Research

We study the 2011 Austrian Pay Transparency Law, which requires firms above a size threshold to publish internal reports on the gender pay gap. Using an event-study design, we show that the policy had no discernible effects on male and female wages, thus leaving the gender wage gap unchanged. The effects are precisely estimated and we rule out that the policy narrowed the gender wage gap by more than 0.4 p.p.. Moreover, we do not find evidence for wage compression within establishments. We discuss several possible reasons why the reform did not reduce the gender wage gap.

This paper studies the behavior of employment dynamics at the establishment level. Using German matched employer-employee data I document a disconnect between the cross-sectional sorting pattern and workforce skill adjustments over time. Whereas larger firms match with higher skilled workers in the cross section, low skilled workers become more valuable to expanding firms as shown by: (i) expanding firms downgrade average workforce skills, (ii) expanding firms use both hires and separation, i.e., they hire worse workers and separate from better workers, (iii) lower skilled workers see larger wage gains in expanding firms. The effects work in reverse for contracting firms. I develop a multi-worker firm model with search frictions, firm dynamics, and worker-firm complementarities and multi-dimensional sorting that replicates these patterns, as well as many other salient features of firm dynamics. My model predicts stronger effects of dismissal taxes compared to more standard labor market models.

We document the consequences of losing a job across countries using a harmonized research design applied to seven matched employer-employee datasets. Workers in Denmark and Sweden experience the lowest earnings declines following job displacement, while workers in Italy, Spain, and Portugal experience losses three times as high. French and Austrian workers face earnings losses somewhere in between. Key to these differences is that Southern European workers are less likely to find employment following displacement. Loss of employer-specific wage premiums explains a substantial portion of wage losses in all countries.

We implement a generalized random forest (Athey et. al. 2019) to a difference-in-difference setting to identify substantial heterogeneity in earnings losses across displaced workers. Using administrative data from Austria over three decades we document that a quarter of workers face cumulative 11-year losses higher than 2 times their pre-displacement annual income, while almost 10% of individuals experience gains. Our methodology allows us to consider many competing theories of earnings losses. We find that the displacement firm's wage premia and the availability of well paying jobs in the local labor market are the two most important factors. This implies that earnings losses can be understood by mean reversion in firm wage premia and losses in match quality, rather than by a destruction of firm-specific human capital. We further show that 94% of the cyclicality of earnings losses is explained by compositional changes of displaced workers over the business cycle.

Additional Resources:

• Accompanying web application to estimate earnings losses conditional on worker and job characteristics: 🔗 link to web app

 We develop an equilibrium search model with a labor force participation decision, job-to-job transitions, and endogenous separations. The calibrated model perfectly matches the observed labor market flows in US data. We use the model to simulate the effects of an extension of unemployment insurance benefits to 99 weeks. The reform leads to a decrease in employment, an increase in the labor force participation and unemployment rate, while it leaves labor productivity roughly constant. Using a modelbased decomposition, as well as comparisons with alternative simplified models, we show that modeling workers’ participation decisions, job-to-job transitions, and endogenous separations together is crucial for a complete and accurate analysis of UI reforms.

Using the universe of Austrian unemployment insurance records until May 2020, we document that the composition of UI claimants during the Covid-19 outbreak is substantially different compared to past times. Using a machine-learning algorithm from Gulyas and Pytka (2020), we identify individual earnings losses conditional on worker and job characteristics. Covid-19-related job terminations are associated with lower losses in earnings and wages compared to the Great Recession, but similar employment losses. We further derive an accurate but simple policy rule targeting individuals vulnerable to long-term wage losses: (i) young workers displaced from employers with high wage premia in areas with relatively low  firm rents and (ii) older workers with long job tenure.