DETERMINANTS OF TRADE CREDIT SUPPLY: THE ...

CIE46 Proceedings, 29-31 October 2016, Tianjin / China

DETERMINANTS OF TRADE CREDIT SUPPLY: THE CASE OF TEXTILE SECTOR OF PAKISTAN Adnan Abbas1, Xiao-guang Luo 2*, Jaleel Ahmed3 1,2School of Economy, Harbin University of Science and Technology, Harbin, China 3 School

of Economy and Management, Harbin Institute of Technology, Harbin, China * [email protected] ABSTRACT The basic objective of this research is to examine the determinants of trade credit supply in textile sector of Pakistan. Trade credit can be affected by firm specifics characteristics. After collecting seven year data from 2006 to 2012 from textile firms of Pakistan, we have applied fixed effect model to estimate regression equation to obtain results. Research found that the following firm specifics independent variables such as current asset, cash & bank balance, gross profit, current liabilities, shareholders equity, inventory and tax expense significantly related to trade credit supply. Keywords: Trade credit, Textile sector, Determinants of trade credit, KSE 1. INTRODUCTION In different countries many Organizations seek various means to finance their capital need in diverse societies. There are two major types of financing which include short term financing and long term financing. Trade credit is one of major source of short term funding in all over the world (Petersen and Rajan, 1995; Van Horen, 2007) [1],[2]. Trade credit and bank loans play an important rule during tight monetary conditions (Guariglia and Mateut, 2006) [3]. Studies found that the companies working in countries having inefficient legal and financial system depend more on trade credit (Rajan and Zinglase, 1995,;Johnson, McMillian and Woodruff, 2002) [4][5], for the contingency effect are higher in this case ( La Porta et al. 1998; Pike et al , 2005)[6] [7]. Trade credit, as explained by Martinez-Sola et al. (2012) is an arrangement between buyer and seller allowing an exchange of goods with some deferred payments terms and condition [8]. Trade credit is an important source of financing for firms, especially for small and medium size firms (Boyery and Gobert 2007) [9]. Trade credit is general term that is used for either supply or demand for credit goods. Trade credit increases benefits for both sellers and buyers part by smoothing or stable out the uncertainties in delivery cycle and simplifying cash management (Schwartz 1974) [10]. Wilson and summers (2002) argue that trade credit is an important marketing tools. The logic is that trade credit could be used to build business relationships in a new market and new entrance could be more easily as compared to building their reputation after year of hard work and marketing their products [11]. According to the study of Rajan & Zingales, (1995) aggregate volume of trade credit of American firms was 17.8% and was gradually increasing before 1990[12]. But in United Kingdom it was about 55% of total credit of the firms based on the trade credit as studied by (Kohler et al. 2000; Guariglia et al. 2006) [13][14]. *

Corresponding Author Xiao‐guang Luo


Trade credit represents more than a quarter of total corporate assets in France, Germany and Italy and it is also vital for an emerging countries like China, where organizations acquire limited support from banks (Ge and Qiu 2007) [15]. The gap of research remains when the trade credit literature is sought in Pakistan. There is no study till date had carried out for detailed about determinates of trade credit analysis and effecting trade credit in non-financial firms listed at Karachi Stock exchange (KSE). KSE is a Pakistan largest stock exchange. The remainder of this paper is planned as following: section 2 reviews of literature that leads us to the gap in the literature. Section 3 discusses the data and empirical methodology. Section 4 reports the results with analysis and at lastly section 5 conclude the study discussion the implications of the findings. 2. LITERATURE REVIEW In every financing policy trade credit always plays a vital role. Funds includes in trade credit supply have always been considered as a profit in current assets. According to Giannetti et al. (2003), account of trade credit supply stands as a quarter of total assets in balance sheets in European firms [16]. Trade credit is a major or important source of financing in underdeveloped countries because these countries have less access to standardized capital market (Beck et al. 2008; Ge and Qiu, 2007) [17], [18]. Jain (2001) argued that trade credit play a significant role between the relationship of financial institutions and business [19]. In case of shortage of financial market, it is beneficial for buyers and suppliers to use this to meet their external financing needs (Frank & Maksimovic, 2004) [20]. France Delannay and Weill (2004): Studied on the determinants of trade credit in transition countries. They used regression, equity, size, profit, and leverage as a variable. They consider regression of the ratio of trade payable to total assets and of the ratio of trade receivable to turnover on a set of variables as methodology. At the end it has been determined that survival of differences between countries concerning theses determinants [21]. Love and Zaidi (2010: Worked on trade bank credit and financial crises in four East Asian countries Thailand, Philippines, Korea and Indonesia. They used size, export, import as variables. It is determined that after the financial crises, firms constrained in bank finance obtain less trade credit in terms of percent of inputs acquired on credit and short time payments [22]. Rajendra R. Vaidya (2011): Studied on determinants of trade credit: Evidence from Indian manufacturing firms. They used dummy variables, explanatory, inventories, fixed assets, profit and size as a variable. The experiential evidence recommends that strong evidence exists to support management of inventory object for the existence of trade credit [23]. Kestens et al. (2012): Investigated trade credit and company performance during the 2008 financial crises in Belgium. They used variables like, size, liquidity and inventory as a variable dummy as variables. They found that financial crises had a negative impact on firm profitability, but this negative impact was lower for companies that reported an increase in trade receivables during the crises [24]. Martinez- Sola et al. (2012): Studied on trade credit policy and firm value in Spain. They used variables like dummy, economic, Control, Growth and Size as Variables. They found that a quadratic relationship between accounts receivable and firm value, furthermore divergence from the required level of account receivables significantly decrease the firm value [25]. Coulibaly et al. (2013): Investigated the financial frictions, trade credit and the 2008-09 global financial crises in USA. They used explanatory, sale and size as Variables. They found that the effectiveness of financial friction in genera and trade credit in particular, on economic activity in their sample of EMEs during the global financial crises. Their focus on trade credit as an alternative


source of economic financing for the period of crises is guided by the finding of existing studies, which documents the function of trade credit in reducing financing constriction during past in emerging market economies (EMES) [26]. Rene Guy and Mazra (2012): Studied the determinants of trade credit demand: An empirical study from Cameroonian Firms. By using transactional element, financial component and socio-cultural variables, they concluded that financial objectives are typically correlated with the organizational behavior [27]. Ahmed et al. (2014): Worked on determinants of trade credit: The case of a developing economy in Pakistan. They use size, liquidity, price, and inventory and sale growth as variable. They have applied three models named, pooled ordinary least square method, fixed effects method and random affects method. They found out that the study adds to the literature by analyzing the determinants of trade credit in context of Pakistan [28]. Many other researchers also work on determinates of trade credit such as Ahmed et al. (2014): Can trade credit serve as a cushion against the financial setbacks in a developing economy. They have found that large firms are willing to grant trade credit but they are not interested in receiving goods on credit [29]. In light of the above mentioned discussion about the determinants of the trade credit supply, we have developed the following hypotheses. H1: There is a positive and significant relationship between current assets and trade credit supply. H2: There is a negative and significant relationship between cash and bank balance and trade credit supply. H3: There is a significant relationship between gross profit and trade credit supply. H4: There is an inverse and significant relationship between inventory and trade credit supply. H5: There is a positive and significant relationship between current liabilities and trade credit supply. H6: There is a significant relationship between shareholder equity and trade credit supply. H7: There is a positive and significant relationship between tax expense and trade credit supply. 3. METHODOLOGY AND DATA To test the hypotheses raised in above paragraph, by following Ahmed et al. (2014), we have applied fixed effects model. Rationality behind using this model is to check more suitable explanation of the results. As our simple regression model is n

Y it     i  X it   it

(1)

i 1

By introducing fixed effects in the Equation 1 n

Yit  i  i  X it   it

(2)

i 1

Where, X is a vector of explanatory variables. After the selection of appropriate explanatory variables we have developed the Equation 3 as TCS it i 1CA it 2CAB it 3GP it 4INV it 5CL it 6SRE it 7TE it it

(3)

Where, “i” is representing ith firm at time t. ai is different for all cross sections. TCS stands for trade credit supply CA stands for current assets. CAB stand for cash and bank balance of a company


during the year, where GP stands for gross profit of a firms, INV stands for inventory, CL stands for current liabilities, SRE stand for shareholder equity and TE stands for trade expense of a firm during the year. E epsilon is error term. To use the equation (3), we used seven year data from 2006 to 2012 for analysis of determinants of trade credit supply for textile sectors of Pakistan. Relevant data has been taken from Balance Sheet analysis (BSA) published by The State Bank of Pakistan (SBP). Secondly, data have been taken from financial statements of non-financial firms listed at Karachi Stock Exchange (KSE). Only those firms are included that provides the complete data in context to the analysis. Firms which have missing values of assets and liabilities were also excluded. By looking at minimum and maximum values of the other variables, they are describing that extreme values do not exist in the ratios of these variables. Moreover we can observe that there is not a pronounced variation in data. TE (tax expensive) has a greater variation than any other variables. Inventory, cash and bank balance lesser variation in their data sets. Table I providing the data of Descriptive statistics and also contains the values of mean, median, maximum, minimum and Std. Dev and we used variables Trade Credit Supply (TCS), Current Asset (CA), Cash and Bank Balance (CAB), Gross Profit(GP), Current Liabilities (CL), Shareholder Equity (SRE) and Tax expenses (TE). TCS

Table 1 descriptive statistics CAB GP CL

CA

SRE

INV

TE

Mean

0.10

0.42

0.02

0.03

0.71

0.29

0.21

0.01

Median

0.07

0.41

0.01

0.09

0.73

0.29

0.19

0.01

Max

0.87

1.00

0.69

0.84

1.00

0.97

0.67

4.99

Min

0.0

0.02

0.0

-8.0

0.08

-1.0

0.00

- 0.3

Std. Dev.

0.09

0.17

0.03

0.54

0.21

0.26

0.12

0.17

Sum

92.600

403

13.9

26.6

673

280

194

13.3

Sums. Dev.

9.11

28.697

1.0180

280.78

38.603

61.482

12.945

26.228

Observation

955

955

955

955

955

955

955

955

To check the correlation among variables a Pearson correlation test has been conducted. Table II is providing the correlation matrix for variables. Trade debt (TCS) is a ratio of account receivable to total assets and it has a positive correlation with (CA), (GP), (SRE), (TE) and (INV). Table 2 correlation matrix

TCS CA CAB GP CL SRE TE INV

TCS

CA

CAB

GP

CL

SRE

TE

1.00 0.51 -0.03 0.11 0.27 0.04 0.01 0.01

1.00 0.11 0.19 0.52 0.17 0.05 0.61

1.00 0.04 0.02 0.01 -0.01 0.07

1.00 0.09 0.02 0.02 0.21

1.00 0.15 0.02 0.37

1.00 -0.01 0.15

1.00 0.02

INV

1.00


TCS have negative correlation with CAB. CA is the ratio of account receivable to sales, it has a positive correlation with all variable.CAB is a ratio of account receivable to asset. It has a negative correlation with TE and positive relation with GP, CL, SRE and INV. GP is an account receivable and it is a ratio of gross profit to sale. It is a positive correlation with CL, SRE, TE and INV. CL stand for current liabilities and it is account payable and calculated as current liabilities to total liabilities. It has positive correlation with all variables SR, TE and INV. SRE stand for shareholder equity and can be calculated as total assets minus total liabilities. It has positive correlation with INV but negative correlation with TE. TE stands for tax expense and be calculated as tax expense to sale.it has positive correlation with INV. TE is positively correlated with inventory. INV can be calculated as inventory to total sales. By looking at the magnitude of the correlations among variables, we can observe that no variables have greater correlation with other variables, which means variables do not have the problem of multi co-linearity. To ensure the contravention of ordinary least square (OLS) assumptions, we apply various tests to identify the problem of multi co-linearity, autocorrelation, and heteroscedasticity. The value of VIF indicates that there is no problem of multi co-linearity because all variables have VIF less than 5. 4. RESULTS Table III is showing the effects of different firm specific variables on trade credit supply. In table III, we can see the results of fixed effect method. The results are in line with the prior conclusion of various studies on trade credit determinants. In Table III the results of fixed effects models are use; the current ratio of CA is calculated by dividing total current asset by total current liabilities. It is highly significant with positive sign of coefficient 0.1371. Positive sign of coefficient is indicating that one unit increase in current asset would leads 13.71percent increase in trade credit supply. The value of t-statistic is significant because its value is more than 2 .value of t-statistic is 7.3709 and p value is 0.0000 therefore at last current assets is significant variables in our results. Results of CA prove our first hypothesis regarding the positive and significant relationship between trade credit supply and CA. In Table III the ratio of CAB is calculated by dividing cash and bank balance by total asset. Negative sign of coefficient is indicating that one unit increase would lead towards 11.06 percent decrease in trade credit supply. The value of t-stat is -2.0018 which more than 2 and p value of CAB is 0.045 thus we consider CAB variables is significant. Results of CAB also prove that our finding hypothesis regarding the relationship CAB. Results of CAB prove our second hypothesis regarding the negative and significant relationship between trade credit supply and CAB. GP is calculated by dividing gross profit by sale. A company total revenue (equivalence to total sales) minus the cost of goods sold. Positive sign of coefficient is indicating that one unit increase in gross profit would be 5.3 percent increase in trade credit supply. In order to increase sales nonfinancial firms in Pakistan tries to grant more credit sales which lead to increase gross profit. The value of t-stat in GP variables are 1.2744 which is less than 2 therefore GP is insignificant. It does not prove our third hypothesis. Variable C CA CAB GP INV CL

Table 3 fixed effect results Coefficient Std. Error 0.0660 0.0094 0.1371 0.0186 -0.1106 0.0552 0.0053 0.0041 -0.2264 0.0271 0.0274 0.0112

t-Statistic 6.9783 7.3709 -2.0018 1.2744 -8.3270 2.4321

Prob. 0.0000 0.0000 0.0456 0.2029 0.0000 0.0152


SRE TE R-squared Adjusted R-squared F-statistic Prob. (F-statistic)

0.0027 0.0163 0.8277 0.7930 23.843 0.0000

0.0128 0.0094

0.2166 1.7328

0.8286 0.0835

In Table III the result of fixed effects model for inventory turn out to be highly significant with negative mark of coefficient. According to the results of fixed effects model, inventory to sale ratio significantly related to trade credit supply. Negative sign of coefficient is showing that one unit increase in inventory would lead towards 22.64 percent decrease in trade credit supply. The value of t-stat is -8.3270 is more than 2 therefore it is significant and the value of p is 0.0000 therefore it is significant. In order to increase sales of non-financial firms in Pakistan and tries to grant more credit on sale which leads towards the decrease in inventory (Ahmed et al. 2014). This statement turned our direction towards the important motive of inventory management. Results of INV prove our fourth hypothesis regarding the negative and significant relationship between trade credit supply and INV. CL is calculated by dividing current liabilities to total liabilities. In Table III our variables is significant because its t value is more than 2 therefore we can consider that our variable is significant. Results of CL prove our fifth hypothesis regarding the positive and significant relationship between trade credit supply and CL. The ratio of SRE used to help determine that how much shareholder receives in the event of company liquidation. Positive sign of coefficient is indicating that one unit increase in shareholder would leads toward 2.7 percent increase in trade credit supply. In Table III the results of fixed effect model for shareholder variables turn out to be insignificant because the value of t-statistics in this variables is 0.2166 which is less than 2 and the p value is 0.82 is also insignificant therefore we consider this variables said to be insignificant. SRE does not prove our six hypotheses because SRE is insignificant. The ratio of TE is calculated dividing tax expense by total sale. In table III the positive sign of coefficient is indicating that one unit increase in tax expense leads towards 0.16 percent to increase in trade credit supply. The value of t-stat is significant because its vale is 1.7328 which is near to 2 so we can observe it is significant but the value of p is 0.835 which is insignificant. Results of TE prove our seventh hypothesis regarding the positive and significant relationship between trade credit supply and TE. The coefficient of determining, indicated by R-squared (R2) that described how much data is fit for further analysis and also the model is in line or not. And R2 of 1 show that the regression line is entirely fit to the data; while an R2 of 0 describes that the line does not fit on the data. It happens because the data is highly non-linear than the curve permit or for the reason that it is random. In the model the value of R-squared is 82.77%. 5. CONCLUSION The main focus of this research paper is to define the trade credit patterns in the textile sector of Pakistan. The study has selected the textile sector as it has a major share in the economy of Pakistan. This study adds the literature by finding the determinants of trade credit supply in context of textile firms of Pakistan. Many studies have been called that trade credit practices should be studied in different cultures in order to have insight into the different patterns used across the nations. This study has tried to analyze the determinants of account receivable of balance sheet in Pakistani textile sector. To find the appropriate results, we used fixed effect model. The results showed the current assets, cash & bank balance, gross profit, inventory and current liabilities are


the significant determinants of trade credit supply. For future research, it is need to explore more factors affecting trade credit supply and trade credit demand in the textile sector of Pakistan.

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