Identifying and Evaluating Contributions to Value within a Firm
Assessing the Relative Economic Importance of Activities and Investments
Economics Partners, LLC
White Paper Series
White Paper 2015-01
Tim Reichert and Erin Hutchinson
Table of Contents
I.
II.
Introduction ................................................................................................................................... 1
Decomposing Value: Overview of Economics Partners’ Research Strategy ....................... 4
A.
Overview of Approach .......................................................................................................... 4
B.
Tobin’s Q.................................................................................................................................. 4
III.
Review of Relevant Literatures ................................................................................................... 7
A.
Introduction............................................................................................................................. 7
B.
Intangible Asset Investments ................................................................................................ 7
1.Early Studies of R&D Investments, Productivity, and Market Value ..................................... 8
2.Studies of the Rate of Return on R&D Investments ................................................................... 9
3.Studies of Marketing and Sales Investments ............................................................................ 13
4.Summary ........................................................................................................................................ 17
C.
Multinational Enterprises .................................................................................................... 17
D.
Cost Efficiency ...................................................................................................................... 20
IV.
Econometric Model ..................................................................................................................... 22
A.
Sample Strategy .................................................................................................................... 22
B.
Model Specification .............................................................................................................. 22
1.Dependent Variable ...................................................................................................................... 22
2.Independent Variables ................................................................................................................. 23
3.Econometric Model ....................................................................................................................... 24
C.
Model Results........................................................................................................................ 24
1.Descriptive Statistics ..................................................................................................................... 24
2.Econometric Results ..................................................................................................................... 25
3.Conclusion ..................................................................................................................................... 26
V.
Attribution of Profits within a Firm ......................................................................................... 27
A.
Overview of Statistical Attribution .................................................................................... 27
B.
General Procedure: Two Steps .......................................................................................... 28
C.
Attribution of Profits to Variables ...................................................................................... 28
D.
Example of Attribution of Variables to Entities ............................................................... 29
1.Factors Attributable to IPCo ........................................................................................................ 30
2.Factors Attributable to OpCo ...................................................................................................... 30
3.Unattributed Factors..................................................................................................................... 30
E.
Summary of Results ............................................................................................................. 30
VI.
Conclusions Reached .................................................................................................................. 32
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I.
Introduction
The past several years have seen an increased emphasis by transfer pricing regulatory bodies
(tax authorities and quasi-regulatory bodies such as the United Nations and the OECD) on the
economic determinants of value within the multinational enterprise. Arguably, this has
corresponded with a de-emphasis, by the same transfer pricing regulators, of intercompany
contracts and the comparability of controlled and uncontrolled transactions.
We have argued elsewhere that an emphasis on the causes of shareholder value, while perhaps
useful in principle, suffers from an inherently high degree of subjectivity. It is easy for both tax
authorities and taxpayers to assert, without reliable quantitative evidence, that one or another
activity or asset is the primary “value driver” within a business. The subjectivity of these sorts
of assertions, or “value statements” as we call them in this paper, is one reason for the
prevalence of seemingly intractable audits and multi-year transfer pricing negotiations.
Such audits tend to devolve into arguments over the relative importance of things like R&D
versus marketing and sales, or of recent R&D versus “early-stage and high risk” R&D, or of
marketing and sales versus manufacturing, and so on. While it is certainly true that R&D
generates value, and it is also true that early stage R&D which has proven successful is often
more valuable than follow-on R&D (and for that matter it is true that marketing and sales give
rise to value), the real question is “what empirical evidence exists regarding the relative values
– or relative claims over operating profit – of these purported sources of value?”
Said differently, “which value statements are credible?” Is it credible, for example, to argue that
manufacturing or assembly is the primary claimant of profit and the primary value driver, and
if so under what conditions and in what industries?
The purpose of this paper is to describe a framework for identifying the primary determinants
of shareholder value and operating profit within a firm, and for estimating an allocation of
operating profit among them.1 Our objective is admittedly a minimalist – i.e., to provide a
framework that can produce empirical benchmarks for assessing the credibility of competing
assertions regarding value creation within the MNE.
Our methodology is straightforward and will be familiar to economists. Following a substantial
economics literature that measures the relationship between “Tobin’s Q” (which is the ratio of
market enterprise value to book value) and various enterprise value drivers, we first show how
to devise a structural model that explains the Q as a function of key value creating activities.
We focus on Tobin’s Q, or simply “Q,” because the ratio of market enterprise value to book
asset value is a direct indicator of the productivity of the firm’s investments. The higher is Q,
the more profitable the firm’s investments are expected to be. In fact, as we show later, there is
For purposes of this study, the term “residual profit” will refer to operating profit earned by a business
in excess of the required operating profit return to physical and financial capital. In short, residual profit,
for purposes of this document, is the return to intangible capital.
1
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a direct arithmetic linkage between Q and operating profit, which means that a model that
relates Q to various value drivers is also a model that relates operating profit to those value
drivers.
Within our model, the structural determinants of Q are chosen because specific academic
literatures exist to support the claim that these are in fact determinants of shareholder value and
operating profit. That is, literatures present and test the hypothesis that these “factors”
constitute investments or activities that are net present value positive (the cost of these activities
or investments is less than the present value of the operating profits that they generate).
Once we have a reasonably accurate and reliable structural model, we then use a technique
called “variance decomposition” to determine the relative importance of our chosen factors. The
term variance decomposition is a synonym in this context for “value attribution.” Variance
decomposition is a way that economists and statisticians use to attribute explanatory power, or
more loosely causality, to individual variables. Variance decomposition gives us the relative
contributions of each variable in an econometric model to the variable of interest – in this case,
operating profit.
As an intuitive example of the way in which variance decomposition works, consider the team
members of an American football team. Across the National Football League, it will almost
always be the case that a team’s kicker will lead the team in total scoring. For example, if a team
scores 23 points in a game, the kicker will perhaps have scored 11 out of the 23 (three field goals
and two extra points, or nearly half of the team’s points).
If we measure a kicker’s relative contribution to the team’s success by looking only at this
statistic, we will grossly overstate the importance of the kicker. Why is this? Because losing a
kicker is not a serious problem for most teams. By imagining what would happen to the team’s
success if the kicker left the team, we see that the kicker is in fact not very important relative to
players in other positions – a kicker can easily be replaced.
Continuing with the example, left tackles score very few points. Left tackles never lead their
team in total scoring, and often go their entire careers without scoring any points. Thus, if we
measure the left tackle’s relative contribution to the team’s success by looking only at this
statistic, we will grossly understate his importance. But losing a left tackle is almost always a
serious problem for a football team. It also explains why left tackles are often taken in the first
round of the NFL draft and are paid tens of millions of dollars, while kickers often go undrafted
and many are paid near the league minimum. In other words, by imagining what would
happen to the team’s success if the left tackle were injured or replaced, we see that the left tackle
is in fact very important on a standalone basis.
This approach – i.e., measuring the effect of losing a “factor” – is exactly what variance
decomposition does. Variance decomposition measures the relative importance of each variable
by examining the effect of not having that variable, or factor, as part of the group of variables
that explain the dependent variable. That is, variance decomposition measures the effect of not
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having a factor as part of the “team,” in order to determine the relative share of the dependent
variable (in this case, economic value) that should be attributed to that factor.
By applying variance decomposition to our structural model we are able to estimate each
variable’s relative importance to Tobin’s Q (and therefore for operating profit). While this could
arguably be used to devise a profit split, we propose that the modest objective of providing a
benchmark against which value statements can be assessed is the more appropriate use of this
approach.
This research paper follows a three step research strategy. First, we review economics literature
related to value creation. Second, we develop an econometric model that relates Q (and
therefore operating profit) to a set of “value drivers,” using a set of comparable publicly-traded
services firms.2 Finally, we use variance decomposition to determine high level value shares
among these value drivers.
This paper proceeds as follows. Section II outlines our research strategy. Section III provides a
review of the relevant literatures. Section IV describes our econometric model. Section V then
describes our use of variance decomposition to estimate a high level attribution of shareholder
value and operating profit to the factors identified in Section IV.
2
By “comparable,” we mean that these firms are comparable to one another.
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II.
Decomposing Value: Research Strategy
A.
Overview of Approach
Our research strategy comprises three primary steps. First, we survey the fields of economics
and finance for literatures that examine the determinants of productivity and profitability.
Second, relying on insights from the literatures surveyed, we develop an econometric model
that predicts a firm’s market enterprise value to book asset value ratio (i.e., its capitalized
residual profit) based upon factors that research economists have identified as value-relevant
over time. This ratio is known in economics as the Q ratio, or Tobin’s Q.
Finally, we apply variance decomposition to the econometric model developed in Step 2 to
attribute profit to the value drivers obtained in Step 2.
B.
Tobin’s Q
Later sections of this paper describe the results of our research for each of the three steps
described above. However, before proceeding to these some discussion of our reasons for
focusing on the market-to-book ratio (Q) are in order.
Within the fields of economics and finance, much emphasis is given to a measure called
“Tobin’s Q.” Tobin’s Q was developed by Nobel Laureate James Tobin in 1969, prompted by
the following passage from John Maynard Keynes’ General Theory.
[The] daily revaluations of the Stock Exchange, though they are primarily made to
facilitate transfers of old investments between one individual and another, inevitably
exert a decisive influence on the rate of current investment. For there is no sense in
building up a new enterprise at a cost greater than that at which a similar existing
enterprise can be purchased; whilst there is an inducement to spend on a new project
what may seem an extravagant sum, if it can be floated off on the Stock Exchange at an
immediate profit.3
Recalling that Keynes’ core macroeconomic concern and research focus was investment. That
is, his observation was that investment would often not match savings. Given this, the idea
behind Tobin’s Q is that corporate investment can be predicted with some degree of accuracy
by examining the Q ratio. Whenever Q exceeds 1, the market value of existing assets exceeds
their replacement cost. This should drive new investment, as it is cheaper to create new assets
(including new firms) than to buy existing assets. Correspondingly, whenever Q is below 1, the
opposite pattern holds (i.e., investment should fall or be negligible, as it is cheaper to buy
existing assets through the stock market than to invest anew).
3
The General Theory of Employment, Interest, and Money. John Maynard Keynes, 1936. p. 151.
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In practice, Tobin’s Q is generally measured as the market enterprise value of a firm (its market
capitalization plus debt) divided by its book value of assets. While the measure should,
technically, have the replacement cost of assets in the denominator, in practice, the replacement
cost of a firm’s existing asset base is very difficult to estimate reliably.
For our purposes, Tobin’s Q is of interest for many reasons. These are as follows.
First, Tobin’s Q involves measures of capitalized value, rather than transitory profit measures.
The numerator of Tobin’s Q is the market capitalization of the firm, which is the net present
value of the market’s expectations for all future profits (available to equity holders) into
perpetuity. Thus it is not dependent on profits in the current or recent past period, which could
be influenced by one-time, non-recurring events. This means that econometric measurements
of the correlation between Tobin’s Q and certain causal factors should be more reliable than
econometric measurements that examine the relationship between profitability and causal
factors – because Tobin’s Q influenced by long term systemic considerations regarding the
firm’s prospects, in a way that annual or quarterly profitability data is not.
Second, Tobin’s Q is market-driven. Therefore, it is the result of an information aggregation
mechanism rather than a function of corporate forecasts. The market capitalization represents
the aggregation of the beliefs of thousands of informed market participants about the future
prospects for a firm (including both level and riskiness of profits) rather than any specific
forecasts prepared by the company. Thus Tobin’s Q represents an objective market expectation,
and cannot be manipulated by firm management.
Third, Tobin’s Q is forward looking. That is, Q capitalizes future profits rather than examining
only the current period or past periods. This means that conclusions regarding the relationship
between Tobin’s Q and a given causal factor are applicable to future time periods.
Fourth, Tobin’s Q corresponds closely to residual profit, and to the value of intangible assets.
Because Tobin’s Q values above (or below) 1 are measuring market enterprise values that are
above (or below) the value of a firm’s physical and financial capital, this is capturing the
market’s value of the firm’s intangible assets (i.e., those assets that are not recorded on the
balance sheet). Since physical and financial assets typically earn their required rate of return
(rarely much higher or lower), they will also tend to trade at or near the present value of the
expected profits associated with that required return. Therefore, a Tobin’s Q above 1 implies
that the market expects total profits in excess of the required return to the physical and financial
capital. In other words, the market expects residual profit. And that in turn implies that the
market recognizes that the firm owns intangible assets, even though those assets do not appear
on the balance sheet.
Fifth, for any given period, Tobin’s Q can be converted from a measure based upon stock values
(i.e., observed market values) into a corresponding measure involving operating profit flows.
This means that if we understand the determinants of Tobin’s Q, we also understand the
determinants of a firm’s steady state operating and residual profit flows. To see this, note that
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∞
Q = TEV / Assets. Therefore, Q*Assets = TEV. TEV = ∫0 𝑁𝑂𝑃𝐴𝑇𝑡 × 𝑒 −𝑟𝑡 𝑑𝑡, and NOPAT =
EBIT*(1-t). Therefore,
𝑄∗𝐴𝑠𝑠𝑒𝑡𝑠
(1−𝑡)
∞
= ∫0 𝐸𝐵𝐼𝑇𝑡 × 𝑒 −𝑟𝑡 𝑑𝑡. The right-hand side of this equation is
simply the present value of operating profits into perpetuity.
Finally, Tobin’s Q is heavily relied upon by investors. For example, see Smithers & Co. in the
UK, which discusses Tobin’s Q at length in its discussion of its investment philosophy. Finance
and other research analysts have also written extensively about Tobin’s Q. See for example
Professor Aswath Damodaran’s “Investment valuation: Tools and techniques for determining
the value of any asset valuation”, or Robert Huebscher of Advisor Perspectives, “The Market
Valuation Q-uestion”.
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III.
Review of Relevant Literatures
A.
Introduction
As outlined in the previous section, the first step in our research strategy is to review available
literature related to determinants of profit and productivity. Our goal is to identify economics
and finance literatures that can be used as a guide to the identification of assets, or activities,
that create value for multinational firms in the IT services industry.
We identified two primary categories of research that pertain to value creation. The first is
research related to Tobin’s Q, which was discussed in the prior section, and the second is
research related to what economists call “Total Factor Productivity” (“TFP”).
Productivity, at its most basic level, is the ratio of output per unit of input. From an economic
perspective, productivity measures output per unit of capital and/or labor.
The term “total factor productivity” refers to output (generally the output of a firm) that cannot
be directly attributed to either labor or capital inputs, as traditionally measured. That is, TFP is
the amount of output that results from combining capital and labor inputs. In plain English, it
is the difference between the “whole” and the “sum of the parts.” TFP is thus a “residual,”
sometimes referred to as the “Solow Residual,” that equals the “extra” output produced when a
firm combines labor and capital to create something more than the sum of the labor and capital
“parts.”
Obviously, there is a close correspondence between the idea of total factor productivity and the
concepts of intangible assets and residual profit. It is obvious that the firm’s organizational,
technological, and customer-based, intangible assets are an important reason why the value of
what a firm produces is often greater than the cost of the inputs that it uses. It should also be
obvious that there is a close relationship between the concept of residual profit and residual
productivity (the Solow Residual).
Through our review of the literature pertaining to TFP, we were able to classify the
determinants of residual value and/or Tobin’s Q into the following three broad categories:



Intangible asset investment;
Contributions of MNEs; and
Cost efficiency.
In the following subsections, we summarize several key articles that examine the determinants
of residual value, as measured by estimates of Tobin’s Q and/or TFP.
B.
Intangible Asset Investments
A key focus of the TFP literature has been on the relationship between investments in intangible
assets and productivity. This literature seeks to explain the relationship between productivity
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and investments in intangible capital, which are not directly reflected in traditionally measured
inputs (i.e., physical capital and labor).
A large portion of this literature on intangible assets and productivity has focused on the
relationship between technology intangible capital, as measured by R&D investment, and
productivity. These studies yield valuable insights into the relationship between intangible
capital and productivity, and provide a useful basis of studying the relationship between
intangible capital and Tobin’s Q.
1.
Early Studies of R&D Investments, Productivity, and Market Value
One of the first studies to analyze the relationship between intangible asset investment and
productivity at the firm level was conducted by Griliches and Mairesse (1984). Griliches and
Mairesse develop a sample of R&D-performing manufacturing and non-manufacturing firms
over time, and examine the relationship between a firm’s productivity and its physical capital
stock, labor, and the unobserved R&D capital stock (i.e., its technology intangible capital).
Specifically, this study analyzes the relationship between productivity and R&D investment
across a broad sample of firms that perform R&D. The study also examines this relationship
over varying time periods, and between different types of firms. While scientific firms (i.e.,
firms operating in the chemical, drug, computer, electronics, and instruments industries) exhibit
higher R&D intensity and productivity growth compared to non-scientific firms, the authors
find that the relationship between R&D investment and productivity is relatively consistent
between scientific and non-scientific firms. Moreover, this study highlights the importance of
R&D capital relative to physical capital in explaining a firm’s productivity and residual profit.4
While Griliches and Mairesse (1984) find that there is a positive relationship between R&D
investment and productivity, Cockburn and Griliches further develop the role of knowledgebased intangible capital by examining the relationship between knowledge-based intangible
capital and stock market valuation. Cockburn and Griliches measure the stock market
valuation of knowledge-based intangible capital by examining examine the relationship
between a firm’s knowledge-based intangible capital, as measured by a firm’s capitalized R&D
expenses and stock of existing patents, and stock market valuation, measured using Tobin’s Q.
The authors find a stronger relationship between R&D expenditure and value than between a
company’s stock of patents and its market value. Markets view R&D expenditures as a stronger
measure of a firm’s ability to create technical innovation than patents as an ex post observation
of technical innovation’s output. In other words, markets do tend to attribute value to R&D, the
input a firm uses to produce technical innovation.5 More fundamentally, Cockburn and
Griliches, Z., and Mairesse, J., (1984), Productivity and R&D at the Firm Level. In R&D, Patents, and
Productivity. Ed. Zvi Griliches. University of Chicago Press, 339-374.
5 Cockburn, I., and Griliches, Z., (1988), Industry Effects and Appropriability Measures in the Stock
Markets Valuation of R&D and Patents, American Economic Review, Proceedings Issue, 419-423.
4
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Griliches find that stock market valuations, and Tobin’s Q specifically, do reflect a firm’s
knowledge-based intangible capital.
Griliches and Mairesse (1984) examine the relationship between R&D expenditures and
productivity, while Cockburn and Griliches (1988) seeks to explain the relationship between
R&D expenditure and stock market valuation using Tobin’s Q. Griliches and Mairesse (1984)
find that R&D expenditures are a key determinant of a firm’s productivity, and Cockburn and
Griliches (1988) further demonstrate that there is a positive relationship between R&D and firm
value. Taken together, Griliches and Mairesse (1984) and Cockburn and Griliches (1988)
highlight the relationship between R&D, productivity, and Tobin’s Q, and demonstrate that
R&D expenditures have a positive relationship with a firm’s productivity and its market
valuation. This is not surprising in light of research by Antonelli and Colombelli (2009), which
shows that TFP generally accounts for Tobin’s Q.
2.
Studies of the Rate of Return on R&D Investments
Economists have been measuring the returns to R&D investments for over 50 years. The first
serious empirical investigation of the returns to R&D investments appeared in the Journal of
Political Economy in 1958 (Griliches, 1958), and most of the literature since that time has followed
the same basic analytical framework.
The methods for evaluating the return to R&D investments fall into three categories.



The production function approach. The production function approach begins from the
premise that a firm can be represented as an output function, or production function, F,
of inputs such as capital, K, labor, L, and R&D. The firm’s output is then simply
modeled as F = F(K,L,R&D), where R&D is the accumulated stock of R&D expenditures,
net of depreciation.6 Given a specific functional form,7 publicly available data are used
across large samples of firms and long time series to econometrically examine the
relationship between changes in the value of firms’ outputs and the investments made
in inputs (such as R&D).
The market value approach. This approach is very similar to the production function
approach, except that the relationship modeled is between firms’ market valuations and
K, L, and R&D.
The accounting approach. This approach uses econometric techniques to examine the
relationship between accounting measures of firm performance (e.g., operating margin,
return on equity, return on assets) and R&D expenditure.
It is customary in the economics literature to treat the depreciation rate of R&D as 15 percent per year.
However, Hall (2007) studies this empirically, and finds that depreciation rates are more likely between
20 and 40 percent per year.
7 Usually Cobb-Douglas.
6
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In general, the accounting approach does not measure all of the inputs into a firm’s production
of goods and services, and therefore does not hold constant these inputs when measuring the
standalone contribution of R&D. In part as a result of this, the accounting approach tends to
find a weak correlation between R&D investments and accounting measures of performance.
For example, a recent study by Booz Allen Hamilton found that the correlation between R&D
and return on equity and R&D and operating margin were both near zero, and that the
relationship between margin growth and R&D was only weakly positive.
In general, academic economists tend to shy away from the accounting approach. Instead,
economists have focused their efforts on the production function approach and the market
value approach. These approaches have the distinct advantages of identifying and holding
constant other basic inputs such as capital and labor, and of greater realism.
The production function and market value approaches both yield a rate of return to R&D by
measuring the effect of changes in the values of inputs (capital, labor, and R&D) on changes in
the value of a firm’s output or market-measured enterprise value. The rate of return to R&D is
then simply the change in output or market value divided by the change in the firm’s net R&D
stock.
Importantly, the production function and market approaches attempt to measure the private
returns to R&D, rather than the social returns. R&D creates social benefits that firms
“externalize,” meaning that they are unable to capture the full social value of the technology
that they create. However, R&D investment decisions are made based upon the “private”
benefits of the R&D, meaning the benefits that firms can capture in their profits.
It is clear from the literature that the gap between private and social returns to R&D is large.
Technology spillovers from one firm to another mean that the measured value of a firm’s
output is not only a function of the firm’s investments in its own R&D, but also a function of the
benefits of R&D by other firms that has “leaked” into the public domain.8
Hall (2009) provides a comprehensive survey of the results of both the production function and
market approaches to the measurement of the rate of return to R&D. The exhibit below
summarizes the findings of 51 studies surveyed by Hall.
As we discuss later, this has important implications for the Investor Model framework. Specifically, if a
share of the value of the firm’s output is generated by the R&D of other firms, then “enterprise value”
methods such as the market capitalization method or the income method that value technology by
subtracting tangible assets from either the firm’s total enterprise value (market capitalization method) or
total operating profit (income method) are including, among other things, the value of other firms’
technologies in the valuation of the subject firm’s technology.
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Exhibit III-1
The exhibit also shows a wide dispersion in the measured rate of return to R&D. This is caused
by variation in time periods covered, industries studied, and econometric models used.
However, the exhibit also shows that the vast majority of the studies find that the rate of return
to R&D is between 10 percent and 50 percent – with 34 of the 51 studies surveyed falling within
this range. It is also clear that almost none of the studies found negative or single digit rates of
return, and only 12 of the 51 studies found rates of return above 50 percent.
The median study found that the rate of return to R&D was 29 percent. The average from the
studies surveyed was 31 percent. According to Hall, these represent reasonable estimates of the
rate of return to R&D.
The exhibit below provides a summary of the studies surveyed by Hall, and their rate of return
findings. Summary statistics are given at the bottom of the exhibit, including the interquartile
range.
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Exhibit III-2
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3.
Studies of Marketing and Sales Investments
While the relationship between investments in technology and productivity is well-studied,
technology is not the only type of intangible asset that creates value and leads to higher
productivity. There is also literature, though not as copious, dedicated to examining the
relationship between customer-based intangible assets and Tobin’s Q.
Ayanian (1983) examines the fact that accounting rates of return tend to be positively correlated
with the advertising-to-sales ratio. That is, firms with higher advertising-per-dollar-of-sales
tend to have persistently higher rates of return over time. This fact seems to contradict the
theory of competitive markets, which states that risk adjusted rates of return of competitors
tend to equality over the long term.9
There are two contending hypotheses for explaining this phenomenon. One is that the positive
correlation between advertising and rates of return is due to the fact that high advertising
creates barriers to entry that allow for possibly persistent “above average” rates of return for
firms that advertise heavily. Put differently, potential competitors will avoid entering
industries that require heavy advertising, thus increasing the likelihood that firms already
operating in the industry earn above normal returns.
The second hypothesis is that accounting bias explains the correlation. The argument here is
that if advertising is a long-lived investment, then expensing of advertising spending (and the
concomitant exclusion of the “advertising asset” from the balance sheet) will bias the rate of
return upward by reducing the asset base on which the return is measured. Thus, firms with
high advertising spending have excluded a relatively large asset from the balance sheet, causing
a significant overstatement of the rate of return.
Ayanian tests the second hypothesis by estimating the advertising rate of economic
obsolescence. If it is found that the annual rate of economic obsolescence is at or near 100
percent, then advertising would not be a long-lived investment and the “accounting bias”
argument would not be supported. If, on the other hand, annual advertising rates of economic
obsolescence are found to be well below 100 percent, then advertising would in fact be a longlived investment and the “accounting bias” hypothesis would be supported to some degree.
Support for the theory of competitive markets does not require that rates of return be identical over the
long term. Limitations in the data and the simple fact that random, uncorrelated factors affect returns on
an annual basis will inevitably cause observed rates of return to differ somewhat. Nonetheless,
economists would expect returns to be “close” to each other and to the average of all companies over
time. On the other hand, economists would not expect one firm to have a systematically and persistently
higher return than another competing firm operating in a similar fashion and under similar
circumstances (unless one firm has a monopoly position of some sort). It turns out, however, that the
observed differences in accounting rates of return are so pronounced that economists have sought some
explanation for these differences.
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Ayanian describes the statistical estimation of an equation that relates industry average
accounting rates of return on stockholders equity to industry advertising-to-sales ratios,
measures of capital requirements, and industry growth. The model uses data for the years
1957-59 and it includes observations for 39 industries representing beverages, food, clothing,
appliances, motor vehicles, cosmetics, furniture, and soaps and detergents as well as other
consumer products industries. The results indicate that the average durability of advertising in
the sample is 6.8 years.10
Hirschey (1982) and Hirschey and Weygandt (1985) extend prior research on whether
advertising expenditures should be viewed as a long-lived capital good that depreciates over
time. Hirschey (1982) contains an interesting critique of earlier studies in which economists
have explored the relationship between sales and advertising. First, Hirschey argues that the
emphasis of product sales and product advertising has ignored the potential impact of
“institutional” advertising on overall aggregate demand for firm product. Institutional
advertising works by improving the firm’s image or aids the firm in interactions with the
public. Thus, institutional advertising, while not directed at promoting specific products, will
potentially have a spillover effect according to which better firm image improves all product
sales. Ignoring such advertising, which (it is claimed) earlier studies have done, will tend to
understate the durability of advertising.
Both papers specify virtually the same econometric model, which posits that a firm’s ratio of
market value to replacement cost11 is explained by advertising spending, R&D spending, the
market concentration ratio, growth, and company beta. The model is estimated using 1977 data
for 390 firms taken from the Fortune 500.
The idea behind the econometric procedure is the following. Firm market value represents the
discounted present value of future expected cash flow. If advertising has a strong positive
effect on market value, then it follows that advertising in the current year must be expected (by
the market) to impact future cash flows for some period of time. The larger is the statistical
relationship, the larger is the impact of advertising, and the longer the life of advertising
spending.
The model provides estimates of a highly statistically significant relationship between firm
market value and advertising. The results hold for both durable and nondurable goods as well
as for the sample as a whole.12 Thus, it appears from the results that current year advertising is
Page 358. Ayanian uses data taken from Comanor and Wilson (1974). Comanor and Wilson find that
advertising has no durability whatsoever: annual economic obsolescence is 100 percent. Ayanian
identifies and corrects a misspecification that is found in the Comanor and Wilson paper, however, and
reaches a conclusion quite different from that of Comanor and Wilson. Ayanian contains various
statistical tests that provide compelling support for its conclusions.
11 Replacement cost and book value are virtually identical.
12 Page 331.
10
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perceived as increasing market value, and therefore future cash flow. That is, the market
believes that advertising imparts a durable effect on cash flow.
Thomas (1989) identifies three primary causes of persistently high profits in heavily advertised
consumer goods: 1) durability of advertising, 2) scale economies, and 3) heterogeneity for
advertising. Thomas suggests that studies showing high durability of advertising use statistical
models that are poorly specified in that these models focus only on buyer behavior and ignore
advertiser behavior. The basic idea is that, while advertising induces buyers to purchase
branded product, greater purchases of branded product induce companies to increase
advertising.
Thomas provides evidence that the premium profits earned by advertising companies are due
to the economies of scale in advertising and not to the durability of advertising. As brand
revenue rises, advertising per dollar of revenue required to maintain sales and margins
declines, yielding higher margins and the observed premium profits.
The evidence is embodied in brand advertising to brand sales ratios in the U.S. soft drink and
cigarette industries. Brands with the highest sales tend to exhibit the lowest advertising to sales
ratios. The data on brand advertising comes from Advertising Age, however, and we doubt
that these data capture all the brand advertising performed by the owners of the brands.
Thomas estimates a model using data for 26 cigarette brands covering the years 1955-70, and 15
soft drink brands cover the years 1962-80. The results show that advertising exhibits a high
degree of durability with an annual rate of economic obsolescence of between 2 percent and 10
percent. Once she augments these equations with a set of advertiser response equations, the
rate of economic obsolescence for cigarettes is virtually 100 percent per annum while for soft
drinks the rate of economic obsolescence is over 80 percent per annum.
Landes and Rosenfield (1994) is a direct response to the Hirschey papers. Landes and
Rosenfield are concerned that the advertising rates of economic obsolescence revealed in other
studies are much too low.
The authors provide estimates of the annual rate of economic obsolescence of advertising by
two-digit SIC code.13 The study examines data for 417 companies covering the years 1982 thru
1986. Rates of economic obsolescence for advertising derived from this first model range from
26.5 percent in Motor Vehicles (#37) to zero percent in several industries such as General
Merchandise stores (#53). The industry of interest here is Chemicals and allied products (#28)
with an estimated rate of economic obsolescence of 10 percent.14
Landes and Rosenfield then alter the model to include a firm-specific dummy variable. The
notion that led to the inclusion of the firm-specific dummy is that firms with higher quality
products will advertise more because the productivity of advertising increases with the quality
13
14
Page 268, Table I.
Page270, Table II.
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of the good. If quality could be included as a separate variable, the measured effect of
advertising on sales might decrease. Product quality is not generally observable, but the firm
dummy variable would in theory reflect differences in quality (if there are no other
unobservable differences that affect advertising and sales). The firm-specific dummy variable is
intended to capture unobserved variables (such as quality of product) that may be positively
correlated with both advertising and sales. Because of this positive correlation, Landes and
Rosenfield argue, omission of the dummy variable from the model results in too high an
attribution of observed sales to lagged advertising expenditures.15
Inclusion the dummy variable raises overall rates of economic obsolescence considerably. The
lowest rate of economic obsolescence estimated is now 25.6 percent in the Paper and Allied
Products industry (#26), while certain other industries reveal 100 percent rates of economic
obsolescence: Holding and other Investment Offices (#67). For Chemicals and Allied Products,
the rate of economic obsolescence rises to 55.4 percent.16
Graham and Frankenberger (2000) examine the asset value of advertising for a sample of 320
firms that reported advertising expenditures for each of the 10 consecutive years ending in 1994.
They estimate econometric equations for three different industry groups: consumer products,
industrial products, and sales and services companies. They find that advertising expenditures
are significantly associated with earnings up to five years following the year of the expenditure.
They further find that asset values are longest lived in the consumer and industrial products
industries, and shorter lived in the sales and services industry.
There are several notable aspects of this article. First, they estimate parameters for all consumer
products. Second, they estimate first an earnings equation whereby firm earnings are related to
present and lagged advertising expenditures; and then, using parameters from the earnings
equation, estimate a market value equation whereby firm market value is related to advertising
asset, among other variables. Third, they include R&D in the model, thus controlling for R&D’s
influence on profitability and value.
Their key result is that the advertising asset does not depreciate over the first two years after the
expenditures take place. In years three, four and five, however, the effect of advertising drops
off significantly and then, presumably goes to zero in year six.
One cannot compute a simple parameter for the rate of economic obsolescence from the
Graham and Frankenberger paper nor do the authors attempt to compute rates of economic
obsolescence. The key result from this paper, however, is that advertising has an effect on
earnings for five years from the date of the advertising spending. Thus, a 20 percent straightline economic obsolescence pattern is roughly consistent with these results.
15
16
Page 272.
Page 270, Table II.
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4.
Summary
In summary, research has shown that investments in intangible assets, both technology and
customer-based, have a positive impact on productivity and therefore create intangible value. It
is clear that the research on the impact of R&D is both broader and deeper than the research on
sales and marketing investments. It is also clear that the research on R&D is quite uniform in its
conclusion that research and development expenditures exhibit a high average rate of return,
and are productive of residual value. By contrast, the research on marketing and sales
investments is less broad, and less clear. Marketing and sales investments are generally found
to give rise to a long-lived asset, but this is not a uniform result. Moreover, the rate of return to
marketing and sales investments is in some studies very low (Landes & Rosenfield).
C.
Multinational Enterprises
Given the critical role of globalization in the IT services industry, the growing research into the
value creation of multinational enterprises is of particular interest to us. Specifically, we
reviewed literature that examines the TFP of MNEs as compared to local firms.
As discussed earlier, MNE’s introduce a number of important advantages and efficiencies to a
business, including capital budgeting, improved management and oversight, economies of
scale, cost savings through offshoring, and other organizational intangibles. Research in the
finance and economics literature has shown that these unique capabilities of MNE’s allow them
to increase productivity and create long-term intangible profits.
One important article that examines the link between globalization and productivity is Carrado,
Lengermann, and Slifman (2009), who found that MNEs reportedly have been able to achieve
significant efficiencies by reorganizing the way they conduct their operations. They attempted
to measure the contribution of MNEs to the aggregate productivity record of the United States.
In particular, they examined the contribution of MNEs to overall labor productivity growth in
the United States.
The authors found that, although the MNEs account for only 40 percent of the total output of
nonfinancial corporations (“NFCs”) between 1977 and 2000, these multinational companies
appear to have accounted for more than three-fourths of the increase in NFC labor productivity
over this same period of time. Moreover, MNEs account for all of the NFC sector’s increase in
labor productivity growth in the late 1990s. Therefore, MNEs account for more than half of the
much-studied acceleration in aggregate labor productivity during this time period.17
Carrado, C., Lengermann, P., and Slifman, L., (2009), The Contribution of Multinational Corporations to
U.S. Productivity Growth, 1977-2000, International Trade in Services and Intangibles in the Era of
Globalization, University of Chicago Press, 331-360.
17
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Criscuolo and Martin (2003)18 document a similar “MNE effect” in the U.K. manufacturing
sector, while Griffith, Redding, and Simpson (2004)19 provide evidence of an MNE productivity
advantage in the U.K. service sector.
Much of the literature in this area attempts to explain why MNEs achieve superior productivity.
This work has focused mostly on two main factors—economies of scale and operational
integration across multiple geographies (i.e., economies of scope). Research by Doms and
Jensen (1998) showed that MNEs tend to be larger than local firms, and they are also
characterized as being more capital intensive and using more sophisticated technology. They
found that, all else equal, MNE characteristics such as size, capital intensity, and technology use
lead to higher labor productivity. This increase in productivity is partly explained by the
greater amount of capital per worker, and partly explained by the fact that the size of a firm and
the technology that it employs can help enhance its organizational efficiency.20
Research by Mataloni (2004) showed that MNEs may also be able to enhance their
organizational efficiency by cross-border integration of their operations. Mataloni’s research
indicates that intra-company trade within MNEs has risen steadily. In 2002, these cross-border
trades within companies accounted for 22 percent of total U.S. exports and 16 percent of total
imports. This vertical integration between parents and foreign affiliates is the phenomenon
known as offshoring or outsourcing. Such offshoring allows MNEs to take advantage of
international factor price differentials and keep costs down.21
One final reason that helps explain the higher productivity enjoyed by MNEs is the transfer of
information and knowledge between parents and affiliates that fosters innovation. Research by
Criscuolo, Haskel, and Slaughter (2005) suggests that MNEs generate more ideas than their
purely domestic counterparts. This is due to the fact that MNEs can draw on an “intra-firm
worldwide pool of information” that is typically larger and more diverse than the stock of ideas
generated by most local companies.22 A similar point is made by Coe, Helpman, and
Hoffmaister (1997) regarding the productivity benefits of international trade.23
Criscuolo, C., and Ralf Martin., “Multinationals and U.S. Productivity Leadership: Evidence from Great
Britain,” CEP Discussion Paper no. dp0672, 2005.
19 Griffith, R., Redding, S., and Simpson, H., (2003), Productivity convergence and foreign ownership at
the establishment level, Center for Economic Policy Research Working Paper no. 3765.
20 Doms, M. E., and Jensen, J.B., (1998), Comparing wages, skills, and productivity between domestically
and foreign-owned manufacturing establishments in the United States. In Geography and ownership as bases
for economic accounting,.ed. R. E. Baldwin, R. E. Lipsey, and J. David Richardson, Studies in Income and
Wealth, Volume 59, University of Chicago Press, 235-258.
21 Mataloni, R. J. Jr., (2004), U.S. multinational companies: Operations in 2002, Survey of Current Business,
U.S. Commerce Department: Bureau of Economic Analysis, 10-29.
22 Criscuolo, C., Haskel, J.E., and Slaughter, M.J., (2005), Global engagement and the innovation activities
of firms, NBER Working Paper no. 11479.
23 Coe, D. T., Helpman, E., and Hoffmaister, A.W., (1997), North-South R&D spillovers. Economic Journal,
Royal Economic Society, 134–49.
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MNEs are also able to easily share firm-specific intangible assets (e.g., R&D) across geographical
boundaries. Research published by Lipsey, Blomström, and Ramstetter (1998) demonstrates
this.24 The sharing of intangible assets and operational know-how across borders occurs
prominently, for example, in service and technology industries where factors of production are
easy to transport.
Bloom and Van Reenen (2010) found further evidence that characteristics of MNEs help explain
persistent differences in productivity at both the firm and national level. The focus of their
research was on management practices, and the authors developed a measure of management
effectiveness to test their hypothesis that differences in management practices contribute to
differences in productivity. Their research showed that management practices do have a
significant impact on firm productivity. Furthermore, in all countries, there research showed
that MNEs were better managed than domestic firms. Their findings are summarized in the
exhibit below, which displays average management scores (based on their measure of
management effectiveness) by country for MNEs and domestic firms.25
Lipsey, R. E., Blomström, M., and Ramstetter, E.D., (1998), Internationalized production in world
output. In Geography and ownership as bases for economic accounting, ed. R. E. Baldwin, R. E. Lipsey, and J.
D. Richardson, Studies in Income and Wealth, vol. 59. University of Chicago Press, 83-135.
25 Bloom, N. and Van Reenen, J., (2010), Why Do Management Practices Differ across Firms and
Countries?, Journal of Economic Perspectives, 203-224.
24
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Exhibit VII-4
Management Effectiveness: MNEs vs. Domestic Firms
In summary, there is substantial evidence from research regarding the productivity of MNEs
that suggests that they are more productive than their purely domestic counterparts. Various
factors, including superior management, increased-knowledge sharing, cross-border
integration, and advantages related to economies of scale, may contribute to the increased
productivity observed in MNEs. However, for purposes of our research, what matters is the
fact that, for whatever reason(s), MNEs display higher levels of intangible value. Therefore, our
econometric model should include a variable to capture the value creation attributed to firms
that are part of a MNE.
D.
Cost Efficiency
Other factors beyond intangible capital and a company’s status as an MNE may affect
productivity and ultimately a firm’s market value. In addition to its absolute productivity, a
firm’s productivity relative to competitors in the industry may be expected to drive market
value. For example, a firm may generate a competitive advantage from its ability to control
costs relative to competitors, or otherwise able to generate greater output per unit input than
the average company.
While Cockburn and Griliches (1988) focus on the relationship between Tobin’s Q and
knowledge-based intangible capital, their model has subsequently been extended to include
other factors that may affect Tobin’s Q, such as a firm’s productivity relative to competitors.
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Following the same approach as Cockburn and Griliches (1988), Dwyer (2001) analyzes the role
of relative cost efficiency in productivity and market value. Dwyer (2001) extends the model
developed by Cockburn and Griliches (1988) to treat the productivity of a firm’s manufacturing
plants as an intangible asset. If highly productive plants truly represent a competitive
advantage to the firm, then this competitive advantage should be reflected in higher stock
market valuations. In other words, productivity should have a price, and firms with high
relative productivity should have higher market valuations, as measured by Tobin’s Q.
To examine the relationship between relative productivity and market value, Dwyer (2001)
measures relative productivity as the difference between a firm’s own productivity and the
average productivity in its industry. In effect, relative productivity measures the additional
output that a firm is able to produce relative to the industry average given the same factor
inputs.26 In order to estimate the impact of relative productivity on market value, Dwyer (2001)
employs the same basic model developed by Cockburn and Griliches (1988), and controls for
the impact of other intangible capital created by R&D and advertising on market value. This
study finds that firms with higher relative productivity do have higher market valuations as
measured by Tobin’s Q. Thus, relative productivity does have a price, and acts like an
intangible asset to the firm.
Moreover, Dwyer’s conclusions regarding relative productivity are important for developing an
empirical model of the factors that affect Tobin’s Q for two reasons. Dwyer shows that a firm’s
relative productivity (i.e., low cost of production) creates a comparative advantage and
functions like an intangible asset. However, this value of relative productivity is not reflected
on the company’s balance sheet, and is fundamentally a proxy for the sources of comparative
advantage that are not measured in other forms of physical and intangible capital. Thus, in
order to capture the value of this relative productivity, it is necessary to measure the factors that
determine a company’s comparative advantage. Dwyer’s relative productivity measures the
ability of a manufacturing firm to produce more output for a given level of factor inputs.
Similarly, an IT services company may derive a comparative advantage from the cost
efficiencies that allow it to deliver the same level of service at a lower cost.
Dwyer, D.W., (2001), Plant-level Productivity and the Market Value of a Firm, Census Bureau CES
working paper.
26
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IV.
Econometric Model
The term “econometric model” is used to describe a statistical technique that allows an
economist to measure functional relationships between economic variables. Econometric
models are also referred to, loosely, as “regression models.”27
The reader with a basic understanding of mathematics will recall that a functional relationship
is one in which one variable (usually denoted “Y”) depends upon another (usually denoted
“X”). An econometric model is a statistical method for “finding” functional relationships in
which one variable that is interesting to a researcher (“Y”) depends on other observed variables
(“X1,” “X2,” and so forth).
We begin our summary of our econometric analysis by describing our sampling approach. That
is, we describe how we obtained the statistical sample from which we developed our
econometric model. Next, we describe the “specification” of the model. “Specification” is
nothing more than “specifying,” or choosing, the variables that are of interest to us. Finally, we
describe the results that we obtained.
A.
Sample Strategy
The primary objective of our model is to identify variables that explain enterprise value (and by
extension profitability) in the Japanese IT services industry. We conducted a search in the
CapitalIQTM database for Japanese firms providing IT services. After applying various screens
for industry, geography, financial results, and data availability, we conducted a review of
business descriptions and arrived at a sample of 41 Japanese IT services firms.
We then constructed a “cross-sectional” data set using the five-year weighted average of market
and financial data from 2007 – 2011, for the 41 firms in our sample. While a cross-section of the
data does not control for unobserved year-over-year differences in a firm’s financial
performance, utilizing the five-year weighted average mitigates this concern. In short, we
endeavor to explain the key contributors to average profitability in Japan, not fluctuations
driven by differences in the business cycle.
B.
Model Specification
1.
Dependent Variable
As we have discussed at length, Tobin’s Q is our chosen measure of value, as it corresponds
closely to residual profit, and directly reflects the value of intangible assets. Further, Tobin’s Q
can be converted from a stock measure (i.e., observed market values) into a corresponding
measure involving operating profit flows. This means that if we understand the determinants of
Regression is a more general term than “econometric,” given that regression is used in fields other than
economics, whereas “econometrics” is specific to economics and finance.
27
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Tobin’s Q, we also understand the determinants of operating profit. For the purposes of this
analysis, we defined Tobin’s Q as the five-year weighted average of the ratio of total enterprise
value to the firm’s reported capital (“K”).28
2.
Independent Variables
Based upon our review of the existing literature, we determined that Tobin’s Q is driven largely
by five key independent variables: research and development, intangible assets and goodwill,
marketing and advertising, relative cost efficiency, and the firm’s status as a multinational
enterprise. The subsections that follow expand on each of these variables.
a)
Research and Development
As noted in our literature review, investments in technology, such as R&D, have been tied to
the growth of TFP, and in turn, TFP is positively linked with Tobin’s Q. Thus, R&D expense is
included in our analysis. R&D is measured as the five-year weighted average of the ratio of
R&D expense to capital.
b)
Goodwill and Intangible Assets
Firms generally report the fair market value, net of amortization, of goodwill and intangible
assets for other companies that they have acquired. In general, the category goodwill and
intangible assets is composed primarily of goodwill, which is the excess of the acquisition price
for an acquired company over the fair market value of the acquired company’s assets.
Goodwill is included in our model because, as discussed earlier, goodwill is the value of the
firm (or an acquired company) over and above the value of its identifiable assets. That is,
goodwill and going concern are “assets” that result from management decisions to structure the
firm so that it can exploit the sales channel across many products and/or exploit the
management asset itself across geographies and products. That is, goodwill and going concern
are closely related to economies of scope – i.e., they are the value associated with exploiting
many assets together.
Intangible assets and goodwill are measured as the five-year weighted average of the ratio of
the book value of acquired intangible assets to capital.
c)
Marketing and Advertising
As discussed earlier, marketing and advertising may drive increases in Tobin’s Q through
enhancements to brand-related equity. Marketing and advertising is measured as the ratio of
marketing and advertising expense to capital.
28
Capital is defined as the sum of net PP&E, accounts receivable, and inventory.
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d)
Relative Cost Efficiency
As noted, TFP has a value that is not captured on the balance sheet. The construction of a
relative cost efficiency variable is meant to reflect the intrinsic value of cost-related advantages.
Relative cost efficiency is measured as the five year weighted average of the difference between
the average ratio of cost of service to capital across all 41 firms and the ratio of cost of service to
capital for a given firm.
e)
Multinational Enterprise
Our review of the literature showed a strong positive link between multinational enterprises
and TFP. Most of this increase can be explained by improved management efficiency. A firm’s
status as a multinational enterprise is defined by a dummy variable (i.e., a binary 0-1 variable),
with “1” indicating that the firm is part of a multinational enterprise.
3.
Econometric Model
We posit the following mathematical (i.e., functional) relationship between Tobin’s Q and the
independent variables.
𝑅&𝐷 𝐸𝑥𝑝𝑒𝑛𝑠𝑒
𝐼𝑛𝑡𝑎𝑛𝑔𝑖𝑏𝑙𝑒𝑠 & 𝐺𝑜𝑜𝑑𝑤𝑖𝑙𝑙
(1) ln(𝑇𝑜𝑏𝑖𝑛′ 𝑠 𝑄) = 𝛽0 + 𝛽1 (
) + 𝛽2 (
)
𝐾
𝐾
𝑀&𝐴 𝐸𝑥𝑝𝑒𝑛𝑠𝑒
+ 𝛽3 (
) +𝛽4 (𝑀𝑁𝐸)+𝛽5 (𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝐶𝑜𝑠𝑡 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦)+𝛽6 (𝑀𝑁𝐸 ∗ 𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝐶𝑜𝑠𝑡 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦) + 𝜀
𝐾
Equation (1) represents a “semi-log” model. In a semi-log model, the dependent variable is
measured as a natural logarithm. This form of estimating equation incorporates the economic
concept of diminishing returns. For example, a semi-log model captures the idea that the
marginal benefit to Tobin’s Q from increases in R&D expenditure at high levels of existing R&D
should be less than the marginal benefit at lower levels of R&D. Further, semi-log models allow
the coefficient estimates on the independent variables (i.e., β1, β2, β3, β4, β5, and β6) to be
interpreted as the percentage change in Tobin’s Q given an absolute change in the independent
variable, all else equal.
The “interaction term” (MNE*Relative Cost Efficiency) represents the contribution to Tobin’s Q
through improvements in relative cost efficiency for multinational enterprises. Interaction
terms are used when the change in the dependent variable (i.e., Tobin’s Q) with respect to one
independent variable (i.e., relative cost efficiency) depends on the level of another independent
variable (i.e., status as a multinational enterprise).
C.
Model Results
1.
Descriptive Statistics
Descriptive statistics for each of the variables are presented in the table below.
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Exhibit VIII-1: Descriptive Statistics
Standard
Variable
Tobin's Q
R&D Expense
GW & Intangibles
Marketing & Advertising
Multinational Enterprise
Relative Cost Efficiency
2.
Deviation
Mean
2.5623
0.0491
3.9427
0.1067
0.1848
0.0842
0.451
8.34E-08
0.2414
0.1126
0.5
1.231
Econometric Results
The econometric results from our OLS regression are presented in the exhibit below.
Exhibit VIII-2: OLS Regression Results
Number of observations
41
F-Statistic (6 , 34)
11.77
Adjusted R-squared
0.6176
Variables
R&D Expense
GW & Intangibles*
Marketing & Adv. Expense**
MNEdummy
Relative Cost Efficiency
MNE*Relative Cost Efficiency**
Intercept
** - significant at 99% level
* - significant at 95% level
Coefficient
Standard Error
T-Statistic
Prob. > |T|
1.1170
0.8363
3.5900
0.1009
-0.1324
0.4896
-0.0906
0.8017
0.4010
0.8631
0.2029
0.1051
0.1558
0.1425
1.39
2.09
4.16
0.5
-1.26
3.14
-0.64
17.3%
4.5%
0.0%
62.2%
21.6%
0.3%
52.9%
95% Confidence Interval
-0.5122
0.0212
1.8359
-0.3115
-0.3459
0.1730
-0.3803
2.7463
1.6213
5.3441
0.5133
0.0811
0.8063
0.1990
Several observations regarding our econometric results are in order. First, of the statistics
shown in the preceding exhibit, the adjusted R-squared is perhaps the most important. The
adjusted R-squared represents the percentage of the variation in Tobin’s Q that is explained by
our econometric model – i.e., the percentage of variation that is explained by research and
development, intangible assets and goodwill, marketing and advertising, relative cost
efficiency, the firm’s status as a multinational enterprise, and the relative cost efficiency of a
multinational enterprise. The econometric model clearly has significant amount of explanatory
power, with an adjusted R-squared of 61 percent.
Another key summary statistic to examine is the F-statistic. The F-statistic represents a
hypothesis test of overall significance of the econometric model. Essentially, the F-statistic is a
test to verify that the entire set of estimated coefficients in each model – or said differently, the
model as a whole – is statistically significant. If an econometric model “fails the F-test,” the
model is indistinguishable from randomness, and is therefore a meaningless model. In our
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model, the F-statistic is quite high, and we can say with 99 percent confidence that our model is
in fact meaningful.
Recall that coefficient estimates in a semi-log model are interpreted as percentage change. For
example, a one unit increase in the five-year weighted average ratio of goodwill and intangibles
to capital would result in an 83.6% increase in Tobin’s Q. A similar chain of logic suggests a 112
percent increase in Tobin’s Q for a one unit increase in R&D, and similarly 359 percent for
marketing and advertising expense. While these coefficients may seem alarmingly high, one
must remember that this is reflects a one unit change in the ratio of these variables to capital.
Increases in these items are likely to increase the ratio to capital by a factor closer to 0.1, rather
than 1.
A key point of focus here is the statistical significance of the coefficient estimates. There were
three highly significant variables in the model: marketing and advertising, the relative cost
efficiency of a multinational enterprise, and goodwill and intangibles. Marketing and
advertising and the relative cost efficiency of a multinational enterprise are significant with 99
percent confidence, and the coefficient on goodwill and intangibles is significant with 95
percent confidence. R&D and relative cost efficiency are significant at the 80 percent and 75
percent confidence levels, respectively.
The statistical significance of the interaction term and relative statistical insignificance of the
individual components of the interaction term may seem odd at first glance.29 However, this
result suggests that the market only rewards multinationals for cost efficiency. Said differently,
the gains of improving relative cost efficiency are only realized when the firm is part of a
multinational enterprise, and the gains of being part of a multinational enterprise are not
realized unless there are improvements in relative cost efficiency.
3.
Conclusion
It is important that the reader keep in mind that econometric models are by their nature
imperfect. They make strong demands of often limited data. It is often difficult for the
researcher to obtain both economic and statistical significance across the board.
In light of these facts, our statistically significant coefficient estimates all produced signs
supported by the literature. Furthermore, the econometric model presented in this section can
be characterized as both reliable and robust, given the strong adjusted R-squared and large Fstatistic for a relatively small sample size.
Recall that the interaction term is the product of relative cost efficiency and the firm’s status as a
multinational enterprise (a binary 0-1 variable).
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V.
Attribution of Profits within a Firm
A.
Overview of Statistical Attribution
Without further modification, an econometric model such as the one described in the previous
section does not give us a model that attributes profitability or value to specific factors and legal
entities within a firm.
The reason for this is that the coefficients of the model – that is, the β1, β2, β3, β4, β5, and β6
variables – are essentially the same thing as slopes. That is, each of these coefficients measures
the rate of change in the dependent variable (Tobin’s Q), per unit change in the independent
variable to which the coefficient is attached. While this information is important, it only tells us
how important one independent variable is given that the other variables are also contributing
to the dependent variable. Said differently, each of the coefficients is a measure of the impact of
one of the independent variables working in conjunction with all of the other variables.
Thus, the econometric model does not provide us with much information about the standalone
importance of each variable. In order to develop this information from an econometric model,
we need something called “variance decomposition.”
The term “variance decomposition” is a fancy name for “attribution.” Variance decomposition
is a way that economists and statisticians use to attribute explanatory power, or causality, to
individual variables. Variance decomposition gives us what we are looking for in this case – the
standalone influence of each variable in an econometric model.
As an example, consider an American football team. There are many contributors to the points
scored during a game or over a season. Across the National Football League, it will almost
always be the case that a team’s kicker will lead the team in total scoring. For example, if a team
scores 23 points in a game, the kicker will have scored 11 out of the 23 (three field goals and two
extra points, or nearly half of the team’s points).
If we measure a kicker’s relative contribution to the team’s success by looking only at this
statistic, we will grossly overstate the importance of the kicker. Even a mediocre kicker scores a
large number of points during a season. Losing a kicker is not a serious problem for most
teams. In other words, by imagining what would happen to the team’s success if the kicker left
the team, we see that the kicker is in fact not very important on a standalone basis. A kicker can
easily be replaced.
Continuing with the example, left tackles score very few points. Left tackles never lead their
team in total scoring, and often go their entire careers without scoring any points. Thus, if we
measure the left tackle’s relative contribution to the team’s success by looking only at this
statistic, we will grossly understate his importance. But losing a left tackle is always a serious
problem for a football team. It also explains why left tackles are often taken in the first round of
the NFL draft and are paid tens of millions of dollars, while kickers often go undrafted and
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many are paid near the league minimum. In other words, by imagining what would happen to
the team’s success if the left tackle were injured or replaced, we see that the left tackle is in fact
very important on a standalone basis.
This approach – i.e., measuring the effect of losing a “factor” – is exactly what variance
decomposition does. Variance decomposition measures the “standalone” importance of each
variable by examining the effect of not having that variable, or factor, as part of the group of
variables that explain the dependent variable. That is, variance decomposition measures the
effect of not having a factor as part of the “team,” in order to determine the amount of the
dependent variable that should be attributed to that factor.
B.
General Procedure: Two Steps
The attribution of profits to specific entities within a firm follows two steps. First, we attribute
Tobin’s Q, and thus residual profits, to specific variables, or factors. Second, we attribute those
factors to specific entities.
C.
Attribution of Profits to Variables
The attribution technique that we use is called “proportional marginal variance decomposition”
(“PMVD”).30 When the dependent variable (i.e., the variable being explained by the regression)
is residual profit (or, more precisely, Tobin’s Q exceeds 1 when capitalized residual profit is
positive), then the PMVD analysis can be interpreted as producing a predicted residual profit
split.
There are two reasons for this. First, PMVD provides each variable’s average contribution to
the model – or how much of what the model explains (in total) is in turn explained by each
variable (separately). As we alluded to earlier, PMVD does this by examining the change in the
explanatory power of our econometric model (our equation) when we eliminate each of the
independent variables, one at a time.
The second reason why the PMVD procedure can be interpreted as a profit split is that it is
exactly the procedure developed by the Nobel Prize winning economist and game theorist
Lloyd Shapley for determining how gains should be split in a cooperative “game” or
cooperative context. Lloyd Shapley developed a solution to the question of how a set of
participants that contribute to a jointly-created surplus (e.g., in a cooperative game setting)
would split the resulting surplus. The Shapley value is a measure of an individual participant’s
marginal contribution to the surplus created by a group (of which the participant is a member).
This value, compared to the Shapley value for all other participants, is also the predicted share
See for example Feldman, Barry E., “A Theory of Attribution” (May 29, 2007). Available at SSRN:
http://ssrn.com/abstract=988860 or http://dx.doi.org/10.2139/ssrn.988860; “Relative importance and
value”, Barry Feldman (working paper, 2005); “Estimators of Relative Importance in Linear Regression
Based on Variance Decomposition”, Ulrike Gromping, The American Statistician, May 2007, Vol. 61, No.
2.
30
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of the surplus that will be captured by each participant. In the same way, the PMVD value for
the regression described in the previous section is the marginal contribution of each variable to
the creation of operating profit. As such, it is also the share of operating profit that would be
expected at arm’s length in a profit split when multiple parties are contributing to the total
operating profit in the system.
One thing that is important to consider is the fact that the sum of the PMVD values for each of
the independent variables in our econometric model equals the R2 of the regression. Recall from
Section IV of this paper that the R2 statistic is always less than one, and represents the
proportion of the variation in the observed values for the dependent variable that is explained
by the independent variables, taken together.
This means that the PMVD values (i.e., how much of the total explanatory power of the model
to attribute to each “factor”) sum to the total explanatory power of the regression model. This is
very similar to the way that so-called “Shapley values” sum to the total surplus created by the
group in a cooperative game.
However, since R2 is a measure of the total explanatory power of the model, and the model does
not explain all of the variation in the dependent variable, application of the PMVD procedure
leaves open the question of the remaining, or unexplained portion of the residual profit. There
are two possibilities we consider with respect to this unexplained portion.
First, the unexplained portion may be due to factors that were not considered, or that cannot be
measured with precision. In these cases, assuming that the relative contributions of the entities
over the explained portion would apply in the same proportion to the unexplained portion is an
unbiased estimate of the total contribution to operating profit for each entity.
Second, it is possible that the unexplained portion is due to factors outside of the entities
considered – such as exogenous forces (e.g., economic conditions in the market) that neither
party could claim at arm’s length.
For purposes of this analysis, we assume that one entity’s contribution to (and thus claim over)
residual profit for the explained portion would apply equally to the unexplained portion,
acknowledging that we are potentially overstating the compensation that the entity could
expect to earn at arm’s length (i.e., bargaining on a standalone basis).
D.
Example of Attribution of Variables to Entities
Here, as an example, we will use the regression model to allocate profits among two entities
within a hypothetical firm – Company A’s IP owner (“IPCo”) and an operating affiliate
provider of IT services in Japan (“OpCo”). The regression model outlined in the prior section
attributes residual profit to six factors. We now must attribute these six factors to the entity that
would claim that factor (and therefore its share of residual profit) at arm’s length. Each of these
factors, and the entity that owns or controls the factor, are described below.
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1.
Factors Attributable to IPCo
Four of the six factors are attributable to IPCo. These are:




R&D – IPCo manages, controls, and develops the technology and know-how.
MNE – IPCo provides access to the global intellectual property, and multi-national
structure, which provides for efficiencies of scope and scale.
MNE*Relative Cost Efficiency – Similar to above, this variable measures how the MNE
status impacts cost efficiency
Goodwill & Intangibles – Intangible assets are developed and maintained by IPCo,
which provide access to and facilitate efficient sharing among individual entities.
2.
Factors Attributable to OpCo
Two of the six factors are attributable to the OpCo in Japan. These are:


Marketing and Advertising – OpCo provides marketing and advertising in the local
market.
Relative Cost Efficiency – Attributed to OpCo to explain the non-MNE (local) portion of
the measure of cost efficiency.
3.
Unattributed Factors
As noted, the PMVD analysis attributes proportional contributions to the factors included in a
model up to the total variance explained by the model. The remaining, unexplained portion is
initially unattributed to either IPCo or OpCo. For purposes of this analysis, we assume that the
relative contributions of IPCo and OpCo apply proportionally to the unexplained portion of the
variance.
However, the unexplained portion is arguably the result of factors actually owned and
controlled outside of IPCo and OpCo. Given that the unexplained portion is at the very least
exogenous to the model as specified, an alternative approach would be to assume that the claim
over residual profit for IPCo and OpCo is limited to the share of the residual profit actually
explained by factors owned and controlled by IPCo and OpCo. This would be the preferred
approach if there were other claimants to the residual profit in the system, such as a parent
company or another entity within the firm that owned additional IP. In our hypothetical model,
there are only two entities, and for purposes of our example we simply allocate all residual
profit to these two entities.
E.
Summary of Results
In summary, the six factors, their relative importance, and their attribution to either IPCo or
OpCo is shown below.
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Exhibit IX-1: PMVD Results
Variable
R&D Expense
Goodwill & Intangibles
Marketing & Advertising Expense
MNE Dummy
Relative Cost Efficiency
MNE*Relative Cost Efficiency
Contribution to Residual Profit Attribute to IPCo? Attribute to OpCo?
4%
20%
42%
10%
3%
21%
Implied Residual Profit Split:
Yes
Yes
No
Yes
No
Yes
No
No
Yes
No
Yes
No
55%
45%
As can be seen, the four factors attributable to IPCo have a combined contribution of 55 percent,
while the two factors attributable to OpCo have a combined contribution of 45 percent. This
analysis therefore suggests that, at arm’s length, OpCo would capture approximately 45 percent
of the system residual profit, while IPCo would capture approximately 55 percent.
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VI.
Conclusions Reached
Recognizing the importance of understanding the primary sources of value creation within an
enterprise when evaluating transfer pricing policies, the purpose of this paper was to provide a
framework for identifying and evaluating the primary determinants of operating and residual
profit within a firm. To that end, we developed a research strategy and completed the
following analyses.



We performed an extensive review of literature related to the determinants of
productivity and profitability.
We developed an econometric model that predicts a firm’s Q ratio (i.e., its capitalized
residual profit) based upon factors that economists have identified as value-relevant
over time.
We used the econometric model to attribute profit to within a hypothetical firm to
particular legal entities within that firm. Specifically, we converted the econometric
model to a profit attribution model to allocate profit between a hypothetical firm in the
IT services industry with a US IP owner and a local Japanese affiliate.
For our hypothetical firm, the results of our analysis indicated the IP Owner would capture
approximately 55 percent of the residual profit in the system, and the local affiliate would
capture approximately 45 percent of the residual profit, assuming no other residual profit
claimants exist in our simple example. While the analysis presented here was for an example
MNE in the IT services industry with just two entities, the approach outlined in this paper can
be applied to other industries and to other MNE structures.
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