- (1990). Since Census data are available at the decennial level, and because Congressional elections are held every two years, we focus on electoral returns for exact Census years from 1940 to 1970. In a number of instances, Congressional election results are not available at the county level. As described in the main text, our analysis is restricted to the 1,263 non-southern counties for which Congressional election data are available for all Census decades between 1940 and 1970 (and with at least one Black American in 1940). However, as documented in Appendix D, all results are unchanged when conducting the analysis with the unbalanced sample of counties. When considering Presidential elections, before taking the first di↵erence with the baseline election decade, we assign the 1948 (resp. 1968) elections to Census year 1950 (resp. 1970).52 51 See also the discussion conducted in Section 2.2 of the paper. 52 Results remain similar when using di↵erent timing conventions.
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- (2017). As for the commonly used DW Nominate scores (Poole and Rosenthal, 1985), legislators are assigned a score that is a function of their past voting behavior and takes more negative (resp. positive) values for more liberal (resp. conservative) positions. We rely on the Bateman et al. (2017) scores for two reasons. First, they are calculated by restricting attention solely to civil rights bills, as classified by Katznelson and Lapinski (2006). Second, they allow the policy content to be Congress specific and to vary over time. Bateman et al. (2017) develop two versions of the scores – one that assumes that the ideal points of legislators remain constant over time, and one that instead does not make such assumption. As shown in the paper, all results are robust to using either of the two versions.
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- (Do you think of any other problems important to you) 1960 and 1964 Against School Integration The government in Washington should stay out of the question of whether white and [Black] children go to the same school. 1960 and 1964 Against Work and Residential Integration If [Blacks] are not getting fair treatment in jobs and housing, the government should see to it that they do. 1960 and 1964 Vote Democratic 1 if voted/intend to vote for the Democratic Party in the last/upcoming Presidential Elections 19561964 Feeling Thermometer Towards [Group] There are many groups in America that try to get the government of the American people to see things more their way.
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- [Black] would you vote for him 1963 Approve Civil Rights Act As you know, a civil rights law was recently passed by Congress and signed by the President.
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- Abramitzky, R., L. Boustan, and M. Rashid (2020). Census Linking Project: Version 1.0 [dataset]. https://censuslinkingproject.org.
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- Adams, H. S. (1966). “The Dingell-Lesinski 1964 Primary Race”. The Western Political Quarterly 19(4), 688–696.
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Adao, R., M. Kolesár, and E. Morales (2019). “Shift-share Designs: Theory and Inference”. The Quarterly Journal of Economics 134(4), 1949–2010.
- Additional Robustness Checks: Congressional Elections (1) (2) (3) (4) (5) (6) (7) Panel A: Change in Democratic Vote Share Change Black Share 1.885*** 1.887*** 1.904*** 1.895*** 2.028*** 2.478*** 2.168*** (0.439) (0.385) (0.447) (0.442) (0.498) (0.529) (0.510) Panel B: Change in Turnout Change Black Share 0.756** 0.637** 0.795** 0.761** 0.675* 0.481 0.558* (0.348) (0.300) (0.356) (0.349) (0.390) (0.354) (0.330) Panel C: First Stage Predicted Change 0.758*** 0.834*** 0.747*** 0.755*** 0.771*** 0.774*** 0.710*** Black Share (0.233) (0.241) (0.232) (0.233) (0.264) (0.229) (0.233) F-Stat 10.57 11.97 10.41 10.51 8.512 11.45 9.260 Observations 3,789 3,789 3,129 3,549 3,712 3,446 3,789 Specification Baseline 1940 Dem Drop IV Equal Drop Black Trim Top 99 Trim Top 95 Southern White Vote Share to 0 Share Equal and Bottom and Bottom In-migration to 0 1 Pctile 5
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- Additional Robustness Checks: CORE Demonstrations Dependent Variable Change in 1[CORE Demonstration] (1) (2) (3) (4) (5) (6) (7) Panel A: 2SLS Change Black Share 0.057*** 0.056*** 0.057*** 0.057*** 0.062*** 0.041* 0.037** (0.018) (0.015) (0.018) (0.018) (0.020) (0.022) (0.018) Panel B: First stage Predicted Change 0.758*** 0.834*** 0.747*** 0.755*** 0.771*** 0.774*** 0.710*** Black Share (0.233) (0.241) (0.232) (0.233) (0.264) (0.229) (0.233) F-Stat 10.57 11.97 10.41 10.51 8.512 11.45 9.260 Observations 3,789 3,789 3,129 3,549 3,712 3,446 3,789 Specification Baseline 1940 Dem Drop IV Equal Drop Black Trim Top 99 Trim Top 95 Southern White Vote Share to 0 Share Equal and Bottom and Bottom In-migration to 0 1 Pctile 5
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- Alesina, A. F. and M. Tabellini (2020). “The Political E↵ects of Immigration: Culture or Economics?”. CEPR Discussion Paper No. DP15486.
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Alesina, A., R. Baqir, and C. Hoxby (2004). “Political Jurisdictions in Heterogeneous Communities”. Journal of political Economy 112(2), 348–396.
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- All regressions are weighed by 1940 county population, and include interactions between period dummies and: i) state dummies; ii) the 1940 Black share; and iii) a dummy equal to 1 if the Democratic vote share in 1940 was higher than the Republican vote share. F-stat is the K-P F-stat for weak instruments. Robust standard errors, clustered at the county level, in parentheses.
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- All regressions are weighed by 1940 county population, and include interactions between period dummies and: i) state dummies; ii) the 1940 Black share; and, iii) a dummy equal to 1 for Democratic incumbency in 1940 Congressional elections. F-stat is the K-P F-stat for weak instruments. Robust standard errors, clustered at the county level, in parentheses.
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- All regressions are weighed by 1940 county population, and include interactions between period dummies and: i) state fixed e↵ects; ii) the 1940 Black share; and iii) a dummy equal to 1 if the Democratic vote share in 1940 was higher than the Republican vote share. F-stat is the K-P F-stat for weak instruments. Robust standard errors, clustered at the county level, in parentheses. Significance levels: ⇤⇤⇤ p< 0.01, ⇤⇤ p< 0.05, ⇤ p< 0.1. Figure E.1. Frequency of CORE Demonstrations, by Type Notes: The figure plots the number of CORE demonstrations as share of all events occurring in our sample period, for each of the four main categories described in the text. The portion of the bar filled with oblique lines (resp. dots) refers to the share of events of each category that involved local (resp. national) issues.
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- All regressions are weighed by 1940 county population.
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- All regressions are weighed by 1940 population, control for state fixed e↵ects, and include i) the 1940 Black share, and ii) a dummy equal to 1 if the Democratic vote share in 1940 was higher than the Republicans vote share. F-stat is the KP F-stat for weak instruments. Robust standard errors, clustered at the CZ level, in parentheses.
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- All regressions are weighed by 1940 population. F-stat refers to the K-P F-stat for weak instruments. Robust standard errors, clustered at the county level, in parentheses.
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- All regressions are weighed with ANES survey weights, include survey year and region fixed e↵ects and individual controls of respondents (gender, age and education fixed e↵ects, and marital status), and control for 1940 state characteristics (Black share; Democratic incumbency in Congressional elections; share in manufacturing; share of workers in the CIO; urban share). F-stat is the K-P F-stat for weak instruments.
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- All regressions include county, state by week, and episode fixed e↵ects, and are weighed by 1940 county population. Columns 1, 3, and 5 focus on episodes that occurred in the southern states that, according to the instrument, had sent more Black migrants to the county over the period. Columns 2, 4, and 6 restrict attention to episodes happening in any other (southern) state. The penultimate row of the table indicates the window over which lynchings occurred. F-stat refers to the K-P F-stat for weak instruments. Robust standard errors, clustered at the county level, in parentheses.
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- All regressions include region and survey year fixed e↵ects, and control for individual characteristics of respondents (gender and age and education fixed e↵ects) as well as for 1940 state characteristics (Black share; Democratic incumbency in Congressional elections; share in manufacturing; share of workers in the CIO; urban share). F-stat refers to the K-P F-stat for weak instruments.
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- Allport, G. W. (1954). The Nature of Prejudice. Oxford, England: Addison-Wesley.
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- As discussed in the main text, we exploit ANES questions concerning political preferences in surveys in years between 1956 and 1964. In particular, individuals were asked to indicate the party they had voted (resp. intended to vote) in the previous (resp. upcoming) elections. From this variable, we create a dummy equal to one if respondents answered that they voted or intended to vote for the Democratic Party (Vote Democratic).
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- As noted in Appendix B, until 1964 (i.e. the end of our sample period), redistricting was unlikely to be strategic (Engstrom, 2013), and was typically mandated at the state level. We exploit the fact that between Congress 78 and Congress 82, five states in our sample (Arizona, Illinois, New York, Maryland, and Pennsylvania) required their CDs to redistrict, and test whether redistricting was systematically correlated with either Black inflows or changes in political conditions (e.g. party switches, changes in legislators’ ideology, etc.).94 In Table D.28, the dependent variable is a dummy equal to 1 if a CD belongs to a state 94 This check cannot be performed between Congress 83 and Congress 88 because most CDs were subject to redistricting in this period.
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- average change in Black and white population during the period for the counties in our sample. The coefficient in column 2 (Panel A) implies that 1,000 more Black residents in a county – or, half of the average change in Black population over the period – were associated with around 300 more white residents. Considering that, on average, the 1940 white population was 62,760, this represents a negligible change (0.4% relative to the baseline white population). Columns 3 and 4 show that results are robust to including only counties with baseline urban share of the population above the sample median (0.320), and to interacting the 1940 urban share of the population with period dummies. Results are also unchanged when estimating long-di↵erence regressions (Table D.4).
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- Black candidate, were her party nominating one (column 2); iii) approves the Civil Rights Act introduced in 1964 (column 3); and iv) thinks that the process of racial integration is occurring at the right pace or not fast enough (column 4). Panel B reports the first stage.
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- Change Black Share 3.895 3.895 3.895 3.895 3.895 3.895 3.895 Notes: In columns 1 to 5 the dependent variable is the 1940-1960 change in the share of white men above 18 not enrolled in school who are: i) high skilled (column 1); ii) employed in manufacturing (column 2); iii) in the labor force (column 3); iv) above the age of 65 (column 4); and v) employed (column 5).
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- Columns 1, 3, and 5 include interactions between period dummies and: i) state dummies; ii) the 1940 Black share; and, iii) a dummy equal to 1 if the Democratic vote share in 1940 was higher than the Republicans vote share.
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- Columns 2, 4, and 6 include county and state by year fixed e↵ects, and control for interactions between period dummies and: i) the 1930 Black share; and, ii) a dummy equal to 1 if the Democratic vote share in 1934 was higher than the Republicans vote share.
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- CORE Demonstrations: Controlling for 1940 County Characteristics Dependent Variable Change in 1[CORE Demonstration] (1) (2) (3) (4) (5) (6) (7) 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS Panel A: 2SLS Change Black Share 0.057*** 0.059*** 0.055*** 0.057*** 0.056*** 0.067*** 0.064** (0.018) (0.019) (0.018) (0.018) (0.017) (0.022) (0.032) Panel B: First Stage Predicted Change 0.758*** 0.723*** 0.743*** 0.756*** 0.761*** 0.598*** 0.458*** Black share (0.233) (0.232) (0.229) (0.228) (0.234) (0.217) (0.175) Control Baseline Coordinates Distance Distance Employment Manufacturing Urban Mason 48ers City to Population Share Share F-stat 10.57 9.744 10.53 11.03 10.57 7.614 6.870 Observations 3,789 3,789 3,789 3,789 3,789 3,789 3,789 Notes: The table replicates the baseline specification for results reported in Table 3 (column 6). Column 1 replicates the baseline results.
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- D Robustness Checks In this section, we present a variety of robustness checks. First, we show that Black in-migration did not systematically trigger white out-migration in the counties in our sample, and that there was no change either in the characteristics of white residents or in their labor market outcomes. Second, we construct alternative versions of the instrument that predict Black out-migration from each southern state exploiting only variation across local push factors and that rely on a county-to-county (instead of state-to-county) migration matrix. The latter exercise allows us to invoke the result obtained in Borusyak et al. (2021) for the validity of shift-share instruments in the presence of a high number of plausibly exogenous “shifts”.
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- Electoral outcomes. As discussed in the main text, we focus on the Democratic vote share in Congressional elections. This choice is motivated by the fact that, since the New Deal, Democrats had become the pro-Black party outside the US South (Caughey et al., 2020; Moon, 1948). Such racial realignment was more likely to emerge in Congressional than in nation-wide Presidential elections (Schickler, 2016).51 In addition to the Democratic vote share, we also consider voter turnout, defined as the share of votes cast in the election over the total number of eligible voters in the county. In Appendix E below, we provide additional results for Presidential elections. Data for both Congressional and Presidential elections are taken from Clubb et al.
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- Engstrom, E. (2013). Partisan Gerrymandering and the Construction of American Democracy. University of Michigan Press.
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- Fifth, we show that results are robust to i) interacting period dummies with a variety of 1940 county characteristics; ii) extending the analysis to the unbalanced sample of counties for which electoral outcomes were not consistently available; iii) omitting potential outliers; iv) considering alternative proxies for support for the Democratic Party; v) estimating di↵erent specifications (including stacked panel regressions in “levels ” rather than a model in stacked first di↵erences); and, vi) clustering standard errors at the CZ level or applying the correction procedure in Adao et al. (2019). Finally, we document that CD-level results: i) are not influenced by pre-existing trends; ii) are robust to using di↵erent timing conventions to map Black inflows to Congress periods; iii) are unchanged when restricting the sample to CDs that span only the counties included in our balanced sample; and, iv) are unlikely to be driven by strategic gerrymandering.
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- Figure 5. Change in Signatures on Discharge Petitions Notes: The figure plots the 2SLS coefficient (with corresponding 95% confidence intervals) for the e↵ects of the 1940-1950 change in the Black share on the corresponding change in the number of signatures on discharge petitions per legislator. The first dot on the left (“All”) includes discharge petitions on employment protection legislation (FEPC), to promote anti-lynching legislation, and to abolish the poll tax. The three remaining dots refer to each of the three issues. Results and details of the specification are reported in Table A.10.
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- Figure 6. Black in-Migration and Political Polarization Panel A: 1940s Panel B: 1950s Notes: Each bar reports 2SLS coefficients (with corresponding 95% confidence intervals) for the e↵ect of changes in the Black share on the change in the probability of electing a member of the House with the corresponding political orientation between Congress 78 and Congress 82 (Panel A) and between Congress 82 and Congress 88 (Panel B). The ideology indicators are defined in the main text (Section 6.2).
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- Figure A.5. 1940 Black Share of the Population Notes: The map plots the 1940 share of Black Americans (divided by county population) for the non-southern counties in our sample.
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- Figure A.6. Black Share in Northern Counties, 1940 vs 1970 Notes: Black share of the population for selected non-southern counties in 1940 (light blue) and in 1970 (black). Source: Authors’ calculation from IPUMS data.
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- Figure A.7. Share of Southern Born Black Migrants in Northern Counties, 1940 Notes: Share of African Americans born in selected southern states living in non-southern counties in 1940. Source: Authors’ calculation from IPUMS data.
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- Figure D.1. Balanced Sample Panel A: 1940-1970 Change in Black Share Panel B: 1940 Black Share Notes: The two maps plot the 1940-1970 change in the Black share and the 1940 Black share of the county population in Panels A and B respectively. The sample is restricted to the 1,263 non-southern counties in the fully balanced (baseline) dataset.
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- Figure D.2. Unbalanced Sample: Electoral Outcomes Panel A: 1940-1970 Change in Black Share Panel B: 1940 Black Share Notes: The two maps plot the 1940-1970 change in the Black share and the 1940 Black share of the county population in Panels A and B respectively. The sample includes the 1,328 non-southern counties for which electoral outcomes are available in at least one decade.
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- Figure D.3. Unbalanced Sample: CORE Demonstrations Panel A: 1940-1970 Change in Black Share Panel B: 1940 Black Share Notes: The two maps plot the 1940-1970 change in the Black share and the 1940 Black share of the county population in Panels A and B respectively. The sample includes the 1,333 non-southern counties that can be used in the analysis of CORE demonstrations.
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- Figure D.5. Black In-Migration and Non-Compactness Notes: The figure presents the 2SLS coefficient with the corresponding 95% confidence interval implied by one standard deviation change in the Black share during the corresponding decade. The dependent variable is the CD non-compactness score from Kaufman et al. (2017). The main regressor of interest is the 1940 to 1950 (resp. 1950 to 1960) change in the Black share for Congresses between 78 and 82 (resp. between 83 and 88), and is instrumented with the shift-share instrument described in the text. All regressions control for state dummies, the 1940 Black share, and a dummy equal to 1 if the district was represented by a Democrat in each Congress.
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- Figures and Tables Figure 1. Change in the Black Share across US Counties, 1940 to 1970 Notes: The map plots the change in the share of Black individuals in the population between 1940 and 1970 for the non-southern counties (1,263) in our sample.
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- Figures D.1, D.2, and D.3 plot the distribution of the 1940-1970 change in the Black share (Panel A) and the 1940 Black share (Panel B) for, respectively: i) the balanced sample used in the main paper; ii) the unbalanced sample of counties for which electoral outcomes are available in at least one decade; iii) the sample without restrictions used for CORE demonstrations.87 The sample included in Figures D.2 and D.3 covers a higher number of counties (and, almost the entire state of California, which is instead missing – except for 4 counties – in our baseline sample).
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- First, in columns 3 and 4 of Tables D.18 and D.19, we restrict attention to counties for which the predicted and the actual Black share was strictly positive in all decades between 1940 and 1970. Not surprisingly, results are unchanged. Next, in column 5 (resp. 6), we exclude counties at the top 1st (resp. 5th ) and at the bottom 99th (resp. 95th ) percentiles of the distribution of changes in Black migration. Also in this case, results remain in line with those of our baseline specification.
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- For this reason, as explained in the main text, we correlate the change in the Black share at the state level with attitudes of white respondents interviewed between the late 1950s and the mid-1960s (when the CRA was passed).53 See Appendix C.2 below for more details.
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- Fouka, V., S. Mazumder, and M. Tabellini (2021). “From Immigrants to Americans: Race and Assimilation during the Great Migration”. The Review of Economic Studies. Forthcoming.
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- Frymer, P. and J. M. Grumbach (2020). “Labor Unions and White Racial Politics”. American Journal of Political Science.
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- Gentzkow, M. (2016). “Polarization in 2016”. Toulouse Network for Information Technology Whitepaper.
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- Gregory, J. N. (2006). The Southern Diaspora: How the Great Migrations of Black and White Southerners Transformed America. University of North Carolina Press.
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- Haines, M. R. et al. (2010). “Historical, Demographic, Economic, and Social Data: the United States, 1790–2002”. Ann Arbor, MI: Inter-university Consortium for Political and Social Research.
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- In both tables, the number of observations is slightly di↵erent than that in our baseline specification reported in the main text. This is because, in Congresses 83 and 87, ideology scores are missing for 4 and 3 CDs respectively. Since the scores constructed in Bateman et al. (2017) use past voting behavior of legislators, it is possible that in a few instances (as it happens for Congresses 83 and 87) there are not enough data points to estimate the ideology scores. Reassuringly, all our results are identical when replicating the baseline specification (with the original timing convention) omitting the CDs missing in Tables D.25 and D.26.
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- In columns 6 and 7, the dependent variable is the 1940-1960 change in the log occupational score and in log wages for white men above 18 not enrolled in school.
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- In general, do you approve or disapprove this law? 1964 Racial Integration at the Right Pace/Not Fast Enough Do you think the Kennedy Administration is pushing racial integration too fast or not fast enough? 1963 Notes: Panel A (resp. B) lists variables and questions taken from the ANES (resp. Gallup).
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- j2South shw jcWhj⌧ (4) where shw jc is the share of whites born in southern state j and living in non-southern county c in 1940, relative to all whites born in j living outside this state; and Whj⌧ is the number of whites who left southern state j during decade ⌧. Then, in column 7 of Tables D.18 and D.19, we augment our baseline specification by separately controlling for the predicted southern white in-migration. Reassuringly, in all cases, results are similar to those in our preferred specification. Table D.18.
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- Jaeger, D. A., J. Ruist, and J. Stuhler (2018). “Shift-share Instruments and the Impact of Immigration”. Working paper 24285, NBER.
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- Katznelson, I. and J. S. Lapinski (2006). “The Substance of Representation: Studying Policy Content and Legislative Behavior”. In The Macropolitics of Congress, pp. 96–126. Princeton University Press Princeton, NJ.
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Kuziemko, I. and E. Washington (2018). “Why Did the Democrats Lose the South? Bringing New Data to an Old Debate”. American Economic Review 108(10), 2830– 67.
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- Local support for civil rights. We obtain measures of local support for the civil rights movement from two sources. First, we use the dataset assembled by Gregory and Hermida (2019) combining a variety of sources that includes the number of non-violent demonstrations organized between 1942 and 1970 by the CORE – an inter-racial group of students from the University of Chicago that coordinated sit-ins and similar forms of civil disobedience mainly across northern cities to protest against segregation in the South. Second, we obtained data on the presence of NAACP chapters from Gregory and Estrada (2019). These data are available only for the early 1940s and the early 1960s. For both CORE and NAACP datasets, we match the geographic coordinates of an event or of a NAACP chapter to the centroid of each county in our sample.
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Logan, T. D. and J. M. Parman (2017). “The National Rise in Residential Segregation”. The Journal of Economic History 77(1), 127–170.
- Lott, Jr, J. R. and L. W. Kenny (1999). “Did Women’s Su↵rage Change the Size and Scope of Government?”. Journal of political Economy 107(6), 1163–1198.
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- McAdam, D. (1982). Political Process and the Development of Black Insurgency, 19301970. Chicago and London: University of Chicago Press.
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- Next, we inspect more directly the possibility that Black inflows led to strategic gerrymandering across CDs. In particular, we rely on the measure of (non-)compactness recently introduced by Kaufman et al. (2017), which is based on the geographic shape of CDs, and captures the “compactness evaluations” made by judges and public officials responsible for redistricting.95 We prefer to use this measure, instead of an alternative proxy based on the vote distribution, because it provides evidence of (potential) gerrymandering at the CD level. In turn, this allows us to investigate the relationship between non-compactness and Black inflows. The measure of compactness can take values between 1 and 100, with higher values indicating less compact districts, i.e. a higher probability of gerrymandering.
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- One interpretation, consistent with the historical evidence (Sugrue, 2008), is that, since higher residential segregation lowered the probability that Black and white pupils shared the same school district, whites’ incentives to create local jurisdictions were stronger in more segregated counties. Coupled with findings in column 1, this suggests that population sorting within counties and the creation of independent jurisdictions might have reduced potential backlash by allowing whites to live in racially homogeneous communities, where the probability of sharing public goods with Black Americans was low.
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- open-ended question the ANES created one specific category that includes racial and public order related issues. For 1960 and 1964, the ANES coded respondents’ answers in categories that reflected their attitudes towards civil rights and integration.59 We use the ANES pre-classified category “Pro integration - anti discrimination in schools, employment, etc.” to create a dummy equal to one if the respondent believes that promoting integration in schools, employment, etc. is one of the top three problems facing the country in that survey year. This is the variable 1[Pro Civil Rights: Most Important Problem] considered in Table 5 in the main text.
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Oster, E. (2004). “Witchcraft, Weather and Economic Growth in Renaissance Europe”. Journal of Economic Perspectives 18(1), 215–228.
- Panel B reports the first stage for the 2SLS results presented in Panel A. All regressions are weighed by 1940 county population, and include interactions between period dummies and: i) state dummies; ii) the 1940 Black share; and, iii) a dummy equal to 1 if the Democratic vote share in 1940 was higher than the Republicans vote share. F-stat is the K-P F-stat for weak instruments. Robust standard errors, clustered at the county level, in parentheses.
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- Panel C reports the first stage for the 2SLS results presented in Panels A and B. All regressions are weighed by 1940 county population, and include interactions between period dummies and: i) state dummies; ii) the 1940 Black share; and, iii) a dummy equal to 1 if the Democratic vote share in 1940 was higher than the Republicans vote share. F-stat is the K-P F-stat for weak instruments. Robust standard errors, clustered at the county level, in parentheses. Significance levels: ⇤⇤⇤ p< 0.01, ⇤⇤ p< 0.05, ⇤ p< 0.1. Table D.16.
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- Pctile Notes: The table replicates the main specification (which is also reported in column 1) for results reported in Table 2 (column 6) by: i) replacing the interaction between period dummies and the 1940 Democratic incumbency dummy with that with the 1940 Democratic vote share in Congressional elections (column 2); ii) considering only counties with predicted (resp. actual) Black share strictly positive in all decades in column 3 (resp. column 4); iii) trimming counties at the top 1 st (resp. 5 th ) and at the bottom 99 th (resp. 95 th ) percentiles of the distribution of changes in Black migration in column 5 (resp. column 6); and iv) controlling for predicted southern white in-migration (column 7). Panel C reports the first stage for the 2SLS results presented in Panels A and B. F-stat is the K-P F-stat for weak instruments. Robust standard errors, clustered at the county level, in parentheses. Significance levels: ⇤⇤⇤ p< 0.01, ** p< 0.05, * p< 0.1. Table D.19.
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- Pearson, K. and E. Schickler (2009). “Discharge Petitions, Agenda Control, and the Congressional Committee System, 1929–76”. The Journal of Politics 71(4), 1238– 1256.
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Peri, G. and C. Sparber (2011). “Assessing Inherent Model Bias: An Application to Native Displacement in Response to Immigration”. Journal of Urban Economics 69(1), 82–91.
- Political variables corresponding to the geography of the 78th Congress for subsequent Congress years are computed by taking the weighed average of the outcomes of the newly formed CDs, using the weights constructed as explained above. In Appendix D, we validate the accuracy of this approach by replicating our (baseline) county-level results for the Democratic vote share using CD level data from Swift et al. (2000). Reassuringly, when conducting the analysis at the CD level, results remain qualitatively and quantitatively similar to those reported in the main text (see Table 2). C Data Appendix In what follows we first provide additional details about the data used in the paper (Appendix C.1), and then describe the survey data from the ANES and Gallup (Appendix C.2).
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- Residential segregation refers to the index constructed in Logan and Parman (2017). In columns 2 to 5 the dependent variable is the (log) number of: i) total jurisdictions (column 2); ii) school districts (column 3); iii) special districts (column 4); and, iv) municipalities (column 5).
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- Results in columns 2, 4, and 6 are unchanged when including interactions using 1940 values (rather than pre-1940 values). F-stat is the K-P F-stat for weak instruments. Robust standard errors, clustered at the county level, in parentheses.
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- Robust standard errors, clustered at the state level, in parentheses. ⇤⇤⇤ p< 0.01, ⇤⇤ p< 0.05, ⇤ p< 0.1. Table 6. Evidence from Northern Newspapers: Cross-sectional Regressions Dependent Variable 1[Any Mention] (1) (2) (3) (4) (5) Panel A: Main Estimates Change Black Share 0.253** 0.135 0.348** 0.532** 0.677** (0.128) (0.086) (0.163) (0.235) (0.301) Panel B: First Stage Predicted Change 1.071*** 1.032*** 1.098*** 1.093*** 1.081*** Black Share (0.289) (0.287) (0.291) (0.291) (0.289) F-stat 13.76 12.95 14.26 14.08 13.96 Observations 311,803 141,332 170,471 79,721 59,665 State FE X X X X X Episode FE X X X X X Week FE X X X X X Weeks 0 to 26 0 to 26 0 to 26 0 to 26 0 to 26 Sample 1940+ 1940-1944 1945+ 1950+ 1955+ Notes: The sample is restricted to the 492 counties in our sample for which newspapers’ data were available.
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- Ruggles, S., K. Genadek, R. Goeken, J. Grover, and M. Sobek (2015). Integrated Public Use Microdata Series: Version 6.0 [dataset]. Minneapolis: University of Minnesota.
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- Schickler, E. (2016). Racial Realignment: The Transformation of American Liberalism, 1932–1965. Princeton University Press.
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Schindler, D. and M. Westcott (2020). “Shocking Racial Attitudes: Black G.I.s in Europe”. The Review of Economic Studies 88(1), 489–520.
- Second, some CDs straddle county boundaries. In such cases, we assign county level values to the CD, weighting them by a county’s area share of the CD.46 Figure B.1 displays the county (gray lines) to CD (Black lines) mapping just described for the 78th Congress, restricting attention to non-southern states. 45 The only di↵erence with their procedure is that we use counties rather than CZs. 46 Following Feigenbaum and Hall (2015), we test the robustness of our results using other weights, such as maximum area. Figure B.1. CD-County Map Notes: The figure presents a map of counties (gray lines) and Congressional Districts (black lines) for the non-South US during the 78th Congress.
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- Second, we exploit the fact that, in 1964, the ANES included questions on respondents ’ “feeling thermometers” towards di↵erent political, demographic, and socioeconomic groups, with higher values reflecting warmer feelings towards a group. 2SLS estimates in Table E.5 show that Black inflows were positively associated with feelings towards Democrats (column 1), Blacks (column 3), and the NAACP (column 4) among white respondents. The 1940-1960 change in the Black share was also positively correlated with whites’ feelings thermometers towards labor unions (column 2), even 102 The number of observations varies substantially across questions, since some of these were asked repeatedly during 1963 and 1964. This was particularly true for the question on the pace of racial integration (column 4). 103 See Table E.4 for the 2SLS coefficients plotted in Figure E.4. though these results are not statistically significant.
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Sequeira, S., N. Nunn, and N. Qian (2020). “Immigrants and the Making of America”. Review of Economic Studies 87(1), 382–419.
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- Significance levels: ⇤⇤⇤ p< 0.01, ⇤⇤ p< 0.05, ⇤ p< 0.1. D.2 Push Factors Instrument D.2.1 Instrument Construction and Zeroth Stage Borusyak et al. (2021) note that the validity of shift-share designs can be guaranteed if the “shifts” – in our case, decadal Black migration from each southern state – are exogenous to local conditions. They propose a correction method, where the “shiftshare ” instrument is expressed in terms of the “shift” components. This method, however, can be implemented only when the number of “shifts” is large. Unfortunately, in our setting, we can only rely on 14 southern states, and so we cannot directly implement the transformation proposed in Borusyak et al. (2021).
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- Significance levels: ⇤⇤⇤ p< 0.01, ⇤⇤ p< 0.05, ⇤ p< 0.1. D.4 Local Pull Shocks and Predicted Economic Growth In Table D.12, we investigate if the instrument constructed in equation (2) in the main text is correlated with county-specific pull factors. We consider two such factors that might have been particularly relevant in this context: WWII contracts and New Deal spending. As discussed in Boustan (2016), the surge in demand across northern and western factories triggered by WWII was one of the pull factors of the Great Migration. Similarly, the generosity of New Deal spending might have influenced the location decision of African Americans prior to 1940, while at the same time having long-lasting e↵ects on political conditions across northern counties.
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- Significance levels: ⇤⇤⇤ p< 0.01, ⇤⇤ p< 0.05, ⇤ p< 0.1. D.7 Additional Robustness Checks D.7.1 Results for the Unbalanced Sample As discussed in Section 3 of the main text, data on Congressional elections are not consistently available for all counties – a problem that is particular evident for California (see Figure 1 in the main text). In our analysis, we consider a strongly balanced sample, which includes only the counties for which data on Congressional elections were available in all Census decades from 1940 to 1970. We now verify that results are unchanged when including all counties for which outcomes are available in at least one Census decade.
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Smith, J. P. and F. R. Welch (1989). “Black Economic Progress After Myrdal”. Journal of economic literature 27(2), 519–564.
- Snyder, J. M. and S. Ansolabehere (2008). The End of Inequality: One Person, One Vote and the Reshaping of American Politics. WW Norton and Company.
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- Specifically, we first compute the share of whites born in each southern state who were living in a non-southern county as of 1940. Next, we interact these shares with the number of white migrants from each southern state in each decade between 1940 and 1970. Finally, for each non-southern county and for each decade, we sum the predicted number of whites moving from each origin over all southern states to obtain the total number of (predicted) white migrants moving to county c during decade ⌧. In formulas, this measure is given by: ZWc⌧ = X
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Steinmayr, A. (2020). “Contact versus Exposure: Refugee Presence and Voting for the Far-Right”. Review of Economics and Statistics, 1–47.
- Sugrue, T. J. (2008). Sweet Land of Liberty: The Forgotten Struggle for Civil Rights in the North. Random House.
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- Sugrue, T. J. (2014). The Origins of the Urban Crisis: Race and Inequality in Postwar Detroit-Updated Edition. Princeton University Press.
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- Swift, E. K., R. G. Brookshire, D. T. Canon, E. C. Fink, J. R. Hibbing, B. D. Humes, M. J. Malbin, and K. C. Martis (2000). Database of Congressional Historical Statistics [computer file]. Ann Arbor, MI: Inter-university Consortium for Political and Social Research.
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Tabellini, M. (2018). “Racial Heterogeneity and Local Government Finances: Evidence from the Great Migration”. Working Paper No. 19-006, Harvard Business School BGIE Unit.
Tabellini, M. (2020). “Gifts of The Immigrants, Woes of The Natives: Lessons from The Age of Mass Migration”. Review of Economic Studies 87(1), 454–486.
- Table D.12. Placebo Checks Dependent Variable Predicted Change in Black Share (1) (2) (3) (4) Panel A: WWII Spending Per Capita 0.049 0.033 0.026 0.108 (0.037) (0.042) (0.037) (0.116) Panel B: New Deal Spending Per Capita-0.122-0.103-0.057-0.283 (0.087) (0.092) (0.084) (0.250) Observations 1,263 1,263 1,263 1,263 Decade 1940-1950 1950-1960 1960-1970 1940-1970 Notes: The dependent variable is the change in the predicted number of Black migrants over 1940 county population. Each column considers the period specific to the decade reported at the bottom of the table. All regressions are weighed by 1940 county population, and control for state dummies, for the 1940 Black share, and for a dummy equal to 1 if the Democratic vote share in 1940 was higher than the Republican vote share.
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- Table E.3. Additional Evidence on Whites’ Attitudes: Gallup Dependent Variable 1[Object to Half 1[Vote for Black 1[Approve Civil 1[Racial Integration: Right Pupils in School] Candidate] Rights Act] Pace vs Not Fast Enough] (1) (2) (3) (4) Panel A: 2SLS Change Black Share -0.043** 0.072 0.038*** 0.000 (0.019) (0.045) (0.010) (0.012) Panel B: First Stage Predicted Change Black Share 2.273*** 2.400*** 2.202*** 2.432*** (0.217) (0.579) (0.348) (0.360) F-Stat 110.2 17.15 40.07 45.53 Observations 851 2,073 931 17,478 Mean Dependent Variable 0.289 0.525 0.706 0.320 Notes: The sample is restricted to white Gallup respondents living in the US North and to years 1963-1964. The dependent variable is a dummy equal to 1 if the respondent: i) objects to having half of the classroom composed of Black pupils (column 1); ii) would vote for a
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- that did not mandate redistricting, and is regressed against: i) changes in the Black share (with OLS in column 1 and with 2SLS in column 2); ii) a dummy if the CD underwent a party switch; and, iii) the change in the Bateman et al. (2017) ideology score (column 4). Since the dependent variable varies at the state level, we cannot control for state fixed e↵ects; yet, we include (as in our baseline specifications) the 1940 Black share and the 1940 Democratic dummy. Reassuringly, the coefficient is never statistically significant, does not display any systematic pattern, and is always quantitatively small. Overall, this exercise thus suggests that neither changes in the Black share nor changes in political conditions were systematically associated with state-mandated redistricting.
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- The analysis is restricted to the 117 CZs for which demographic variables were available from the 1960 5% sample of the micro-census.
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- The bottom rows of Table D.3 report the average 1940 white population and the 69 CZs have become the standard measure of “labor markets” in the US since the work by Autor and Dorn (2013). CZs were developed by Tolbert and Sizer (1996) using commuting patterns to create clusters of counties characterized by strong commuting ties within CZs and weak commuting ties across CZs. 70 Panels B and C report, respectively, results for turnout and the first stage. Coefficients for turnout are no longer statistically significant, but the point estimate remains close to that reported in the main text. 71 The point estimate in Panel B indicates that one additional predicted Black migrant is associated with 2.5 more Black residents in the county. The magnitude of the coefficient is smaller than, but in line with, that reported in Boustan (2010).
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- The dependent variable in Table D.12 is the change in predicted Black in-migration, scaled by 1940 county population. The main regressor of interest is WWII spending per capita (Panel A) and generosity of New Deal (Panel B). Columns 1 to 3 consider each decade separately, whereas column 4 focuses on the long di↵erence (1940-1970) change in predicted Black in-migration. We always include the set of controls used in our most preferred specification – i.e., state dummies, the 1940 Black share, and a dummy equal to 1 if in 1940 the Democratic vote share was higher than the Republican vote share in Congressional elections – and weigh regressions by 1940 county population. Reassuringly, in all cases the coefficient is imprecisely estimated and quantitatively small.
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- The main regressor of interest is the 1940 to 1960 (resp. 1940-1950 and 1950-1960) change in the Black share in columns 1 and 2 (resp. in columns 3-4 and 5-6) interacted with an indicator for weeks 0 and above (POST). The change in the Black share is instrumented with the shift-share instrument described in equation (2) in the text.
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- The regressor of interest is the state level 1940-1960 change in the Black share, which is instrumented with the predicted number of Black migrants over 1940 state population. Each column restricts attention to white respondents who belong to the group reported at the bottom of the table. Panel B reports the first stage.
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- The remaining columns include the interaction between period dummies and, respectively: i) latitude and longitude of county centroid (column 2); ii) distance from the Mason-Dixon line (column 3); iii) distance from the closest city where Forty-Eighters settled (column 4); iv) the 1940 male employment to population ratio (column 5); v) the 1940 share of employment in manufacturing (column 6); and vi) the 1940 urban share (column 7).
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- The remaining columns include the interaction between period dummies and, respectively: i) latitude and longitude of county centroid (column 2); ii) distance from the Mason-Dixon line (column 3); iii) distance from the closest city where Forty-Eighters settled (column 4); iv) the 1940 male employment to population ratio (column 5); v) the 1940 share of employment in manufacturing (column 6); and vi) the 1940 urban share (column 7).
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- The table reports 2SLS results for the 1940-1960 change in the Black share, instrumented with the shift-share instrument described in equation (2) in the text.
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- The table reports 2SLS results replicating the baseline specification, which includes interactions between period dummies and: i) 1940 Black share; ii) 1940 Democratic incumbency dummy; iii) state dummies.
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- The wording reported for variables Most Important Problem, Against School Integration, and Against Work and Residential Integration in Panel A is taken from the 1960 survey, but remains almost identical in all other years considered.
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- Then, political outcomes (e.g. ideology scores, number of discharge petitions signed by legislators, etc.) are collapsed to the 78th Congress using a weighting procedure similar to that adopted when matching counties to CDs. The logic of our strategy is simple: we fix the 1944 (i.e. the 78th Congress) geography of CDs, and we link them to CDs that represented the same geographic area in subsequent (or previous) Congress years.50 Then, we calculate a weighed average of political outcomes that correspond to the area originally represented by CDs according to the 1944 map.
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- Third, we document that the instrument is uncorrelated with county-specific pull factors that might have influenced pre-1940 Black settlements, and that results are unchanged when simultaneously controlling for local economic growth, predicted using a Bartik methodology. Fourth, we verify that our findings are not driven either by pre-existing trends or by the simultaneous inflow of southern born white migrants.
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Tiebout, C. M. (1956). “A Pure Theory of Local Expenditures”. Journal of political economy 64(5), 416–424.
- To illustrate this procedure, we ask how the 78th Congress would have looked like, had its geography persisted until Congress 86. We now explain how we proceed to collapse the political outcomes corresponding to the geography of Congress 86 “back” to that of Congress 78. Suppose that the area represented by a single CD in Congress 78 gets split in two separate CDs by Congress 86. To assign political variables of new CDs back to the level of the original CD, we adopt a weighting procedure, based on 48 While our analysis focuses on years after 1940, we also construct the cross-walk for the pre-1940 decade in order to perform several robustness and falsification checks. 49 The reason to consider the 88th Congress in the second decade is that this was the Congress that approved the CRA. 50 When states have more than one district, we drop at-large Congressional seats from the spatial merge (e.g. at-large seats for the state of New York are dropped between 1933 and 1945).
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- Together with results in Appendix D.2, this exercise increases the confidence that our main findings are not driven by local pull shocks simultaneously correlated with the pre-1940 distribution of Black settlements across northern counties. 81 As for the baseline instrument, the denominator of the initial shares of African Americans includes all individuals from the origin county who were living in any other county – in or out the US South – by 1930. 82 Panel B reports the first stage. When using the alternative instrument that relies on predicted migration flows, the F-stat falls slightly below conventional levels. Table D.11.
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- Trende, S. (2012). The Lost Majority: Why the Future of Government Is Up for Grabs-and Who Will Take It. St. Martin’s Press.
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Troy, L. (1957). “Distribution of Union Membership among the States, 1939 and 1953”. NBER Books.
- Using the approach just described, in columns 5 to 7 of Table D.8, we also show that Black inflows did not increase labor market competition for white residents.75 This confirms existing evidence that northern labor markets were highly segmented along racial lines, and African Americans rarely – if at all – directly competed for jobs with whites (Boustan, 2009). 75 As before, we restrict attention to men of age 18 or more who were not in school. Since data on employment, occupation, or wages are separately available by race (and gender or age) only from micro-censuses, we focus on years 1940 and 1960, and conduct the analysis at the CZ level.
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Voigtländer, N. and H.-J. Voth (2012). “Persecution Perpetuated: the Medieval Origins of anti-Semitic Violence in Nazi Germany”. The Quarterly Journal of Economics 127(3), 1339–1392.
Wasow, O. (2020). “Agenda Seeding: How 1960s Black Protests Moved Elites, Public Opinion and Voting”. American Political Science Review, 1–22.
- We start by analyzing descriptively the trends of non-compactness between Congress 71 and Congress 90 in Figure D.4. Consistent with the existing literature discussed in Appendix B, for the period considered in our analysis – between Congress 78 and Congress 88 – average compactness changes very little. Reassuringly, other aggregate measures, such as the standard deviation and the interquantile range, do not show any detectable changes either (not shown). Interestingly, and again consistent with existing studies, non-compactness starts to increase precisely after Congress 88, suggesting that after 1964 strategic gerrymandering might have become gradually more common.
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- We would like to get your feelings toward some of these groups... Where would you put (group) on the thermometer? 1964 Panel B. Gallup Object to Half Black Pupils in School Would you, yourself, have any objection to sending your children to a school where half of the children are [Black] 1963 Black Candidate There’s always much discussion about the qualifications of presidential candidates -their education, age, religion, race and the like... If your party nominated a generally well-qualified man for president and he happened to be a
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- weights constructed in four steps. First, we overlay the map of the initial CD to that of the two CDs in Congress 86, and divide the area in cells derived by this spatial merge. Second, we assign the 1940 county population to each cell in proportion to the area share of the cell that is included in the county. Third, we sum over all cells that compose the CD to obtain an estimate of CD population as of Congress 78. Finally, we divide the area of each cell by such estimated CD population.
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Whatley, W. C. (1985). “A History of Mechanization in the Cotton South: the Institutional Hypothesis”. The Quarterly Journal of Economics 100(4), 1191–1215.
- Wheaton, B. (2020). “Laws, Beliefs, and Backlash”. Working Paper.
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- Whites’ attitudes. We collect data on whites’ racial attitudes and stance on civil rights from two, nationally representative surveys: the ANES and Gallup. Both are cross-sectional datasets that report individuals’ socioeconomic and demographic characteristics as well as their political ideology. Starting from the mid to late 1950s, both surveys began to elicit respondents’ views on racial equality and their support for civil rights. The ANES contains respondents’ county of residence, while Gallup only records their state. However, even in the ANES, we are unable to exploit county-level information, due to the very limited number of counties (56) included in the survey.
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- Wright, G. (2013). Sharing the Prize. Harvard University Press.
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- Zieger, R. H. (2000). The CIO, 1935-1955. University of North Carolina Press.
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