Inference After Ranking with Applications to Economic Mobility

TL;DR

This paper develops a new inference framework for populations ranked within a range of values, extending previous winner-takes-all methods, with applications to economic mobility and validated through simulations.

Contribution

It introduces a two-step inference method for ranking-based population analysis, accommodating broader applications and degeneracy in covariance structures.

Findings

Finite-sample validity in a normal model

Asymptotic uniform validity over distribution classes

Difficulty distinguishing mobility levels after correction

Abstract

This paper considers the problem of inference after ranking. In our setting, we are interested in any population whose rank according to some random quantity, such as an estimated treatment effect, a measure of value-added, or benefit (net of cost), falls in a pre-specified range of values. As such, this framework generalizes the inference on winners setting previously considered in Andrews et al. (2023), in which a winner is understood to be the single population whose rank according to some random quantity is highest. We show that this richer setting accommodates a broad variety of empirically-relevant applications. We develop a two-step method for inference, which we compare to existing methods or their natural generalizations to this setting. We first show the finite-sample validity of this method in a normal location model and then develop asymptotic counterparts to these results by proving uniform validity over a large class of distributions satisfying a weak uniform integrability condition. Importantly, our results permit degeneracy in the covariance matrix of the limiting distribution, which arises naturally in many applications. In an application to the literature on economic mobility, we find that it is difficult to distinguish between high and low-mobility census tracts when correcting for selection. Finally, we demonstrate the practical relevance of our theoretical results through an extensive set of simulations.