{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/80608"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/80608","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Spatial Analysis of Shifts in U.S. Manufacturing Employment and Income Inequality","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Choi, Sungwoong; 0000-0001-9506-7782"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Bagchi-Sen, Sharmistha","Geography"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-10-28T19:46:54Z","date_published":"2019-10-28T19:46:54Z","updated_at":"2026-07-27T19:05:23Z","subjects":["geography"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/80608","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bagchi-Sen, Sharmistha","Geography"]},{"key":"dc:creator","label":"Author","values":["Choi, Sungwoong; 0000-0001-9506-7782"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-10-28T19:46:54Z","2019","2019-08-06 01:14:41"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["geography"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/80608"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","In the 21st century, the U.S. economy has continued to experience declines in manufacturing employment. As a key source of export and innovation, manufacturing still plays a leading part in providing stable jobs and wages for regional and national economies. Although some studies cover what causes and triggers of recent manufacturing decline, geographical unevenness of such decline is not fully addressed. In this regard, this study has two research objectives. First, it explores spatial disparities of manufacturing decline between 2006 and 2016. With a Partitioning Around Medoids (PAM), the U.S. counties are categorized into several groups that share similar features of absolute decline, relative decline, and manufacturing specialization. By doing so, demographic and socio-economic characteristics of the groups are compared to provide broader understanding of patterns of regional disparities. Second, the dissertation provides an analysis of the association of manufacturing specialization and sectoral change with household income inequality. A Bayesian network is applied to examine the above relationships based on probabilities of increasing and decreasing income inequality. Specifically, the technology level of manufacturing and service industries is considered to look at specific associations. The main findings of this study show that disparities of employment decline between manufacturing-specialized regions may result from the growth of high-tech manufacturing sectors. Next, significant disparities in the earnings and poverty rate as well as education level are found across regions. Third, manufacturing decline regardless of technology level consistently shows higher probabilities of increasing income inequality. On the other hand, a growth of service sectors, based on non-high-technology in particular, reveals a negative relationship with income inequality. This study contributes to the discussion of manufacturing decline by emphasizing the importance of manufacturing industries for regional economic well-being."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Spatial Analysis of Shifts in U.S. Manufacturing Employment and Income Inequality"]}]}],"canonical_facts":{"dc:contributor":["Bagchi-Sen, Sharmistha","Geography"],"dc:creator":["Choi, Sungwoong; 0000-0001-9506-7782"],"dc:date":["2019-10-28T19:46:54Z","2019","2019-08-06 01:14:41"],"dc:description":["Ph.D.","In the 21st century, the U.S. economy has continued to experience declines in manufacturing employment. As a key source of export and innovation, manufacturing still plays a leading part in providing stable jobs and wages for regional and national economies. Although some studies cover what causes and triggers of recent manufacturing decline, geographical unevenness of such decline is not fully addressed. In this regard, this study has two research objectives. First, it explores spatial disparities of manufacturing decline between 2006 and 2016. With a Partitioning Around Medoids (PAM), the U.S. counties are categorized into several groups that share similar features of absolute decline, relative decline, and manufacturing specialization. By doing so, demographic and socio-economic characteristics of the groups are compared to provide broader understanding of patterns of regional disparities. Second, the dissertation provides an analysis of the association of manufacturing specialization and sectoral change with household income inequality. A Bayesian network is applied to examine the above relationships based on probabilities of increasing and decreasing income inequality. Specifically, the technology level of manufacturing and service industries is considered to look at specific associations. The main findings of this study show that disparities of employment decline between manufacturing-specialized regions may result from the growth of high-tech manufacturing sectors. Next, significant disparities in the earnings and poverty rate as well as education level are found across regions. Third, manufacturing decline regardless of technology level consistently shows higher probabilities of increasing income inequality. On the other hand, a growth of service sectors, based on non-high-technology in particular, reveals a negative relationship with income inequality. This study contributes to the discussion of manufacturing decline by emphasizing the importance of manufacturing industries for regional economic well-being."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/80608"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["geography"],"dc:title":["Spatial Analysis of Shifts in U.S. Manufacturing Employment and Income Inequality"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:23Z"}