Research

Working Papers


“Profit Puzzles and the Fall of Public-Firm Profit Rates,” with Carter Davis and Alex Sollaci

Why are aggregate profit rates and factor shares divorced from financial-market measures of the cost of capital? We propose a novel explanation: National accounts track all firms, while financial markets track public firms only. In contrast to stable aggregate profits rates, we show public-firm profit rates have fallen since 1980, matching financial markets and suggesting low market power. The public-firm share of capital is stable; implied private-firm profit rates have risen. Size and sector differences cannot explain the divergence, though R&D intensity or capital wedges might. Our results indicate substantial biases in extrapolating public-firm trends to the aggregate economy.

“Labor Market Power and Technological Change in US Manufacturing,” with Ivan Kirov

We estimate time-varying plant-level production functions with Census microdata to separately identify labor and product market power in the US manufacturing sector. Wage markdowns rose substantially from 1 in 1972 to 2 in 2014, while price markups stayed flat at 1. Wage markdowns rose because marginal-revenue-product growth speeds up, not because wage growth stagnates. In local labor markets, wage-markdown growth is uncorrelated with employer-concentration growth. We document strong associations with direct measures of information and communication technologies and indirect measures of management and automation technologies. Altogether, the evidence points to technological threat as a key driver of labor market power.

Honors: 2018–19 Stigler Center PhD Dissertation Award, Bradley Fellowship 

Blogs: ProMarket


“Measuring Markups with Revenue Data,” with Ivan Kirov and Paolo Mengano

When output prices are unobserved, standard production-based markup estimators are biased and inconsistent because they’re unable to distinguish whether firms have higher revenues due to higher prices or higher quantities. Building on work designed for competitive environments, we propose a novel method that solves this problem using only revenue data. We flexibly model markups as a specified function of observables and fixed effects, supporting a broad class of variable-markup frameworks. We explicitly adopt a Markovian revenue productivity process, a commonly implicit assumption in the literature. Our suggested two-step approach is simple in concept and implementation, requiring only common regression techniques.

“The Beginning of the Trend: Interest Rates, Profits, and Markups,” with Anton Bobrov

Recent research uses time series evidence to argue the decline in interest rates led to a large rise in economic profits and markups. We show the size of these estimates is sensitive to the sample start date: The rise in markups from 1984 to 2019 is 14% larger than from 1980 to 2019, a difference amounting to a $3000 change in income per worker in 2019. The sensitivity comes from a peak in interest rates in 1984, during a period of heightened volatility.

“Seven Million Demand Elasticities,” with Jordan Rosenthal-Kay and Uyen Tran

The household’s price elasticity of demand is a key input to many economic models’ construction of markups and the assessment of consumer surplus. We measure the price elasticity of demand for around 14,000 products by region-year using retail scanner data. In all, we estimate over 7.5 million demand elasticities. We find that the distribution of these elasticities is stationary over time. However, we document substantial spatial heterogeneity in consumers’ price sensitivity: consumers in the largest markets are the most price elastic. As demand elasticities are a key input into the measurement of markups, our results suggest that any conclusions that markups are rising in retail markets must be driven by assumptions on conduct.

“Measuring Markups with Production Data,” with Zach Flynn and Amit Gandhi

We show standard methods to estimate production functions do not identify markups. This nonidentification creates spurious skewness in estimated markup distributions. We also show that ex-ante structure on the returns to scale solves the identification problem. In US public firm data and in a Monte Carlo experiment, we find that applying constant returns to scale performs remarkably well and reduces the skewness in the markup distribution among public-firm by as much as half in comparison to nonidentified estimates. This results in half the efficiency losses in output and labor shares when calibrated to a recent macroeconomic model.

“Is Aggregate Market Power Increasing? Production Trends Using Financial Statements”

Recent work in macroeconomics argues that firm market power dramatically increased since the 1980s. Using financial statement data, I find that public firm markups increased only modestly over this time period, and are within historical variation. These estimates improve on earlier work by accounting for marketing and management expenses, which I document are a rising share of costs in firm production. Markups are increasing in firm size and vary by sector. Reasonable calibrations accounting for the representativeness of public firms show a flat or even decreasing aggregate markup.

Blogs: Marginal Revolution, ProMarket 1, ProMarket 2, Chicago Booth Review, Quartz



Publications


“The Production Approach to Markup Estimation Often Measures Input Distortions,” with Arshia Hashemi and Ivan Kirov

Economics Letters 217 (2022)

The production approach recovers markups using the output elasticity for a variable and undistorted input. We show using the revenue elasticity for a variable input recovers that input’s wedge. Our result has two implications. First, in the canonical setting with CES demand and monopolistic competition, past research using the production approach with revenue data should be recast as evidence of input, rather than output, distortions. Second, future research can use the production approach with revenue data to study input distortions, provided researchers can measure inputs in physical units. A promising application pertains to labor market distortions.

“Resolving 'Too Big to Fail',” with Nicola Cetorelli

Journal of Financial Services Research 60, no. 1 (2021)

Blogs: Liberty Street Economics


“Evolution in Bank Complexity,” with Nicola Cetorelli and Jamie McAndrews

Economic Policy Review 20, no. 2 (2014)

Blogs: Liberty Street Economics


“Do 'Too-Big-to-Fail' Banks Take On More Risk?,” with Gara Afonso and João Santos

Economic Policy Review 20, no. 2 (2014)

Blogs: Liberty Street Economics



Other Work


“How Practical Are Biden’s Proposals to Promote Labor Market Competition?,” with Jordan Rosenthal-Kay

ProMarket (2022)