The impact of party affiliation of US governors on immigrants’ labor market outcomes

Abstract

Do immigrants have better labor market outcomes under Democratic governors? By exploiting variations associated with close elections in a regression discontinuity (RD) design applied on gubernatorial elections in 50 states over the last two decades, we find that immigrants are more likely to be employed, work longer hours and more weeks, and have higher earnings under Democratic governors. Results are robust to a number of different specifications, controls, and samples.

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Notes

  1. 1.

    Using RD designs to estimate program effects in a variety of contexts have become quite popular in economics. Lee and Lemieux (2010, 2014) provide a comprehensive review of the literature by discussing identification, interpretation, and estimation issues related to RD designs.

  2. 2.

    The literature on this subject is vast. Important contributions are Garand (1988), Besley and Case (1995), Knight (2000), and Alt and Lowry (2000) among many others. Besley and Case (2003) provides an early review of the literature.

  3. 3.

    Ferreira and Gyourko (2009) investigate whether cities are as politically polarized as states. Their RD analysis shows that whether the mayor is a Democrat or Republican has an insignificant impact on the size of local government, the composition of local public expenditure, or crime rate.

  4. 4.

    See Hemming et al. (2002) for an earlier review of this literature.

  5. 5.

    State governments also have judicial branch that is responsible for administering the laws of the state and resolving legal conflicts. More information about these branches can be found on https://www.whitehouse.gov/1600.

  6. 6.

    States without term limits are Connecticut, Idaho, Illinois, Iowa, Massachusetts, Minnesota, New Hampshire, New York, North Dakota, Texas, Utah, Vermont, Washington, and Wisconsin. Particularity differs from states to states. More information on term limits for governors and state legislatures are available at https://www.termlimits.org.

  7. 7.

    Top-coded incomes for years 1994 and 1995 are multiplied by 1.5; but no correction made for the subsequent years. This is because, starting in 1996, top-coded income values are assigned the mean of all top-coded earners, and these numbers are substantially higher than top-coded income values reported in the previous years. The analysis without top-coded earners yields mostly the same results. Following Autor et al. (2008), workers with income below $3.35 per hour (in 2009 dollars) are dropped. In addition, to prevent measurement errors related to hours and weeks reported, in each year, the maximum hourly income of workers is limited to the top-coded annual income divided by 2000 (hours per year). In this way, we also prevent part-time workers from having a higher feasible wage than full-time, full-year workers (see Autor et al. 2008). Our results are not sensitive to such corrections.

  8. 8.

    For Texas, for example, the 2006 election results (the political party of the winner and the margin of victory) are used in regressions for 2007, 2008, 2009 and 2010. We exclude observations where neither a Democrat nor a Republican won. We assume that F j (M V ) is a third-order polynomial function and F j (M V ) is allowed to differ on either side of the threshold. However, considering first- or second-order polynomials yields very similar results. Results are also similar using local linear regression discontinuity (see Section 5).

  9. 9.

    We also consider labor force status (i.e., in labor force or not) as an outcome variable. Our estimate of β D I is − 0.0042 (0.0033), where the number in parentheses is the standard error based on clustering data at state level. Thus, the party affiliation has no impact on the labor force status of immigrants relative to white natives.

  10. 10.

    Immigrants’ location choices may be affected by voting shares of parties (Damm 2009), but our identification relies on close elections. Further, one may argue that immigrants with respect to their skill levels, time spent in the US, and country of origin may choose where to live based on their political preferences. Table 2B reports results from our RD regressions based on these characteristics. Note that all estimated coefficients are statistically insignificant.

  11. 11.

    We also run the simple OLS regressions, and the results are given in Table 13 in the appendix. According to the OLS estimates, the Democratic Party has no significant impact on immigrants’ labor market outcomes. However, these estimates suffer from biases as there are many potential unobserved factors affecting the party affiliation and outcome variables.

  12. 12.

    Table 24 in the appendix replicates Table 3, but presents results for all covariates, except state and year fixed effects.

  13. 13.

    While our primary focus is the impact of party affiliation on immigrants relative to whites, one can easily calculate the total impact on immigrants by adding the estimated coefficients on D and D s t × I m g i s t . In this case, the total impact for Emp Status is 0.0174∗∗∗ (0.0050), Hours per week 0.0047 (0.0062), total weeks 0.01963∗∗∗ (0.0059), total hours 0.0214∗∗ (0.0098), hourly income 0.0400∗∗∗ (0.0129), weekly income 0.0445∗∗∗(0.0142), and annual income 0.0480∗∗∗ (0.0144).

  14. 14.

    Our findings have economically significant impact on immigrants’ labor market outcomes. For example, the average annual labor income of immigrants in states where the Republican governors barely won is about $38,000. According to our estimates, their annual labor income would be about $1,500 higher if the Democratic governors won the elections.

  15. 15.

    However, our findings that Democratic governors have a positive and significant impact on hourly and weekly income of black workers are different from his, and our analysis shows that these differences mainly stem from studying different time periods. Beland uses the same data sources over the period of 1977–2008, and his sample covers only the prime working age group (i.e., individuals between 20 and 55 years old). Our sample starts in 1993, following the availability of the immigrant identifier in CPS, and covers a wider age group as in Autor et al. (2008). When his sample is restricted 1993 to 2008, the results are qualitatively similar to those reported in Table 3. Some differences on white native labor inputs are also due to different time periods. Considering only the prime working age group does not have significant impact on our results (see Table 19 in the appendix).

  16. 16.

    Relatedly, we also explored heterogeneity among immigrants with respect to their country of origin, since immigration from different countries can have different effects on voting shares of the Democratic and Republican Party (Mayda et al., 2015). We assigned immigrants into 7 regions where they come from: Canada, Mexico, the rest of America, Asia, Africa, Europe, and Other. Table 14 in the appendix reports the results from this exercise, and for the sake of brevity we only report the coefficients related to these variables. Note that the impact of Democratic governors on labor market outcomes of Mexican immigrants (who constitutes the largest share in the population) is generally higher than that on all immigrants reported in Table 3.

  17. 17.

    We find that for all regressions of Table 6, the coefficients for Img-skilled ×Dem and Img-unskilled ×Dem are not statistically different from each other at the 5% level. Running separate RD regressions for skilled and unskilled workers yields qualitatively similar results as shown in Table 15A and B. Table 15A and B both shows that under Democratic governors, there is improvement in labor market outcomes of immigrants, blacks and others (relative to white natives) In Table 15A, our reference group is the low-skill, white, natives. According to Table 15A, labor market outcomes of low-skill, white natives are relatively better under Democratic governors.

  18. 18.

    We also run regressions where skilled and unskileld immigrants are included as in Table 6. Our results using private sector data are very similar to those reported in Table 6. We do not find any impact of Democratic governors on skilled and unskilled immigrants’ labor market outcomes in the public sector.

  19. 19.

    The impact of spending on infrastructure is insignificant, but the estimated coefficient is very similar to that on education and health. Although Democrats have strong political ties with unions (e.g., Dark 2001), their impact on unionization is insignificant, which is consistent with Beland and Unel (2017). Finally, the last column look at the impact of party affiliation on state EITC.

  20. 20.

    As discussed above, Democratic governors spend more on education and health, and unions are strong in these sectors. In addition, their minimum wage and tax policies are most likely to affect occupations such as maintenance and repair, farming, food preparation and serving, construction and assemblers & operators. Therefore, our first sample includes the following occupations: maintenance and repair, farming, food preparation and serving, personal care, health care, teaching, construction and assemblers and operators. The other sample includes managers and CEOs, business and finance specialists, architects, engineers, scientists, technicians, sales specialists, and administrative support. Each sample contains about 800,000 individuals.

  21. 21.

    We also run an RD design where the outcome variable is a dummy variable that equals one if an individual is in the sample that is more likely affected by governors’ policies, and zero otherwise. Our estimated coefficients for Img and Img ×Dem respectively are 0.1438 (0.0084) and − 0.0059 (0.0060), indicating that immigrants are more likely to hold occupations listed in the first sample, but the impact of Democratic governors on their occupational choice (i.e., the first or second sample) is insignificant.

  22. 22.

    We also investigate skill heterogeneity as in Table 6 for our two different samples. Tables 16 and 17 in the appendix report the results, and note that they are largely consistent with the conclusion on Tables 9 and 10.

  23. 23.

    We also verified that states where Democrats barely won and states where Democrats barely lost are not statistically different from each other in their pre-treatment covariates. To address the issues raised in Caughey and Sekhon (2011), using data from Jensen and Beyle (2003), we found that campaign spending is not different when Democrats barely wins than when they barely lost. In addition, for close elections to be regarded as random, such elections won by Democratic governors should not be more likely to come with a Democratic House or Senate. We checked and confirmed that those variables are not statistically different when Democrats barely won.

  24. 24.

    In our non-parameteric RD analysis, we calculate the optimal bandwith using procedures developed by Imbens and Kalyanaraman’s (2012) and Calonico et al. (2012). As Table 11 shows, they yield qualitatively similar results.

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Acknowledgments

We thank the editor Klaus F. Zimmermann and three anonymous referees for their valuable comments and suggestions.

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Correspondence to Louis-Philippe Beland.

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Responsible editor: Klaus F. Zimmermann

Appendix

Appendix

Table 13 OLS estimates: impact of party affiliation on labor markets over 1993–2013
Table 14 RD estimates: impact of party affiliation on labor markets over 1993–2013, by Country of Origins
Table 15 RD estimates: impact of party affiliation on labor markets over 1993–2013
Table 16 RD estimates: party affiliation on labor markets, occupations more likely affected by gov. policies
Table 17 RD estimates: party affiliation on labor markets, occupations less likely affected by gov. policies
Table 18 Robustness of RD Estimates: Using Second-Order Polynomials
Table 19 Robustness of RD Estimates: Using Prime Age (20–55 Years Old) Group
Table 20 Robustness of RD Estimates: Including Region-Specific Time Trends
Table 21 Robustness of RD Estimates: Using After-Tax Income
Table 22 Robustness of RD Estimates: Including Additional Controls
Table 23 Robustness of RD Estimates: Governors and Legislatures are from the Same Party
Table 24 RD estimates: impact of party affiliation on labor markets over 1993–2013, all Covariates

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Beland, L., Unel, B. The impact of party affiliation of US governors on immigrants’ labor market outcomes. J Popul Econ 31, 627–670 (2018). https://doi.org/10.1007/s00148-017-0663-y

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Keywords

  • Earning gaps
  • Immigration
  • Labor market outcomes
  • Political parties
  • Regression discontinuity

JEL Classification

  • J15
  • J21
  • J31
  • D72