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Better Bunching, Nicer Notching
We study the bunching identification strategy for an elasticity parameter that summarizes agents' response to changes in slope (kink) or intercept (notch) of a schedule of incentives. A notch identifies the elasticity but a kink does not, when the distribution of agents is fully flexible. We propose new non-parametric and semi-parametric identification assumptions on the distribution of agents that are weaker than assumptions currently made in the literature. We revisit the original empirical application of the bunching estimator and find that our weaker identification assumptions result in ...
Robust Inference in First-Price Auctions : Experimental Findings as Identifying Restrictions
In laboratory experiments bidding in first-price auctions is more aggressive than predicted by the risk-neutral Bayesian Nash Equilibrium (RNBNE) - a finding known as the overbidding puzzle. Several models have been proposed to explain the overbidding puzzle, but no canonical alternative to RNBNE has emerged, and RNBNE remains the basis of the structural auction literature. Instead of estimating a particular model of overbidding, we use the overbidding restriction itself for identification, which allows us to bound the valuation distribution, the seller's payoff function, and the optimal ...
The Dynamics of Global Sourcing
This paper studies an import model that incorporates both static crosscountry interdependence and dynamic dependence in firm-level decisions. I find that the benefit of sourcing from one country increases as a firm imports from more countries. Furthermore, using a partial identification approach under the revealed preferences assumption, I provide evidence for the sunk costs of importing, which make establishing relationships with new sellers costlier than maintaining existing ones. The coexistence of cross-country interdependence and sunk costs implies that temporary trade policy changes can ...
Bunching Estimation of Elasticities Using Stata
A continuous distribution of agents that face a piecewise-linear schedule of incentives results in a distribution of responses with mass points located where the slope (kink) or intercept (notch) of the schedule changes. Bunching methods use these mass points to estimate an elasticity parameter, which summarizes agents' responses to incentives. This article introduces the command bunching, which implements new non-parametric and semi-parametric identification methods for estimating elasticities developed by Bertanha et al. (2021). These methods rely on weaker assumptions than currently made ...