Migração de acadêmicos: o desenvolvimento econômico não leva necessariamente à fuga de cérebros
Uma equipe de pesquisadores do Instituto Max Planck de Pesquisa Demográfica (MPIDR) em Rostock, Alemanha, desenvolveu um banco de dados sobre migração internacional de acadêmicos para avaliar padrões e tendências de emigração para esse...
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e+lk3QnfAjT5zvdIv0ZoWxcDjZHw/Sn0WL0GrEqY1muD+Jpah98U56SU2xTWmjDdx24nWS69GZqGJcFoX5Df9PFiTd0tmHKntn0UL0Z9ZX9C4G7ZCP3xYnyFBscWssONu9svTn1kl6lLKUbQsISra/UXsJVuZfpyj9oTv3Zdf3Ov8q//8QAJhEAAQMDBQADAQADAAAAAAAAAQACEQMQMRIgITBAE0FRImFwgP/aAAgBAgEBPwH/AIJ1BT63GAvkPh1KVKygBvLQVjyVMWHW6pYBSVKlBAFOTRvLpwiIQ8dTFh1ObqQpD7Rpja0hPKBhDaTCcJCDU8iUPHUxYdhUrUFqClC2lN2kKVKJlBxagZCNQynOmwqFDnm4UdNTFhjsqO+ts/iaz9QEbjUQbIWlFp+kIARcEf7EBfHwtCFLeZnfUxYY7Kg52sbHR/MrUU3kKFoT4AQcQhUM2+SN3IMrl2+piwx2PREJjdS+MJrYd0OM8BaYsE7CkohABFhCkwnFNqEL5RCaZHXUxYY7aoVMgIOk9IEIiVpQRwtKyFCEzZ7I5s2n+ou0ppM9VTFhju0BY6RKc5GomvlPxYOjY4SFSFniQtOkWweipiw8D0130pU7tJTmFMEHY0HZFnIcomFkdFTFh4HZtNg7pehneRKZwYTgZX+Laxufiw8DghsZ0OEpmeghc3LZ3PxYeAiUBBRE3bjo1fSHUTCJlauE3G1+LDwv4Qq/qKDZ6JQ5PU47Gna/Fh4X4sCgI6CmjqdlaSiIs0bamLDwuxam2PMRK0bqmLDHiOfQBFudhErR43snlA+1uf8ATv8A/8QAPBAAAQMCAwYFAwIGAQIHAAAAAQACESExAxASEyIwQVFxIDJAYYEEQpEjoRRQUmBisTOCwSRDcnOS0eH/2gAIAQEABj8CVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlb0Je6wQe2x4wwoMkT4dE73RPwwDLPGGFw1Hl4i91gtRwywcp5+DYwZifAWBw1DkntAO4YM5Bk7x5ZyQTNAAtTmFh6HxaTJd0AWthookx1iil7gAiWWCLaki8CyDmGQVqeYCDRIJtIQDpk8gtTChhh8uPRAP1V6BBo1Sfbjn0GFgdTLuyxvp+hlqxtbv1MMxKwsI4mlzhLnnktlttq0iQUMV2LQfb1TnH6sbQfYLLAZhnS7FEk9FhYTsXW0+yf/AOJGCGmAOql1wYQwRibIRJcsfD2m00s1Neh9Rt7coTfqdrf7IosNgO6WzCe9uLs2tMAQsR5fvB9055xWkui9A1YYb9Rtg6jqWRG1MxMx+y+q1GW4dgv4jbe+mKL6Y4Z07S6bhPxdoHhPc0wQFtjigy2gPJNcPqdo6d5qYNqQSKGLI4W22TW3PVYzQdo7D8ruqY7D+pD3fcx1ESeSOM3G0f0thDFmIuOqGp+qUMM4myZHm6rEe9zcSPK4c03Fbi6ya6IWznd0TCc/+JGF/S1YD2GHOdBWGx2LtG4nsnjamRcxdfVfcWHdCa7D+pDn82OomjakEihiy2bsbYsihi6/Uc13Rw5rCOs1dAHRYOEzFq6ZJTPpm4u9EufC2GK/WHCQU9+3I0k8l/EbaI5AJjzzCxMY/ddfUYpGnaVhf9KwTiNDobzTv/UjscAu1VMJrnPkE8lzLQVh4eH/AOWZc7osWeTV9Q3kQvpgxoArk/F+3D3W8c+gxH/UNp9tVhv+nbT7qoOH/G6C74TMXCgubyPNS/CZhtjldbN9DVH6cYbI/qm6+nc2Nrh0g81hbRoaYsCn7PDbiMcZryX6gaHf4oY+EGupBaVi68NjNTYaAtlG/CbhNG8AFh4kbobCxBhBj2EzU+VPaypL0GNuOSwXOwmtDDYFbZjQ4EQa2X1Ovy4ll/DhjYtrnkvpgyowzVYWIButFU9rbkIYNnQm4eya0M97rDxsJuqBEStthMa/UN5pT5DMNx8sJodhMY4GuICoKODhta4cnTZbFlXJoPIKWtbi4Z+wrGD4ZtLNHJNwoayPvlbX7dMIsZhMxG8ieSwKNlr5dCwHizJlHEa0Oa+9bL6iukvMtKa12CxrhfElMxsJodAiJR3G42GftPJO1wNRmByWFoHlfJWC8DdbdNx8GC4UIPNbfGAbAhrViMcKklHCI34TGuuAp2YexvlEp7MfDDARyMr+G0t0/wBcoNHIQi14rqlOaAMUGxJstLjUmSowPMVpZ9Oz/wCS2+CA6RBCxMXFjW/kOSwXgbrbp2y8xTWfnjn0QwwJNz7eGMQTClgr1ObZ+4x4S4gybwVpaIAz1HrHh2QaSeZ6eBzYO74C5xgDxBrnbx5KTZaRMmokXWkTUwDFCtNbxMUzkpkSQ+xA8LZ+4xkXHkJQd1E8IuAmOSDm2Poj6L6ojzC34WE4Eb3m/UnV8KC4ljZ2fuvp3tedoXQarHxWl2oOIvZO0vDGln9er5RbOk7O4dqHdU8+zJBDpDlhvYRJFf1J1fC+mfrl7n71UxpnedF4WKzaAAYkCtO0rEbBEG2qQFjOaYLDcv8A9BOKxGsJg4JN19JDp3TNfZfTO1OlzoNVjMk6Q9vOyxv4dxI2c3mCiWkAHDM/qapTXVlwEyU/F1nbh9BP7LFpYBMYfLpmNWmV9O17pG/ZyxH6nThvhtbL6neIGpsnoFitw3U2PVYLak4lavgWTG6jG2ihX1TawyCK2QDSHNDfJrj5RcCfJzuvpjhvJc7zVTHyA/VUl/7Qn4hJnW4X90z/ANxqxGg72my+l0Gd0/6X0zAd7WKLHZ92s0+UZnpQwvqBhmzQaOmE4MxCZw50zPyvpALGZqm4Mk7GSffosN8jUTvE4l/hYY1C/lJjUsNkuDNpDpP/AHTWMxC4bXrZY7GOIaC34WKzCdLNkSaysNh5YQNXwF9OMRzqsM1ReXat/wAzXVHtCbWN8LHGA46Q0G9k/SQGnDNNpq+ULnUJMlNwK/pEn/6WG+Rq1bxL7/CbiNNdrFX1/CxXkmQ8gVXtqMeiPoto0xSHe6JDGyfZDdFLUsmiAXD7oVAEdLGiegW4wDsFuMA7BagxoPWFOhs9lDgCPdEaGwfZQxoA9lJY0nsoIBCmKrdY0fCA0iBai8or7LcaB2R0saJvRQFr0DV1hTFSoe0HuhuilvZEaRB9k7TDS7nCc5xBkRAbAWlzQR0Xkb1sjuit/dAFjYFqJzLSIQgDVETC16G6usKghVErac4hbrQJ6BagwausLXoGrrCgiQt1gHwtxoE9AqMaI9kTFStWhurrChwBHutGkaekIDSIFqI0FbohrGgH2QljTFqIQ0CLUWrQ3V1hATEOBUNaAPZGGNE3oqJ73u1Od7LXobq6wp0NnsnDDhpPNBjbD0R/uQ/3If7kP9sQ50LzrzprmGkhEzQOiFvGZaHeM+tlxhTqlUBVMP8Adf8AH+6pgn8ryFeQryfkr7QqQfj/APV/xyqqfBVeYZ1MKAR4qlUPrvjNk9AtUVW6PGfURpcfhQRA6ytAxA0dlpLyD7I6cRzp9rIaidTeip/tV3QqDV3W82Oy3eXi0tFSoaI8O9+VUNI8V1QHOqut15VQuap4Lq49L8Zs7cE+gqQqHw+UklSSWyt7fHQqmCJU6Gk/4iy3gC6wkLehzjcQvI1AhrfwqGucW8J01I/ZanGT/rgeYeCOS6nKglWRrTw0Oe9RclBAI7KjYV15h4IJ43xmztwTxoEKpzufyryvOVOorzKXmD7BUM/C3tXZUL9ItVSEAGgu6rViNJPRTaft6Ks/ClqlTYTOcCp6KwHypdU3VT4oZ+VVVV1vBWoqCV5Qr+hoV5lcq6rVVoqHh/GbO3BPEuvNPx4IHBoPlV/PJb8R/UqYw9oCh10W4jYiyhohOHspCgCT0W8ZKoIycS3UCiBhtbPgqVS2U4jZJsM7D1W9ZS1ybQntwvjNnbgnhm0ZUXRUKjg63Vzhv/UVIuhqjEKDuoz3T8FSbnxwXCch2VroFokoGxC837ensrKoMoBSq34nxmztwTwt2XdlQBqq4kqmZPBHTogBMq8noi7rmGTXhS4wi6TB5hSLKyM70/stbzHshpcpPpq+CevG+M2dlpW74z6DELjvO4Ti7oi7r4GioPCmw6rZzuhEOo1GHEDIehoqjgTyVMqW4vxmzsiepupcIhob4z6CfuyuVcq5Vyrqh8MxREENA/dfpsDndYRNZ58MhuGGj+pS4zlKt6CAFMKoVlcrdCr4Aonj/GbO3BPoPbg0XTKoPYKS7StI4cak6oOUQoIhSKgqcpKpEKTbrlVVUBVoqHwyFvDwQI8IyrxvjNnbgnj6eFZQFJPGOQeUSh1HNabhSJE2CmBAVldWReyh6BTNUHVCDn/hFzPxlIFOh5oamweitHhoVfOBkBc5Qq343xm3twTxx24ccaFUXW9uhQFZEMGmF+pYIjnyRDvx0VlYqgUHKHBQ1Q5QBvrQSI6rTFQpHBleUQm1tbPVxvjNvbgnj3twYCk1PHnwmhznnlrdyUuNSbKT4aBE9FqPNSOFBsoW7lXjfGbO3BPoHg35eGyqY+FVD0EeOS6VKjUVGo18AfyOcO1SqE6emUK3Egcf4zZ24J9AHTdRn3z7JoaW9jx9IUgFRPB6oPZ5XZkOqzoteqGoOAMFRpP4VQQvfPopJ1BUj4Wp3i0c/Q/GbeyNKAwqumWB3jPoIVbjIZVKJFY6LygD8oFwiOfGkq9VCd2CpnMwoltP3UOoUSrq62RqiOmcFxW7UdMoco8BATdQ8xhaeS9lEKQqBSUfQ/Gbey1RXqt1oHjPoSFXKsypjPTvA9qKG6gOyqCR2UwR34cJvbL4y1BdTk3sqprQd12YcbLas+cpaKLS5a3CnLOyOUDODcKei3IlDZhAHIaufofjNvbgn0VVQwpBTWtfzRg6ovA9A4reMtXlA+VJiii/ZVAA756jl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)
O mapa inclui valores para os 100 países com maior número de pesquisadores ativos ao longo do tempo e com uma população de pelo menos 0,5 milhão de pessoas. A maioria dos países tem taxas de emigração abaixo de 40 por 1.000 acadêmicos. Sudão, Malawi, Zimbábue, El Salvador e República Dominicana têm as taxas mais altas. Outros casos notáveis ??com altas taxas de emigração acadêmica incluem Canadá, Peru, Suíça, Irlanda, Quênia, Uganda, Arábia Saudita, Omã, Iêmen, Geórgia, Bangladesh e Nepal. Crédito: MPIDR
Uma equipe de pesquisadores do Instituto Max Planck de Pesquisa Demográfica (MPIDR) em Rostock, Alemanha, desenvolveu um banco de dados sobre migração internacional de acadêmicos para avaliar padrões e tendências de emigração para esse grupo-chave de inovadores. O artigo foi publicado no PNAS em 18 de janeiro.
Como primeiro passo, a equipe produziu um banco de dados que contém o número de acadêmicos que publicam artigos regularmente e os fluxos e taxas de migração para todos os países que incluem acadêmicos que publicaram artigos listados no banco de dados bibliográfico Scopus. O banco de dados de migração foi obtido aproveitando metadados de mais de 36 milhões de artigos de periódicos e revisões publicadas de 1996 a 2021.
"Este banco de dados de migração é um recurso importante para avançar nossa compreensão da migração de acadêmicos", disse o pesquisador do MPIDR, Ebru Sanliturk. O cientista de dados Maciej Danko acrescenta: "Embora os dados subjacentes sejam proprietários, nossa abordagem gera conjuntos de dados de nível agregado anônimos que podem ser compartilhados para fins não comerciais e que estamos disponibilizando publicamente para pesquisa científica."
O pesquisador do MPIDR Aliakbar Akbaritabar explica como eles processaram os dados bibliográficos para receber informações sobre os padrões de migração de acadêmicos: "Usamos os metadados do título do artigo, nome dos autores e afiliações de quase todos os artigos e resenhas publicados no Scopus desde 1996 . Acompanhamos cada um dos cerca de 17 milhões de pesquisadores listados no banco de dados bibliográfico ao longo dos anos e notamos mudanças na afiliação e, usando essa tática, sabemos quantos acadêmicos deixaram um determinado país a cada ano."
A análise empírica dos pesquisadores centrou-se na relação entre emigração e desenvolvimento econômico , indicando que os padrões de configuração acadêmica podem diferir amplamente dos padrões populacionais.
A literatura anterior mostrou que, à medida que os países de baixa renda se tornam mais ricos, as taxas gerais de emigração inicialmente aumentam. A certa altura, o aumento abranda e inverte-se a tendência, com as taxas de emigração a diminuir.
Isso significa que favorecer o desenvolvimento econômico tem o efeito contra-intuitivo de inicialmente aumentar a migração de países de baixa e média renda, em vez de diminuí-la.
Esse padrão também é geralmente válido para a migração de cientistas?
Na verdade, não.
Os pesquisadores descobriram que, ao considerar os acadêmicos, o padrão é o oposto: em países de baixa e média renda, as taxas de emigração diminuem à medida que o produto interno bruto (PIB) per capita aumenta. Então, partindo de cerca de 25.000 dólares americanos no PIB, a tendência se inverte e a propensão à emigração aumenta à medida que os países ficam mais ricos.
O diretor do MPIDR, Emilio Zagheni, acrescenta: "Os acadêmicos são um grupo crucial de inovadores cujo trabalho tem efeitos econômicos relevantes. Mostramos que sua propensão a emigrar não aumenta imediatamente com o desenvolvimento econômico - na verdade, diminui até um ponto de virada de alta renda e depois aumenta. Isso implica que o aumento do desenvolvimento econômico não leva necessariamente a uma fuga de cérebros acadêmicos em países de baixa e média renda ."
Revelar esses e outros padrões relacionados e abordar grandes questões científicas com implicações sociais só foi possível devido ao trabalho meticuloso na preparação desse novo banco de dados global de migração de acadêmicos. "Estamos dando os retoques finais em um banco de dados ainda mais abrangente, o Scholarly Migration Database, que será lançado em seu próprio site em breve", disse o desenvolvedor de software Tom Theile.
Mais informações: Ebru Sanliturk et al, Padrões globais de migração de acadêmicos com desenvolvimento econômico, Proceedings of the National Academy of Sciences (2023). DOI: 10.1073/pnas.2217937120
Informações do periódico: Proceedings of the National Academy of Sciences