Working Capital Management and Profitability: The Case of Industrial Firms in Jordan

European Journal of Economics, Finance and Administrative Sciences ISSN 1450-2275 Issue 36 (2011) © EuroJournals, Inc. 2011 http://www.eurojournals.com

Working Capital Management and Profitability: The Case of Industrial Firms in Jordan Mamoun M. Al-Debi'e The University of Jordan, Faculty of Business Department of Accounting, Amman, Jordan E-mail: [email protected] Tel: +962-6-5355000; Fax: +962-6-5330695 Abstract The study aims at examining the relationship between profitability and working capital management measures for industrial companies listed on Amman Stock Exchange in Jordan during the period 2001-2010. Industrial companies in Jordan invest significantly in working capital. Therefore, efficient working capital management is expected to enhance the profitability of these companies. The results show that less profitable companies wait longer to sell their products, to collect credit sales, and to pay their supplies of goods. Moreover, the results show that regardless of the level of profitability industrial companies in Jordan pay their suppliers before collecting credit sales. The control variables (Size, Leverage, and GDP growth) included in all regression models were significant and have the expected signs. Profitability increases with Size and GDP growth and decreases with leverage.

Keywords: Working capital management, Profitability, Cash Conversion Cycle, Industrial companies, Jordan.

1. Introduction The ultimate goal of all business organizations is to be a going concern. However, achieving this ultimate goal requires business organizations to be simultaneously profitable and solvent. Solvency is classified into short-term solvency; or what is referred to as liquidity, and long-term solvency. Liquidity is defined as the ability of the business organization to meet its short-term debts and obligations when they come due. Or it can be defined as the average time period required to convert non-cash current assets into cash; the shorter the period required the stronger the liquidity position of the business organization. The relationship between current asset items and current liability items is called the working capital of the business organization. Working capital can be looked at as the excess of total current assets over total current liabilities, i.e., the remaining current assets after paying all current liabilities. Thus, the larger the amount of working capital the stronger the liquidity position of the business organization. Related literature has identified other measures of the liquidity position of business organizations such as the current ratio, the acid-ratio, the current cash debt coverage ratio, the Receivables Conversion Period (RCP), the Inventories Conversion Period (ICP), and the Cash Conversion Cycle (CCC). Working capital management (WCM) focuses on the quality of current asset items and takes into consideration the trade-off between risk and return. The quality of current assets hinges on the

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composition of these assets, the magnitude of their amounts, and the average time period required to converting each of them into cash. Current asset items can be sub-classified into cash and cash equivalent items; such as cash and marketable securities, near–to-cash items; such as short-term receivables, far-from-cash items; such inventories, and finally non-cash items; such as short-term prepayments. Furthermore, quality of the working capital of business organizations takes into consideration whether current assets are sufficient to cover current liabilities. Working capital management (WCM) also requires making a trade-off between risk and return. It is known that the nearer the asset to the cash state the lower its riskiness and the lower its expected return. Therefore, it is logical to expect a negative relationship between profitability and the length of period over which resources of business organizations are held in non-cash current assets. Given the importance of such aforementioned concepts to business success, this study aims at examining the relationship between profitability and selected liquidity measures that signals the management of the working capital of the business organization. The rest of the study is organized as follows. The researcher starts with reviewing related previous literature on the relationship between profitability and working capital management (WCM) measures. Then the study methodology is introduced including the study sample and period, the variables under examination, and models of the study. The final part of the study reports the empirical results and conclusions of the study.

2. Previous Research Long-term investing and financing decisions received most of the researchers' attention compared with the attention given to short-term investing and financing decisions which reflect the WCM of the business organization. However, several previous studies, conducted in different countries, examined the relationship between WCM measures and profitability. Researchers believe and have proved the importance of efficient WCM on the profitability of the business organization. Richards and Laughlin (1980) argued that the static liquidity measures; such as the current ratio and the acid-ratio, are measures of liquidation rather than the going concern of the business organization. They further argued that the operating cash flow coverage approach, rather than the asset liquidation value, is crucial element in the liquidity analysis. They managed to construct a measure of the cash cycle concept by reflecting the net time interval between actual cash expenditures on purchases and the ultimate recovery of cash receipts from products sale and they called it the Cash Conversion Cycle (CCC) which is an additive measure of the Receivables Conversion Period (RCP) plus the Inventories Conversion Period (ICP) less the Payables Deferral Period (PDP). Shin and Soenen (1998) investigated the relationship between the company's net trade cycle (NTC) and its profitability using a Compustat sample of 58985 company-year observations over the period 1975-1994. Correlation and regression analysis were employed. The net trade cycle is basically equal to the CCC whereby all three components of the CCC; the receivables conversion period, the inventories conversion period and the payables deferral period, are expressed as a percentage of sales. This is done because the denominators for those three components are different which makes the addition not really useful. The Pooled and Cross-sectional regression models ran included the levels and changes in the following independent variables besides NTC; the current ratio, the total debt to total assets ratio, and sales growth. They tested the relationship between those independent variables and two dependent variables; profitability and stock returns. Profitability was measured as operating income minus depreciation deflated by either total assets or total sales. The results, using the levels and changes of dependent and independent variables, showed that a company with a short NTC is more profitability and has a higher stock return. The results also showed that profitability is significantly negatively related to the current ratio and leverage, and significantly positively related to sales growth. Weinraub and Visscher (1998) aimed at examining whether a significant difference exists in the aggressive / conservative working capital policies between industries. Quarterly levels of current assets to total assets ratio and current liabilities to total assets ratio for ten industries over the period 1984-

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1993 were used. Data were obtained from Compustat database. The number of companies ranged between 15 and 33 in each industry, with a total of 216 in their final sample. One-way ANOVA was applied to the set of ten year average ratio means. The differences in the means were significant indicating a distinctive difference in the asset management policies between industries. They further examined the stability of those relative differences between policies over time using Rank Order Correlations. They concluded that differences in working capital policies not only exist but they persist over extended periods of time. Regarding the relationship between asset and financing policies, they found that industries with relatively aggressive asset policies follow relatively conservative financing policies. Deloof (2003) investigated the relationship between WCM measures and profitability for a sample of 1009 large Belgian non-financial companies over the period 1992-1996. He used regression models with fixed effects1 and OLS regressions with dummy variable for time and industries. The dependent variable was measured using gross profit deflated by (total assets minus financial assets), the WCM measures used were the RCP, the ICP, the PDP, and the CCC. Control variable were also included to the regression models measuring size, financial leverage, sales growth, and financial assets to total assets ratio. The results for the regression models estimated with fixed effects showed that profitability increases with the decrease in the RCP, the ICP, PDP, and financial leverage. On the other hand, profitability increases with the increase in company size, sales growth, and fixed financial assets ratio. The CCC was not significant. The results of the OLS regressions were not significantly different from those mentioned above. However, the CCC was significant and showed a negative sign. Eljelly (2004) tested the relationship between profitability and liquidity measures for 27 Saudi companies, from three non-financial sectors, over the period 1996-2000. The independent variables used in the regression models as measures of liquidity were the current ratio and the CCC. Size was included as a control variable. The dependent variable was measured using net operating income before depreciation deflated by sales. The overall results showed that liquidity measures are significant and have negative relationship with profitability, and the importance of those measures differ across industries. Filbeck and Krueger (2005) hypothesized that differences exist among industries with respect to the measures of working capital efficiency identified by the annual ratings of working capital management published in CFO magazine2, and that working capital measures for companies within an industry change across time. Data was collected for the years 1996-1999. The findings of their research supported their hypotheses; averages of WCM measures vary across industries and over time within an industry. They argued that their findings may be explained in part by macroeconomic factors; such as changes in interest rates, rate of innovation, and competition. Lazaridis and Tryfonidis (2006) investigated the relationship between profitability and WCM using a sample of 131 industrial companies listed on the Athens Stock Exchange during the period 2001-2004. The dependent variable used in all regression models was gross profit deflated by total assets after excluding financial assets. The independent variables were the CCC and its components (i.e., the RCP, the ICP, and the PDP). They have also introduced control variable for the size of the company, leverage, and the ratio of financial assets to total assets. The regression models were ran with dummy variable for sub classification of the industrial sector. The results showed a significant and negative relationship between profitability and the CCC and its individual components. However, the ICP was not significant. The control variables were all significant; the size of the company and the ratio of financial assets to total assets had a positive relationship with profitability while the leverage variable had a negative relationship with profitability.

1

2

Fixed effects estimation assumes company specific intercepts, which capture the effects of those variables that are particular to each company and that are constant over time, Deloof (2003). Working capital efficiency is measured using cash from operations to sales ratio, the net trade ratio; (receivables +inventories–payables)(365/sales), receivables conversion period, inventories conversion period, and payables deferral period.

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Padachi (2006) examined the relationship between profitability and selected WCM measures using 58 small manufacturing Mauritian companies over the period 1997-2003. They sub-classified their sample companies into five sub-classifications for analysis purposes. The study models were estimated using the regression based framework (Fixed and Pooled ordinary least squares). The dependent variable used in all models was the return on assets. The independent variables were the RCP, the ICP, the PDP, and the CCC. The models also included control variables that may affect the profitability of the company, these control variables were; size, gearing ratio, working capital turnover ratio, current assets to total assets ratio, and current liabilities to total assets ratio. The results of all types of the regression models used showed that the only significant, with the expected negative sign, WCM measure is the average RCP. Size and the current assets to total assets ratio were the only significant control variables with positive signs. Raheman and Nasr (2007) studied the effects of selected WCM and liquidity measures on the profitability of 94 Pakistani companies listed on Karachi Stock Exchange over the period 1999-2004. They ran pooled least squares and generalized least squares regression models with cross section weights to test the relationship between profitability; the dependent variable, measured as the net operating income deflated by total assets and the following independent variables; the RCP, the ICP, the PDP, the CCC, and the current ratio. They have also used size, leverage, and the ratio of financial assets to total assets as control variables. The results showed significant and negative relationships between profitability and all WCM and liquidity measures. Furthermore, size showed a significant and positive relationship with profitability, leverage and the ratio of financial assets to total assets showed significant and negative sign with profitability. Garcia_Teruel and Martinez-Solano (2007) aimed at providing empirical evidence on the effects of WCM on the profitability of a sample of small and medium-sized Spanish companies. Using panel data methodology on a sample of 8,872 small to medium-sized companies over the period 19962002, they ran multiple regression models which included, as a proxy for profitability, the return on assets as a dependent variable. The independent variables capturing WCM were the RCP, the ICP, the PDP, and the CCC. These independent variables were introduced individually to the models. They have also included in all models control variables for size of the company measured as the logarithm of total assets, sales growth measured as the percentage annual change in sales, leverage measured as the ratio of debt to liabilities, and finally an indicator of economic conditions measured as the annual growth in GDP. They first divided their sample into for quartiles ranging from the least profitable companies to the most profitable companies using the average values of the return on assets. The results showed that the average values for all WCM measures are shorter for the most profitable companies compared with the least profitable companies. The results of the regression models showed significant and negative relationship between profitability and all WCM measures. In regards to the control variables; they were all significant and showed a positive relationship with profitability except for leverage which showed a negative sign. The overall result is that the relationship between profitability and WCM measures is important in the case of small and medium-sized companies. Falope and Ajilore (2009) tested the relationship between WCM and profitability using panel data methodology for a sample of 50 listed companies in Nigeria Stock Exchange for the period 19962005. The dependent variable used in all regression models, as a measure of profitability, was the return on assets. The independent variables that were introduced to the models individually are; the RCP, the ICP, the PDP, and the CCC. They have also included in all models the following four control variables; size measured as the logarithm of total assets, leverage measured as the ratio of total debt to total assets, sales growth measured as the percentage annual change in sales, and a macro economic indicator measured as the growth in the annual GDP. The results of the study show significant and positive association between profitability and the RCP, the ICP, and the cash conversion cycle, and a significant and positive association with the PDP. The results in regards to the control variables were in general insignificant and showed positive and negative relationships with profitability. Finally, and after decomposing their sample companies into two portfolios according to the market capitalization of

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companies; they failed to prove the existence of a difference in the relationship between profitability and WCM measures for small and large companies. Dong and Su (2010) studied the relationship between WCM and profitability using the fixed effects model for a sample of 130 companies listed in Vietnam Stock Market for the period 2006-2008. The dependent variable used in all regression models, as a measure of profitability, was gross profit deflated by total assets excluding financial assets. The independent variables that were introduced to the models individually are; the RCP, the ICP, the PDP, and the CCC. They have also included in all models the following three control variables; size of the company, financial leverage, and the ratio of financial assets to total assets. The results of the study show a significant and negative relationship between profitability and the RCP, the ICP, and the CCC. However, the relationship between profitability and the PDP was significant and positive, implying that the longer the period suppliers offered the company to pay its obligations the better its profitability. Furthermore, all three control variables were found significant and positively associated to the profitability of the company. Karaduman et. al. (2010) examined the effects of WCM on profitability using panel data methodology for a sample of 140 companies listed in the Istanbul Stock Exchange for the period 20052008. They ran multiple regression models that include control variables besides each of the independent variables that reflect WCM of the company. The results show significant and negative relationships between profitability; measured by the return on assets, and the RCP, the ICP, the PDP, and the CCC3. They concluded that shortening the lengths of these periods individually enhances the profitability of the company. Regarding the control variables, they found a significant and positive relationship between profitability and both the company's size and the economic growth rate (GDP), on the other hand they found a significant and negative relationship between profitability and the company's financial leverage.

3. Research Method 3.1. Study Sample The study sample includes all Industrial companies listed on Amman Stock Exchange (ASE) during the period 2000-2010. Required data to calculate all study variables must be available for two consecutive years at least to include the company in the sample. Applying this criterion resulted in including (86) companies in the study sample out of the (98) industrial companies listed on ASE in the year (2011). The final number of companies included in all analyses is (77) companies and (552) company-year observations after deleting outliers defined as the top and bottom 1% of the observations on each of the study variables. While arbitrary, exclusion of extreme observations is consistent with similar practice in previous research. 3.2. Variables Measurement The dependent variable, representing the profitability of the company, is the Gross Operating Income (GOI), and is measured as follows: GOIit=OIit + DepExpit / AvAssets Where: GOIit: Gross operating income for company i in year t; OIit: Operating income for company i in year t; DepExpit: Depreciation expenses for company i in year t; AvAssetsit: Average of total assets for company i, calculated by dividing (2) into the sum of Total assets at the end of year t-1 and Total assets at the end of year t; 3

The result regarding the relationship between profitability and the PDP contradicts some of previous empirical research; (Dong and Su, 2010; and Falope and Ajilore, 2009), and agrees with some other pervious empirical research; (GarciaTeruel and Martinez-Solano; 2007, and Deloof, 2003).

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The independent variables used in this study to measure WCM are based on the Cash Conversion Cycle (CCC) approach introduced by Richards and Laughlin (1980), and they are measured as follows: (1) The Cash Conversion Cycle CCCit = OCit – PDPit Where: CCCit: the Cash Conversion Cycle; OCit: the Operating Cycle for company i in year t; PDPit: the Payables Deferral Period. OCit =RCPit + ICPit Where: RCPit: the Receivables Conversion Period for company i in year t; ICPit: the Inventories Conversion Period for company i in year t. (2) The Receivables Conversion Period RCP = 365(AvRecit / Sit) Where: Sit: Net credit sales for company i in year t; AvRecit: Average of receivables for company i, calculated by dividing (2) into the sum of Receivable at the end of year t-1 and Receivables at the end of year t; (3) The Inventories Conversion Period ICPit = 365(AvInvit / CGSit) Where: CGSit: Cost of goods sold for company i in year t; AvInvit: Average of inventories for company i, calculated by dividing into (2) the sum of Inventories at the end of year t-1 and Inventories at the end of year t. (4) The Payables Deferral Period PDPit = 365(AvPayit / Pit) Where: Pit: Net credit purchases for company i in year t; AvPayit: Average of payables for company i, calculated by dividing (2) into the sum of Payables at the end of year t-1 and Payables at the end of year t. I have also included in all study models the following three control variables; size measured as thenatural logarithm of total assets, leverage measured as the ratio of total liabilities to total assets, and a macro economic indicator measured as the growth in the annual GDP4. 3.3. Study Models The following four models were run to examine the relationship between profitability and working capital management measures. Model (1) examines the relationship between profitability and the average time period required to collect receivables (RCP). It is expected that the relationship will be significant and negative; the longer the period of converting receivables into cash the lower is the operating income of the organization. GOI it = α 0it + α 1it RCPit + α 2it Sizeit + α 3it Leverageit + α 4it GDPG + ε 1it ...... (1) Model (2) examines the relationship between profitability and the average time period required to sell inventories (ICP). It is expected that the relationship will be significant and negative; the longer the period of converting inventories into receivables the lower is the operating income of the organization. 4

Annual changes in GDP at constant prices were calculated using information published in the Monthly Statistical Bulletins issued by The Central Bank of Jordan, Different Issues for the years 2000-2010.

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GOIit = λ0it + λ1it ICPit + λ2it Sizeit + λ3it Leverageit + λ4it GDPG + ε 21it ......(

(2) Model (3) examines the relationship between profitability and the average time period required to pay Payables (PDP). It is expected that the relationship will be significant and negative; the longer the period required to pay supplies the lower is the operating income of the organization. GOI it = β 0it + β1it PDPit + β 2it Sizeit + β 3it Leverageit + β 4it GDPG + ε 3it ...... (3) Model (4) examines the relationship between profitability and Cash Conversion Period (CCC). It is expected that the relationship will be significant and negative; the longer the CCC the lower is the operating income of the organization. GOI it = φ 0it + φ1it CCC it + φ 2it Sizeit + φ3it Leverageit + φ 4it GDPG + ε 4it ......( (4) Regarding the control variables, it is expected that the relationship between profitability and both of Size and GDPG will be significant and positive. Large organizations enjoy the benefits of the economies of scale and organizations benefit from good economic conditions. However, the relationship between profitability and Leverage is expected to be significant and negative. Table (1) reports descriptive statistics for all study variables. The mean and median GOI is 8% of average assets. Companies collects credit sales from customers after (100) days on average (median is 76 days), and it takes on average (174) days for companies to sell their inventories (median is 146 days). Companies wait (79) days to pay for credit purchases (median is 54 days). Therefore, the average CCC is (195) days (median is 159 days). It is clear from table (1) that, on average, the sample companies pay credit purchases before collecting from customers on account which may reflect negatively on the efficiency of managing the working capital of the company. Although the highest observation for Leverage is (0.945), the use of debt to finance the assets of companies seems to be moderate in the sample companies; the mean and median for Leverage is (0.30). Jordan experienced during the period 2000-2010 an average GDP growth of (6%) (median is 5.8%). However, year 2009 experienced the lowest GDP growth (2.3%) and years 2004 and 2007 experienced the highest GDP growth (8.5%). To shed light on the magnitude and relative importance of inventories, Accounts receivable, and accounts payable in the sample companies, I calculated the ratio of the sum of inventories and receivables to total assets and the ratio of accounts payable to total assets for all observations. The result shows that Jordanian industrial companies invest, on average, (33%) of total assets in inventories and accounts receivable, and have, on average, (9%) of total assets debts to suppliers of products. Table 1:

Descriptive Statistics

(77 Industrial companies listed on ASE, 2001-2010, 552 company-year Observations) Variable Percentile 1 Minimum Mean Median Maximum Percentile 99 Std. Deviation Kurtosis -0.125 -0.221 0.084 .085 0.362 0.304 0.083 0.969 GOI 7.500 4 100.610 76.540 703 377.740 81.934 11.739 RCP 14.100 11 174.270 146.830 617 552.090 116.255 1.232 ICP 4.730 3 79.160 54.260 622 416.700 79.936 11.636 PDP -147.320 -255 195.720 159.710 805 709.940 157.645 1.399 CCC 5.999 5.922 7.133 7.091 9.003 8.679 0.544 0.848 Size 0.029 .015 0.322 0.300 0.945 0.821 0.192 -0.296 Leverage 2.300 2.300 6.132 5.800 8.500 8.500 2.201 -1.246 GDPG Notes: The table reports descriptive statistics for the main variables in the study after deleting outliers defined as the top and bottom 1% of the observations on each of the study variables. GOI is Gross Operating Income ((Operating income + Depreciation expenses)/Average of total assets), RCP is Receivables Conversion Period ((365*(Average receivables/Net credit sales)), ICP is Inventories Conversion Period ((365*(Average inventories/Cost of sales)), PDP is Payables Deferral Period ((365*(Average payables/Net credit purchases)), CCC is Cash Conversion Period (RCP+ICP-PDP), Size is the natural logarithm of Total Assets, Leverage is the ratio of Total Liabilities to Total Assets, GDPG is Annual growth in GDP.

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4. Empirical Analyses 4.1. Correlation Analysis Table (2) reports Pearson correlation coefficients for all study variables. There is a significant and negative correlation between GOI on one hand and all working capital management measures; RCP, ICP, PDP, and CCC on the other hand. The strongest correlation is between GOI and PDP, RCP, ICP, CCC respectively. It is known that the nearer the asset to the cash state the lower its riskiness and the lower its expected return. Therefore, it is logical to expect a negative relationship between profitability and the length of period over which resources of business organizations are held in non-cash current assets. These negative correlations are consistent with the view that the longer the time required to sell inventories and collect from customers on account the lower is the profitability of the company. The negative correlation between GOI and PDP can be interpreted that less profitable companies wait longer to pay their credit purchases. The correlation coefficients between GOI and the control variables have the expected signs. GOI is positively correlated with size and GDPG meaning that the larger the company and the better the economic conditions of the country, the more profitable is the company. However, the correlation between GOI and GDPG is insignificant. Finally, the correlation coefficient between GOI and Leverage is significant and negative indicating that less profitable companies rely on borrowed funds to finance their operations. Table 2:

Pearson Correlations (77 Industrial companies listed on ASE, 2001-2010, 552 company-year Observations) GOI RCP ICP PDP CCC Size

RCP

-0.282**

ICP

-0.236**

0.287**

PDP

-0.297**

0.319**

0.164**

CCC

-0.170**

0.570**

0.803**

-0.220**

Size

0.383**

-0.122**

-0.243**

-0.085*

-0.199-**

Leverage

-0.127**

-0.037

-0.115**

0.196**

-0.203**

Leverage

0.223**

0.078 -0.018 0.000 -0.104* 0.043 -0.031 -0.033 GDPG Notes: The table reports Pearson correlation coefficients for the main variables in the study after deleting outliers defined as the top and bottom 1% of the observations on each of the study variables. GOI is Gross Operating Income ((Operating income + Depreciation expenses)/Average of total assets), RCP is Receivables Conversion Period ((365*(Average receivables/Net credit sales)), ICP is Inventories Conversion Period ((365*(Average inventories/Cost of sales)), PDP is Payables Deferral Period ((365*(Average payables/Net credit purchases)), CCC is Cash Conversion Period (RCP+ICP-PDP), Size is the natural logarithm of Total Assets, Leverage is the ratio of Total Liabilities to Total Assets, GDPG is Annual growth in GDP. ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

4.2. Regression Analysis Table (3) reports the results of the four regression models of the study. Model (1) aims at examining the relationship between profitability (GOI) and the Receivables Conversion Period (RCP) taking into consideration the size of the company, the ratio of total liabilities to total assets (Leverage) and the annual growth in GDP (GDPG). The results show that RCP affects the profitability of the company negatively and the coefficient is highly significant. This result implies that the longer the credit terms of the company or the time required to collect accounts receivable the less profitable is the company. However, the coefficient on RCP is near to zero implying that the increase in the RCP by one day would not affect the profitability of the company significantly. The overall explanatory power of the

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model is relatively high; adj-R2 is equal to (0.252). The control variables are all significant and show the expected signs. Model (2) examines the relationship between profitability (GOI) and the Inventories Conversion Period (ICP) in the presence of the same three control variables. The ICP is highly significant and shows the expected negative sign. The longer the period it takes the company to sell its inventories the lower its profitability. Again the coefficient on the ICP variable is near to zero indicating the insignificant effect of the increase by one day in the ICP on the company's profitability. The adj-R2 of the model is equal to (0.222) and all control variables are significant and show the expected signs. Model (3) examines the relationship between profitability (GOI) and the Payables Deferral Period (PDP) with all control variables included in the regression model. As with the previous two working capital management measures; the coefficient on the PDP is significant and negative, and the value of the coefficient is near to zero. All control variables except for GDPG are significant. The adjR2 of the model is equal to (0.242). Table 3:

Regression analysis Results (77 Industrial companies listed on ASE, 2001-2010, 552 company-year Observations) Model 1 Model 2 Model 3 0.000** (-6.446) 0.000** (-4.287) 0.000** (-5.797)

RCP ICP PDP CCC Size Leverage GDPG Constant

0.062** (10.677) -0.097** (-5.918) 0.003* (2.127) 0-.323** (-7.576) 0.252

0.061** (10.031) -0.101** (-6.001) 0.003* (2.186) -0.317** (-7.010) 0.222

0.062** (10.525) -0.074** (-4.398) 0.002 (1.645) -0.331** (-7.706) 0.242

Model 4

-7.496E-5** (-3.623) 0.063** (10.502) -0.106** (-6.229) 0.003* (2.338) -0.339** (-7.650) 0.214

Adj-R2 Notes: The table reports the results of the four regression models of the study after deleting outliers defined as the top and bottom 1% of the observations on each of the study variables. The models are:

GOI it = α 0it + α 1it RCPit + α 2it Sizeit + α 3it Leverageit + α 4it GDPG + ε 1it GOI it = λ0it + λ1it ICPit + λ2it Sizeit + λ3it Leverageit + λ4it GDPG + ε 21it

GOI it = β 0it + β1it PDPit + β 2it Sizeit + β 3it Leverageit + β 4it GDPG + ε 3it GOI it = φ0it + φ1it CCCit + φ 2it Sizeit + φ3it Leverageit + φ 4it GDPG + ε 4i

(1) (2) (3) (4)

GOI is Gross Operating Income ((Operating income + Depreciation expenses)/Average of total assets), RCP is Receivables Conversion Period ((365*(Average receivables/Net credit sales)), ICP is Inventories Conversion Period ((365*(Average inventories/Cost of sales)), PDP is Payables Deferral Period ((365*(Average payables/Net credit purchases)), CCC is Cash Conversion Period (RCP+ICP-PDP), Size is the natural logarithm of Total Assets, Leverage is the ratio of Total Liabilities to Total Assets, GDPG is Annual growth in GDP. t-values are between parenthesis. ** Significant at the 0.01 level. * Significant at the 0.05 level.

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The final model includes the Cash Conversion Cycle (CCC) which is the summary indicator of the effects of the previous three measures. The coefficient on the CCC is highly significant and negative. This is consistent with the view that shortening the time lag between paying the cost of goods purchased and the collection of the price of sales increases the profitability of the company. All control variables are significant and show the expected signs. The adj-R2 of the model is equal to (0.214). 4.3. Examining the Direction of the Relation between GOI and WCM Measures One would argue whether profitability of the company affects the working capital management it employs rather than profitability being affected by working capital management measures. To examine this argument, all observations were partitioned by GOI into deciles. Median values for all WCM measures were calculated for each of the GOI deciles. Table (4) reports the median values for RCP, ICP, PDP, and CCC for each of the deciles. Figure (1) also depicts the behavior of WCM measures as we move from the least profitable companies (group No. 1) to the most profitable companies (group No. 10). It is very clear that the most profitable companies have the shortest CCC compared with all other less profitable companies. The same observation can be made regarding the other WCM measures. This is strong evidence that working capital management affects profitability and not vice versa. This result also confirms the interpretation of the negative relationship between profitability and PDP; less profitable companies take longer periods to pay their accounts payable. Furthermore, longer RCP and ICP for less profitable companies indicate triggering credit extensions because of difficulties in selling the company's products, unexpected sales decrease, or loss of inventory control, or growth of obsolete inventory items. Table 4:

Median values for all working capital management measures (RCP, ICP, PDP, and CCC) partitioned by GOI Deciles.

GOI Deciles

RCP

ICP

PDP

CCC

1

97.73

165.29

88.92

132.06

2

116.6

179.46

55.99

221.23

3

112.91

178.36

70.55

216.05

4

92.68

169.99

92.11

170.65

5

84.18

181.95

62.26

206.33

6

77.96

169.1

51.54

170.89

7

69.41

129.23

53.53

132.53

8

60.04

111.13

41.74

130.17

9

73

138.2

50.45

162.82

10

50.66

83.21

45.17

90.66

Notes: GOI Deciles are ranked from the least profitable companies (group No. 1) to the most profitable companies (group No. 10). GOI is Gross Operating Income ((Operating income + Depreciation expenses)/Average of total assets), RCP is Receivables Conversion Period ((365*(Average receivables/Net credit sales)), ICP is Inventories Conversion Period ((365*(Average inventories/Cost of sales)), PDP is Payables Deferral Period ((365*(Average payables/Net credit purchases)), CCC is Cash Conversion Period (RCP+ICP-PDP).

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Figure 1: Median RCP, ICP,and CCC, Partitioned by GOI Deciles (77 Industrial companies listed on ASE, 2001-2010,552 company-year Observation)

5. Summary and Concluding Remarks The findings of this study indicate that profitability of business organizations is affected negatively by the length of time required to sell their products, the length of time required to collect their accounts receivable, and the length of time required to pay their accounts payable. Furthermore, the study concluded that the measures of working capital management significantly affect profitability and not vice versa. The study also found that industrial companies in Jordan invest significantly in working capital. Sample companies over the study period have, on average, third of total assets invested in inventories and receivables, and have, on average (9%) of total assets financed through accounts payable. Accordingly, it can be expected that the way working capital is managed will have a significant impact on the profitability of the company. Surprisingly, industrial companies in Jordan pay their suppliers of goods before collecting from customers on account regardless of the level of profitability of the company. The difficult economic conditions in Jordan, the relatively small market, and competition forces companies to loosen their credit terms when dealing with customers, however, this strategy seems not to be the same when manufactures deal with wholesalers. Overall the results of the study are consistent with similar studies conducted in different countries worldwide which roles out that such relationship is country specific or that the relationship differs between developed and emerging markets.

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