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Hazard Versus Linear Probability Difference-in-Differences Estimators for Demographic Processes.
1Department of Sociology, New York University, New York, NY, USA.
Demography
|September 15, 2022
Summary
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.
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.
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