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Abstract:
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Public policy interventions are commonly evaluated using the difference-in-differences (DiD) approach. However, this approach does not directly account for the effect of the policy "spilling over" to neighboring regions. For example, the implementation of an excise tax on sweetened beverages in Philadelphia was shown to be associated with a substantial decrease in volume sales of taxed beverages in Philadelphia but also showed an increase in beverage sales in non-taxed bordering counties, suggesting potential cross-border shopping among Philadelphia residents. To address these important concerns, we extend DiD methods to identify the causal effects of policy interventions under various spillover conditions. We propose doubly robust estimators for the average treatment effect on the treated and on the neighboring control. The new estimators relax the standard assumptions on interference and model specification. In addition, we formally define a new causal estimator for the average treatment effect on the treated as a function of neighborhood exposure to the policy intervention. We apply these methods to understand the causal effects of the aforementioned Philadelphia beverage tax.
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