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Inverting the Logic of Economic Migration: Happiness Among Migrants Moving from Wealthier to Poorer Countries in Europe

Abstract

Migration from a poorer country to a wealthier one often results in a lower relative economic status for the migrant (even when it increases their incomes in an “absolute” sense)—and thus perhaps results also in a decrease in his/her happiness. By the same logic, migration from a wealthy country to a poorer one might bring a higher status position for the migrant and so might raise his/her happiness. This paper investigates happiness among migrants who move from northern European countries to Spain, Portugal, Greece and Cyprus, comparing them to stayers in the origin countries (Belgium, Switzerland, France, Germany, Britain, and the Netherlands). The analysis shows that migrants are less happy than stayers, in a bivariate comparison and a conventional regression model. A consideration of results from “treatment models” and matching analyses suggests that the difference represents a decrease in happiness for the migrants (and not a difference in happiness prior to migration), contrary to an expectation rooted in an anticipated increase in economic status. Migrants have lower relative incomes than stayers; when relative income is controlled, the happiness disadvantage of migrants is smaller. Controlling additionally for absolute income does not lead to further change in that difference.

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Notes

  1. 1.

    Stark and Taylor drew on a well-established sociological concept of relative deprivation (Merton 1968; Runciman 1966)—an idea that resonates strongly with the emphasis of happiness studies on relative income.

  2. 2.

    The economic differences between the various European origin and destination countries are small compared to the larger international differences apparent at a global level. It is possible that the change in relative status for intra-European migrants is not sufficiently large to lead to a gain in happiness.

  3. 3.

    Immigrants in the northern countries were removed from the dataset.

  4. 4.

    In effect, then, the survey design implied that earning €300 a month in Romania was equivalent to earning €300 a month in France.

  5. 5.

    As with most survey data, non-response rates for the income question are decidedly non-trivial, with roughly twenty per cent declining to answer in the sample considered here. The structure of non-response was explored using techniques of multiple imputation (Rubin 1987; Royston 2004). The data created via imputation provided no basis for believing that non-response was selective according to income level; for migrants there was no difference whatsoever in the mean, and for stayers the difference was trivial. The analysis below, then, reports results from the original data, without imputation (thus proceeding via listwise deletion).

  6. 6.

    See Cong and Drucker (2001) for details of Stata’s “treatreg” routine and Maddala (1983) for the underlying model.

  7. 7.

    Rates for vocational qualifications do not differ for the two comparisons, and so those who selected the vocational response are removed from these figures for presentational reasons.

  8. 8.

    The specification here uses the more conservative “two-step” option in place of full-information maximum likelihood. Use of sample weights is then not possible.

  9. 9.

    In regression analysis one might aspire to maximize r-squared, the proportion of variation explained by the model. That aspiration, in combination with the desire to avoid “omitted variable bias”, can lead one to add more control variables—perhaps without thinking clearly about what doing so means in the context of one’s research question.

  10. 10.

    In a probit model of migration, all of those variables except gender are statistically significant at conventional levels.

  11. 11.

    It might be disconcerting not to see a table providing more detailed information about these results—but the Stata output for matching analyses does not give any additional useful information (the only other figures reported are the standard error of the coefficient, the associated “z” and “p”, and a confidence interval).

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Acknowledgments

I am grateful to Marcy Brink-Danan for comments that helped to clarify the core idea for this paper.

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Correspondence to David Bartram.

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Bartram, D. Inverting the Logic of Economic Migration: Happiness Among Migrants Moving from Wealthier to Poorer Countries in Europe. J Happiness Stud 16, 1211–1230 (2015). https://doi.org/10.1007/s10902-014-9554-z

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Keywords

  • International migration
  • Happiness
  • Subjective well-being
  • Europe
  • Matching methods