Publications

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.

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 and organizational changes call for a better understanding of work skill attributes and how they characterize modern jobs. In this paper, I analyze a large and highly unstructured novel data source containing the near-universe of online job vacancies posted in Italy in 2014-2019. I exploit information on the type of contract, industry, occupation, and location associated to given job postings, as well as on hundreds of granular skill requirements contained within them, that are given in the form of short strings univocally identified by tags. I first assign these short strings to a set of skill categories through a keyword-based matching routine based on the taxonomy proposed by Deming and Kahn (2018). Then, I illustrate the skill content of the obtained skill categories, examine the demand for each of them, and analyze how the latter varies across several dimensions. Next, I use a bag-of-tags approach to implement two unsupervised machine learning methods that detect skill requirements co-occurrence patterns across job postings and produce groupings of the data based on skill similarity. 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 expresses job postings as a weighted average of different topics. I show that the groupings obtained with the two methods are strongly consistent. This work exemplifies how dimensionality reduction techniques can be used to draw meaningful insights from large and unstructured datasets.

Differences in skill utilization across firms and labor markets have been associated with wage inequality, but how this relationship reflects differences in worker and 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.