the wage effects for blacks are identified from limited variation in local immigrant shares. As a consequence, the black regression results, particularly those for the 1950 samples in which only a subset of individuals were asked wage information, should be interpreted with some caution. However, the reported standard errors are clustered at the metropolitan area, and the estimates are very similar to those obtained by regressing metro-average residual (from regressions of log annual wages on individual covariates) wages on metro-level immigrant shares and covariates (these regressions are presented in Appendix 3: Table 17).¹⁰ Below, I also show that the individual-level regressions are robust to a number of tests designed to assess their sensitivity to outliers and the use of different estimation samples.

For native whites, the evidence is somewhat mixed. Both the OLS and IV estimates suggest a weak positive effect of black immigration on the wages of Northern-born whites, although this effect disappears in the IV estimate of specification (4), which includes the most covariates. At the same time, there is some evidence of a negative own-race effect for whites, with the coefficients on the white immigrant share of the labor force uniformly negative. However, these coefficients are never statistically significant when metro-level covariates are included. The estimates for whites also suggest that the apparent negative effect of immigration on the wages of native blacks cannot be explained by selection into low-wage areas: the coefficients on the black immigrant share variables are all positive, consistent with a either positive cross-race effects of immigration or selection into high-wage areas, but not selection into low-wage areas.

I also estimate models of the impact of immigration on weekly wages (annual wages divided by weeks worked). The overall and race-specific estimates, presented in Appendix 3: Tables 20 and 21, are similar to the annual wage results: Southern immigration put substantial downward pressure on native blacks’ wages, an effect driven by competition from black immigrants, with no discernible impact on whites’ wages. The key difference between the annual and weekly estimates is that the magnitudes of the effects of immigration on blacks’ weekly wages are much smaller. In the 1950 sample, for example, the coefficient on the (overall) proportion Southern for specification (4), which includes the full set of covariates, is about −1.3, compared to about −2.3 for annual wages. This difference, which implies that adjustments along the intensive margin of labor supply were an important part of the response of local labor markets to Southern immigration, helps explain the magnitude of the estimated annual wage elasticities. The prominence of adjustments along this margin also raises the possibility that both the annual and weekly wage estimates understate the true impact of Southern immigration, which may have caused some natives to exit the labor force, and consequently, my estimation samples.

These wage impact estimates suggest that black and white labor were, effectively, imperfect substitutes during the sample period. Under perfect substitution, the effects of overall, own-, and cross-race immigration would be the same for each racial group. Instead, there is a large black-white disparity in the estimated impact of overall immigration, a robustly negative effect of own-race immigration on native blacks’ wages, and only tenuous, specification-sensitive evidence of an own-race effect for native whites or cross-race effects for either group. This pattern cannot be explained by outmigration; under perfect substitution, these effects would be symmetric regardless of whether outmigration attenuated relative local labor supply shocks. Nor can it be explained by measurement error in the construction of local immigrant shares, which Aydemir and Borjas (2011) show can attenuate local labor markets estimates. The estimated effect of black immigration on native blacks’ wages is substantially larger than the cross-race effect even though the black samples are smaller than the white ones and therefore likely to be more error-ridden. This implies that, to the extent that my estimates are attenuated by measurement error, the underlying own-race effect for blacks must be larger than the cross-race effect.

Because Southern immigrants were disproportionately black relative to the native labor force, segmentation between the markets for black and white labor in the North would have concentrated the effects of immigration on blacks. A simple measure of the separation between these markets is the correlation between black and white employment shares within industry-occupation cells throughout the North. This correlation, which does not reflect segregation at lower levels of aggregation, was.65 in 1940 and.41 in 1950. In the next section, I provide direct evidence of imperfect substitution: treating the entire North as an aggregate labor market, I estimate a finite elasticity of substitution between blacks and whites. The wage regressions, employment share correlations and structural elasticity estimates imply that black and white labor were utilized as though they were imperfectly substitutable, though this effective imperfect substitution may not have been due to actual productivity differences. Discrimination against blacks, for example, may have caused occupational segregation that prevented the substitution of black for white labor. Racial differences in educational attainment, and educational quality conditional on attainment, may have also rendered blacks and whites imperfect substitutes. Boustan (2009) finds that controlling for measures of educational quality can explain two thirds of the apparent imperfect substitution.

2.4 Native outmigration results

As discussed in the introduction, if natives move to different labor markets in order to avoid competition from immigrants, immigration-induced labor supply shocks will be distributed across many labor markets, as will the effect of these shocks on wages. As a consequence, estimates of the impact of immigration on wages based on comparisons between local labor markets will be attenuated. Note that the possibility of spatial arbitrage among native blacks during the Great Migration is less troubling since the wage regressions reveal an appreciable effect of immigration. At worst, these regressions identify an upper bound on the underlying impact of Southern immigration on blacks’ wages. On the other hand, these regressions do not suggest an own-race effect for whites or any cross-race effects. It is possible that outmigration arbitraged away local differences in these effects, even if they were present at an aggregate level in the North.

To test for outmigration, I model the propensity of natives to change locales in response to Southern immigration. Both the 1940 and 1950 Censuses provide information on individuals’ recent migration behavior. The 1940 data give the metropolitan area of residence 5 years prior to enumeration while the 1950 data give the area of residence 1 year prior. For each Census decade and racial group, I regress an indicator for leaving one’s pre-Census metropolitan area on the recent immigrant share of the local labor force (the number of Southerners living in the metropolitan area at the time of the Census but not in the pre-Census period as a fraction of the area’s pre-Census population). Specification (1) includes age and education indicators as covariates; specification (2) includes the contemporaneous metro-average covariates used in the wage regressions and described above.¹¹

Since some of the outmigration that took place from 1935 to 1940 and 1949 to 1950 was likely in response to competition from immigrants who arrived before these periods began, the estimates in Table 5 may overstate native whites’ outmigration response.¹² This effect may be less pronounced in the 1940 samples since a longer period of observation is more likely to capture natives’ response to recent immigration. For example, the specification (2) IV estimate of the white outmigration response to white immigration is 2.13 in the 1950 sample, while the 1940 estimate of.77 is more reasonable, though it still implies a considerable outmigration response.

In summary, there is no evidence of black outmigration, but strong evidence of a native white response to Southern white immigration. My local labor markets estimates of the impact of Southern immigration on native blacks’ wages are therefore unattenuated by spatial arbitrage. Although my estimates do not suggest that outmigration among native whites completely offset incoming Southern whites, the possibility remains that spatial arbitrage obscures an own-race effect for whites or cross-race effects for both groups (e.g., if inflows of whites combined with black-white imperfect substitution put upward pressure on blacks’ wages). Since immigration only increased the supply of white labor in the North modestly, the underlying wage impacts may have been small and difficult to detect even absent outmigration, making the influence of spatial arbitrage on my estimates for whites unclear. However, the national labor market analysis below results in similarly small cross-race and white own-race effect estimates, suggesting that spatial arbitrage does not explain why the white wage regression coefficients are small.¹³

2.5 Robustness and sensitivity tests

Although 1920 settlement patterns are necessarily uncorrelated with the conditions of local labor markets in the North that were idiosyncratic to 1940 and 1950, if 1920 immigrants systematically selected into Northern labor markets with above- or below-average wages and the factors that determine local average wages are serially correlated, immigrant shares predicted from these settlement patterns may be spuriously correlated with wages in future periods. In the wage regressions above, I account for this possibility by estimating models that include lagged metro-average wages. The evidence presented so far is consistent with selection into high wage areas: including covariates or instrumenting for immigration decreases the estimated wage effects, and the coefficients on the proportion Southern black in the native white regression samples are positive. These patterns also suggest that any remaining selection leads my wage regressions to understate the effects of immigration, in which case they can be interpreted as upper bounds on the underlying effects.

Both the fixed-effects and first-differenced estimates suggest much larger wage effects for blacks than the decade-specific wage regressions discussed previously, although given their standard errors they are also consistent with the main regression results presented above. One explanation for this difference is that, because these models use the variation in changes in immigrant shares (rather than levels, as the decade-specific models use), they identify the effects of recent immigration, which may be larger if labor markets adapt to immigration over time, for example through endogenous adjustments to capital stock or industrial composition. However, the magnitudes of the point estimates may also indicate that the fixed-effects and first-differenced models are misspecified. As I show in Appendix 1, if wages follow a lagged dependent variable structure, estimates of a misspecified model that assumes a first-differenced/fixed-effects structure will be biased down. The intuition for this result is that if the initial wave of immigrants is attracted to high wage areas, future waves are attracted to areas with established enclaves, and wages are mean reverting, areas with declining wages will receive more immigrants, inducing a spurious correlation between changes in wages and changes in immigration. This possibility makes the lagged dependent variable estimates, which can be interpreted conservatively as upper bounds, preferable.

During the period that I study, blacks were clustered into a small number of metropolitan areas in the North. For example, since the 1950 Census only asked wage questions of a subset of households, my sample for that year is relatively small, containing only 22 metropolitan areas with at least ten native blacks meeting the other selection criteria. This clustering is advantageous from an identification standpoint since it eliminates concerns about spatial arbitrage among blacks working in the North. However, since it also implies that the effects of Southern immigration on blacks’ wages are identified from variation across a limited number of labor markets, it also raises the concern that the estimated effects are artifacts of a small number of outlying metropolitan areas. Figure 3, which plots metro-average residual wages against residual immigration for blacks and whites in 1950, presents graphical evidence that this is not the case.¹⁵ These plots show that neither group’s estimated effect is driven by outliers.

I also estimate the annual wage regressions using two additional samples in order to further assess the sensitivity of my results to the number of metropolitan areas included in the regression samples (in the interest of brevity, I do not present these results, though they are available upon request). The first sample includes both US- and foreign-born men. Including the foreign born slightly increases the number of metropolitan areas in the black regression samples (to 30 in 1940 and 23 in 1950), and the overall pattern of the estimates is similar. The point estimates on the proportion Southern are slightly smaller, possibly because foreign- born blacks are imperfect substitute for US-born blacks, and therefore less affected by competition from Southern immigration. The second sample relaxes the exclusion of metropolitan areas with fewer than ten native born blacks or whites. When this restriction is relaxed, the black wage regressions include observations from 87 metropolitan areas in 1940 and 72 areas in 1950. Because the additional metropolitan areas contain few native blacks, the results are nearly identical to those presented in Table 3.

3.1 Data

My national labor market results are also based on extracts of the US Census provided by IPUMS (Ruggles et al. 2015). To make the national and local estimates comparable, I use the same sample restrictions for each approach (US-born black and white men living in the North, aged 16–64, with nonzero wages in the year before enumeration). I add the 1960 Census to the estimation sample in order to generate time-series variation in wages and labor supply, which the national approach requires to estimate the elasticities.¹⁶ In order to compare my results to those in Boustan (2009), who studies the 1940–1970 period, I also use samples that include the 1970 Census. However, my focus is on comparing my national and local estimates, for which the shorter sample is more appropriate.

Table 2 shows that average education was low during the period that I study, making the schemes used by studies of contemporary immigration to assign individuals to education groups inappropriate. Instead, I divide the sample into five education groups: less than fifth grade, fifth-to-eighth grade, some high school, high school, and greater than high school. I impute labor market experience as age minus six minus the number of years of schooling an individual in the midpoint of the education group would have received and assign individuals to one of eight 5-year experience groups. See Appendix 2 for further detail.

The most disaggregated labor nest is the education-experience-race-birthplace group. In this nest, I measure labor supply as the number of individuals in the group and wages as group-average annual wages (note that this allows me to capture adjustments to the intensive margin of labor supply). Labor supplies and wages for higher nests are calculated as implied by the nested-CES structure (see Appendix 2).

3.2 Elasticity estimates

where \(\bar {w}_{exrit}\) is the average wage paid to members of an education-experience-race-birthplace group, \(\hat {L}_{exrit}\) is the (calculated) size of the group, λ _exr is an education-experience-race-group fixed effect, λ _ert is a decade-specific education-race-group fixed effect, and λ _xrt is a decade-specific experience-race-group fixed effect. The fixed effects are included to absorb as much variation as possible in the relative productivity terms. Note that in these models, only within-education-experience-race group variation over time in relative labor supply can be endogenous—this would require that, for example, only in some year, black immigrants with a high school degree and 5 years of experience supplied more labor than their native counterparts, or more immigrant than native blacks with 5 years of experience completed high school, in response to a productivity shock that favored immigrants, but black immigrants with different skills and otherwise-similar white immigrants did not.¹⁷

where \(\bar {w}_{exrt}\) and \(\hat {L}_{exrt}\) are education-experience-race group wages and labor supplies. Based on the evidence that immigrants and natives are perfect substitutes, I construct the \(\hat {L}_{exrt}\) as the sum of immigrant and native labor supplies within education-experience groups in each decade.¹⁸ To account for potentially endogenous black-white relative labor supply within education-experience groups, I use two instrumental variable strategies, both based on the premise that Southern immigration shifts relative supply curves. The first, following Borjas (2003) and Ottaviano and Peri (2012), is the immigrant component of black-white relative labor supply within education-experience groups. The second, following Boustan (2009), is the ratio of blacks to whites among the national stock of Southerners within education-experience groups (i.e., among all Southerners, regardless of whether they have migrated North). The immigrant component instrument is appropriate if within-group labor supply is endogenous with respect to productivity shocks that affect the North and South alike, since these shocks would not have attracted immigrants. The national stock instrument is more appropriate for productivity shocks local to the North, which would be unlikely to elicit a labor-supply response among many Southerners. Although my immigrant-native elasticity estimates imply that skill-group-average wages should be calculated using all workers, I also estimate (3) using only natives’ wage to allow for the possibility that immigrant-biased productivity shocks affected black-white relative labor supply.

As I note in the introduction, although occupational segregation arising from discrimination may have contributed to effective imperfect substitution between blacks and whites, analysis of the Great Migration can still provide externally valid insight into the effects of an influx of immigrants with a different skill mix than the native population. Discrimination may also create the appearance of imperfect substitution if, as in Becker (1971), heterogeneity in employers’ tastes for discrimination meant that blacks were paid less in labor markets or skill groups containing more blacks. In this case, the Great Migration would only be informative about contemporary flows of immigrants who belong to racial, ethnic, or other minorities. The race-specific elasticity estimates in Table 14 are inconsistent with this hypothesis. If the estimated elasticities were an artifact of heterogeneous tastes for discrimination, we would expect them to be driven primarily by variation in black labor supply and wages; we observe the opposite.

3.3 Wage impact results

where Δ L _exrs /L _exrs is the proportional change in immigration to group (e,x,r), s _exr is the share of the wage bill accruing to skill group (e,x,r), s _ex is the share of the wage bill accruing to the aggregate group with shared skills (e,x), and so on.²⁰ Expression (4) clarifies the channels through which immigration affects wages in a nested-CES economy: as long as substitutability increases with nest depth, immigrants depress wages for those who share their skills and increase wages for those who do not.²¹

I use (4) to compute the effects of Southern immigration from 1940–1960 and 1940–1970 on wages in the North. Skill-group-specific proportional changes in immigration are directly observable (I list them in Appendix 3: Table 22). To assess the sensitivity of wage-effect simulations to the racial elasticity of substitution, I use the values of σ _r implied by both the OLS and national-stock IV estimates of (4) obtained using the full wage sample. I use the IV estimates of σ _x and σ _e . While it was typical for a Northern worker to have less than a high school education in 1940 (see Table 2), the proportional changes in immigration were larger in more-educated skill groups, reflecting increasing educational attainment over time. Using 1940 wage-bill shares to compute (4) may therefore understate the effects of immigration over the 20- and 30-year periods that I analyze by underweighting skill groups that were initially small. A simple solution is to weight skill groups by their wage-bill shares in 1950, the midpoint of the shorter sample period, though I also report results obtained using 1940 shares. I weight skill-group-specific effects by wage-bill shares in order to present education-race, race, and overall average wage impacts (this weighting scheme is suggested by Ottaviano and Peri (2012)).

The average wage decline for blacks was driven by even larger declines among blacks with at least some high school. Less-educated black labor aggregates, which received few Southern immigrants, saw trivial changes in wages. Because immigrants comprised a small fraction of the white labor force, the proportional changes in white immigration were relatively small, and the white immigrant and native skill distributions were broadly similar, the effects of Southern immigration were small across all white labor aggregates. For the same reasons, the Great Migration would have had an equally negligible impact on blacks’ wages had black and white labor been perfectly substitutable.

6.1 Local labor markets appendix

7.1 National labor market appendix