Delis, Manthos D. and Iosifidi, Maria and Tasiou, Menelaos (2021): Efficiency of government policy during the COVID-19 pandemic.
Preview |
PDF
MPRA_paper_107046.pdf Download (1MB) | Preview |
Abstract
Using data envelopment analysis and stochastic frontier models, we introduce a new country-month index of efficiency of government policy in dealing with the COVID-19 pandemic. Our indices cover the period May 2020 to March 2021 for 81 countries. Our framework assumes that governments impose stringent policies with the ultimate goal of saving lives. We use policies listed in the Oxford COVID-19 Containment and Health Index as government policy input and a deaths-based measure as the output. Importantly, we estimate our output to account for country-month variations in the quality of death reporting. Based on their average efficiency, the top 5 countries are Taiwan, Japan, Estonia, Finland and New Zealand. We also examine the correlates of our new indices and find that important and positive ones are institutions, democratic principles, political stability, high public spending in health, female participation in the workplace, and economic equality. Within the efficient jurisdictions, the most efficient ones are those with cultural characteristics of low power distance and high patience. The new index and its correlates produce several avenues for future research.
Item Type: | MPRA Paper |
---|---|
Original Title: | Efficiency of government policy during the COVID-19 pandemic |
Language: | English |
Keywords: | Government efficiency, COVID-19 pandemic, Data envelopment analysis, Stochastic frontiers, Oxford COVID-19 Government Response Tracker, determinants of efficiency |
Subjects: | I - Health, Education, and Welfare > I1 - Health > I18 - Government Policy ; Regulation ; Public Health |
Item ID: | 107046 |
Depositing User: | Dr Menelaos Tasiou |
Date Deposited: | 11 Apr 2021 16:55 |
Last Modified: | 11 Apr 2021 16:55 |
References: | Ashraf, Q., & Galor, O. (2011). Cultural diversity, geographical isolation, and the origin of the wealth of nations (No. w17640). National Bureau of Economic Research. Aigner, D., Lovell, C. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21-37. Barro, R.J., Ursúa, J F., Weng J., 2020. The coronavirus and the great influenza pandemic: Lessons from the “Spanish flu” for the coronavirus’s potential effects on mortality and economic activity. Working Paper 26866, National Bureau of Economic Research. Baveja, A., Kapoor, A., Melamed, B., 2020. Stopping Covid-19: A pandemic-management service value chain approach. Annals of Operations Research 289, 173-184. Beauchamp, Z., 2020. The myth of authoritarian coronavirus supremacy. Vox. Available at: https://www.vox.com/2020/3/26/21184238/coronavirus-china-authoritarian-system-democracy. Breitenbach, M.C., Ngobeni, V., Aye, G., 2020. Efficiency of healthcare systems in the first wave of COVID-19 - a technical efficiency analysis. MPRA Paper, 101440. Cabinet Office. (2021). COVID-19 Response - Spring 2021. Retrieved from https://www.gov.uk/government/publications/covid-19-response-spring-2021/covid-19-response-spring-2021 Chang, R. and Hong, J. (2021). Inside Bloomberg’s Covid Resilience Ranking. Retrieved from https://www.bloomberg.com/news/articles/2020-11-24/inside-bloomberg-s-covid-resilience-ranking Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA)–Thirty years on. European journal of operational research, 192(1), 1-17. Cooper, W. W., Seiford, L. M., & Zhu, J. (2011). Data envelopment analysis: History, models, and interpretations. In Handbook on data envelopment analysis (pp. 1-39). Springer, Boston, MA. Correia, S., Luck, S., Verner, E., 2020. Pandemics depress the economy, public health interventions do not: Evidence from the 1918 Flu. Available at: https://ssrn.com/abstract=3561560. Daraio, C., & Simar, L. (2007). Advanced robust and nonparametric methods in efficiency analysis: Methodology and applications. Springer Science & Business Media. Gaganis, C., Pasiouras, F., Tasiou, M., & Zopounidis, C. (2021). CISEF: A composite index of social, environmental and financial performance. European Journal of Operational Research, 291(1), 394-409. Greene, W.H., 2005. Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. Journal of Econometrics 126, 269-303. Haug, N., Geyrhofer, L., Londei, A. et al., 2020. Ranking the effectiveness of worldwide COVID-19 government interventions. Nature Human Behaviour 4, 1303-1312. Ivanov, D., 2020. Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review 136, 101922. Ivanov, D., Dolgui, A., 2020. OR-methods for coping with the ripple effect in supply chains during COVID-19 pandemic: Managerial insights and research implications. International Journal of Production Economics 232, 107921. Jamison, D. T., Lau, L. J., Wu, K. B., & Xiong, Y. (2020). Country performance against COVID-19: rankings for 35 countries. BMJ global health, 5(12), e003047. Kontis, V., Bennett, J.E., et al., 2020. Magnitude, demographics and dynamics of the effect of the first wave of the COVID-19 pandemic on all-cause mortality in 21 industrialized countries. Nature Medicine 26, 1919-1928. Li, W., Liang, L., Cook, W. D., & Zhu, J. (2016). DEA models for non-homogeneous DMUs with different input configurations. European Journal of Operational Research, 254(3), 946-956. Lowy Institute (2021). Covid Performance Index. Deconstructing Pandemic Responses. Retrieved from: https://interactives.lowyinstitute.org/features/covid-performance/?fbclid=IwAR1nGkrg5DGC4WAkza3fv4KSYkCKaooIQjNqR8lol6kFEBRhrdhRixe0IoQ#methodology Meeusen, W., & van Den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review, 18(2), 435-444. Mérieau, E., 2020. COVID-19, authoritarianism vs. democracy: What the epidemic reveals about the orientalism of our categories of thought. SciencesPo, Center for International Studies. Nikolopoulos, K., Punia, S., Schäfers, A., Tsinopoulos, C., Vasilakis, C., 2020. Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Operational Research 290, 99-115. Queiroz, M.M., Ivanov, D., Dolgui, A., Wamba, S.F., 2020. Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of Operations Research, published online. Simar, L., & Wilson, P. W. (1999). Of course we can bootstrap DEA scores! But does it mean anything? Logic trumps wishful thinking. Journal of Productivity Analysis, 93-97. Simar, L., & Wilson, P. W. (2000). A general methodology for bootstrapping in non-parametric frontier models. Journal of applied statistics, 27(6), 779-802. Simar, L., & Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of econometrics, 136(1), 31-64. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/107046 |
Available Versions of this Item
- Efficiency of government policy during the COVID-19 pandemic. (deposited 11 Apr 2021 16:55) [Currently Displayed]