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Res Popul Econ ; 7: 113-28, 1991.
Artigo em Inglês | MEDLINE | ID: mdl-12317025

RESUMO

PIP: The author gathered 1965 and 1975 socioeconomic data on 71 developing countries from a variety of sources such as UNESCO and the World Bank. He applied these data to strict tests to confirm the nonlinear fertility literacy relationship. The total fertility rate (TFR) was the dependent variable. In 1975, the TFR was as low as 2.2 in Singapore and as high as 8.1 in Rwanda. The unweighted mean stood at 5.8. Independent variables included real gross domestic product/worker (earnings); ratio of female literacy over male literacy; natural logarithm of fraction of labor force engaged in agriculture, forestry, hunting, and fishing; population density; male literacy; and strength of the family planning program. The statistical regression revealed that fertility rose slightly (mean=0.25 of a child) with an initial growth in the literacy rate. In fact, it peaked when 25-50% of the adult male population were literate. As the adult male literacy rate increased to the point where almost everyone was literate, fertility fell swiftly to 2 children. These changes were stable across the board. Between 1965-1975, the male literacy rate increased from 46-57% which resulted in a reduction of .51 children in the TFR. Algeria, Nigeria, and Pakistan whose male literacy levels ranged only from 23-42% stalled at high fertility levels. Fertility decreased considerable in countries where family planning programs were valued at least 4.7, even when no significant changes occurred in literacy, earnings, density, or agricultural composition. Morocco, valued at 4, only experienced a change of -.561 whereas China, valued at 25, experienced a change of -1.506. Thus to reduce fertility, policy makers must set high literacy levels as goals recognizing, however, that fertility will 1st rise as literacy bigins its ascent. Further they should either estiablish or strengthen existing family planning programs.^ieng


Assuntos
Agricultura , Coeficiente de Natalidade , Comparação Transcultural , Coleta de Dados , Escolaridade , Fertilidade , Planejamento em Saúde , Densidade Demográfica , Dinâmica Populacional , Análise de Regressão , Salários e Benefícios , África , América , Ásia , América Central , Demografia , Países em Desenvolvimento , Economia , Emprego , Serviços de Planejamento Familiar , Fiji , Geografia , Mão de Obra em Saúde , América do Norte , Ilhas do Pacífico , Polinésia , População , Pesquisa , Classe Social , Fatores Socioeconômicos , América do Sul , Estatística como Assunto
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