Research

“Estimating Labor Market Monopsony Power from a Forward-looking Perspective” [PDF, Online Appendix], Economics Letters, 2024. (This is a shortened version of the second chapter of my PhD dissertation.)

“Human Capital Transferability and Employer Monopsony Power” [Job Market Paper, PDF]

[Click for Abstract]

In this paper, I study the sources of employer monopsony power from the perspective of imperfect human capital transferability, defined as the portability of skills across occupations. Utilizing a task decomposition approach, I construct a measure of human capital transferability across occupations, and integrate this into a dynamic two-sided model of the labor market. Workers in this model make job-switch decisions over their life-cycle, cognizant of the depreciation in human capital value upon changing occupations. Imperfect transferability of skills gives firms some market power and allows them to reduce the wage, but it also makes it harder to replace skilled workers, reducing that power. Employers, in turn, post wage profiles that maximize their lifetime profits. I estimate the model using the Sample of Integrated Employer-Employee Data and Task Operationalization data from German Institute of Employment Research. I have three main findings. First, occupational switches are associated with significant wage penalties, suggesting that human capital gained in one occupation will be penalized in other occupations. Second, I find the life-cycle profile of wage markdown exhibits a U shape, where mid-aged workers suffer the smallest markdown. Third, I show that restoring perfect skill transferability lowers wage markdown for senior workers but increases that for younger workers. Further policy analyses show that a set of Active Labor Market Policies and education policies that feature both general education and vocational training have the potential to reduce labor market power.

“Growing Up Together: Sibling Correlation, Parental Influence, and Intergenerational Educational Mobility in Developing Countries” with Nazmul Ahsan, Shahe Emran, Hanchen Jiang, and Forhad Shilpi [SSRN working paper, 2024, submitted]