Understanding rural poverty clusters: the intersection of agriculture, economic structure and locality under postindustrialism
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Analysis seeks to understand rural poverty within the context of agricultural and postindustrial economic structure. Detailed socioeconomic data are analyzed for 4,610 non-metropolitan census tracts in the north central United States. Statistical cluster analysis is used to group tracts according to their similarity along four poverty measures. Multinomial logistic regression is used to predict cluster membership by taking into account agriculture, industry and occupation structure. Results found that both agriculture self-employment and wage-employment reduced near poverty. Core/basic industry employment tended to reduce near poverty, except for information that increased poverty. Semi-core industries tended to increase poverty while also decreasing near poverty. Periphery/non-basic industries tended to reduce near poverty, except the leisure industry which increased poverty. In terms of occupation, the professional-managerial class was associated with low poverty clusters, except cultural workers that increased poverty. Working class occupations were associated with average poverty clusters. Lower services class occupations were associated with poverty and near poverty cluster membership.