Abstract
There is wide spread concern about a growing gap between top-performing publicly listed firms and the rest of the economy and the implications of this for rising inequality in the U.S. Using conventional return calculations, there is indeed a widening gap between star firms (defined as those in top 10 percent of return on invested capital in any year) and the rest of the economy over time, especially in industries that rely on a skilled labor force. However, once measurement error in intangible capital is accounted for, this gap shrinks dramatically and has not been widening over time. While pricing power, as measured by markups, predicts star firm status, a large fraction of star firms have low markups and there is no evidence that star firms are cutting output or investment more than other firms for the same markup. The effect of star status is persistent. Five years later, star firms have higher growth, profits, and Tobin?s Q. A small subset of exceptional firms may pose more pressing policy concerns with much higher returns and the potential to exercise market power in the future.
Highlights
A great deal of attention has been paid to two trends: (1) the emergence of star firms that have pulled away from the rest of the economy (Furman and Orszag [2015], Koller, Goedhart, and Wessels [2017], Autor, Dorn, Katz, Patterson, and Van Reenen [2017]) and (2) introduction of new technologies together with a fundamental structural change towards a more intangible intensive economy (Corrado and Hulten [2010]) with corresponding implications for corporate investment and the overall economy.1 we have little systematic evidence on the characteristics of the star firms
We focus on the following three measures of human capital: CPS (Complex Problem Solving) which is identifying complex problems and reviewing related information to develop and evaluate options and implement solutions; NRCOG (Non-routine Cognitive Analytical skills) from Keller and Utar [2016] which is the sum of Mathematical Reasoning, Inductive Reasoning, Developing Objectives and Strategies, and Making Decisions and Solving Problems; and RMAN (Routine Manual) from Keller and Utar [2016] which is the sum of Spend time making repetitive motions, Pace Determined by Speed of Equipment, Manual Dexterity, and Finger Dexterity
We begin by comparing markups generated using different measures of marginal costs - COGs as in De Loecker and Eeckhout [2017], OPEX as in Traina [2018], and our own measure of operating expenses adjusted for investments in intangible capital following Peters and Taylor [2017], OPEX*
Summary
A great deal of attention has been paid to two trends: (1) the emergence of star firms that have pulled away from the rest of the economy (Furman and Orszag [2015], Koller, Goedhart, and Wessels [2017], Autor, Dorn, Katz, Patterson, and Van Reenen [2017]) and (2) introduction of new technologies together with a fundamental structural change towards a more intangible intensive economy (Corrado and Hulten [2010]) with corresponding implications for corporate investment and the overall economy. we have little systematic evidence on the characteristics of the star firms. Hall [2018] studies mega firms, defined as firms with more than 10,000 workers and finds no evidence that industries with high proportion of these firms have high markups but does find that markups increase in sectors with rising share of mega firms.8 In contrast to these papers, we use a market based measure of returns on invested capital to characterize star firms, which is the definition that studies highlighting the emergence of star firms have focused on such as Furman and Orszag [2015], Koller et al [2017], Council of Economic Advisors [2016]. We find little evidence for the hypothesis that these firms are restricting investment
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