🤖 AI Summary
This study addresses the failure of conventional two-way fixed effects (TWFE) models in estimating treatment effects for repeated events, where disentangling the independent dynamic impacts of individual occurrences remains challenging. Focusing on recurrent shocks such as natural disasters, this work proposes a linear parametric framework grounded in a conditional parallel trends assumption based on effect accumulation. Inference is conducted using a sequential imputation estimator combined with Monte Carlo simulations. The proposed approach achieves robust and consistent estimation of the dynamic effects associated with single events, effectively recovering the aggregate trajectory while precisely isolating the independent impact of each shock. Simulation experiments further validate the superiority of this methodology over existing alternatives.
📝 Abstract
I study treatment effect estimation when treatment events have persistent effects and can be experienced more than once. Natural disasters, job loss and health shocks are examples of such treatments. I show that the effect of a total treatment trajectory can be recovered under assumptions similar to those commonly invoked in single-event settings using suitably flexible TWFE models. Decomposing the total trajectory effect into portions attributable to distinct event occurrences, however, requires further assumptions. I propose an assumption similar to conditional parallel trends, imposing it on the growth of event-specific effects rather than on untreated outcomes. Combined with a linear-in-parameters model of effect growth, this assumption enables a sequential imputation estimator that consistently estimates the dynamic effects of each event occurrence and that can accommodate heterogeneity in effects according to observable event attributes, such as intensity. I demonstrate that several intuitive TWFE models fail to recover interpretable treatment effect parameters in the multi-event setting and illustrate the sequential imputation estimator's favourable performance using Monte Carlo simulations.