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I am a Ph.D. Candidate in Economics at the University of Luxembourg (Department of Economics and Management) and the Luxembourg Institute of Socio-Economics Research - LISER (Labor Market Department).

I conduct empirical research on labor economics. I study the variation of skill demand across labor markets, its reaction to labor market policies, and the way it influences wage inequality.

I am on the 2022-2023 economics job market.

You can download my CV here.

Work in Progress

The Impact of Restricting Fixed-Term Contracts on Labor and Skill Demand

We study how increasing the relative cost of fixed-term contracts affects firms’ labor and skill demand. We exploit a 2018 Italian labor law reform, which increased the cost of fixed-term contracts while leaving that of permanent contracts unchanged. Using rich data covering the near-universe of online job vacancies in Italy, we are able to characterize the demand for labor, human capital, and specific skill requirements under different contract types. Identification is based on a difference-in-differences research design that exploits variation in firms’ exposure to the reform stemming from their heterogeneous reliance on fixed-term contracts due to varying reactions to earlier labor market reforms. We find that the increase in the cost of hiring under temporary contracts led to a decline in the relative demand for temporary workers and an increase in that for permanent workers, accompanied by rising demand for human capital and specific skill requirements. When offering jobs under a permanent contract, firms increased their demand for workers with a college degree and social skills, while reducing demand for workers with a high school degree and no work experience. When offering jobs under a fixed-term contract, firms increased demand for workers with some work experience and social skills. These findings suggest that, while restricting fixed-term contracts promoted the hiring of permanent workers, this type of reform may have unintended consequences by raising hiring standards for entry into jobs, thereby reducing employment opportunities for less qualified workers.

Demand for skills and wage inequality

Differences in skill utilization across firms and labor markets have been associated with wage inequality, but whether this relationship reflects differences in worker or firm heterogeneity is still unclear. Combining linked employer-employee data from Italy with detailed information on skill demand extracted from online job vacancies, we study the relationship between wages and the demand for cognitive and social skills across labor markets defined by province, sector, and occupation. We then estimate the worker- and firm-pay components of the wage process through an AKM model and investigate their relationship with skill demand at the labor market level. We find a strong and positive association between wages and the demand for cognitive and social skills, which is pronounced when both skills are required jointly for the same job position revealing their complementarity. Our decomposition suggests that higher wages in markets in which firms more frequently demand cognitive and social skills jointly are driven by worker effects, reflecting the higher market value of a hybrid skill set, rather than by firm pay policies. Conversely, markets in which firms demand more of either cognitive or social skills only are associated to higher firm effects, suggesting that more rents are shared to specialized workers, while the market value of these specialized skills for workers is lower. These results highlight the role of worker and firm heterogeneity as channels through which skill demand differences affect overall wage inequality.

Heterogeneous Job and Skill Landscapes in the Italian Labor Market

Traditional labor market data provide very limited knowledge about the type and skill content of jobs. Yet, the profound labor market transformations due to rapid technological change call for a better understanding of work skill attributes and how they characterize modern jobs. In this paper, I use machine learning methods to analyze a large and highly unstructured novel data source of online job vacancies posted in Italy in 2014-2019. I exploit information on industry of posting firms, location and occupation of job positions, as well as on hundreds of granular skill requirements given in the form of short strings univocally identified by tags. I assign these short strings to skill categories in the standard Deming and Kahn (2018) taxonomy through a keyword- based matching routine. Next, I use a bag-of-tags approach in two natural language processing routines to detect patterns among jobs based on the similarity of their skill requirements. Firstly, I run a K-means model, which assigns jobs to mutually exclusive clusters representing job types. Secondly, I run a Latent Dirichlet Allocation model, which probabilistically assigns skill tags to mutually exclusive latent topics representing work domains and returns their prevalence within jobs. The two methods capture different nuances in the data while delivering comparable job groupings. Lastly, I explore variation in job types and work domains across locations, industries, and occupations to describe the heterogeneous job and skill landscapes in the Italian labor market.

Teaching

University of Luxembourg - Teaching Assistant

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