Estimating government worker skills (Working paper coming soon!!)

This paper provides a new approach to estimate government worker skills that allows to quantify the quality of government workers and their selection. The approach estimates skills from wages in comparable jobs in the private sector, relates these skills to skill-related observables using Machine Learning tools and then predicts government worker skills out-of-sample. The method is applicable in settings where government output is unobserved and government wages are uninformative about skill differences. I then apply the approach to rich Indonesian household-level panel data from 1988 to 2014, showing two main applications. First, I quantify that government workers are highly selected, that their skills have increased, but that their skill premium has declined over time. Second, I analyze government wage setting: the Indonesian government pays a wage premium of at least 30% conditional on skills, about 1/3 of which is driven by the large gender wage gap in Indonesia’s private sector.