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Estimating Heterogeneous Effects: Applications to Labor Economics

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arxiv 2404.01495 v1 pith:OYOO6CTW submitted 2024-04-01 econ.EM

Stephane Bonhomme , Angela Denis This is my paper · ORCID
classification econ.EM
keywords effectsmodelapplicationsdifferentdiscussfirmsheterogeneousmoving
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A growing number of applications involve settings where, in order to infer heterogeneous effects, a researcher compares various units. Examples of research designs include children moving between different neighborhoods, workers moving between firms, patients migrating from one city to another, and banks offering loans to different firms. We present a unified framework for these settings, based on a linear model with normal random coefficients and normal errors. Using the model, we discuss how to recover the mean and dispersion of effects, other features of their distribution, and to construct predictors of the effects. We provide moment conditions on the model's parameters, and outline various estimation strategies. A main objective of the paper is to clarify some of the underlying assumptions by highlighting their economic content, and to discuss and inform some of the key practical choices.

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Cited by 2 Pith papers

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  1. Debiased Machine Learning for Unobserved Heterogeneity: High-Dimensional Panels and Measurement Error Models

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    A sliced-Wasserstein and k-nearest-neighbor minimum-distance estimator for the distribution of random coefficients β is consistent with polynomial-in-dimension computation, while its diffusion and causal extensions re...

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