Are immigrants really attracted to the welfare state? Evidence from OECD countries


This paper examines the impact of fiscal policies on both the size and educational levels of immigrants in destination countries. We find that whether or not a country’s policies are attracting highly educated immigrants goes beyond the issue of the “welfare state”. Immigrants are making important distinctions between the different benefits provided by a receiving country’s government. Health and education spending are found to have a positive impact on the education levels of immigrants while the reverse is true for unemployment and retirement benefits. Welfare programs are found to be insignificant once other government programs/taxes and other factors are taken into account. These results imply that countries should be less concerned about whether they are a “big government” with regards to attracting immigrants, and more concerned with what types of benefits they offer.

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  1. 1.

    Massey et al. (1994) and Taylor and Yunez-Naude (1999) provide a good review of different migration theories.

  2. 2.

    Some examples of the empirical Tiebout research includes papers such as Banzhaf and Walsh (2008), Buchanan and Goetz (1972), Cebula (2009), Cebula and Kafoglis (1986), Cushing (1993), Day (1992), Day and Winer (2001), Flatters et al. (1974), Koven and Shelley (1989), Mazzaferro and Zanardi (2008), Ott and Shadbegian (1993), Shaw (1986) and Starrett (1980).

  3. 3.

    Given the nature of international data, local property tax rates such as those utilized by Oates (1969) cannot be studied. Housing values, while measured on a macro scale, are not available for enough destination countries to be utilized in analyses during the timeframe of our study.

  4. 4.

    For examples see Docquier et al. (2008); Fan and Stark (2007); Lien and Wang (2005); or Yabuuchi and Chaudhuri (2007).

  5. 5.

    See Borjas et al. (1992); Chiswick (1999); Chiquiar and Hanson (2002); or Hunt and Mueller (2004).

  6. 6.

    We thank an anonymous referee for pointing out that in countries where health care is not provided by the government, it is possible that some immigrants may choose to not participate in private health care coverage, and therefore allow for a higher level of private consumption. However, even in countries such as the United States that do not have nationalized health care systems, there is still de facto government spending on health care for those who cannot afford it, including immigrants, implying that government spending on health care will still indirectly increase. In addition, given the cross-section of countries that are considered destination countries, the vast majority have some form of government supported (or fully nationalized) health care, making the number of countries with strict private health insurance the only option relatively small. While disaggregating the different types of health care coverage among countries is an interesting question, it goes beyond the scope of this paper.

  7. 7.

    We assume that there is no correlation between the likelihood of an immigrant finding employment in a destination country and the generosity of that country’s immigrant-eligible benefits (that γ w and γ B behave independent of one another).

  8. 8.

    The γ term is only necessary for the destination countries in Eq. (12), as we assume that immigrants have full knowledge regarding employment prospects, government benefits and eligibility in their source country. Additionally, this simplification is made due to developing country data limitations.

  9. 9.

    The idea of using expected wage differentials in migration models is well-established in economics, beginning with the work of Harris and Todaro (1970). One can consider the notion of expected benefits to be an extension of that concept.

  10. 10.

    The destination countries are Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, and United States.

  11. 11.

    Beine et al. (2007) were able to source the education for a subset of immigrants with tertiary education and results were found not to change as compared to the original data.

  12. 12.

    We converted to log form to scale the variable.

  13. 13.

    Note that the theoretical model has fiscal variables entering as differences between destination and source countries. However, these fiscal variables are unavailable for many developing countries, making the use of source country fiscal variables infeasible. This is unavoidable and is common in the literature (see Marfouk 2008 and Peridy 2006 for examples). Difference variables are available and included for many of the non-fiscal variables.

  14. 14.

    Although wages are separated from the direct and indirect costs which defined the vector X sd in the theory section, to simplify the presentation of our estimated model we will lump the wage variable into the vector X sd .

  15. 15.

    To convert the wage difference data to logs, we had to first normalize it since some differences were negative.

  16. 16.

    Countries in the sample with a high percentage of asylum applications accepted also tend to receive a high number of applications. The correlation in the sample between # of asylum applications and percentage accepted is 0.83. This data is from the United Nations High Commissioner for Refugees (1999).

  17. 17.

    The United States is one of very few countries which seek to tax its citizens income earned while working abroad. However, this is mitigated by tax treaties (to avoid double-taxation) which the United States has with all of the destination countries in our sample. The amount of foreign earned income automatically exempted is high enough that even without a tax treaty, less than 10 % of U.S. citizens would have any tax liability from income earned abroad.

  18. 18.

    Results available on request.

  19. 19.

    The first step involves identifying the regressor(s) suspected to be endogenous. Next, a regression is run with the suspected endogenous variable as the dependent variable. The predicted residuals from this regression are then used as an independent variable in the original equation. If the coefficient on the residuals variable is significant, then the variable is likely endogenous.

  20. 20.

    The Hausman-Wu test rejects the null of exogeneity with a p-value of 0.00 for both welfare spending and health/education spending for both dependent variables M sd and E sd .

  21. 21.

    Note that fiscal variables such as P and H, B, and R are available only for destination countries, hence the d subscript. This is also true of the instruments (Z). The vector X of control variables also contains some variables that are only available for destination countries. Others are differences between values in source and destination countries. Still others pertain to source countries. For convenience we subscript the X variable with sd.

  22. 22.

    This is accomplished through using Stata’s reg3 command with the 2sls option specified.

  23. 23.

    An alternative specification (results on request) included inequality squared to test if this would change the unexpected sign, as suggested by Borjas (1987) and Peridy (2006), but it did not.

  24. 24.

    Note the caveat mentioned earlier about the European Parliament directive (2003) aggregating pensions after 2003. Our data is, however, prior to that directive.


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Correspondence to Michael A. Quinn.


Appendix A–Instrument survey questions

Five of the instruments are based on survey questions. ISSP survey questions as a predictor of attitudes on public expenditure programs has precedence in the work of Mazzaferro and Zanardi (2008). These questions are on the topics of education, care for the elderly, health, housing and unemployment compensation. These survey instruments were chosen because they would be directly related to the income assistance and health/education spending variables but not to the number or composition of immigrants. These are cross-country survey questions, administered in a yes/no format. The variable values are the percentage of “yes” answers to each question. The text of the questions are:

  1. a.)

    Is it the responsibility of the government to provide free education to all people?

  2. b.)

    Is it the responsibility of the government to provide care and support for the elderly?

  3. c.)

    Is it the responsibility of the government to provide health care for all people?

  4. d.)

    Is it the responsibility of the government to ensure adequate housing for all people?

  5. e.)

    Is it the responsibility of the government to provide a decent standard of living for the unemployed?

Appendix B–First stage results

Table 5.

Table 5 First stage regression results

Appendix C–Results with additional variables

By adding three additional variables, we attempted to gain further insight into fiscal effects. The first two variables were property and sales taxes in the destination country as a percentage of GDP. These are measured using tax revenues from all levels of government. The property and sales tax variables are taken from the OECD Tax Revenue Statistics and OECD Consumption Tax Trends, respectively (OECD 2005a, 2012). The motivation behind these variables is that immigrants may respond differently to varying types of taxes. Taxes can be collected and services provided at different levels of government. Some countries use a federal (versus unitary) system of government that allows for more tax and spending decisions to be made at sub-national levels of government. Thus, we created a dummy variable equal to one for those eight destination countries in our sample that use a federal system of government. The federal system countries are Australia, Austria, Belgium, Canada, Germany, Spain, Switzerland and the United States (Forum of Federations 2012).

Results are robust to the inclusion of the new variables although there are a few changes worth noting. For the number of immigrants analysis, including the new variables causes the coefficients on the income tax, Gini coefficient and skill-based visa program to become insignificant. The new property tax variable is insignificant in the number of immigrants regression but the sales tax is negative and significant. The federal system is also negative and significant. There are no significant changes in the impact of government spending programs in this regression.

For the educational level analysis, the property tax and federal variables are both positive and significant and the income tax variable also becomes positive. The sales tax variable is insignificant in this case. Income assistance programs become negative and significant, which was expected by the model but not found in the paper’s main results. Interestingly, both economic freedom and income inequality (Gini coefficient) change from being positive to negative. The asylum variable becomes insignificant.

These unexpected changes raised our suspicions about multicollinearity. The VIF statistics for the results in the body of the paper were quite low (1.5–2.7) but for this supplemental analysis were rather high (1.2–8.8) suggesting the presence of significant multicollinearity. Further explanatory regressions uncovered complex relationships involving these new variables. Specifically, it was found that countries with higher economic freedom scores and/or higher rates of income inequality were significantly more likely to have higher property taxes and low sales taxes. Federal systems were more likely to have both lower property and lower sales taxes. It is likely that these relationships are the source of the increased multicollinearity. Due to the multicollinearity issue, one should use caution in interpreting these results.

Table 6.

Table 6 Results with additional tax and federalism variables

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Jackson, A.L., Ortmeyer, D.L. & Quinn, M.A. Are immigrants really attracted to the welfare state? Evidence from OECD countries. Int Econ Econ Policy 10, 491–519 (2013).

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  • Migration
  • European union
  • Fiscal
  • Welfare

JEL codes

  • J1
  • J6
  • F2