Working Papers

Markups, Firm Scale and Distorted Economic Growth, October 2023 (draft forthcoming)

(with Jean-Felix Brouilette and Mohamad Adhami)

We study the consequences of markups for long-run economic growth in a model of firm-driven endogenous technological change. In this framework, differentiated firms engage in monopolistic competition, charge heterogeneous markups, and make forward-looking investments in R&D to improve their process efficiency. Markups distort the scale at which these firms operate and therefore affect their incentives to invest in R&D. With dispersion in markups, the introduction of size-dependent subsidies inducing firms to price at marginal cost can alter both the aggregate and cross-firm allocations of such investments. Using firm-level administrative data from France to discipline our model, we find that this intervention increases the long-run growth rate of productivity by 1.2 percentage points per year. Nearly 75% of this faster productivity growth can be achieved by simply reallocating R&D resources across firms, revealing that it is the dispersion in markups, rather than their average level, that is more detrimental to economic growth. 

Protect or Prevent? Non-Compete Agreements and Innovation, May 2023

(with Kate Reinmuth) 

A large proportion of the US workforce is covered by non-compete agreements (NCAs), but recently their use has become one of intense policy debate, with the FTC recently proposing to ban them. In this paper, we examine what effect such a policy change might be expected to have on innovation, and what the optimal policy would be. We consider four main channels that make the impact of NCAs on innovation ex-ante theoretically ambiguous: incumbent innovation incentives, entry, external spillovers, and internal spillovers. Our work suggests that at their current levels, NCAs have a significant negative impact on innovation, in contrast to what is often assumed in policy discussions. The impact is not only strongly statistically significant but also economically significant – for the mean observed change in state-level enforcement in our sample, patenting would be expected to move in the opposite direction by 11.8%. Moreover, this effect does not appear to simply be a story of NCAs restricting entry, with the fall in innovation almost exclusively being driven by incumbents. This suggests a potentially central role for labour markets and employee networks as a source of innovation spillovers. Therefore, we develop a GE model with endogenous innovation, search and matching frictions, and heterogeneous firms, allowing us to study optimal policy.

Household Debt and Labour Supply, September 2021, Bank of England Working Paper No. 941

(with Phil Bunn, Jagjit Chadha, Thomas Lazarowicz and Stephen Millard)

In this paper, we first develop a theoretical framework with three types of household: outright homeowners, mortgagors and renters. We then examine empirically how household debt affects the response of labour supply to shocks to income, mortgage interest rates and house prices for each type of household. In line with our framework, we find that negative income shocks lead to lower participation among outright homeowners while increasing mortgagors’ desired hours; surprise rises in interest rates lead to increases in desired hours that are larger the higher is the household’s debt level; and falls in house prices increase mortgagors’ desired hours.

Work in Progress

AI Adoption and Inequality, (draft forthcoming)

(with Carlo Pizzinelli and Marina M. Tavares

Using household microdata, we document how the exposure to AI varies across the income distribution and how it compares to the previous wave of routine-biased automation. We show that high-income workers are more exposed to AI displacement than low-income workers whereas automation was more concentrated at the bottom of the income distribution. However, high-income workers in exposed jobs also possess substantial sources of non-labor income (wealth and equity) and may thus benefit from higher returns to capital from AI. As such, the net effect of AI on total income inequality, factoring in capital income, depends on several competing channels which must be disciplined structurally. To this end, we calibrate a task-based model of production with heterogeneous households to the UK. The model shows that, unlike the fall in wages at the bottom of the income distribution induced by automation, under AI middle- and high-income workers are likely to experience declining wages. However, for the highest-income households this negative shock is more than offset by increased capital income. As a result, though wage inequality is expected to fall in response to AI, wealth inequality is likely to increase. Finally, we extend the model to the case of endogenous adoption of new technologies to study optimal capital taxation to balance the trade-off between redistribution and productivity growth.

Labor Market Exposure to AI: Cross-country Differences and Distributional Implications, (draft forthcoming)

(with Mauro Cazzaniga, Carlo Pizzinelli and Marina M. Tavares

Misallocation and Inequality

(with Pete Klenow and Emmanuella Kyei Manu)