Net Operating Working Capital
Behavior: A First Look
Matthew D. Hill, G. Wayne Kelly, and Michael J. Highfield∗
Net operating working capital captures multiple dimensions of firms’ adjustments to operating and
financial conditions. Sales growth, uncertainty of sales, costly external financing, and financial
distress encourage firms to pursue more aggressive working capital strategies. Firms with greater
internal financing capacity and superior capital market access employ more conservative working
capital policies. Results are robust to unobserved heterogeneity and industry effects. The evidence
suggests that operating and financing conditions should be considered when evaluating working
capital behavior, not just industry averages. Additionally, industry concentration magnifies the
effect of sales growth.
A substantial body of research examines individual components of operating working capital
in isolation. Long, Malitz, and Ravid (1993), Deloof and Jegers (1996), and Ng, Smith, and Smith
(1999) study trade credit policy and find support for the contracting cost motive with receivables
used as a product quality indicator. Petersen and Rajan (1997) demonstrate that receivables are
directly tied to profitability and capital market access, and Deloof and Jegers (1999) study the
demand side of trade credit and illustrate that payables are directly related to financing deficits.1
An examination of the motives behind operating working capital strategy accounting for the
net influence of receivables, inventory, and payables, is absent from the finance literature.2 While
existing studies typically focus on individual components of working capital, we integrate the
components to investigate factors influencing the net investment in operating working capital
using the working capital requirement (WCR), defined as the sum of accounts receivable and
We appreciate the helpful comments from the editor (Bill Christie), two anonymous referees, Dan Bradley, Ken Cyree,
Corie Hulen-Hill, Julia Ingram, Bonnie Van Ness, Robert Van Ness, Jim Washam, and John Zietlow as well as session
participants at the 2007 Southern Finance Association Annual Meeting and the 2008 Financial Management Association
Annual Meeting. Financial support and research assistance for this project was provided by the College of Business
and Industry and the Warren Chair of Real Estate at Mississippi State University. A previous version of this paper was
titled “Determinants of Operating Working Capital Policy: A First Look.” All errors remain the sole property of the
authors.
∗
Matthew D. Hill, CTP, is an Assistant Professor of Finance at the University of Mississippi in Oxford, Mississippi, USA.
G. Wayne Kelly is an Associate Professor of Finance at Mississippi State University in Starkville, Mississippi, USA.
Michael J. Highfield, CFA, is an Assistant Professor of Finance and Interim Robert W. Warren Chair of Real Estate at
Mississippi State University in Starkville, Mississippi, USA.
1
Please see the following for a full review of the trade credit literature: Melzer (1960), Smith (1987), Mian and Smith
(1992), Lee and Stowe (1993), Biais and Gollier (1997), Emery and Nayar (1998), Frank and Maksimovic (1998),
Burkhart and Ellingsen (2004), Atanasova (2007), Love, Preve, and Sarria-Allende (2007), Cunat (2007), and Molina
and Preve (2009).
2
Theoretical integrative working capital studies include Richards and Laughlin (1980), Sartoris and Hill (1983), and
Gentry, Vaidyanathan, and Lee (1990).
Financial Management • Summer 2010 • pages 783 - 805
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3
inventories net of accounts payable. Operating assets and liabilities ultimately must be managed
in concert rather than individually, a condition this paper attempts to reflect.
As discussed by Atanasova (2007) and shown by Molina and Preve (2009), most firms supply
and demand trade credit simultaneously; firms with better access to trade credit find it easier to
finance receivables and inventory. We use the WCR as it is comprehensive. By focusing on the
operating component of working capital, the results and implications are clearly distinguishable
from those presented in corporate cash holdings research that is necessarily limited to the causes
and consequences of holding cash.4 A positive WCR, or conservative working capital policy,
indicates a need for additional capital that firms can finance internally using free cash flow, or
externally via commercial paper or lines of credit. Thus, conservative working capital policies
imply either opportunity costs or explicit financing costs. Alternatively, a negative working capital
gap means the firm’s net operating working capital provides financing for long-term assets, an
aggressive strategy.
Using a sample of 20,710 firm-year observations for 3,343 companies over the 1996 to 2006
period, we find that the mean WCR is $296 million, or 23% of capital structure, on average.
This statistic suggests that working capital behavior deserves closer scrutiny, particularly in
light of Fazzari and Petersen’s (1993) finding that increased net working capital reduces fixedinvestment and evidence that profitability and risk-adjusted returns are negatively related to the
cash conversion cycle (e.g., Shin and Soenen, 1998; Deloof, 2003).5
The dependent variable across all models is the WCR, which focuses the study on the drivers of
net operating working capital behavior and accounts for the joint effect of receivables, inventory,
and payables. The results provide evidence of strong relationships between net operating working
capital and operating conditions (e.g., sales growth and sales volatility). Since a positive WCR
must be financed, management must devise a strategy to acquire funds internally or externally.
Accordingly, the results indicate that the WCR depends on internal financing resources, external
financing costs, capital market access, negotiating ability, and financial distress. Additionally,
by adjusting the dependent variable by the annual mean industry-level WCR-to-sales ratio, we
confirm that the results are robust to industry effects. Finally, we present evidence regarding the
relation between working capital behavior and the degree of industry competitiveness. The results
are robust to econometric specification, firm-specific heterogeneity, and time effects.
Our results suggest that operating conditions and financing ability influence the WCR even
after adjusting for industry working capital norms, and we infer that quantifying the efficiency of
working capital management using traditional methods, such as industry-level ratio comparisons,
may lead to questionable inferences and should be accompanied by even greater caveats than is
typical. Hence, the models presented here provide an improved method to benchmark operating
working capital behavior.
The remainder of the paper is organized as follows. Section I discusses the hypothesized
relationship between the WCR and various corporate financial characteristics. Section II describes
3
Shulman and Cox (1985) define the working capital requirement as current assets net of cash minus current liabilities
net of notes payable and current long-term debt due, thus including prepaid expenses and accruals. We use the modified
version of the working capital requirement for two reasons. First, the simplified version parallels the cash conversion
cycle. Second, theoretical implications of holding prepaid expenses and accruals are not well developed in the extant
literature.
4
Kim, Mauer, and Sherman (1998), Opler et al. (1999), Ozkan and Ozkan (2004), and Dittmar and Mahrt-Smith (2007)
exemplify typical methods and findings of corporate cash holdings research.
5
Specifically, Shin and Soenen (1998) use the net trade cycle, a variation of the cash conversion cycle, while Deloof and
Jegers (2003) use the cash conversion cycle. Also, Kieschnick, LaPlante, and Mousawwi (2008) estimate the value of net
operating working capital.
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the sample and descriptive statistics and specifies the model. Section III presents the results and
Section IV provides our conclusions.
I. Hypothesis Development
A. Operating Conditions
1. Sales Growth
Sales growth affects working capital behavior, but the relationship between the WCR and
sales growth is complicated by potential endogeneity problems. For example, relaxed credit and
inventory policies can stimulate sales causing reverse causality when using contemporaneous
sales growth as an independent variable. Similar to Love, Preve, and Sarria-Allende (2007) and
Molina and Preve (2009), we mitigate this problem by lagging sales growth.6
Molina and Preve (2009) find trade credit granted is inversely related to lagged sales growth,
and they suggest that firms with greater prior period growth tighten credit policy as they achieve
planned levels of sales growth. With respect to the spontaneous sources of funds generated by
sales growth, Petersen and Rajan (1997) and Deloof and Jegers (1999) indicate that payables are
directly related to growth, supporting the expected relation between the variables. Since the WCR
is the net result of the firm’s operating working capital behavior, and the effect of sales growth on
inventory is not clear from the previous literature, we expect an inverse relationship between the
WCRs and lagged sales growth. Lagged sales growth is defined as the lagged percentage change
in sales over the period t−1 to t.
2. Contribution Margin
Over the typical operating cycle, firms acquire and finance inventory via payables and then
finance the marked-up product to customers through receivables before collecting on the sale.
Since the dollar value of a good in terms of receivables generally exceeds the dollar value of a
good in payables, each unit sold contributes to an increased WCR. Thus, we expect the WCR and
contribution margin are directly related. Since most sampled firms sell multiple products or are
conglomerates, the lagged gross profit margin (GPM), defined as the ratio of sales minus cost of
goods sold to sales, is used to proxy contribution margin.7
3. Sales Volatility
Higher deviations in demand make the optimal inventory level more difficult to determine
making increased inventory a rational response to sales volatility. Emery (1987) confirms that
not all firms will find it advantageous to increase inventory in response to increased sales
volatility. For example, firms with cost advantages in financing receivables may find it optimal to
extend additional credit to customers. That is, to avoid the buildup of inventory due to decreased
demand, firms can move inventory by offering more attractive credit terms. Widespread access to
financial innovations such as asset-backed commercial paper affords cost advantages in financing
receivables, meaning receivables would be cheaper to finance than inventory for many firms.
Empirical findings are mixed. Using a sample of industrial firms, Long, Malitz, and Ravid
6
7
When applicable, in an effort to mitigate endogeneity concerns, we lag all independent variables used in this study.
Gross profit margin aggregates contribution margins for individual product, and Compustat offers no way to individualize
these margins by product/division.
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(1993) provide empirical support for Emery’s (1987) operational view of trade credit. In contrast,
Deloof and Jegers (1996) find no relation between receivables and sales volatility for a sample of
Belgian industrial and wholesale firms. Also, results presented by Ng, Smith, and Smith (1999)
suggest that firms generally do not respond to deviations in demand by altering credit terms.8
Spontaneous financing demanded by firms should relate directly to sales volatility; firms with
more unpredictable revenues have greater difficulty forecasting day-to-day liquidity needs and
subsequent funding. Thus, firms with volatile sales tend to rely on payables to enhance cash flow.
Due to conflicting empirical evidence reported by Long, Malitz, and Ravid (1993), Deloof and
Jegers (1996), and Ng, Smith, and Smith (1999), the link between the net investment in operating
working capital and sales volatility is an empirical question; therefore, we do not predict the sign
of the sales volatility variable.
Sales volatility is the standard deviation of a firm’s annual net sales over a rolling five-year
period prior to each of the sample years. For example, the standard deviation of sales for 2006
is calculated over the five-year period of 2001-2005. Firm-year observations are included in the
sample for a given year if the firm has at least three observations during the previous five-year
period. Thus, sales volatility is designed as a backward looking measure alleviating endogeneity
concerns. The sales variability measure is scaled by net assets, defined as total assets minus cash
and short-term investments.
B. Ability to Finance Operating Working Capital
1. Operating Cash Flow
Positive operating cash flow enables firms to finance a positive WCR, allowing a more conservative operating working capital strategy, thereby facilitating future sales growth. However,
firms with negative operating cash flows must finance a positive WCR through other sources.
Love, Preve, and Sarria-Allende (2007) estimate a direct correlation between net trade credit
and cash flow for a sample of firms in emerging market countries. Consequently, we expect a
positive association between WCRs and cash flow. To relieve endogeneity concerns, cash flow
is measured as lagged operating income before depreciation minus income taxes scaled by net
assets.
2. Asymmetric Information and Costs of External Financing
Myers and Majluf (1984) demonstrate that capital markets extract a premium for the external
financing of firms with greater informational asymmetries as such firms’ projects and cash flows
are more difficult to value leading firms to follow a financing pecking order and exhausting the
lowest cost sources of capital first. A positive WCR must be financed. We expect less transparent
firms to have a reduced WCR since firms with greater informational asymmetries typically pay
greater rates to borrow. The lagged market-to-book ratio is used as a proxy for the degree of
asymmetric information, where market-to-book is defined as the sum of market value of equity
and total liabilities minus payables scaled by net assets. We expect an inverse relation between
the WCR and the market-to-book ratio.
3. Capital Market Access
Creditworthy firms with superior capital market access are more capable of financing the
working capital gap externally. Brennan and Hughes (1991) argue that larger firms are covered
8
Ng, Smith, and Smith (1999) use survey data for firms in various industries.
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more intensely by analysts, whose increased monitoring reduces informational asymmetries,
implying that larger firms enjoy ready access to capital relative to smaller firms. Since this study
examines the determinants of net operating working capital, we emphasize commercial paper
issues and bank debt.
While larger firms find it easier to finance relaxed credit and inventory policies, smaller firms
are less able to issue commercial paper or negotiate lines of credit. With fewer ways to finance
receivables, smaller firms rely on factoring more than large firms.9 Whited (1992) finds that larger
firms face fewer borrowing constraints than smaller firms since the former have better capital
market access.10 Petersen and Rajan (1997) suggest that receivables are directly related to size
and Deloof and Jegers (1999) report that payables are insignificantly related to size. Accordingly,
we expect a direct relationship between the net investment in operating working capital and size.
We use the natural logarithm of the lagged annual inflation-adjusted market value of equity to
proxy size.
4. Market Power
The length of trade credit terms are directly related to market power as more valuable customers
can negotiate more generous credit terms with suppliers. Additionally, firms with greater market
share can stretch the credit terms offered by suppliers with little repercussion as contracts with
industry leaders are critical to the viability of smaller suppliers. Similarly, strong relationships
with vendors allow firms with greater market power to hold less inventory.
Suppliers with more market power relative to customers can negotiate shorter terms with
customers for at least two reasons. First, the level of competition from rival firms is reduced for
firms with large market share, decreasing the likelihood of losing customers over a reduction in
credit terms. Second, suppliers with greater market share are more likely to have forged longer
relationships with clients implying higher costs of switching suppliers. These switching costs
include learning and transactions costs as documented by Klemperer (1987) and Chevalier and
Scharfstein (1996). Molina and Preve (2009) indicate that, when compared to firms in competitive
industries, firms in concentrated industries tighten credit policies to a greater extent when facing
financial distress.
Overall, we expect firms with greater negotiating power to have more payables, fewer receivables, and less inventory. That is, the net impact of increased market power is a reduced WCR.
We measure market power as the lagged ratio of a firm’s annual sales to the total annual sum of
sales in a given industry with greater ratios implying increased negotiating ability. Industries are
defined by the Fama-French (1997) classifications. We expect WCRs to be inversely related to
market power. Market power is a firm-specific proxy for the firm’s ability to negotiate bilaterally
as both client and supplier.
5. Financial Distress
Distressed firms have limited financial slack and cash generating ability, and the strain of
financial distress may cause firms to reduce investment in operating working capital by collecting
on receivables, tightening credit terms, liquidating existing inventory, and stretching credit terms
granted by suppliers. Molina and Preve (2009) demonstrate that financially distressed firms have
significantly reduced levels of trade credit relative to their nondistressed counterparts. We expect
the WCR to correlate inversely with financial distress.
9
Factoring is not explicitly accounted for and so its influence is not observed here due to data availability.
10
Many studies use size as an inverse proxy of financial constraint (Fazzari, Hubbard, and Petersen, 1988; Almeida,
Campello, and Weisbach, 2004; Faulkender and Wang, 2006).
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Following Molina and Preve (2009), a firm must satisfy two criteria to be classified as financially distressed: 1) the firm must have difficulty covering interest payments and 2) it must be
overleveraged. The first component is having a coverage ratio calculated as operating income
before depreciation divided by interest expense less than one for two consecutive years or less
than 0.80 in any given year. Second, a firm is considered overleveraged if its leverage ratio is in
the top two deciles of its industry’s leverage ratio in a given year. If a firm meets both conditions
in a given year, then Distresst−1 , an indicator variable, equals one and zero otherwise.11
II. Data and Methods
A. Data Source and Description
The initial sample includes all nonfinancial, nonutility, non-ADR, and SIC-classifiable firms
covered by the Compustat database over the period 1991-2006.12 We eliminate firm-years with
missing financial data, nonpositive assets, nonpositive sales, negative market value of equity,
negative interest expense, and duplicate values. We include firm-years in the sample if the firm
makes at least three appearances in the panel during the five-year period preceding a given
year. Thus, observations from the first five years, while not directly studied in the analysis, are
necessary to construct the first cross-sectional set in 1996.13 Finally, to mitigate the influence
of outliers, firm-level ratios are winsorized at the 1% level. The final sample consists of 20,710
firm-year observations for 3,343 companies from 1996 to 2006, an unbalanced panel data set
with maximum, minimum, and mean number of individual firm appearances equal to 11, 1, and
6.20 panels, respectively.
The descriptive statistics for the variables used to estimate the determinants of operating
working capital behavior are displayed in Table I. The mean WCR-to-sales ratio is approximately
19.8%. Thus, approximately $0.20 of each dollar in sales is tied up in net operating working
capital equating to just over $296 million, a nontrivial amount given the reduction in free cash
flow and market value this represents.
Though unreported, the mean WCR-to-total-assets ratio is 22.7%, illustrating the magnitude
of a firm’s capital structure devoted to operating working capital. This statistic is as striking
as the mean cash-to-total-assets ratio of 22.0% reported by Dittmar and Mahrt-Smith (2007),
yet the financial component of working capital receives much more attention than its operating
component. Given the size and implications of the reported average WCR-to-total-assets ratio, it
11
Molina and Preve (2009) use three proxies for financial distress. First, FINDIST is a binary variable equal to one if a
firm’s coverage ratio, calculated as operating income before depreciation divided by interest expense, is less than one for
two consecutive years, or if the coverage ratio is less than 0.80 in any given year. Second, FDLEV is a binary variable
equal to one if the firm is in the top two deciles of industry leverage in a given year and meets the conditions described
by FINDIST above. Third, the authors define financial distress as a firm that has had three consecutive years of losses,
LOSSFD. We consider FDLEV the most stringent of the distress measures and we use this definition as the variable
Distress.
12
The Compustat mnemonic and codes for variables used in the analysis are as follows: Sales growth (SALECHG1,
none), Sales (SALE, A12), Operating Income Before Depreciation (OIBDP, A13), Income Taxes-Total (TXT, A16), Total
Assets (AT, A6), Cash and Short-Term Investments (CHE, A1), Receivables (RECT, A2), Inventory (INVT, A3), Accounts
Payable (AP, A70), Market Value of Equity (MKTVALF, none), Total Liabilities (LT, A181), Interest Expense (XINT,
A15), Cost of Goods Sold (COGS, A41), Preferred Stock (PSTK, A130), Deferred Taxes-Income Account (TXDI, A50),
and Debt-Convertible Total (DCVT, A79).
13
The use of lagged variables does not constrain the data set since the first three observations for each firm are lost due
to the methodology used to estimate sales volatility.
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Table I. Descriptive Statistics
This table provides the sample characteristics of 20,710 firm-years across 3,343 unique companies over
the period 1996-2006. WCR is the ratio of receivables plus inventory minus payables to sales at the end
of each year. WCRq is the ratio of the average end-of-quarter WCR to end-of-year sales. IndAdj WCR is
the difference between the annual WCR and industry average annual WCR for the respective year. IndAdj
WCRq is the difference between the quarterly WCR and industry average quarterly WCR for the respective
year. Growth is the percentage change in sales over the previous year. GPM is the ratio of sales minus
cost of goods sold to sales. SalesVAR is the standard deviation of sales. OCF is operating income before
depreciation minus taxes scaled by net assets. M/B is the ratio of market value of equity plus total liabilities
minus payables to net assets. Size is the market value of equity in 2006 dollars. MktShare is firm sales as a
percentage of aggregate sales in the firm’s industry. Distress equals one if a firm meets Molina and Preve’s
(2009) definition of financial distress and zero otherwise. Industry dependent calculations follow the Fama
and French (1997) 48 industry classification system.
Variables
WCR (%)
WCRq (%)
IndAdj WCR (%)
IndAdj WCRq (%)
Growtht−1 (%)
GPM t−1 (%)
SalesVar (%)
OCF t−1 (%)
M/Bt−1 (ratio)
Sizet−1 ($M)
MktSharet−1 (%)
Distresst−1 (binary)
N
Mean
Standard
Deviation
Minimum
Median
Maximum
20,710
20,710
20,710
20,710
20,710
20,710
20,710
20,710
20,710
20,710
20,710
20,710
19.785
19.258
0.001
−0.312
14.372
32.412
31.168
5.153
2.324
1,957.146
1.407
0.051
17.336
16.287
14.574
13.917
35.085
33.962
44.326
29.938
2.919
5,605.622
3.334
—
−163.256
−156.184
−181.776
−171.784
−70.86
−78.193
1.361
−353.085
0.481
0.789
0.000
0.000
18.895
18.236
0.998
−1.341
8.694
32.688
18.427
11.054
1.493
252.138
0.215
0.000
95.964
98.369
81.301
84.521
321.508
87.983
571.823
46.635
37.716
65,449.510
29.349
1.000
is hard to rationalize the relative lack of attention devoted to the operating component of working
capital.
Although discussed in more detail later, we use several measures of the WCR. WCRq is the ratio
of the average end-of-quarter WCR to end-of-year-sales. IndAdj WCR is the difference between
the firm’s annually reported WCR, and the firm’s industry average annual WCR for the respective
year. IndAdj WCRq is the difference between the firm’s WCRq and the firm’s industry average
annual WCR for the respective year.14
Summary statistics for the remaining variables are similar to prior studies. The average lagged
annual sales growth rate is 14.4% with a median of 8.7%. Mean lagged GPM is 32.4%, and the
average standard deviation of sales to net assets is 31.2%. Sales volatility exhibits substantial
skewness. Lagged operating cash flow is negatively skewed indicating that many firms have weak
operating cash flows or operating losses. The mean lagged market-to-book ratio is 2.3. While
this value may seem large, it should be noted that the base is net assets defined as total assets
minus cash. Mean and median lagged market capitalizations in the sample are $2.0 billion and
$252 million, respectively. Also, the average of the lagged market share variable is 1.4%. Finally,
5.1% of the observations qualify as financially distressed.
Table II provides the distribution of the sample across time. The maximum and minimum number of observations for a given year is 2,278 and 1,152 occurring in 2005 and 1996, respectively.
14
We reestimate the models after scaling all WCR measures by net assets. The results are robust.
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Table II. Time Distribution of Sample
This table provides the distribution of the sample across time for 20,710 firm-years across 3,343 unique
companies over the period 1996-2006. WCR is the ratio of receivables plus inventory minus payables to
sales at the end of each year. WCRq is the ratio of the average end-of-quarter WCR to end-of-year-sales.
IndAdj WCR is the difference between the annual WCR and industry average annual WCR for the respective
year. IndAdj WCRq is the difference between the quarterly WCR and industry average quarterly WCR for
the respective year. Mean values of IndAdj WCR and IndAdj WCRq are not reported since they approximate
zero by construction.
Sample Year
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Observations
Unique firms
N
1,152
1,516
1,616
1,783
1,871
1,924
2,042
2,128
2,232
2,278
2,168
20,710
3,343
Mean Values
Median Values
WCR
WCRq
WCR
WCRq
IndAdj
WCR
IndAdj
WCRq
21.657
21.342
21.697
21.202
21.165
19.438
19.211
18.252
18.583
17.754
17.594
19.785
20.883
20.874
21.246
21.106
20.798
20.164
19.602
17.999
17.566
17.196
16.953
19.258
20.719
20.308
20.702
20.207
20.153
18.582
17.928
17.333
17.638
17.228
16.819
17.336
20.314
19.962
20.202
19.995
20.032
19.363
17.956
16.787
16.617
16.793
16.172
16.287
−0.831
−1.411
−1.187
−1.333
−1.232
−0.617
−1.345
−0.834
−1.192
−0.695
−0.667
0.053
−1.564
−1.545
−1.152
−1.581
−1.467
−0.037
−1.059
−1.159
−2.053
−1.219
−1.357
−0.312
Column 3 illustrates a general downward trend in the WCR as the mean WCR decreases by 18.8%
from 1996 to 2006. This decline suggests that firms have become more efficient in managing
working capital.
Table III provides the distribution of WCR by industry affiliation consistent with the Fama
and French (1997) four-industry classification system. Business services has the most firm-year
appearances with 1,976. Restaurants, hotels, and motels have the smallest mean WCR. The
maximum industry-level WCR is agriculture with a mean of 44.0%. The substantial variation of
operating working capital behavior across industries echoes the findings of Hawawini, Viallet,
and Vora (1986) and suggests that working capital behavior is at least partly industry specific.
Certain firms have nonpositive positions in net operating working capital. The WCR is not a
use of funds for these firms, but instead finances long-term assets. Table IV provides univariate
comparisons of firm characteristics to gain a better understanding of the differences in firms
having positive and nonpositive WCRs. Roughly 6.8% of the observations have nonpositive
WCR during the sample period. This subset consists of 520 unique firms, 306 of which appear
at least twice in the nonpositive WCR subset. Figures in the table are variable means for each
subset. The last column provides t-statistics testing for differences in mean firm characteristics.
The mean WCR for the nonpositive WCR subset is −11.3% while the average is 21.8% for
the positive WCR subset. The WCR is significantly different across subsets of firms. Further,
the positive and nonpositive WCR subsets are significantly different at the 1% level for each
of the financial characteristics considered except lagged size. With respect to operating conditions variables, the positive WCR firms have lower growth rates, sales variability, and greater
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Table III. Industry Distribution of Sample
This table provides the distribution of the sample across industries for 20,710 firm-years across 3,343 unique
companies over the period 1996 to 2006. Industry classifications follow the Fama-French (1997) 48-industry
classification system. Utilities, banking, insurance, real estate, and trading firms are omitted. WCR is the
ratio of receivables plus inventory minus payables to sales at the end of each year. WCRq is the ratio of
the average end-of-quarter WCR to end-of-year sales. IndAdj WCR is the difference between the annual
WCR and industry average annual WCR for the respective year. IndAdj WCRq is the difference between the
quarterly WCR and industry average quarterly WCR for the respective year. Mean values of IndAdj WCR
and IndAdj WCRq are not reported since these approximate zero by construction.
Industry Focus
Agriculture
Food products
Candy and soda
Beer and liquor
Tobacco products
Recreation
Entertainment
Print. & publishing
Consumer goods
Apparel
Healthcare
Medical equip.
Pharmaceutical
Chemicals
Rubber & plastic
Textiles
Const. materials
Construction
Steel works
Fabricated prod.
Machinery
Electrical equip.
Autos & trucks
Aircraft
Shipbuilding
Defense
Precious metals
Mining
Coal
Oil and nat. gas
Communication
Personal services
Business services
Computers
Electronic equip.
Measuring equip.
Business supplies
Shipping cont.
Transportation
Wholesale
Retail
Restaurants, etc.
Other
Observations
Unique firms
N
57
487
69
73
23
296
350
255
514
491
471
778
895
590
290
111
555
357
476
99
1,066
544
446
150
52
40
80
96
31
971
602
273
1,976
820
1,602
614
397
87
706
973
1,243
515
189
20,710
3,343
Mean Values
Median Values
WCR
WCRq
WCR
WCRq
IndAdj
WCR
IndAdj
WCRq
44.004
14.976
9.251
27.760
19.799
28.502
2.228
14.292
25.997
27.856
14.391
30.971
18.306
22.143
19.915
25.093
22.284
28.436
22.871
23.195
30.074
29.449
20.889
33.202
17.984
23.189
18.843
21.478
21.478
4.587
6.239
8.487
12.639
21.444
25.813
32.664
17.773
16.579
7.983
20.016
15.610
1.494
12.939
19.785
42.505
15.903
8.618
27.188
19.566
28.831
2.079
13.758
26.145
28.998
13.591
29.942
17.201
21.782
19.543
26.452
22.163
26.935
22.574
23.098
30.007
29.570
20.643
32.495
18.252
23.754
20.104
21.402
2.678
5.887
8.203
11.974
14.697
21.964
25.348
31.998
16.937
16.180
7.452
19.858
15.451
0.761
12.675
19.258
48.623
13.568
8.044
15.486
17.490
26.891
0.602
11.983
23.763
25.553
13.905
30.478
20.592
21.209
19.415
23.408
22.255
20.740
21.909
22.678
27.757
27.048
18.270
27.719
13.882
21.640
19.088
19.569
7.228
6.648
8.376
10.201
14.940
20.193
24.222
30.892
16.729
16.729
16.729
16.729
15.169
7.495
18.262
17.336
49.444
14.656
8.005
15.226
16.553
27.768
0.614
12.000
23.620
27.610
13.015
29.303
19.367
20.817
19.551
24.675
21.545
18.987
21.644
22.630
28.024
27.201
18.187
28.170
14.441
21.829
18.829
19.089
7.249
5.960
8.154
9.164
14.317
19.820
23.487
30.279
15.580
15.580
15.580
15.580
14.545
7.116
17.948
16.287
4.824
−1.321
−0.724
−4.622
0.000
−0.836
−1.532
−2.093
−2.157
−1.872
−0.142
−0.324
2.672
−1.050
−0.721
−1.774
0.111
−7.306
−1.329
−0.232
−2.050
−2.210
−2.562
−4.144
−4.055
−1.958
−2.909
−1.749
0.000
0.386
0.369
−0.830
−0.129
−0.584
−1.469
−1.426
−1.057
−1.057
−1.057
−1.057
−1.275
−0.593
−1.527
0.053
3.089
−0.376
−0.781
−7.450
1.147
−0.732
−1.624
−2.131
−2.310
−0.191
−0.994
−1.675
1.467
−1.161
−0.930
−0.214
−0.784
−8.698
−1.472
0.604
−1.997
−2.267
−2.632
−4.900
−3.611
−0.517
−1.962
−1.523
−0.454
−0.188
−0.104
−2.460
−0.870
−0.813
−1.963
−2.066
−1.684
−1.684
−1.684
−1.684
−1.724
−1.003
−1.916
−0.312
792
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Table IV. Differences in Means: Positive WCR versus Nonpositive WCR Firms
This table provides the sample characteristics of 20,710 firm-years across 3,343 unique companies over the
period 1996-2006. Positive WCR firms report a WCR greater than zero. Nonpositive WCR firms report a
WCR less than or equal to zero. All t-tests invoke the assumption of unequal variances and Satterthwaite’s
approximation formula. WCR is the ratio of receivables plus inventory minus payables to sales at the end
of each year. WCRq is the ratio of the average end-of-quarter WCR to end-of-year sales. IndAdj WCR is
the difference between the annual WCR and industry average annual WCR for the respective year. IndAdj
WCRq is the difference between the quarterly WCR and industry average quarterly WCR for the respective
year. Growth is the percentage change in sales over the previous year. GPM is the ratio of sales minus cost of
goods sold to sales. SalesVAR is the standard deviation of sales. OCF is operating income before depreciation
minus taxes scaled by net assets. M/B is the ratio of market value of equity plus total liabilities minus payables
to net assets. Size is the market value of equity in 2006 dollars. MktShare is sales divided by industry sales.
Distress is one if a firm meets Molina and Preve’s (2009) definition of financial distress and zero otherwise.
Industry-dependent calculations follow the Fama and French (1997) 48-industry classification system.
Variables
WCR (%)
WCRq (%)
IndAdj WCR (%)
IndAdj WCRq (%)
Growtht−1 (%)
GPM t−1 (%)
SalesVar (%)
OCF t−1 (%)
M/Bt−1 (ratio)
Sizet−1 ($M)
MktSharet−1 (%)
Distresst−1 (binary)
∗∗∗
Positive WCR
Nonpositive WCR
Difference in Means
(Pos.) – (Nonpos.)
N
Mean
N
Mean
Difference
T-Stat
19,295
19,295
19,295
19,295
19,295
19,295
19,295
19,295
19,295
19,295
19,295
19,295
21.835
21.274
1.568
1.007
13.998
33.534
30.289
6.651
2.249
1,975.065
1.452
0.046
1,415
1,415
1,415
1,415
1,415
1,415
1,415
1,415
1,415
1,415
1,415
1,415
−11.314
−8.226
−21.386
−18.299
19.474
17.110
43.143
−15.268
3.344
1,712.798
0.792
0.159
33.149∗∗∗
29.501∗∗∗
22.955∗∗∗
19.306∗∗∗
−5.476∗∗∗
16.424∗∗∗
−12.853∗∗∗
21.919∗∗∗
−1.095∗∗∗
262.267∗
0.660∗∗∗
−0.113∗∗∗
56.928
55.217
35.072
32.768
−4.127
7.809
−6.837
13.230
−8.393
1.781
11.732
−11.503
Significant at the 0.01 level.
Significant at the 0.10 level.
∗
contribution margins. Firms with conservative working capital policies (again, the positive WCR
subset) have greater cash flows, size, market power, smaller market-to-book ratios, and are less
likely to be in distress. These comparisons support several of the hypotheses as the nonpositive
WCR subset is more restricted in terms of internal financing ability and capital market access,
have greater costs of external capital, and are more likely to be in financial distress. Only lagged
market share is opposite our expectations. Overall, these results suggest that a nonpositive WCR
is not necessarily obtained by design. In fact, it may be forced on struggling firms.
Table V displays the matrix of Pearson correlation coefficients for the variables in this study.
The correlations are largely consistent with our expectations. The WCR is negatively correlated
with lagged sales growth and sales variability but directly related to lagged GPM. The WCR
is negatively associated with lagged values of market-to-book, size, market share, and financial
distress and is directly related to lagged cash flow. Most correlation coefficients are significant at
the 1% level. Only size deviates from our expectations. Although many of the independent variables are significantly correlated, none of the correlation coefficients are of sufficient magnitude
to suggest a collinearity problem.
Significant at the 0.01 level.
0.0242∗∗∗
−0.0709∗∗∗
0.1417∗∗∗
0.0954∗∗∗
0.0426∗∗∗
−0.0195∗∗∗
−0.0383∗∗∗
Growtht−1
GPM t−1
SalesVar
MBt−1
Sizet−1
OCF t−1
MktSharet−1
Distresst−1
∗∗∗
Growtht−1
WCR
−0.0314∗∗∗
0.1420∗∗∗
−0.1451∗∗∗
−0.0702∗∗∗
−0.0260∗∗∗
0.1248∗∗∗
−0.0327∗∗∗
−0.1164∗∗∗
Variables
−0.0712∗∗∗
−0.0954∗∗∗
0.0848∗∗∗
0.3468∗∗∗
−0.0008
−0.0928∗∗∗
GPM t−1
0.0925∗∗∗
−0.2712∗∗∗
−0.2465∗∗∗
−0.1146∗∗∗
0.2136∗∗∗
SalesVar
0.0120∗∗∗
−0.3915∗∗∗
−0.0703∗∗∗
0.1078∗∗∗
MBt−1
0.2669∗∗∗
0.4686∗∗∗
−0.2745∗∗∗
Sizet−1
0.1088∗∗∗
−0.3562∗∗∗
OCF t−1
−0.0815∗∗∗
MktSharet−1
r
This table provides Pearson correlation coefficients for the 20,710 firm-years across 3,343 unique companies over the period 1996-2006. WCR is the ratio of
receivables plus inventory minus payables to sales at the end of each year. WCRq is the ratio of the average end-of-quarter WCR to end-of-year sales. IndAdj
WCR is the difference between the annual WCR and industry average annual WCR for the respective year. IndAdj WCRq is the difference between the quarterly
WCR and industry average quarterly WCR for the respective year. Growth is the percentage change in sales over the previous year. GPM is the ratio of sales
minus cost of goods sold to sales. SalesVAR is the standard deviation of sales. M/B is the ratio of market value of equity plus total liabilities minus payables to
net assets. Size is market value of equity in 2006 dollars. OCF is operating income before depreciation minus taxes as a percentage of net assets. MktShare is
sales divided by the aggregate sales in the firm’s industry. Distress is one if a firm meets Molina and Preve’s (2009) definition of financial distress and zero
otherwise.
Table V. Pearson Correlation Coefficients
Hill, Kelly, & Highfield
Net Operating Working Capital Behavior: A First Look
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B. The Determinants of WCR
Our primary variable of interest is the WCR-to-sales ratio measured as the ratio of the annual
sum of accounts receivable and inventory minus accounts payable to sales. We estimate the
following empirical model:
WCRi,t = β0 + β1 Growthi,t−1 + β2 GPM i,t−1 + β3 SaleVARi,t + β4 OCF i,t−1 β5 M/Bi,t−1
+ β6 Sizei,t−1 + β7 MktSharei,t−1 + β8 Distressi,t−1 + β j Controlsi,t + εi ,
(1)
where sales growth, Growth, is the annual percentage change in sales during the previous year.
Gross profit margin, GPM, is the ratio of sales minus cost of goods sold to sales. Sales volatility,
SaleVart , is the ratio of the standard deviation of sales to net assets. Operating cash flow, OCF,
is operating income before depreciation minus income taxes, scaled by net assets. The marketto-book ratio, M/B, is the ratio of the sum of market value of equity and total liabilities minus
payables to net assets. Firm size, Size, is the natural logarithm of market value of equity in
inflation-adjusted 2006 dollars. The ability to negotiate credit terms, MktShare, is the ratio of
annual firm-level sales to the industry’s annual sum of sales. Finally, Distres equals one if the
firm is in financial distress and zero otherwise.
The variables SalesVar, OCF, and M/B, are scaled by total assets net of cash as it is likely that
cash and the WCR are jointly determined as increased receivables and inventories reduce internally
generated cash. By construction, this joint determination can lead to a negative correlation between
WCR and variables divided by total assets when cash is included; hence, total assets net of cash is
preferred. Equation (1) also includes a set of annual binary variables to control for time-specific
macroeconomic factors influencing WCR.
III. Empirical Results
A. Panel Model Results: WCR Specification
The variation in WCR across firms may be a result of firm-specific unobservable factors,
which, if correlated with the independent variables, can cause pooled OLS regression results to
suffer from heterogeneity bias. A Breusch and Pagan (1980) Lagrange multiplier test rejects the
use of pooled OLS with a single intercept. Next, a Hausman’s (1978) test determines whether the
unobservable heterogeneity is correlated with the independent variables by testing for systematic
differences in the fixed and random effects coefficient vectors. The null hypothesis of equality
in the fixed and random effects coefficient vectors is rejected suggesting that fixed effects is the
preferred specification for these data.
Table VI presents fixed effects regression results for the determinants of net operating working
capital behavior analysis. Column 1 exhibits the estimates from Equation (1) using the WCRsto-sales ratio as the dependent variable. The models evaluate the factors influencing the WCR for
the full sample of 20,710 firm-years for 3,343 unique firms over the 1996-2006 period. Annual
binary variables are included in the fixed effects models.
1. Results: Lagged Sales Growth
The fixed effects results for Equation (1) in Table VI demonstrate an inverse relation between
the WCR and lagged sales growth. The coefficient is statistically significant at the 1% level. The
negative correlation between the WCR and sales growth is consistent with Molina and Preve’s
−0.008∗∗∗
(−4.430)
0.007∗∗
(2.140)
−0.018∗∗∗
(−9.410)
0.037∗∗∗
(9.730)
−0.339∗∗∗
(−10.300)
1.001∗∗∗
(10.420)
0.091
(1.540)
−1.690∗∗∗
(−5.140)
20,710
0.10
−0.010∗∗∗
(−5.000)
0.001
(0.031)
−0.040∗∗∗
(−19.400)
0.023∗∗∗
(5.470)
−0.128∗∗∗
(−3.510)
0.490∗∗∗
(4.590)
−0.089
(−1.350)
−1.621∗∗∗
(−4.450)
20,710
0.14
Growtht−1 (%)
∗∗
Significant at the 0.01 level.
Significant at the 0.05 level.
∗
Significant at the 0.10 level.
∗∗∗
Observations
R2
Distresst−1 (Binary)
MktSharet−1 (%)
Sizet−1 (Ln($M))
MBt−1 (Ratio)
OCF t−1 (%)
SalesVar (%)
GPM t−1 (%)
WCRq (%)
WCR (%)
−0.010∗∗∗
(−4.85)
0.002
(0.44)
−0.039∗∗∗
(−19.180)
0.022∗∗∗
(5.300)
−0.135∗∗∗
(−3.750)
0.496∗∗∗
(4.690)
−0.129∗∗
(−1.970)
−1.534∗∗∗
(−4.250)
20,710
0.15
IndAdj WCR (%)
−0.008∗∗∗
(−4.240)
0.008∗∗
(2.280)
−0.017∗∗∗
(−9.010)
0.036∗∗∗
(9.520)
−0.347∗∗∗
(−10.560)
1.011∗∗∗
(10.510)
0.052
(0.880)
−1.602∗∗∗
(−4.890)
20,710
0.11
IndAdj WCRq (%)
−0.015∗∗∗
(−5.330)
−0.004
(−0.830)
−0.043∗∗∗
(−15.100)
0.034∗∗∗
(5.830)
−0.179∗∗∗
(−3.910)
0.599∗∗∗
(3.970)
−0.246∗∗∗
(−2.670)
−2.169∗∗∗
(−4.320)
11,730
0.13
Manufacturing
−0.018∗∗∗
(−3.130)
0.046∗∗∗
(3.720)
−0.039∗∗∗
(−7.870)
−0.010
(−1.160)
0.121
(1.420)
0.682∗∗
(2.110)
0.205
(0.580)
−2.834∗∗∗
(−2.780)
2,249
0.20
Service
0.001
(0.140)
0.073∗
(1.650)
−0.047∗∗∗
(−7.420)
−0.027∗∗
(−2.110)
0.035
(0.200)
−0.115
(−4.000)
0.569
(0.960)
−0.512
(−0.570)
1,243
0.09
Retail
WCR (%): Industry Subsample
r
Variables
This table presents firm fixed effects regressions with WCR, WCRq, IndAdj WCR, and IndAdj WCRq as dependent variables. WCR is the ratio of receivables
plus inventory minus payables to sales at the end of each year. WCRq is the ratio of the average end-of-quarter WCR to end-of-year sales. IndAdj WCR is the
difference between the annual WCR and industry average annual WCR for the respective year. IndAdj WCRq is the difference between the quarterly WCR and
industry average quarterly WCR for the respective year. Estimates for WCR are recalculated based on Retail, Service, and Manufacturing industry subsamples
as defined in Fama-French (1997). Growth is the percentage change in sales over the previous year. GPM is the ratio of sales minus cost of goods sold to sales.
SalesVAR is the standard deviation of sales. OCF is operating income before depreciation minus taxes as a percentage of net assets. M/B is the ratio of market
value of equity plus total liabilities minus payables to net assets. Size is the market value of equity in 2006 dollars. MktShare is sales divided by the aggregate
sales in the firm’s industry. Distress is one if a firm meets Molina and Preve’s (2009) definition of financial distress, and zero otherwise. Industry-dependent
calculations follow the Fama and French (1997) 48 industry classification system. Annual binary variables (not reported) control for time effects. The sample
consists of 20,710 firm-years across 3,343 unique firms over the period 1996-2006. T-values are in parentheses below coefficients.
Table VI. Fixed Effects Results
Hill, Kelly, & Highfield
Net Operating Working Capital Behavior: A First Look
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(2009) finding that firms tighten their credit policy as they achieve planned levels of sales growth.
Further, this result suggests that prior period sales growth provides net financing. As shown by
Petersen and Rajan (1997) and Deloof and Jegers (1999), payables are directly correlated with
growth as suppliers are willing to offer more credit with better terms to high-growth firms in
hopes of building relationships. High-growth firms need not relax trade credit terms as sales
are already growing. Molina and Preve (2009) confirm that receivables are negatively related to
lagged sales growth. These initial results suggest that, on average, lagged sales growth provides
financing.15
2. Results: Lagged Gross Profit Margin
Although Petersen and Rajan (1997) find that receivables are directly related to GPM, we
find that lagged GPM has no statistically distinguishable effect on the WCR after controlling
for other independent variables. Since we use GPM to proxy contribution margin, the lack of
a significant relationship is unexpected as greater contribution margins should mechanically
increase the WCR. It is feasible that the explanatory power of GPM is captured by the fixed
effect. Alternatively, lagged GPM may be a poor proxy for the contribution margin since most of
the sampled firms sell multiple products or are conglomerates.
3. Results: Sales Volatility
The WCR is negatively associated with sales volatility with a t-statistic of −19.40. Since sales
volatility represents expected deviations in demand, the inverse correlation between WCR and
sales volatility suggests that managers react to greater sales volatility by managing working capital
more aggressively. This finding is consistent with the Deloof and Jegers (1996) and Ng, Smith,
and Smith (1999) view that receivables policy is largely unaffected by deviations in demand
and the intuition that sales variability should increase a firm’s dependence on payables. This
result does not invalidate Emery’s (1987) hypothesis that firms with superior ability to finance
receivables will loosen credit policy in response to variable demand; however, it implies that
increased sales volatility causes firms to reduce their net investment in operating working capital.
Plausibly, the incremental cash flow provided by reducing the working capital gap is needed most
by firms with volatile sales.
4. Results: Lagged Operating Cash Flow
The estimated correlation between the WCR and lagged operating cash flow is positive and
significant at the 1% level, suggesting that firms with greater operating cash flows manage
working capital more conservatively. Similarly, Love, Preve, and Sarria-Allende (2007) report a
direct association between net trade credit and cash flow. A potential benefit of a conservative
working capital approach is increased profitability. Looser inventory policies and more lenient
credit standards can be associated with increases in sales and profits. The direct relationship
between the WCR and operating cash flow indicates that firms with stronger operating cash flows
are more likely to enjoy the benefits of a less restrictive working capital policy than firms with
15
We also estimate the model using conditional growth variables as in Petersen and Rajan (1997) and Molina and Preve
(2009). The results further reiterate the aforementioned result and accompanying theory as firms with lagged positive
sales growth reduce their investment in net operating working capital, while negative sales growth has a positive effect
on the WCR. To check robustness, we also used the growth of market share in place of sales growth, but due to low
cross-sectional variation, particularly for competitive industries, the results are negative, yet insignificant. Both sets of
alternative results are available upon request.
Hill, Kelly, & Highfield
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Net Operating Working Capital Behavior: A First Look
797
weaker cash flows, as a positive WCR must be financed. Conversely, firms for which internal
cash flow matters most (i.e., those with reduced internal financing ability) manage operating
working capital more aggressively.
5. Results: Lagged Market-to-Book Ratio
The association between the WCR and the lagged market-to-book ratio is negative and significant at the 1% level. Taking the market-to-book ratio as the degree of asymmetric information
faced by firms in capital markets, hence a proxy for the cost of external finance, the estimated
inverse relationship between the WCR and market-to-book supports the view that firms with
greater costs of external finance seek to reduce the WCR, probably to avoid costly external
financing. Alternatively, the market-to-book ratio proxies for investment opportunities; firms
with superior prospects will strive to reduce their net investment in operating working capital to
unlock cash flow needed to invest in positive NPV projects. Further, this result complements the
estimated inverse connection between the WCR and sales growth, as high-growth firms typically
have greater market-to-book ratios.
The negative correlation between WCR and the market-to-book ratio parallels the positive
relation between cash and market-to-book ratio presented in the cash literature in that cash
provides financing, while the WCR requires financing (Kim, Mauer, and Sherman, 1998; Opler
et al., 1999; Ozkan and Ozkan, 2004). Thus, for high market-to-book ratio firms, cash reduces
external financing needs. For these firms, external financing needs increase with an increased
WCR.
6. Results: Lagged Firm Size
WCR varies directly with lagged firm size and the association is significant. We expect this
relationship as size is a proxy for capital market access. Smaller firms are limited in their choices
for financing a positive WCR as they are less able to issue commercial paper or obtain lines
of credit. In the research examining corporate cash holdings, Opler et al. (1999) indicate that
cash and size are inversely related since larger firms have less need to hold cash as they have
better access to short-term debt markets. Since a positive WCR must be financed, smaller firms
will more closely monitor operating working capital strategies since they have fewer alternatives
available to finance the working capital gap relative to larger firms. Also, the direct correlation
between the WCR and size supports prior working capital results such as Petersen and Rajan
(1997) and Deloof and Jegers (1999).
7. Results: Lagged Market Share
WCR and lagged market share are not significantly related. Firms with greater negotiating
ability have superior bargaining position regarding credit terms extended to customers and received from suppliers. Since we believe market share proxies negotiating ability, the lack of an
affiliation between WCRs and market share is unexpected. As with lagged GPM, it is possible
that the firm-specific heterogeneity absorbed the effect of negotiating ability.16
8. Results: Lagged Financial Distress
WCR is negatively related to lagged financial distress. Molina and Preve (2009) demonstrate
that financially distressed firms have significantly reduced levels of trade credit relative to their
16
In later regressions, the relation between operating working capital behavior and negotiating ability/market concentration
is more fully analyzed using industry concentration.
798
Financial Management
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17
nondistressed counterparts. We infer that distressed firms manage operating working capital
more aggressively than nondistressed firms. A more restrictive working capital policy is a rational
response to financial distress due to the limited financial slack and cash generating ability of
distressed firms. As such, distressed firms are likely to reduce investment in operating working
capital by collecting on receivables, tightening credit terms, liquidating inventory, and stretching
supplier credit.
This result has additional economic meaning. The average WCR of distressed firms is 1.6%
lower than that of nondistressed firms. This implies that nondistressed firms have a $31 million
additional investment in net operating working capital relative to their distressed counterparts, on
average.18
9. Summary of Initial Results
The results indicate that the WCR is inversely related to sales growth, uncertain demand, cost of
external financing, and financial distress and is directly related to operating cash flow and capital
market access. Next, we discuss two potential problems with our dependent variable specification.
The first deals with variation in ending fiscal years, and the second concerns industry effects.
To this point, we have allowed the fixed effect to absorb the industry effect on working capital
behavior, a matter discussed in more detail below.
B. Additional Results: Averaged Quarterly WCR Specification
A potential problem with the measurement of WCRs is that not all firms use the same fiscal
year-end date for their annual financial statements introducing arbitrary differences in the WCR
for firms in the same industry.19 To attenuate this concern, sample firms’ quarterly receivables,
inventory, and payables data are averaged, smoothing variations in operating cycles caused by
varying fiscal year ends.
The results using the quarterly averaged WCR (WCRq) as the dependent variable appear in
Column 2 of Table VI. Overall, the results are consistent with the earlier results in that WCRq is
inversely related to lagged sales growth, sales volatility, lagged market-to-book ratio, and lagged
financial distress and directly related to lagged operating cash flow and size. As before, lagged
market share is insignificant. One difference is that lagged GPM is positive and significant for
the quarterly averaged model. We expect this result since GPM proxies contribution margin and
increased contribution margins imply greater receivables relative to payables. This finding echoes
Petersen and Rajan’s (1997) result of a direct correlation between receivables and GPM.
C. Additional Results: Industry-Adjusted WCR Specification
Hawawini, Viallet, and Vora (1986) indicate that working capital behavior is industry dependent.
For example, operating working capital policies of manufacturing firms are markedly different
from service firms since the former typically carry substantial inventory levels and the latter carry
virtually no inventory. Industry effects would ordinarily be captured by indicator variables, but the
time-invariant nature of these variables precludes them from fixed effects estimation. In Columns
1 and 2 of Table VI, we assume the industry effect loads on the fixed effect. To explicitly control
17
We also estimate the models using Molina and Preve’s (2009) other measures of financial distress. The results are
quantitatively and qualitatively similar and are available upon request.
18
The mean sales level for sampled firms is $1.94 billion.
19
We thank an anonymous reviewer for bringing this point to our attention.
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Net Operating Working Capital Behavior: A First Look
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for industry effects, the annual industry average WCR is netted from the preceding versions of
the WCR where industries are defined according to the Fama and French (1997) 48 industry
classifications.20 Columns 3 and 4 report results using deviations from industry averages as
dependent variables. Column 3 illustrates the estimates of Equation (1) using the annual WCR
minus the industry averaged WCR as the dependent variable (IndAdj WCR), while Column
4 shows the estimates of Equation (1) using the quarterly averaged WCR minus the industry
average WCR as the dependent variable (IndAdj WCRq).
The results in Column 3 echo many of the findings in Columns 1 and 2. Similar to Column 1,
lagged GPM is not significant. A notable difference is lagged market share, which is negative and
significant suggesting that firms with greater market share have lower net investment in operating
working capital. This finding meets our expectations because firms with increased market share
are in an improved position to negotiate more generous credit terms with suppliers, as well as
stretch the terms offered by those suppliers. Similarly, increased market share implies improved
relationships with vendors, allowing for lower inventory levels. Finally, increased market share
implies a reduced need to offer generous credit terms. In all, the benefits of increased market
share yield a reduced net investment in operating working capital.
Results in Column 4 are similar to our previous results. Two exceptions include lagged GPM,
which is positive and significant as in Column 2, and lagged market share, which is not significant
as in Column 3. Results from Columns 3 and 4 suggest that even after controlling for industrylevel working capital benchmarks, certain firm-level financial characteristics strongly influence
operating working capital behavior. This is worth emphasizing as it reinforces a frequently voiced,
but often ignored, caveat against the exclusive use of means of industry ratios as benchmarks
for financial analysis. Insightful analysis should encompass firm-level operating conditions,
financing ability, and managerial decision making at the most fundamental level available given
the purpose of the analysis.21
To better understand the variation in factors influencing working capital behavior across industries, we estimate Equation (1) separately for firms classified as manufacturing, service, and
retail appearing in Columns 5-7, respectively, of Table VI. The results are generally consistent
with those in Column 1. In fact, the sign of the coefficients and significance level for the manufacturing subsample (Column 5) are identical to those in Column 1 except for the market share
variable, which is negative and significant. The results for service and retail firms in Columns 6
and 7, respectively, vary from those reported in column 1; however, the significant coefficients’
signs are consistent with theory.
The models in Table VI account for firm-specific heterogeneity as well as time effects.22 The
estimated coefficients are generally robust across dependent variable specifications as subsequent
models account for the variation in fiscal year ends and industry benchmarks. Overall, the fixed
effects results strongly support many of the hypotheses.
D. Additional Results: Industry Competitiveness/Concentration
The degree of market concentration within an industry influences management of operating
working capital in response to operating conditions and financing ability. Firms in concentrated
industries have improved negotiating ability; thus, they are able to dictate trade credit terms
20
We use 43 of the 48 Fama-French (1997) industry classifications in the study as utility and financial firms are
discarded from the sample. The Fama-French industry classifications are taken from Kenneth French’s web page at
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/.
21
For example, CFO.com produces an annual working capital benchmarking analysis that relies on industry-level means.
22
We note that the results are robust to scaling all variables, including dependent variables, by net assets.
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granted and received, and inventory policies. As a result, the effects of the independent variables
in Equation (1) could depend on the degree of concentration within the industry. Molina and
Preve (2009) demonstrate that the effect of financial distress on credit policy is most pronounced
for firms in concentrated industries.
To determine whether the results vary according to degree of industry competitiveness, regression models presented in columns 1-4 of Table VI are estimated separately for firms in competitive
and concentrated industries in Table VII. Similar to Molina and Preve (2009), we define a concentrated industry as one whose Herfindahl index exceeds the median industry Herfindahl index
for the year; otherwise, the industry is considered competitive. As before, the models account for
fixed and time effects.
The results in Table VII generally echo those in Table VI with a few important distinctions.
Sales volatility, operating cash flow, size, and financial distress retain the same signs and general
significance as the results in Table VI. However, the significance of market-to-book varies across
dependent variable specification. While the market-to-book coefficient is negative in all models, it
is significant only in the quarterly averaged results (WCRq and IndAdj WCRq). Another interesting
result concerns market share. Firms in concentrated industries with greater market share should
be able to reduce WCR, but this is not supported by the results.
A few other findings deserve note. First, sales growth is negative and marginally significant
for one competitive subsample (Column 3) but negative and significant at the 1% level for each
concentrated subsample. This suggests that sales growth for firms in concentrated industries, those
with presumably more market power, generates more spontaneous financing than spontaneous
uses of funds. That is, sales growth provides net financing for firms in concentrated industries.
Second, GPM is positive and significant for each concentrated subsample. This has intuitive
appeal in that firms in concentrated industries typically enjoy greater profit margins that by
definition lead to a higher WCR.
Although the results in Table VII provide valuable insights, it would be helpful to know the
marginal effects of industry concentration on the WCR. As illustrated in Table VIII, we interact
each independent variable in Equation (1) with an indicator variable for concentration, where the
indicator variable equals one if the firm is in a concentrated industry and zero otherwise. The
results in Table VIII are derived from estimating Equation (1) plus an industry concentration
dummy variable and the aforementioned interaction terms using the four dependent variables
described earlier. As before, the models account for fixed and time effects.
The relation between the WCR and the interaction between lagged sales growth and the
industry concentration dummy variable is negative and significant for each model, suggesting
that sales growth causes a greater reduction in net investment in operating working capital for
concentrated firms. Thus, sales growth provides net financing for firms in concentrated industries.
This significant negative interaction between lagged sales growth and industry concentration is
consistent with our expectations. Growing firms in concentrated industries have less need to
loosen credit and inventory policies to facilitate increases in sales. Meanwhile, suppliers are more
likely to offer increases in credit and better terms to these firms.
Columns 1 and 3 of Table VIII demonstrate that the marginal effect of lagged GPM on the
WCR is greater for concentrated firms. Firms in concentrated industries are more likely to have
greater contribution margins increasing their WCR. The results in Columns 2 and 4 indicate that
firms in concentrated industries finance less of their net investment in operating working capital
with operating cash flow. This is likely due to concentrated firms receiving additional trade credit
from suppliers. Contrary to expectations, the interaction between industry concentration and
lagged market share is insignificant for each model. However, the results in Column 3 confirm
that the industry-adjusted WCR is negatively related to market share, whereas this variable
was insignificant for each model in Table VI. This finding is not robust across the dependent
IndAdj WCR (%)
IndAdj WCRq (%)
−0.004
(−1.520)
−0.008∗
(−1.760)
−0.041∗∗∗
(−14.030)
0.020∗∗∗
(3.590)
−0.059
(−1.260)
0.334∗∗
(2.120)
0.071
(0.450)
−1.600∗∗∗
(−3.110)
10,862
0.10
−0.017∗∗∗
(−5.690)
0.041∗∗∗
(5.530)
−0.040∗∗∗
(−12.900)
0.014∗
(1.940)
−0.029
(−0.047)
0.475∗∗∗
(3.020)
−0.088
(−1.180)
−2.768∗∗∗
(−5.320)
9,848
0.15
−0.005∗
(−1.930)
−0.001
(−0.050)
−0.020∗∗∗
(−7.630)
0.036∗∗∗
(7.260)
−0.257∗∗∗
(−6.070)
0.869∗∗∗
(6.070)
0.256∗
(1.790)
−1.930∗∗∗
(−4.120)
10,862
0.10
−0.010∗∗∗
(−3.950)
0.035∗∗∗
(5.400)
−0.014∗∗∗
(−5.210)
0.020∗∗∗
(3.320)
−0.302∗∗∗
(−5.490)
1.047∗∗∗
(7.630)
0.041
(0.064)
−2.710∗∗∗
(−5.960)
9,848
0.08
−0.003
(−1.090)
−0.008∗
(−1.800)
−0.040∗∗∗
(−13.720)
0.018∗∗∗
(3.390)
−0.063
(−1.370)
0.287∗
(1.830)
−0.126
(−0.810)
−1.535∗∗∗
(−2.990)
10,862
0.11
−0.017∗∗∗
(−5.560)
0.041∗∗∗
(5.540)
−0.040∗∗∗
(−13.110)
0.015∗∗
(2.180)
−0.037
(−0.590)
0.455∗∗∗
(2.940)
−0.079
(−1.090)
−2.851∗∗∗
(−5.560)
9,848
0.16
−0.004
(−1.460)
−0.001
(−0.080)
−0.019∗∗∗
(−7.250)
0.035∗∗∗
(7.030)
−0.262∗∗∗
(−6.200)
0.822∗∗∗
(5.760)
0.059
(0.420)
−1.864∗∗∗
(−3.980)
10,862
0.11
−0.010∗∗∗
(−3.710)
0.035∗∗∗
(5.230)
−0.014∗∗∗
(−5.230)
0.022∗∗∗
(3.570)
−0.310∗∗∗
(−5.640)
1.028∗∗∗
(7.510)
0.050
(0.770)
−2.792∗∗∗
(−6.160)
9,848
0.09
Net Operating Working Capital Behavior: A First Look
∗∗
WCRq (%)
Competitive Concentrated Competitive Concentrated Competitive Concentrated Competitive Concentrated
Significant at the 0.01 level.
Significant at the 0.05 level.
∗
Significant at the 0.10 level.
∗∗∗
Observations
R2
Distresst−1 (binary)
MktSharet−1 (%)
Sizet−1 (Ln($M))
MBt−1 (Ratio)
OCF t−1 (%)
SalesVar (%)
GPM t−1 (%)
Growtht−1 (%)
WCR (%)
r
Variables
This table presents firm fixed effects regressions using WCR, WCRq, IndAdj WCR, and IndAdj WCRq as dependent variables. These results are similar to
Columns 1-4 presented in Table VI, but this table controls for the competitiveness/concentration of the industry by sub-setting the sample into concentrated
and competitive industries. A competitive (concentrated) industry is the half-sample of firms in industries whose Herfindahl Index is below (above) the year
median. WCR is the ratio of receivables plus inventory minus payables to sales at the end of each year. WCRq is the ratio of the average end-of-quarter WCR
to end-of-year sales. IndAdj WCR is the difference between the annual WCR and industry average annual WCR for the respective year. IndAdj WCRq is the
difference between the quarterly WCR and industry average quarterly WCR for the respective year. Growth is the percentage change in sales over the previous
year. GPM is the ratio of sales minus cost of goods sold to sales. SalesVAR is the standard deviation of sales. OCF is operating income before depreciation minus
taxes as a percentage of net assets. M/B is the ratio of market value of equity plus total liabilities minus payables to net assets. Size is the market value of equity
in 2006 dollars. MktShare is sales divided by the aggregate sales in the firm’s industry. Distress is one if a firm meets Molina and Preve’s (2009) definition
of financial distress, and zero otherwise. Industry-dependent calculations follow the Fama and French (1997) 48 industry classification system. Annual binary
variables (not reported) control for time effects. The sample is 20,710 firm-years across 3,343 unique firms over the period 1996-2006. The t-values are in
parentheses below coefficients.
Table VII. Fixed Effects Results: Conditional on Industry Concentration
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Table VIII. Fixed Effects Results: Marginal Effect of Industry Concentration
This table presents firm fixed effects regressions using interaction terms to test for differences between
concentrated and competitive industries. A competitive (concentrated) industry is the half sample of firms
in industries whose Herfindahl Index is below (above) the year median. WCR is the year-end ratio of
receivables plus inventory minus payables to sales. WCRq is the ratio of the average end-of-quarter WCR to
end-of-year sales. IndAdj WCR is the difference between the annual WCR and industry average annual WCR
for the respective year. IndAdj WCRq is the difference between the WCR and industry average WCR for the
respective year, quarterly. Growth is the percentage change in sales over the previous year. GPM is the ratio
of sales minus cost of goods sold to sales. SalesVAR is the standard deviation of sales. OCF is operating
income before depreciation minus taxes as a percentage of net assets. M/B is the ratio of market value of
equity plus total liabilities minus payables to net assets. Size is the market value of equity in 2006 dollars.
MktShare is sales divided by aggregate industry sales. Distress is one if a firm meets Molina and Preve’s
(2009) definition of financial distress and zero otherwise. IndCon is binary and equal to one if the firm
is in a concentrated industry and zero otherwise. A multiplication symbol (×) indicates interaction terms.
Annual binary variables (not reported) control for time effects. Industry-dependent calculations follow the
Fama and French (1997) 48 industry classification system. The sample is 20,710 firm-years across 3,343
unique firms over the period 1996-2006. The t-values are in parentheses below coefficients.
Variables
WCR (%)
WCRq (%)
IndAdj WCR (%)
IndAdj WCRq (%)
IndCon (binary)
−1.517
(−0.790)
−0.005∗∗
(−2.030)
−0.013∗∗∗
(−3.250)
−0.003
(−0.790)
0.015∗∗
(2.280)
−0.041∗∗∗
(−16.550)
0.004
(1.060)
0.026∗∗∗
(5.290)
−0.011
(−1.350)
−0.129∗∗∗
(−3.120)
−0.002
(−0.030)
0.459∗∗∗
(4.010)
0.070
(0.680)
−0.116
(−1.290)
0.029
(0.410)
−1.498∗∗∗
(−3.280)
−0.366
(−0.520)
20,710
0.14
−2.081
(−1.190)
−0.006∗∗∗
(−2.580)
−0.006∗
(−1.660)
0.005
(1.380)
0.007
(1.150)
−0.019∗∗∗
(−8.510)
0.006
(1.550)
0.044∗∗∗
(9.940)
−0.024∗∗∗
(−3.240)
−0.328
(−8.780)
−0.044
(−0.720)
0.961∗∗∗
(9.290)
0.122
(1.310)
0.087
(1.06)
−0.004
(−0.006)
−1.515∗∗∗
(−3.670)
−0.506
(−0.790)
20,710
0.11
−0.910
(−0.480)
−0.005∗
(−1.810)
−0.013∗∗∗
(−3.380)
−0.002
(−0.560)
0.013∗∗
(1.990)
−0.040∗∗∗
(−16.200)
0.003
(0.680)
0.024
(4.920)
−0.007
(−0.890)
−0.136∗∗∗
(−3.300)
−0.004
(−0.006)
0.481∗∗∗
(4.240)
0.025
(0.250)
−0.163∗
(−1.830)
0.046
(0.650)
−1.327∗∗∗
(−2.930)
−0.596
(−0.850)
20,710
0.15
−1.474
(−0.850)
−0.005∗∗
(−2.320)
−0.006∗
(−1.770)
0.006∗
(1.650)
0.005
(0.810)
−0.018∗∗∗
(−8.000)
0.004
(1.120)
0.042
(9.520)
−0.020∗∗∗
(−2.730)
−0.335∗∗∗
(−8.980)
−0.046
(0.760)
0.983∗∗∗
(9.530)
0.077
(0.830)
0.039
(0.490)
0.013
(0.210)
−1.344∗∗∗
(−3.270)
−0.736
(−1.160)
20,710
0.12
Growtht−1 (%)
Growtht−1 × IndCon
GPM t−1 (%)
GPM t−1 × IndCon
SalesVar (%)
SalesVar × IndCon
OCF t−1 (%)
OCF t−1 × IndCon
MBt−1 (ratio)
MBt−1 × IndCon
Sizet−1 (Ln($M))
Sizet−1 × IndCon
MktSharet−1 (%)
MktSharet−1 × IndCon
Distresst−1 (binary)
Distresst−1 × IndCon
Observations
R2
∗∗∗
Significant at the 0.01 level.
Significant at the 0.05 level.
∗
Significant at the 0.10 level.
∗∗
Hill, Kelly, & Highfield
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Net Operating Working Capital Behavior: A First Look
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variable specifications presented in Table VIII. Another result worth noting is that the industry
concentration indicator variable has no distinguishable effect on the WCR.
In summary, the results in Table VIII echo many of those in Tables VI and VII as the WCR
is inversely related to lagged sales growth, sales volatility, lagged market-to-book, and lagged
financial distress and is directly related to operating cash flow and size. The interaction between
industry concentration and lagged sales growth is the only significant interaction that is robust
across the models in Table VIII. We conclude that the effects of the other independent variables
are not significantly different for concentrated and competitive firms.
IV. Conclusion
Firms adopt working capital policies to address market imperfections over the operating cycle
and incur costs and accrue benefits that affect cash flow and ultimately shareholder wealth. This
study seeks a better understanding of the factors influencing working capital behavior as reflected
in the WCR.
For our sample of firms, the average WCR represents $296 million in untapped cash. Our
empirical models relate the WCR ratio to operating conditions and financing ability. Concerning
the operating conditions variables, our results indicate that increases in sales growth and sales
volatility cause firms to manage operating working capital more aggressively. We find limited
support for a direct correlation between GPM and WCR. The results also indicate that working
capital behavior is influenced by financing capabilities. Specifically, WCR is directly related to
operating cash flow and size and is inversely related to the market-to-book ratio and financial
distress. A weak negative correlation exists between WCR and market share; however, the result
is not robust. Together, these outcomes suggest that firms with weaker internal financing ability,
limited capital market access, and greater costs of external financing will more aggressively use
payables relative to receivables and inventory. These results are consistent after using a quarterly
averaged WCR and they are robust to unobserved heterogeneity.
The results are also robust after industry-adjusting WCR and highlight the need to consider
financial characteristics besides industry affiliation when examining working capital levels for
optimality. The implication is that factors other than just industry benchmarks should be considered when setting or evaluating working capital behavior. Finally, we examine the effect of
industry-level concentration on working capital behavior and find that the interaction between
lagged sales growth and industry concentration reduces firms’ net investment in operating working capital.
Our models should be helpful to future research since they are the first to investigate the factors
influencing the determinants of the net investment in operating working capital. Furthermore,
these models can be employed to benchmark optimal working capital levels since they jointly
control for operating conditions and the ability to seek and acquire capital. A question left
to future research considers the impact of changes in working capital holdings on changes in
market value. The corporate cash holdings literature demonstrates that the marginal value of cash
varies according to various financial characteristics such as the degree of financial constraint
and corporate governance. Similar arguments may apply to the value of a marginal dollar in net
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