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A Local Projections Approach to Difference-in-Differences Event Studies

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  • Jordà , Ã’scar
  • Dube, Arindrajit
  • Girardi, Daniele
  • Taylor, Alan

Abstract

Many of the challenges in the estimation of dynamic heterogeneous treatment effects can be resolved with local projection (LP) estimators of the sort used in applied macroeconometrics. This approach provides a convenient alternative to the more complicated solutions proposed in the recent literature on Difference-in-Differences (DiD). The key is to combine LPs with a flexible ‘clean control’ condition to define appropriate sets of treated and control units. Our proposed LP-DiD estimator is clear, simple, easy and fast to compute, and it is transparent and flexible in its handling of treated and control units. Moreover, it is quite general, including in its ability to control for pre-treatment values of the outcome and of other time-varying covariates. The LP-DiD estimator does not suffer from the negative weighting problem, and indeed can be implemented with any weighting scheme the investigator desires. Simulations demonstrate the good performance of the LP-DiD estimator in common settings. Two recent empirical applications illustrate how LP-DiD addresses the bias of conventional fixed effects estimators, leading to potentially different results.

Suggested Citation

  • Jordà , Ã’scar & Dube, Arindrajit & Girardi, Daniele & Taylor, Alan, 2023. "A Local Projections Approach to Difference-in-Differences Event Studies," CEPR Discussion Papers 18141, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:18141
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    2. Alexander Rodnyansky & Yannick Timmer & Naoki Yago, 2023. "Intervening against the Fed," CESifo Working Paper Series 10575, CESifo.
    3. Rodnyansky, A. & Timmer, Y. & Yago, N., 2023. "Intervening against the Fed," Cambridge Working Papers in Economics 2357, Faculty of Economics, University of Cambridge.
    4. Jordà, Òscar & Nechio, Fernanda, 2023. "Inflation and wage growth since the pandemic," European Economic Review, Elsevier, vol. 156(C).
    5. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
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    7. Cheick Camara, 2023. "Gender Budgeting and Health Spending Efficiency in Indian States: A Staggered Difference-in-Differences Analysis," CERDI Working papers hal-04294262, HAL.
    8. Amorim, Guilherme & Britto, Diogo & Fonseca, Alexandre & Sampaio, Breno, 2024. "Job Loss, Unemployment Insurance, and Health: Evidence from Brazil," IZA Discussion Papers 16790, Institute of Labor Economics (IZA).
    9. João Pedro Vieira & Ricardo Dahis & Juliano Assunção, 2023. "The Role of Sanctions and Spillovers in Forest Conservation," Monash Economics Working Papers 2023-16, Monash University, Department of Economics.
    10. Natalia Emanuel & Emma Harrington, 2023. "Working Remotely? Selection, Treatment, and the Market for Remote Work," Staff Reports 1061, Federal Reserve Bank of New York.

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    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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