{"id":{"repo_id":"missouri","oai_identifier":"oai:mospace.umsystem.edu:10355/10920"},"canonical_url":"https://search.dev.ndltd.org/etd/missouri/oai:mospace.umsystem.edu:10355/10920","repository":{"repo_id":"missouri","name":"University of Missouri","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Spatial analysis of poverty and prosperity in the U.S. counties","abstract":"[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Prosperity is often associated with income. This study goes beyond the economic determinant of prosperity and develops a prosperity index by using indicators of education, employment, housing occupancy, and poverty status for all contiguous U.S. counties and non-metro counties. A spatial approach has been used to analyze the data as the data was spatially distributed. Using OLS, spatial lag, and spatial error methods, three models were developed and compared. Spatial error model explained higher percent of variation among three models. Labor markets variables were found to be important predictors of prosperity in all-counties and non-metro counties. The results showed that high prosperous counties had higher economic opportunities, higher urban influence, higher social capital, lower income inequality, lower percent of minority population, higher percent of employed female population, higher civic agricultural activities, lower percent of people working in low paying service sectors, and more jobs in manufacturing sectors. A major contribution of this study to sociology is that spatial effect should be taken into consideration while dealing with spatially correlated data.","abstract_html":"[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Prosperity is often associated with income. This study goes beyond the economic determinant of prosperity and develops a prosperity index by using indicators of education, employment, housing occupancy, and poverty status for all contiguous U.S. counties and non-metro counties. A spatial approach has been used to analyze the data as the data was spatially distributed. Using OLS, spatial lag, and spatial error methods, three models were developed and compared. Spatial error model explained higher percent of variation among three models. Labor markets variables were found to be important predictors of prosperity in all-counties and non-metro counties. The results showed that high prosperous counties had higher economic opportunities, higher urban influence, higher social capital, lower income inequality, lower percent of minority population, higher percent of employed female population, higher civic agricultural activities, lower percent of people working in low paying service sectors, and more jobs in manufacturing sectors. A major contribution of this study to sociology is that spatial effect should be taken into consideration while dealing with spatially correlated data.","abstract_has_math":false,"creators":["Khatiwada, Lila Kumar, 1969-"],"institution":"University of Missouri--Columbia","degree_name":"Ph. 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