I’m an economist at the OECD, working in the Centre for Entrepreneurship, SMEs, Regions and Cities.
My research centers on understanding firms’ decision-making processes by integrating applied microeconometric techniques with cutting-edge machine learning and deep learning methods.
Working Papers
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National Firms, Local Effects: Spillovers from
Multi-Establishment Employers' Expansion
Job Market Paper.
[abstract]
Abstract: This paper examines how the expansion of large, multi-establishment (national) employers influences the wage and hiring policies of smaller, local firms. Although these expansions are firms' independent decisions, understanding their spillover effects is important for evaluating policies that aim to attract new large employers. Using administrative data from Brazil that cover firms' wages and employment across different locations and occupations, I conduct a matched event study to assess how local employers respond to significant, idiosyncratic labor demand shifts by national employers. The findings reveal that when national employers increase wages in large cities by 8 log points (relative to other employers in large cities), they simultaneously raise wages by 5 log points and expand employment in other locations. This expansion pressures local employers to increase wages by 2 log points, resulting in a 1.5 log point wage growth for incumbent workers. Despite local employers reducing employment, workers are not adversely affected because they reallocate to the expanding national employers. -
Spooky Boundaries at a Distance: Inductive
Bias, Dynamic Models, and Behavioral Macro.
with Mahdi Ebrahimi Kahou, Jesus Fernandez-Villaverde, Sebastian Gomez-Cardona and Jesse
Perla, Revise and Resubmit, Journal of Monetary Economics
[abstract] [NBER Working Paper]
Abstract: In the long run, we are all dead. Nonetheless, when studying the short-run dynamics of economic models, it is crucial to consider boundary conditions that govern long-run, forward-looking behavior, such as transversality conditions. We demonstrate that machine learning (ML) can automatically satisfy these conditions due to its inherent inductive bias toward finding flat solutions to functional equations. This characteristic enables ML algorithms to solve for transition dynamics, ensuring that long-run boundary conditions are approximately met. ML can even select the correct equilibria in cases of steady-state multiplicity. Additionally, the inductive bias provides a foundation for modeling forward-looking behavioral agents with self-consistent expectations. -
Heterogeneous Productivity,
Monopsony Power, and Spillovers: Unpacking the Local Effects of Industry
Shocks.
with Sudipta Ghosh and Xiaojun Guan
[abstract]
Abstract: We develop a unified theoretical framework to quantify how firms in local labor markets respond to industry-level productivity shocks. By extending the standard monopsony model of Card et al. (2018) to a general equilibrium setting, we incorporate cross-employer spillovers that allow firms to respond endogenously to changes in workers' outside options, which arise from productivity shifts in other local industries. Using German administrative data, we estimate the model using national industry wage premiums and their employment-weighted local averages as proxies for industry-specific and aggregate local productivity shocks. Our analysis reveals strong outside option effects and limited variation in local monopsony power. Our parameter estimates indicate that a 1% increase in an industry's productivity raises wages in that industry by approximately 0.47%, while a 1% increase in average local labor market productivity raises all local wages by about 0.53%. Additionally, a 1% increase in productivity above the local labor market average increases employment share by 1.04%. A simulation of a 10% subsidy to the machinery sector demonstrates substantial wage spillovers varying from 0.01% to 1.2% and significant labor reallocation.