Abstract
AbstractThere are large variations between and within countries’ performance in coping with the Covid-19 pandemic. This study assesses the efficiency of different provinces in Sweden in managing the pandemic. Its objective to estimate the relative efficiency of provinces during the pandemic and identify the key determinants of the level and variations in their performance. Performance is measured as efficiency in keeping the number of negative outcomes low and the number of positive outcomes high for given resources. It uses a parametric distance function approach with multi-input, multi-output, and a flexible functional form for estimating the provinces’ efficiency and the variations in this efficiency over time. Variations in their performance are attributed to the observable characteristics of their socioeconomic, locational, demographic, and healthcare resources. The empirical part is based on a panel data of the population in 21 provinces observed on a weekly basis from January 2020 to September 2021. In particular, the paper estimates the effects of public support and vaccinations on a reduction in the number of deaths and the spread of new cases. The level and variations in outcomes are explained by various provinces and local and national policies. The results show large variations in provinces’ performance and their persistence/transitory nature attributed to their observable characteristics. The paper suggests some policy recommendations to help cope with the threat of the pandemic post the removal of restrictions.
Publisher
Springer Science and Business Media LLC
Subject
Management of Technology and Innovation,Computational Theory and Mathematics,Management Science and Operations Research,Statistics, Probability and Uncertainty,Strategy and Management,Modeling and Simulation,Numerical Analysis
Reference64 articles.
1. Alam-Siddik MdN (2020) Economic stimulus for Covid-19 pandemic and its determinants: evidence from cross-country analysis. Heliyon 6(12):e05634
2. Almlöf E, Rubensson I, Cebecauer M, Jenelius E (2021) Who continued travelling by public transport during Covid-19? Socioeconomic factors explaining travel behaviour in Stockholm 2020 based on smart card data. Eur Trans Res Rev 31:1–13
3. Anell A, Glenngard AH, Merkur S (2012) Sweden: health system review. Health Syst Transit 14(5):1–159
4. Askitas, N., Tatsiramos, K., and B. Verheyden (2020). Lockdown Strategies, Mobility Patterns and Covid-19. IZA DP No. 13293.
5. Baccini L, Brodeur A, Weymouth S (2021) The Covid-19 pandemic and the 2020 US presidential election. J Popul Econ 34:739–767