🤖 AI Summary
This study addresses the failure of the markup estimation method proposed by De Loecker and Warzynski (2012) when its key identifying assumptions are falsified. To overcome this limitation, the paper introduces a general framework that requires no additional assumptions: by continuously relaxing the standard identifying conditions, it constructs a non-empty set of non-falsified models and expresses markups as a function of the relaxation parameters, thereby generating an adaptive identification set. This approach extends conventional point identification to interval identification that reflects potential violations of underlying assumptions. Applying this method to Chilean firm-level data from Raval (2023), the empirical analysis successfully constructs robust markup identification sets, effectively mitigating the identification risks inherent in the original approach. The framework thus offers a novel contribution by enhancing model robustness while preserving identification rigor.
📝 Abstract
In this paper we provide a constructive way for researchers to salvage the classic De Loecker and Warzynski (2012) markup recovery procedure when falsified. To do this, we consider continuous relaxations of the standard assumptions behind markup estimation. By computing the values of the markup as a function of the relaxations across the set of non-falsified models, we obtain an identified set for the markup which generalizes the standard baseline markup estimand to account for possible falsification without the need to impose additional assumptions. We illustrate our results using Chilean data from Raval (2023).