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Inequality and Immigration Policy

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Abstract

How does inequality between capital and labor affect immigration policy? Increasing inequality can heighten anti-immigrant attitudes among host-state workers. Yet, for labor-intensive firms, their increasing share of the value added provides more business opportunities to expand their production, which in turn leads to increased business support for open immigration. Given these countervailing pressures of rising inequality in immigration policymaking, we argue that a country’s level of economic development holds the key to the causal mechanism between inequality and immigration policy openness. In less-developed economies where local and immigrant workers compete for the same jobs, rising inequality leads to more restrictive immigration policy. In advanced economies where local and immigrant workers are complements, rising inequality leads to less restrictive immigration policy. Using data on the capital share of the value added in the industrial sector as a measure of inequality in 24 democracies from 1947 to 2006, we find support for our argument.

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Notes

  1. Our use of “low-wage” reflects that highly skilled immigrants may end up working in low-paying jobs and that many jobs referred to as “low-skill” may require a great number of skills and specialized knowledge but are not compensated for their skills (e.g., childcare workers, agricultural workers). We still use the term “skill” when discussing specific skills needed for a position or a specific industry.

  2. The capital share of the value added is defined as one minus the ratio of compensation of employees to the value added, which is the labor share.

  3. See Peters (2014, 2015, 2017) and Shin (2019) on how increasing demand for labor leads to increased business support for immigration.

  4. Our use of “host-country workers” instead of “native workers” reflects that mass publics in the states of our interest may not be part of indigenous groups but are rather descendants of immigrants themselves.

  5. Neither our dataset nor others of which we are aware include low-income countries, making our prediction for these countries impossible to evaluate. However, poor countries are less likely to be immigrant destinations.

  6. A notable exception in the literature is Borjas (2003) who argues that immigration inflows have substantial negative effects on locals’ wages but see Clemens and Hunt (2017) for a rebuttal. Edo and Toubal (2015) argue that high-wage immigrants can be detrimental to high-wage local workers and beneficial to low-wage local workers. See Card (2009) for a concise review of the literature.

  7. For instance, see Somerville and Goodman (2010) for the role of employers and businesses in shaping the UK’s economic migration policy.

  8. See Grossman and Oberfield (2022) for a review of the literature on the declining labor share.

  9. Immigration may still raise total inequality in the country as immigrants take the place of host-country citizens at the low end of the income distribution. This substitution effect stretches the overall income distribution—including both immigrants and host-country citizens—while shrinking the income distribution among host-country citizens. Piketty (2014) argues that immigrants have played this role in the USA.

  10. Dustmann et al. (2008, 478) argue the belief that immigrants hurt unskilled host-country workers is widely accepted by the public.

  11. In contrast, high-wage/high-income locals benefit from increased inequality and their opinions on immigration should be relatively unaffected.

  12. For an excellent discussion of these indices, see Goodman (2015).

  13. It has been also documented that migrant-sending states’ dual citizenship policies encourage emigration (e.g., Alarian and Goodman 2017).

  14. See Peters (2017) and Shin (2019) for more details on collecting and coding the data.

  15. We use Houle’s (2009) imputed version of the data

  16. The compensation-productivity gap—the gap between real hourly compensation and labor productivity—widens when labor share falls (Fleck et al. 2011).

  17. We have retrieved data on GDP per capita from Haber and Menaldo (2011) who assembled the data using the Penn World Tables (PWT).

  18. There are very wealthy states, such as the states of the Gulf Cooperative Council, that do not have high levels of development.

  19. We have alternatively run both models with (1) the lagged dependent variable but without country fixed effects and (2) without the lagged dependent variable but with country fixed effects and found substantively similar results.

  20. Docquier et al. (2007) have data on flows by skill level but only for 1990 and 2000.

  21. Most of the data on import duties over imports come from Clemens and Williamson (2004). Peters (2015) and Shin (2019) collected and updated the data to fill in missing values.

  22. We retrieved and updated the taxation data from Cusack and Beramendi (2006) and the party data from Swank (2014). Note that the original taxation dataset in Cusack and Beramendi (2006) is only available for country-year observations from 1950 to 1995. Our updated taxation dataset is available up to 2015, but since the inequality dataset is available up to 2006, our analysis covers country-years observations between 1950 and 2006. To measure right-wing populist influence in each country, we use the lower-chamber vote shares of national right-wing populist parties defined by Betz (1994), excluding more traditional neo-fascist parties (e.g., German National Democratic Party) and centrist and left-wing nationalist parties (e.g., the True Finns). Left governing party seats are coded as a share of all legislative seats. Left parties include communist, socialist, social democratic, labor, and other various left-wing parties (e.g., left-libertarian parties).

  23. On the one hand, less-developed states may have a larger low-income constituency that should be more opposed to immigration in general (i.e., income effect). On the other hand, more developed countries typically have higher levels of productivity and a larger knowledge sector, both of which Peters (2014, 2015, 2017) argues should lead to a less open policy (i.e., development effect).

  24. We use the education data collected by Barro and Lee (2013). The data on education are available only at five-year intervals from 1950 to 2010. Since educational attainment tends to follow a linear trend, we use linear interpolation to fill in missing values.

  25. We find similar results in Models 3 and 5.

  26. Fig. A6 in the Supplementary Materials shows how inequality and immigration have varied over time in the wealthiest countries (income above about $22, 000 per capita).

  27. Interestingly, once we account for country-specific time trends in Table A5 in the Supplementary Materials, the coefficient of the Polity score gains more statistical significance. The differences in results between Table 2 and Table A5 in the Supplementary Materials may owe to that country-specific time trends can clarify the correlation between the dependent variable and the Polity score by simultaneously accounting for downward trends of immigration policy and upward trends of democratization in several countries.

  28. The migration stock data come from United Nations Department of Economic and Social Affairs (2020).

  29. See Fig. A4 in the Supplementary Materials.

  30. See Table A7 in the Supplementary Materials. Autocracies tend to have more open immigration policy than democracies. See Shin (2017) for more information.

  31. Data for the top 10% income share, the top 5% income share, and the top 1% income share are taken from the World Wealth and Income Database (WID) assembled by Alvaredo et al. (2016). The Gini coefficient measure is taken from Deininger and Squire (1996). The top 10% and 1% wealth shares are retrieved from Roine and Waldenström (2015)’s dataset. Lastly, top inheritance and income tax data, are obtained from Scheve and Stasavage (2016).

  32. The capital share of the value added measures the level of inequality between capital and labor since it is the proportion of the value added in production accruing to capital owners. Higher values indicate more inequality against the interests of the labor class.

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Acknowledgements

We would like to thank Charlotte Cavaillé and Adam Harris for providing comments and suggestions on previous versions of the manuscript. In addition, we would like to thank panelists and audiences at the 2015 International Political Economy Society Conference (IPES), the 2016 European Political Science Association Conference, and the 2017 American Political Science Association Annual Meeting for their questions and comments. Finally, we also thank Samantha Moya and Brendan Connell for their research assistance. All errors remain our own.

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  1. Department of Political Science, University of California, Los Angeles, Los Angeles, CA, USA

    Margaret E. Peters

  2. Department of Political Science at the University of Colorado Boulder, Boulder, CO, USA

    Adrian J. Shin

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Peters, M.E., Shin, A.J. Inequality and Immigration Policy. St Comp Int Dev 58, 224–251 (2023). https://doi.org/10.1007/s12116-022-09372-x

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