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Immigration and the pattern of public spending: evidence from OECD countries

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Abstract

Using a panel of OECD countries, we show that immigration systematically alters the composition of public spending in the destination country. To mitigate bias from the endogenous sorting of immigrants, we use an IV estimation strategy. The instrument is constructed by estimating a bilateral migration model for 24 destination and 208 source countries. We find that the host country responds to the increase in immigrants by adjusting various expenditures, such as by reallocating resources from social welfare to national defense and public order. Our findings imply that (1) immigration affects policy outcomes in areas with a low ethnic or redistributive dimension and (2) immigration may have an insubstantial effect on the total size of government.

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Notes

  1. See Alesina and La Ferrara (2005), Böheim and Mayr (2005), Coen-Pirani (2011), Razin et al. (2002), Speciale (2012), Soroka et al. (2016), Stichnoth and Van der Straeten (2013) and Tabellini (2020).

  2. In recent decades, the main sources of migration inflows to OECD countries changed from labor migration to refugees or family reunion immigrants from less-developed countries (Chiswick and Hatton 2003). These latter groups often have lower skills and higher unemployment rates than the natives (Pedersen et al. 2008). Thus, (unskilled) immigrants are overrepresented among welfare-dependents in welfare states (Borjas and Hilton 1996; Borjas 1999; Boeri 2010; Hansen and Lofstrom 2003).

  3. Increased immigration also reduces the support for public education potentially because immigrant students (with limited language proficiency) shift resources away from native students (Betts and Fairlie 2003; Speciale 2012).

  4. In the Italian national elections, for instance, immigration inflows increased votes for the center-right coalition with a political platform less favorable to immigrants and these changes in voters’ preferences were partly driven by competition for public services (Barone et al. 2016).

  5. For instance, immigration has a positive impact on police spending although there is a very weak relationship between immigration and crime rates (Bell et al. 2013; Bianchi et al. 2012).

  6. General government expenditures by function (COFOG) are not available for Canada, Chile, Iceland, Mexico, New Zealand and Turkey. Data on bilateral migration stock are unavailable for Estonia, Ireland, Israel and Slovenia.

  7. In addition, countries define immigrants differently. For instance, some OECD countries define immigrants by country of birth (i.e., foreign-born population), while others define immigrants by citizenship or self-reported nationality (i.e., foreign population) (see Pedersen et al. 2008). The former definition includes foreign-born (naturalized) citizens, while the latter definition excludes foreign-born citizens but includes native-born (unnaturalized) foreigners. Depending on the system of citizenship and naturalization, each definition may overestimate or underestimate the number of immigrants (OECD International Migration Outlook 2016).

  8. We measure political corruption using the Corruption Perceptions Index by Transparency International. The index measures the extent to which corruption is perceived to exist among public officials on a reverse scale from 0 (least corrupt) to 10 (most corrupt).

  9. Thus, unskilled immigrants to the EU are overrepresented in countries with generous welfare states (Boeri 2010). In general, the European countries where immigrants have lower skill levels than natives tend to have more generous welfare benefits (Nannestad 2007).

  10. Borjas (1999) shows that immigrant welfare recipients are more heavily clustered in high-benefit US states than those who do not receive welfare. Using data from Norwegian local governments, Fiva (2009) also finds that welfare policy affects the residential choices of welfare recipients. On the contrary, using microdata from the National Longitudinal Survey of Youth, Levine and Zimmerman (1999) find little evidence that welfare benefits have a significant effect on migration decisions across US states.

  11. Both distance and cultural data were taken from the CEPII Gravity Dataset.

  12. Aleksynska and Tritah (2015) control for destination country income in the bilateral migration equation because their main equation examines the effect of immigration on the destination country income. In any case, the causal effect of supply-side determinants is not our main interest.

  13. See Kenny and Winer (2006) for the case of tax revenue.

  14. Tabellini (2020) finds that the US immigration wave in the early twentieth century caused the reduction in public spending on the services that would benefit poorer immigrants more than the native residents.

  15. Specifically, Chevalier et al. (2018) find that high-inflow cities shifted their spending toward social welfare and away from local infrastructure and schools. The potential reason is that those migrants, unlike most international migrants, were German citizens with full voting rights and were poorer than the average West Germans.

  16. Similarly, Luttmer (2001) finds that respondents increase their support for redistribution as the share of welfare recipients from their own ethnic groups increases.

  17. Countries respond to political instability by increasing military spending that will deter internal rebellion and conflicts (Gebremedhin and Mavisakalyan 2013).

  18. Governments typically determine the current year’s budget (e.g., 2008) in the prior year (i.e., 2007).

  19. We aggregate bilateral migration stocks of OECD (non-OECD) source countries to generate an OECD immigrants (non-OECD immigrants) variable for a given destination country.

  20. In general, immigrants from non-OECD origin countries are more likely to be unemployed (11.3%) than those from OECD origin countries (8.7%) (OECD Database on Immigrants in OECD Countries). This difference also holds for all levels of education (tertiary, secondary and primary).

  21. In column 2, the coefficient of the simple right-wing government term (0.789) indicates the impact of having a right-wing government if the immigration share is 0. Evaluated at the mean value of immigrant share, the marginal effect of a right-wing government dummy becomes negative and significant (not reported).

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Acknowledgements

This article is based on the first author’s doctoral dissertation at Sungkyunkwan University. We would like to thank an anonymous reviewer, Seok-ju Cho (Kyung Hee University), Woo-Hyung Hong (Hansung Univeristy), Hyunchul Kim, Minseong Kim, Minsoo Park (Sungkyunkwan University), Hee-Seung Yang (Yonsei University) and participants at the meetings of the Korean Association of Public Finance for valuable comments and suggestions.

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Authors and Affiliations

  1. Migration Research & Training Centre, Seoul, Korea

    Dowon Kim

  2. Department of Economics, Sungkyunkwan University, Seoul, Korea

    Dongwon Lee

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  1. Dowon Kim
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Correspondence to Dongwon Lee.

Appendix

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Table 7 List of 24 destination countries
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Table 8 Summary statistics
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Table 9 First stage regression results
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Kim, D., Lee, D. Immigration and the pattern of public spending: evidence from OECD countries. Int Tax Public Finance 28, 1014–1034 (2021). https://doi.org/10.1007/s10797-020-09638-x

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