ABACUS, Vol. 45, No. 3, 2009 doi: 10.1111/j.1467-6281.2009.00289.

BARUCH LEV, SURESH RADHAKRISHNAN AND WEINING ZHANG

Organization Capital abac_289 275..298

We develop a firm-specific measure of the most important intangible

asset—organization capital—and document that organization capital is
associated with five years of future operating and stock return perfor-
mance, after controlling for other factors. Thus, our organization capital
measure captures firms’ fundamental ability to generate abnormal perfor-
mance. We also find that executive compensation is positively associated
with our measure of organization capital, showing that the measure indeed
reflects managerial ability.
Key words: Abnormal performance; Executive compensation; Measure;
Organization capital.

Intangible assets—sources of future benefits that lack physical embodiment—are

the hallmark of modern economies and business enterprises. Various estimates
indicate the significance of such assets in the modern economy. For example, Naka-
mura (2000) estimates the value of U.S. corporate investment in intangibles during
2000 to be around $1.0 trillion, making it roughly equal to the total investment of the
non-financial sector in property, plant and equipment. Hall (2000) estimates the total
value of intangible capital as ranging between half to two-thirds of the total market
value of publicly traded corporations, as indicated by the q ratio (market value to
replacement cost of physical assets). Nakamura (1999, 2000) argues that the major
growth in value and impact of intangible capital started roughly in the mid-80s, with
the emergence of major ‘intangible industries’ (software, biotech, internet, etc.) and
continues unabated, though with considerable fluctuations, to the present. Gu and
Lev (2001) show that firm-specific estimates of intangible capital improve signifi-
cantly the association between capital market values and accounting-based mea-
sures of performance and value (e.g., earnings or book values). More recently,
McGrattan and Prescott (2007) emphasize the importance of considering intangible
investments in explaining the real economic growth in the 1990s.1 Overall, it is widely
accepted that intangible assets are the major drivers of national as well as corporate
1
McGrattan and Prescott (2007) find abnormally high investment at the macro level, even when
corporate profits were falling as output was rising. They interpret this as suggesting that investment in
tangible capital was abnormally high. They also find that compensation per hour was particularly low
in the 1990s, suggesting ‘sweat equity’. Sweat equity is defined as ‘investment financed by the worker-
owners who allocate effort and time to their business and receive compensation at less than their
market rate’.

Baruch Lev (blev@stern.nyu.edu) is Philip Bardes Professor of Accounting and Finance, Stern School of
Business, New York University, Suresh Radhakrishnan Professor of Accounting and Information Man-
agement, School of Management, University of Texas at Dallas, and Weining Zhang a PhD candidate,
Accounting and Information Management, School of Management, University of Texas at Dallas.

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value and growth, as most forms of physical and financial assets are essentially
commodities, yielding on average the cost of capital.
A framework developed by Lev (2001) for intangible capital classifies intangible
assets into the following four groups.
1. Discovery/learning intangibles—technology, know-how, patents and other assets
emanating from the discovery (R&D) and learning (e.g., reverse engineering)
processes of business enterprises, universities and national laboratories.
2. Customer-related intangibles—brands, trademarks and unique distribution chan-
nels (e.g., internet-based sales), which create abnormal (above cost of capital)
earnings.
3. Human-resource intangibles—specific human resource practices such as training
and compensation systems, which enhance employee productivity and reduce
turnover.
4. Organization capital—unique structural and organizational designs and business
processes generating sustainable competitive advantages.
While economic and business research as well as popular managerial writings on
the first three classes of intangibles (discovery, customers and human resources) are
voluminous, systematic research and knowledge about organization capital is in its
infancy.
It is widely observed that within industries some companies systematically out-
perform their competitors and maintain their leading position for long periods of
time, despite significant economic changes: Wal-Mart in retail, Microsoft in software,
Southwest among airlines, DuPont in chemicals, Exxon in oil and gas, Intel in
microprocessors, and the list goes on. Such superior performance in terms of growth
in sales, earnings and stock returns cannot be attributed to monopoly power or
government subsidies because these firms operate in a competitive environment.
How can such enterprises achieve/maintain their superior performance and leading
role? We argue that such enterprises have a stealth asset: organization capital—the
agglomeration of business processes and systems, as well as a unique corporate
culture, that enables them to convert factors of production into output more effi-
ciently than competitors. Anecdotal examples of such business processes and
systems abound: Wal-Mart’s supply chain, where the reading of barcodes of pur-
chased products at the checkout register is directly transmitted to suppliers who are
in turn responsible for inventory management and product provision to Wal-Mart
stores; Cisco’s internet-based product installation and maintenance system, esti-
mated by Cisco’s CFO to have saved $1.5 billion over three years (Economist, 26
June 1999); Zara, a successful clothes and accessories retailer, which transmits in real
time customers’ choices to its suppliers worldwide. This agglomeration of business
processes and systems that cannot be easily mimicked by competitors is the intan-
gible asset, organization capital.
Even though terms like ‘firm’s reputation’, ‘value of leadership’, ‘capacity to
innovate’, etc. that capture certain elements of organization capital abound in the
management literature, little in terms of operating measures of organization capital
and empirical evidence is available. In essence, there is sparse systematic evidence

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on the magnitude and impact of organization capital, in contrast to the substantial

evidence on other intangibles, such as R&D, patents or brands.2 Lev and
Radhakrishnan (2005) develop a methodology to estimate organization capital and
provide preliminary evidence on the role of this asset in market values. This measure
is based on the contribution of organization capital to revenue growth.
In this paper, we extend the methodology developed by Lev and Radhakrishnan
(2005) to estimate firm-specific organization capital, by incorporating a firms’ poten-
tial to save operating costs as well. Thus, the new measure of organization capital
incorporates a firm’s potential to generate super-normal revenues as well as cost
savings. We validate the measure by examining its association with future firm
performance measures, such as operating income growth, sales growth and abnormal
returns. We find that the organization capital measure is associated with five years of
future operating and stock return performance, after controlling for other factors.
Thus, the organization capital measure indeed captures the notion of a firm’s ability
to maintain its leadership position.
We also examine the association of the organization capital with executive com-
pensation. Successful organization capital is in fact a reflection of managerial ability,
since managers are responsible for the creation and maintenance of this asset.
Executive compensation is an incentive mechanism as well as a measure of the
executive’s ability that should manifest in business processes and systems, that is, the
way of doing business. We find that executive compensation is positively associated
with our measure of organization capital. This is important because it suggests that
organization capital proxies for the elusive concept of ‘managerial quality’. Thus, the
compensation committee of the board can use the organization capital measure as
an aggregate proxy for managerial ability and quality for compensation purposes.

BACKGROUND: WHAT IS ORGANIZATION CAPITAL?

Evenson and Westphal (1995, p. 2337) provide a succinct definition of organization

capital: ‘organization capital . . . [is] the knowledge used to combine human skills
and physical capital into systems for producing and delivering want-satisfying pro-
ducts’. Accordingly, organization capital relates to firms’ underlying systems and
processes of operating, investment and innovation capabilities that enable them to
generate outputs. While all enterprises require a certain organization capital, to
convert resources into outputs we use the term ‘organization capital’ to refer to the
ability of firms to deliver and sustain super-normal performance. That is, organiza-
tion capital enables superior operating, investment and innovation performance,
represented by the agglomeration of technologies—business practices, processes
and designs.
An important feature of organization capital as an enabler of super-normal per-
formance is its tacit nature. In essence, for organization capital to provide firms with

2
Exceptions are Brynjolfsson and Yang (1999) estimating the impact of information technology invest-
ment on market value of companies, and ascribing the large estimated multiple (roughly 10:1) to the
organization capital enabled by information technology, and Hall (2000).

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a competitive edge, the capabilities encompassed in business processes cannot be

completely codified and transferred to other organizations or imitated by them.
Evenson and Westphal (1995, p. 2213) emphasize this feature: ‘Much of the knowl-
edge about how to perform elementary processes and about how to combine them
in efficient systems is tacit, not physically embodied and neither codified nor readily
transferable’.The retail industry demonstrates the difficulties in imitating and adopt-
ing others’ organization capital. With all that has been written about Wal-Mart’s
business processes, such as vendor-managed inventory and supply chains, electronic
data exchange systems, K-Mart was largely unsuccessful in imitating these business
processes. Wal-Mart continues to be the leader in efficient supply chain manage-
ment, as is Apple in innovation and Exxon in exploration. Overall, the knowledge or
information underlying the business operations is not easily transferable across
companies, even over extended time periods. This feature of organization capital is
referred to in the management literature as management culture and capability and
thus is recognized as an important intangible asset.
The tacit and idiosyncratic nature of organization capital makes it particularly
hard to measure. Different enterprises can have different areas of superior capabili-
ties. For instance, some enterprises have superior business processes and culture
enhancing the innovation capabilities, while others may have superior processes to
tap into the human capabilities. Loosely speaking, there are different roads that lead
to a sustained superior performance, and a direct measure of organization capital is
difficult to obtain.
Adding to this fundamental difficulty of measuring organization capital—its tacit
nature—is the fact that the investment (input) in organization capital is not fully
tracked by firms (e.g., the cost of on-the-job training) nor reported publicly. Even in
the realm of innovation expenditures, such as research and development, firms at
most provide the total expenditures, but no information is available on the nature of
research and development activities. Therefore, a direct measure of organization
capital based on inputs is not feasible. As such, our measure of organization capital
provides a relative measure of firms’ capabilities.
We proceed to develop the measure of organization capital.

FIRM-SPECIFIC ESTIMATE OF ORGANIZATION CAPITAL

All companies use similar resources (inputs) to generate revenues: physical capital,
labour and periodic expenditures for generating and maintaining organization
capital. While all companies use similar resources, there are significant differences
across companies in the efficiency of use, or the contribution of the resources to
revenues. For example, while companies A and B use employees, Company A’s
employees may generate more revenue than B’s because they are better trained and
compensated. Similarly, while both A and B have physical capital, Company A may
generate more revenue per unit of physical capital than B because A uses more
sophisticated IT and optimization models. In short, there are many reasons why
companies differ in the efficiency of resource usage, but most of these reasons
(better IT, higher quality employees, improved incentive and compensation systems)

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are related to the organization capital. Accordingly, the way to measure organization
capital is to compare across companies the efficiency of using the resources in
generating revenues as well as in cost containment. The procedure is described
below.

Estimating Organization Capital

We model the firm’s output—sales (SALE)—as a function of physical capital (PPE:
property, plant, and equipment) and labour (EMP). We use for estimation the
following constant returns to scale production function (see Hall, 2000):
SALEit = a0 it PPEit b1it EMPit b 2 it eit , (1)
where SALEit is the revenues of firm i in year t, PPEit is net plant, property and
equipment, EMPit is number employees, and eit is an error term. The constant, a0it is
the productivity parameter, which we model as a function of the instrumental
variable, SGAit as follows:
log ( a0 it ) = b0 t + b0 st log ( SGA it ) , (2)
where SGAit is the sales, general, and administrative capital, computed by capital-
izing and amortizing the annual SG&A expense over three years. The firm’s SG&A
expenses include outlays related to the building of organization capital, such as
information systems, employee training, research and development, consultants’
fees, and brand promotion. We allow for two types of contributions of organization
capital to revenue: (a) the contribution common to all firms (b0t), and consequently
is available to all firms (e.g., a certain level of employee education, the prevailing
legal and institutional setting, etc.), and (b) a firm-specific contribution of organiza-
tion capital to revenue (b0stlog(SGAit/SGAi,t-1)) which is developed and enhanced by
each firm (e.g., coded knowledge, production blueprints, business processes and
procedures, marketing networks and channels, etc.). That is, the total factor produc-
tivity, a0it, is firm-specific and influenced by SGAit (as in equation (2)).
We estimate equation (1) by taking logarithms of annual changes, after substitut-
ing equation (2) into (1):
log ( SALEit SALEit − 1 ) = b0 t + b0 st log ( SGA it SGA it − 1 ) + b1t log ( PPEit PPEit − 1 )
+ b2 t log ( EMPit EMPit − 1 ) + log ( eit eit − 1 ) . (3)
Equation (3) is estimated annually and cross-sectionally for each industry. The
coefficient estimates indicate the average contributions of organization capital to
revenue growth. In other words, these coefficient estimates provide us with the
industry average efficiency with which companies use the resources to generate
revenues.
We then use the coefficient estimates from equation (3) to obtain a firm-specific
monetary measure of the contribution of organization capital to revenues. For each
company in a given year, equation (3) with estimated coefficients allows us to predict
the revenues under the average efficiency assumption without organization capital.
We then subtract the predicted firm’s revenues without firm-specific organization
capital from the firm’s actual revenues to get AbSALEit, which is the contribution of
organization capital to revenue of firm i in year t.

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Similar to the above computation of the contribution of organization capital to

revenues, we compute the contribution of organization capital to cost containment,
and measure the latter by first predicting the costs based on the industry average cost
of the resources utilized to achieve the sales. Then, the difference between the
predicted cost and the actual cost, AbCOSTit, is the contribution of organization
capital to cost containment.
The contribution of organization capital to operating profits is thus given by
AbProfitit = AbSALEit + AbCOSTit. The contribution of organization capital to
revenues (AbSALEit) and to cost containment (AbCOSTit) yields the firm a com-
parative advantage over a period of time. In other words, organization capital is not
fickle in the sense that it contributes to AbSALEit and AbCOSTit in one year but not
for the next. Viewed differently, AbSALEit and AbCOSTit are not due to a company
being lucky or unlucky in a particular year. To account for these aspects, we capi-
talize and amortize AbProfitit over five years to get our estimate of organization
capital from the annual contributions. We scale the capitalized contributions of
organization capital by the total assets in year t, which is referred as OCit (organi-
zation capital).
Equations (3) and its cost counterpart are estimated each year for each of the
twelve industry categories specified by Fama–French.The sample consists of all firms
with sales and total assets greater than $5 million from 1971 to 2006. Data are
obtained from the Compustat Annual Database. For stock returns tests, we obtain
data from CRSP. The sample consists of 147,524 firm-year observations. The com-
position of sample by the twelve industry groups is provided in Panel A of Table 1.
Panel B of Table 1 provides the descriptive statistics of the variables used to
estimate equation (3). The mean (median) SALEit and COSTit are $1,542 ($150) and
$1,281 ($127) million, respectively, indicating that the sample consists of many small
firms and the empirical distribution is skewed to the right. Correspondingly, the
resources also exhibit this skewness: the mean (median) PPEit and SGAit are $659
($32) and $414 ($35) million, respectively; and the mean (median) EMPit is 8.82
(1.38) thousand. The mean growth in SALEit is roughly 11.64 per cent [=exp(0.1101)
- 1] and COST is 11.98 per cent. It is interesting to note that on average the SALEit
and COSTit growth are similar. However, in the left tail of the distribution the sale
growth is much lower than the cost growth, and in the right tail the sale growth is
higher than the cost growth. On average, the growth in resources, that is, capital and
labour, is lower than of sale growth and cost growth, while the growth in the
capitalized selling, general and administrative expenses is much higher than the sale
growth and cost growth, because selling, general and administrative expenses con-
tains resources that influence organization capital. This likely indicates the impor-
tance of the role of intangibles.

The Dell Example

Since the contribution of organization capital is derived from the firm’s perfor-
mance, the question arises whether the organization capital measure adds useful
information beyond that in typical financial data such as sales and net income. This
question is addressed comprehensively in the next section, but the following case of

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Table 1

MEASURE OF ORGANIZATION CAPITAL

Panel A: Industry distribution

Industry name Total number of observations

Consumer nondurables 11,697

Consumer durables 5,079
Manufacturing 22,487
Oil, gas, and coal extraction and products 5,852
Chemicals and allied products 3,910
Business equipment 23,197
Telephone and television transmission 3,652
Utilities 7,469
Wholesale, retail, and some services 18,076
Healthcare, medical equipment, and drugs 9,179
Finance 17,701
Other 19,225
Total 147,524

Panel B: Descriptive statistics of variables used to estimate organization capital

M SD Median Q1 Q3

SALEit ($ millions) 1542.27 7289.60 150.25 43.10 632.71

COSTit ($ millions) 1281.96 6209.79 127.58 37.93 525.94
EMPit (hundreds) 8.82 32.55 1.38 0.37 5.05
PPEit ($ millions) 659.59 3299.62 32.62 7.42 188.84
SGAit ($ millions) 414.57 2140.60 35.25 8.64 143.74
Log (SALEit/SALEit-1) 0.1101 0.2459 0.0994 0.0017 0.2103
Log (COSTit/COSTit-1) 0.1132 0.2396 0.1006 0.0052 0.2110
Log (EMPit/EMPit-1) 0.0505 0.2554 0.0278 -0.0422 0.1289
Log (PPEit/PPEit-1) 0.0996 0.2884 0.0632 -0.0309 0.1995
Log (SGAit/SGAit-1) 0.1558 0.2581 0.1131 0.0328 0.2302

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Table 1

CONTINUED

Panel C: Descriptive statistics of organization capital and firm performance

Variablea M SD Median Q1 Q3

Organizational capital variables

OCit 0.1439 0.2471 0.1118 0.0201 0.2382
OCit ($ millions) 246.63 1370.35 42.55 5.69 179.95
Operating performance and market performance
OIGrowthit+1 0.0217 0.0548 0.0149 -0.0035 0.0421
SALEGrowthit+1 0.1368 0.2219 0.0992 0.0175 0.2066
ABRETit 0.0306 0.3929 -0.0288 -0.2002 0.1764
RETit 0.1951 0.4726 0.1267 -0.0889 0.3774
Firm characteristics
SIZEit 6.0014 1.8647 5.9412 4.6561 7.2302
DIVit 0.0148 0.0179 0.0092 0.0000 0.0244
RDCAPit 0.1087 0.1537 0.0689 0.0307 0.1343
EPit 0.0769 0.0579 0.0674 0.0410 0.1014
D_EPit 0.0755 0.2642 0.0000 0.0000 0.0000
BMit 0.6973 0.4917 0.5922 0.3681 0.8888
BETAit 1.0543 0.5130 1.0611 0.7269 1.3108
VOLit 0.0151 0.0216 0.0117 0.0065 0.0182
a
Variable definitions are provided in the Appendix.

Dell Corp., which fell on hard times in 2005, provides a rough idea. Figure 1 presents
the sales, net income, organization capital and stock price data for Dell from 2001 to
2007. We scale all these variables by their respective levels as of 2001. Thus, stock
price in July of 2005 is the stock price of July divided by the stock price as of January
of 2001. It follows that all variables begin with a value of 1, so as to highlight the
relative trends of these variables over the period. As shown in Figure 1, sales, net
income and stock prices exhibit similar trends from 2002 to 2005. In fact they track
each other quite consistently, even though the stock price is a forward looking
measure while net income and sales are backward looking numbers. Nevertheless
the trends exhibited by the accounting-based and stock-based performance mea-
sures are similar. Starting in 2005, the stock price starts to decline precipitously up
until the beginning of 2006, due to serious concerns over the continued growth of
Dell, its governance practices and accounting irregularities.
Interestingly, the organization capital measure shows a different trend. Starting
from 2001, the organization capital measure drops, up until 2004, and flattens out for
2005. This is starkly different from the backward looking sales and net income
variables, which exhibit an increasing trend throughout the period. This drop in
organization capital corresponds to the coming to light of the slowing growth and

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Figure 1

EXAMPLE OF DELL

Note: All variables indexed to be equal to one on 1/2/2001.

the consequent accounting irregularities in 2005/2006.3 That is, the ‘real’ perfor-
mance of Dell was not as good as the accounting numbers reflected; when this came
to light as a ‘surprise’ in 2005/2006 the stock price dropped. The organization capital
measure, at least in Dell’s case, provided an early warning of the real performance.
We now generalize and examine the relationship between organization capital and
future firm performance for the entire sample.

RELATING ORGANIZATION CAPITAL TO FUTURE PERFORMANCE

We relate the organization capital measure (OC) to the five future years perfor-
mance. We use growth of operating income (OIGrowthi,t+i) and growth of sales
(SALEGrowthi,t+i) as measures of operating performance, and use the size and
book-to-market adjusted annual return and raw annual return as measures of
market performance. Operating income growth (OIGrowthi,t+i) is defined as average
operating income for years t + 1 and t + i, minus the operating income in year t scaled
by total assets in year t. Sales growth (SALEGrowthi,t+i) is computed as average sales
3
It is typical that slowing growth companies try to obscure the bad news from investors by earnings and
sales manipulation.

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for years t + 1 and t+i, minus the sales in year t scaled by sales in year t. We compute
the size and book-to-market adjusted excess returns using the companion portfolio
approach, where firms are grouped by the book-to-market ratio into five equal
groups at the end of June each year, and the size breakpoints are determined by
classifying the NYSE companies into five equal groups in June each year. Thus, we
have five groups of book-to-market ratio and five groups of firm size to determine
the companion portfolio. The monthly excess returns are then computed as the
difference between the firm’s monthly return and the companion portfolio’s
monthly return. The annual excess returns are obtained by compounding the
monthly excess returns each fiscal year. For this analysis we require that firms have
an OC (organization capital) estimate as well as the performance data be available
for the next five years. The final sample consists of 27,701 firm-year observations.
Table 1, Panel C provides the descriptive statistics of organization capital and firm
performance. The mean (median) organization capital is 0.1439 (0.1118), indicating
that on average organization capital represents roughly 14 per cent of the total assets.
In unreported analysis, the mean (median) contributions of organization capital to
sales and cost containment are 0.2185 and -0.0745 (0.1740 and -0.0568), respectively.
The contribution of organization capital through cost containment is negative for
roughly 80 per cent of the firm-year observations. This suggests that, in general,
organization capital enables firms to achieve additional output, and presumably firms
have to ‘pay extra’ in terms of cost to get the additional contribution to output.
Table 1, Panel C also provides descriptive statistics of other variables used in the
multivariate analysis. The mean (median) firm-size measured as the logarithm of
market value of equity is 6.0014 (5.9412), which corresponds to a market value of
equity of $404 ($380) million. The firms considered in our sample are reasonably
large, because we require data for five prior years to compute OC and for five future
years: a ten-year survival requirement. The mean (median) book-to-market ratio is
0.6973 (0.5922), suggesting that the sample firms are on average high growth firms.
Roughly 8 per cent of the sample contains firms with negative bottom-line earnings:
mean D_EPit is 0.0755. Overall, the firm characteristics indicate that the sample
contains reasonably mature firms with reasonable growth potential.

Organization Capital and Operating Performance

If the organization capital measure captures an attribute of efficiency with which
firms convert their resources into outputs, then this capability should result in
superior firm performance in future years. In other words, if organization capital is
simply a backward-looking measure that does not capture the attribute of differen-
tial efficiency that can be sustained in the future, then it should not be systematically
associated with the future operating performance. To examine this, we form ten
portfolios based on the OC measure. In particular, we classify each firm into the
respective industry-year, decile based on OC: OC is the capitalized abnormal oper-
ating profit scaled by total assets.We then track the operating performance measures
for the five years subsequent to the year of portfolio formation.
Table 2, Panel A provides the mean of cumulative growth in operating income
(OIGrowthit+i) for each OC group for one, two, three, four and five years following

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Table 2

ORGANIZATION CAPITAL AND FUTURE OPERATING PERFORMANCE,

UNIVARIATE TESTS

Panel A: Operating performance is OIGrowthit+i

Portfolio of OCit i is years after portfolio formation

i=1 i=2 i=3 i=4 i=5

1: Bottom 0.0220 0.0332 0.0435 0.0538 0.0647

2 0.0166 0.0259 0.0349 0.0438 0.0524
3 0.0154 0.0245 0.0333 0.0422 0.0513
4 0.0160 0.0247 0.0335 0.0428 0.0528
5 0.0190 0.0294 0.0401 0.0511 0.0631
6 0.0194 0.0302 0.0407 0.0520 0.0641
7 0.0217 0.0336 0.0472 0.0614 0.0757
8 0.0248 0.0381 0.0520 0.0671 0.0832
9 0.0277 0.0437 0.0614 0.0810 0.1016
10: Top 0.0353 0.0532 0.0734 0.0965 0.1207

Top minus Bottom 0.0134* 0.0200* 0.0299* 0.0427* 0.0560*

t-value 7.32 8.45 10.2 11.91 13.02

Panel B: Operating performance is SALEGrowthit+i

Portfolio of OCit i is years after portfolio formation

i=1 i=2 i=3 i=4 i=5

1: Bottom 0.1228 0.1901 0.2556 0.3264 0.4033

2 0.1109 0.1731 0.2365 0.3007 0.3677
3 0.1181 0.1846 0.2504 0.3190 0.3937
4 0.1194 0.1841 0.2533 0.3255 0.4036
5 0.1272 0.1974 0.2734 0.3525 0.4362
6 0.1323 0.2065 0.2819 0.3629 0.4467
7 0.1429 0.2241 0.3138 0.4075 0.5045
8 0.1529 0.2330 0.3196 0.4151 0.5163
9 0.1627 0.2598 0.3665 0.4808 0.6013
10: Top 0.1793 0.2849 0.3986 0.5257 0.6615

Top minus Bottom 0.0565* 0.0948* 0.1430* 0.1993* 0.2582*

t-value 8.12 9.55 10.94 11.9 12.4

Note: Variable definitions are provided in the Appendix.

the year of portfolio formation. Firms in OC group 1 (10) are the firms in the lowest
(highest) decile in their industry for that year. The mean OIGrowthit+i of firms in the
lowest (highest) OC group for one, two, three, four and five years ahead are: 2.20 per
cent, 3.32 per cent, 4.35 per cent, 5.38 per cent and 6.47 per cent (3.53 per cent, 5.32

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per cent, 7.34 per cent, 9.65 per cent and 12.07 per cent), respectively. The last two
rows show the difference between the top and bottom OC groups and the related
t-statistic.While the difference is positive and statistically significant for all five years,
there is a steady increase in the difference. This increase is attributable to a more
marked increase in the top OC group of about 2.41 [=(12.07 - 3.53)/3.53] times
compared to an increase in the bottom group of about 1.94 [=(6.47 - 2.20)/2.20]
times. In unreported analysis, for the one-year ahead OI growth, the percentage of
negative OI growth is roughly 30 per cent in the bottom OC group and 20 per cent
in the top OC group; while for the cumulative five-year ahead OI growth the
percentage of negative OI growth is roughly 28 per cent and 23 per cent. This
indicates that, on average, firms in the bottom OC group appear to exhibit a low
propensity to grow their operating income.
Table 2, Panel B provides the mean sales growth (SALEGrowthit+i): sales growth
shows a similar trend to income: firms in the top OC group exhibit a higher sales
growth than firms in the bottom OC group throughout the future five years. Figure 2
portrays the difference in IOGrowthit+1 and SALEGrowthit+1 across the top 30 per
cent and bottom 30 per cent of the OC groups, and shows that the difference is
substantial. Overall, these results show that organization capital is associated with
future operating performance of firms.
The univariate tests presented in Table 2 have the advantage of not having a
functional form. However, to the extent that OC measure is associated with other
Figure 2

DIFFERENCE IN FUTURE OPERATING PERFORMANCE ACROSS THE TOP AND

BOTTOM ORGANIZATION CAPITAL FIRMS

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firm characteristics, such as firm size (e.g., smaller firms having more OC or vice
versa), the relationships in the univariate tests may not be conclusive. We augment
Lev and Nissim’s (2004) model by adding the rank of OC and D_EP (Penman and
Zhang, 2002) to the analysis. In particular, we estimate the following equation:
Growth it + i = a0 + a1 R_OC it + a2 SIZEit + a3 DIVit + a4 RDCAPit + a5 EPit + a6 D_EPit
+ a7 BM it + eit , (4)
where Growth = {OIGrowthit+1, SALEGrowthit+1}. OIGrowthit+i is the average differ-
ence between operating income in year t + i minus operating income in year t, scaled
by total assets in year t. SALEGrowthit+i is the average difference between sales in
year t + i minus operating income in year t, scaled by sales in year t. R_OCit is the
industry-year based decile rank of OCit, and OCit is the contribution of organization
capital to revenues and cost containment. R_OCit is scaled to be between 0 and 1.
SIZEit is the natural log of market value of equity. DIVit is dividend to common
shares, scaled by total assets. RDCAPit is the sum of R&D expenditures and capital
expenditures, scaled by sales. EPit is net income divided by market value of equity if
the ratio is greater than 0, and 0 otherwise. D_EPit is an indicator variable that equals
one if net income divided by market value of equity is less than 0, and zero other-
wise. BMit is the book value of equity divided by market value of equity.
Table 3, Panel A provides the mean coefficient estimates obtained from the
annual estimation of equation (4), where the t-statistics are based on the standard
errors of the annual coefficient estimates, that is, the Fama–MacBeth procedure (see
Fama and MacBeth, 1972). The coefficient estimates on R_OCit for one, two, three,
four and five years ahead of operating income growth are 0.0074, 0.0113, 0.0177,
0.0263 and 0.0355, respectively, and all are statistically significant. These coefficient
estimates indicate the difference between the top and bottom OC groups, because
R_OCit is scaled to be between 0 and 1. The results indicate that, after controlling for
other major factors that are associated with future operating performance, organi-
zation capital still contributes to future growth in operating income. Compared to
the univariate analysis, the difference across the top and bottom OC groups is
smaller in magnitude in the multivariate tests (Table 3) than the univariate tests (see
Table 2), suggesting that the other factors that influence growth are also correlated
with the organization capital, yet don’t subsume it.
Table 3, Panel B provides the results of estimating equation (4) when the depen-
dant variable is sales growth (SALEGrowthit+i). The first two years OC is not asso-
ciated with sales growth after controlling for the other factors. However, over three
years ahead OC is significant and exhibits and increasing trend, similar to the OI
growth. Overall, the results are qualitatively similar to those of Panel A. In summary,
our OC estimate is associated with future operating performance, an indication that
the organization capital measure captures fundamental efficiency attributes affect-
ing long-term performance.

Organization Capital and Future Stock Returns

The results in Tables 2 and 3 show that our measure of organization capital is
associated with future operating performance. So, OC, as the Dell example shows, is

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Table 3

ORGANIZATION CAPITAL AND FUTURE OPERATING PERFORMANCE,

MULTIVARIATE TESTS

Panel A: Dependent variable is OIGrowthit+i

i is years after portfolio formation

i=1 i=2 i=3 i=4 i=5

Coef. t-value Coef. t-value Coef. t-value Coef. t-value Coef. t-value

INTERCEPT 0.0598* 13.12 0.0899* 13.98 0.1193* 14.23 0.1487* 13.65 0.1780* 13.45
R_OCit 0.0074* 4.17 0.0113* 4.83 0.0177* 5.83 0.0263* 6.96 0.0355* 7.77
SIZEit -0.0027* -6.85 -0.0044* -8.39 -0.0061* -9.61 -0.0080* -9.82 -0.0100* -9.73
DIVit -0.2181* -6.90 -0.3226* -7.01 -0.4434* -6.94 -0.5516* -6.62 -0.6509* -6.37
RDCAPit 0.0176* 3.16 0.0315* 4.82 0.0448* 6.98 0.0606* 8.99 0.0786* 9.58
EPit -0.1435* -8.53 -0.2044* -9.23 -0.2567* -9.42 -0.3020* -8.94 -0.3428* -8.33
D_EPit 0.0112* 5.02 0.0085* 2.95 0.0064 1.88 0.0043 1.10 0.0028 0.63
BMit -0.0233* -9.00 -0.0322* -8.72 -0.0412* -8.69 -0.0510* -8.50 -0.0614* -8.48
Adj. R2 0.0682 0.0853 0.0988 0.1074 0.1118

Panel B: Dependent variable is SALEGrowtht+i

i is years after portfolio formation

i=1 i=2 i=3 i=4 i=5

Coef. t-value Coef. t-value Coef. t-value Coef. t-value Coef. t-value

INTERCEPT 0.2998* 17.26 0.4646* 18.03 0.6355* 18.95 0.8143* 19.53 1.0029* 20.27
R_OCit 0.0090 1.08 0.0222 1.82 0.0457* 2.83 0.0778* 3.71 0.1123* 4.39
SIZEit -0.0091* -6.21 -0.0160* -7.42 -0.0239* -8.45 -0.0330* -9.33 -0.0436* -10.15
DIVit -2.1746* -14.41 -3.2522* -14.07 -4.4270* -13.32 -5.6400* -12.86 -6.8185* -12.57
RDCAPit 0.1401* 4.67 0.2389* 6.47 0.3343* 8.07 0.4428* 9.04 0.5657* 9.67
EPit -0.3934* -5.51 -0.5825* -5.44 -0.7611* -5.14 -0.9269* -4.95 -1.0929* -4.84
D_EPit -0.0666* -9.10 -0.0976* -8.13 -0.1221* -6.60 -0.1438* -6.14 -0.1590* -5.45
BMit -0.1032* -10.82 -0.1524* -11.52 -0.2032* -11.98 -0.2549* -12.16 -0.3079* -12.23
Adj. R2 0.0838 0.0995 0.1102 0.1169 0.1202

* Significant at the 5% level.

Note: The coefficient estimates are the mean of the annual regressions; the t-statistics are based on the standard errors
of the annual coefficient estimates, and variable definitions are provided in the Appendix.

forward looking. The natural question is: Do investors fully understand and incor-
porate the information in organization capital? In other words, does the stock return
incorporate contemporaneously the elements of OC? These are important ques-
tions, because hardly any useful information is provided by firms to investors about
intangibles in general, or about organization capital, which is an aggregator of most
intangibles, in particular.

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For this purpose, similar to the analysis in Table 2, we track the mean cumulative
excess stock returns of the ten OC groups in the five subsequent years. For excess
returns, ABRET, we use the size and book-to-market adjusted annual returns. The
two years ahead cumulative excess returns, CUMABRETit+i, is the sum of ABRET
for years t + 1 and t + 2; similarly, the five year ahead cumulative excess returns is the
sum of ABRET from years t + 1 to t + 5.
Table 4, Panel A shows the cumulative excess returns, CUMABRETit+i, for the ten
OC deciles from years t + 1 to t + 5. The difference in cumulative excess returns
between the top and bottom OC groups for one year ahead are: 3.53 per cent, 6.49
per cent, 8.33 per cent, 10.12 per cent and 11.54 per cent, respectively. For the fourth
and fifth years the difference in cumulative excess returns across the top and bottom
OC groups flattens out considerably. This occurs because the bottom OC groups’
cumulative return flattens out while the top OC group exhibits a considerably lower
excess returns than in the first two years. We will examine this reversal of excess
returns, that is, the flattening out of the cumulative excess returns in Table 4, Panel
B. It is also interesting to note that the cumulative excess returns for groups 2, 3 and
4 are markedly lower than those of groups 7, 8 and 9, and all of these intermediate
groups’ cumulative excess returns also flatten out in the fourth and fifth years. The
excess returns for the top and bottom groups are large compared to the intermediate
groups, even in the fifth year: this likely indicates that some of the excess return is
likely attributable to inadequate adjustment for risk.While for the bottom OC group
the risk is likely to be primarily associated with business risk, for the top OC group
the risk is likely to be mainly attributable to poor information, that is, information
risk. Figure 3 shows the difference in CUMABRETit+i across the top 30 per cent and
the bottom 30 per cent of the OC groups for the five subsequent years: the results are
similar to those of the top and bottom 10 per cent of the OC groups.
Table 4, Panel B provides the ABRETit for the ten OC groups to examine the
reversal of hedge returns. The difference in future excess returns across the top and
bottom OC groups, one and two years after portfolio formation, are: 3.53 per cent
and 2.95 per cent, and is zero for three, four and five years ahead. This is similar to
the cumulative excess returns shown in Panel A. We show the annual excess returns
to highlight the reversion of hedge returns, that is, the difference across the top and
bottom OC groups tends to zero. This is important, since the reversal of the excess
returns indicates that the difference in excess returns across OC groups is attribut-
able to mispricing, which is likely to occur due to lack of information on the
intangibles, rather than to inappropriate risk adjustment in our analysis.
We also test the relationship between OC and future cumulative excess returns
with multivariate regressions. We augment the Lev and Nissim (2004) model by
adding the rank of OC and D_EP. Accordingly, we run Fama–MacBeth regression
for the following equation:
CUMABRETit + i = a0 + a1R_OC it + a2 SIZEit + a3 BETA it + a4 VOL it + a5EPit
+ a6 D_EPit + a7 BM it + eit , (5)
where CUMABRETit+1 is sum of excess return adjusted for the companion size and
book-to-market from year t to year t + i. R_OCit is the scaled decile rank of OCit.

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Table 4

ORGANIZATIONAL CAPITAL AND FUTURE EXCESS RETURNS

Panel A: Future cumulative excess returns, CUMABRETit+i

Portfolio of OCit i is years after portfolio formation
i=1 i=2 i=3 i=4 i=5
1: Bottom 0.0332 0.0576 0.0860 0.1038 0.1170
2 0.0222 0.0504 0.0583 0.0615 0.0607
3 0.0123 0.0278 0.0247 0.0258 0.0232
4 0.0175 0.0313 0.0443 0.0483 0.0461
5 0.0277 0.0526 0.0582 0.0818 0.0834
6 0.0304 0.0604 0.0711 0.0817 0.0870
7 0.0211 0.0478 0.0769 0.0917 0.0921
8 0.0230 0.0371 0.0636 0.0768 0.0918
9 0.0517 0.0957 0.1248 0.1580 0.1713
10: Top 0.0685 0.1225 0.1693 0.2050 0.2325
Top minus Bottom 0.0353* 0.0649* 0.0833* 0.1012* 0.1154*
t-value 2.85 3.77 4.02 4.31 4.44

Panel B: Future annual excess returns, ABRETit

Portfolio of OCit i is years after portfolio formation
i=1 i=2 i=3 i=4 i=5
1: Bottom 0.0332 0.0244 0.0284 0.0178 0.0132
2 0.0222 0.0282 0.0079 0.0032 -0.0008
3 0.0123 0.0155 -0.0031 0.0011 -0.0025
4 0.0175 0.0138 0.0130 0.0040 -0.0022
5 0.0277 0.0249 0.0057 0.0236 0.0015
6 0.0304 0.0300 0.0107 0.0106 0.0053
7 0.0211 0.0267 0.0292 0.0147 0.0004
8 0.0230 0.0141 0.0265 0.0132 0.0150
9 0.0517 0.0440 0.0291 0.0331 0.0134
10: Top 0.0685 0.0540 0.0467 0.0357 0.0275
Top minus Bottom 0.0353* 0.0295* 0.0184 0.0179 0.0143
t-value 2.85 2.49 1.59 1.58 1.27

Panel C: Dependant variable is CUMABRETt+ia

i is years after portfolio formation
i=1 i=2 i=3 i=4 i=5
Coef. t-value Coef. t-value Coef. t-value Coef. t-value Coef. t-value
INTERCEPT 0.0751 1.84 0.1112* 2.22 0.1413* 2.36 0.1768* 2.84 0.1458* 1.99
R_OCit 0.0299* 2.93 0.0509* 3.44 0.0750* 4.08 0.0925* 4.54 0.1100* 4.67
SIZEit -0.0169* -5.39 -0.0294* -6.98 -0.0385* -7.55 -0.0460* -7.90 -0.0477* -7.77
BETAit 0.0118 0.62 0.0248 1.22 0.0349 1.45 0.0328 1.09 0.0466 1.35
VOLit 1.0212* 3.01 2.4292* 4.74 3.3668* 6.10 4.3802* 6.03 5.0360* 5.82
EPit 0.1522 1.00 0.1727 1.03 0.1218 0.69 0.1773 0.89 0.1195 0.58
D_EPit 0.0815* 3.33 0.1205* 3.50 0.1421* 4.25 0.1623* 4.57 0.1773* 4.37
BMit 0.0010 0.08 0.0183 1.11 0.0311 1.54 0.0246 1.18 0.0413 1.59
Adj. R2 0.0124 0.0201 0.0269 0.0293 0.0309

aIn panel C, the coefficient estimates are the mean of the annual regressions and the t-statistics are based on the standard errors
of the annual coefficient estimates.
Note: Variable definitions are provided in the Appendix.

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Figure 3

DIFFERENCE IN FUTURE EXCESS RETURNS ACROSS THE TOP AND BOTTOM

ORGANIZATION CAPITAL FIRMS

SIZEit is the natural log of market value of equity. BETAit is the slope coefficient
obtained from estimating a market model using the previous sixty monthly returns.
VOLit is the firm-specific variance of the monthly returns computed over the pre-
vious sixty months. EPit is net income divided by market value of equity if the ratio
is greater than zero, and 0 otherwise. D_EPit is an indicator variable that equals one
if net income divided by market value of equity is less than 0, and zero otherwise.
BMt is the book value of equity divided by market value of equity.
Table 4, Panel C provides the results of estimating equation (5). The mean coef-
ficient estimates obtained from the annual estimation of equation (5) and the
t-statistics are computed using the standard errors of the annual coefficient esti-
mates, that is, the Fama–MacBeth procedure (see Fama and MacBeth, 1972). The
coefficient estimate on R_OCit indicates the difference between the top and bottom
OC groups, controlling for other factors. In particular, the coefficient estimates on
R_OCit for the next five years are 2.99 per cent, 5.09 per cent, 7.50 per cent, 9.25 per
cent, and 11.00 per cent, respectively, indicating a trend of flattening out but not as
markedly as in the univariate analysis discussed above.
Summarizing, the tests document a systematic association between our estimate of
firm-specific organization capital, OC, and future accounting-based performance
measures as well as market-based measures. While these results support the validity

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of the OC measure as reflecting fundamental efficiency dimensions, the implications

of the two tests are different. In particular, the stock return tests indicate that despite
the importance of the OC indicator, investors don’t fully comprehend this informa-
tion in a timely manner. The OC measure, therefore, both reflects important oper-
ating dimensions and is largely unknown to investors.
We next proceed to examine the association between executive compensation and
organization capital to corroborate the attribute of organization capital as a measure
of management quality.

RELATING ORGANIZATION CAPITAL TO EXECUTIVE

COMPENSATION

Our findings thus far indicate that the measure of organization capital, which is an
aggregate measure of intangibles, is related to firms’ future operating and stock
performance. In this section, we examine whether the organization capital can be used
as a measure of managerial quality for decisions such as executive compensation.
Our measure of organization capital reflects the aggregate value of management
quality, that is, the intangibles that enable physical and human resources to be used
more efficiently. As such, the measure of organization capital should be associated
with the ability of management, and if executive compensation is a reward to
managerial ability, then the measure of organization capital should be associated
with executive compensation. Accordingly, we examine the association between
executive compensation and organization capital.
For this purpose, we use two different measures of CEO compensation: total
compensation scaled by firms’ total assets, and the pay-for-performance sensitivity
(PPS) of the executives’ equity holdings. Total compensation is the sum of salary,
bonus, other annual benefits, restricted stock grants, long-term incentive plan (LTIP)
payouts, options grants based on Black–Scholes value and all other benefits. We
obtain data for total compensation from EXECOMP database.
We estimate the pay-for-performance sensitivity of equity (PPS) using the pro-
cedure of Core and Guay (1999). PPS is defined as the change in the dollar value
of the CEO’s stock and options for a 1 per cent change in the stock price. While
computing this measure is straightforward for stockholdings, because stock value
increases by 1 per cent for each 1 per cent increase in the stock price, computing
this measure for options is not as straightforward, because the percentage increase
in the value of an option is less than the percentage increase in the stock price, and
depends upon the parameters embedded in the option contract. Following Core
and Guay, we estimate PPS of options as the partial derivative of one option’s
value with respect to stock price (the option delta), and then multiply the option
delta by 1 per cent of the firm’s stock price. Thus, we estimate the incentives from
CEO’s option portfolio as the sum of the option deltas multiplied by 1 per cent of
price; and PPS is the sum of the incentives from the stock holdings and option
holdings. We obtain data on CEOs’ equity holding and accumulated option grants
from EXECOMP database. In the empirical analysis we use the annual industry

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median as the benchmark to adjust the total compensation and PPS measures, so
as to account for industry-specific variation.
Total compensation is the annual ‘flow’ of compensation, and thus captures the
reward to CEOs based on their ability. The PPS measures the exposure of CEOs to
the stock price movements based on the CEOs’ current and potential equity holding.
PPS should also be influenced by the CEOs’ ability because higher ability CEOs are
more likely to capture more rents. In other words, higher ability CEOs are likely to
have more risk in their current and potential equity portfolios, and thus as a com-
pensation for risk the corresponding reward is also likely to be higher. The PPS
measure is a stock of reward, that is, all outstanding equity grants from pervious
periods are accumulated in computing PPS. As such, PPS measures the exposure of
the CEO to the firm’s stock price.
We obtain the CEOs’ total compensation and variables required to estimate
PPS from EXECOMP database from 1992 to 2006. The data required to calculate
firm size, sales growth and firm’s market share are obtained from Compustat. The
variable of corporate governance index (G-Index) is obtained from Corporate
Governance of IRRC database, and is based on the measure developed by
Gompers et al. (2003). This index is comprised of twenty-four indicators reflecting
the shareholder rights and is reverse ordered, that is, higher index values indicate
weaker shareholder rights. While the G-Index measure is available for 1990, 1993,
1995, 1998, 2000, 2002 and 2004, we use the latest available G-Index value for the
years where data are not available. We adjust firm size, sales growth and G-Index
with the annual industry median as the benchmark so as to account for industry
practices and factors.
We examine the relationship between our organization capital measure in year t+1
and executive compensation in year t. We do this because compensation presumably
motivates the CEO to direct his/her attention to generate organization capital in
future years. If organization capital is the abnormal contribution to operating profits
arising from the conglomeration of intangibles of which managerial ability is key,
then the compensation in year t must motivate managers to get the abnormal
contribution to profits in future years.4 Our test design captures this notion of
rewards/incentives embedded in executive compensation.
Panel A of Table 5 provides descriptive statistics of the variables used in the
analyses. The mean of OCit+1 is 0.0733, indicating that firms in general have positive
organization capital. The mean (median) of CEO’s total compensation scaled by
firm’s total asset and CEO’s PPS are 2.4873 (1.2329) and 782.96 (188.48), indicating
that the executive compensation variables are right skewed.
To examine the association between executive compensation and organization
capital, we form five portfolios each year based on the industry adjusted measures of
executive compensation. We then track the organization capital of each portfolio in

4
The results are qualitatively similar when we use the organization capital in year t and year t + 5,
instead of organization capital in year t + 1. We consider OC in year t for the sensitivity analysis
because if compensation is not an incentive mechanism but a reward for current performance, then the
organization capital must be associated with current compensation. We consider OC in year t + 5
because manager’s ability to contribute the organization capital may take a substantial amount of time.

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Table 5

DESCRIPTIVE STATISTICS FOR EXECUTIVE COMPENSATION AND

ORGANIZATION CAPITAL

Panel A: Descriptive statistics

M SD Median Q1 Q3

OCit+1 0.0733 0.2645 0.0568 -0.0042 0.1591

TCit 2.4873 3.8247 1.2329 0.4624 2.8321
PPSit 782.96 2032.79 188.48 62.54 564.49
SIZEit 7.5317 1.5630 7.4513 6.4587 8.5181
SALEGROWit 0.1140 0.2639 0.0790 0.0050 0.1786
GINDEXit 9.3199 2.6958 9.0000 7.0000 11.0000
ADJ_ TCit 0.9936 3.6225 0.0000 -0.6133 1.1507
ADJ_PPSit 553.40 2013.59 0.00 -110.43 314.78
ADJ_SIZEit 0.0993 1.4894 0.0000 -0.9052 1.0093
ADJ_SALEGROWit 0.0265 0.2019 0.0000 -0.0684 0.0874
SHAREit 0.0054 0.0134 0.0015 0.0006 0.0046
ADJ_GINDEXit -0.0214 2.6560 0.0000 -2.0000 2.0000

Panel B: Portfolio based on ADJ_TC = Total compensation / Total assets in year t

Portfolio ADJ_TCit OCit OCit+1

1: Bottom -1.173 0.061 0.060

2 -0.657 0.074 0.070
3 0.005 0.079 0.069
4 1.107 0.083 0.074
5: Top 5.719 0.111 0.094

Top minus Bottom 6.892 0.049 0.035

t-value 62.55 7.14 4.87

Panel C: Portfolio based on ADJ_PPSit = Pay for performance sensitivity of equity holding in year t

Portfolio ADJ_PPSit OCit OCit+1

1: Bottom -198.893 0.014 0.012

2 -122.613 0.063 0.057
3 5.512 0.088 0.073
4 297.555 0.104 0.095
5: Top 2810.964 0.141 0.128

Top minus Bottom 3,009.857 0.127 0.116

t-value 42.95 19.24 17.03

Note: The variable definitions are contained in Appendix.

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years t and t+1. Panel B of Table 5 provides the results of the portfolio based on the
total compensation scaled by total assets in year t. The difference in the organization
capital between the top and bottom groups are 0.049 (t-value = 7.14) and 0.035
(t-value = 4.87), in year t and t + 1, respectively. This indicates that higher executive
compensation is associated with higher subsequent organization capital. Panel C of
Table 5 provides the results of the portfolio based on pay-performance-sensitivity of
equity holdings: The results are qualitatively similar to those obtained with levels of
executive compensation. Overall, these results are consistent with the notion that
managers with higher compensation have higher ability as reflected by higher orga-
nization capital.
Organization capital can be attributed to other intangibles that may not be related
to manger’s ability/management quality. In other words, organization capital may be
associated with firm characteristics, such as firm size, sales growth, market share and
corporate governance, the same characteristics that are associated with executive
compensation. Thus, we need to control for these factors to focus on the association
between executive compensation and organization capital. For this purpose, we
estimate the following equation.
OC it + 1 = a0 + a1 EXECOMPit + a2 ADJ_SIZEit + a3 ADJ_SALEGROWit
(6)
+ a4 SHAREit + a5 ADJ_GINDEX it + ε it ,
where OCit+1 is the organization capital in year t + 1. EXECOMPit = {R_TCit,
R_PPSit}. R_TCit is the industry-year based quintile rank of total compensation
scaled by total assets in year t. R_PPSit is the industry-year based quintile rank of
pay-performance-sensitivity of holding equity (Core and Guay, 1999) in year t.
ADJ_SIZEit is industry-year median-adjusted firm size. ADJ_ SALEGROWit is
industry-year median-adjusted sales growth. SHAREit is market share of a firm,
equal to firm’s sale divided by total industry sales in year t. ADJ_GINDEXit is
industry-year median adjusted corporate governance index (G-index).
Table 6, Panel A reports the results of estimating equation (6) with R_TCit. The
coeffcient on R_TCit is 0.0623 (t-value = 6.46) in column (1) and is 0.0616 (t-value =
6.40) in column (2), indicating that after controling for other factors that are asso-
ciated with compensation, OC is still associated with compensation. The results in
Panel B using R_PPSit also provide qualitatively similar results.

CONCLUDING REMARKS

Intangibles or knowledge assets are major drivers of corporate and national growth.
Organization capital enables productive interactions among various resources (both
tangible and intangible) for creating economic value and growth. Organization
capital—a major form of intangibles, embodied in unique organizational designs and
processes—is the least documented type of intangible assets. We develop a measure
for a company’s organization capital, incorporating a firm’s potential to generate
super-normal revenues and cost savings. We validate the measure by examining its
association with future performance measures, such as operating income growth,
sales growth and abnormal returns. We find that the organization capital measure is

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Table 6

REGRESSIONS OF ORGANIZATION CAPITAL ON EXECUTIVE COMPENSATION

Panel A: Dependent variable = OCt+1 and executive compensation = R_TCit

Coef. t-value Coef. t-value

INTERCEPT 0.0303* 5.57 0.0310* 5.74

R_TCit 0.0623* 6.46 0.0616* 6.40
ADJ_SIZEit 0.0262* 7.93 0.0269* 7.93
ADJ_SALEGROWit 0.4628* 21.37 0.4597* 21.40
SHAREit -0.5636* -2.65 -0.6412* -2.93
ADJ_GINDEXit -0.0028* -2.32
Adj. R2 0.1547 0.1554

Panel B: Dependent variable = OCt+1 and executive compensation = PPSit

Coef. t-value Coef. t-value

INTERCEPT 0.0439* 8.18 0.0455* 8.42

R_PPSit 0.0384* 3.80 0.0359* 3.50
ADJ_SIZEit 0.0143* 3.93 0.0154* 4.06
ADJ_SALEGROWit 0.4684* 21.78 0.4659* 21.84
SHAREit -0.6814* -3.05 -0.7550* -3.28
ADJ_GINDEXit -0.0026* -2.08
Adj. R2 0.1514 0.1520

* Significant at the 5% level.

Note: The regressions include fixed-year effects. The standard errors are corrected using the Huber–
White procedure to compute the t-statistics. The variable definitions are contained in Appendix.

associated with five years of future operating and stock return performance, after
controlling for other factors. Thus, our organization capital measure captures fun-
damental efficiency dimensions of operations.
We also examine the association of the organization capital measure with execu-
tive compensation. Executive compensation is an incentive mechanism as well as a
measure of the executive’s ability that should manifest in business processes and
systems, that is, the way of doing business. We find that executive compensation is
positively associated with our measure of organization capital.
Collectively the results show that organization capital is an important intangible
asset that is related to firm value and important corporate decisions. While we have
developed the measure from a firm-level aggregate set of input and output mea-
sures, providing supplementary disclosure on inputs and outputs at a segment or
divisional level would help to improve the measurement and tracking of the firm’s
important intangible asset, its organization capital. In general, evidence exists that
investment in intangibles, and organization capital in particular, is relevant for
investors and managers. However, such information is disclosed in a haphazard

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manner, similar to the disclosure of segment reporting before it was standardized.

Thus, it would be useful if standard setting bodies develop templates for disclosure
of intangibles, such as inputs and outputs, so that it can be disseminated in a
standardized fashion.

references

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APPENDIX

VARIABLE DEFINITIONS

Variables Definitions

AbCOSTit Predicted cost and actual cost

AbProfitit AbSALEit + AbCOSTit
ABRETit Annual excess return adjusted for the companion size and book-to-market
portfolio returns

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APPENDIX

CONTINUED

Variables Definitions

AbSALEit Actual sale minus predicted sale without organization capital

ADJ_ SALEGROWit Industry-year median adjusted sales growth
ADJ_GINDEXit Industry-year median adjusted corporate governance index
ADJ_SIZEit Industry-year median adjusted firm size
BETAit Slope coefficient obtained from estimating a market model using previous
sixty monthly returns
BMit Book value of equity divided by market value of equity
COSTit Cost of goods sold plus the selling, general and administrative expense
CUMABRETit+i Cumulative excess returns, ABRET cumulated from year t to year t + i
D_EPit Indicator variable that equals one if net income divided by market value
of equity is less than 0, and zero otherwise
DIVit Dividend to common shares scaled by total assets
EMPit Number of employees
EPit Net income divided by market value of equity if the ratio is greater than
0, and zero otherwise
OCit Organization capital, computed by capitalizing and amortizing AbProfit
over five years scaled by total assets
OIGrowthit+i Average difference between operating income in year t+i minus operating
income in year t, scaled by total assets in year t
PPEit Gross plant, property, and equipment
PPSit Pay-for-performance sensitivity of CEO
R_OCit The industry-year based decile rank of OC
R_PPSit Industry-year based quintile rank of Pay-Performance-Sensitivity of
holding equity
R_TCit Industry-year based quintile rank of total compensation scaled by total
assets in year t
RDCAPit Research and development expenditure plus and capital expenditure,
scaled by sales
SALEGrowthit+i Average difference between sale in year t+i minus sale in year t, scaled by
total assets in year t
SALEit Sales revenues
SGAit Selling, general, and administrative (SG&A) capital computed by
capitalizing and amortizing SG&A expense over three years
SHAREit Market share of a firm, equal to firm’s sale divided by total industry sales
in year t
SIZEit Log of market value of equity
TCit Total compensation of CEO, scaled by total assets
VOLit Variance of the monthly return of a firm for previous sixty months

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