Impact of the collection mode on labor income data. A study in the times of COVID-19

El estricto confinamiento implementado por el Gobierno Nacional de Colombia para contener la expansión de la pandemia provocada por el COVID-19 generó desafíos en las operaciones de recolección de datos a través de encuestas de hogares. En consecuencia, las encuestas con la modalidad de recolección...

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Tipo de recurso:
Fecha de publicación:
2022
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/35991
Acceso en línea:
https://doi.org/10.48713/10336_35991
https://repository.urosario.edu.co/handle/10336/35991
Palabra clave:
Encuestas de hogares en Colombia
Sesgo de medición
Ingresos laborales
Datos administrativos
COVID-19
Economía
C83
C81
J31
Household surveys
Measurement bias
Labor income
Administrative data
COVID-19
Colombia
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License
http://creativecommons.org/licenses/by-nc-nd/2.5/co/
id EDOCUR2_a65fdbf2ebd208bc2fc629d704178d25
oai_identifier_str oai:repository.urosario.edu.co:10336/35991
network_acronym_str EDOCUR2
network_name_str Repositorio EdocUR - U. Rosario
repository_id_str
dc.title.none.fl_str_mv Impact of the collection mode on labor income data. A study in the times of COVID-19
title Impact of the collection mode on labor income data. A study in the times of COVID-19
spellingShingle Impact of the collection mode on labor income data. A study in the times of COVID-19
Encuestas de hogares en Colombia
Sesgo de medición
Ingresos laborales
Datos administrativos
COVID-19
Economía
C83
C81
J31
Household surveys
Measurement bias
Labor income
Administrative data
COVID-19
Colombia
title_short Impact of the collection mode on labor income data. A study in the times of COVID-19
title_full Impact of the collection mode on labor income data. A study in the times of COVID-19
title_fullStr Impact of the collection mode on labor income data. A study in the times of COVID-19
title_full_unstemmed Impact of the collection mode on labor income data. A study in the times of COVID-19
title_sort Impact of the collection mode on labor income data. A study in the times of COVID-19
dc.subject.none.fl_str_mv Encuestas de hogares en Colombia
Sesgo de medición
Ingresos laborales
Datos administrativos
COVID-19
Economía
C83
C81
J31
Household surveys
Measurement bias
Labor income
Administrative data
COVID-19
Colombia
topic Encuestas de hogares en Colombia
Sesgo de medición
Ingresos laborales
Datos administrativos
COVID-19
Economía
C83
C81
J31
Household surveys
Measurement bias
Labor income
Administrative data
COVID-19
Colombia
description El estricto confinamiento implementado por el Gobierno Nacional de Colombia para contener la expansión de la pandemia provocada por el COVID-19 generó desafíos en las operaciones de recolección de datos a través de encuestas de hogares. En consecuencia, las encuestas con la modalidad de recolección presencial migraron a una modalidad remota, a través de encuestas telefónicas, lo que pudo haber cambiado los posibles sesgos de reporte de variables como los ingresos. Este trabajo estudia el efecto del cambio en el modelo de recolección de información en la Gran Encuesta Integrada de Hogares (Gran Encuesta Integrada de Hogares) de Colombia sobre el reporte de ingresos laborales. Para ello, aprovechamos la variación geográfica en la implementación de métodos de recolección y una integración de la encuesta con un registro administrativo de seguridad social para cuantificar la variación en el reporte.
publishDate 2022
dc.date.none.fl_str_mv 2022-09-13
2022-09-14T21:36:12Z
dc.type.none.fl_str_mv info:eu-repo/semantics/workingPaper
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_b1a7d7d4d402bcce
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_8042
dc.identifier.none.fl_str_mv https://doi.org/10.48713/10336_35991
https://repository.urosario.edu.co/handle/10336/35991
url https://doi.org/10.48713/10336_35991
https://repository.urosario.edu.co/handle/10336/35991
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://ideas.repec.org/p/col/000092/020396.html
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/2.5/co/
http://purl.org/coar/access_right/c_abf2
dc.format.none.fl_str_mv 30 pp
application/pdf
dc.publisher.none.fl_str_mv Universidad del Rosario
Facultad de Economía
publisher.none.fl_str_mv Universidad del Rosario
Facultad de Economía
dc.source.none.fl_str_mv Abowd, J. M., & Stinson, M. H. (2013). Estimating measurement error in annual job earnings: A comparison of survey and administrative data. Review of Economics and Statistics, 95(5), 1451– 1467.
Angel, S., Disslbacher, F., Humer, S., & Schnetzer, M. (2019). What did you really earn last year?: explaining measurement error in survey income data. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(4), 1411–1437.
Angel, S., Heuberger, R., & Lamei, N. (2018). Differences between household income from surveys and registers and how these affect the poverty headcount: Evidence from the Austrian SILC. Social Indicators Research, 138(2), 575–603.
Bollinger, C. R. (1998). Measurement error in the current population survey: A nonparametric look. Journal of Labor Economics, 16(3), 576–594.
Bound, J., Brown, C., Duncan, G. J., & Rodgers, W. L. (1994). Evidence on the validity of crosssectional and longitudinal labor market data. Journal of Labor Economics, 12, 345–368.
Bound, J., Brown, C., & Mathiowetz, N. (2001). Measurement error in survey data. In: Handbook of econometrics (Vol. 5, pp. 3705–3843). Elsevier.
Bound, J., & Krueger, A. B. (1991). The extent of measurement error in longitudinal earnings data: Do two wrongs make a right?. Journal of labor economics, 9(1), 1-24.
Bowling, A. (2005). Mode of questionnaire administration can have serious effects on data quality. Journal of public health, 27(3), 281-291.
Burton, J., Crossley, T. F., Fisher, P., Gaia, A., & Jäkle, A. (2020). Understanding and reducing income reporting error in household survey (No. 2020-01). Understanding Society at the Institute for Social and Economic Research.
Caeyers, B., Chalmers, N., & Weerdt, J. (2010). A Comparison of CAPI and PAPI through a Randomized Field Experiment. Available at SSRN 1756224.
Canberra Group. (2011). Canberra Group Handbook on Household Income Statistics. United Nations.
Chesher, A., & Schluter, C. (2002). Welfare measurement and measurement error. The Review of Economic Studies, 69(2), 357-378.
Consolini, P., & Donatiello, G. (2013). Improvements of data quality through the combined use of survey and administrative sources and micro simulation model. M. Jäntti, 1000, 125-139.
de Leeuw, E. D. (1992). Data quality in mail, telephone and face to face surveys. TT Publikaties, Plantage Daklaan 40, 1018CN Amsterdam.
Departamento Nacional de Estadísticas [DANE]. (2020). Comunicado de Prensa – Acciones para garantizar la continuidad de la Gran Encuesta Integrada de Hogares (GEIH).
Economic and Social Research Council [ESRC]. (2019). ESRC Question Bank FACTSHEET 6. https://www.ukdataservice.ac.uk/use-data/guides/methods-and-software-guides
Economic Commission for Latin America and the Caribbean [ECLAC]. (2020). Recommendations for eliminating selection bias in household surveys during the coronavirus disease (COVID-19 pandemic).
Ellis, C. H., & Krosnick, J. A. (1999). Comparing telephone and face to face surveys in terms of sample representativeness: a Meta-Analysis of Demographics Characteristics. Universidad de Michigan, NES.
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reponame:Repositorio Institucional EdocUR
instname_str Universidad del Rosario
institution Universidad del Rosario
reponame_str Repositorio Institucional EdocUR
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repository.mail.fl_str_mv
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spelling Impact of the collection mode on labor income data. A study in the times of COVID-19Encuestas de hogares en ColombiaSesgo de mediciónIngresos laboralesDatos administrativosCOVID-19EconomíaC83C81J31Household surveysMeasurement biasLabor incomeAdministrative dataCOVID-19ColombiaEl estricto confinamiento implementado por el Gobierno Nacional de Colombia para contener la expansión de la pandemia provocada por el COVID-19 generó desafíos en las operaciones de recolección de datos a través de encuestas de hogares. En consecuencia, las encuestas con la modalidad de recolección presencial migraron a una modalidad remota, a través de encuestas telefónicas, lo que pudo haber cambiado los posibles sesgos de reporte de variables como los ingresos. Este trabajo estudia el efecto del cambio en el modelo de recolección de información en la Gran Encuesta Integrada de Hogares (Gran Encuesta Integrada de Hogares) de Colombia sobre el reporte de ingresos laborales. Para ello, aprovechamos la variación geográfica en la implementación de métodos de recolección y una integración de la encuesta con un registro administrativo de seguridad social para cuantificar la variación en el reporte.The strict confinement implemented by the National Government of Colombia to contain the expansion of the pandemic caused by COVID-19 generated challenges in data collection operations through household surveys. As a result, the surveys with face-to-face collection methods migrated to a remote mode, through telephone surveys, which could have changed the possible reporting biases of variables, such as income. This paper studies the effect of the change in the information collection model in the Great Integrated Household Survey (Gran Encuesta Integrada de Hogares) of Colombia on the report of labor income. To do this, we exploit the geographical variation in implementing collection methods and an integration of the survey with a social security administrative record to quantify the variation on the report.Universidad del RosarioFacultad de Economía2022-09-132022-09-14T21:36:12Zinfo:eu-repo/semantics/workingPaperhttp://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_804230 ppapplication/pdfhttps://doi.org/10.48713/10336_35991https://repository.urosario.edu.co/handle/10336/35991Abowd, J. M., & Stinson, M. H. (2013). Estimating measurement error in annual job earnings: A comparison of survey and administrative data. Review of Economics and Statistics, 95(5), 1451– 1467.Angel, S., Disslbacher, F., Humer, S., & Schnetzer, M. (2019). What did you really earn last year?: explaining measurement error in survey income data. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(4), 1411–1437.Angel, S., Heuberger, R., & Lamei, N. (2018). Differences between household income from surveys and registers and how these affect the poverty headcount: Evidence from the Austrian SILC. Social Indicators Research, 138(2), 575–603.Bollinger, C. R. (1998). Measurement error in the current population survey: A nonparametric look. Journal of Labor Economics, 16(3), 576–594.Bound, J., Brown, C., Duncan, G. J., & Rodgers, W. L. (1994). Evidence on the validity of crosssectional and longitudinal labor market data. Journal of Labor Economics, 12, 345–368.Bound, J., Brown, C., & Mathiowetz, N. (2001). Measurement error in survey data. In: Handbook of econometrics (Vol. 5, pp. 3705–3843). Elsevier.Bound, J., & Krueger, A. B. (1991). The extent of measurement error in longitudinal earnings data: Do two wrongs make a right?. Journal of labor economics, 9(1), 1-24.Bowling, A. (2005). Mode of questionnaire administration can have serious effects on data quality. Journal of public health, 27(3), 281-291.Burton, J., Crossley, T. F., Fisher, P., Gaia, A., & Jäkle, A. (2020). Understanding and reducing income reporting error in household survey (No. 2020-01). Understanding Society at the Institute for Social and Economic Research.Caeyers, B., Chalmers, N., & Weerdt, J. (2010). A Comparison of CAPI and PAPI through a Randomized Field Experiment. Available at SSRN 1756224.Canberra Group. (2011). Canberra Group Handbook on Household Income Statistics. United Nations.Chesher, A., & Schluter, C. (2002). Welfare measurement and measurement error. The Review of Economic Studies, 69(2), 357-378.Consolini, P., & Donatiello, G. (2013). Improvements of data quality through the combined use of survey and administrative sources and micro simulation model. M. Jäntti, 1000, 125-139.de Leeuw, E. D. (1992). Data quality in mail, telephone and face to face surveys. TT Publikaties, Plantage Daklaan 40, 1018CN Amsterdam.Departamento Nacional de Estadísticas [DANE]. (2020). Comunicado de Prensa – Acciones para garantizar la continuidad de la Gran Encuesta Integrada de Hogares (GEIH).Economic and Social Research Council [ESRC]. (2019). ESRC Question Bank FACTSHEET 6. https://www.ukdataservice.ac.uk/use-data/guides/methods-and-software-guidesEconomic Commission for Latin America and the Caribbean [ECLAC]. (2020). Recommendations for eliminating selection bias in household surveys during the coronavirus disease (COVID-19 pandemic).Ellis, C. H., & Krosnick, J. A. (1999). Comparing telephone and face to face surveys in terms of sample representativeness: a Meta-Analysis of Demographics Characteristics. Universidad de Michigan, NES.instname:Universidad del Rosarioreponame:Repositorio Institucional EdocURenghttps://ideas.repec.org/p/col/000092/020396.htmlhttp://creativecommons.org/licenses/by-nc-nd/2.5/co/http://purl.org/coar/access_right/c_abf2García Suaza, Andrés FelipeLobo, JoséMontoya, SergioOrdoñez-Herrera, JuanOviedo Arango, Juan Danieloai:repository.urosario.edu.co:10336/359912022-09-20T10:34:31Z

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