Research
Publications:
Large Firm Dynamics and the Business Cycle (joint with Vasco M. Carvalho)
American Economic Review. Apr 2019, Vol. 109, No. 4: Pages 1375-1425
Do large firm dynamics drive the business cycle? We answer this question by developing a quantitative theory of aggregate fluctuations caused by firm-level disturbances alone. We show that a standard heterogeneous firm dynamics setup already contains in it a theory of the business cycle, without appealing to aggregate shocks. We offer an analytical characterization of the law of motion of the aggregate state in this class of models – the firm size distribution – and show that aggregate output and productivity dynamics display: (i) persistence, (ii) volatility and (iii) time-varying second moments. We explore the key role of moments of the firm size distribution – and, in particular, the role of large firm dynamics – in shaping aggregate fluctuations, theoretically, quantitatively and in the data.
Production Networks and Economic Policy (joint with Julien Sauvagnat)
Oxford Review of Economic Policy. Winter 2019, Vol. 35, Issue 4: Pages 638–677
In this paper, we show how to combine data on input-output tables and recent insights from the theory of production networks in order to inform policy. We first describe the information contained in input-output tables complied by statistical agencies, and show how to derive relevant statistics of production networks. We then discuss the implications of production networks for policy intervention in a series of domains, such as fiscal policy, industrial policy, or, finance. Finally, we present a quantitative exercise applied to French data in order to illustrate that production networks shape the overall impact of competition policy on the economy.
Sectoral Effects of Social Distancing (joint with Jean-Noël Barrot and Julien Sauvagnat)
American Economic Association P&P. May 2021, Vol. 111, Pages 277–281
[Covid Economics] [replication file] [toolbox]
The outbreak of the Covid-19 virus has led many states to take drastic measures of social distancing. Using US executive order, occupation and survey data, we measure the fall in labor supply due to these measure. Starting from a model of production networks, we analyze the sectoral effects of these labor shocks for the United States. We find that non-linearities in the production network account for around half of the drop in GDP associated to the implementation of social distancing measures. The model also generates realistic dispersion in sectoral output change.
Causal Effects of Closing Businesses in a Pandemic [WP version] (joint with Jean-Noël Barrot, Maxime Bonelli and Julien Sauvagnat)
Journal of Financial Economics. April 2024, Vol. 154
We study whether state-level mandatory business closures implemented in response to the outbreak of the Covid-19 causally affect economic and health outcomes. Using plausibly exogenous variations in exposure to these restrictions, we find that they impose substantial losses to firms and workers, the former bearing approximately two thirds of the cost, consistent with firms partially insuring their workers. We show that mandatory business closures have a significant negative causal effect on mortality rates, particularly in areas featuring contact-intensive occupations. We discuss the assumptions under which the health benefits of business closures exceed their associated economic costs.
Working Papers:
The EU Miracle: When 75 Million Reach High Income [new]
[VoxEU column] [OFCE Blog in French] [IEP@BU]
In 2004, 75 million people across 10 countries joined the European Union (EU). Over the subsequent 15 years, their GDP per capita had increased by 90\%. Using a synthetic control method, I show that half of this increase is attributed to the EU accession. I found that without joining the EU, the GDP per capita of the new members would have been 8,433~USD or 24\% lower in 2019. The same methodology does not identify a robust effect on the 15 countries that were already members of the EU before 2004. These findings are robust to various tests and specifications: a leave-one-out test, an in-country placebo, an in-time placebo, and alternative donor pools. A simple growth accounting decomposition shows that the contribution of the Solow residual to growth of the new member countries is three times larger. The data shows convergence in investment, consumption, government spending, export/import shares, employment rate, FDI, and regulations indices. The TFP of the new member states has been growing at a higher rate since 2004. These results raise the question of why accession to the EU had such a large impact on TFP.
The Hitchhiker's Guide to Markup Estimation: Assessing Estimates from Financial Data [new version], (with Maarten De Ridder and Giovanni Morzenti)
RR @ Econometrica
Macroeconomic outcomes depend on the distribution of markups across firms and over time, making firm-level markup estimates key for macroeconomic analysis. Methods to obtain these estimates require data on the prices that firms charge. Firm-level data with wide coverage, however, primarily comes from financial statements, which lack information on prices. We use an analytical framework to show that trends in markups or the dispersion of markups across firms can still be well-measured with such data. Finding the average level of the markup does require pricing data, and we propose a consistent estimator for such settings. We validate the analytical results with simulations of a quantitative macroeconomic model and firm-level administrative production and pricing data. Our analysis supports the use of financial data to measure trends in aggregate markups.
Bottom-up Markup Fluctuations (joint with Ariel Burstein and Vasco M. Carvalho)
RR (2nd round) @ Quarterly Journal of Economics
We study markup cyclicality in a granular macroeconomic model with oligopolistic competition. We characterize the comovement of firm, sectoral, and economy-wide markups with sectoral and aggregate output following firm-level shocks. We then quantify the model’s ability to reproduce salient features of the cyclical properties of markups in French administrative firm-level data, from the bottom (firm) level to the aggregate level. Our model helps rationalize various, seemingly conflicting, measures of markup cyclicality in the French data.
Firm-level productivity shocks can help understand sector- and macroeconomic-level outcomes. Capturing the market power of these firms is important: it determines how productivity gains translate into prices and markups. In existing models, firms do not internalize the impact of their systemic size. This paper explores the alternative oligopolistic market structure. To this end, I build a tractable multi-sector heterogeneous-firm general equilibrium model featuring oligopolistic competition and an input-output (I-O) network. By affecting price and markup, firm-level productivity shocks propagate both to the downstream and upstream sectors. Sector-level competition intensity affects the strength of these new propagation mechanisms. The structural importance of a firm is determined by the interaction of (i) the sector-level competition intensity, (ii) the firm's sector position in the I-O network, and (iii) the firm size. In a calibration exercise, the aggregate volatility arising from independent firm-level shocks is 34% of the one observed in the data.
Why Risky Sectors Grow Fast (joint with Jean Imbs)
Because they are populated by a few large firms and many small ones. We construct a model of idea flows in which growth and volatility both depend on the prevalence of large firms in a sector. There is a finite number of firms that choose between a "local" and a "global" technology. The "local" technology means producing using a random technology, given by a discrete Markov deviation from its earlier value. In the limit, "local" firms define an expanding technology frontier. The "global" technology means drawing technology from the pool of existing producers. In equilibrium, the "local" technology is chosen by large enough firms only, and growth increases in their share. Since the "local" technology has stochastic consequences, so does volatility. The model's key predictions are born out in US firm-level data: growth and volatility both increase in the share of large firms, which can explain a sizeable fraction of the positive link between growth and volatility at microeconomic level.