Manufacturing exports,profitability and working capital

Abstract

This study examines the effect of working capital (WC) on the profitability of manufacturing firms engaged in exports. Over the 2009–2015 time periods, the data gathered from 270 such firms in Pakistan ascertain a curvilinear relation between profitability and WC. Namely the generalized method of movement suggests an optimum working capital for manufacturing firms, estimated at about eight percent of sales revenue. Moreover, the exports’ destination region also affects the association between working capital and profitability, as exports to stable economies sustain working capital, while exports to less-stable economies yield a lower WC. Despite the study’s qualifications, exporting is a vital business component that affects a manufacturing firm’s working capital, with multiple implications for business practice and research.

Keywords

Working capital profitability exports performance optimal

Ashfaq Habib received his Master degree in commerce from University of Azad Jammu and Kashmir, Pakistan in 2007. He completed his M.Phil degree in finance from Muhammad Ali Jinnah University Islamabad, Pakistan in 2011. His major research areas are working capital, and investment analysis. He served as lecturer in Mohi-Ud-Din Islamic University after that he joined the University of Poonch Rawalakot as a Research Associate. He is currently doing Ph.D in Management Sciences at Dongling School of Economics and Management, University of Science and Technology, China.

Prof. Dr. Xiaoxia Huang earned her PhD degree from Beijing Institute of Technology, China. She has published many research articles in the area of investment analysis and portfolio management of science citation index journals. She is also the author of famous book, Portfolio Analysis: From Probabilistic to Credibilistic and Uncertain Approaches, Berling: Springer-Verlag, 2010. She is currently serving as Professor of Management Science and Engineering in Dongling School of Economics and Management, University of Science and Technology Beijing, China.

1 Introduction

In Pakistan, the manufacturing sector is playing an essential role in the economic growth, which contributes 15.3% to the employment of total labor force in the country. The contribution of the manufacturing sector in Gross Domestic Product (GDP) has been documented 13.6% in the financial year 2015-16. The growth in the manufacturing sector of Pakistan was effected by the international financial crisis in 2008. In the fiscal year 2006-07, the total contribution of the manufacturing sector in GDP was 19.66% and decline to reach 14.45% in 2011 [1, 2].

Since 2008, the manufacturing sector in Pakistan has suffered potentially serious problems. First, the industry has suffered from the distress of economic uncertainty and weak demand for manufacturing products following the global financial crisis. Similarly, the depreciation in Pakistani currency has extensively increased the prices of imported material, while raises energy prices further increases the overall cost of production [3]. Moreover, the higher rate of inflation in Pakistan economy is also hampering the growth rate in the manufacturing sector [4].

In this study, we focus on investment in working capital of Pakistan manufacturing industry, which considers a significant determinant of growth [5]. Pakistan’s manufacturing industry maintains a lower level of working capital 14.70% of sales and a significant amount of trade debts (18.71% of sales) in our sample period. The industry also maintains a higher percentage of inventories (24.43% of sales) in the selected sample. Conversely, the manufacturing firms have a lower level of accounts receivable (8.98% of sales).

It means manufacturing firms in Pakistan are collecting the receivables immediately to pay their short-term dues and more rely on trade creditors to finance the growth opportunity. It can be the justification of limited access to capital resources and financial constraints [6]. The firms may also face the severe competition in the local and international markets, which restricts to increase the price of the product, even in the conditions where the product cost increased. Ultimately, it reduces the product margin and limits the internal source of financing [7].

Working capital is an essential part of the capital structure and performs an important role to stimulate the operational performance of the business. A growing standard of academic research examines the different aspect of working capital, which may directly affect the operational performance of a business [8 –12]. The purpose was to identify a minimum level of working capital that a firm’s needs to meet its operational expenses and remain in a position to pay short-term financial dues. Therefore, the firms need to critically examine all aspects of working capital to fulfill the operational requirements of the business.

Working capital is the combination of current assets and current liabilities and frequently uses as a measure of liquidity. The working capital’s components are cash, accounts receivable, and inventory used to finance the trade credits and short-term debts [13]. Liquidity is a prerequisite to ensure that a firm pays their short-term obligations and avoids the threat of bankruptcy [14]. Inadequate liquidity can cause to a bankruptcy and excess of liquidity can damage to a firm profitability [15]. Therefore, the firms are seeking the efficient working capital management, where a firm can discharge their capital to achieve the strategic purpose, reduces the cost of financial distress and improves profitability [16]. In fact, there are two competitive views to invest in the working capital management. One view suggests a positive level of working capital allows a firm to boost up sales by implementing the liberal trade credit strategy and avails discounts to make early payment [17, 18]. By holding a large inventory, firms can also control the input price fluctuations and avoids interruption in the manufacturing process [19]. However, the maintaining a higher level of working capital requires the additional financing, which associates with higher interest expenses and opportunity cost. Hence, it may increase the risk and reduce the profitability of the firms [20].

Alternatively, enforcing the aggressive credit policy, firms diminish the cyclical period of accounts receivable. By dropping the stock of inventory, companies reduce the insurance, warehouse rent, and security expenses [21]. Moreover, a lower level of cash holding is permitting to reduce the interest expenses and opportunity cost.

But, if a firm maintain the lower level of inventory, the costly stock may be out of market, which can become a cause of disruption in the production process and firm may also have a risk of losing sales [22]. Similarly, with a lower level of cash holding, firms may have not enough cash to pay their short-term financial commitments in an unexpected economic conditions and increase the chance of bankruptcy [23].

Consequently, these positive and negative effects of working capital on profitability develop the predictions of a non-monotonic relation between working capital and profitability [24]. The hypothesis is that an inverse relation may exist if both effects are sufficiently robust. Hence, the profitability of the firm can be enhanced not only through the efficient working capital but also by maintaining the optimal working capital. However, the determining and holding an optimal working capital is a difficult task because the level of working capital is affected by different economic conditions and firm-specific components.

Although, a comprehensive set of studies develops plenty of theoretical justifications to comprehend the association between working capital and firm’s profitability [17 , 25–29]. But still, we believe there is a gap at least in Pakistan at firm-level analysis associating with exports performance and practices in term of working capital. This research contributes to the existing literature in a variety of ways.

By using a large panel of Pakistan manufacturing firms, this study examines the relation between working capital and profitability of manufacturing firms that engage in exports business activities. The research examines whether the working capital is affected by the exports performance of the firms and identify the optimal level of working capital that a firm needs to manage the day-to-day operations of the business. The purpose is to explore whether the relation between working capital and profitability is affected by the destination region of exports.

2 Literature review

2.1 Working capital and exports

Due to globalization and international trade agreements, working capital may also be affected by the exports performance of the firms. The exports oriented firms are facing the high-risk decisions which include the unsustainable profit due to the exchange rate fluctuations, sunk costs, local competition, limited knowledge about external market conditions, and cultural restrictions [30, 31]. The firms that focus to exports need to be more productive, highly skills and capital intensive to enter and survive in the foreign market [32]. Further, Zhang et al., [33] also argued that cross-border trading is one of an important element that may affect the capital structure of the firms.

The firms have lack of liquidity cannot stay and compete in the foreign market. The firms maintain liquidity not only to cover the fixed cost, but a sufficient amount of liquidity is needed to cover the variable cost also [34]. The sales on the foreign market is highly uncertain and very difficult to monitors by debt holders since the firm agency cost of debt or bankruptcy costs increased when it moves to finance the foreign operations [35]. Minetti et al., [36] suggested that firms maintain the lower level of leverage and more liquidity to enter and compete in the foreign market.

According to Burgman [37] after controlling the firms’ size and industry related characteristics, multinational companies face higher agency cost, which restricts the multinational firms to hold less-leverage than the domestic firms. Myers [38] reports that multinational enterprises have higher growth opportunities than local firms, which increases the agency cost of debt as a result hold less leverage. In contrast, the multinational companies can increase their short-term financing to smooth out the cash flow fluctuations to exports on the diversified market. The short-term financing may be the substitute for long-term financing to finance the growth opportunities and minimizing the agency cost of debt [32].

The firm capital structure may also be affected by the exports designation. The firms face different risk and return to exports across the countries and the regions [39]. According to Kwok and Reeb [40] multinational firms’ capital structure decisions are affected by the risk of the firms’ home country and target country. They explained, that if a firm is based on the less-stable economy and exports to a more stable economy, the risk of the firm decrease because the risk of the exports market is lower than the average risk of the firm. Further, Wagner [41] argued that agency cost of exports-firms decrease if the host country is highly income and well-access to capital market. Such firms expected cost of bankruptcy is reduced and increased their capacity to take more debt. The overall risk of the firm is also decreased, since the cash flows from foreign operations are not perfectly correlated [42]. The Dae Mello [43] and Chiou [44] found that lower level of risk and easy accessibility of external finance allow to invest more in working capital to accelerate sales in the foreign market.

The higher level of working capital allows to invest more in inventories to control the inputs price fluctuation, reduce the carrying cost of inventory, and avail an adequate amount of discount from suppliers. It also supports to provide the quality of product at low price on the foreign market [17]. Similarly, the firms have the low-cost accessibility of external financing permits to adopt the liberal trade credit policies to stimulate the potential customers in foreign market. The firms may also increase the cash holding to pay creditors on time and minimize the risk of bankruptcy. On the other hand, the positive working capital may increase the interest expenses and opportunity cost which develops the negative effects of working capital on profitability [22].

Hypothesis 1: If a firm is based on less-stable economy and exports to a more-stable economy, the working capital will develop the negative relation with profitability.

On the other hand, Kwok & Reeb [40] also stated that if a firm is based on stable economy and exports to a less-stable economy, the risk of the firm increases, because the risk of the exports market is higher than average risk of the company. The agency cost of capital also increases to exports on a less-stable economy. Burgman [37] and Chen [45] states that higher agency cost of debt and the threat of bankruptcy enforce multinational companies to take a lower level of debt. Such firms generate the short-term financing internally by managing the cash flow efficiently and maintaining the lower level of working capital to compete in global market. The companies maintain the lower level of inventory, implement the tied trade credit policy about customers, and delay payment to the suppliers as much as possible. In the negative working capital, the firms set the minimum level of working capital to achieve the target revenues without considering for any contingencies and maintain provisions for the uncertain situation. The lower level of working capital reduces the interest expenses, opportunity cost and bankruptcy cost, which may develop the positive effect of working capital on firm profitability [24].

Hypothesis 2: if a firm is based on a stable economy and exports to a less-stable economy, the working capital will develop the positive relation with profitability.

3 Data and methodology

3.1 Data explanation

In this paper, we use the data of listed joint stock companies of Pakistan manufacturing industry to examine the relation between working capital and profitability. The study only focuses the manufacturing firms that exports on the international markets. The data has been abstracted from the balance sheet analysis of joint stock companies compiled by the central bank of Pakistan and Pakistan Economic Survey of listed manufacturing companies. The study only considered the firms have a complete dataset available for analysis. There are 270 listed companies have seven years of complete dataset from 2009 to 2015 become the part of our sample. Out of 270 firms, 172 firms’ exports on less-stable economies and remaining 98 companies are exports on stable economies.

Based on the export’s destination, the firms are segregated into stable and less-stable economies. The firms’ exports to European countries, Unites States, China, and Japan included in the sample of the stable economy. These countries have substantial financial markets and a relatively high economic growth. These countries are also pursuing the consistent monetary policy to bring stability in the prices and exchange rate. On the other hand, firms’ exports to some weak economic Asian countries and low-income African countries included in the sample of less-stable economies. To diminish the effects of extreme observations, we dropped 1% outlier in the entire dataset. Further, Cranbach Alpha has been used to test accuracy and consistency in the dataset. The Alpha value (0.77) shows that data is consistent and reliable for this research.

3.2 Variables interpretation

Return on Assets (ROA) is used as an explained variable to measure the performance of listed manufacturing firms in the presence of exports performance. ROA is calculated as operating profit before interest, taxes, amortization, and depreciation divided by the total assets. Working Capital (WC) is used in this study as an independent variable and segregated into stable and less-stable economies on the base of exports. The working capital in term of exports in the stable economy (WCSE) is calculated as current assets minus current liabilities divided by the exports to a stable economy. The working capital in term of exports on less-stable economy (WCLSE) is calculated as current assets minus current liabilities divided by the exports to the less-stable economy.

Although this study focuses on the relation between operating earnings and working capital, we also use the set of control variables are leverage, firms’ size, asset tangibility, and capital structure, which traditionally used in research to determine the relation between working capital and accounting profit. Table 1 shows the list of variables and their acronym use in this study. The redundant test is applied on all the explanatory variables to check the reliability of explanatory variables in the equation. All explanatory variables are found statistically significant in the redundant test and must be the part of an equation. The multicollinearity is checked by applying the Tolerance (Toler) and Variance Inflationary Factor (VIF). The test values of Toler and VIF of all explanatory variables are greater than 0.5 and less than 2 respectively, which indicate that the independent variables are not seriously correlated to each other. Table 1 also presents the results of multicollinearity tests of explanatory variables.

Table 1
Variables estimation and their acronym

Variables Acronym Formula Multicollineariy

Tolr VIF

Return on assets ROA Earnings before interest and taxes/total assets

Working capital WC Current assets/current liabilities 0.87 1.14

Working capital square WC² Square of working capital 0.82 1.16

Working capital in stable economies WCSE Working capital/exports on stable economies 0.81 1.19

Working capital in less-stable economies WCLSE Working capital/exports on less-stable economies 0.80 1.23

Leverage LEV Long-term debt/total assets 0.80 1.23

Firm size FS Log of total assets 0.96 1.03

Assets tangibility AT (Non-current assets+ inventories)/total assets 0.85 1.17

Capital structure ratio CR Non-current liabilities/equity 0.85 1.16

Solvency ratio SVR Equity/total liabilities 0.74 1.35

Inventory INVR Inventory/exports

Accounts ARR Accounts receivables/exports

Accounts payable APR Accounts payables/exports

Variables	Acronym	Formula	Multicollineariy
Return on assets	ROA	Earnings before interest and taxes/total assets
Working capital	WC	Current assets/current liabilities	0.87	1.14
Working capital square	WC²	Square of working capital	0.82	1.16
Working capital in stable economies	WCSE	Working capital/exports on stable economies	0.81	1.19
Working capital in less-stable economies	WCLSE	Working capital/exports on less-stable economies	0.80	1.23
Leverage	LEV	Long-term debt/total assets	0.80	1.23
Firm size	FS	Log of total assets	0.96	1.03
Assets tangibility	AT	(Non-current assets+ inventories)/total assets	0.85	1.17
Capital structure ratio	CR	Non-current liabilities/equity	0.85	1.16
Solvency ratio	SVR	Equity/total liabilities	0.74	1.35
Inventory	INVR	Inventory/exports
Accounts	ARR	Accounts receivables/exports
Accounts payable	APR	Accounts payables/exports

3.3 Methodology

After checking the reliability of explanatory variables, this study applies the panel data methodology to test the curvilinear relation between working capital and profitability. The panel data methodology has several advantages including the better estimation of the equation, control of heterogeneity, minimize the sample biases and measurement errors [46].

Panel data can be analyzed under three regression models namely the Ordinary Least Squares (OLS), Random Effect (RE) or Fixed Effect (FF) and Generalized Method of Moments (GMM). First, we apply the panel Ordinary Least Squares (OLS) to estimate the equation-1. To minimize the effect of the serial correlated error, this study also applies the panel FE to estimate the equation-2. The Hausman test is used to find the exogeneity of the unobserved errors and to choose the random or fixed effect model. The null hypothesis of Hausman test is rejected as the Cross Section Fixed Chi.Sq.Statistics (7.78) value is significant. Since the random effect is inappropriate and fixed effect is preferred.

Further, this study has applied the panel Generalized Method of Moments (GMM) to remove the endogeneity problems in the equation-3. The Breusch-Godfrey Serial Correlation LM Test is used to check the endogeneity problems in the model. The LM test value (Obs*R-Squared 38.45-Prob.Chi-Square 0.0279) is significant at the 5% level, which indicates that the endogeneity problem exists in the model.

The endogeneity problem arises when one or more explanatory variable is correlated with the disturbance term, which can be the cause of omitted variables, measurement errors or simultaneously between the explained variables and explanatory variables. We apply the Arellano and Bond 1991, two steps GMM, where a lagged difference of explained variables can be used as instruments and the lagged difference of explanatory variables may also be used as instruments to remove the endogeneity problem. Finally, the following models used in this study to test the hypothesis.

1- Panel Ordinary Least Square (OLS) $\begin{matrix} ROA = β_{0} + β_{1} (WC) + β_{2} {(WC)}^{2} + β_{3} (WCSE) \\ + β_{4} (WCLSE) + β_{5} (LEV) + β_{6} (FS) \\ * β_{7} (CR) + β_{8} (SVR) + β_{9} (AT) + ɛ \end{matrix}$ 2- Panel Fixed Effects (FE) $\begin{matrix} {ROA}_{i, t} = β_{0} + β_{1} ({WC}_{i, t}) + β_{2} {({WC}_{i, t})}^{2} \\ + β_{3} ({WCSE}_{i, t}) + β_{4} ({WCLSE}_{i, t}) \\ + β_{5} ({LEV}_{i, t}) + β_{6} ({FS}_{i, t}) β_{7} ({CR}_{i, t}) \\ + β_{8} ({SVR}_{i, t}) + β_{9} ({AT}_{i, t}) + a_{i} + ɛ_{i, t} \end{matrix}$

3-Panel Generalized Method of Moments (GMM), $\begin{matrix} {ROA}_{i, t} {= β}_{0} + β_{1} ({ROA}_{i, t - 1}) {+ β}_{2} ({WC}_{i, t}) \\ + β_{3} {({WC}_{i, t})}^{2} + β_{4} ({WCSE}_{i, t}) \\ + β_{5} ({WCLSE}_{i, t}) + β_{6} ({LEV}_{i, t}) \\ + β_{7} ({FS}_{i, t}) + β_{8} ({CR}_{i, t}) + β_{9} ({SVR}_{i, t}) \\ + β_{10} ({AT}_{i, t}) + a_{i} + ɛ_{i, t} \end{matrix}$

Instruments for differenced variables: GMM types: ROA_i,t-1 $\begin{matrix} Standard : Δ {WC}_{i, t}, Δ {WCSE}_{i, t}, Δ {WCLSE}_{i, t} \\ Δ {LEV}_{i, t}, Δ {FS}_{i, t}, Δ {CR}_{i, t}, Δ {SR}_{i, t}, Δ {AT}_{i, t} \end{matrix}$

4 Results and discussion

4.1 Correlation analysis

The Tables 2 and 3 present the correlation matrix among the variables. The significant negative correlation reveals between ROA and WC (–0.16) in Table 2. Among the WC components, only ARR develops the significant positive relation (0.08) with ROA. Whereas, INVR (–0.13) and APR (–0.11) are negatively associated with ROA. All WC components are INVR (0.44), APR (0.37), and ARR (0.39) developed the significant positive correlation with WC. The control variables are also significantly associated with ROA.

Table 2
Pearson correlation for entire sample

ROA WC INVR APR ARR LEV FS CR SVR AST

ROA 1

WC –0.16^* 1

INVR –0.11^* 0.44^* 1

APR –0.13^* 0.37^* 0.59^* 1

ARR 0.08^* 0.39^* 0.35^* 0.32^* 1

LEV 0.13^* –0.09^* –0.10^* –0.10^* –0.05^* 1

FS 0.10^* 0.04^* 0.07^* –0.03^* 0.03^* 0.08^* 1

CSR –0.19^* –0.08^* 0.04^* –0.06^* –0.03^* 0.06^* 0.05^* 1

SOR 0.18^* 0.07^* –0.13^* –0.01^* 0.03^* –0.16^* 0.09^* –0.21^* 1

AT –0.16^* 0.02^* 0.05^* 0.04^* –0.02^* –0.06^* –0.02^* 0.04^* 0.11^*** 1

	ROA	WC	INVR	APR	ARR	LEV	FS	CR	SVR	AST
ROA	1
WC	–0.16^***	1
INVR	–0.11^***	0.44^***	1
APR	–0.13^***	0.37^***	0.59^***	1
ARR	0.08^***	0.39^***	0.35^***	0.32^***	1
LEV	0.13^***	–0.09^***	–0.10^***	–0.10^***	–0.05^***	1
FS	0.10^***	0.04^***	0.07^***	–0.03^***	0.03^***	0.08^***	1
CSR	–0.19^***	–0.08^***	0.04^***	–0.06^***	–0.03^***	0.06^***	0.05^***	1
SOR	0.18^***	0.07^***	–0.13^***	–0.01^***	0.03^***	–0.16^***	0.09^***	–0.21^***	1
AT	–0.16^***	0.02^***	0.05^***	0.04^***	–0.02^***	–0.06^***	–0.02^***	0.04^***	0.11^***	1

^***Significant at 0.01; ^**Significant at 0.05; ^*Significant at 0.10.

In Table 3, the figure for stable economies correlation presents the upper part of triangle and figure for less stable economies correlation shows the lower part of triangle. The results show that there is a negative relation between ROA and WC (–0.20) in the stable economies, but a significant positive relation (0.28) in the less-stable economies, which suggests that an inverse relation exists between profitability and working capital. In WC components, INVR (–0.15), and APR (–0.12) negatively relate with ROA, whereas ARR (0.06) positively associates with ROA in stable economies. But in less-stable economies, all WC components INVR (–0.09), APR (–0.18) and ARR (–0.15) are significant negatively associated with ROA. Both stable and less-stable economies, control variables also develop the significant relation with ROA in Table 3 in upper and lower triangle respectively. These results are related to the study of [24] and [48].

Table 3

Correlation coefficient matrix in the stable economies and less-stable economies

Variables	ROA	WC	INVR	APR	ARR	LEV	FS	SCR	SOR	AST
ROA	1	–0.20^***	–0.15^***	–0.12^***	0.06^***	–0.17^***	0.11^***	–0.09^***	0.29^***	–0.15^***
WC	0.28^***	1	0.63^***	0.64^***	0.41^***	0.00^**	–0.03^***	–0.09^***	0.03^***	–0.06^***
INVR	–0.09^***	–0.02^***	1	0.59^***	0.35^***	0.17^***	–0.01^***	0.05^***	–0.20^***	0.05^***
APR	–0.18^***	–0.34^***	0.50^***	1	0.60^***	–0.02^***	–0.05^***	–0.06^***	–0.07^***	–0.21^***
ARR	–0.15^***	–0.16^***	0.63^***	0.47^***	1	–0.05^***	0.05^***	–0.03^***	–0.01^***	–0.09^***
LEV	–0.21^***	–0.03^***	–0.15^***	–0.06^***	–0.09^***	1	–0.09^***	0.11^***	–0.04^***	0.08^***
FS	0.11^***	–0.02^***	0.01^***	0.09^***	0.00^***	–0.07^***	1	0.06^***	0.14^***	0.11^***
SCR	–0.04^***	0.08^***	–0.07^***	–0.08^***	–0.09^***	0.16^***	0.05^***	1	–0.09^***	0.03^***
SOR	0.29^***	0.26^***	–0.16^***	–0.21^***	–0.23^***	–0.11^***	0.13^***	–0.17^***	1	0.02^***
AT	–0.16^***	–0.13^***	–0.06^***	–0.15^***	–0.35^***	0.18^***	0.11^***	0.19^***	0.02^***	1

Note. Figure for stable economies WC group in the upper triangle and figure for less-stable economies WC. group in the lower triangles. ^***Significant at 0.01; ^**Significant at 0.05; ^*Significant at 0.10.

4.2 Regression analysis

The Table 4 shows the results of the working capital group for the whole sample (WC), working capital group in the stable economy (WCSE), and working capital group in the less-stable economy (WCLSE) under the OLS, FE and GMM by using equations 1–3. The study reveals that WC creates the significant positive relation with profitability in OLS (WC; 0.082), FE (WC; 0.075) and in GMM (WC; 0.064) as presented in Table 4. The WC² is used to find the curvilinear relation between working capital and profitability. The significant negative effects of WC² on profitably in OLS (–0.161), FE (–0.136), and GMM (–0.098) show that working capital develops the inverted curvilinear relation with profitability in Pakistan manufacturing industry. The Fig. 1 has also been plotted to show the curvilinear relation between WC and ROA. The spikes in the Fig. 1 present that inverse relations exist between working capital and profitability. The curvilinear curve has been drawn on the basis of average spikes which explains that a non-linear exist between working capital and profitability. The optimal level of working capital is found by taking the partial derivation of WC² in term of sales in each method. The firms reach the optimal level, where WC² is 0.15% of sales in OLS, 0.11% of sales in FE and 0.08% of sales in GMM.

Table 4
Analysis the relation between working capital and profitability

Dependent Variable OLS FE GMM

RA WC WCSE WCLSE WC WCSE WCLSE WC WCSE WCLSE

WC 0.082^* 0.075^* 0.064^*

WC² –0.161^* –0.136^* –0.098^*

WCSE –0.008^* –0.006^* –0.005^*

WCLSE 0.138^* 0.143^* 0.142^*

LEV –0.055^* 0.053^* –0.045^** –0.049^* 0.045^* 0.033^* –0.041^* 0.031^* –0.220^***

FS –0.086^* 0.009^ –0.255^* –0.068^ 0.001^ –0.134^ –0.045^ 0.092^ –0.046^*

CR 0.057^* 0.091^* –0.047^* 0.030^ 0.098^* 0.038^ 0.047^ 0.082^* 0.031^

SVR 0.081^* 0.023^* 0.083^* 0.087^* 0.024^* 0.074^ 0.091^ 0.020^* 0.018^*

AT –0.045^ –0.082^* –0.044^ –0.031^ –0.116^* –0.119^ –0.025^ –0.081^* –0.034^*

R-Sq. 0.10 0.33 0.18 0.14 0.22 0.16

Obs. 1890 690 1200 1890 690 1200 1490 483 840

Arellano-Bond

1st Order –5.11^ –4.65^* –3.91^***

2nd Order –0.124 0.946 –0.875

Dependent Variable	OLS	FE	GMM
WC	0.082^***			0.075^***			0.064^***
WC²	–0.161^***			–0.136^***			–0.098^***
WCSE		–0.008^***			–0.006^***			–0.005^***
WCLSE			0.138^***			0.143^***			0.142^***
LEV	–0.055^***	0.053^***	–0.045^****	–0.049^***	0.045^***	0.033^***	–0.041^***	0.031^***	–0.220^***
FS	–0.086^*	0.009^**	–0.255^***	–0.068^**	0.001^**	–0.134^**	–0.045^**	0.092^**	–0.046^***
CR	0.057^*	0.091^***	–0.047^***	0.030^**	0.098^***	0.038^**	0.047^**	0.082^***	0.031^**
SVR	0.081^***	0.023^***	0.083^***	0.087^***	0.024^***	0.074^**	0.091^**	0.020^***	0.018^***
AT	–0.045^**	–0.082^***	–0.044^**	–0.031^**	–0.116^***	–0.119^**	–0.025^**	–0.081^***	–0.034^***
R-Sq.	0.10	0.33	0.18	0.14	0.22	0.16
Obs.	1890	690	1200	1890	690	1200	1490	483	840
Arellano-Bond
1st Order							–5.11^**	–4.65^***	–3.91^***
2nd Order							–0.124	0.946	–0.875

^***Significant at 0.01; ^**Significant at 0.05; ^*Significant at 0.10.

Fig.1

Curvilinear relation between working capital and profitability.

In WC group, control variables LEV (–0.055 in OLS, –0.049 in FE and –0.041 in GMM), FS (–0.086 in OLS, –0.068 in FE and –0.045 in GMM) develop the significant negative relation with ROA. Similarly, the AT (–0.045 in OLS, –0.031 in FE and 0.025 in GMM) also finds the significant negative relation with profitability. In contrast, CR (0.057 in OLS), (0.030 in FE), and (0.047 in GMM), SR (0.081 in OLS, 0.087 in FE and 0.091 in GMM) are significant positively associated with ROA. Our findings are consistent with the previous studies of [24 , 47–49].

Further, the relation between working capital and profitability has been analyzed in the presence of exports destinations [40]. For this, the whole sample is segregated into WCSE group and WCLSE group based on the exports to stable and exports to less-stable economies respectively.

The coefficients of working capital in the stable economy group develop the significant negative relation with profitability in OLS (WCSE; –0.008), FE (WCSE; –0.006), and in GMM (WCSE; –0.005) as explain in hypothesis 1. The results guide that when a firm is based on the less stable economy and exports to a stable economy, the risk of the firm decreases because the risk of the exports market is lower than the average risk of the company. The firms focus on long-term financing to sustain the growth and avail investment opportunities. The low-cost accessibility of external financing permit to invest more in working capital to boost-up sales and provide a quality of product in the foreign market. The higher level of working capital increases the interest expenses and opportunity cost which negatively affect the profitability.

On the other hand, when a firm is based on a stable economy and exports to a less-stable economy, the working capital develops the significant positive relation with profitability in OLS (WCLSE; 0.138), FE (WCLSE; 0.143), and in GMM (WCLSE; 0.142) as presents in hypothesis 2. According to Kwok & Reeb [40] the risk of a firm increases to exports on less-stable economies. The agency cost of capital also increases to export on a less-stable economy. Burgman [37] and Chen, et al., [45] state that higher agency cost of debt and the threat of bankruptcy enforce multinational companies to take a lower level of debt. Such firms generate the short-term financing internally by managing the cash flow efficiently and maintain the lower level of working capital to compete in global market. The lower level of working capital reduces the interest expenses and opportunity cost, which positively effects the profitability. The results in both stable and less-stable economies are similar to the research of [24 , 49].

Arellano-Bond serial correlation test has been applied to remove the endogeneity problem in GMM. The test results in Table 4 shows that instruments used to remove the endogeneity problem are well define and correctly specified. Hence, the null-hypothesis (endogeneity does not exist) of Arellano-Bond serial correlation test cannot be rejected at 2nd order in OLS and FE. Therefore, the instrument variables do not correlate with the errors in GMM and gives the most robust results to control the endogeneity problem, which cannot be controlled in OLS and FE. Hence, the results estimated under the GMM in Table 4 are more significant and reliable than the OLS and FE.

5 Conclusion

This study examined the relation between working capital and profitability of Pakistan manufacturing firms by using the OLS, FE, and GMM. The study revealed that working capital develops the significant positive relation with profitability. The WC² has been used to find the curvilinear relation between working capital and profitability. The significant negative effect of WC² on profitability shows that inverse relation exists between working capital and profitability in Pakistan manufacturing industry. The optimal level of working capital is derived by taking the partial derivation of WC² in term of sales in each method. The firms reach the optimal level, where WC² is 0.15% of sales in OLS, 0.11% of sales in FE and 0.08% of sales in GMM.

Further, the effect of working capital on profitability is analyzed in the presence of firms’ exports performance. For this, the whole sample is divided into exports on stable and less-stable economies. The firms’ exports to European countries, Unites States, China, and Japan included in the sample of the stable economy. These countries have substantial financial markets and a relatively high economic growth. These countries are also pursuing the consistent monetary policy to bring stability in the prices and exchange rate. The firms’ exports to stable economies have a lower level of risk than the average risk of the firm. The agency cost of capital and bankruptcy cost also decrease as cash flows from foreign operations are not perfectly correlated. Hence, the overall risk of the firms reduces to exports on the stable economies. The low cost accessibility of external finance provides an opportunity to invest more in working capital to accelerate sales in the foreign market. The study revealed that firms’ exports on stable economies maintain the higher level of working capital to boost-up sales in the foreign markets. The results suggest that the higher level of working capital increases the interest expenses and opportunity cost which turn negative on profitability. Hence, the working capital develops the negative relation with profitability to exports on stable economy as explained in hypothesis 1.

On the other hand, firms’ exports to some weak economic Asian countries and low-income African countries have been included in the sample of less-stable economies. The firms’ exports to less-stable economies face the higher level of risk. The agency cost of capital and bankruptcy cost also increase to exports on less-stable economies. Hence, the increases cost of capital and bankruptcy cost reduce the borrowing power of a firm to raise finance externally. The study revealed that such firms generate the short-term financing internally by managing the cash flow efficiently and maintaining a lower level of working capital to finance the day-to-day operations of the business. The results suggest that lower level of working capital reduces the interest expenses and opportunity cost which turn the positive effect of working capital on profitability as explained in hypothesis 2. This implies that exports corporate activity is one of an important component which may effects the working capital of a firm. Therefore, the relation between working capital and profitability also depends upon the destination region of exports.

6 Managerial implication

The study draws some important managerial and theoretical implication for the manufacturing industry. Theoretically, the study explains that a non-linear relation exists between working capital and profitability of Pakistan manufacturing industry. The study finds that exports corporate activity plays an essential role to determine the level of working capital. The findings suggest that firms are based on less-stable economies, and exports to the stable economies maintain the positive working capital to enhance the firm profitability. On the other hand, the firms are based on stable economies and exports to less-stable economies hold the negative working capital to increase the profitability of the firms.

In the perspective of managerial implication, when a firm is based on less-stable economies and exports to stables economies it is beneficial to chase the Cash Conversion Cycle either by investing more in inventories, adopt the liberal trade credit policies and paying-off the trades’ payable earlier. However, when a firm has good capabilities to generate cash flow from operations, it is not beneficial to hold too much cash to finance the accounts receivables and inventories. On the other hand, when the firms do not have the sufficient amount of working capital, it is important to secure assets more quickly either by reducing expenses, delaying payments to the suppliers, or raise short-term loan to evade the negative effects of cash shortage.

7 Study limitations and future work

Although this study has significantly contributed towards the better understanding of working capital management for manufacturing firms, but it has also some unavoidable limitations. The study only focuses the manufacturing firms that engaged in exports business activities and, thus, the verdicts of this research may not directly applicable to other industries such as finance, IT, or other local manufacturing firms that maintain higher level of working capital to manage the business operation. In additional, the finding may not be effective to other industries, because the government has announced some special grants for exports-firms such as low cost accessibility of electricity, declare the non-power shortage zone, easy availability of raw material to boot-up the exports at national level. The government also assists the exports-firms to raise the short-term finance at favorable terms and conditions from the public sector banks to increase the exports performance. The future research can be conducted in the other growing industries such as IT, leasing, finance or other traditional manufacturing firms that maintain the higher level of working capital to manage the day-to-day operations of the business.

Footnotes

Acknowledgments

This work was supported by the national social science foundation of China No. 17BGL052, fundamental research fund for the central universities No. FRF-BR-16-002B, and USTB-NTUT Joint Research Program No. TW201709.

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