RoyaltyStat Blog

The Profit Margin of US Retailers in Transfer Pricing

Posted by Ednaldo Silva

Segue a reliable method to determine the arm's length profit margin of each selected comparable company to benchmark the tested party. For each selected comparable company, we measure Total Costs (Lato) = COGS + XSGA + (DP – AM). In Standard & Poor's Global (Compustat) mnemonics, COGS is cost of goods sold, XSGA is operating expenses, DP is the depreciation of property, plant & equipment (PPENT), including AM that is the amortization of acquired intangibles. Denote C as Total Costs (Lato) and S as Net Sales, which for each selected company is the sum of the unit price of the individual goods and services offered by the enterprise during the fiscal year multiplied by the respective quantity supplied:

     (1)     S(t) = C(t) + P(t)

for t = 1 to T fiscal periods.

Equation (1) represents an accounting identity that in each fiscal period net sales are equal to total costs (lato sensu) plus operating profits after depreciation (but excluding the amortization of acquired intangibles because they may not be integral to the business operations under transfer pricing audit) (EBIT).

To simplify exposition, we hide the comparable i-th subscript.

Add a behavioral equation that the net profits after depreciation (EBIT exclusive of amortization or OMBA) are proportional to the company’s net sales during the same period:

     (2)     P(t) = μ S(t) + U(t)

where the slope μ = OMBA is the net profit margin and U(t) is a random error.

Transfer pricing analysts estimate structural equation (2), which is misconceived. A correct procedure is to substitute (2) into (1) and obtain a reduced-form equation, whose parameters we can estimate using regression analysis:

     (3)     S(t) = λ C(t) + V(t)

where λ = 1 / (1 – μ) > 1 is the net profit markup and V(t) is a transformed random variable.

The displacement λ ± (2 × SE(λ)), where SE denotes standard error, measures the confidence interval for the slope coefficient of regression equation (3). See Wonnacott & Wonnacott ((1969), pp. 132, 244 and James et. al. (2013), p. 66.

The net profit margin is obtained by indirect least squares from equation (3) using the formula:

     (4)     μ = (λ – 1) / λ = OMBA (our nomenclature, not Compustat)

See https://blog.royaltystat.com/profit-margin-in-the-markup-pricing-model

Like John Wallis (1616-1703), “We Test and See it to be so”. See Wallis (1643), pp. 60-61.

Profit Markup of Major US Retailers

The net operating profit markup of several major US retailers is estimated using all available data to fit equation (3). The OMBA ratio can be calculated by using equation (4), which we leave to the reader as an exercise. However, we report the profit margin per selected company on the charts below.

Equation (3) was run with the intercept but they are suppressed on the table below because they are weak or insignificant. The t-statistics are Newey-West estimators that correct for serial correlation among the residuals. See Zeileis (2004) and Green (2018), Section 20.5.2, pp. 998-999 (“The White [1980] and Newey-West [1987] estimators are standard in the econometrics literature.”).

Company             GVKEY                  Period               Count             λ                        t-statistics          R2          

Best Buy                   2184                  1983-2019           37                 1.0473                  282.2                    0.9998

Conns’s                156614                  2002-2018           17                 1.0818                     74.2                    0.993

Costco                   29028                  1992-2019            28                 1.0322                   887.7                    0.9999

Home Depot           5680                  1980-2019            40                 1.1423                    72.1                     0.9985

Kohl’s                     25283                  1991-2019            29                 1.093                      78.1                     0.9985

Lowe’s                      6929                  1978-2019            42                 1.0948                  238.6                    0.9995

Macy’s                      4611                  1978-2019            42                 1.0864                    96.7                     0.9983

PriceSmart            65343                  2001-2019            24                 1.0583                   201.5                    0.9997

Target                      3813                  1978-2019            42                 1.0746                   235.8                    0.9997

Walmart                11259                  1978-2019            42                 1.049                     243.2                    0.9999

All 10 Retailers                                  1978-2019          658                 1.0515                   289.7                    0.9996

These regression results were computed using RoyaltyStat's online (interactive) scatterplot function.

Home Depot is the only large US retailer in our sample showing double digits net operating profit markup, λ = 14.2% or OMBA = μ = 12.5%.

The OLS regression results are reliable measured by two tests:

First, the Newey-West t-statistics are high compared to the 1.96 rule-of-thumb. Think of the t-statistics as a coefficient of variation defined as the ratio of the regression coefficient (λ) divided by its standard error. The higher the t-statistics the more reliable is the estimate of the partial regression coefficient measuring the relationship between the dependent variable and the selected independent variable.

We provide a chart of OMBA (EBIT margin, excluding AM) considering all available annual data per company from 1978 to 2019.

For comparison, we provide a chart of OMBD (EBITDA margin), including the profit margin for the top 10 US retailers in our sample. This range of EBITDA margins (6.7% ± 0.5%), which reflects the business operations of the top 10 US retailers, include 658 annual observations. According to the statistical law of large numbers, this large sample produces very reliable results. E.g., the Newey-West t-statistics is 344.3, the OLS (ordinary least squares) t-statistics is 1,220.3, and the R2 is 0.9996. 

Second, the R2 of every company assayed is close to one, which is its maximum value. The R2 measures the explanatory power of the regression equation, indicating that in our application of equation (3) the residual left to chance is negligible.

In RoyaltyStat, we have integrated scatterplot, multiple regression and other useful statistical functions with our distribution license of Standard & Poor's Global (Compustat) database of listed company financials. RoyaltyStat's built-in (interactive) scatterplot and multiple regression functions include the reporting of Newey-West standard errors of the regression coefficients.

We believe that RoyaltyStat offers for subscription the most effective transfer pricing interactive software as a service (SaaS) in the industry.

References

William Green, Econometric Analysis (8th edition), Pearson, 2018.

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, An Introdution to Statistical Learning, Springer, 2013 (corrected at 4th printing 2014).

John Wallis, Truth Tried, London, Samuel Gellibrand, 1643, 128 pages. Quote from Amir Alexander, Infinitesimal, Scientific American, 2014, p. 327. Wallis was one of the mathematical progenitors of Isaac Newton. For fun, read John Wallis, The Arithmetic of Infinitesimals [1656], translated from Latin to English with an Introduction by Jacqueline Stedall, New York, Springer-Verlag, 2004. See also: http://www-history.mcs.st-and.ac.uk/Biographies/Wallis.html

Thomas Wonnacott & Ronald Wonnacott, Introductory Statistics, Wiley, 1969.

Achim Zeileis, “Econometric Computing with HC and HAC Covariance Matrix Estimators,” Journal of Statistical Software, Vol. 11, Issue 10, November 2004. Accessed: https://www.jstatsoft.org/article/view/v011i10/v11i10.pdf

Chart of OMBA US Retailers 2020-04-04

Chart of OMBD (EBITDA) 2020-04-03

 

Published on Oct 6, 2019 4:41:01 PM

Ednaldo Silva (Ph.D.) is founder and managing director of RoyaltyStat. He helped draft the US transfer pricing regulations and developed the comparable profits method called TNNM by the OECD. He can be contacted at: esilva@royaltystat.com

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Topics: Net Profit Indicator