The Performance of Recent Methods for Estimating Skill Prices in Panel Data

TL;DR

This paper evaluates various methods for estimating skill prices in panel data, highlighting their robustness and limitations depending on skill accumulation modeling and occupational switching.

Contribution

It systematically compares estimation methods for skill prices, emphasizing the importance of modeling skill accumulation and occupational switches in panel data analysis.

Findings

Skill price estimates are robust when skill accumulation is modeled correctly and occupational switches are rare.

Performance of estimation methods varies significantly when occupational switching is frequent.

Proper modeling of skill accumulation enhances the reliability of skill price estimates.

Abstract

This paper explores different methods to estimate prices paid per efficiency unit of labor in panel data. We study the sensitivity of skill price estimates to different assumptions regarding workers' choice problem, identification strategies, the number of occupations considered, skill accumulation processes, and estimation strategies. In order to do so, we conduct careful Monte Carlo experiments designed to generate similar features as in German panel data. We find that once skill accumulation is appropriately modelled, skill price estimates are generally robust to modelling choices when the number of occupations is small, i.e., switches between occupations are rare. When switching is important, subtle issues emerge and the performance of different methods varies more strongly.