Exploring unobserved household living conditions in multilevel choice modeling: an application to contraceptive adoption by Indian women

Detalhes bibliográficos
Autor(a) principal: Dias, J. G.
Data de Publicação: 2018
Outros Autores: de Oliveira, I. T.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10071/16096
Resumo: This research analyzes the effect of the poverty-wealth dimension on contraceptive adoption by Indian women when no direct measures of income/expenditures are available to use as covariates. The index-Household Living Conditions (HLC)-is based on household assets and dwelling characteristics and is computed by an item response model simultaneously with the choice model in a new single-step approach. That is, the HLC indicator is treated as a latent covariate measured by a set of items, it depends on a set of concomitant variables, and explains contraceptive choices in a probit regression. Additionally, the model accounts for complex survey design and sample weights in a multilevel framework. Regarding our case study on contraceptive adoption by Indian women, results show that women with better household living conditions tend to adopt contraception more often than their counterparts. This effect is significant after controlling other factors such as education, caste, and religion. The external validation of the indicator shows that it can also be used at aggregate levels of analysis (e.g., county or state) whenever no other indicators of household living conditions are available.
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spelling Exploring unobserved household living conditions in multilevel choice modeling: an application to contraceptive adoption by Indian womenThis research analyzes the effect of the poverty-wealth dimension on contraceptive adoption by Indian women when no direct measures of income/expenditures are available to use as covariates. The index-Household Living Conditions (HLC)-is based on household assets and dwelling characteristics and is computed by an item response model simultaneously with the choice model in a new single-step approach. That is, the HLC indicator is treated as a latent covariate measured by a set of items, it depends on a set of concomitant variables, and explains contraceptive choices in a probit regression. Additionally, the model accounts for complex survey design and sample weights in a multilevel framework. Regarding our case study on contraceptive adoption by Indian women, results show that women with better household living conditions tend to adopt contraception more often than their counterparts. This effect is significant after controlling other factors such as education, caste, and religion. The external validation of the indicator shows that it can also be used at aggregate levels of analysis (e.g., county or state) whenever no other indicators of household living conditions are available.Public Library of Science2018-06-11T16:18:44Z2018-01-01T00:00:00Z20182018-06-11T09:41:14Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/16096eng1932-620310.1371/journal.pone.0191784Dias, J. G.de Oliveira, I. T.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-25T17:35:13ZPortal AgregadorONG
dc.title.none.fl_str_mv Exploring unobserved household living conditions in multilevel choice modeling: an application to contraceptive adoption by Indian women
title Exploring unobserved household living conditions in multilevel choice modeling: an application to contraceptive adoption by Indian women
spellingShingle Exploring unobserved household living conditions in multilevel choice modeling: an application to contraceptive adoption by Indian women
Dias, J. G.
title_short Exploring unobserved household living conditions in multilevel choice modeling: an application to contraceptive adoption by Indian women
title_full Exploring unobserved household living conditions in multilevel choice modeling: an application to contraceptive adoption by Indian women
title_fullStr Exploring unobserved household living conditions in multilevel choice modeling: an application to contraceptive adoption by Indian women
title_full_unstemmed Exploring unobserved household living conditions in multilevel choice modeling: an application to contraceptive adoption by Indian women
title_sort Exploring unobserved household living conditions in multilevel choice modeling: an application to contraceptive adoption by Indian women
author Dias, J. G.
author_facet Dias, J. G.
de Oliveira, I. T.
author_role author
author2 de Oliveira, I. T.
author2_role author
dc.contributor.author.fl_str_mv Dias, J. G.
de Oliveira, I. T.
description This research analyzes the effect of the poverty-wealth dimension on contraceptive adoption by Indian women when no direct measures of income/expenditures are available to use as covariates. The index-Household Living Conditions (HLC)-is based on household assets and dwelling characteristics and is computed by an item response model simultaneously with the choice model in a new single-step approach. That is, the HLC indicator is treated as a latent covariate measured by a set of items, it depends on a set of concomitant variables, and explains contraceptive choices in a probit regression. Additionally, the model accounts for complex survey design and sample weights in a multilevel framework. Regarding our case study on contraceptive adoption by Indian women, results show that women with better household living conditions tend to adopt contraception more often than their counterparts. This effect is significant after controlling other factors such as education, caste, and religion. The external validation of the indicator shows that it can also be used at aggregate levels of analysis (e.g., county or state) whenever no other indicators of household living conditions are available.
publishDate 2018
dc.date.none.fl_str_mv 2018-06-11T16:18:44Z
2018-01-01T00:00:00Z
2018
2018-06-11T09:41:14Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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url http://hdl.handle.net/10071/16096
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1932-6203
10.1371/journal.pone.0191784
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv Public Library of Science
publisher.none.fl_str_mv Public Library of Science
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