• Pension eligibility rules and the local causal effect of retirement on cognitive functioning

      Fé, Eduardo; email: eduardo.fe@manchester.ac.uk (2021-03-23)
      Abstract: We propose an identification framework to evaluate the exclusion restriction in a fuzzy regression discontinuity setting, by adopting results from the literature on partial identification with invalid instrumental variables. With this framework, we provide new estimates of the effect of retirement on cognitive functioning and the first empirical analysis of the validity of an age‐based instrumental variable for retirement. Point estimates suggest an insignificant negative effect of retirement on cognitive functioning. Partial identification regions qualify this finding by suggesting that if retirement is, in fact, detrimental for cognitive functioning, then large drops are unlikely. Second, data alone cannot identify the sign of the treatment effect. In fact, our results support improvements in cognitive functioning following retirement. The bounds analysis suggest that, when studying the impact of retirement, the validity of eligibility as an instrumental variable depends on the time period considered for the analysis and that violations of the exclusion restriction are likely already in very small intervals of 8 months around the cut‐off in regression discontinuity designs.
    • Severity of the COVID‐19 pandemic in India

      Imai, Katsushi S.; orcid: 0000-0001-7989-8914; email: Katsushi.Imai@manchester.ac.uk; Kaicker, Nidhi; Gaiha, Raghav (2021-05-18)
      Abstract: The main objective of this study is to identify the socioeconomic, meteorological, and geographical factors associated with the severity of COVID‐19 pandemic in India. The severity is measured by the cumulative severity ratio (CSR)—the ratio of the cumulative COVID‐related deaths to the deaths in a pre‐pandemic year—its first difference and COVID infection cases. We have found significant interstate heterogeneity in the pandemic development and have contrasted the trends of the COVID‐19 severities between Maharashtra, which had the largest number of COVID deaths and cases, and the other states. Drawing upon random‐effects models and Tobit models for the weekly and monthly panel data sets of 32 states/union territories, we have found that the factors associated with the COVID severity include income, gender, multi‐morbidity, urbanization, lockdown and unlock phases, weather including temperature and rainfall, and the retail price of wheat. Brief observations from a policy perspective are made toward the end.