Happiness and International Migration

Abstract

In this paper, we consider the extent to which the aggregate happiness of a country affects the flow of people across its borders. We merge data from the World Values Survey, which produces happiness indices for 84 countries between 1981 and 2004, with three different migration datasets: emigration rates from the Organization for Economic Cooperation and Development, immigration rates from the U.S. Census, and net migration rates from the United Nations. We find that happiness has a U-shaped relationship with emigration rates: emigration rates fall in happiness for relatively unhappy countries, but rise for relatively happy countries. The U-shaped relationship also holds for migrant flows into the U.S. When analyzing net migration rates, we find that the reverse relationship exists. Net migration is associated with an increase in happiness for relatively unhappy countries, but after a threshold level of happiness, net migration is associated with a decrease in happiness. Our findings are robust to various empirical specifications and datasets.

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Notes

  1. 1.

    Hatton and Williamson (2005) have a nice discussion about how the pressures to limit migration have increased globally during this period, even though actual migrant flows have not decreased.

  2. 2.

    http://www.un.org/esa/population/publications/2006Migration_Chart/Migration2006.pdf.

  3. 3.

    Frey and Stutzer (2002) and Di Tella and MacCulloch (2006) offer excellent reviews of this literature.

  4. 4.

    http://www.worldvaluessurvey.org/.

  5. 5.

    The WVS also reports a life-satisfaction measure, but there is no clear pattern in the economics literature about the use of happiness indices compared to measures of life-satisfaction. Life satisfaction is measured on a 10-point scale compared to the 4-point scale of the happiness index. Not surprisingly, the two indices are highly correlated. We report results using the happiness indices for ease of interpretation. However, results using the life satisfaction data are almost identical and can be obtained from the authors.

  6. 6.

    Specifically, we computed weighted averages of each country-year cell using the ‘s018’ weight. Weights are defined at: http://www.worldvaluessurvey.org/services/index.html.

  7. 7.

    http://www.oecd.org/document/51/0,2340,en_2649_37415_34063091_1_1_1_37415,00.html.

  8. 8.

    We use this measure to be consistent across datasets.

  9. 9.

    GDP data is not available for Iraq in 1995. Instead of dropping the observations, we used the 1997 GDP level to compute its growth rate. Thus, Iraq grew 58.7% between 1997 and 1999!

  10. 10.

    Regions are classified using the UN groupings, http://esa.un.org/unpp/index.asp?panel=5.

  11. 11.

    Source: United Nations Migration Report 1996.

  12. 12.

    http://esa.un.org/unpp/index.asp?panel=2.

  13. 13.

    These data were obtained from Weil (2005) and the WDI.

  14. 14.

    We use a 5-year period in calculating GDP growth rates to represent the recent growth experience of the country. The results are robust to using annual GDP growth rates.

  15. 15.

    We considered turning Model 2 into a model of immigration, where the independent variables would be unique to the destination country (the U.S.) But since the destination country is the same across the observations, we do not get enough variation in the variables to yield significant coefficient estimates. We also considered a model where the independent variables are in relative terms (for example, relative happiness was defined as U.S. happiness divided by the source country happiness). Once again, there was little variation in U.S. happiness over the time period. Thus, we arrive at Model 2 that represents the flow of people out of country i and into the U.S. in each year as a fraction of country i’s population.

  16. 16.

    For all three models, various statistical models were considered, specifically, OLS models with log and square root transformations as well as generalized linear models with Gaussian and gamma families and multiple link functions. Using AIC as a model-selection criterion, none of these models were substantially better than the OLS model, and due to the ease of interpretation of the OLS model, it was chosen. In fact, the predictions from all models considered gave similar results.

  17. 17.

    We calculate the inflection point by differentiating the estimated models. h* is based on the estimated coefficients for the happiness indicator (β1) and its square (β2), holding all other variables constant. That is, h* = −β1/2β2.

  18. 18.

    Note that we also consider past GDP growth. The results are similar to those presented. Ideally, we would use GDP in the previous 5 years to be consistent with the other models, but in doing so we lose eight observations.

  19. 19.

    The set of control variables considered is smaller than in Model 1 because we do not have time series data on variables such as Gini coefficients, corruption, rule of law, etc.

  20. 20.

    These thresholds are calculated for specification C in all three cases.

  21. 21.

    In their study, the life orientation test was used to measure optimistic versus pessimistic attitudes towards the future.

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Acknowledgments

We wish to thank Michelle Wiggins for excellent research assistance. This paper benefited from helpful comments made by Takao Kato, Giovanni Peri, Ann Own, seminar participants at Lafayette College, the Midwest Economic Association 2007 meetings in Minneapolis, and the American Economic Association 2009 meetings in San Francisco, CA. All errors are our own.

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Correspondence to Linnea A. Polgreen.

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Polgreen, L.A., Simpson, N.B. Happiness and International Migration. J Happiness Stud 12, 819–840 (2011). https://doi.org/10.1007/s10902-010-9229-3

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Keywords

  • International migration
  • Happiness
  • Human development
  • Income