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Brain Drain in Developing Countries Frederic Docquier, Olivier Lohest, and Abdeslam Marfouk An original data set on international migration by educational attainment for 1990 and 2000 is used to analyze the determinants of brain drain from developing countries. The analysis starts with a simple decomposition of the brain drain in two multiplicative components, the degree of openness of sending countries (measured by the average emigration rate) and the schooling gap (measured by the education level of emigrants compared with natives). Yet recent theoretical studies emphasize several compensatory effects, showing that a limited but positive skilled emigration rate can be beneficial for sending countries (Commander, Kangasniemi, and Winters 2004; Docquier and Rapoport 2007; Beine, Docquier, and Rapoport 2001, forthcoming; Schiff 2005 provides a critical appraisal of this literature). However, without reliable comparative data Frederic Docquier (corresponding author) is a research associate at the National Fund for Economic Research; professor of economics at the Universite Catholique de Louvain, Belgium; and research fellow at the Institute for the Study of Labor, Bonn, Germany; his email address is docquier ires.ucl.ac.be. Olivier Lohest is a research is a researcher at the Institut Wallon de l'Evaluation, de la Prospective et de la Statistique, Regional Government of Wallonia Section I presents the data set on the brain drain, as measured by the emigration rate of post-secondary-educated workers, and describes the average brain drain from developing countries by income group and country size. Section II decomposes the brain drain into two multiplicative components: the degree of openness, measured by the average emigration rate of workingage natives, and the schooling gap, measured by the relative education attainment of emigrants compared with natives. 202 THE WORLD BANK ECONOMIC REVIEW The Docquier Marfouk (2006) study, which collected census, registry, and survey data from all OECD countries, enables the size of these biases for developing countries to be evaluated. 40 million) 1990 Worlda High-income countries Developing countries Low-income countries Lower medium-income countries Upper-medium-income countries Least developed countries Landlocked developing countries Small island developing economies Large developing countries (. Cross-Section Regression Results (2000 data) OLS-1 General model Variable Country size Native population (logs) Small island developing economies Level of development Proportion of post-secondary educated natives 100 (logs) GNI per capita (logs) Least developed country Oil exporting country Sociopolitical environment Political stability Government effectiveness Religious fractionalization Geographic and cultural proximity Distance from selectiveimmigration countries (logs) Distance from EU15 countries ( The analysis starts with a simple multiplicative decomposition of the brain drain into two components: degree of openness of sending countries, as measured by average or total emigration rate, and schooling gap, as measured by the relative education level of emigrants compared with natives.

Relying on an original data set on international migration by educational attainment for 1990 and 2000, we analyze the determinants of the brain drain from developing countries. We start from a simple decomposition of the brain drain in two multiplicative components, the degree of openness of sending countries (as measured by their average emigration rate) and the schooling gap (as measured by the relative education level of emigrants compared to natives). Using various regression models, we put forward the determinants of these components and explain cross-country differences in skilled migration. Unsurprisingly, the brain drain is strong in small countries which are not too distant from the major OECD regions, which share colonial links with OECD countries and which send most of their migrants to host countries where quality-selective immigration programs exist. More interestingly, the brain drain increases with political instability and the degree of fractionalization at origin; it globally decreases with natives' human capital.

Relying on an original data set on international migration by educational attainment for 1990 and 2000, we analyze the determinants of the brain drain from developing countries. We start from a simple decomposition of the brain drain in two multiplicative components, the degree of openness of sending countries (as measured by their average emigration rate) and the schooling gap (as measured by the relative education level of emigrants compared to natives). Using various regression models, we put forward the determinants of these components and explain cross-country differences in skilled migration. Unsurprisingly, the brain drain is strong in small countries which are not too distant from the major OECD regions, which share colonial links with OECD countries and which send most of their migrants to host countries where quality-selective immigration programs exist. More interestingly, the brain drain increases with political instability and the degree of fractionalization at origin; it globally decreases with natives' human capital.

In this paper, we analyze the distribution of the brain drain in the LAC region (Latin America and the Caribbean), Asia and Africa. We rely on an original data set on international migration by educational attainment for 1990 and 2000. Our analysis reveals that the brain drain is strong in Eastern, Middle and Western Africa, Central America and the Caribbean. However, the Kernel approach suggests that the dispersion and the intradistribution dynamics of skilled migration rates strongly differ across regions. We then tautologically disentangle the brain drain into two multiplicative components, the global migration rate and the selection bias. Among the most affected countries, LAC countries suffer from high migration rates whilst most African countries suffer from high selection biases. Finally, exploratory Moran's tests reveal strong spatial, political and cultural autocorrelations in migration rates and selection biases. The latter result suggests that skilled workers react differently than unskilled workers to a large set of variables.

In this paper, we analyze the distribution of the brain drain in the LAC region (Latin America and the Caribbean), Asia and Africa. We rely on an original data set on international migration by educational attainment for 1990 and 2000. Our analysis reveals that the brain drain is strong in Eastern, Middle and Western Africa, Central America and the Caribbean. However, the Kernel approach suggests that the dispersion and the intradistribution dynamics of skilled migration rates strongly differ across regions. We then tautologically disentangle the brain drain into two multiplicative components, the global migration rate and the selection bias. Among the most affected countries, LAC countries suffer from high migration rates whilst most African countries suffer from high selection biases. Finally, exploratory Moran's tests reveal strong spatial, political and cultural autocorrelations in migration rates and selection biases. The latter result suggests that skilled workers react differently than unskilled workers to a large set of variables.

Résumé Cet article repose sur une base de données originale mesurant les stocks et les taux d'émigration par niveau de qualification pour tous les pays du monde et pour la majorité des territoires dépendants en 1990 et en 2000 (Docquier et Marfouk [2005]). Nous analysons ici le rôle de l'Union européenne à quinze membres ( ue 15) dans la mobilité internationale des travailleurs qualifiés. Par rapport au reste de l' ocde , l' ue 15 se distingue par la faible qualification moyenne de ses immigrés. Néanmoins, en tant que terre d'accueil privilégiée des émigrants africains, elle joue un rôle significatif dans ce débat. L' ue 15 attire une part importante des migrants diplômés en provenance de pays proportionnellement très affectés par l'exode de leurs cerveaux. L'estimation non paramétrique des densités de taux de migration confirme ce résultat. Au total, si l'Europe enregistre une large perte nette de capital humain dans ses échanges avec les grandes nations d'immigration, elle compense quasiment ce déficit en attirant des travailleurs diplômés en provenance des pays en développement. L'effet net est toutefois dérisoire comparativement aux larges gains observés aux États-Unis, au Canada et en Australie.

In this paper, we analyze the distribution of the brain drain in the LAC region (Latin America and the Caribbean), Asia and Africa. We rely on an original data set on international migration by educational attainment for 1990 and 2000. Our analysis reveals that the brain drain is strong in Eastern, Middle and Western Africa, Central America and the Caribbean. However, the Kernel approach suggests that the dispersion and the intradistribution dynamics of skilled migration rates strongly differ across regions. We then tautologically disentangle the brain drain into two multiplicative components, the global migration rate and the selection bias. Among the most affected countries, LAC countries suffer from high migration rates whilst most African countries suffer from high selection biases. Finally, exploratory Moran's tests reveal strong spatial, political and cultural autocorrelations in migration rates and selection biases. The latter result suggests that skilled workers react differently than unskilled workers to a large set of variables.