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Stymied ambition: does a lack of economic freedom lead to migration?

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Abstract

We investigate the relationship between economic freedom and international migration for the 1980–2010 period using a dataset on migration from 91 emerging countries to the 20 most attractive OECD destination countries. We find that more economic freedom at home discourages high-skilled migration, but not low-skilled migration. The negative association between economic freedom and high-skilled emigration also holds when we estimate (dynamic) panel models that allow for endogeneity in the economic freedom-migration nexus. In sum, our findings suggest that high-skilled migration is especially responsive to the economic incentives resulting from economic freedom.

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Notes

  1. Throughout this paper, we employ the definition of economic freedom used by Gwartney et al. (2014: 1):

    “Economic freedom is present when individuals are permitted to choose for themselves and engage in voluntary transactions as long as they do not harm the person or property of others. […] The cornerstones of economic freedom are (1) personal choice, (2) voluntary exchange coordinated by markets, (3) freedom to enter and compete in markets, and (4) protection of persons and their property from aggression by others.”

  2. Adjacent to our study are previous empirical efforts that analyze the interaction between migration and political (rather than economic) freedom in the context of migration and asylum-seeking (e.g. Neumayer 2005; Hatton 2009; Docquier et al. 2016). In our analysis, we control for the effect of political variables when studying the economic freedom-migration nexus.

  3. For overviews of the economic theory of migration, see Borjas (1989, 1999), Bauer and Zimmermann (1998), Van Dalen et al. (2005: 744–749) and Bodvarsson and Van den Berg (2009: 27–57).

  4. Classifying migration determinants as push or pull factors harks back to Lee (1966). For an economic application of these ideas, see Zimmermann (1996) who discusses the idea of push and pull migration in the context of European migration.

  5. For one, being able to be entrepreneurially active is likely to increase personal income. For another, mental satisfaction from entrepreneurship may also affect a migrant’s utility considerations. There may be cases of “lifestyle migration,” meaning that migration decisions are made to follow a certain lifestyle centering on opening up a business. For instance, Stone and Stubbs (2007) in their field study in France and Spain find that lifestyle considerations are indeed relevant to the decision of immigrants to become entrepreneurs.

  6. Migrants may also become self-employed out of necessity, e.g., due to discrimination in the labor market (Constant and Zimmermann 2006).

  7. Borjas (1999) discusses the Roy model and the idea of migrant self-selection in more detail.

  8. A list of all source countries is provided in Appendix 2.

  9. Chile has been an OECD member only since 2010. OECD countries receive the majority of high-skilled migrants and about half of all international migration (Docquier and Rapoport 2012: 684).

  10. The analytical approach taken in our study, where only conditions in sending countries are considered, has previously been taken in, e.g., Docquier et al. (2007), Dreher et al. (2011), Dimant et al. (2013), and Cooray and Schneider (2016). Future research, however, may explicitly analyze the relationship between economic freedom and migration in a gravity model using dyadic data. As pointed out by a referee, it may not only be interesting to understand whether low levels of economic freedom in the source country predict migration but also whether relative differences in economic freedom between source and target countries play a role. The IAB Dataset, however, only provides data on a limited set of 20 OECD target countries that are very similar with respect to their level of economic freedom, making a gravity approach unpromising.

  11. Time-invariant variables (colonial ties, common languages, etc.) are also often used to explain migration (Mayda 2010). We account for these variables via a fixed-effect approach. Furthermore, a number of other potential migration determinants have been named in relevant literature, e.g., demographic pressures such as rapid population growth (Hatton and Williamson 2003). As a robustness check, we amend our baseline model with additional variables for trade openness, economic structure (measured by the value added in agriculture), population density, and population growth. Adding these variables does not change the main findings of our study concerning the relationship between economic freedom and migration (results available upon request).

  12. Economic freedom and per capita income are moderately strongly correlated for our sample (r = 0.46). Consequently, as a robustness check, we drop per capita income from our regression models. This does not change our main findings concerning the effect of economic freedom on migration (results available upon request).

  13. The Freedom House dataset also provides information on the lack of civil liberties (concerning, e.g., the prevalence of religious discrimination, infringements of free speech, poor rule of law). Substituting the variable measuring the lack of political freedom with one indicating the lack of civil liberties as a robustness check, however, delivers results virtually identical to the ones reported in the main text. This is unsurprising given the very high correlation between both indicators (r = 0.91). As another robustness check, we replace the political rights variable with the Polity2 score from the Polity4 Dataset (Marshall et al. 2014), which is an alternative measurement of political development more strongly focusing on key constitutional elements. Here, we again detect a high correlation between this measure and our political rights variable (r = −0.84) and find that our main results also remain robust to interchanging the indicators (all robustness checks available upon request).

  14. A similar argument is made by Docquier et al. (2016) concerning feedback between migration and political freedom. Docquier and Rapoport (2012) offer an overview of economic channels through which migration may affect sending countries, e.g., remittances, return migration, diaspora effects, and the transfer of knowledge and institutions.

  15. The system-GMM estimator does not directly eliminate country-specific effects but uses appropriate instruments (lagged differences of the explanatory variables) to control for them. These instruments are valid under the assumption that the correlation between the error term and the levels of the explanatory variables is constant over time, so there is no correlation between the differences of the explanatory variables and the country-specific effects.

  16. The system-GMM estimator is described in more detail in Bond (2002) and Roodman (2009).

  17. As pointed out by a referee, system-GMM estimates may depend upon the instrument set employed. We consequently experimented with different instrument sets to assess the robustness of the system-GMM estimates to such changes; the results of these efforts are in line with those reported in the main text (results available upon request).

  18. In addition to low- and high-skilled migration, we also run analyses with the total and medium-skilled migration rates as dependent variables. As shown in Appendix 1 (Supplementary Table 1), these two migration rates also share no consistent association with economic freedom, a finding mirroring our results for the effect of economic liberty on low-skilled migration.

  19. In Appendix 1 (Supplementary Table 2), we show that the migration-dampening effect of economic freedom is also present when we differentiate between male and female high-skilled migration rates. That is, both high-skilled male and female migration are negatively associated with economic freedom, where the size of the respective effects is comparable.

  20. An increase in economic freedom by one standard deviation is equivalent to an increase in economic freedom by 18%. For our observation period (1980–2010), such improvements in economic freedom can be observed for a variety of emerging economies in Eastern Europe (e.g., Slovakia), Asia (e.g., South Korea), the Middle East (e.g., Jordan), Latin America (e.g., Guatemala), and Sub-Saharan Africa (e.g., Ghana). We expect such improvements to correlate with less high-skilled out-migration. Indeed, to give a concrete example, in Singapore, economic freedom between 1980 and 2012 improved by 13.5%, while high-skilled emigration declined by 45.24%. Of course, such changes in emigration rates are only illustrative and may be driven by other demographic, political, and economic effects as well.

  21. In our case, the mean skill-mix can be derived as the mean high-skilled emigration rate (0.156) divided by the mean low-skilled emigration rate (0.034). The change is derived by keeping the low-skilled emigration rate constant, while diminishing the high-skilled emigration rate by 14.35% (which is the System-GMM result).

  22. For a further discussion of the potential economic costs (“brain drain“) and benefits (“brain gain”) of high-skilled migration, see Docquier and Rapoport (2012).

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Acknowledgments

The authors want to thank three anonymous referees, Klaus F. Zimmermann as well as the participants of the 2016 European Public Choice Society Meeting, the 2016 Annual Congress of the European Economic Association and the 2016 Annual conference of the Verein fuer Socialpolitik for their very helpful comments.

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  1. University of Freiburg, Wilhelmsstraße 1b, 79098, Freiburg, Germany

    Daniel Meierrieks & Laura Renner

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  1. Daniel Meierrieks
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Correspondence to Daniel Meierrieks.

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Responsible editor: Klaus F. Zimmermann

Appendices

Appendix 1. Additional Estimates

Table 6 Other migration variables and economic freedom
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Table 7 High-skilled migration by males and females and economic freedom
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Appendix 2

Table 8 List of source countries
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Appendix 3. Main variables

3.1 Migration rates

The emigration rates in the IAB brain drain dataset are measures for the share of immigrants from one source country living in the 20 OECD countries relative to the sum of residents and emigrants from the source country with the same level of education (Brücker et al. 2013: 5).

Formally, Brücker et al. (2013: 6) define the emigration rate for skill level e with gender s from source country i in period t as:

$$ {m}_{i, t, e, s}=\frac{M_{OECD{20}_{s, e, t}}^i}{R_{s, e, t}^i + {M}_{OECD{20}_{s, e, t}}^i} $$
(A1)

R i s,e,t denotes the total number of residents in source country i, \( {M}_{OECD{20}_{s, e, t}}^i \) measures the stock of immigrants from i summed over all 20 destination countries.

Data on migrant population is obtained from the respective National Statistics Offices or the IPUMS (Integrated Public Use Microdata Series) website (a detailed overview is given in Brücker et al. (2013:7–11)). Only persons born abroad and aged 25 years or older are considered. The imputation method in case of missing data is in detail explained by Brücker et al. (2013:4). For the calculation of R i s,e,t , the population shares of high, medium, and low skilled individuals from Barro and Lee (2013) are applied (Brücker et al. 2013: 5).

Data and educational categories by Barro and Lee (2013) are widely used as their dataset is one of the most comprehensive sources for measures on international human capital. For an overview, see Hanushek (2013). The educational categories considered by Brücker et al. (2013: 2) are primary (low skilled: includes lower secondary, primary, and no schooling); secondary (medium-skilled: high-school leaving certificate or equivalent) and tertiary education (high-skilled: higher than high-school leaving certificate or equivalent). These categories are consistent with those used by Barro and Lee (2013) who in turn rely on internationally agreed definitions, e.g., the UNESCO International Standard Classification of Education.

3.2 Economic freedom

The Fraser Institute index of economic freedom is a summary index constructed from five components. The components in turn make use of 42 distinct variables. For instance, to construct the first component of the economic freedom index (government size) information on, inter alia, government consumption, tax rates and the share of government subsidies and transfers to total economic activity are used. An overview of all 42 variables is given in Gwartney et al. (2014: 4).

The five major components of economic freedom are:

  1. 1.

    Size of government. This component measures the influence of the political process on resource allocation. More government interference into the economic life (e.g., in the form of transfers) reduces economic freedom (Gwartney et al. 2014: 3).

  2. 2.

    Legal system and property rights. This component measures the security of property rights and the strength of the legal system, e.g., by considering the impartiality and independence of courts. A stronger legal system and more secure property rights correspond to higher levels of economic freedom (Gwartney et al. 2014: 5).

  3. 3.

    Sound money. This component considers the reliability of monetary policies. For example, price stability results in more economic freedom (Gwartney et al. 2014: 5).

  4. 4.

    Freedom to trade internationally. This component measures barriers to international trade, e.g., in the form of tariffs. Fewer barriers correspond to higher levels of economic freedom (Gwartney et al. 2014: 6).

  5. 5.

    Regulation. This component measures regulatory restraints that limit the freedom of exchange in credit, labor, and other market regulations (e.g., business starting costs). Fewer regulatory restraints mean that economic freedom is higher (Gwartney et al. 2014: 6).

Table 9 List of countries with mean high-skilled migration rates and mean economic freedom over time
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Meierrieks, D., Renner, L. Stymied ambition: does a lack of economic freedom lead to migration?. J Popul Econ 30, 977–1005 (2017). https://doi.org/10.1007/s00148-017-0633-4

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