Análise contrafactual dos impactos da COVID-19 na arrecadação dos entes subnacionais e políticas de isolamento social e lockdown
estimando impactos contrafactuais temporais da COVID-19 na arrecadação
DOI:
https://doi.org/10.55532/1806-8944.2022.140Palavras-chave:
Covid-19, Arrecadação, Taxa de Mortalidade, Inferência ContrafactualResumo
O objetivo deste estudo é estimar os impactos contrafactuais da pandemia do Covid-19 na arrecadação estadual e taxas de mortalidade das UF do Brasil, considerando a adoção de políticas de lockdown e de isolamento social. Os modelos DiD, indicam que consumo diário de energia elétrica esteve inversamente associado as taxas de mortalidade estaduais. Os resultados ArCo evidenciaram que quanto maior a severidade pandêmica maiores foram os impactos em arrecadação, e quanto maior a taxa de isolamento social menores foram as taxas de mortalidade contrafactuais. Não há evidências significantes da política de lockdown sobre arrecadação ou taxa de mortalidade estadual.
Referências
Abadie, A., Diamond, A. and Hainmueller, J.: 2010, Synthetic control methods for comparative case studies: Estimating the effect of california’s tobacco control program, Journal of the American statistical Association 105(490), 493–505.
Abadie, A., Diamond, A. and Hainmueller, J.: 2015, Comparative politics and the synthetic control method, American Journal of Political Science 59(2), 495–510.
Abadie, A. and Gardeazabal, J.: 2003, The economic costs of conflict: A case study of the basque country, American economic review 93(1), 113–132.
Al-Awadhi, A. M., Al-Saifi, K., Al-Awadhi, A. and Alhamadi, S.: 2020, Death and contagious infectious diseases: Impact of the covid-19 virus on stock market returns, Journal of Behavioral and Experimental Finance p. 100326.
Albulescu, C.: 2020a, Coronavirus and financial volatility: 40 days of fasting and fear, arXiv preprint arXiv:2003.04005.
Albulescu, C. T.: 2020b, Covid-19 and the united states financial markets’ volatility, Finance Research Letters p. 101699.
Alchian, A. A.: 1953, The meaning of utility measurement, The American Economic Review 43(1), 26–50.
Angrist, J. D. and Pischke, J.-S.: 2008, Mostly harmless econometrics: An empiricist’s companion, Princeton university press.
Arellano, M.: 2003, Panel data econometrics, Oxford university press.
Arellano, M. and Bond, S.: 1991, Some tests of specification for panel data: Monte carlo evidence and an application to employment equations, The review of economic studies 58(2), 277–297.
Arellano, M. et al.: 1987, Computing robust standard errors for within-groups estimators, Oxford bulletin of Economics and Statistics 49(4), 431–434.
Ashenfelter, O. and Card, D.: 1985, susing the longitudinal structure of earnings to estimate the effect of training programs, review of economics and statistics; 67 (4).
Baker, S. R., Bloom, N., Davis, S. J., Kost, K., Sammon, M. and Viratyosin, T.: 2020, The unprecedented stock market reaction to covid-19, The Review of Asset Pricing Studies .
Baumol, W. J.: 1951, The neumann-morgenstern utility index–an ordinalist view, Journal of Political economy 59(1), 61–66.
Baumol, W. J.: 1958, The cardinal utility which is ordinal, The Economic Journal 68(272), 665–672.
Bertrand, M., Duflo, E. and Mullainathan, S.: 2004, How much should we trust differences-in-differences estimates?, The Quarterly journal of economics 119(1), 249–275.
Canova, F.: 1998, Detrending and business cycle facts, Journal of monetary economics 41(3), 475–512.
Card, D. and Krueger, A. B.: 2000, Minimum wages and employment: a case study of the fast-food industry in new jersey and pennsylvania: reply, American Economic Review 90(5), 1397–1420.
Carvalho, C., Masini, R. and Medeiros, M. C.: 2018, Arco: an artificial counterfactual approach for high-dimensional panel time-series data, Journal of econometrics 207(2), 352–380.
Casado, M. G., Glennon, B., Lane, J., McQuown, D., Rich, D. and Weinberg, B. A.: 2020, The effect of fiscal stimulus: Evidence from covid-19. Working Paper.
Choi, I.: 2001, Unit root tests for panel data, Journal of international money and Finance 20(2), 249–272.
Clemens, J. and Miran, S.: 2012, Fiscal policy multipliers on subnational government spending, American Economic Journal: Economic Policy 4(2), 46–68.
Clemens, J. and Veuger, S.: 2020, Implications of the covid-19 pandemic for state government tax revenues. Working Paper.
Coates, B., Cowgill, M., Chen, T. and Mackey, W.: 2020, Shutdown: estimating the covid-19 employment shock, Grattan Institute, Melbourne, Victoria .
Corbet, S., Hou, G., Hu, Y., Oxley, L. and Xu, D.: 2020, Pandemic-related financial market volatility spillovers: Evidence from the chinese covid-19 epicentre, Available at SSRN 3618736 .
Cribari-Neto, F.: 2004, Asymptotic inference under heteroskedasticity of unknown form, Computational Statistics & Data Analysis 45(2), 215–233.
De Vito, A. and Gomez, J.-P.: 2020, Estimating the covid-19 cash crunch: Global evidence and policy, Journal of Accounting and Public Policy p. 106741.
Debreu, G.: 1954, Representation of a preference ordering by a numerical function, Decision processes 3, 159–165.
Demsetz, H.: 1968, Why regulate utilities?, The Journal of Law and Economics 11(1), 55–65.
Doudchenko, N. and Imbens, G. W.: 2016, Balancing, regression, difference-in-differences and synthetic control methods: A synthesis, Technical report, National Bureau of Economic Research.
Fairlie, R. W., Couch, K. and Xu, H.: 2020, The impacts of covid-19 on minority unemployment: First evidence from april 2020 cps microdata.
Ferman, B. and Pinto, C.: 2016, Revisiting the synthetic control estimator, Working Paper FGV .
Ferman, B. and Pinto, C.: 2019, Inference in differences-in-differences with few treated groups and heteroskedasticity, Review of Economics and Statistics 101(3), 452–467.
Ferman, B., Pinto, C. and Possebom, V.: 2020, Cherry picking with synthetic controls, Journal of Policy Analysis and Management 39(2), 510–532.
Fernandes, N.: 2020, Economic effects of coronavirus outbreak (covid-19) on the world economy, Available at SSRN 3557504 .
Fezzi, C. and Fanghella, V.: 2020, Real-time estimation of the short-run impact of covid-19 on economic activity using electricity market data, Environmental and Resource Economics 76(4), 885–900.
Friedman, J., Hastie, T. and Tibshirani, R.: 2010, Regularization paths for generalized linear models via coordinate descent, Journal of statistical software 33(1), 1.
Garcin, M., Klein, J. and Laaribi, S.: 2020, Estimation of time-varying kernel densities and chronology of the impact of covid-19 on financial markets, arXiv preprint arXiv:2007.09043 .
Gobillon, L. and Magnac, T.: 2016, Regional policy evaluation: Interactive fixed effects and synthetic controls, Review of Economics and Statistics 98(3), 535–551.
Gómez, V. and Maravall, A.: 1996, Programs tramo and seats, instruction for user (beta version: September 1996), Technical report, Banco de España.
Hsiao, C., Steve Ching, H. and Ki Wan, S.: 2012, A panel data approach for program evaluation: measuring the benefits of political and economic integration of hong kong with mainland china, Journal of Applied Econometrics 27(5), 705–740.
Ibikunle, G. and Rzayev, K.: 2020, Volatility, dark trading and market quality: evidence from the 2020 covid-19 pandemic-driven market volatility, Available at SSRN 3586410 .
Knight, F. H.: 1921, Risk, uncertainty and profit, Vol. 31, Houghton Mifflin.
Kurmann, A., Lale, E. and Ta, L.: 2020, The impact of covid-19 on us employment and hours: Real-time estimates with homebase data, May). http://www. andrekurmann. com/hb_covid .
Laffont, J.-J. and Martimort, D.: 2009, The theory of incentives: the principal-agent model, Princeton university press.
Le Quéré, C., Jackson, R. B., Jones, M. W., Smith, A. J., Abernethy, S., Andrew, R. M., De-Gol, A. J., Willis, D. R.,Shan, Y., Canadell, J. G. et al.: 2020, Temporary reduction in daily global co 2 emissions during the covid-19 forced confinement, Nature Climate Change pp. 1–7.
LeRoy, S. F. and Singell Jr, L. D.: 1987, Knight on risk and uncertainty, Journal of political economy 95(2), 394–406.
Lutz, B., Molloy, R. and Shan, H.: 2011, The housing crisis and state and local government tax revenue: Five channels, Regional Science and Urban Economics 41(4), 306–319.
Mandal, I. and Pal, S.: 2020, Covid-19 pandemic persuaded lockdown effects on environment over stone quarrying and crushing areas, Science of The Total Environment 732, 139281.
Milani, B., Bender Filho, R., Ceretta, P. S., Vieira, K. M. and Coronel, D. A.: 2014, Spillover de liquidez no mercado brasileiro, Revista Pensamento Contemporâneo em Administração 8(2), 55–74.
Morgenstern, O. and Von Neumann, J.: 1953, Theory of games and economic behavior, Princeton university press.
Norouzi, N., de Rubens, G. Z., Choubanpishehzafar, S. and Enevoldsen, P.: 2020, When pandemics impact economies and climate change: Exploring the impacts of covid-19 on oil and electricity demand in china, Energy Research & Social Science 68, 101654.
Ozili, P. K. and Arun, T.: 2020, Spillover of covid-19: impact on the global economy, Available at SSRN 3562570 .
Poterba, J. M.: 1994, State responses to fiscal crises: The effects of budgetary institutions and politics, Journal of political Economy 102(4), 799–821.
Roberton, T., Carter, E. D., Chou, V. B., Stegmuller, A. R., Jackson, B. D., Tam, Y., Sawadogo-Lewis, T. and Walker, N.: 2020, Early estimates of the indirect effects of the covid-19 pandemic on maternal and child mortality in low-income and middle-income countries: a modelling study, The Lancet Global Health .
Romano, F.: 2020, An estimate of the economic impact of covid-19 on australia, Available at SSRN 3581382 .
Snow, J.: 1855, On the mode of communication of cholera, John Churchill.
Sumner, A., Hoy, C., Ortiz-Juarez, E. et al.: 2020, Estimates of the impact of covid-19 on global poverty, UNU-WIDER, April pp. 800–9.
Topcu, M. and Gulal, O. S.: 2020, The impact of covid-19 on emerging stock markets, Finance Research Letters p. 101691.
Tristão, J. A. M.: 2003, A administração tributária dos municípios brasileiros: uma avaliação do desempenho da arrecadação, PhD thesis.
Wang, Q. and Su, M.: 2020, A preliminary assessment of the impact of covid-19 on environment–a case study of china, Science of the Total Environment p. 138915.
Zhang, D., Hu, M. and Ji, Q.: 2020, Financial markets under the global pandemic of covid-19, Finance Research Letters p. 101528.
Abadie, A., Diamond, A. and Hainmueller, J.: 2015, Comparative politics and the synthetic control method, American Journal of Political Science 59(2), 495–510.
Abadie, A. and Gardeazabal, J.: 2003, The economic costs of conflict: A case study of the basque country, American economic review 93(1), 113–132.
Al-Awadhi, A. M., Al-Saifi, K., Al-Awadhi, A. and Alhamadi, S.: 2020, Death and contagious infectious diseases: Impact of the covid-19 virus on stock market returns, Journal of Behavioral and Experimental Finance p. 100326.
Albulescu, C.: 2020a, Coronavirus and financial volatility: 40 days of fasting and fear, arXiv preprint arXiv:2003.04005.
Albulescu, C. T.: 2020b, Covid-19 and the united states financial markets’ volatility, Finance Research Letters p. 101699.
Alchian, A. A.: 1953, The meaning of utility measurement, The American Economic Review 43(1), 26–50.
Angrist, J. D. and Pischke, J.-S.: 2008, Mostly harmless econometrics: An empiricist’s companion, Princeton university press.
Arellano, M.: 2003, Panel data econometrics, Oxford university press.
Arellano, M. and Bond, S.: 1991, Some tests of specification for panel data: Monte carlo evidence and an application to employment equations, The review of economic studies 58(2), 277–297.
Arellano, M. et al.: 1987, Computing robust standard errors for within-groups estimators, Oxford bulletin of Economics and Statistics 49(4), 431–434.
Ashenfelter, O. and Card, D.: 1985, susing the longitudinal structure of earnings to estimate the effect of training programs, review of economics and statistics; 67 (4).
Baker, S. R., Bloom, N., Davis, S. J., Kost, K., Sammon, M. and Viratyosin, T.: 2020, The unprecedented stock market reaction to covid-19, The Review of Asset Pricing Studies .
Baumol, W. J.: 1951, The neumann-morgenstern utility index–an ordinalist view, Journal of Political economy 59(1), 61–66.
Baumol, W. J.: 1958, The cardinal utility which is ordinal, The Economic Journal 68(272), 665–672.
Bertrand, M., Duflo, E. and Mullainathan, S.: 2004, How much should we trust differences-in-differences estimates?, The Quarterly journal of economics 119(1), 249–275.
Canova, F.: 1998, Detrending and business cycle facts, Journal of monetary economics 41(3), 475–512.
Card, D. and Krueger, A. B.: 2000, Minimum wages and employment: a case study of the fast-food industry in new jersey and pennsylvania: reply, American Economic Review 90(5), 1397–1420.
Carvalho, C., Masini, R. and Medeiros, M. C.: 2018, Arco: an artificial counterfactual approach for high-dimensional panel time-series data, Journal of econometrics 207(2), 352–380.
Casado, M. G., Glennon, B., Lane, J., McQuown, D., Rich, D. and Weinberg, B. A.: 2020, The effect of fiscal stimulus: Evidence from covid-19. Working Paper.
Choi, I.: 2001, Unit root tests for panel data, Journal of international money and Finance 20(2), 249–272.
Clemens, J. and Miran, S.: 2012, Fiscal policy multipliers on subnational government spending, American Economic Journal: Economic Policy 4(2), 46–68.
Clemens, J. and Veuger, S.: 2020, Implications of the covid-19 pandemic for state government tax revenues. Working Paper.
Coates, B., Cowgill, M., Chen, T. and Mackey, W.: 2020, Shutdown: estimating the covid-19 employment shock, Grattan Institute, Melbourne, Victoria .
Corbet, S., Hou, G., Hu, Y., Oxley, L. and Xu, D.: 2020, Pandemic-related financial market volatility spillovers: Evidence from the chinese covid-19 epicentre, Available at SSRN 3618736 .
Cribari-Neto, F.: 2004, Asymptotic inference under heteroskedasticity of unknown form, Computational Statistics & Data Analysis 45(2), 215–233.
De Vito, A. and Gomez, J.-P.: 2020, Estimating the covid-19 cash crunch: Global evidence and policy, Journal of Accounting and Public Policy p. 106741.
Debreu, G.: 1954, Representation of a preference ordering by a numerical function, Decision processes 3, 159–165.
Demsetz, H.: 1968, Why regulate utilities?, The Journal of Law and Economics 11(1), 55–65.
Doudchenko, N. and Imbens, G. W.: 2016, Balancing, regression, difference-in-differences and synthetic control methods: A synthesis, Technical report, National Bureau of Economic Research.
Fairlie, R. W., Couch, K. and Xu, H.: 2020, The impacts of covid-19 on minority unemployment: First evidence from april 2020 cps microdata.
Ferman, B. and Pinto, C.: 2016, Revisiting the synthetic control estimator, Working Paper FGV .
Ferman, B. and Pinto, C.: 2019, Inference in differences-in-differences with few treated groups and heteroskedasticity, Review of Economics and Statistics 101(3), 452–467.
Ferman, B., Pinto, C. and Possebom, V.: 2020, Cherry picking with synthetic controls, Journal of Policy Analysis and Management 39(2), 510–532.
Fernandes, N.: 2020, Economic effects of coronavirus outbreak (covid-19) on the world economy, Available at SSRN 3557504 .
Fezzi, C. and Fanghella, V.: 2020, Real-time estimation of the short-run impact of covid-19 on economic activity using electricity market data, Environmental and Resource Economics 76(4), 885–900.
Friedman, J., Hastie, T. and Tibshirani, R.: 2010, Regularization paths for generalized linear models via coordinate descent, Journal of statistical software 33(1), 1.
Garcin, M., Klein, J. and Laaribi, S.: 2020, Estimation of time-varying kernel densities and chronology of the impact of covid-19 on financial markets, arXiv preprint arXiv:2007.09043 .
Gobillon, L. and Magnac, T.: 2016, Regional policy evaluation: Interactive fixed effects and synthetic controls, Review of Economics and Statistics 98(3), 535–551.
Gómez, V. and Maravall, A.: 1996, Programs tramo and seats, instruction for user (beta version: September 1996), Technical report, Banco de España.
Hsiao, C., Steve Ching, H. and Ki Wan, S.: 2012, A panel data approach for program evaluation: measuring the benefits of political and economic integration of hong kong with mainland china, Journal of Applied Econometrics 27(5), 705–740.
Ibikunle, G. and Rzayev, K.: 2020, Volatility, dark trading and market quality: evidence from the 2020 covid-19 pandemic-driven market volatility, Available at SSRN 3586410 .
Knight, F. H.: 1921, Risk, uncertainty and profit, Vol. 31, Houghton Mifflin.
Kurmann, A., Lale, E. and Ta, L.: 2020, The impact of covid-19 on us employment and hours: Real-time estimates with homebase data, May). http://www. andrekurmann. com/hb_covid .
Laffont, J.-J. and Martimort, D.: 2009, The theory of incentives: the principal-agent model, Princeton university press.
Le Quéré, C., Jackson, R. B., Jones, M. W., Smith, A. J., Abernethy, S., Andrew, R. M., De-Gol, A. J., Willis, D. R.,Shan, Y., Canadell, J. G. et al.: 2020, Temporary reduction in daily global co 2 emissions during the covid-19 forced confinement, Nature Climate Change pp. 1–7.
LeRoy, S. F. and Singell Jr, L. D.: 1987, Knight on risk and uncertainty, Journal of political economy 95(2), 394–406.
Lutz, B., Molloy, R. and Shan, H.: 2011, The housing crisis and state and local government tax revenue: Five channels, Regional Science and Urban Economics 41(4), 306–319.
Mandal, I. and Pal, S.: 2020, Covid-19 pandemic persuaded lockdown effects on environment over stone quarrying and crushing areas, Science of The Total Environment 732, 139281.
Milani, B., Bender Filho, R., Ceretta, P. S., Vieira, K. M. and Coronel, D. A.: 2014, Spillover de liquidez no mercado brasileiro, Revista Pensamento Contemporâneo em Administração 8(2), 55–74.
Morgenstern, O. and Von Neumann, J.: 1953, Theory of games and economic behavior, Princeton university press.
Norouzi, N., de Rubens, G. Z., Choubanpishehzafar, S. and Enevoldsen, P.: 2020, When pandemics impact economies and climate change: Exploring the impacts of covid-19 on oil and electricity demand in china, Energy Research & Social Science 68, 101654.
Ozili, P. K. and Arun, T.: 2020, Spillover of covid-19: impact on the global economy, Available at SSRN 3562570 .
Poterba, J. M.: 1994, State responses to fiscal crises: The effects of budgetary institutions and politics, Journal of political Economy 102(4), 799–821.
Roberton, T., Carter, E. D., Chou, V. B., Stegmuller, A. R., Jackson, B. D., Tam, Y., Sawadogo-Lewis, T. and Walker, N.: 2020, Early estimates of the indirect effects of the covid-19 pandemic on maternal and child mortality in low-income and middle-income countries: a modelling study, The Lancet Global Health .
Romano, F.: 2020, An estimate of the economic impact of covid-19 on australia, Available at SSRN 3581382 .
Snow, J.: 1855, On the mode of communication of cholera, John Churchill.
Sumner, A., Hoy, C., Ortiz-Juarez, E. et al.: 2020, Estimates of the impact of covid-19 on global poverty, UNU-WIDER, April pp. 800–9.
Topcu, M. and Gulal, O. S.: 2020, The impact of covid-19 on emerging stock markets, Finance Research Letters p. 101691.
Tristão, J. A. M.: 2003, A administração tributária dos municípios brasileiros: uma avaliação do desempenho da arrecadação, PhD thesis.
Wang, Q. and Su, M.: 2020, A preliminary assessment of the impact of covid-19 on environment–a case study of china, Science of the Total Environment p. 138915.
Zhang, D., Hu, M. and Ji, Q.: 2020, Financial markets under the global pandemic of covid-19, Finance Research Letters p. 101528.
Downloads
Publicado
22-03-2022
Como Citar
Maranhão, A. (2022). Análise contrafactual dos impactos da COVID-19 na arrecadação dos entes subnacionais e políticas de isolamento social e lockdown: estimando impactos contrafactuais temporais da COVID-19 na arrecadação. CADERNOS DE FINANÇAS PÚBLICAS , 21(03). https://doi.org/10.55532/1806-8944.2022.140
Edição
Seção
artigos
Licença
Os artigos podem ser copiados, desde que citada a fonte.
