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I4R Discussion Paper Series #16


Carl Bonander (University of Gothenburg), Gabriella Chauca Strand (University of Gothenburg), Niklas Jakobsson (Karlstad University)

Direct Replication and Additional Sensitivity Analyses for Altindag et al. (2022): A Replication Report from the Oslo Replication Games

This report presents a replication of Altindag et al. (2022) performed at the Olso Replication Games in 2022. Altindag et al. (2022) estimate the effects of an age-specific lockdown on mental health outcomes and mobility among adults aged 65 and older in Turkey, using a regression discontinuity design. The authors find a decline in mobility with a one-day decrease in the number of days being outside and an increase in the probability of never going out by 30 percentage points. These point estimates are statistically significant at the 1% level. The mobility restrictions lead to a worsening in mental health outcomes of approximately 0.2 standard deviations, statisti-cally significant at the 10% level in their preferred specification. In this paper we accomplish two things. First, we successfully reproduce Altindag et al.’s main findings. Second, we test the ro-bustness of the results to a small number of changes to their preferred estimations by (1) not clustering the standard errors on the running variable, (2) not including control variables, and (3) calculating the optimal bandwidth using another technique. Point estimates for mobility outcomes are stable to all three manipulations, and standard errors only change marginally. Point estimates and standard errors for the mental health outcomes are somewhat more sensitive, especially to changing the optimal bandwidth selection method. However, the observed changes are reason-ably expected when applying data-driven model selection methods to noisy data (to avoid over-fitting, it is likely preferable to apply a less data-driven approach like the original authors did). Our general impression is that the original analyses and results are both theoretically plausible and credible, despite some defensible model dependencies.