Estimating government worker skills: Application to worker selection and wage setting in Indonesia
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摘要
This paper develops a three-step method to estimate “government-job skills”: (1) extract noisy individual skill signals from wages in private-sector jobs comparable to government roles, adjusting for experience and a time-varying skill price; (2) use machine learning (GBM preferred) to learn the mapping from rich observable skill-related covariates to the noisy skill signal; (3) predict out-of-sample skills for government workers and all other workers. Applying the method to IFLS (1988–2014) shows (i) government workers are positively selected but their relative skills decline vs. the private sector over time (driven by the private sector’s growing right tail), (ii) government wages are somewhat less informative about skills but—after accounting for life-cycle profiles—still predictive, and (iii) a conditional government wage premium of ≈43% (partly explained by a large private-sector gender wage gap). [page::1][page::2][page::26]
