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What's Logs Got to do With it: On the Perils of log Dependent Variables and Difference-in-Differences

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  • Brendon McConnell

Abstract

The log transformation of the dependent variable is not innocuous when using a difference-in-differences (DD) model. With a dependent variable in logs, the DD term captures an approximation of the proportional difference in growth rates across groups. As I show with both simulations and two empirical examples, if the baseline outcome distributions are sufficiently different across groups, the DD parameter for a log-specification can be different in sign to that of a levels-specification. I provide a condition, based on (i) the aggregate time effect, and (ii) the difference in relative baseline outcome means, for when the sign-switch will occur.

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  • Brendon McConnell, 2023. "What's Logs Got to do With it: On the Perils of log Dependent Variables and Difference-in-Differences," Papers 2308.00167, arXiv.org, revised Aug 2023.
    • Handle: RePEc:arx:papers:2308.00167
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    1. Jesse Matheson & Brendon McConnell & James Rockey & Argyris Sakalis, 2023. "Do Remote Workers Deter Neighborhood Crime? Evidence from the Rise of Working from Home," Working Papers 2023020, The University of Sheffield, Department of Economics.

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