The effects of working capital management on the profitability of Nigerian manufacturing firms.
Aregbeyen, Omo
1. Introduction
A significant (and positive) development in corporate financial
management over the recent years has been an increased emphasis on WCM
decisions. According to Appuhami (2008) WCM is a very important
component of corporate finance for two main reasons. The first is that a
typical firm's current assets account for a substantial proportion
of total assets. The second being that the maintenance of adequate
levels of current assets is required to optimize returns on investment.
Indeed, firms with inadequate levels of current assets may incur
shortages and have difficulties in smoothly maintaining day-to-day
operations (Van Horne and Wachowicz 2000, 2004). However, funds tied up
in working capital (WC) are hidden reserves that can be used to fund
growth strategies, such as capital expansion. Firms that have earned
profits and grown have underscored efficient WCM in reacting quickly and
appropriately to unanticipated changes in market variables and gain
competitive advantages over rivals.
Consequently, there have been several studies on the effects of WCM
on the profitability of firms across countries. This study also examined
the relationship between efficiency of WCM and profitability of
manufacturing firms in Nigeria. The motivation is that a previous study
by Falope and Ajilore (2009) bears a number of significant weaknesses.
First, the sample lumped together purely manufacturing firms and service
rendering firms (e.g. hospitals, aviation firms, trading companies)
without taking due cognizance of the fact that WCM requirements and
practices differ across broad categories of firms. The second is the
single use of ROA as measure profitability. The literature identified
alternative measures Exploring these alternatives, I believe would
provide more insightful results. The last but fundamental is that while
the study reportedly covered the period 1996-2005 for 50 firms, the
reported results was for 694 firm year observations instead of 500 for
which the sample and year of study suggest. This makes the reported
results very questionable and doubtful.
This study has attempted to fix these weaknesses by focusing only
on manufacturing firms and explored alternative measures of
profitability. The study therefore, aims to expand and contribute new
findings to the existing literature particularly on Nigeria and at
large.
The rest of the paper is organized as follows. Section two reviews
the literature on the imperatives of efficient WCM and the empirical
evidences on WCM and firm profitability. The study sample and data
collection as well as the method of analysis are indicated in section
three. Section four presents and discusses the results obtained, while
section five gives the summary and conclusion of the study.
2. Literature review
2.1. Theoretical insights
Smith (1973) noted that the failure of a large number of firms can
be attributed to inefficient WCM due to the inability of financial
managers to properly plan and control WC. WC is commonly understood as
the fund needed to meet the day-to-day expenses of an enterprise.
Technically, it is defined as the difference between a firm's
current assets and liabilities (Guthman and Dougall 1948; Park and
Gladson, 1963; Bhattacharya 2009). In Deloof (2003), Planware (2010) and
Lukkari (2011), the major measures of WC indicated include: number of
days inventories are turnover, account receivable and payable; current
ratio (CR); quick ratio; WC ratio; net liquidity balance; WC
requirement; and the CCC, first introduced by Gitman (1974) and later
refined by Gitman and Sachdeva (1984).
The CCC captures the time lag between the expenditure for the
purchase of raw materials and the collection from the sale of finished
goods (Shin and Soenen 1998). Longer cash cycle means more investment in
WC while shorter cycle implies otherwise. Reducing the CCC to a
reasonable minimum generally leads to improved profitability, but in
some cases longer cash cycle might increase profitability because it
leads to higher sales (Deloof 2003).
The effective and/or efficient management of WC is therefore
necessary to achieve both the long and short term goals of firms. WCM
has been severally defined. For instance, Eljelly (2004) says efficient
WCM involves planning and controlling current assets and liabilities in
a manner that eliminates the risk of inability to meet due short term
obligations on one hand and avoids excessive investment in these assets
on the other. Very succinctly, KPMG (2005) sees WCM as comprising all
efforts directed towards optimizing the time span during which WC is
tied up in the company. The WC that a firm would need is affected by a
number of factors, both internal and external (Ali and Khan 2011).
Several researchers (see for example Hawawini et al. 1986; Chiou and
Cheng 2006; Michalski 2007; Nazir and Afza 2008, 2009) identified nature
and size, manufacturing cycle, business fluctuations, production policy,
turnover of circulating capital, operating efficiency, price level
changes, growth and expansion activities, and credit terms/policy, among
others.
2.2. Previous studies
The survey of previous studies was done to cover studies on several
countries. However, the rendition is ordered by the year.
Deloof (2003) studied a sample of 1009 large Belgian firms from
1992-1996. WCM was measured by the CCC, while profitability was measured
by NOI and GOI. The analysis combined the Pearson's correlation and
regression analyses. The results indicate that WCM is not efficient and
thereby negatively affect profitability. In addition, less profitable
firms wait longer to pay their bills.
Seyaduzznmen (2006) reported that the efficiency of WCM of the
British American Tobacco Bangladesh Company Ltd was positively related
to profitability. The study was conducted for two periods of 1999-2000
and 2002-2003. Similarly, Lazaridis and Tryfonidis (2006) investigated
the profitability effect of WCM in a sample of 131 companies listed in
the Athens Stock Exchange for the period of 2001-2004. The results
showed significant relationship between profitability, measured by GOI
and the CCC. They concluded that managers can create profits by handling
correctly the CCC and keeping each different component to an optimum
level.
Padachi (2006) also examined the trends in WCM and its impact on
performance for a sample of 58 Mauritian small manufacturing firms,
using panel analysis for the period 1998-2003. ROA was used to measure
profitability and the CCC for WCM. The results showed that high
investment in inventories and receivables is associated with lower
profitability.
Raheman and Nasr (2007) studied 94 Pakistani firms during
1999-2004. WCM was measured alternatively by ACP, APP, ITID, CCC and CR.
Profitability was measured by NOI. Pearson's correlation and
regression analyses were utilized. Strong negative relationship between
WCM and profitability was reported. The study recommended WCM be
improved.
Garcia-Teruel and Martinez-Solano (2007) examined a panel of 8,872
Spanish SMEs during the period 1996-2002. WCM was also measured by the
CCC and profitability by ROA. Univariate and multivariate analyses were
conducted. A significant negative relation between profitability and ACP
and ITID exist. However, there was no confirmation that APP affects ROA,
as this relation losses significance when possible endogeneity problem
were controlled for. Nevertheless, it was concluded that
improved/efficient WCM would improve the firms' profitability.
Mathuva (2009) considered 30 firms listed on the Nairobi Stock
Exchange for the periods 1993 to 2008. He used Pearson and
Spearman's correlations, the pooled ordinary least square, and the
fixed effects regression models to conduct data analysis. He found
highly significant (i) negative relationship between the ACP and
profitability, and (ii) positive relationship between ITID,APP and
profitability.
Danuletiu (2010) analyzed the efficiency of WCM of companies from
Alba County over the period 2004-2008. The Pearson correlation analysis
was utilized. Weak negative linear correlation between WCM and
profitability was reported.
Gill et al.(2010) studied 88 American firms for a period of 3 years
from 2005 to 2007. Significant relationship between the CCC and
profitability was reported. They concluded that managers can create
profits for their companies by handling correctly the CCC and by keeping
accounts receivables at an optimal level.
For Turkey, Karaduman et al. (2010) investigated selected companies
in the Istanbul Stock Exchange for the period of 2005-2008.The panel
data methods were employed. Profitability of the firms was measured by
ROA while WCM was alternatively measured by CCC, ACP, APP and ITID. All
the measures bear significant negative relationship with profitability,
indicating WCM was not efficient. The need for the companies to improve
on WCM in order to increase profitability was emphasized.
Charitou et al. (2010) studied firms listed on the Cyprus Stock
Exchange for the period 1998-2007. They hypothesized that WCM leads to
improved profitability. Using multivariate regression analysis, the
reported results supported the hypothesis. Specifically, the results
indicate that the CCC and all its major components are associated with
profitability.
Most recently, Sharma and Kumar (2011) for India examined 263
non-financial Bombay Stock Exchange listed firms from 2000 to 2008.
Panel regression analysis was conducted. The findings revealed that WCM
and profitability are positively correlated. Furthermore, ITID and APP
are negatively correlated with profitability, whereas ACP and the CCC
exhibit positive relationships.
2.3. Summary of the literature
The efficiency of WCM has implications for profitability. The main
goal of WCM is to maintain a level of cash inflows and outflows that
would create balance between each element of WC, without which it is
impossible to move or make firms function in a proper way. In addition,
reliable and incessant monitoring of components of WC is required to
achieve such balance. CCC is the popular and most comprehensive measure
of WCM. The optimal level of CCC vis-a-vis profitability differs from
firm to firm. The empirical literature indicates that firms that are
efficient with WCM recorded increased profitability and vice versa.
Therefore, improvement in WCM increases profitability.
3. Methodology
3.1. The study sample and data collection
The study population included manufacturing firms that have been
listed since 1997 and remained in existence up till 2005. The time frame
covered a thirteen year period from 1993 to 2005. Using 1997 as the
basis for drawing our sample is informed by the fact that all firms
listed on the NSE will have available financial records dated back at
the limit to 1993. A total of 114 firms fit into this frame. However,
only 48 for which we obtained complete data set constituted the study
sample.
Data were sourced from the individual firm's various issues of
Annual Report and Statement of Accounts. The literature indicated
various measures of profitability. This study used three measures namely
NOI, GOI and ROA with each serving as check on the other. The CCC as a
comprehensive measure of WCM was also used.
3.2. Method of analysis
Most previous studies combined descriptive/statistical and
econometrics analyses. This study followed this lead by employing both
approaches. The former was used to characterize the profitability and
WCM patterns of the firms as well as the relationship between the two.
The latter provided estimates of the association between the firms'
profitability and WCM by specifying a model and applying an appropriate
estimation technique. The general form of our model following in the
footsteps of Deloof (2003), Raheman and Nasr (2007) and Samiloglu and
Demigunes (2008) and others is as follows:
[PRT.sub.it] = [a.sub.0] + [summation][[beta].sub.i][X.sub.it] +
[summation][l.sub.i][Z.sub.it] + [U.sub.it], (1)
where [PRT.sub.it] = profitability of firm i at time t; i=1,2 ...
48 firms
[a.sub.0] = the intercept of equation
[[beta].sub.i] = coefficients of X variables.
[l.sub.i] = coefficient of Zlt variables.
[X.sub.it] = the different independent variables for WCM of firm i
at time t
[Z.sub.it] = the different independent variables serving as control
variables of firm i at time t
t = time: 1, 2 ... 13 years
Ut = The error term
I then converted the above general least squares model into
specified variables model as:
[PRT.sub.it] = [a.sub.0]+ ([a.sub.1][ACP.sub.it] +
[a.sub.2][APP.sub.it] + [a.sub.3][ITID.sub.it] + [a.sub.4][CCC.sub.it])
+ [a.sub.5]([CR.sub.it] + [a.sub.6][LEV.sub.it] + [a.sub.7][SIZE.sub.it]
+ [a.sub.8][GRT.sub.it] + [a.sub.9][FATA.sub.it]) + [U.sub.it] (2)
where:
PRT : Profitability measured by NOI (Total Sales minus Cost of
goods sold, including depreciation and amortization /Total
Asset-Financial Asset), GOI (Operating Income plus depreciation/Total
Assets minus Financial Assets) and ROA (Net Income/Total Asset).
ACP : Average Collection Period (= Account receivables x 365/sales)
APP : Average Payment Period (= Accounts payable x 365/purchases)
ITID: Inventory Turnover in Days (= Inventory x 365/cost of sales)
CCC : Cash Conversion Cycle (= ACP plus ITID minus APP)
CR : Current Ratio - current liabilies/current asset
LEV : Total Debt/ Total Assets
SIZE: Natural Logarithm of Total Asset
GRT : Percent Changes in Sales
FATA: Financial Assets/Total Assets
3.3. A prior expectations on the model
In this section, I discuss the likely direction of relationship
between profitability and WCM variables and others in our model. Given
the focus of the study, we begin with the WCM variables.
Starting with account receivable, a negative relationship is
plausible when customers take more time to assess the quality of
products they buy from firms, particularly, those with declining
profitability. So it is presumed that higher profits should lead to more
accounts receivable, because firms with higher profits have more cash to
lend to customers. The study of Deloof and Jergers (1996) provided
empirical validation that firms with shortage of cash reduce investment
in accounts receivable.
For account payable, delaying payments to suppliers and other
creditors allows firms to assess the quality of bought products and can
be an inexpensive and flexible source of financing. In this instance, it
can enhance the prospect of firms' profitability. However, late
payment of invoices can be costly if a discount for early payment is
offered. Loosing such discounts makes firms less profitable. Less
profitable firms in turn wait longer to pay their bills.
Large inventory and a generous trade credit may lead to higher
sales and reduce the risk of a stock-out. Trade credit may stimulate
sales because it allows customers to assess product quality before
paying (Long et al. 1993; Deloof and Jegers 1996; Deloof 2003; Raheman
and Nasr 2007). The flip side is that money is locked up in WC with
implications for profitability (Deloof 2003). A negative relationship
would ensure with declining sales, leading to lower profits and more
inventory.
A longer CCC might increase profitability because it leads to
higher sales. However, profitability might also decrease with the CCC,
if the costs of higher investment in WC rise faster than the benefits of
holding more inventories and/or granting more trade credit to customers.
Therefore, to have desired positive impact on profitability, the optimal
CCC must be attained. With optimal CCC, a firm would be efficient, and
this will lead to increasing its profitability.
CR is expected to be negatively signed with profitability. This is
because the two objectives of liquidity and profitability have inverse
relationships. So, firms need to maintain a balance or trade-off between
the two. When a balance is achieved, CR would be positively related to
profitability on the presumption that debt facilities offer opportunity
to actualize investment opportunities and expansion activities that they
may not otherwise be able to accomplish with own funds. With a
trade-off, CR and profitability would be negatively related because debt
must be repaid and at a cost. With high a level of debt and servicing
cost, there could be a negative relation.
On size and profitability, there are three contending postulations.
The first is the traditional theory which suggests a negative
relationship and premised on the assumption that large or big firms
supposedly operate close to the optimum level and so would grow very
little and might even have to shrink. But small firms are far below the
optimum size and would need to grow faster. The second is the
Gilbrat's (1931) "Law of Proportionate Effect". which
states that both big and small firms have equal chance of growing at a
given rate during any period of time. The third is the "bigger the
better" theory that postulates that large firms have an advantage
over the smaller ones because larger firms can enter into all the
product lines that the smaller firms enter, while the reserve is not
true owing to the presence of size and scale advantages. In addition,
big firms generally have easier access to capital and money markets than
less well-known small firms (Biggs et al. 1996).
4. Data analysis and discussion of results
The data collected were analyzed descriptively and quantitatively.
The results are discussed in this section.
4.1. Descriptive analysis
The descriptive analysis shows the average and standard deviation
of the different variables as well as the minimum and maximum values.
Table 1 presents descriptive statistics for 48 listed Manufacturing
firms for thirteen years from 1993 to 2005 and for a total of 624
firms' year observations. GOI is on average 38.7% of total
asset-financial asset with a median of 34.5%. Mean and median NOI and
ROA are 34.7% and 18.9%; and 5.5% and 5.6%, respectively. The average
CCC is 27.3 days with a median of--10.3 days. Firms receive payment on
sales after an average of 71.4 days (the median is 60.8 days). It takes
on average 191.5 days to sell inventory (median is 163.9 days) and firms
wait on average 271.3 days to pay for purchases (the median is 233.1
days).
Mean GRT is 24.0% and a median of 15.3%. On average, just about
6.8% of all assets are financed with financial debt with a median value
of 6.4%. Similarly, the mean and median FATA are 6.6% and 6.7%,
respectively. This suggests that most of the firms have large proportion
of total assets as fixed assets. Maximum LEV stood at 5.691 with an
average value of 0.676. Size variation showed a wide margin from a
minimum value of about 10 to a maximum of 21, with mean and median
values of 15 and 14, respectively.
4.2. Empirical analysis
The empirical analysis is bifurcated. The first is the correlation
analysis, while the second is the regression analysis
4.2.1. Correlation analysis
Table 2 presents Pearson correlation coefficients for all
variables. There is a negative and significant relation between the
profitability and WCM measures. In specific terms, the coefficients
between ACP the three measures of profitability are negative: GOI -0.12,
NOI -0.07 and ROA -0.21. These indicate that longer ACP has negative
impacts on profitability. The results for APP also bear negative and
significant correlation with the profitability measures suggesting that
the less profitable firms are, the longer they take to pay bills.
Similarly, the coefficients between ITID and PRT are also negatively
signed implying that it took longer time on the average in selling
inventory or stocks, which adversely affect profitability. The CCC also
bear negative coefficients with the three profitability measures, though
with weaker statistical strength: -0.02 for (GOI), -0.05 for (NOI) and
-0.06 for ROA. These results are indicative that the CCC has not been
optimal and efficient, and impacted profitability positively as desired.
CR is divergently correlated with the profitability measures. While
it is negatively correlated with GOI, it is positive with both NOI and
ROA. FATA is positively correlated with all the three measures of
profitability. This shows that FATA was sufficient enough to enable the
firms seize profitable investment opportunities, and thereby facilitated
profitability. GRT as expected bears positive correlations with all
measures of profitability. The coefficients obtained are 0.07 for GOI,
0.17 for NOI and ROA and suggest that GRT increased profitability. LEV
is both positively and negatively correlated with profitability. For
GOI, it is positive with a coefficient value of 0.28. It is negative
with respect to NOI and ROA with coefficient values of -0.14 and -0.22,
respectively. All the coefficients are significant. The positive
correlation with GOI is indicative that debt enabled the firms to
actualized some of their investment plans and expansion activities,
while the negative relation to NOI and ROA suggest that the cost of
servicing the debt vis-a-vis the return on the investment, had reducing
effect on profitability.
Lastly, size is negatively correlated with profitability. The
relationship is significant in respect of GOI and NOI. These results
point to the validation of the traditional theory which suggests a
negative relation between size and firms performance.
4.2.2. Regression analysis
The regression analysis was explored with two estimation
techniques/approaches. The first is the pooled regression, while the
second is the fixed effect regression with cross-section weights. The
cross-section weights were explored to fix the heteroskedasticity
issues/problems in the data/estimation. The fixed effects regression was
considered because of the simple reason that all the variables of the
model are endogenous in nature. The results obtained from both, and for
each variant of the model are presented in Tables 3a, 3b and 3c,
respectively.
Starting with Table 3a, the pooled regression results show that
profitability measured by GOI, is negatively and significantly related
to ACP and APP, while that of ITID and the CCC are positive but
insignificant. This therefore implies WCM practices have not been
efficient. Similarly, CR, LEV, and size bear significant negative
relationship with GOI. FATA and GRT are positively linked with GOI.
However, only that of FATA is significant.
The results from the fixed effects regression present a stronger
evidence of the inefficiency of the WCM practices given that all the
measures of WCM are negatively and significantly signed with GOI. The
positive and negative impacts of FATA and SIZE, respectively like in the
pooled regression are also significant. CR retains its negative impact
but with significance only on APP. Similarly, LEV retains its positive
impact but lost its significance.
In Table 3b, the results reflect a somewhat transpose of those in
table 3 a. This is because the pooled regression results in this
instance provide a stronger evidence of the inefficiency in the WCM
practices. All the measures of WCM show significant negative
relationship to NOI. In contrast, the results from the fixed effects
regression present weak evidences of inefficient WCM except for ACP with
a significant impact. This result suggests that ACP is longer than the
minimum required and as such had negative impact. FATA and GRT bear
significant positive impact while LEV and SIZE exerted significant
negative influences in both the pooled and fixed effects regressions.
The impact of CR showed mixed results. It is positive in the pooled
regression but negative in the fixed effects regression. However, while
its impact in the pooled estimation was largely insignificant, it was
significant in the fixed effect regression. This implies a trade-off
between CR and profitability.
The results obtained with the use of ROA as the measure of
profitability (Table 3c) parallel that obtained from using NOI. Results
from both the pooled and fixed effect regressions showed that the WCM is
inefficient and has therefore caused reduction in profitability. Both
FATA and GRT had significant positive impacts. In contrast, CR, LEV and
SIZE had significant negative impacts.
In summary, the WCM of the firms have been inefficient and thereby
negatively affected their profitability. This result/conclusion is
similar to the findings of Shin and Soenen (1988), Deloof (2003),
Padachi (2006), Raheman and Nasr (2007) and Karaduman et al. (2010).
5. Summary and conclusion
This paper investigated the relationship between efficiency of WCM
and profitability of a sample of 48 manufacturing firms in Nigeria
during the period 1993 to 2005. The motivation is underpinned by the
inadequacy and weaknesses in a previous study by Falope and Ajilore
(2009). Thus, the study is aimed at contributing to expanding and
enriching the literature particularly on Nigeria and at large.
The investigation examined the responses of the firms'
profitability to WCM and a number of augmenting factors namely CR, LEV,
GRT, FATA and size. Profitability was alternatively measured by GOI, NOI
and ROA. Likewise, WCM was measured by the ACP, APP, ITID and
comprehensively by the CCC.
The results indicated the firms' have been inefficient with
their WCM and caused reductions in their profitability. This is similar
to the findings of Shin and Soenen (1988), Deloof (2003), Padachi
(2006), Raheman and Nasr (2007) and Karaduman et al. (2010). In
addition, CR, LEV, GRT, FATA and size also have significant effect on
the firms' profitability just as reported by Samiloglu and
Demigunes (2008).
On the strength of the results, the paper concluded that improving
the efficiency of WCM is essential for the firms. The paper, therefore,
recommended that manufacturing firms in Nigeria should shorten the ACP,
APP and ITID, and reduce CCC towards enhancing their profitability. In
addition, financial managers generally must accord importance to
maintaining optimal and efficient WCM to guarantee profitability. For
the investors with eye on high returns on their investment, proper
scrutiny of WCM policies and management practices of firms should be
done prior to making investments. Since the core of financial management
consultancy is to provide informed opinion and guidance to clients, it
is imperative that financial management consultants utilize the findings
of empirical research like this paper in their practices.
However, some limitations of the study are recognized. First, is
the small sample size of 48 firms. Second, the period covered
(1993-2005) is somewhat dated. The third and the most significant is the
lumping of all manufacturing firms together. There are ten sub sector
classifications of the manufacturing sector in Nigeria. A sub-sector
study would provide more insightful results. It is, therefore, suggested
that further research focusing on sub sector analysis be conducted.
doi: 10.3846/16111699.2011.651626
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Omo Aregbeyen
Department of Economics, University of Ibadan, Ibadan, Nigeria
E-mail:
[email protected]
Received 13 August 2011; accepted 14 December 2011
Omo AREGBEYEN holds a Ph.D. in Economics from the University of
Ibadan, Ibadan, Nigeria. He has taught and conducted researches in a
number of leading universities and institutions in the country. His
teachings and research activities have focused more on
Development/Public Sector Economics and Industrial Economics. Many of
the research outputs of Dr Aregbeyen have appeared in learned journals
both locally and internationally. He has also served as a
consultant/resource person in a number of commissioned studies/projects
nationally and internationally. Presently, he is with the Department of
Economics, University of Ibadan, Ibadan and the Director of postgraduate
academic programmes.
Table 1. Descriptive statistics--48 listed Nigerian manufacturing
firms (1993-2005)
Minimum Maximum Mean Median Std. Dev.
ACP 0.66 321.24 71.36 60.84 52.00
APP 0.15 963.16 271.30 233.12 159.04
CCC -103.89 805.89 27.40 -10.30 106.79
CR 0.02 13.87 1.40 1.24 0.90
FATA 0.04 4.30 0.66 0.67 0.24
GOI -0.65 7.68 0.39 0.35 0.44
GRT -73.44 924.33 24.01 15.31 55.20
ITID 3.91 969.47 191.50 163.90 135.60
LEV 0.00 5.69 0.68 0.64 0.48
NOI -7.75 8.42 0.35 0.19 0.97
ROA -2.79 0.52 0.06 0.06 0.16
SIZE 10.07 21.05 14.65 14.49 2.01
Table 2. Pearson correlation coefficients for 48 listed Nigerian
manufacturing firms (624 firm-year observations)
ACP APP CCC CR FATA GOI
ACP 1 .39 ** .23 ** .10 ** .06 -.12 **
(.00) (.00) (.01) (.08) (.00)
APP 1 -.14 ** -.20 ** -17 ** -.12 **
(.00) (.00) (.00) (.00)
CCC 1 39 ** .03 -.02
(.00) (.22) (.32)
CR 1 .21 ** -.02
(.00) (.28)
FATA 1 .52 **
(.00)
GOI 1
GRT
ITID
LEV
NOI
ROA
SIZE
GRT ITID LEV NOI
ACP -.10 ** .22 ** -.03 -.07 *
(.01) (.00) (.20) (.04)
APP -0.02 .36 ** .10 ** -.16 **
(.36) (.00) (.01) (.00)
CCC -14 ** .69 ** -.20 ** -.05
(.00) (.00) (.00) (.11)
CR -.02 14 ** -.31 ** .19 **
(.28) (.00) (.00) (.00)
FATA .04 -.01 .25 ** .30 **
(.14) (.37) (.00) (.00)
GOI .07 * -.02 .28 ** .12 **
(.04) (.30) (.00) (.00)
GRT 1 -.10 ** .01 .17 **
(.01) (.42) (.00)
ITID 1 -.16 ** -.09 *
(.00) (.02)
LEV 1 -14 **
(.00)
NOI 1
ROA
SIZE
ROA SIZE
ACP -.21 ** -.09 *
(.00) (.01)
APP -.19 ** .04
(.00) (.17)
CCC -.06 -.160 **
(.07) (.00)
CR 11 ** -.19 **
(.00) (.00)
FATA .21 ** -.17 **
(.00) (.00)
GOI .31 ** -.13 **
(.00) (.00)
GRT .17 ** .03
(.00) (.22)
ITID -.05 -.10 **
(.12) (.01)
LEV -.22 ** -.01
(.00) (.39)
NOI .53 ** -.15 **
(.00) (.00)
ROA 1 -.01
(.45)
SIZE 1
* (**) Significant at 1% (5%) level; p-value are in parenthesis
Table 3a. Regression of gross operating profit on working capital
variables
Regressions/ Pooled Regression
Variable
1 2 3 4
C 0.08 0.04 -0.03 -0.04
(0.63) (0.32) (-0.25) (-0.32)
ACP -0.001 -- -- --
(-4.24) *
APP -- -0.0002 -- --
(-1.87) **
CCC -- -- 0.0001 --
(0.85)
ITID -- -- -- 0.0001
(0.56)
CR -0.04 -0.05 -0.06 -0.05
(-2.42) * (-2.88) * (-2.78) * (-2.69) *
FATA 0.92 0.89 0.91 0.91
(13.92) * (13.15) * (13.60) * (13.58) *
GRT 0.0003 0.0004 0.0004 0.0004
(0.96) (1.36) (1.48) (1.43)
LEV 0.11 0.12 0.12 0.12
(3.27) * (3.48) * (3.38) * (3.36) *
SIZE -0.02 -0.01 -0.01 -0.01
(-2.09) * (-1.84) ** (-1.71) ** (-1.75) **
[R.sup.2] 0.328 0.312 0.309 0.309
ADJ. [R.sup.2] 0.322 0.306 0.302 0.302
F-STAT 50.23 46.73 46.06 45.97
(0.00) (0.00) (0.00) (0.00)
Fixed effects -- -- -- --
test (F-test)
Regressions/ Fixed Effect Regression
Variable (with cross-section weights)
1 2 3 4
C 0.53 0.67 0.54 0.63
(4.71) * (6.19) * (4.60) * (5.48) *
ACP -0.001 -- -- --
(-6.65) *
APP -- -0.0004 -- --
(-8.50) *
CCC -- -- -0.00001 --
(-2.02) *
ITID -- -- -- -0.0002
(-3.85) *
CR -0.01 -0.03 -0.02 -0.01
(-1.25) (-2.82) * (-1.38) (-1.25)
FATA 0.47 0.47 0.45 0.46
(10.85) * (10.93) * (9.98) * (9.99) *
GRT 0.0003 0.0004 0.001 0.001
(3.74) * (4.78) * (4.58) * (4.41) *
LEV 0.02 0.02 0.02 0.03
(1.07) (1.12) (1.04) (1.14)
SIZE -0.03 -0.03 -0.03 -0.04
(-3.85) * (-4.91) * (-4.35) * (-5.03) *
[R.sup.2] 0.694 0.711 0.675 0.684
ADJ. [R.sup.2] 0.666 0.684 0.645 0.655
F-STAT 24.40 26.45 22.38 23.27
(0.00) (0.00) (0.00)
Fixed effects 14.79 * 16.97 * 17.91 * 18.47 *
test (F-test) (0.00) (0.00) (0.00) (0.00)
* (**) Significant at 5% (10%) level
Note: Null hypothesis for the fixed effects test: Fixed effects are
not significant.
Table 3b. Regression of net operating profit on working capital
variables
Regressions/ Pooled Regression
Variable
1 2 3 4
C 0.48 0.50 0.41 0.57
(1.51) (1.54) (1.32) (1.78) **
ACP -0.002 -- -- --
(-2.57) *
APP -- -0.001 -- --
(-2.03) *
CCC -- -- -0.001 --
(-3.31) *
ITID -- -- -- -0.001
(-3.17) *
CR 0.05 0.03 0.09 -0.06
(1.16) (0.73) (2.02) ** (1.23)
FATA 1.29 1.23 1.26 1.28
(8.09) * (7.57) * (7.93) * (8.00) *
GRT 0.003 0.003 0.003 0.003
(4.12) * (4.37) * (3.93) * (4.08) *
LEV -0.42 -0.40 -0.44 -0.45
(-5.10) * (-4.84) * (-5.34) * (-5.41) *
SIZE -0.05 -0.05 -0.05 -0.05
(-2.64) * (-2.51) * (-2.82) * (-2.74) *
[R.sup.2] 0.181 0.177 0.186 0.185
ADJ. [R.sup.2] 0.173 0.169 0.179 0.177
F-STAT 22.69 22.19 23.57 23.38
(0.00) (0.00) (0.00) (0.00)
Fixed effects test -- -- -- --
(F-test)
Regressions/ Fixed Effect Regression
Variable (with cross-section weights)
1 2 3 4
C 1.40 1.37 1.33 1.35
(11.43) * (9.84) * (8.99) * (9.33) *
ACP -0.001 -- -- --
(-3.46) *
APP -- -0.0001 -- --
(-0.71)
CCC -- -- 0.0002 --
(1.59)
ITID -- -- -- 0.0001
(1.50)
CR -0.03 -0.04 -0.04 -0.04
(-2.21) * (-2.52) * (-2.64) * (-2.40) *
FATA 0.60 0.59 0.59 0.58
(11.83) * (10.89) * (10.97) * (10.74) *
GRT 0.001 0.001 0.001 0.001
(7.59) (7.54) * (7.88) * (8.14) *
LEV -0.35 -0.35 -0.35 -0.35
(-9.18) * (-8.75) * (-8.71) (-8.72) *
SIZE -0.08 -0.08 -0.08 -0.08
(-10.45) * (-8.88) * (-8.37) * (-9.04) *
[R.sup.2] 0.670 0.651 0.648 0.654
ADJ. [R.sup.2] 0.639 0.618 0.616 0.622
F-STAT 21.85 20.03 19.82 20.32
(0.00) (0.00) (0.00) (0.00)
Fixed effects test 11.91 12.85 13.23 13.60
(F-test) (0.00) (0.00) (0.00) (0.00)
* (**) Significant at 5% (10%) level
Note: Null hypothesis for the fixed effects test: Fixed effects
are not significant.
Table 3c. Regression of return on assets on working capital
variables
Regressions/ Pooled Regression
Variable
1 2 3 4
C 0.02 0.01 -0.03 -0.02
(0.42) (0.15) (-0.53) (-0.28)
ACP -0.001 -- -- --
(-6.14) *
APP -- -0.0001 -- --
(-3.30) *
CCC -- -- -0.0002 --
(-2.66) *
ITID -- -- -- -0.0001
(-1.86) **
CR -0.003 -0.01 0.001 -0.01
(-0.40) (-1.19) (0.13) (-0.63)
FATA 0.20 0.18 0.19 0.19
(7.46) * (6.48) * (6.98) * (7.03) *
GRT 0.0004 0.001 0.0004 0.001
(3.79) * (4.30) * (3.92) * (4.11) *
LEV -0.10 -0.10 -0.11 -0.11
(-7.51) * (-6.91) * (-7.41) * (-7.36) *
SIZE 0.001 0.002 0.002 0.002
(0.39) (0.57) (0.49) (0.61)
[R.sup.2] 0.196 0.162 0.157 0.152
ADJ. [R.sup.2] 0.188 0.154 0.148 0.143
F-STAT 25.09 19.84 19.10 18.39
(0.00) (0.00) (0.00) (0.00)
Fixed effects -- -- -- --
test (F-test)
Regressions/ Fixed Effect Regression
Variable (with cross-section weights)
1 2 3 4
C 0.33 0.32 0.34 0.34
(8.41) * (7.65) * (8.17) * (7.93) *
ACP -0.0002 -- -- --
(-4.13) *
APP -- -0.00001 -- --
(-3.66) *
CCC -- -- 0.00003 --
(1.01)
ITID -- -- -- 0.00001
(0.45)
CR -0.014 -0.014 -0.014 -0.013
(-4.01) * (-3.79) * (-3.68) * (-3.68) *
FATA 0.13 0.13 0.12 0.12
(8.33) * (8.04) * (7.65) * (7.65) *
GRT 0.0003 0.0003 0.0003 0.0003
(6.87) * (6.50) (7.15) (7.03) *
LEV -0.08 -0.08 -0.08 -0.08
(-6.93) (-6.94) * (-6.97) * (-6.99)
SIZE -0.02 -0.02 -0.02 -0.02
(-7.79) * (-7.01) * (-7.97) * (-7.92) *
[R.sup.2] 0.675 0.660 0.663 0.664
ADJ. [R.sup.2] 0.645 0.628 0.631 0.633
F-STAT 22.38 20.87 21.12 21.29
(0.00) (0.00) (0.00) (0.00)
Fixed effects 15.16 * 15.78 17.09 17.11
test (F-test) (0.00) (0.00) (0.00) (0.00)
* (**) Significant at 5% (10%) level
Note: Null hypothesis for the fixed effects test: Fixed effects
are not significant.
