Research

I show that unobserved sorting patterns of firms and workers across space can account for the tight link between rising aggregate wage inequality and rising spatial inequality in West Germany. Two-sided sorting patterns of workers and firms interact with a change in technology to produce a spatially concentrated increase in inequality, driving up regional disparities. These sorting patterns are determined jointly in equilibrium and depend on theoretical objects that are difficult to measure in the data. This paper develops a novel bi-clustering method to recover these objects empirically and uses these results to structurally estimate a dynamic spatial search model with two-sided sorting. I find that regional sorting of firms is more pronounced than regional sorting of workers and the former is an important determinant of workers' job ladders and lifetime values. Compensating differentials between regions are large, driven in part by better labor market outcomes in rich places. The model allows me to consider the redistributive effects of spatial policy, which I find to be strong.

This paper proposes selection in the labor market as a solution to the puzzle of slow and near-linear recoveries. A simple twist in the matching technology of an otherwise standard matching model delivers such selection and generates the same recovery unemployment dynamics as in the data. Early in the recovery, composition effects and separations depress job creation incentives and therefore job finding rates. As observed empirically, this effect becomes much stronger for less productive, unemployed workers who under slack markets often get outranked by their more productive, employed peers. As these workers struggle to find jobs, negative composition effects create a feedback loop that slows down the adjustment of  unemployment back to steady state. The model is able to match the last 6 recovery processes in the US economy closely.

We introduce aggregate shocks to the value of job amenities in a frictional equilibrium model of the labor market with on-the-job search, where the job creation cost is sunk and quits create vacancies. We examine how key labor market indicators respond to this shock: when the valuation of the amenity is heterogeneous in the population, labor reallocation ensues. A calibrated version of the model can quantitatively account for many peculiar traits of the post-pandemic labor market recovery through three aggregate shocks: a temporary fall in productivity to account for the short, but sharp, downturn; a rise in the opportunity cost of work; and, crucially, a persistent increase in the value that workers put on job amenities. Cross-sectoral patterns of vacancies, quit rates, and job-filling rates where sectors are ranked by the share of teleworkable jobs offer support to the view that the key amenity in question is the ability to work remotely.

We study a general equilibrium model of the labor market in which agents slowly learn about their suitability for jobs. Our model reproduces desirable features of the data, many of which standard models fail to replicate. We explore how, in such an environment, asymmetric information can lead to substantial misallocation. We calibrate our model to US data and quantify the welfare loss arising from misallocation due to informational frictions. The tractability of the model allows us to explore the responsiveness of wages and employment to an aggregate shock. We find that wage rigidity arises endogenously because of protracted learning, and in line with the data, the model is able to generate a larger and more persistent employment response.

We propose a new identification strategy to assess the efficacy of macroprudential measures. We use a novel instrumental variable based on the idea that a politically sensitive macroprudential measure is more likely to be implemented if a politically independent institution, such as a central bank, is in charge. Our results show that borrower-based macroprudential measures have had a strong and statistically significant dampening effect on credit growth in the European Union. 

Advances in technology such as recent progress in AI raise concerns about job displacement. While many studies estimate task and occupation exposure to technological innovations, the impact on individual earnings depends on prospects for alternative employment, shaped by workers' task-specific skills. These skills are typically unobserved. We develop a two-step approach to address this challenge: First, we infer task-specific skills from widely available micro data on individual employment histories and occupational task composition. Second, we use these measures with a structural, dynamic labor market model to quantify worker-level impacts of task-specific innovations. This approach enables analysis of historical patterns and simulations of future scenarios, providing insights into the heterogeneous effects of AI-driven automation on individuals’ earnings.