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A unique survey which tracks worldwide the best and brightest academic performers from three Pacific countries is used to assess the extent of emigration and return migration among the very highly skilled, and to analyze, at the microeconomic level, the determinants of these migration choices. Although we estimate that the income gains from migration are very large, not everyone migrates and many return. Within this group of highly skilled individuals the emigration decision is found to be most strongly associated with preference variables such as risk aversion, patience, and choice of subjects in secondary school, and not strongly linked to either liquidity constraints or to the gain in income to be had from migrating. Likewise, the decision to return is strongly linked to family and lifestyle reasons, rather than to the income opportunities in different countries. Overall the data show a relatively limited role for income maximization in distinguishing migration propensities among the very highly skilled, and a need to pay more attention to other components of the utility maximization decision. Keywords: AbstractA unique survey which tracks worldwide the best and brightest academic performers from three Pacific countries is used to assess the extent of emigration and return migration among the very highly skilled, and to analyze, at the microeconomic level, the determinants of these migration choices. Although we estimate that the income gains from migration are very large, not everyone migrates and many return. Within this group of highly skilled individuals the emigration decision is found to be most strongly associated with preference variables such as risk aversion, patience, and choice of subjects in secondary school, and not strongly linked to either liquidity constraints or to the gain in income to be had from migrating. Likewise, the decision to return is strongly linked to family and lifestyle reasons, rather than to the income opportunities in different countries. Overall the data show a relatively limited role for income maximization in distinguishing migration propensities among the very highly skilled, and a need to pay more attention to other components of the utility maximization decision..

Measuring the gain in income from migration is complicated by non-random selection of migrants from the general population, making it hard to obtain an appropriate comparison group of non-migrants. This paper uses a migrant lottery to overcome this problem, providing an experimental measure of the income gains from migration. New Zealand allows a quota of Tongans to immigrate each year with a lottery used to choose amongst the excess number of applicants. A unique survey conducted by the authors in these two countries allows experimental estimates of the income gains from migration to be obtained by comparing the incomes of migrants to those who applied to migrate, but whose names were not drawn in the lottery, after allowing for the effect of non-compliance among some of those whose names were drawn. We also conducted a survey of individuals who did not apply for the lottery. Comparing this non-applicant group to the migrants enables assessment of the degree to which nonexperimental methods can provide an unbiased estimate of the income gains from migration. We find evidence of migrants being positively selected in terms of both observed and unobserved skills. As a result, non-experimental methods are found to overstate the gains from migration, by 9 to 82 percent. A good instrumental variable works best, while difference-in-differences and bias-adjusted propensity-score matching also perform comparatively well.

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Popular DMSP night lights data are flawed by blurring, top-coding, and lack of calibration. Yet newer and better VIIRS data are rarely used in economics. We compare these two data sources for predicting Indonesian GDP at the second sub-national level. DMSP data are a bad proxy for GDP outside of cities. The city lights-GDP relationship is twice as noisy using DMSP as using VIIRS.Spatial inequality is considerably understated with DMSP data. A Pareto adjustment to correct for top-coding in DMSP data has a modest effect but still understates spatial inequality and misses key features of economic activity in Jakarta.