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This study reveals that the linear probability difference-in-differences estimator can be misleading when applied to continuous-time hazard processes. Understanding data-generating processes is crucial for accurate causal identification in economic research.

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Area of Science:

  • Econometrics
  • Causal Inference
  • Survival Analysis

Background:

  • The linear probability difference-in-differences (DiD) estimator is widely used in econometrics.
  • Its application to data from continuous-time hazard processes may lead to biased estimates.
  • Implicit assumptions about data generation are often overlooked.

Purpose of the Study:

  • To analyze the properties of the linear probability DiD estimator under a single-decrement, continuous-time hazard process.
  • To reexamine economic studies on the causal effect of divorce using the linear probability DiD estimator.
  • To demonstrate how hazard process characteristics can explain observed patterns in treatment effects.

Main Methods:

  • Formal derivations of the linear probability DiD estimator's behavior.
  • Simulation and reanalysis of existing economic studies.
  • Focus on a two-group, two-period setting with pre- and post-treatment observations.

Main Results:

  • The linear probability DiD estimator's validity is compromised when data follow a continuous-time hazard process.
  • An increasing then decreasing pattern of treatment effects, as observed in divorce studies, can arise from time-invariant treatment effects within a proportional hazard model.
  • The study highlights the sensitivity of DiD estimates to underlying data-generating mechanisms.

Conclusions:

  • Implicit assumptions about data-generating processes are critical for valid causal identification.
  • Researchers must carefully consider the suitability of the linear probability DiD estimator for their specific data.
  • A deeper understanding of hazard processes can improve the accuracy of causal inference in economics.