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Endogenous treatment effects for count data models with endogenous participation or sample selection
Massimiliano Bratti1, Alfonso Miranda
1Department of Economics, Business and Statistics, Università degli Studi di Milano, Milan, Italy.
Health Economics
|August 11, 2011
Summary
This study introduces a new statistical method to analyze how a treatment affects outcomes when participation or selection is endogenous. Ignoring these factors leads to biased estimates of treatment effects, particularly in health economics research.
Area of Science:
- Econometrics
- Health Economics
- Biostatistics
Background:
- Endogeneity in treatment effects and outcomes is a common challenge in empirical research.
- Sample selection and endogenous participation can bias estimates in statistical models.
- Accurate estimation is crucial for understanding treatment impacts, especially in health-related studies.
Purpose of the Study:
- To propose a novel estimator for models with endogenous dichotomous treatments affecting count outcomes.
- To account for endogeneity in both treatment participation/selection and the main outcome.
- To address limitations of existing methods in health economics and related fields.
Main Methods:
- Development of a maximum simulated likelihood estimator.
- Modeling the effect of treatment on participation/selection and the outcome simultaneously.
- Application to health economics data, specifically physician advice on alcohol consumption.
Main Results:
- Neglecting treatment endogeneity results in incorrect effect sizes for physician advice on drinking intensity.
- Ignoring endogenous participation leads to upward-biased treatment effect estimates.
- Physician advice influences the intensity of drinking but not its prevalence.
Conclusions:
- The proposed estimator effectively addresses endogeneity in treatment and participation/selection.
- Accurate modeling is essential to avoid biased conclusions in health economics.
- Findings highlight the nuanced impact of physician advice on alcohol consumption patterns.
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