A Classifier-Lasso Approach for Estimating Production Functions with Latent Group Structures

TL;DR

This paper introduces a data-driven classifier-Lasso method to estimate production functions with unknown latent group structures, effectively identifying group memberships and revealing significant heterogeneity among firms.

Contribution

The paper develops a novel estimation approach combining classifier-Lasso with recent identification strategies for production functions with latent groups, addressing unknown group memberships.

Findings

High accuracy in assigning firms to correct latent groups

Significant differences in production function estimates compared to industry classification

Method performs well in simulation and real data applications

Abstract

I present a new estimation procedure for production functions with latent group structures. I consider production functions that are heterogeneous across groups but time-homogeneous within groups, and where the group membership of the firms is unknown. My estimation procedure is fully data-driven and embeds recent identification strategies from the production function literature into the classifier-Lasso. Simulation experiments demonstrate that firms are assigned to their correct latent group with probability close to one. I apply my estimation procedure to a panel of Chilean firms and find sizable differences in the estimates compared to the standard approach of classification by industry.