Léonard Bocquet

How fast do labor markets adjust to technology shocks? In this paper, I introduce a new network framework to model skill frictions between occupations. Using data on skills, I construct an occupation network and find it is sparse, divided in clusters of similar occupations with 'bridge occupations' linking distinct clusters. Leveraging French administrative data, I show that workers transitioning through these 'bridges' move to distant occupations with higher wages and lower unemployment. Next, I build a tractable model of job search with networked labor markets. My main finding is that job-finding rates in bridge occupations—a measure of local accessibility—have large effects on the overall speed of reallocation, with slower reallocation leading to significant welfare losses. To quantify these effects, I augment the model with quantitative extensions, leveraging hat-algebra methods to solve counterfactuals without having to estimate large numbers of parameters. Calibrated to French data, the model predicts that robot adoption induces slow reallocation, around 40 quarters, and that this sluggish reallocation reduces welfare gains by approximately 40%— an order of magnitude higher than previous estimates. However, policies targeting bridge occupations can speed-up reallocation, and much more so than policies targeting tight occupations directly. These findings highlight the crucial role of the occupation network in shaping reallocation dynamics and provide new insights for the design of labor market policies.

Retraining programs have large positive effects on workers who participate, but they can also create negative spillovers on others, by shifting congestion across labor markets. This paper quantifies these spillovers to evaluate the overall effects of retraining programs. Using exhaustive French administrative data on retraining, I first compare the mobility patterns of retrained and non-retrained workers. Next, I extend a job search model with heterogeneous occupations to include retraining. Calibrating the model with French data, I simulate counterfactual scenarios to assess how labor market dynamics following structural shocks would change in the absence of retraining programs. I identify the programs with the greatest impact on reallocation speed and welfare. This economy-wide cost-benefit analysis offers insights for the design of retraining programs, especially as their costs come under increased scrutiny.

The aging population in advanced economies poses new challenges for labor markets, as senior workers have significantly lower employment rates than other age groups—a phenomenon known as the senior employment gap. This paper quantifies the competing drivers of this gap using rich administrative data from France. First, we document new stylized facts on age-based differences in worker mobility. We then extend a job search model to incorporate age heterogeneity, identifying four channels that contribute to the senior employment gap: learning ability, productivity, time-discounting, and wage expectations. Finally, we calibrate the model to quantify each channel’s impact. Our findings offer insights for labor market policies, as distinct drivers may call for different policy interventions.

Switching occupations requires workers to access and process vast amounts of labor market information, such as wages and job-finding probabilities in different labor markets. This paper examines how worker learning affects worker mobility in response to structural shocks. We extend a job search model in heterogeneous occupations to incorporate bounded rationality, where agents gradually learn about their environment. A key parameter—the speed of learning—determines how quickly workers approach the full information rational expectation benchmark and can be estimated from data. Using French administrative data to calibrate the model, we run counterfactuals varying learning speeds to quantify their effects on labor market adjustment. Our findings shed light on how bounded rationality may shape the economy's response to structural shocks.

Standard Schumpeterian theory suggests that small unproductive firms are more likely to default when real interest rates rise, resulting in a market-cleansing effect. However, empirical evidence show that default rates increased more for large firms following monetary policy tightening episodes. In this paper, we study how monetary policy influences firm default. To do so, we develop a model of heterogeneous firms with endogenous default, incorporating both financial and real frictions. Our findings show that the interaction of these frictions is key to explaining the observed non-cleansing default patterns. Intuitively, this stems from a leverage effect: financial frictions allow only large, productive firms to take on high leverage, making them more vulnerable to monetary shocks as real frictions make downward capital adjustment costly. These results suggest that firm selection may endogenously worsen during recessions, highlighting a potential need for support for large firms in distress.

Coming soon!