Using Difference-in-Differences to Identify Causal Effects of COVID-19 Policies
By Andrew Goodman-Bacon, Jan Marcus. Social Science Research Network, 2020
207 citations recorded by the source corpus.
Policymakers have implemented a wide range of non-pharmaceutical interventions to fight the spread of COVID-19. Variation in policies across jurisdictions and over time strongly suggests a difference-in-differences (DD) research design to estimate causal effects of counter-COVID measures. We discuss threats to the validity of these DD designs and make recommendations about how researchers can avoid bias, interpret results accurately, and provide sound guidance to policymakers seeking to protect public health and facilitate an eventual economic recovery.
Subfields: General Health Professions, Modeling and Simulation, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/d38a98bc591a16a1adbe2dc07b250a8af102deba
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Citations in the corpus
- Face masks considerably reduce COVID-19 cases in Germany (2020, methodology): “Hence, in analogy to our SCM approach, the main idea for our two‐step approach is to give those control regions a larger sample weight, which have similar characteristics as Jena before the baseline treatment starts (51).”
- Poverty and economic dislocation reduce compliance with COVID-19 shelter-in-place protocols (2020, unknown): “The use of event study designs enables us to assess the plausibility of the common trends assumptions using microlevel changes in cellphone movement ( Goodman-Bacon and Marcus, 2020 ).”