Titulo:
“Quédate en casa (si puedes)”: empleo informal y COVID-19 en México
.
Sumario:
Este artículo explora la relación entre el confinamiento residencial para reducir la propagación del virus COVID-19, visto como una política pública, y cómo afecta al sector laboral informal, así como la respuesta de los individuos a la pandemia en los estados de México. La formación de paneles para varios niveles de informalidad aplicada al panel vectorial autorregresivo (PVAR) muestra que la permanenciaen el hogar como política pública es más efectiva a medida que disminuye la informalidad. Además, la respuesta de los individuos a un aumento de la propagación de la pandemia depende del nivel de informalidad: para los estados con menores tasas de informalidad, los individuos responden a una mayor concentración del confinamiento residencial... Ver más
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2011-7663
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2023-03-23
135
155
Carlos Emmanuel Saldaña Villanueva, Joana Cecilia Chapa Cantú, Edgar Mauricio Luna Domínguez - 2023
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
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“Quédate en casa (si puedes)”: empleo informal y COVID-19 en México “Stay at home (if you can)”: informal employment and COVID-19 in Mexico Este artículo explora la relación entre el confinamiento residencial para reducir la propagación del virus COVID-19, visto como una política pública, y cómo afecta al sector laboral informal, así como la respuesta de los individuos a la pandemia en los estados de México. La formación de paneles para varios niveles de informalidad aplicada al panel vectorial autorregresivo (PVAR) muestra que la permanenciaen el hogar como política pública es más efectiva a medida que disminuye la informalidad. Además, la respuesta de los individuos a un aumento de la propagación de la pandemia depende del nivel de informalidad: para los estados con menores tasas de informalidad, los individuos responden a una mayor concentración del confinamiento residencial. Pero para los estados con un mayor nivel de informalidad, la evidencia no es significativa. El documento considera el papel de la informalidad en el desarrollo de una política pública eficaz. This paper explores the relationship between residential confinement to reduce the spread of the COVID-19 virus, seen as a public policy, and how it affects the informal labor sector, as well as the responseof individuals to the pandemic in the states of Mexico. Forming panels for various levels of informality applied to panel vector auto-regressive (PVAR) shows that staying at home as public policy becomes more effective as informality decreases. In addition, the response of individuals to an increase in the spread of the pande-mic depends on the level of informality: for states with lower rates of informality, individuals respond to a higher concentration of residential confinement. But for states with a higher level of informality, the evidence is not significant. The paper considers the role of informality in the development of an effective public policy. Chapa Cantú, Joana Cecilia Saldaña Villanueva, Carlos Emmanuel Luna Domínguez, Edgar Mauricio Covid-19 stay-at-home panel VAR informal employment México COVID-19 Var de panel empleo informal México 15 1 Artículo de revista Journal article 2023-03-23T00:00:00Z 2023-03-23T00:00:00Z 2023-03-23 text/html application/pdf text/xml Universidad Católica de Colombia Revista Finanzas y Política Económica 2248-6046 2011-7663 https://revfinypolecon.ucatolica.edu.co/article/view/4287 10.14718/revfinanzpolitecon.v15.n1.2023.6 https://doi.org/10.14718/revfinanzpolitecon.v15.n1.2023.6 eng https://creativecommons.org/licenses/by-nc-sa/4.0 Carlos Emmanuel Saldaña Villanueva, Joana Cecilia Chapa Cantú, Edgar Mauricio Luna Domínguez - 2023 Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0. 135 155 Abrigo, M. R. M., & Love, I. (2016). Estimation of panel vector autoregression in Stata. Stata Journal, 16(3), 778-804. https://doi.org/10.1177/1536867x1601600314 Akaike, H. (1969). Fitting autoregressive models for prediction. Annals of the Institute of Statistical Mathematics, 21(1), 243-247. https://doi.org/10.1007/BF02532251 Altamirano, Á., Azuarra, O., González, S., & Banco Interamericano de Desarrollo (BID). (2020). ¿Cómo impactará la COVID-19 al empleo?: Posibles escenarios para América Latina y el Caribe. Banco Interamericano de Desarrollo, 7. https://publications. iadb.org/publications/spanish/document /Cómo_impactará_la_COVID-19_al_em-pleo_Posibles_escenarios_para_América_Latina_y_el_Caribe.pdf Andersen, M. (2020). Early evidence on social distancing in response to COVID-19 in the United States. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3569368 Banxico (2020). Reporte sobre las economías regionales julio-septiembre 2020. https://www.banxico.org.mx/publicaciones-y-prensa/reportes-sobre-las-economias-regionales/%7B8427BCB2-D8F2-C28A-8DD4-EB8DD9770681%7D.pdf Bargain, O., & Aminjonov, U. (2020). Trust and compliance to public health policies in times of COVID-19. Journal of Public Economics, 104316. https://doi.org/10.1016/j.jpubeco.2020.104316 Bloomberg (2020a). Coronavirus pandemic: Ranking the best, worst places to be. Bloomberg. https://www.bloomberg.com/graphics/covid-resilience-ranking/?utm_medium=social&cmpid=socialflow-twitter-business&utm_source=twitter&utm_campaign =socialflow-organic&utm_content=business Bloomberg (2020b). Inside Bloomberg’s covid resilience ranking - Bloomberg. Bloomberg. https://www.bloomberg.com/news/articles/2020-11-24/inside-bloomberg-s-covid-resilience-ranking Brotherhood, L., Kircher, P., Santos, C., & Tertilt, M. (2020). An economic model of the Covid-19 epidemic: The importance of testing and age-specific policies. IZA Institute of Labor Economics - Discussion Paper Series, 13265, 1–71. www.RePEc.org Busso, M., Camacho, J., Messina, J., & Montenegro, G. (2021). Social protection and informality in Latin America during the COVID-19 pandemic. PLoS ONE, 16(11November). https://doi.org/10.1371/journal.pone.0259050 Catherine, S., Miller, M., & Sarin, N. (2020). Relaxing household liquidity cons- traints through social security. Journal of Public Economics, 189, 104243. https://doi.org/10.1016/j.jpubeco.2020.104243 Chapa, J. (2020). Impacto Económico del COVI-19 en las regiones de México. Revista Ciencia UANL, 23(102). https://doi.org/10.29105/cienciauanl23.102-1 CONAGUA. (2020). Servicio Meteorológico Nacional. https://smn.conagua.gob.mx/es/ Dingel, J. I., & Neiman, B. (2020). How many jobs can be done at home? Journal of Public Economics, 189. https://doi.org/10.1016/j.jpubeco.2020.104235 Engle, S., Stromme, J., & Zhou, A. (2020). Staying at home: mobility effects of COVID-19. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3565703 Esquivel, G. (2020). Los impactos económicos de la pandemia en México. EconomíaUNAM, 17(51), 28–44. https://doi.org/10.22201/FE.24488143E.2020.51.543 Esquivel, G., & Campos-Vázquez, R. M. (2020). Consumption and geographic mobility in pandemic times: Evidence from Mexico. Cepr Press Covid Economics, 38, 218-252. Estados Unidos Mexicanos (2020a). DOF - Diario Oficial de la Federación. https://www. dof.gob.mx/nota_detalle.php?codigo=5589479&fecha=16/03/2020&print=true Estados Unidos Mexicanos. (2020b). DOF - Diario Oficial de la Federación. https://www.dof.gob.mx/nota_detalle.php?codigo=5590914&fecha=31/03/2020&print=true Ferraresi, M., Kotsogiannis, C., Rizzo, L., & Secomandi, R. (2020). “Lockdown” and institutions COVID-19. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports . Gasca, N. C., Reyes-Garza, J., Lozano-Esparza, S., del Pino, P. O., Olivas-Martínez, A., Ulloa-Pérez, E., Garbuno-Inigo, A., & Arroyo, J. (2022). Effect of Mexico’s vaccination program on Covid-19 cases, hospitalizations, and deaths among older adults in Mexico City. Salud Pública de México, 64(4, jul-ago), 424-428. https://doi.org/10.21149/13402 Gausman, J., & Langer, A. (2020). Sex and gender disparities in the COVID-19 pandemic. Journal of Women’s Health, 29(4), 465-466. https://doi.org/10.1089/jwh.2020.8472 Gobierno de México, (GobMx), & Secretaría de Salud, (SALUD). (2020). Coronavirus gob.mx. Gobierno de México (GobMx). Google LLC. (2020). Google COVID-19 community mobility reports. Https://www.Google.Com/Covid19/Mobility/ Accessed: <18 May 2020>. ILO, I. L. O. (2020). COVID-19 and the world of work. ILO Monitor Fourth Edition. INEGI. (2020a). Banco de datos. Banco de Información Economica. https://www.inegi.org.mx/sistemas/bie/ INEGI. (2020b). Indicacadores de ocupación y empleo. Cifras oportunas durante enero del 2020. Febrero. INEGI. (2020c). Investigación - Estado de ánimo de los tuiteros. INEGI. https://www.inegi. org.mx/app/animotuitero/#/app/multiline Kong, E., & Prinz, D. (2020). Disentangling policy effects using proxy data: Which shutdown policies affected unemployment during the COVID-19 pandemic? Journal of Public Economics, 189, 104257. https://doi.org/10.1016/j.jpubeco.2020.104257 Langer, A., Meleis, A., Knaul, F. M., Atun, R., Aran, M., Arreola-Ornelas, H., Bhutta, Z. A., Binagwaho, A., Bonita, R., Caglia, J. M., Claeson, M., Davies, J., Donnay, F. A., Gausman, J. M., Glickman, C., Kearns, A. D., Kendall, T., Lozano, R., Seboni, N., ... Frenk, J. (2015). Women and health: The key for sustainable development. The Lancet, 36(9999), 1165–1210. Lancet Publishing Group. https://doi.org/10.1016/S0140-6736(15)60497-4 Leyva, A. G. (2020). Propuesta metodológica del indicador “Grado de Felicidad Local”, asociación de indicadores relativos a la felicidad, bienestar y estado de ánimo. Interconectando Saberes. https://doi.org/10.25009/is.v0i10.2663 Loayza, N., & Pennings, S. M. (2020). Macroeconomic policy in the time of COVID-19: A primer for developing countries. World Bank Research and Policy Briefs, 147291. https://ourworldindata.org/coronavirus-source-data , Luque Zúñiga, B. G., Moreno Salazar Calderón, K. A. B., & Lanchipa Ale, T. M. (2021). Impactos del COVID-19 en la agricultura y la seguridad alimentaria. Centro Agrícola. http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S0253-57852021000100072 Lütkepohl, H. (2005). New introduction to multiple time series analysis. New introduc- tion to Multiple Time Series Analysis. Springer. https://doi.org/10.1007/978-3-540-27752-1 Maloney, W., & Taskin, T. (2020). Determinants of social distancing and economic activity during COVID-19: A global view. World Bank Policy Research Working Paper, 9242. http://www.worldbank. Mendoza Cota, J. E. (2019). COVID-19 y el empleo en México: impacto inicial y pronósticos de corto plazo. Contaduría y Administración, 65(4), 1-18. https://doi.org/10.22201/fca.24488410e.2020.3028 Milani, F. (2020). COVID-19 Outbreak, social response, and early economic effects: A global VAR analysis of cross-country interdependencies. https://doi.org/10.1101/2020.05.07.20094748 Milani, F. (2021). COVID-19 outbreak, social response, and early economic effects: a global VAR analysis of cross-country interdependencies. Journal of Population Economics, 34(1), 223-252. https://doi.org/10.1007/s00148-020-00792-4 Moreno Salazar Calderón, K. A. B. (2021). Seguridad alimentaria en tiempos de COVID-19: Una visión desde la cadena productiva de recursos hidrobiológicos. Revista Estudios del Desarrollo Social: Cuba y América Latina, 9(1). http://scielo.sld.cu/scielo.php?pid=S2308-01322021000100021&script=sci_arttext&tlng=en Müller, S., & Rau, H. A. (2020). Economic preferences and compliance in the social stress test of the corona crisis. SSRN Electronic Journal, 104322. https://doi.org/10.2139/ssrn.3575633 Narula, R. (2020). Policy opportunities and challenges from the COVID-19 pandemic for economies with large informal sectors. Journal of International Business Policy, 3(3), 302-310. https://doi.org/10.1057/s42214-020-00059-5 OECD. (2020). COVID-19 in Latin America and the Caribbean: Regional socioeconomic implications and policy priorities. OECD, 1–12. https://www.oecd.org/coronavirus/policy-responses/covid-19-in-latin-america-and-the-caribbean-regional-socio-economic-implications-and-policy-priorities-93a64fde/ Ohnsorge, F., & Yu, S. (2022). The Long shadow of informality: challenges and policies. The Long Shadow of Informality: Challenges and Policies. https://doi.org/10.1596/978-1-4648-1753-3 Peluffo, C., & Viollaz, M. (2020). Intra-Household Insurance in the Time of Covid-19: Lessons from Mexico. Piguillem, F., & Shi, L. (2020). Optimal COVID-19 quarantine and testing policies. CEPR Discussion Papers. https://www.researchgate.net/publication/340226829 Rangel González, E., Llamosas-Rosas, I., Fonseca, F. J., Rangel González, E., Llamosas- Rosas, I., & Fonseca, F. J. (2021). Aislamiento social y el COVID-19 en las regiones de México. EconoQuantum, 18(2), 1-22. https://doi.org/10.18381/EQ.V18I2.7227 Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1. https://doi.org/10.2307/1912017 Tejedor Estupiñán, J. M. (2021). Vacunación y desarrollo en tiempos de la COVID-19. Revista Finanzas y Política Económica, 13(1), 9-13. https://doi.org/10.14718/REVFINANZPOLITECON.V13.N1.2021.1 Testa, P. F., Snyder, R., Rios, E., Moncada, E., Giraudy, A., & Bennouna, C. (2021). Who stays at home? The politics of social distancing in Brazil, Mexico, and the United States during the COVID-19 pandemic. Journal of Health Politics, Policy and Law, 46(6). https://doi.org/10.1215/03616878-9349100 The World Bank. (2015). World Development Indicators | DataBank. DataBank. Yilmazkuday, H. (2020). Stay-at-home works to fight against COVID-19: international evidence from Google mobility data. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3571708 Zhu, D., Mishra, S. R., Han, X., & Santo, K. (2020). Social distancing in Latin America during the COVID-19 pandemic: an analysis using the Stringency Index and Google community mobility reports. Journal of Travel Medicine, 2020, 1-3. https:// doi.org/10.1093/jtm/taaa125 https://revfinypolecon.ucatolica.edu.co/article/download/4287/4679 https://revfinypolecon.ucatolica.edu.co/article/download/4287/4623 https://revfinypolecon.ucatolica.edu.co/article/download/4287/4680 info:eu-repo/semantics/article http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/resource_type/c_2df8fbb1 http://purl.org/redcol/resource_type/ART info:eu-repo/semantics/publishedVersion http://purl.org/coar/version/c_970fb48d4fbd8a85 info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 Text Publication |
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UNIVERSIDAD CATÓLICA DE COLOMBIA |
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https://nuevo.metarevistas.org/UNIVERSIDADCATOLICADECOLOMBIA/logo.png |
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Colombia |
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Revista Finanzas y Política Económica |
| title |
“Quédate en casa (si puedes)”: empleo informal y COVID-19 en México |
| spellingShingle |
“Quédate en casa (si puedes)”: empleo informal y COVID-19 en México Chapa Cantú, Joana Cecilia Saldaña Villanueva, Carlos Emmanuel Luna Domínguez, Edgar Mauricio Covid-19 stay-at-home panel VAR informal employment México COVID-19 Var de panel empleo informal México |
| title_short |
“Quédate en casa (si puedes)”: empleo informal y COVID-19 en México |
| title_full |
“Quédate en casa (si puedes)”: empleo informal y COVID-19 en México |
| title_fullStr |
“Quédate en casa (si puedes)”: empleo informal y COVID-19 en México |
| title_full_unstemmed |
“Quédate en casa (si puedes)”: empleo informal y COVID-19 en México |
| title_sort |
“quédate en casa (si puedes)”: empleo informal y covid-19 en méxico |
| title_eng |
“Stay at home (if you can)”: informal employment and COVID-19 in Mexico |
| description |
Este artículo explora la relación entre el confinamiento residencial para reducir la propagación del virus COVID-19, visto como una política pública, y cómo afecta al sector laboral informal, así como la respuesta de los individuos a la pandemia en los estados de México. La formación de paneles para varios niveles de informalidad aplicada al panel vectorial autorregresivo (PVAR) muestra que la permanenciaen el hogar como política pública es más efectiva a medida que disminuye la informalidad. Además, la respuesta de los individuos a un aumento de la propagación de la pandemia depende del nivel de informalidad: para los estados con menores tasas de informalidad, los individuos responden a una mayor concentración del confinamiento residencial. Pero para los estados con un mayor nivel de informalidad, la evidencia no es significativa. El documento considera el papel de la informalidad en el desarrollo de una política pública eficaz.
|
| description_eng |
This paper explores the relationship between residential confinement to reduce the spread of the COVID-19 virus, seen as a public policy, and how it affects the informal labor sector, as well as the responseof individuals to the pandemic in the states of Mexico. Forming panels for various levels of informality applied to panel vector auto-regressive (PVAR) shows that staying at home as public policy becomes more effective as informality decreases. In addition, the response of individuals to an increase in the spread of the pande-mic depends on the level of informality: for states with lower rates of informality, individuals respond to a higher concentration of residential confinement. But for states with a higher level of informality, the evidence is not significant. The paper considers the role of informality in the development of an effective public policy.
|
| author |
Chapa Cantú, Joana Cecilia Saldaña Villanueva, Carlos Emmanuel Luna Domínguez, Edgar Mauricio |
| author_facet |
Chapa Cantú, Joana Cecilia Saldaña Villanueva, Carlos Emmanuel Luna Domínguez, Edgar Mauricio |
| topic |
Covid-19 stay-at-home panel VAR informal employment México COVID-19 Var de panel empleo informal México |
| topic_facet |
Covid-19 stay-at-home panel VAR informal employment México COVID-19 Var de panel empleo informal México |
| topicspa_str_mv |
COVID-19 Var de panel empleo informal México |
| citationvolume |
15 |
| citationissue |
1 |
| publisher |
Universidad Católica de Colombia |
| ispartofjournal |
Revista Finanzas y Política Económica |
| source |
https://revfinypolecon.ucatolica.edu.co/article/view/4287 |
| language |
eng |
| format |
Article |
| rights |
https://creativecommons.org/licenses/by-nc-sa/4.0 Carlos Emmanuel Saldaña Villanueva, Joana Cecilia Chapa Cantú, Edgar Mauricio Luna Domínguez - 2023 Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0. info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 |
| references_eng |
Abrigo, M. R. M., & Love, I. (2016). Estimation of panel vector autoregression in Stata. Stata Journal, 16(3), 778-804. https://doi.org/10.1177/1536867x1601600314 Akaike, H. (1969). Fitting autoregressive models for prediction. Annals of the Institute of Statistical Mathematics, 21(1), 243-247. https://doi.org/10.1007/BF02532251 Altamirano, Á., Azuarra, O., González, S., & Banco Interamericano de Desarrollo (BID). (2020). ¿Cómo impactará la COVID-19 al empleo?: Posibles escenarios para América Latina y el Caribe. Banco Interamericano de Desarrollo, 7. https://publications. iadb.org/publications/spanish/document /Cómo_impactará_la_COVID-19_al_em-pleo_Posibles_escenarios_para_América_Latina_y_el_Caribe.pdf Andersen, M. (2020). Early evidence on social distancing in response to COVID-19 in the United States. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3569368 Banxico (2020). Reporte sobre las economías regionales julio-septiembre 2020. https://www.banxico.org.mx/publicaciones-y-prensa/reportes-sobre-las-economias-regionales/%7B8427BCB2-D8F2-C28A-8DD4-EB8DD9770681%7D.pdf Bargain, O., & Aminjonov, U. (2020). Trust and compliance to public health policies in times of COVID-19. Journal of Public Economics, 104316. https://doi.org/10.1016/j.jpubeco.2020.104316 Bloomberg (2020a). Coronavirus pandemic: Ranking the best, worst places to be. Bloomberg. https://www.bloomberg.com/graphics/covid-resilience-ranking/?utm_medium=social&cmpid=socialflow-twitter-business&utm_source=twitter&utm_campaign =socialflow-organic&utm_content=business Bloomberg (2020b). Inside Bloomberg’s covid resilience ranking - Bloomberg. Bloomberg. https://www.bloomberg.com/news/articles/2020-11-24/inside-bloomberg-s-covid-resilience-ranking Brotherhood, L., Kircher, P., Santos, C., & Tertilt, M. (2020). An economic model of the Covid-19 epidemic: The importance of testing and age-specific policies. IZA Institute of Labor Economics - Discussion Paper Series, 13265, 1–71. www.RePEc.org Busso, M., Camacho, J., Messina, J., & Montenegro, G. (2021). Social protection and informality in Latin America during the COVID-19 pandemic. PLoS ONE, 16(11November). https://doi.org/10.1371/journal.pone.0259050 Catherine, S., Miller, M., & Sarin, N. (2020). Relaxing household liquidity cons- traints through social security. Journal of Public Economics, 189, 104243. https://doi.org/10.1016/j.jpubeco.2020.104243 Chapa, J. (2020). Impacto Económico del COVI-19 en las regiones de México. Revista Ciencia UANL, 23(102). https://doi.org/10.29105/cienciauanl23.102-1 CONAGUA. (2020). Servicio Meteorológico Nacional. https://smn.conagua.gob.mx/es/ Dingel, J. I., & Neiman, B. (2020). How many jobs can be done at home? Journal of Public Economics, 189. https://doi.org/10.1016/j.jpubeco.2020.104235 Engle, S., Stromme, J., & Zhou, A. (2020). Staying at home: mobility effects of COVID-19. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3565703 Esquivel, G. (2020). Los impactos económicos de la pandemia en México. EconomíaUNAM, 17(51), 28–44. https://doi.org/10.22201/FE.24488143E.2020.51.543 Esquivel, G., & Campos-Vázquez, R. M. (2020). Consumption and geographic mobility in pandemic times: Evidence from Mexico. Cepr Press Covid Economics, 38, 218-252. Estados Unidos Mexicanos (2020a). DOF - Diario Oficial de la Federación. https://www. dof.gob.mx/nota_detalle.php?codigo=5589479&fecha=16/03/2020&print=true Estados Unidos Mexicanos. (2020b). DOF - Diario Oficial de la Federación. https://www.dof.gob.mx/nota_detalle.php?codigo=5590914&fecha=31/03/2020&print=true Ferraresi, M., Kotsogiannis, C., Rizzo, L., & Secomandi, R. 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