June 29th, Miguel ZERECERO ANTON's PhD Defense

June 29, 2021 Research

Miguel ZERECERO ANTON will defend his thesis on June 29th at 10:30 AM

Title: « Essays on Labor Economics » 

By videoconferencing (TSE) 

Supervisor: Christian Hellwig

To attend the meeting, please contact Elvire JALRAN

Memberships are: 

- Jan Eeckhout, Professor University of Pompeu Fabra 

- Thomas Chaney, Professor, SciencesPo 

- Patrick Fève, Professor, UT1 Capitole - TSE 

- Christian Hellwig, TSE Scientific Director, UT1 Capitole

Abstract :

This thesis contains three essays on the macroeconomic effects of labor markets with a special emphasis on the determinants of internal migration, spatial inequality, labor market power, and the determination of wages. 

In the first chapter, I study a potential reason of why workers stay in economically distressed areas: people like to live close to what they call home. Using administrative data for France, I find: (i) the share of migrants who return to their birthplace is almost twice as large as the share of migrants who go to any other particular location; (ii) there is a negative relationship between labor flows and distance from the workers’ birthplace; and (iii) workers accept a wage discount between 9 to 11 percent to live in their home location. To understand the implications of these findings, I build a dynamic quantitative migration model into which I introduce home bias, understood as a utility cost of living away from one’s birthplace. I use the model to separately identify home bias and migration costs from the data. I find that differences in birth location lead to average welfare differences of up to 30 percent in consumption-equivalent terms, and explain 43 percent of the total dispersion in welfare. Finally, I show that a migration model without home bias overstates the migration response of agents. This underestimates the pass-through of local productivity to real wages and overestimates the efficiency costs associated with place-based policies. 

In the second chapter, Miren Azkarate-Askasua and I study the efficiency and welfare effects of employer and union labor market power. We use data of French manufacturing firms to first document a negative relationship between employment concentration and wages and labor shares. At the micro-level, we identify the effects of employment concentration thanks to mass layoff shocks to competitors. Second, we develop a bargaining model in general equilibrium that incorporates employer and union labor market power. The model features structural labor wedges that are heterogeneous across firms and potentially generate misallocation of resources. We propose an estimation strategy that separately identifies the structural parameters determining both sources of labor market power. Furthermore, we allow different parameters across industries which contributes to the heterogeneity of the wedges. We show that observing wage and employment data is enough to compute counterfactuals relative to the baseline. Third, we evaluate the efficiency and welfare losses from labor market distortions. Eliminating employer and union labor market power increases output by 1.6% and the labor share by 21 percentage points translating into significant welfare gains for workers. Workers’ geographic mobility is key to realize the output gains from competition. 

In the third chapter, Miren Azkarate-Askasua and I propose a bias correction method for estimations of quadratic forms in the parameters of linear models. It is known that those quadratic forms exhibit small-sample bias that appears when one wants to perform a variance decomposition such as decomposing the sources of wage inequality. When the number of covariates is large, the direct computation for a bias correction is not feasible and we propose a bootstrap method to estimate the correction. Our method accommodates different assumptions on the structure of the error term including general heteroscedasticity and serial correlation. Our approach has the benefit of correcting the bias of multiple quadratic forms of the same linear model without increasing the computational cost and being very flexible. We show with Monte Carlo simulations that our bootstrap procedure is effective in correcting the bias and we compare it to other methods in the literature. Using administrative data for France, we apply our method by doing a variance decomposition of a linear model of log wages with person and firm fixed effects. We find that the person and firm effects are less important in explaining the variance of log wages after correcting for the bias and depending on the specification the correlation becomes positive after the correction.