Networks and migrants’ intended destination

The share of the world population that does not reside in its country of birth stands at around 3.3 percent. This figure has barely changed over the past decades, as the growth in the absolute number of international migrants has been roughly in line with the growth of the world population. It furthermore appears quite low given the extent of economic (and demographic) imbalances between different countries of the world. Imagine that you have to play a variant of the popular “Who’s Who?” game, where you have to guess the position of a person - selected by the other player but whose identity is unknown to you - in the world income distribution. The rules of this game allow you asking just one single question about this person, and you wonder which question you should opt for. Or, to put it differently, what is the information that allows you to refine your guess about his or her level of income? The empirical evidence provided by Milanovic (2015) provides a clear guidance: ask in which country this person was born. Within-country income inequalities pale with respect to differences in average income across countries, so that the country of birth largely pins down a person’s position in the world income distribution.

Differences in income across countries can reflect either differences in the characteristics (notably, the level of education) of individuals living in different countries, or differences in the productivity of an identical worker across countries, or both. Clemens, Pritchett and Montenegro (forthcoming) provide evidence of the extent to which the wages of workers with identical characteristics vary across countries. A low-skilled male worker would, on average, increase his annual income by more than $13,700 per worker per year if he migrated to the United States. Natural or policy-induced barriers prevent individuals from moving from lower to higher-income countries, thus climbing up the world income distribution from the point that is determined by the country in which they were born.

Which are the factors that allow (just) 3.3 percent of the world population to overcome the cliff that exists along most borders? An article that is forthcoming on the Journal of Economic Geography provides novel empirical evidence about the role that migrant networks play in shaping the initial step in the migration process, namely the choice of the intended destination. Bertoli and Ruyssen (forthcoming) rely on individual-level data from 419 surveys conducted by Gallup in 147 countries of the world between 2007 and 2011 to shed light on how direct connections to individuals residing in other countries of the world influence the choice of the preferred destination for the respondents who intend to move abroad. Specifically, the Gallup World Polls contain the following question: “Ideally, if you had the opportunity, would you like to move to another country, or would you prefer to continue living in this country?”, and ``To which country would you like to move?'' for the individuals who provide an affirmative answer to the first question. Furthermore, participants to the surveys are also asked: “Do you have relatives or friends who are living in another country whom you can count on to help you when you need them, or not?”, allowing them to list up to three countries in which they can rely on what Bertoli and Ruyssen (forthcoming) call a distance-one connection.

The natives aged 15 to 49 intend to migrate towards 185 different countries in the world, with a (highly) uneven distribution of intending migrants across (intended) destinations. Specifically, 29.3 percent of the individuals would ideally migrate to the United States, followed by the United Kingdom (7.9), Canada (6.5), France (5.7) and Australia (4.8). The first 20 countries are chosen by around 87.5 percent of all intending migrants, while the total share of the 95 countries at the bottom of the list stands at just 1.0 percent. Initial analyses of the data drawn from the Gallup World Polls strengthen the case for using the answer to these questions. Furthermore, variations over time in bilateral migration intentions correlate well with variations in actual bilateral migration flows to OECD destinations even after controlling for the time-varying conditions at origin and at destination, and for the usual dyadic determinants of actual migration flows. This, coupled with the fact that the distribution of distance-one connections across countries closely mirrors the actual distribution of migrants coming from the same origin, corroborates the case that the analysis of migration intentions can be informative about the role played by migrant networks in shaping actual migration decisions.

Bertoli and Ruyssen (forthcoming) estimate an origin-specific conditional logit model that describes the choice of intending migrants among the alternative destinations, and that controls for the dependency of the attractiveness of each destination on the size of the bilateral migrant stock, the variable that is traditionally used in the literature. Having a distance-one connection in a country is, on average, associated with an increase in the relative odds of opting for that destination by six to eight times. Distance-one connections are able to invert the ranking of two destinations defined on the basis of their attractiveness for intending migrants in 56.5 percent of the cases.

Direct connections in a potential destination appear to be a key resource for crossing an international border, thus increasing the difficulty for a player of the “Who’s Who” game that we described above to guess a person’s position in the world income distribution.
 

References

Bertoli, Simone and Ilse Ruyssen. “Networks and migrants’ intended destination.” Journal of Economic Geography, forthcoming.

Clemens, Michael A., Montenegro, Claudio and Pritchett, Lant. “Bounding the Price Equivalent of Migration Barriers." Review of Economics and Statistics, forthcoming.

Milanovic, Branko. "Global inequality of opportunity: How much of our income is determined by where we live?." Review of Economics and Statistics 97.2 (2015): 452-460.