Assessing the effectiveness of international government responses to the COVID-19 pandemic

Lopez-Medoza, Hector and González-Álvarez, Maria A. and Montañés, Antonio (2023): Assessing the effectiveness of international government responses to the COVID-19 pandemic.

Abstract

This paper studies the effectiveness of the non-pharmaceutical measures adopted by governments in order to control the evolution of the COVID-19 pandemic. To that end, we estimate a Panel VAR model for 50 countries and test for causality between the 7 day cumulative incidence, the mortality rate and a stringency index that measures government actions. The use of Granger-type statistics provides evidence that the evolution of the COVID-19 pandemic caused the measures taken by governments; however, we cannot find evidence of the reverse situation. This result suggests that the government measures were not very effective in controlling the pandemic. This does not necessarily imply that the government responses were useless. However, our results show a considerable lack of effectiveness, a lesson that governments should learn and correct if similar events occur again.

• Item Type: MPRA Paper
• Language: English
• Keywords: Government response index; stringency indexes; Granger causality; incidence, SARS-CoV-2 infection, COVID-19
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal Models; H - Public Economics > H0 - General

References

Alfano, V. (2021). Work ethics, stay-at-home measures and COVID-19 diffusion. The European Journal of Health Economics, 1-9. Alfano, V., & Ercolano, S. (2020). The efficacy of lockdown against COVID-19: a cross-country panel analysis. Applied health economics and health policy, 18(4), 509-517. Alfano, V., & Ercolano, S. (2022). Stay at home! Governance quality and effectiveness of lockdown. Social indicators research, 159(1), 101-123. Alfano, V., Capasso, S., Ercolano, S., & Goel, R. K. (2022). Death takes no bribes: Impact of perceived corruption on the effectiveness of non-pharmaceutical interventions at combating COVID-19. Social Science & Medicine, 301, 114958. Alfano, V., Ercolano, S., & Cicatiello, L. (2021). School openings and the COVID-19 outbreak in Italy. A provincial-level analysis using the synthetic control method. Health Policy, 125(9), 1200-1207. Alfano, V., Ercolano, S., & Pinto, M. (2022). Fighting the COVID pandemic: National policy choices in non-pharmaceutical interventions. Journal of Policy Modeling, 44(1), 22-40. Allen, D. W. (2022). Covid-19 lockdown cost/benefits: A critical assessment of the literature. International Journal of the Economics of Business, 29(1), 1-32. Abrigo, M. R., & Love, I. (2016). Estimation of panel vector autoregression in Stata. The Stata Journal, 16(3), 778-804. Andrews, D. W., & Lu, B. (2001). Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models. Journal of econometrics, 101(1), 123-164. Arellano, M., & 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., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of econometrics, 68(1), 29-51. Barnett, L., Barrett, A. B., & Seth, A. K. (2018). Misunderstandings regarding the application of Granger causality in neuroscience. Proceedings of the National Academy of Sciences, 115(29), E6676-E6677. Bendavid, E., Oh, C., Bhattacharya, J., & Ioannidis, J. P. (2021). Assessing mandatory stay-at-home and business closure effects on the spread of COVID-19. European journal of clinical investigation, 51(4), e13484. Bhattacharya, S. (2020). The Social Impact of the COVID-19 Pandemic. Observer research foundation. Bloom, D. E., Kuhn, M., & Prettner, K. (2022). Modern infectious diseases: macroeconomic impacts and policy responses. Journal of Economic Literature, 60(1), 85-131. Bjørnskov, C. (2021). Did lockdown work? An economist’s cross-country comparison. CESifo Economic Studies, 67(3), 318-331. Brauner, J. M., Mindermann, S., Sharma, M., Johnston, D., Salvatier, J., Gavenčiak, T., ... & Kulveit, J. (2021). Inferring the effectiveness of government interventions against COVID-19. Science, 371(6531), eabd9338. Brodeur, A., Gray, D., Islam, A., & Bhuiyan, S. (2021). A literature review of the economics of COVID‐19. Journal of Economic Surveys, 35(4), 1007-1044. Chaudhry, R., Dranitsaris, G., Mubashir, T., Bartoszko, J., & Riazi, S. (2020). A country level analysis measuring the impact of government actions, country preparedness and socioeconomic factors on COVID-19 mortality and related health outcomes. EClinicalMedicine, 25, 100464. Chaudhuri, A. (2022). Nudged into Lockdown?: Behavioral Economics, Uncertainty and Covid-19. Edward Elgar Publishing. Dergiades, T., Milas, C., & Panagiotidis, T. (2022). Unemployment claims during COVID-19 and economic support measures in the US. Economic Modelling, 105891. Elgin, C., Basbug, G., & Yalaman, A. (2020). Economic policy responses to a pandemic: Developing the COVID-19 economic stimulus index. Covid Economics, 1(3), 40-53. Frijters, P., Foster, G., & Baker, M. (2021). The great Covid panic: what happened, why, and what to do next. Austin: Brownstone Institute. Gibson, J. (2022). Hard, not early: putting the New Zealand Covid-19 response in context. New Zealand Economic Papers, 56(1), 1-8. Hale, T., Petherick, A., Phillips, T., Webster, S., 2020. Variation in government responses to COVID-19: version 2.0. Blavatnik School of Government Working Paper. Hale, T., Angrist, N., Goldszmidt, R., Kira, B., Petherick, A., Phillips, T., ... & Tatlow, H. (2021). A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nature human behaviour, 5(4), 529-538. Haug, N., Geyrhofer, L., Londei, A., Dervic, E., Desvars-Larrive, A., Loreto, V., ... & Klimek, P. (2020). Ranking the effectiveness of worldwide COVID-19 government interventions. Nature human behaviour, 4(12), 1303-1312. Herby, J., Jonung, L., & Hanke, S. (2022). A literature review and meta-analysis of the effects of lockdowns on COVID-19 mortality. Studies in Applied Economics, (200). Hagihara, A., Onozuka, D., Miyazaki, S., & Abe, T. (2015). Influenza newspaper reports and the influenza epidemic: an observational study in Fukuoka City, Japan. BMJ open, 5(12), e009900. Huang, J., Fisher, B. T., Tam, V., Wang, Z., Song, L., Shi, J., ... & Rubin, D. M. (2022). The Effectiveness Of Government Masking Mandates On COVID-19 County-Level Case Incidence Across The United States, 2020: Study examines the effectiveness of US government masking mandates during a portion of the COVID-19 pandemic. Health Affairs, 41(3), 445-453. Holtz-Eakin, D., Newey, W., & Rosen, H. S. (1988). Estimating vector autoregressions with panel data. Econometrica: Journal of the econometric society, 1371-1395. Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of econometrics, 115(1), 53-74. Liu, Y., Morgenstern, C., Kelly, J., Lowe, R., & Jit, M. (2021). The impact of non-pharmaceutical interventions on SARS-CoV-2 transmission across 130 countries and territories. BMC medicine, 19(1), 1-12. Meenagh, D., & Minford, P. (2021). A structural model of coronavirus behaviour for testing on data behaviour. Applied Economics, 53(30), 3515-3534. Nickell, S. (1981). Biases in dynamic models with fixed effects. Econometrica: Journal of the econometric society, 1417-1426. Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross‐section dependence. Journal of applied econometrics, 22(2), 265-312. Pesaran, M. H. (2015). Testing weak cross-sectional dependence in large panels. Econometric reviews, 34(6-10), 1089-1117. Pesaran, M. H. (2021). General diagnostic tests for cross-sectional dependence in panels. Empirical Economics, 60(1), 13-50. Petherick, A., Kira, B., Hale, T., Phillips, T., Webster, S., Cameron-Blake, E., ... & Angrist, N. (2020). Variation in government responses to COVID-19. Blavatnik Centre for Government Working Paper, University of Oxford. Park, J. Y., Kim, H. I., Kim, J. H., Park, S., Hwang, Y. I., Jang, S. H., ... & Jung, K. S. (2021). Changes in respiratory virus infection trends during the COVID-19 pandemic in South Korea: the effectiveness of public health measures. The Korean journal of internal medicine, 36(5), 1157. Pingle, M. (2022). Addressing threats like Covid: why we will tend to over-react and how we can do better. Mind & Society, 1-15. Rees, E. E., Avery, B. P., Carabin, H., Carson, C. A., Champredon, D., de Montigny, S., ... & Ogden, N. H. (2022). Effectiveness of non-pharmaceutical interventions to reduce SARS-CoV-2 transmission in Canada and their association with COVID-19 hospitalization rates. CCDRCANADA, 48(10), 438. Sato, K., Mano, T., Iwata, A., & Toda, T. (2021). Need of care in interpreting Google Trends-based COVID-19 infodemiological study results: potential risk of false-positivity. BMC medical research methodology, 21(1), 1-10. Schippers, M. C., Ioannidis, J. P. A., & Joffe, A. R. (2022). Aggressive Measures, Rising Inequalities and Mass Formation During the COVID-19 Crisis: An Overview and Proposed Way Forward. ERIM Report Series Research in Management, (ERS-2022-004-LIS). Seong, H., Hong, J. W., Hyun, H. J., Yoon, J. G., Noh, J. Y., Cheong, H. J., ... & Song, J. Y. (2021). Correlation between the Level of Social Distancing and Activity of Influenza Epidemic or COVID-19 Pandemic: A Subway Use-Based Assessment. Journal of clinical medicine, 10(15), 3369. Sims, C. A. (1980). Macroeconomics and reality. Econometrica: journal of the Econometric Society, 1-48. Spiliopoulos, L. (2022). On the effectiveness of COVID-19 restrictions and lockdowns: Pan metron ariston. BMC public health, 22(1), 1-15. Stokes, P. A., & Purdon, P. L. (2017). A study of problems encountered in Granger causality analysis from a neuroscience perspective. Proceedings of the national academy of sciences, 114(34), E7063-E7072. Sun, J., Zheng, Y., Liang, W., Yang, Z., Zeng, Z., Li, T., ... & Zhong, N. (2022). Quantifying the Effect of Public Activity Intervention Policies on COVID-19 Pandemic Containment Using Epidemiologic Data From 145 Countries. Value in Health, 25(5), 699-708. Vickers, D. M., Baral, S., Mishra, S., Kwong, J. C., Sundaram, M., Katz, A., ... & Williamson, T. (2022). Stringency of containment and closures on the growth of SARS-CoV-2 in Canada prior to accelerated vaccine roll-out. International Journal of Infectious Diseases, 118, 73-82. Yang, Q. C., Chen, X., Chang, C. P., Chen, D., & Hao, Y. (2021). What is the relationship between government response and COVID-19 pandemics? Global evidence of 118 countries. Structural Change and Economic Dynamics, 59, 98-107.