Determinants and Consequences

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Corporate working capital management:

Determinants and Consequences*

Rabih Moussawi

Wharton Research Data ServicesUniversity of Pennsylvania

Philadelphia, PA 19104Email: [email protected]

Mark LaPlanteSchool of Management, SM31University of Texas at Dallas

Richardson, Texas 75080Email: [email protected]

Robert KieschnickSchool of Management, SM31University of Texas at Dallas

Richardson, Texas 75080Email: [email protected]

Nina BaranchukSchool of Management

University of Texas at DallasRichardson, TX 75080

[email protected]

Current Draft: November 4, 2006

* The authors wish to thank Alexander Butler, Jarrod Harford, and Robert MacDonaldfor helpful comments on an earlier version of this paper.


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Corporate working capital management:

Determinants and Consequences

ABSTRACT

Recent business surveys suggest that firms over-invest in working capital. We examinethis suggestion, and find evidence consistent with it. Given this evidence, we then focuson what factors influence corporate working capital management. We find that industrypractices, firm size, future firm sales growth, the proportion of outsider directors on aboard, executive compensation (current portion), and CEO share ownership significantlyinfluence the efficiency of a companys working capital management. Overall, ourevidence suggests that managers respond positively to incentives and monitoring inmanaging their firms working capital.


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1. Introduction

Several recent business studies suggest that corporations, on average, over-invest

in working capital. For example, REL Consultancy Group has for years conducted an

annual survey of corporate working capital management practices for CFO Magazine,

which CFO Magazine then reports. TheREL 2005 Working Capital Survey concludes

that U.S. corporations had roughly $460 billion unnecessarily tied up in working capital.

Similarly, ITworld.com recently posted the results of a study arguing that poor working

capital management practices cost IT companies billions of dollars annually

(ITworld.com, 2002).

Do US corporations over-invest in working capital? If so, to what extent is this

due to agency problems? We address both of these questions in this study. To see how

important the efficiency of a corporations working capital management can be, we use

an example given in Shin and Soenen (1998). Shin and Soenen (1998) point out that

Wal-Mart and Kmart had similar capital structures in 1994, but because Kmart had a cash

conversion cycle of roughly 61 days while Wal-Mart had a cash conversion cycle of 40

days, that Kmart likely faced an additional $198.3 million per year in financing

expenses.1 Such evidence demonstrates that Kmarts poor management of its working

capital contributed to its going bankrupt. As their 2005 U.S. survey report points out,

there is a high positive correlation between the efficiency of a corporations working

capital policies and its return on invested capital.

To present our examination of these issues, we organize this paper as follows. In

Section 2, we briefly review the literature on working capital management. Our sample

and data sources are described in Section 3. Section 4 provides an analysis of the effect

of working capital management on firm value, and Section 5 provides an analysis of what

factors may influence working capital management. Finally, Section 6 concludes the

paper with a summary of its principal findings.

Using data on a panel of U.S. corporations from 1990 through 2004, we find

evidence of a significantly negative relationship between firm value and investment in

working capital that is consistent with over-investment in working capital. According to

our estimated equations, an additional $1 million investment in working capital is

1 Shin and Soenen (1998), page 37.


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associated with a roughly 119 to 129 thousand dollar reduction in firm value. To put this

in perspective, a firm that under-utilizes debt by $1 million, can increase firm value by

roughly $140 thousand at current rates by increasing its interest tax shield. Consequently,

it is clear that working capital management decisions have corporation valuation effects

of the same magnitude of corporate capital structure decisions and so probably warrant

just as much attention.

Turning to what influences a firms management of working capital, we find that

a firms working capital policy is influenced by its industrys working capital policies, its

size, its expected sales growth, the proportion of outside directors on its board, the

current compensation of its CEO, and its CEOs share ownership. Consequently,

managerial incentives and the monitoring of management are significant influences on a

firms working capital management performance.

2. Review of Prior Literature

Shin and Soenen (1998) point out that a corporations working capital is the result

of the time lag between the expenditure for the purchase of raw materials and the

collection from the sale of finished goods. As such, it involves many different aspects of

corporate operational management: management of receivables, management of

inventories, use of trade credit, etc. Consequently, there are streams of research on

individual aspects of working capital management (cash and marketable securities, e.g.

Mauer, Sherman and Kim (1998), trade credit, e.g. Rajan and Peterson (1997), etc.).2

However, Schiff and Lieber (1974), Sartoris and Hill (1983), and Kim and Chung (1990)

all emphasize the need to consider the joint effects of these individual policies,

particularly with respect to inventory and credit decisions. For this reason, we only

discuss the prior literature that focuses on overall working capital management.

With respect to the effect of working capital management on firm value, we find

no direct evidence. While Schiff and Lieber (1974), Sartoris and Hill (1983), and Kim

and Chung (1990) model the effects of working capital management practices on firm

2 Some authors include cash and marketable securities in their characterization of working capital and otherexclude these items. We will follow the later tradition as it is consistent with Schiff and Lieber (1974),Sartoris and Hill (1983), Kim and Chung (1990) and other papers that focus on the joint effects of workingcapital management practices and it is consistent with the business survey described earlier.


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value, they do not provide evidence on whether firms actually do maximize their value by

their working capital management choices. The study that comes nearest to addressing

this issue is the study by Shin and Soenen (1998), which examines the relation between

different accounting profitability measures and net trade cycles, a summary efficiency

measure of a firms working capital management. Shin and Soenens study implies,

without providing direct evidence, that firms that manage their working capital more

efficiently (i.e., shorter net trade cycle) experience higher operating cash flow and are

potentially more valuable.3 However, this last implication does not necessary follow

because firms that have longer net trade cycles are also investing in short-term assets

which may pay off in subsequent periods. Further, their evidence does not speak to

whether the market sees firms as over-investing in net working capital. So the question

as to whether firms over-invest in net working capital on average iis unanswered by prior

research.

As for the determinants of working capital practices, we find even less prior

evidence on which to draw. Nunn (1981) uses the PIMS database to examine why some

product lines have low working capital requirements, while other product lines have high

working capital requirements. In addition, Nunn is interested in permanent rather than

temporary working capital investment as he uses data averaged over four years. Using

factor analysis, he identifies factors associated with the production, sales, competitive

position, and industry. Reinforcing the role of industry practices on firm practices,

Hawawini, Viallet, and Vora (1986) examine the influence of a firms industry on its

working capital management. Using data on 1,181 U.S. firms over the period 1960 to

1979, they conclude that there is a substantial industry effect on firm working capital

management practices that is stable over time. From these studies, we conclude that sales

growth and industry practices are important factors influencing a firms investment in

working capital.

What the above review illustrates is that while there are models to describe how

working capital management practices influence firm value, there is practically no

3 Shin and Soenens measures of firm profitability are ratios of its operating income plus depreciation overeither total assets or total sales. One interesting aspect of Shin and Soenen s evidence is their conclusionthat their net trade cycle variable is measuring something different than what is measured by the currentratio, which typically includes cash and marketable securities.


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evidence that firms manage their working capital so as to maximize their value. Further,

there is little evidence on what factors influence a firms management of working capital,

particularly whether agency cost issues are concerns.

3. Sample and Sample Data

To address these questions, we examine samples of U.S. public corporations from

1990 through 2004. We begin by identifying all U.S. corporations with Compustat and

CRSP data over this time period. Next, we exclude all firms in financial service

industries (SIC 6000 to SIC 6800) as working capital has a very different meaning in

these industries. Table 1 column 2 presents the number of firms in this sample by year.

This sample is what we examine when we study the effect of investment in net working

capital on firm value.

To study what influences working capital management performance, we add data

from a number of different data sources, which reduces our sample in different analyses.

First, we use the Investor Responsibility Research Center (IRRC) Governance database to

obtain data on certain corporate governance features over the 1990 through 2004 time

period. 4 The availability of these data underlies our choice of time period to study.

Specifically, IRRC collects data on governance provisions in effect for at least 3,155

major US corporations consisting of the S&P 1500 firms and other companies selected

primarily on the basis of market capitalization and high institutional ownership levels

over the years 1990 to 2004.5

As the original IRRC data is biennial and sometimes

triennial, we use the filling method adopted by Gompers Ishii and Metrick (2003) and

lately followed by Bebchuk Cohen and Ferrell (2004) in building our sample for all the

4 IRRCs governance database is an electronic translation of a part of IRRCs published Corporate

Takeover Defenses volumes (September 1990, July 1993, July 1995, February 1998, November 1999,February 2002, and January 2004)5 As IRRC does not provide a manual of its dataset on WRDS, we refer to studies on IRRC data for moreinformation on sampling and individual provision specifications. Some of these sources are Gompers, Ishiiand Metrick (2003), Gillan, Hartzell and Starks (2003), and others. Note here that Gompers Ishii andMetrick (2003) assume that though IRRC governance data is a noisy measure of a firms governanceprovisions, there is no reason to suspect any systematic bias (p. 113). Gompers Ishii and Metrick (2003)report that over the sample period from 1990 to 2002, IRRC tracked more than 93 percent of the totalcapitalization of the combined New York Stock Exchange (NYSE), American Stock Exchange (AMEX),and NASDAQ markets (p. 111).


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years over the sample period. The maximum number of firms included in this restricted

sample by year is identified in column 3 of Table 1.6

We say the maximum number because the number of firms with IRRC data is

larger than the number of firms with S&P Execucomp data and the number of firms with

IRRCs Directors data.7 We use the IRRCs Directors database to collect information on

the board of directors of sample firms. We use S&P Execucomp database to collect

information on CEO compensation and share ownership. We will discuss the use of

these data further in our analysis of what factors influence a firms working capital

performance.

4. Do firms over-invest in working capital?

4.1 Capital Cash Flow Analysis

While earlier examples focused on the costs associated with investment in

working capital, they do not address the potential benefits. Clearly a company has to

have stock on hand in order to make some sales. Further, competition between firms may

require them to provide customers with interim financing in the form of trade credit

which becomes a receivable to the supplier. Thus, the net effect of investment in

working capital is not as straightforward as earlier examples suggest.8

To discern if firms over-invest in working capital, we use the capital cash flow

valuation model used in Kaplan and Ruback (1995) as our guide. Assume, as they did,

the following simple DCF valuation model:

(1) ( )( )

( ) ( )

( )1 1

( )( )0 (0) (0)

1 1

L s

F t tt t

OCF t INV t INV t CCF t PV CASH CASH

r r

= =

= + = ++ +

where PVF(0) is the current value of the firm; CASH(0) is the current value of its cash

assets; CCF(t) equals its Capital Cash Flows; OCF(t) equals REV(t) EXP(t) + OTH(t),

REV(t) equals net revenues; EXP(t) equals Cost of Goods Sold + SGA + Taxes; OTH(t)

6 It is worth noting that our inferences in the valuation section of our paper are unchanged if we restrict theanalysis to just this sub-sample. In fact, the magnitude of the reduction in firm value from over-investmentin working capital is even larger.7 Execucomp data starts in 1992 and is constrained to the S&P 500 companies until 1994 when Execucompcoverage extends to all S&P 1500 constituents. IRRCs Directors data, on the other hand, tracks directorsof S&P 500, S&P Midcap, and S&P Smallcap companies, starting in 1996.8 See Schiff and Lieber (1974), Sartoris and Hill (1983), or Kim and Chung (1990) for further discussion ofsome of these trade-offs.


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= Depreciation + Amortization + After tax cash from asset sales; INVL(t)= investment in

long-term assets; and INVS(t)= investment in net working capital, where net working

capital equals accounts receivable plus inventory less accounts payable and accrued

expenses. Like Kaplan and Ruback, we add the firms current cash balances and use a

net working capital definition that excludes investment in cash balances. This approach

is particularly valuable in our context as it allows us to separate out cash management

issues, which have been the focus of a separate literature (e.g., Faulkender and Wang

(2005) and Pinkowitz and Williamson (2005)), from working capital management issues.

Based upon this DCF valuation model, we develop two regression models to

ascertain the relationship between firm investment in net working capital and its market

value. Thus we are able to address the question of whether or not the market sees firms

as over-investing in net working capital.

Regression model 1:

Assume that the firms capital cash flows grow at a constant rate, so that we can

rewrite (1) as:

(2) ( )( ) ( )

( )

( 1) 1 1( )

L s

F

OCF t INV t INV t PV t CASH t

r g

+ + + = +

which can be re-written as:

( ) ( ) ( )1 1 1

( ) ( 1) 1 1F L S

PV t CASH t OCF t INV t INV t r g r g r g

= + + + +

.

This relationship can then be re-expressed as the following regression model:

(3) ( ) ( ) ( )* * *0 1 2 3 4( | ) ( ) ( 1) 1F L SE PV t X CASH t OCF t INV t INV t = + + + + + +

where:

( ) ( )* * *1 1 1

( ) ( ) ; ( ) ; ( ) .L L S SOCF t OCF t INV t INV t and INV t INV t

r g r g r g

= = =

To interpret the marginal effect of INV(t), we would need to recognize that:

(4)( )

( ) ( ) ( )4

( ) 1 1 ( ) 11F

s s s

PV t OCF t OCF t

INV t INV t r g r g INV t r g

= = =


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So, investment in working capital is at the optimal level if4 is insignificantly different

from zero. Since ( )1

r g

>0, then 4 > 0 implies that there is under-investment in net

working capital and 4 < 0 implies that there is over-investment in net working capital.

Regression model 2:

Our first regression model follows the DCF valuation approach taken in Kaplan

and Ruback (1995). However, the analysis ignores incremental investment in cash and

marketable securities. Whether such incremental investment should be included or

excluded is unclear because some formulations of the DCF valuation framework use

definitions of working capital that includes cash and marketable securities and some do

not. Consequently, to address formulations that do, we modify equation (2) as follows:

(5) ( )( ) ( ) ( )

( )

( 1) 1 1 1( )

L S C

F

OCF t INV t INV t INV t PV t CASH t

r g

+ + + + = +

Basically, we have taken the broader definition of investment in working capital and

broken it into investment in cash and investment in working capital, as we define it. We

can re-write this expression as:

( ) ( ) ( ) ( )1 1 1 1

( ) ( 1)F L S CPV t CASH t OCF t INV t INV t INV t r g r g r g r g

= + +

We can re-express this relationship as the following regression model:

(6) ( ) ( ) ( ) ( )* * * *0 1 2 3 4 5( | ) ( ) ( 1) 1F L S CE PV t X CASH t OCF t INV t INV t INV t = + + + + + + +

where:

( ) ( )

( )

* * *

*

1 1 1( ) ( ) ; ( ) ; ( ) ;

1( ) .

L L C C

S S

OCF t OCF t INV t INV t INV t INV t r g r g r g

and INV t INV t

r g

= = =

=

For regression model 2 we again interpret 4 < 0 and 5 < 0 as evidence of over-

investment in net working capital and cash, respectively.

To estimate these two regression models, we use Compustat data. Selected

summary statistics for each variable based on these data are provided in Table 2. The

first variable, MVF(t), represents the market value of the firm computed as in Fama and


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French (2002). Specifically, we start with total assets, subtract the book value of equity

and add back the market value of equity as of the end of fiscal year.

To estimate, CASH(t), we use the cash and marketable securities balance of the

firm at the same point in time as we measure its market value. We do this to be

consistent with the way that Kaplan and Stein measured cash balances in their valuation

model.

To estimate OCF(t+1), we use the two approaches described in Kaplan and Stein

(1995), and like their paper, we only report the results based on the second approach as

the results are similar. Specifically, we start with net income; add back depreciation and

amortization expense, interest expense, and the proceeds from the sale of fixed assets.

To estimateINVL(t+1), we use the firms investment in long-term assets (PPE)

from its cash flow statement. Using changes in PPE as an alternative measure does not

change our conclusions and so we only report results using this measure.

To estimateINVS(t+1), we use a definition of net working capital that is consistent

with the one in Kaplan and Steins paper. Specifically, we use current assets minus cash

and marketable securities, minus accounts payable, and minus accrued expenses. There

are two important points to note about this definition. First, we are separating out

investment in cash and marketable securities. Second, we are focused on the investment

in current assets that must be financed with non-spontaneous or outside sources of

financing. This definition is consistent with our valuation model.

To estimateINVC(t+1), we compute the change in the balance of cash and

marketable securities between fiscal years. It is important to note that we estimate

OCF(t+1),INVL(t+1),INVS(t+1), andINVC(t+1) for the fiscal year subsequent to the date

on which we measure the value of a firm and its cash balances. We do this to be

consistent with our valuation model.

The next issue that we have to confront is how to specify the data generating

process for our regression models. It should be fairly obvious that MVF(t) is a non-

negative random variable. While some researchers have scaled MVF(t) by the book

value of assets to create an estimate of Tobins q. We do not take this approach, though

we will see that it does not later, as it introduces additional problems when there is more

variation in book values of assets than in any of the explanatory variables. One


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alternative is to take the logarithm of the dependent variable and use OLS to estimate a

linear regression on it. Unfortunately, as Manning (1997) explains, this is not always

appropriate, and can lead to biased estimates of the marginal effects of the explanatory

variables.9 Consequently, we follow the recommendation of Hardin and Hilbe (2001)

and use a generalized linear model approach with a log link assumption. Specifically, we

adopt a general estimating equation approach (a GMM approach) using the logarithm link

function, ln(E(y|x)), and estimate the standard errors using the Rogers/Huber/White

estimators adjusted for clustering at the firm level.10

The results of estimating our two regression models using this estimation

approach are reported in Table 3. Both models give fairly similar results. Current cash

balances, operating cash flow, and investment in fixed assets are all positively priced.

The last inference suggests that additional investment in fixed assets for most firms

increases their value. Interestingly, all these inferences are consistent with those that

would be derived from estimates reported in either Faulkender and Wang (2005) or

Pinkowitz and Williamson (2005). More importantly, for our study, we find that the

coefficient on the investment in working capital variable is significantly negative.

Following the interpretation of equation (4), this result implies that at the margin, firms

tend to over-invest in working capital on average. Such a conclusion is consistent with

both the ITworld.com survey mentioned earlier, as well as various annual REL Working

Capital Surveys.

Apparently, the market recognizes this over-investment and discounts firms for it.

According to our estimated equations, evaluated at the mean values of the explanatory

variables, an additional $1 million investment in working capital is associated with a

roughly $129 thousand reduction in firm value. To put this in perspective, a firm that

under-utilizes debt by $1 million, can increase firm value by roughly $140 thousand at

current rates by increasing its interest tax shield.11 Consequently, it is clear that working

9 Nevertheless, it is worth pointing out that our subsequent conclusions do not change if we use thisapproach.10 We should note that we did use the transformed dependent variable method to test for whether fixedeffects were important and fail to reject the use of the random effects model for these data. Later, in ouranalysis of the cash conversion cycles, we face this problem and so use a different specification.11 This estimate assumes new 10 year, AAA rated debt and a 34% tax rate, which is likely an over-estimategiven actually corporate tax rates are significantly less.


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with a basis for comparison. While Pinkowitz and Williamson uses the methodology of

Fama and MacBeth (1973) to estimate their regression models, we use the panel data

approach advocated in Peterson (2005) as it appears superior to the Fama and MacBeth

methodology. Thus all our standard errors are estimated using Roger estimators adjusted

for clustering on the firm level. Our results for their base specification are somewhat

different than theirs as our estimated coefficient on the level of cash variable is 0.701,

rather than 0.97 as in their study. This difference may be due to the difference in time

period studied: their regression covers 1950 to 1999, while ours covers 1990 through

2004. Other than differences in numerical values, our estimates share the same signs as

their estimates.

Building on this base specification, we next estimate a regression model with net

assets reduced by investment in accounts receivable and inventory and then add a

variable for investment in net working capital, defined by accounts receivable plus

inventory minus accounts payable as this mimics our prior definition of net working

capital. The results of this estimated regression model are reported in column 3 of Table

5. The negative and significant coefficient on the level of net working capital investment

is consistent with our prior estimated valuation model result in that it suggests that firms

on average over-invest in net working capital.

Before reaching a conclusion on how much, we next estimate a specification that

includes prior and future changes in net working capital investment and report the results

in column 4 of Table 5. The reported results suggest that prior and future investment in

net working capital increase firm value. Such estimated coefficients suggest that

Pinkowitz and Williamson interpretation of their change variables is somewhat

questionable as we should not observe a negative sign on the coefficient associated with

the current level of investment in net working capital if their interpretation was correct.

Nevertheless, we can estimate the total effect of investment in net working capital

on firm value by evaluating its effect through current, past and future investment in net

working capital. Evaluated at our sample averages, the total effect is that an additional

$1 million investment in net working capital overall reduces firm value by roughly

$119,326. What is striking about this estimate is that it is close to the $129,000 estimate

that we derive from our prior valuation analysis. Given this consistency, we conclude


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that our valuation analysis suggests that the market views firms as over-investing in net

working capital on average.

5. What influences the efficiency of a firms working capital management?

Given the above evidence that corporations over-invest in net working capital, the

next question is why. One obvious possibility is that managers do not expend the effort

necessary to minimize net working capital because of incentive compatibility problems,

or agency problems. Prior literature suggests that there are three likely sources of

misalignment: (1) CEO incentives, (2) board incentives, and (3) the structure of corporate

governance.

We will explore each of these possibilities, but before we do we must first

develop a basic model to identify potential control variables. Thus, we conduct this

analysis in a series of steps. We first develop a core model, and then we explore the

influence of board characteristics, CEO compensation and ownership, and finally

corporate charter provisions on a corporations efficiency in managing its working

capital.

As our dependent variable in these regressions, we use a firms cash conversion

cycle (i.e., the inventory conversion period plus the receivables collection period minus

the payables deferral period using Compustat data) as our measure of the efficiency of its

working capital management. While there are alternatives, such as Shin and Soenens

NTC measure, the cash conversion cycle measure (CCC) is standard in many corporate

finance textbooks and is used in theREL Working Capital Surveys as a summary

measure. Consequently, we follow industry and textbook practice and use this measure

for the efficiency of a firms overall working capital management. Summary statistics for

this variable, and all our other variables used in this analysis are reported in Table 5.

Note that we winsorize this and all of our accounting and compensation variables at the

1% level to avoid distortions due to outliers.

For our core model, we conjecture that the following factors are significant

influences on a firms working capital management. First, prior research such as

Hawawini, Viallet, and Vora (1986) suggests that industry practices are significant

determinants of a firms working capital management practices. The working capital


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policies of say a software company are going to be quite different from those of a retail

shoe company. Consequently, it is important to control for the influence of industry

practices on a firms working capital practices. To do this, we use the median cash

conversion cycle of firms within a firms industry, CCCM, to proxy for the typical

working capital practices within such industry. For our identification of a firms industry

we use the Fama and French (1997) 48 industry delineations set out in Appendix B.

Second, firm size may influence the efficiency of a firms working capital

management. Larger firms may require larger investments in working capital because of

their larger sales levels. Or, alternatively, larger firms may be able to use their size to

forge relationships with suppliers that are necessary for reductions in investments in

working capital. Current supply chain management practices require a lot of coordination

between companies and are typically easier for a larger firm to implement than for a

smaller firm to implement. Thus, firm size is likely to influence the efficiency of a firms

working capital management, though the direction of the effect is an open question. We

use a firms total assets, TA, as our proxy for its size.

Third, the proportion of a firms assets accounted for by fixed assets might

exercise an influence on a firms working capital performance. For example, the

inventory problems of an automobile parts manufacturer are likely to be quite different

from that of a software manufacturer. Further, the receivables problems of these types of

companies are also likely to be different. To measure this variable, we take the ratio of a

firms property, plant and equipment to its total assets, and name it PTA.

Fourth, based upon Nunns (1981) evidence, we expect firm sales to influence a

firms working capital management. In this connection, and consistent with our earlier

regression results, we expect a firms expected future sales to influence its working

capital investment, and so its cash conversion cycle. For example, a firm might build up

inventories in anticipation of future sales growth, and as a result, may also increase its

use of trade credit. To proxy for such growth, we use the firms percentage sales growth

over the future two years and name this variable, FSG.

Finally, some might argue that firms with some degree of market power are able

to work deals with suppliers and customers that give them an advantage over competitors.

To capture this possibility, we compute the Herfindahl-Hirschman index using sales data


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for each firms industry, again using Fama and Frenchs (1997) 48 industry delineations.

The more concentrated the industry the more likely this will influence the cash

conversion cycles of firms within it. We denote this variable asHHI.

To determine the relevance of the above core factors to the efficiency of a firms

working capital management, we regress the firms cash conversion cycle, CCC, on these

above factors. Before conducting this analysis, we must address the specification of the

data generating process as CCCis a non-negative random variable. While we would

prefer to use the same data generating process specification used in our valuation

analysis, it does not appear appropriate for these data. A Hausman type test indicates that

a random effects model is inappropriate in this case, and so we use a fixed effect model

on a logarithmic transformed dependent variable and estimate Rogers/Huber/White

standard errors adjusted for firm level clustering.

The results of estimating our core regression model,Model 1, for corporate cash

conversion cycles are reported in column 2 of Table 6. These results suggest that firms

do not use their size or market power to reduce their cash conversion cycle. If anything,

they use their position to relax their efforts. Of the factors examined, industry practices

are the main determinant of a firms working capital practices. In addition, positive

future sales growth is associated with increased investment in net working capital. And

finally, firms with more tangible long-term assets reduce their investment in net working

capital. Interestingly, this result appears to mirror the conclusion of the ITworld.com

survey mentioned earlier as firms with more intangible long-term assets appear more lax

in their management of their working capital.

Given these results, we now add the board characteristics of a firm to our core

regression model to extend it. We use two characteristics to capture the essential features

of a corporations board: its size measured by the number of directors (DIR), and its

proportion of outsiders on the board (POD). Prior literature leads us to expect that larger

boards might be lax in monitoring management and so be associated with longer cash

conversion cycles than other firms in their industry. Conversely, prior literature suggests

that more outsiders on the board lead to greater monitoring of management, which we

expect will result in shorter cash conversion cycles for these firms. The results of

estimating this expanded regression model,Model 2, are reported in column 3 of Table 6.


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These results suggest that board size is not a significant influence, but that board

composition is. The greater the proportion of outsiders on the board, the better the

performance of the firms working capital management. This result is consistent with the

monitoring role of outsider directors.

Continuing this line of inquiry, we now include the compensation and share

ownership of the CEO in our expanded regression model. The more the CEO is paid, the

more likely they will have incentives to reduce a firms cash conversion cycle.

Consequently, we expect the firms cash conversion cycle to be negatively correlated

with the CEOs total current compensation. We measure such compensation that

comprises of CEOs salary and bonus using the Execucomp database and denote it as

TCCOMP. Note that we exclude their current period stock option grants from this

measure and only focus on their current non-stock compensation.

We exclude their current stock option grants because we instead focus on their

total unexercised stock option holdings. Stock options granted in the past might be just

as important an influence as current stock option grants in our attempt to capture

managerial incentive alignment with shareholder interests. Consequently, it might be

better to recognize a CEOs total unexercised stock option position. To estimate this

quantity, we use the Execucomp database to estimate the dollar value of the CEOs

unexercised stock options and denote this variable as TUO.

Finally, we can expect the CEOs current shareholdings to influence the

management of the firms cash conversion cycle. For this reason, we construct the

proportion of stocks held by the CEO and call it CEOPS. Unfortunately, the effect of this

variable is less clear as it could either create incentives for the manager to tightly control

this cycle, or if it could create incentives for managers to expend less effort on this

activity if they have the power to avoid the expenditure of such effort (Morck, Shleifer,

and Vishny (1988)).

The results of estimating this further expanded regression model,Model 3, are

reported in column 4 of Table 6. While both CEO compensation components have a

negative influence on their firms cash conversion cycle, only the total current

compensation component has a statistically significant effect. In some ways this is

consistent with our earlier expectation that a firms investment in working capital


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16

primarily influences its performance in the current and near future periods.

Consequently, we should expect current CEO compensation to have a greater influence

on the firms cash conversion cycle, while we might expect their unexercised stock

options to influence their long-term investment decisions. Interestingly, the CEO share

ownership is significantly positively related to their firms cash conversion cycle. So, the

incentive effect of stock ownership appears to be dominated by other effects of CEO

stock ownership.

Expanding our regression model further, we now include a consideration of the

firms corporate charter provisions. Such provisions have figured prominently in recent

literature on cash management (e.g., Dittmar and Mahrt-Smith (2003) and Harford,

Mansi, and Maxwell (2004)) as result of Gompers, Ishii, and Metricks (2003) evidence

on the relationship between these corporate characteristics and equity returns. To begin

this analysis, we follow Harford, Mansi, and Maxwell and simply include Gompers, Ishii

and Metricks governance index, GINDEX, as an additional regressor and report the

results in column 5 of Table 6 (i.e.,Model 4). The reported evidence does not suggest

that these firm characteristics are significant influences on a corporations working

capital performance.

Whether this conclusion is correct is somewhat unclear as the GINDEX assumes

that all of charter provisions have the same influence on a firms cash conversion cycle

and that assumption has been subject to criticism in recent governance literature

(Bebchuk, Cohen and Ferrell (2004)). For example, executive severance agreements such

as golden parachutes can give management an incentive to agree to a takeover, while

poison pills ostensibly are intended to deter takeovers. More importantly, some

provisions (e.g., advance notice requirements) are intended to primarily influence internal

changes in corporate governance, while other provisions (e.g., supermajority

requirements for a merger) are intended to solely deter external changes in corporate

control without any impact on internal governance. Consequently, we create several

indices which group governance features by intended purpose.

Our component indices are: internal provisions, external provisions, compensation

and liability provisions, minority voting provisions, and state laws. The components of

each of these indices are presented in Appendix A. The rationale for each is as follows.


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The internal provisions index,INTERNAL, includes provisions that limit the

constitutional rights of shareholders, like staggered boards, limitations on shareholder

rights to amend charter and bylaws, and advance notice requirements. Therefore, the

internal provisions index focuses on provisions that primarily influence internal

governance or changes in the internal control of a firm. The external provisions index,

EXTERNAL, which is constituted of provisions like poison pills, supermajority

requirements to approve mergers, fair price, and anti-greenmail, focuses on provisions

that are primarily used to thwart external control contests (i.e., takeover bids). The

compensation and liability provisions index, C&L, focuses on provisions that primarily

influence directors legal liability costs, or compensation received by officers and

directors in the event of a control change. The minority voting provisions index,MVP,

focuses on shareholder voting rules, mainly cumulative and confidential voting rules.

The state laws index, SLAWS, focuses on antitakeover provisions endorsed state law.

Because these antitakeover statutes are often implemented by default in all firms

incorporated in a particular state, and are sometimes redundant with the presence of firm-

level antitakeover defenses, it is not clear that they add much.

We report in column 6 of Table 6 (i.e.,Model 5) the regression results from

substituting these component indices for the GINDEX. While the negative sign on both

the internal provisions index and the compensation and liabilities index are consistent

with the arguments in Baranchuk, Kieschnick, and Moussawi (2005) in that such

provisions help managers to maximize the value of potential growth options in their

establishments, neither coefficient is statistically significant. Further, none of the

coefficients of the different corporate charter indices are statistically significant and so

we conclude that the corporate charter characteristics of a corporation do not significantly

influence its working capital management performance.

Overall, our evidence for the compensation and governance variables suggest that

monitoring of management and managerial compensation are more important influences

on a firms management of its working capital.


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6. Summary and Conclusions

Several recent business studies suggest that US corporations, on average, over-

invest in working capital. If correct, and if recognized by the market, then one should

observe a negative relation between investment in working capital and firm value. We

address this issue by examining data on samples of U.S. corporations from 1990 through

2004. We find that on average firms have over-invested in their working capital in the

sense that additional investment in working capital is associated with a reduction in firm

value. Such a conclusion appears consistent with the various annual surveys by REL

Consultancy for the CFO Magazine on corporate working capital performance, and the

recent ITworld.com survey of IT firms working capital practices. Apparently the market

recognizes this over-investment and discounts firms for it. However, one can also view

the flip side of this evidence and explain why firms like Wal-Mart suggest that their

working capital management practices are a source of their value.

Given this evidence, we then turn to the question of what factors influence the

efficiency of a corporations working capital management. We find that the inefficiency

of a firms working capital management is positively correlated with firm size and

uncorrelated with its industrys concentration. We interpret these results as suggesting

that firms are not using their market power at the margin to improve the efficiency of

their working capital management practices. Instead, they tend to follow the practices of

their industry. Further, they tend to invest in working capital in anticipation of future

sales growth. Expanding this analysis to include different firm governance features, we

find evidence that the larger the proportion of outsiders on a firms board, the better its

working capital management performance. Such evidence is consistent with the

monitoring of management role of outside directors. Taking the CEOs compensation

and stock ownership also proves important. The larger the CEOs current compensation

the better the firms working capital management performance. However, the larger the

CEOs share of the firms stock, the contrary behavior is shown. Finally, taking

corporate charter features in account, we find no evidence that any such features are

significant influences on a corporations working capital management practices.

Consequently our evidence appears to emphasize the role of board monitoring of

management and managements compensation in its control of the firms working capital.


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One question that is raise by our study is what determines industry practices, as it

is clear from our firm level analyses that industry practices are a critical determinant of

firm practices. We defer this issue to future research.


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Table 1

Number of Sample Firms by Year and by Analysis

The number of firms in our full sample of Compustat and CRSP firms, which is used inour valuation analysis in Section 4, is reported in column 2. After merging with IRRC

Governance, IRRC Directors, and with Execucomp datasets, the maximum number offirms in our subsample, which is used in our cash conversion analysis in Section 5, isreported in column 3.

YearNumber of Firms in the

Valuation AnalysisMaximum Number of Firms

in the CCC Analysis

1990 5,969 1,361

1991 6,071 1,327

1992 6,377 1,319

1993 6,664 1,306

1994 6,945 1,3721995 7,657 1,422

1996 7,780 1,371

1997 7,584 1,359

1998 7,713 1,734

1999 7,627 1,592

2000 7,205 1,585

2001 6,701 1,520

2002 6,311 1,810

2003 5,874 1,748

2004 5,043 1,800


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Table 2

Basic Summary Statistics

MVF(t) represents the market value of a firm, as defined by Fama and French (2002), attime t. CASH(t) represents the amount of cash and marketable securities reported for thefirm at time t. Time t is the end of the fiscal year prior to the fiscal year (t+1). OCF(t+1)

represents net income plus depreciation and amortization expense plus interest expenseplus sales of fixed assets. INVL(t+1) represents investment in fixed assets (property,plant and equipment) during year t+1. INVS(t+1) represents investment in net workingcapital, where net working capital is defined as current assets minus cash minus accountspayable minus accrued expenses. INVC(t+1) represents investment in cash andmarketable securities during year t+1. All variables are in millions, and were winsorizedat the 1% level.

Mean Median Std Deviation

MVF(t) 1416.184 114.270 4501.54

CASH(t) 84.400 5.59 662.209

OCF(t+1) 102.126 4.908 345.019

INVL(t+1) 61.095 3.334 202.212

INVS(t+1) 3.929 0.249 64.417

INVC(t+1) 7.801 0.037 62.691


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Table 3

Analysis of Firm Valuation: Capital Cash Flow ModelMVF(t) represents the market value of a firm, as defined by Fama and French (2002), attime t. CASH(t) represents the amount of cash and marketable securities reported for the

firm at time t. Time t is the end of the fiscal year prior to the fiscal year (t+1). OCF(t+1)represents net income plus depreciation and amortization expense plus interest expenseplus sales of fixed assets. INVL(t+1) represents investment in fixed assets (property,plant and equipment) during year t+1. INVS(t+1) represents investment in net workingcapital, where net working capital is defined as current assets minus cash minus accountspayable minus accrued expenses. INVC(t+1) represents investment in cash andmarketable securities during year t+1. All variables based upon Compustat data werewinsorized at the 1% and 99% levels. The regression model is a general estimatingequations model with a log link (i.e., ln(E(y|x))) with standard errors estimated usingRogers/Huber-White estimators adjusted for firm level clustering. P-values associatedwith the null hypothesis that the coefficient equals zero are reported within parentheses.

Model 1 Model 2

Constant 7.964791(0.00)

7.987754(0.00)

CASH(t) 0.0005372(0.00)

0.000567(0.00)

OCF(t+1) 0.0003641(0.00)

0.0003309(0.00)

INVL(t+1) 0.0004833

(0.00)

0.0004894

(0.00)INVS(t+1) -0.0000405

(0.01)

-0.0000101

(0.02)

INVC(t+1) 0.0001917(0.00)

2 1210.44

(0.00)1202.87(0.00)


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Table 4

Basic Summary Statistics for Fama-French Valuation Analysis

The definitions of most of these variables follow those in Pinkowitz and Williamson(2005). X(t) is the level of variable X in year t divided by the level of assets in year t.dX(t) is the change in the level of X from year t-2 to year t divided by total assets in year

t. dX(t+2) is the change in the level of X from year t to year t+2 divided by total assets inyear t. M is the market value of equity. E is earnings before extraordinary tems, plusinterest, deferred tax credits and investment tax credits. NA is assets minus cash. NNAis NA minus accounts receivable and inventory. RD is research and developmentexpense. I is interest expense. DIV is common dividends. C is cash and marketablesecurities. NWC is account receivable plus inventory minus accounts payable. Allvariables are in millions, and were winsorized at the 1% level.


M(t) 1.714 0.939 2.318

E(t) -0.117 0.042 0.477

dE(t) 0.008 0.009 0.353dE(t+2) -0.023 0.013 0.467

dNA(t) 0.501 0.493 0.252

dNA(t+2) 0.138 0.067 0.898

dNNA(t) 0.181 0.168 0.423

dNNA(t+2) 0.172 0.030 0.710

RD(t) 0.062 0 0.129

dRD(t) 0.003 0 0.062

dRD(t+2) 0.025 0 0.119

I(t) 0.032 0.018 0.043

dI(t) -0.0005 0 0.033

dI(t+2) 0.008 0 0.048DIV(t) 0.005 0 0.017

dDIV(t) 0.00005 0 0.007

dDIV(t+2) 0.00017 0 0.009

C(t) 0.176 0.076 0.220

NWC(t) 0.207 0.191 0.211

dNWC(t) -0.0003 0.016 0.201

dNWC(t+2) 0.089 0.01 0.308


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Table 5

Analysis of Firm Valuation: Fama-French ApproachThis analysis follows Pinkowitz and Williamsons (2005) variation of Fama and Frenchs (1998) valuationmodel. X(t) is the level of variable X in year t divided by the level of assets in year t. dX(t) is the change inthe level of X from year t-2 to year t divided by total assets in year t. dX(t+2) is the change in the level ofX from year t to year t+2 divided by total assets in year t. M is the market value of equity. E is earnings

before extraordinary tems, plus interest, deferred tax credits and investment tax credits. NA is assets minuscash. NNA is NA minus accounts receivable and inventory. RD is research and development expense. I isinterest expense. DIV is common dividends. C is cash and marketable securities. NWC is accountreceivable plus inventory minus accounts payable. Following Peterson (2005), each regression model is afixed effects model with standard errors estimated using Rogers estimators adjusted for firm levelclustering. P-values associated with the null hypothesis that the coefficient equals zero are reported withinparentheses.

M(t) M(t) M(t)

Constant 1.453(0.00)

1.565(0.00)

1.422(0.00)

E(t) -0.754(0.00)

-0.546(0.00)

-0.800(0.00)

dE(t) 0.522(0.00)

0.470(0.00)

0.405(0.00)

dE(t+2) -0.202

(0.00)

-0.168

(0.00)

-0.344

(0.00)dNA(t) -0.862

(0.00)

dNA(t+2) 0.276(0.00)

dNNA(t) -0.955(0.00)

-0.854(0.00)

dNNA(t+2) 0.343(0.00)

0.500(0.00)

RD(t) 3.847(0.00)

3.89(0.00)

3.696(0.00)

dRD(t) 1.757(0.00)

2.086(0.00)

1.477(0.00)

dRD(t+2) 4.128(0.00)

4.569(0.00)

3.897(0.00)

I(t) 0.144

(0.87)

-0.146

(0.86)

-0.011

(0.99)dI(t) -3.572

(0.00)-2.399(0.00)

-3.885(0.00)

dI(t+2) -0.132(0.81)

2.478(0.00)

-0.277(0.00)

DIV(t) 9.509(0.00)

10.376(0.00)

9.256(0.00)

dDIV(t) -1.202(0.21)

-1.172(0.23)

-1.436(0.14)

dDIV(t+2) 7.988(0.00)

9.361(0.00)

7.762(0.00)

dM(t+2) 0.203(0.00)

.183(0.00)

.208(0.00)

C(t) 0.701(0.00)

.773(0.00)

.709(0.00)

NWC(t) -1.439

(0.00)

-1.110

(0.00)dNWC(t) 0.907

(0.00)

dNWC(t+2) 1.244(0.00)

R2 0.27 0.29 0.30


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Table 6

Basic Summary Statistics

CCC represents the cash conversion cycle for a firm, defined as the inventory conversionperiod plus the receivables collection period minus the payables deferral period usingCompustat data. CCCM represents the median cash conversion cycle for a firms

industry using Fama and French (1997) 48 industry classification where firm is allocatedto one of the industries identified in Appendix B. TA represents the total assets (bookvalue) of a firm. PTA is the ratio or the firms property, plant and equipment, to its totalassets. FSG is the firms sales growth over the future two years, computed usingCompustat data. HHI represents the Herfindahl-Hirschman index for a firms industry(using the Fama and French delineations) using Compustat sales data. DIR represents thefirms number of directors, collected from IRRC Directors database. POD is theproportion of board members who are outsiders, collected from IRRCs Directorsdatabase. TCCOMP represents the CEOs total current compensation, excluding stockoptions. TUO represents the CEOs total unexercised stock options. CEOPS representsthe proportion of stock held by the CEO. GINDEX represents Gompers, Ishii and

Metrick (2003) governance index. INTERNAL, EXTERNAL, MVP, C&L, and SLAWSrepresent governance indices identified in Appendix A.


CCC 915.678 143.494 2807.534

CCCM 948.266 149.435 2950.86

TA 7656.707 1427.94 20990.76

PTA 0.310 0.257 0.242

FSG 0.078 0.061 0.174

HHI 0.068 0.051 0.070

DIR 9.745 9 3.085

POD 0.625 0.666 0.186

TCCOMP 1522138 1102150 1109268

TUO 15300000 2361523 39400000

CEOPS 0.027 0.002 0.078

GINDEX 9.065 9 2.785

INTERNAL 1.958 2 1.545

EXTERNAL 1.963 2 1.092

MVP 0.221 0 0.437

C&L 2.269 2 1.569

SLAWS 1.743 1 1.307


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Table 7

Analysis of Factors Influencing Management of Working CapitalThe dependent variable in each regression is Ln(CCC), which represents the logarithm of the cash conversion cycle fora firm, defined as the inventory conversion period plus the receivables collection period minus the payables deferralperiod using Compustat data. Ln(CCCM) represents the logarithm of the median cash conversion cycle for a firmsindustry using Fama and French (1997) 48 industry classification where firm is allocated to one of the industriesidentified in Appendix B. TA represents the total assets (book value) of a firm. PTA is the ratio or the firms property,plant and equipment, to its total assets. FSG is the firms sales growth over the future two years, computed usingCompustat data. HHI represents the Herfindahl-Hirschman index for a firms industry (using the Fama and Frenchdelineations) using Compustat sales data. DIR represents the firms number of directors, collected from IRRCDirectors database. POD is the proportion of board members who are outsiders, collected from IRRCs Directorsdatabase. Ln(TCCOMP) represents the logarithm of the CEOs total current compensation, excluding stock options.Ln(TUO) represents the logarithm of the CEOs total unexercised stock options. CEOPS represents the proportion ofstock held by the CEO. GINDEX represents Gompers, Ishii and Metrick (2003) governance index. INTERNAL,

EXTERNAL,MVP, C&L, and SLAWS represent governance indices identified in Appendix A. Note that all accountingvariables were winsorized at the 1% level. The below regression is estimated using a fixed effects model with standarderrors estimated using Rogers/Huber/White estimators corrected for firm level clustering. P-values for the nullhypothesis that the coefficient equals zero are reported within parentheses.

Model 1 Model 2 Model 3 Model 4 Model 5

Constant 1.623859

(0.00)

0.687534

(0.01)

0.918074

(0.00)

0.918085

(0.00)

0.899097

(0.00)Ln(CCCM) 0.603557

(0.00)0.662138

(0.00)0.676244

(0.00)0.675400

(0.00)0.673348

(0.00)

Ln(TA) 0.068978(0.00)

0.1437(0.00)

0.142941(0.00)

0.143153(0.00)

0.145330(0.00)

PTA -0.302807(0.00)

-0.079926(0.31)

-0.119592(0.17)

-0.122012(0.17)

-0.125612(0.16)

FSG 0.276770(0.00)

0.316564(0.00)

0.271339(0.00)

0.271929(0.00)

0.271927(0.00)

HHI 0.064759(0.62)

DIR 0.000529(0.79)

POD -0.066063(0.02)

-0.084497(0.00)

-0.081428(0.01)

-0.081808(0.01)

Ln(TCCOMP) -0.016490(0.00)

-0.016399(0.00)

-0.016260(0.00)

Ln(TUO) -0.002773(0.29)

-0.002767(0029)

-0.002768(0029)

CEOPS 0.093137(0.01)

0.091170(0.02)

0.088009(0.02)

GINDEX -0.001766(0.66)

INTERNAL -0.004930(0.42)

EXTERNAL 0.004064

(0.64)MVP 0.043138

(0.11)

C&L -0.007786(0.27)

SLAWS 0.003331(0.78)

F statistic 202.10(0.00)

62.52(0.00)

33.40(0.00)

33.40(0.00)

23.40(0.00)


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Appendix A: Classification of Antitakeover ProvisionsThis table describes our index building methodology and classification of antitakeover provisions, which issymmetric to IRRC provisions in Gompers, Ishii and Metrick (2003), and is outlined in Bebchuk Cohen andFerrell (2004). The classifications are used to create explanatory variables that count the number ofprovisions within each classification that are in place.

Provision Class and Index IRRC Provision Notes

Internal Provisions Index INTERNAL

1 Unequal voting provisionsUnequal Voting (Dual Classstructures, Time phased, Others)

2 Classified Board with Staggered Terms

3 Limits to Amend Charter

4Limits on Shareholder Rights

Limits to Amend Bylaws

5 Limits to Call Special Meeting

6 Limits for Written Consent

7

Limits on Internal Governance

Advance Notice Requirements

External Provisions Index EXTERNAL1 Poison Pill

Does not require shareholderratification

2 Super Majority to Approve Merger

3 Antigreenmail

4 Director Duties (Stakeholder Clause)

5 Fair Price

Compensation and Liability Provisions Index C&L

1 Director Liability

2 Director Indemnification

3

Director Liability & Indemnification

Indemnification Contracts

4 Severance Agreements w/Change in Control

5Severance Agreements (Empl.Contracts)

6

Executive Severances (GoldenParachutes)

Compensation Plans withChange in Control

(Some) Do not require shareholderratification

Minority Voting Provisions Index MVP

1 Confidential Voting

2 Cumulative Voting [or not(Eliminate Cumulative Voting)] Default rule in only 5 states

State Laws Index SLAWS

1 Business Combination (Freezeout) Law 33 States

2 Fair Price Law 27 States - Similar to fair price3 Control Share Acquisition Law 27 States - Similar to supermajority

4 Recapture of profits Law 7 States - Similar to anti-greenmail

5 Control Share Cash out Law 3 States - Similar to fair price

6 Director Duties (Stakeholder Clause) Law 2 States - Similar to director duties


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32

Appendix B: Industrial Classifications

These industrial classifications are similar to those used in Fama and French (1997).More documentation is available athttp://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html .

Code DescriptionCorresponding 4-Digit SIC Code (Compustat's

DNUM)

1 Agriculture 0100-0799, 2048-2048

2 Food Products2000-2046, 2050-2063, 2070-2079, 2090-2095, 2098-2099

3 Candy & Soda 2064-2068, 2086-2087, 2096-2097

4 Beer & Liquor 2080-2085

5 Tobacco Products 2100-2199

6 Recreation 0900-0999, 3650-3652, 3732-3732, 3930-3949

7 Entertainment 7800-7841, 7900-7999

8 Printing and Publishing 2700-2749, 2770-2799

9 Consumer Goods

2047-2047, 2391-2392, 2510-2519, 2590-2599, 2840-2844, 3160-3199, 3229-3231, 3260-3260, 3262-3263,3269-3269, 3630-3639, 3750-3751, 3800-3800, 3860-3879, 3910-3919, 3960-3961, 3991-3991, 3995-3995

10 Apparel2300-2390, 3020-3021, 3100-3111, 3130-3159, 3965-3965

11 Healthcare 8000-8099

12 Medical Equipment 3693-3693, 3840-3851

13 Pharmaceutical Products 2830-2836

14 Chemicals 2800-2829, 2850-2899

15 Rubber and Plastic Products 2830-2836

16 Textiles 2200-2295, 2297-2299, 2393-2395, 2397-2399

17 Construction Materials

0800-0899, 2400-2439, 2450-2459, 2490-2499, 2950-2952, 3200-3219, 3240-3259, 3261-3261, 3264-3264,3270-3299, 3420-3442, 3446-3452, 3490-3499, 3996-3996

18 Construction 1500-1549, 1600-1699, 1700-1799

19 Steel Works Etc 3300-3369, 3390-3399

20 Fabricated Products 3400-3400, 3443-3444, 3460-3479

21 Machinery 3510-3536, 3540-3569, 3580-3599

22 Electrical Equipment3600-3621, 3623-3629, 3640-3646, 3648-3649, 3660-3660, 3691-3692,3699-3699

23 Automobiles and Trucks2296-2296, 2396-2396, 3010-3011, 3537-3537, 3647-3647, 3694-3694, 3700-3716, 3790-3792, 3799-3799

24 Aircraft 3720-372925 Shipbuilding, Railroad Equip. 3730-3731, 3740-3743


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33

Appendix B: Industrial Classification (continued)

Code DescriptionCorresponding 4-Digit SIC Code (Compustat's

DNUM)

26 Defense 3480-3489, 3760-3769, 3795-379527 Precious Metals 1040-1049

28 Non-Metal & Ind. Metal Mining 1000-1039, 1060-1099, 1400-1499

29 Coal 1200-1299

30 Petroleum and Natural Gas 1310-1389, 2900-2911, 2990-2999

31 Utilities 4900-4999

32 Communication 4800-4899

33 Personal Services

7020-7021, 7030-7039, 7200-7212, 7215-7299, 7395-7395, 7500-7500, 7520-7549, 7600-7699, 8100-8199,8200-8299, 8300-8399, 8400-8499, 8600-8699, 8800-8899

34 Business Services

2750-2759, 3993-3993, 7300-7372, 7215-7299, 7395-

7395, 7500-7500, 7520-7549, 7600-7699, 8100-8199,8200-8299, 8300-8399, 8400-8499, 8600-8699, 8800-8899

35 Computers 3570-3579, 3680-3689, 3695-3695, 7373-7373

36 Electronic Equipment 3622-3622, 3661-3679, 3810-3810, 3812-3812

37 Measuring and Control Equip. 3811-3811, 3820-3830

38 Business Supplies2520-2549, 2600-2639, 2670-2699, 2760-2761, 3950-3955

39 Shipping Containers 2440-2449, 2640-2659, 3210-3221, 3410-3412

40 Transportation4000-4099, 4100-4199, 4200-4299, 4400-4499, 4500-4599, 4600-4699, 4700-4799

41 Wholesale 5000-5099, 5100-5199

42 Retail 5200-5299, 5300-5399, 5400-5499, 5500-5599, 5600-5699, 5700-5736, 5900-5999

43 Restaurants, Hotels, Motels5800-5813, 5890-5890, 7000-7019, 7040-7049, 7213-7213

44 Banking 6000-6099, 6100-6199

45 Insurance 6300-6399, 6400-6411

46 Real Estate 6500-6553

47 Trading 6200-6299, 6700-6799

48 Miscellaneous 3900-3900, 3990-3990, 3999-3999, 9900-9999

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