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Unintended Consequences of Lockdowns, COVID-19 and the Shadow Pandemic in India. A Reproduction Study of Ravindran and Shah

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  • McWay, Ryan

Abstract

Ravindran and Shah (2023) assesses the effect of COVID-19 lockdowns on violence against women (VAM) in India. Using a dynamic fixed effects methodology exploiting spatio-temporal variation in the timing and intensity of 'stay-at-home' mandates in Indian districts, the authors find that government-mandated lockdowns increased complaints of domestic violence in districts with the strictest lockdown rules. I successfully computationally reproduce their results. Further, I test the replicability of the results by re-analyzing the results using a Poisson estimator as opposed to the ordinary least squares (OLS) estimator used by the original authors. I find that lower bound censoring of the outcome measure resulted in attenuation of the marginal effect of government-mandated lockdowns. The effect of mobility restrictions on VAW are understated in the original study. This provides support for the internal validity of Ravindran and Shah (2023)'s results by suggesting that may present lower bound estimates of an under-reported consequence of mobility restrictions during the COVID-19 pandemic.

Suggested Citation

  • McWay, Ryan, 2025. "Unintended Consequences of Lockdowns, COVID-19 and the Shadow Pandemic in India. A Reproduction Study of Ravindran and Shah," I4R Discussion Paper Series 230, The Institute for Replication (I4R).
    • Handle: RePEc:zbw:i4rdps:230
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    References listed on IDEAS

    as
    1. McWay, Ryan & Nchare, Karim & Sun, Pu, 2024. "Market Access and Quality Upgrading: Evidence from Four Field Experiments. A Replication Study of Bold et al. (American Economic Review, 2022)," Journal of Comments and Replications in Economics (JCRE), ZBW - Leibniz Information Centre for Economics, vol. 3, pages 1-17.
    2. Brodeur, Abel & Mikola, Derek & Cook, Nikolai & Brailey, Thomas & Briggs, Ryan & de Gendre, Alexandra & Dupraz, Yannick & Fiala, Lenka & Gabani, Jacopo & Gauriot, Romain & Haddad, Joanne & Federice, A, 2024. "Mass Reproducibility and Replicability: A New Hope," I4R Discussion Paper Series 107, The Institute for Replication (I4R).
    3. Zacharias Maniadis & Fabio Tufano & John A. List, 2017. "To Replicate or Not To Replicate? Exploring Reproducibility in Economics through the Lens of a Model and a Pilot Study," Economic Journal, Royal Economic Society, vol. 127(605), pages 209-235, October.
    4. Anna Dreber & Magnus Johannesson, 2025. "A framework for evaluating reproducibility and replicability in economics," Economic Inquiry, Western Economic Association International, vol. 63(2), pages 338-356, April.
    5. Brodeur, Abel & Valenta, David & Marcoci, Alexandru & Aparicio, Juan P. & Mikola, Derek & Barbarioli, Bruno & Alexander, Rohan & Deer, Lachlan & Stafford, Tom & Vilhuber, Lars & Bensch, Gunther & Gold, 2025. "Comparing Human-Only, AI-Assisted, and AI-Led Teams on Assessing Research Reproducibility in Quantitative Social Science," I4R Discussion Paper Series 195, The Institute for Replication (I4R).
    6. Saravana Ravindran & Manisha Shah, 2023. "Unintended consequences of lockdowns, COVID-19 and the Shadow Pandemic in India," Nature Human Behaviour, Nature, vol. 7(3), pages 323-331, March.
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