U.S. Operating Profit Markup, 1950-2022

March 05, 2024 by Ednaldo Silva
About the Author
Ednaldo Silva
Ednaldo Silva
(Ph.D. Econ.) is a leading economist with over 25 years’ experience in transfer pricing.
He is founder and former managing director of RoyaltyStat, an online database of royalty rates extracted from license agreements. Dr. Ednaldo Silva was the first Sr. Economic Advisor at the IRS Office of Chief Counsel, a drafting member of U.S. 26 IRC section 1.482 (1992, 1993, 1994) transfer pricing regulations. He introduced the “comparable profits method” (CPM in the US and TNMM in the OECD), “best method” rule, multiyear profit analysis, and the concept of arm’s-length represented by a range of results, rather than a point estimate.
Read more

More articles by this author

Find Ednaldo Silva on LinkedIn

“You better stop the things you do, I tell you, I ain’t lying.”

− Jay Hawkins, “I Put a Spell on You” (1956)

Microeconomics is a tool of miseducation. Students are taught that individual single-product entities maximize profits, subject to cost constraints. Average and marginal costs are assumed to be increasing with increasing quantity supplied, a restricted assumption that can be regarded as special. Textbooks based on restricted assumptions have become thick and obscure. Parables tells us that, under competition (like the statistical law of large numbers), the single product entity’s price equals its marginal cost. 

In microeconomics, corporate profits are hidden, embodied, or obfuscated in marginal cost. In classical economics, profits were visible, and regarded as the residual property income, separate from costs. Aside from axiomatic profit maximization, the analysis of corporate profit behavior is absent from microeconomics. Profits get few references in the indices of microeconomics textbooks.

Like Western art, economics has moved from romantic (ideal competition model, an atavist obsession with an imagined past) to abstract representations distant from living corporate reality. Realist art (or literature) reflecting reality has been dissonant in the established order, like realist economics. Here, I explore the operating profits (before depreciation and amortization) of a major group of U.S. corporations (consolidated entities) operating as oligopolies, characterized by the ability to preserve stable long-term operating profits markup irrespective of business cycles.

The law of large numbers in statistics posits that large data samples have two attractive convergence properties. First, the center of mass (mean value, regression slope coefficient) becomes free from bias; second, the variance tends to zero (see Jaynes, 2004, pp. 113, 188, 199). Here are the robust (Huber algorithm) regression results from large U.S. corporations:

     (1)    Revenue = (1.1532 ± 0.0001) XOPR + 215.9 [USD in millions]

            t-statistics     8,416.7                    55.0

The R2 = 0.9764, Residual Standard Error (RSE) = USD 19.3 million, Count = 20,416 pairs of XOPR and Revenue numbers. Revenue excludes non-operating income, and XOPR is the acronym for total cost, that is, COGS plus XSGA (operating expenses). 

The data sample includes 472 U.S. corporations from the Standard & Poor’s (S&P) 500 stock price index. I excluded corporations domiciled abroad. Together, they constitute a large fraction of U.S. annual GDP. The period covered is from 1950 to 2022.

A straight line through the scatter plot of XOPR versus Revenue shows no inflection point, indicating the enduring ability of the sampled corporations to pass on increased costs by increasing product prices. Hence, there is no need to test switching slope coefficients (no shifts in structural parameters can be observed).

For comparison with the robust regression (1), consider the ordinary least squares (OLS) regression with Newey-West corrected residual errors:

     (2)    Revenue = (1.1492 ± 0.0135) XOPR + 860.4 [USD in millions]

            t-statistics     85.0                    8.6

The R2 = 0.9768, RSE = USD 4.7 million, Count = 20,416 pairs of XOPR and Revenue numbers.

The robust regression (1) produces a more reliable standard error of the slope coefficient, which is the 15.3% ± 0.01% profit markup for this large and influential group of U.S. corporations. The t-statistics, the ratio of the estimated coefficient to its standard error, is the accepted measure of reliability. The t-statistics of the robust regression (1) is much larger than the t-statistics of the OLS (with error correction) regression (2).

Yet, despite this stable, lucrative business history in which the long-run operating profit markup is 15% measured by the robust or OLS regression, certain corporate income tax planners want to convince the tax authorities (under secretive proceedings) that “comparables” to controlled economic activities operate with low profits, and multi-year operating losses are acceptable comparables. The S&P 500 group of corporations is used as a performance benchmark, except in transfer pricing.

The contrived low profits are lower than the realistic alternative available to controlled operating loss corporations, which is to invest in T-bills and earn a historical 3% on the invested capital.

As Michael Jackson warned in “Billie Jean,” a lie can become truth only if fiction is addled with reality. Fiction, such as the “magic realism” of Gabriel García Márquez, One Hundred Years of Solitude (1967), or the epic film 2001: A Space Odyssey (1968) by Stanley Kubrick and Arthur Clarke, must be plausible to be convincing.

 

References

Compare James Henderson & Richard Quandt, Microeconomics Theory (A Mathematical Approach), McGraw-Hill, 1958, 283 pages to the massive textbook by Andras Mas-Collel, Michael Winston & Jerry Green, Microeconomic Theory, Oxford University Press, 1995, 970 bulkier pages.

The progress of microeconomic theory between the publication of these two influential textbooks is disappointing, except that the presentation of the same implausible principles has become more dense and abstract from reality. See also David Kreps, A Course in Microeconomic Theory, Princeton University Press, 1990, § 7.2 (The profit function).

Edwin Jaynes, Probability Theory (The Logic of Science), Cambridge University Press, 2004, p. 113: “As the number N of tests increases, these [50%, 90%, 99% confidence] intervals shrink, ... proportional to 1 / √ N, a common rule that arises repeatedly in probability theory.” Jaynes calls probability theory the logic of science.

A realist reading of art can start with George Thomson, Aeschylus and Athens, Lawrence & Wishart (2nd edition), 1946, Gene Weltfish, The Origins of Art, Dobbs-Merrill, 1953; and Frederick Antal, Classicism & Romanticism, Basic Books, 1966.

Much knowledge can be obtained from reading Ian Watt, The Rise of the Novel (Defoe, Richardson, and Fielding), University of California Press, 1965, and the myth-breaking article by Stephen Hymer, “Robinson Crusoe and the Secret of Primitive Accumulation” in Edward Nell (Editor), Growth, Profits & Property, Cambridge University Press, 1980. I heard Stephen Hymer discuss his revealing article, originally published in Monthly Review (September 1971), during the seminar of James O’Connor, while O’Connor was writing his book, The Fiscal Crisis of the State, St. Martin's Press, 1973.

The dominant paradigm of art history is found in Horst Janson, History of Art, Prentice-Hall, 1962, Etienne Gilson, Painting and Reality, Pantheon Books, 1957; and Ernst Gombrich, Art and Illusion, Pantheon Books, 1960. Aside from Joseph Schumpeter, History of Economic Analysis, Oxford University Press, 1954, there is no grand survey of economics. In context, see the four-volume survey by Albert Boime, A Social History of Modern Art, Chicago University Press, Vol. 1 (1987), Vol. 2 (1990), Vol. 3 (2004), and Vol. 4 (2007), almost the same title as the prior multi-volume art history survey by Arnold Hauser (1962).

Headquarters
EdgarStat LLC
5425 Wisconsin Ave., Suite 600
Chevy Chase, MD, 20815-3577
USA
Customer Support
support@edgarstat.com