Research
Published Work
The Review of Economic Studies, 91(3), May 2024, pp. 1807–1841. NBER Working Paper, Replication Package.
This paper explores the relationship between the duration of a vacancy and the starting wage of a new job, using linked data on vacancies, the posting establishments and the workers eventually filling the vacancies. The unique combination of large-scale, administrative worker-, establishment- and vacancy-data is critical for separating establishment- and job-level determinants of vacancy duration from worker-level heterogeneity. Conditional on observables, we find that vacancy duration is negatively correlated with the starting wage and its establishment component, with precisely estimated elasticities of -0.07 and -0.21, respectively. While the negative relationship is qualitatively consistent with search-theoretic models where firms use the wage as a recruiting device, these elasticities are small, suggesting that firms’ wage policies can account only for a small fraction of the variation in vacancy filling across establishments.
Working Papers
Presentation at the ASSA Annual Meeting 2023.
This paper uses large-scale high-frequency data on vacancy flows from Austria to document the cyclicality of the flows of new vacancies, vacancy fillings and vacancy lapses. We apply a decomposition analysis of the time series variation of the stock of vacancies, similar to the one used by Shimer (2012) for the unemployment rate. We find that vacancy inflows explain at least two-thirds of the time series variation in the vacancy stocks, whereas the remainder is explained by vacancy fillings. Vacancy lapses, while accounting for about 20% of vacancy outflows, are acyclical. We set up a search-and-matching model with a fixed cost of vacancy posting and variable recruiting intensity and calibrate it to match the averages and cyclicalities of vacancy flows. The calibrated model has important implications for the cyclical behavior of recruiting intensity and the observed shifts in the Beveridge curve.
Optimal Unemployment Insurance with Repeated Unemployment
Despite its importance, we have a limited understanding of repeat unemployment and its consequences for optimal unemployment insurance. We provide novel empirical evidence that individuals with multiple periods of unemployment respond differently to changes in the generosity of unemployment insurance (UI). Using a sufficient-statistics approach, we evaluate the implications of repeat unemployment for the optimal design of UI policies. Our results imply that repeated unemployment spells should have more generous UI benefits, in contrast to existing unemployment insurance systems.
Work in Progress
Short-Time Work Incidence and Consequences: Individual- and Firm-Level Evidence from Austria
This paper uses rich matched employer-employee data from the Austrian social security registers to document the short- and long-run effects of short-time work vs. temporary layoffs for workers and firms during the Great Recession. Descriptive evidence points to substantial heterogeneity in short-time work take-up across workers and firms’ observable characteristics. We leverage the rich dataset at hand to estimate a structural model to guide a comparative analysis of short-time work and temporary layoffs policies in terms of efficiency and redistribution. This contributes to the topical debate on whether workers or jobs should be insured during recessions.
Identifying Administrative Firm Changes using Worker Flows in the Austrian Social Security Data
This paper demonstrates how worker flows can be used in order to identify administrative firm identifier changes in the Austrian Social Security Data. Relative to the approach used by Fink et al. (2010), the procedure improves by using daily worker flows between different firms in order to identify administrative renames, spinoffs and takeovers. I apply this approach using daily worker flows to the time period 1972-2020, which allows to correctly identify firm-level and aggregate separations as well as hirings in the Austrian Social Security Data.