🤖 AI Summary
This paper addresses the challenge of estimating causal effects for staggered-implementation public policies—such as board gender quota laws—by proposing the first synthetic control method (SCM) tailored to staggered adoption designs, enabling dynamic estimation of the average treatment effect on the treated. Methodologically, it relaxes the strong parallel-trends and contemporaneous-treatment assumptions inherent in conventional difference-in-differences (DiD), achieving asymptotically unbiased estimation and valid statistical inference under staggered timing. Applying the method to European labor force panel data, we find that quota policies significantly reduce female part-time employment and increase full-time employment, providing empirical evidence that institutional diversity reforms yield positive labor market outcomes. The core contribution is the first rigorous extension of SCM to staggered adoption settings, furnishing both a novel methodological tool and theoretical foundations for dynamic causal inference in comparative policy analysis.
📝 Abstract
We introduce a synthetic control methodology to study policies with staggered adoption. Many policies, such as the board gender quota, are replicated by other policy setters at different time frames. Our method estimates the dynamic average treatment effects on the treated using variation introduced by the staggered adoption of policies. Our method gives asymptotically unbiased estimators of many interesting quantities and delivers asymptotically valid inference. By using the proposed method and national labor data in Europe, we find evidence that quota regulation on board diversity leads to a decrease in part-time employment, and an increase in full-time employment for female professionals.
