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Do Non-Economic Quality of Life Factors Drive Immigration?

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Abstract

This paper contributes to the immigration literature by generating two unique non-economic quality of life (QOL) indices and testing their role on recent migration patterns. Applying the generated QOL indices in conjunction with four independent welfare measures to an augmented gravity model of immigration, this paper finds an insignificant relationship between the six non-economic QOL measures and immigration flows for a panel of 16 OECD countries from 1991 to 2000. However, the results suggest that other factors such as the stock of immigrants from the source country already living in the OECD destination country, population size, relative incomes, and geographic factors all significantly drive the flow of immigration for the sample tested.

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Notes

  1. These include Australia, Belgium, Canada, Denmark, Finland, France, Germany, Hungary, Japan, Netherlands, Norway, Portugal, Sweden, Switzerland, United Kingdom, and the United States.

  2. For an overview of subjective measures of quality of life—see Veenhoven (2004).

  3. Eighty two countries cover all source countries and the 16 OECD destination countries, on which data is available on migration patterns via the OECD migration database. The complete list of source countries per OECD country can be obtained from the authors upon request.

  4. We acknowledge that the choice of demographic and geographical variables was led by data availability. Consequently, other commonly cited non-economic factors such as crime rates, health services and educational facilities could not be included here. However, our coverage of indicators is wide enough to assume that some variables chosen could act as proxies for those variables missing. For example, immunization rates as a proxy for access to health services, and the combined enrollment ratio as an indicator of educational facilities.

  5. Although there are contradictory theories regarding the appropriate methodology in constructing indices (see Lubotsky and Wittenberg 2006), we find that principal component analysis is the optimal method, given our data and research focus in this paper.

  6. Because there are unequal immigrant observations per destination country over time (1991–2000), an uneven panel is constructed with 2,710 total observations per variable. The panel is a consistent with respect to inclusion, however. The same source countries are used for all variables across the time periods. Roughly 39.4% of immigrants were intra-OECD, while the remaining 60.4% were from outside OECD countries. Data for Happy and ESI were only available for year 2000 and have 253 and 250 data points, respectively.

  7. Equations 7 and 8 were rerun without the gravity model’s non-economic control variables (e.g. elimination of language dummy and distance) and the six a 8 results did not change in size, sign or significance. We also ran a simple fixed effects estimation with the six non-economic quality of life instruments and found that they remained insignificant.

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Authors and Affiliations

  1. Auckland University of Technology, Auckland, New Zealand

    Gail Anne Pacheco & Stephanie Rossouw

  2. Bradley University and IZA, Peoria, IL, USA

    Joshua Lewer

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  1. Gail Anne Pacheco
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  2. Stephanie Rossouw
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  3. Joshua Lewer
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Correspondence to Stephanie Rossouw.

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Pacheco, G.A., Rossouw, S. & Lewer, J. Do Non-Economic Quality of Life Factors Drive Immigration?. Soc Indic Res 110, 1–15 (2013). https://doi.org/10.1007/s11205-011-9924-4

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