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Dynamic Modeling of Labor Market Mobility and Human Capital Accumulation

Periodic Reporting for period 2 - DYMOLAMO (Dynamic Modeling of Labor Market Mobility and Human Capital Accumulation)

Reporting period: 2020-05-01 to 2021-10-31

In today’s globalized world, labor mobility is at the core of the political debate and a centerpiece for economic policy. The design of migration policies, such as selective, skill-biased, immigration policies, policies to encourage the integration of immigrants, or ones that facilitate geographical mobility to increase labor market opportunities of disadvantaged workers, requires a good understanding of a more fundamental issue: understanding the role of internal migration and immigration in shaping the career paths and human capital accumulation of workers. This project aims at providing a coherent analysis that allows us to understand the interactions between labor mobility and human capital accumulation, and their implications for economic policy design.

This project focuses on three main issues: labor mobility, labor market effects of immigration, and labor market assimilation. Our questions are: (a) What are the role of temporary and permanent contracts in shaping career paths and geographic mobility of workers? (b) Does the forgone human capital accumulation during a recession produce a lost generation? Is this alleviated by geographical mobility? (c) What is the role of geographical and occupational mobility in spreading or containing the effects of technological progress on wage inequality? (d) To what extent selective immigration policies maximize native workers’ prospects and wellbeing? (e) How can we increase degree of assimilation of immigrants?

To address these questions, we develop dynamic equilibrium models that explicitly characterize human capital accumulation decisions of workers and how these decisions interact with migration. Our proposed models introduce rich labor market structures and a variety of economic shocks. They require the implementation of novel estimation methods, which we also develop. The estimated models are used to evaluate and design key economic policies for the labor market.
A total of 21 papers at different stages are already in progress, two of which are near submission, one is submitted, and four are already published. The PI has also co-edited a book on immigration and macroeconomics. Exploration for new lines of research that will hopefully lead to new projects and grant applications after the project ends have also been initiated. Several of the papers have been presented at research visits and conferences. Four PhD theses are being written within the project.
We developed new estimation methodologies for equilibrium dynamic discrete choice structural models that are orders of magnitude faster than standard approaches. Thanks to these estimation techniques, we can estimate models that would not be estimable otherwise. The more advanced papers already delivered interesting preliminary results. Among other interesting results, we show that high skilled immigration generates endogenous technological progress that is skill-biased, that is, that increases the demand of workers in STEM (Science, Technology, Engineering, and Math) occupations and reduces the demand for blue collar workers. We also show that the “skill-quailty” of recent immigrant cohorts in the United States is not worse than earlier ones as previously thought, and their skill assimilation is not slower than that of previous cohorts. Instead, we uncovered a new mechanism that drives an important part of the patterns observed in the data: increasing competition induced by growing cohorts of immigrants. Since immigrants tend to work on different occupations than natives, new cohorts of immigrants add stronger competition to incumbent immigrants. This additional competition drifts wages of immigrants away from those of natives, which can explain a large portion of the differences between the two.