Estimating Government Worker Skills

TL;DR

This paper introduces a machine learning-based method to estimate government worker skills using private sector wages and applies it to Indonesian data, revealing skill decline and wage premiums.

Contribution

It presents a novel approach linking observable wages to skills in a setting with unobservable output and uninformative wages, applied to Indonesian data.

Findings

Government skills have declined relative to the private sector.

Most skilled workers have moved to the private sector.

Indonesian government pays a 43% wage premium conditional on skills.

Abstract

We propose a new approach to estimate government worker skills, a setting where output is hard to observe and wages may be uninformative about skills. The approach uses wages in comparable jobs in the private sector and machine learning tools to link skills to skill-related observables. We apply the approach to rich Indonesian household-level panel data from 1988-2014, showing two main applications. First, government skills have continuously declined relative to the private sector, driven by the most skilled workers ending up in the private sector. Second, the Indonesian government pays a wage premium of 43% conditional on skills.