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.

Working Papers:

Bottom-up Markup Fluctuations (joint with Ariel Burstein and Vasco M. Carvalho) [appendix]

RR@ 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.

The Hitchhiker's Guide to Markup Estimation , (with Maarten De Ridder and Giovanni Morzenti)

Is it feasible to estimate firm-level markups from administrative data? Common methods to measure markups hinge on a production function estimation, but most datasets do not contain data on the quantity that firms produce. We use a tractable analytical framework, simulation from a quantitative model, and firm-level administrative production and pricing data to study the biases in markup estimates that may arise as a result. The level of markup estimates from revenue data is biased, but they do correlate highly with true markups. They also display similar correlations with variables such as profitability and market share in our data. Finally, we show that imposing a Cobb-Douglas production function or simplifying the production function estimation may reduce the informativeness of markup estimates.

Causal Effects of Closing Businesses in a Pandemic (joint with Jean-Noël Barrot, Maxime Bonelli and Julien Sauvagnat)

RR@ Journal of Financial Economics

Typical government responses to pandemics involve social distancing measures implemented to curb disease propagation. We evaluate the impact of state-mandated business closures in the context of the Covid-19 crisis in the US. Using state-level variations in the set of sectors defined as non-essential and forced to shut down, and geographic variations in industry composition, we estimate the effects of business closure decisions on firms' market value, and on infection and death rates. We find that a 10 percentage point increase in the share of restricted labor is associated with a drop by 3 percentage points in April 2020 employment, a 1.87% drop in firms' market value, and 0.15 and 0.011 percentage points lower Covid-19 infection and death rates, respectively. An extrapolation of these preliminary findings suggests that state-mandated business closures might have cost $700 billion and saved 36,000 lives so far.

IO in I-O: Size, Industrial Organization, and the Input-Output Network Make a Firm Structurally Important

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.