. International Journal of Economics and Financial Issues ISSN: 2146-4138 available at http: www.econjournals.com International Journal of Economics and Financial Issues, 2016, 6(2), 414-419. International Journal of Economics and Financial Issues | Vol 6 • Issue 2 • 2016414 Determinants of Corporate Trade Credit: An Empirical Study on Korean Firms Woo Sung Kim* Department of Business Administration, College of Business Administration, Konkuk University, 120, Neungdong-ro Gwangjin-gu, Seoul 05029, Korea. *Email: ab1212@konkuk.ac.kr ABSTRACT This study is designed to determine the motives for trade credit in Korean firms. Based on data collected from 14,660 firm-year observations running from 1992 to 2011 on the Korean Stock Exchange, this paper finds strong evidence on determinants of trade credit based on financial characteristics. The principal result is that older firms with larger size, lower growth, and higher profits tend to extend accounts receivable. This evidence, while consistent with the access to financing hypothesis, is difficult to reconcile with the growth hypothesis and price discrimination hypothesis. Second, this paper provides evidence that firms with larger size and greater leverage, as well as young firms, appear to use accounts payable. This finding, while consistent with the financial constraint hypothesis, is difficult to harmonize with the financing and growth hypothesis. The paper contributes to the argument about trade credit motives. It may help managers in making financial policy concerning improving firm value in the Korean market. Keywords: Trade Credit, Accounts Receivable, Accounts Payable JEL Classifications: 30, 32 1. INTRODUCTION Trade credit is a very important corporate finance issue. In general, trade credit is measured by accounts receivable (AR) and accounts payable (AP). Trade credit research has garnered little attention in corporate finance literature. According to Nadri (1969), one reason for this neglect is that trade credit is buried in the firm’s distribution activity. Trade credit, like other working capital components, is related to short-term external finance. Moreover, trade credit makes up a large share of total assets in manufacturing companies. Mian and Smith (1992) document that AR are 21.0% of total assets in US manufacturing corporations, a substantial fraction of corporate assets. Likewise, AR are approximately 19% of total assets in Korea manufacturing companies. Specifically, trade credit management, such as AR, is one of the most important and time-consuming activities for a financial manager. Why do firms then extend or use trade credit? Prior literature has many theoretical explanations for trade credit determinants. Interestingly, it is mixed with both financial and non-financial descriptions. First, the non-financial explanation suggests that trade credit arises from price discrimination and acts as a warranty for product quality, and the persistence of long-term relationships with customers (Long et al., 1993; Shin and Soenen, 1998; Wilson and Summers, 2002; Deloof, 2003; Barine, 2012; Hill et al., 2012). Meanwhile, financial explanations highlight cost, monitoring, and informational advantages over banks (Biais and Gollier, 1997; Burkart and Ellingsen, 2004). Furthermore, prior studies empirically examine the determinants of trade credit concentrating on the preferences and expectations of both suppliers and buyers (Deloof and Jegers, 1996; Petersen and Rajan, 1997; Love et al., 2007; Giannetti et al. 2008; Bougheas et al., 2009; Molina and Preve, 2009; García-Teruel, and Martínez-Solano, 2010). More specifically, Ferrando and Mulier (2011) noted that companies that are more likely to be financially constrained are more dependent on the trade credit channel to finance growth. The goal of this paper is to examine determinants of corporate trade credit in Korea. Empirical trade credit research has been applied mainly to developed countries. As with most emerging countries, for Korean companies, trade credit is an important source of Kim: Determinants of Corporate Trade Credit: An Empirical Study on Korean Firms International Journal of Economics and Financial Issues | Vol 6 • Issue 2 • 2016 415 financing. However, empirically little attempt has been made to verify the effect of trade credit on the motives behind trade credit. This paper tests our hypotheses using a sample of 14,660 firm- year data points across non-financial Korean companies for the period from 1992 to 2011. Methodologically, this paper adopts a large panel data set of trade credit for these firms, considering the fixed effects for each firm and each year (to consider unobserved relationships). This study groups corporate trade credit hypotheses into two categories: Account receivables and account payables. Our main results are as follows. First, this paper finds that firm size and age have a significantly positive impact on AR, whereas growth and profitability have a significantly negative effect on AR. Second, the results demonstrate that firm size and leverage are significantly positively related to AP, whereas firm age has a significantly negative effect on AP. The paper contributes to the growing literature on corporate trade credit motives. The sample includes firms from emerging countries that prior literature has not widely studied. Moreover, the paper explains trade credit motives. It also may help managers, such as the chief financial officer, to make effective financial policy to improve firm performance on the Korean market. The paper is organized as follows. In Section 2, this paper suggests the hypotheses to be tested. In Section 3, data description and methodologies are presented. Section 4 presents empirical results on reasons why firms extend trade credit. Section 5 summarizes and concludes the paper. 2. HYPOTHESIS 2.1. Determinants of AR 2.1.1. Access to financing hypothesis In a capital system, larger firms are more creditworthy than other firms (Petersen and Rajan, 1997). Large established firms have better reputations on the capital markets (Diamond, 1991). Specifically, older firms may have more access to financial markets than younger firms. Well-established firms with better access to financial markets extend more trade credit than other firms (Niskanen and Niskanen, 2006; Baños-Caballero et al., 2010). Thus, firm size and age are positively related to the level of AR. This study tests the following hypotheses: H1.1: Firm size is positively related with AR. H1.2: Firm age is positively related with AR. 2.1.2. Growth hypothesis A firm willing to grow may choose a strategy of extending trade credit with longer re-payment periods than its competitors (Niskanen and Niskanen, 2006). In general, firms may perform a growth strategy using trade credit terms, such as the credit period, which is the total length of time credit extended to the customer. For instance, a way to beat competitors may be to grant credit if the customer is expected to be a repeat customer. Therefore, this study tests the following hypothesis: H.1.3: Firm growth is positively related with AR. 2.1.3. Price discrimination hypothesis Trade credit may be offered even if the supplier does not have a financing advantage over financial institutions, because trade credit may be used for price discrimination (Mian and Smith, 1992; Petersen and Rajan, 1997). In particular, since firms with low credit quality have bad credit terms, trade credit lowers the effective price of goods and services. Thus, firms with larger operating margins have a larger incentive to generate additional cash flows by financing the sales of additional units to their poorer customers by extending trade credit (Niskanen and Niskanen, 2006). For example, wealthy customers pay early and get a discount. Monopolists can use trade credit as a price discrimination tool. Therefore, this study tests the following hypothesis: H1.4: Profitability is positively related with AR. 2.2. Determinants of AP 2.2.1. Financing hypothesis Large and old firms may have more collateral assets than small and young firms (Barclay et al., 2003). These firms are inclined to have more stable cash flows. Moreover, large and old firms may have a high incentive for AP, because of low information asymmetry compared with small and young firms. Hence, larger firms and older firms are positively related to the level of AP. Therefore, this study tests the following hypotheses: H2.1: Firm size is positively related with AP. H2.2: Firm age is positively related with AP. 2.2.2. Growth hypothesis Suppliers appear to have some advantage in financing growing firms. That is, firms with high growth opportunity may be a source of future business to a supplier, and suppliers are more willing to provide credit in anticipation of capturing this future business. Theoretically, it may be argued that rapidly growing firms have better investment opportunities than other firms do, and are thus willing to use more trade credit as a partial financing source for their new investments (Niskanen and Niskanen, 2006). Therefore, firms with high growth have a greater possibility to increase AP (Howorth and Reber, 2003; Cunat, 2006). Therefore, this study tests the following hypothesis: H.2.3: Firm growth is positively related with AP. 2.2.3. Financial constraint hypothesis In general, firms facing financial distress have difficulty acquiring financing on the capital market. In other words, financially constrained firms have less access to capital and face more costly external financing, since they have a higher default risk and tighter monetary conditions. Firms under financial distress use a significantly larger amount of trade credit to substitute for alternative financing sources (Molina and Preve, 2012). This suggests a positive effect relative to equity and financial debt when firms are in financial distress. Accordingly, firms experiencing Kim: Determinants of Corporate Trade Credit: An Empirical Study on Korean Firms International Journal of Economics and Financial Issues | Vol 6 • Issue 2 • 2016416 financial distress may extend AP to avoid high equity costs of issuing stock and high debt costs. Therefore, this study tests the following hypothesis: H2.4: Leverage is positively related with AP. 3. DATA AND METHODOLOGY This paper is based on data collected from 763 non-financial firms listed on the Korean Stock Exchange for the period from 1992 to 2011. The panel data set is based on 14,660 firm-year data points across listed Korean non-financial firms. Sample data has been collected from the data analysis, retrieval and transfer System supplied by the Financial Supervisory Service and financial data obtained from KISVALUE, supplied by National Information and Credit Evaluation. We exclude issues offered by financial companies from our sample. This study performs a t-test to analyze firm specifics and industry characteristics for trade credit. Furthermore, the study uses the panel regression model to test the hypotheses. The advantage of panel data methodology allows us to control for unobservable heterogeneity. To model trade credit determinants, the estimated equations take the following form. Trade Creditit = β0 + β1LnSaleit + β2LnAgeit + β3AssetGrit + β4Profitit + β5Leverageit + ui + λt + eit (1) Where, trade credit is Accounts Receivable and Accounts Payable for each firm. AR is defined as AR divided by the book value of total assets (Kestens et al., 2012). AP is defined as AP divided by the book value of total assets. LnSale is measured as the natural logarithm of the book value of total sales. LnAge is measured as the natural logarithm of the difference between year 2011 and the year when the firm was first established. Trade credit may be particularly important for firms with financial market imperfections, such as young or small firms. Asset growth rate (AssetGR) is measured by subtracting total assets in year t−1 from total assets in year t divided by total assets in year t−1 ([total assets in year t − total assets in year t−1]/total assets in year t−1). Profit is earnings before interest and tax divided by total assets, which is the profitability of assets-in-place. Leverage is measured by total debt divided by total assets. The study includes the fixed effects for each firm and each year to consider unobserved relationships. The parameter ui is the firm’s unobservable individual effects, so we can control for the unique characteristics of each firm. The parameter λt is a time dummy variable that aims to capture the influence of economic factors that may also affect corporate trade credit determinants and firm performance, but which firms cannot control. Furthermore, the parameter eit is random disturbance. Table 1 presents a description of the variables. 4. EMPIRICAL RESULTS 4.1. Descriptive of Statistics Table 2 provides descriptive statistics for sample data. The mean of AR is 0.181 (18.1%), indicating that AR is relatively higher than reported for а related U.S. study presented by Petersen and Rajan (1997) where the mean value is 11.6%. Additionally, the mean of AP is 0.115 (11.5%), suggesting that AP is slightly lower than reported in the previously mentioned work of Petersen and Rajan (1997) where it is documented as 18.5%. The mean of AssetGr is 0.126 (12.6%), which implies a relatively high firm performance of Korean companies. The means of the ratio of LnSale and LnAge are 25.473 and 3.283, respectively. The means of Leverage and Profit are 0.558 (55.8%) and 0.056 (5.6%). 4.2. Firm and Industry Characteristics for Trade Credit Table 3 illustrates firm and industry characteristics for trade credit. The current study performs the t-tests to establish if there are differences in the mean values of comparing samples. In Panel A of Table 3, this paper have compared financial characteristics of firms ranging from large to small ones. The results show that there are significant differences in the mean values between the two groups for all the variables except AssetGr at 1% significant level. More precisely, the mean values of AR and AP are significantly higher for small firms than for large firms, indicating that trade credit plays a more important role for small firms than it does for large ones in short-term financing. On the other hand, the mean values of Leverage and LnSale and Profit and LnAge are significantly lower for small firms than they are for large ones at 1% significant level. In Panel B of Table 3, the study compares financial characteristics of firms with high leverage to those of firms with low leverage. The results indicate that there are significant differences in mean values between the two groups for all variables except AR at 1% significant level. Moreover, the AP of firms with high financial distress is slightly higher than the AP of low-financial-distress companies. The mean value of Profit is significantly higher for firms with high leverage than it is for firms with low leverage. On the contrary, the mean values of LnSale and AssetGr and LnAge Table 1: Description of variable Variable name Description Expected sign coefficient AR AP AR Account receivable divided by the book value of total assets AP Account payable divided by the book value of total assets LnSale The natural logarithm of the book value of total sales + + LnAge Natural logarithm of the difference between year 2011 and the year when the firm had first been established + + AssetGr Subtracting total assets in year t−1 from total assets in year t divided by total assets in year t−1 ([total assets in year t−total assets in year t−1)/total assets in year t−1 + + Profit EBIT divided by total assets which is the profitability of assets-in-place + Leverage Total debt divided by total assets + EBIT: Earnings before interest and tax Kim: Determinants of Corporate Trade Credit: An Empirical Study on Korean Firms International Journal of Economics and Financial Issues | Vol 6 • Issue 2 • 2016 417 Table 2: Descriptive statistics Variable Observations Mean Median Maximum Minimum SD AR 14,639 0.181 0.159 0.802 0.000 0.121 AP 14,639 0.115 0.086 1.334 0.000 0.101 AssetGr 13,927 0.126 0.081 31.212 −1.000 0.420 LnSale 14,635 25.473 25.329 32.425 17.263 1.582 LnAge 14,644 3.283 3.367 4.745 0.000 0.517 Leverage 14,660 0.558 0.557 26.477 0.000 0.417 Profit 14,639 0.056 0.055 0.550 −2.753 0.083 The sample consists of 14,660 firms-year observations from1992 to 2011 excluding financial and regulated firms. All market and accounting data are for the end of the fiscal year to the issue, unless otherwise indicated. AR is defined as account receivable divided by the book value of total assets. And AP is defined as account payable divided by the book value of total assets. Leverage is measured by total debt divided by total assets. LnSale is measured as the natural logarithm of the book value of total sales. Profit is measured by EBIT divided by total assets which is the profitability of assets-in-place. AssetGr is measured by subtracting total assets in year t−1 from total assets in year t divided by total assets in year t−1 ([total assets in year t−total assets in year t−1]/total assets in year t−1. LnAge is measured by natural logarithm of the difference between year 2011 and the year when the firm had first been established. SD: Standard deviation Table 3: Firm and industry characteristics of trade credit Panel A: Large firms versus small firms Variables Large firm Small firm Difference (mean) t value Significant levelObservations Mean Observations Mean AR 1725 0.120 7157 0.186 −0.066*** −21.846 0.000 AP 1725 0.097 7157 0.104 −0.007*** −3.010 0.003 Leverage 1725 0.561 7157 0.487 0.074*** 5.749 0.000 LnSale 1725 28.076 7157 25.237 2.839*** 95.331 0.000 Profit 1725 0.056 7157 0.042 0.014*** 5.991 0.000 AssetGr 1725 0.105 7157 0.094 0.011 1.340 0.180 LnAge 1725 3.637 7157 3.405 0.232*** −7.578 0.000 Panel B. High leverage firms vs. Low leverage firms Variables High‑leverage firm Low leverage firm Difference (mean) t value Significant levelObservations Mean Observations Mean AR 7316 0.182 7323 0.18 0.002 0.748 0.455 AP 7316 0.144 7323 0.087 0.057*** 35.297 0.000 LnSale 7316 0.05 7323 0.062 −0.012*** −8.597 0.000 Profit 7316 0.971 7344 0.922 0.049*** 4.305 0.000 AssetGr 7302 3.178 7342 3.388 −0.21*** −25.12 0.000 LnAge 4935 31.511 6233 37.000 −5.489*** −14.73 0.000 Panel C: High‑tech firms versus low‑tech firms Variables High‑tech firms Low‑tech firms Difference (mean) t value Significant levelObservations Mean Observations Mean AR 8001 0.202 6638 0.155 0.047*** 23.648 0.000 AP 8001 0.118 6638 0.112 0.006*** 3.767 0.000 Leverage 8020 0.542 6640 0.577 −0.035*** −5.080 0.000 LnSale 7998 25.233 6637 25.762 −0.529*** −20.436 0.000 Profit 8001 0.059 6638 0.052 0.007*** 5.278 0.000 AssetGr 7619 0.135 6308 0.116 0.020*** 2.828 0.005 LnAge 8013 3.221 6631 3.358 −0.136*** −16.012 0.000 Panel D: Manufacturing firms versus service firms Variables Manufacturing firms Service firms Difference (mean) t value Significant levelObservations Mean Observations Mean AR 10,760 0.192 3879 0.151 0.040*** 17.940 0.000 AP 10,760 0.110 3879 0.131 −0.021*** −10.904 0.000 Leverage 10,780 0.541 3880 0.604 −0.062*** −7.999 0.000 LnSale 10,759 25.282 3876 26.002 −0.720*** −24.794 0.000 Profit 10,760 0.058 3879 0.049 0.009*** 5.909 0.000 AssetGr 10,241 0.125 3686 0.129 −0.004 −0.521 0.602 LnAge 10,764 3.277 3880 3.300 −0.022** −2.314 0.021 The sample consists of 14,660 firm-year observations from1992 to 2011 excluding financial and regulated firms. All market and accounting data are for the end of the fiscal year to the issue, unless otherwise indicated. AR is defined as account receivable divided by the book value of total assets. And AP is defined as account payable divided by the book value of total assets. Leverage is measured by total debt divided by total assets. LnSale is measured as the natural logarithm of the book value of total sales. Profit is measured by EBIT divided by total assets which is the profitability of assets-in-place. AssetGr is measured by subtracting total assets in year t−1 from total assets in year t divided by total assets in year t−1 ([total assets in year t−total assets in year t−1]/total assets in year t−1. LnAge is measured by natural logarithm of the difference between year 2011 and the year when the firm had first been established. ***,** and * represent 1%, 5% and 10% significance levels, respectively Kim: Determinants of Corporate Trade Credit: An Empirical Study on Korean Firms International Journal of Economics and Financial Issues | Vol 6 • Issue 2 • 2016418 are significantly higher for firms with low leverage than those of firms with high leverage. In Panel C of Table 3, the study correlates the industry characteristics of high-tech firms to those of low-tech ones. The results reveal that there are significant differences in mean values between the two groups for all variables. This is especially valid for the mean values of AR and AP, which are substantially higher for high-tech firms than the ones of low-tech ones. The mean values of Leverage and LnSale and LnAge are considerably higher for low-tech firms than those for high-tech firms. Additionally, the mean values of Profit and AssetGr are way higher for high-tech firms than those of low-tech ones. Finally, in Panel D of Table 3, the paper compares the industry characteristics of manufacturing firms to those of service firms. The results demonstrate significant distinction between the mean values of the two groups for all variables except AssetGr at 1% and 5% significance level. More specifically, the mean values of AR are quite higher for manufacturing firms than those for service firms; the mean values of AP are much higher for service firms than those for manufacturing firms. The mean values of Leverage and LnSale and LnAge are substantially higher for service firms than those for manufacturing ones. Also, the mean values of Profit are significantly lower for service firms than those of manufacturing ones. 4.3. Determinants of Corporate Trade Credit The current study uses panel regressions to examine determinants of corporate trade credit. Table 3 shows the results of panel regression for the dependent variables AR and AP. Column (2) of Table 4 presents the results of the determinants of corporate trade credit dependent variable AR. The coefficient (0.024, 19.022 [t-statistics]) of LnSale is positive and statistically significant at 1% level, indicating that large firms tend to extend AR. This outcome strongly supports H1.1. The coefficient (0.028, 19.022 [t-statistics]) of LnAge is positive and statistically significant at 1% level, suggesting that older firms tend to increase their AR as this paper implied in H1.2. Meanwhile, the coefficient (−0.007, −3.581 [t-statistics]) of AssetGr is negative and statistically significant at 1% level, indicating that firms with growth are inclined to reduce their trade credit receivable. This result is inconsistent with H1.3. The coefficient of profitability (−0.057, −6.184 [t-statistics]) is negative and statistically significant at 1% level, which means that firms with high operating margin are likely to decrease their AR. This outcome is inconsistent with H1.4. Column (3) of Table 4 presents the results for the dependent variable AP. The coefficient (0.022, 33.923 [t-statistics]) of LnSale is positive and statistically significant at 1% level, which is a sign that large firms tend to use AP. This result strongly supports H2.1. The coefficient (−0.071, −8.922 [t-statistics]) of LnAge is positive and statistically significant at 1% level, indicating that older firms are likely to decrease their AP as this paper hypothesized in H2.2. Meanwhile, the coefficient (0.00009, 0.627 [t-statistics]) of AssetGr is positive and statistically insignificant. This result is inconsistent with H2.3. The coefficient (0.054, 42.309 [t-statistics]) of Leverage is positive and statistically significant at 1% level, supporting the notion that firms with high financial stress are inclined to increase their AP. This result is consistent with H2.4. In short, hypotheses H1.1, H1.2, H2.1, and H2.4 are strongly supported by the panel regression results. 5. CONCLUSION The general state of the determinants of corporate trade credit is still unresolved. The current paper examines the determinants of corporate trade credit in the Korea Stock Exchange Market. Based on a panel data set from 14,660 firms in Korea, this study provides strong evidence that financial characteristics affect trade credit policy. More specifically, it compares industry characteristics of high-tech firms to those of low-tech ones. The results indicate that AR and AP are higher for high-tech firms than those of low-tech firms, proving that firms requiring more time to observe product quality extend more trade credit that those where product quality is easy to observe (Long et al., 1993). This evidence implies that trade credit can reduce information asymmetry concerning product quality by allowing buyers to assess the quality of goods before remitting payment. It also correlates industry characteristics of manufacturing firms to those of service firms. The outcome shows that AR are higher for manufacturing firms than those for service firms, while the AP are higher for service firms compared to those for manufacturing firms. First, the main results show that firms with larger size, lower growth, lower profit and longer corporate presence tend to extend AR. This evidence, while consistent with the access to financing hypothesis, is difficult to reconcile with the growth hypothesis and price discrimination hypothesis. Second, this paper provides evidence that firms with larger size, higher leverage and shorter Table 4: Panel regression estimating determinants of corporate trade credit Independent variable Dependent variable AR AP Intercept −0.537*** −0.263*** (−12.146) (−7.491) LnSale 0.024*** 0.022*** (19.022) (33.923) LnAge 0.028*** −0.071*** (19.022) (−8.922) AssetGr −0.007*** 0.00009 (−3.581) (0.627) Profit −0.057*** (−6.184) Leverage 0.054*** (42.309) Fixed effects Firm and time Firm and time Adjusted R2 0.694 0.676 F value 34.397*** 32.711*** The sample consists of 14,660 firm-year observations from 1992 to 2011 excluding financial and regulated firms. All market and accounting data are for the end of the fiscal year to the issue, unless otherwise indicated. AR is defined as account receivable divided by the book value of total assets. And AP is defined as account payable divided by the book value of total assets. Leverage is measured by total debt divided by total assets. LnSale is measured as the natural logarithm of the book value of total sales. Profit is measured by EBIT divided by total assets which is the profitability of assets-in-place. AssetGr is measured by subtracting total assets in year t−1 from total assets in year t divided by total assets in year t−1 ([total assets in year t - total assets in year t−1]/total assets in year t−1). LnAge is measured by natural logarithm of the difference between year 2011 and the year when the firm had first been established. ***,** and * represent 1%, 5% and 10% significance levels, respectively Kim: Determinants of Corporate Trade Credit: An Empirical Study on Korean Firms International Journal of Economics and Financial Issues | Vol 6 • Issue 2 • 2016 419 market presence appear to use AP. This finding, while consistent with the financial constraint hypothesis, does not correspond to the financing and growth hypothesis. Before all else, this finding suggests that trade credits do not act as an effective financial policy to firm growth in Korea. Moreover, these results indicate that trade credit is used as an alternative source of financing as well as an operational vehicle for marketing. The paper contributes to the growing literature on motives of corporate trade credit. Additionally, the sample includes firms from emerging countries that prior literature has not studied thoroughly. It may also be useful to managers such as chief financial officers in developing financial policies toward improving firm performance in the Korean market. REFERENCES Baños-Caballero, S., García-Teruel, P.J., Martínez-Solano, P. (2010), Working capital management in SMEs. Accounting and Finance, 50 (3), 511-527. Barclay, M., Marx, L., Smith, C. (2003), The joint determination of leverage and maturity. Journal of Corporate Finance, 9, 149-167. Barine, M.N. (2012), Working capital management efficiency and corporate profitability: Evidence from quoted Nigerian firms. Journal of Applied Finance and Banking, 2(2), 215-237. Biais, B., Gollier, C. (1997), Trade credit and credit rationing. Review of Financial Studies, 10(4), 903-937. Bougheas, S., Mateut, S., Mizen, P. (2009), Corporate trade credit and inventories: New evidence of a trade-off from accounts payable and receivable. Journal of Banking and Finance, 33(2), 300-307. Burkart, M., Ellingsen, T. (2004), In-kind finance: A theory of trade credit. American Economic Review, 94, 569-590. Cunat, V. (2006), Trade credit: Suppliers as debt collectors and insurance providers. Review of Financial Studies, 20(2), 491-527. Diamond, D. (1991), Monitoring and reputation: The choice between bank loans and directly placed debt. Journal of Political Economy, 99(4), 689-721. Deloof, M. (2003), Does working capital management affect profitability of Belgian firms? Journal of Business Finance and Accounting, 30, 573-588. Deloof, M.M., Jegers, M. (1996), Trade credit, product quality, and intra group trade: Some European evidence. Financial Management, 25, 33-43. Ferrando, A., Mulier, K. (2013), Do firms use the trade credit channel to finance growth?. Journal of Banking and Finance, 37, 3035-3046. García-Teruel, P.J., Martínez-Solano, P. (2010), Determinants of trade credit: A comparative study of European SMEs. International Small Business Journal, 28(3), 215-233. Giannetti, M., Burkart, M., Ellingsen, T. (2011), what you sell is what you lend? Explaining trade credit contracts. Review of Financial Studies, 24(4), 1261-1298. Hill, M.G., Wayne, K., Lockhart, G.B. (2012), Shareholder returns from supplying trade credit. Financial Management, 41, 255-280. Howorth, C., Reber, B. (2003), Habitual late payment of trade credit: An empirical examination of UK small firms. Managerial and Decision Economics, 24(6-7), 471-482. Kestens, K., Cauwenberge, P.V., Bauwhede, H.V. (2012), Trade credit and company performance during the 2008 financial crisis. Accounting and Finance, 52(4), 1125-1151. Long, M.S., Malitz, I.B., Ravid, S.A. (1993), Trade credit, quality guarantees, and product marketability. Financial Management, 22(4), 117-127. Love, I., Preve, L.A., Sarria-Allenda, V. (2007), Trade credit and bank credit: Evidence from recent financial crises. Journal of Financial Economics, 83(2), 453-469. Mian, S.L., Smith, C.W. (1992), Accounts receivable management policy: Theory and evidence. The Journal of Finance, 47(1), 169-200. Molina, C., Preve, L. (2009), Trade receivables policy of distressed firms and its effect on the costs of financial distress. Financial Management, 38, 663-686. Molina, C., Preve, L. (2012), An empirical analysis of the effect of financial distress on trade credit. Financial Management Spring, 41, 187-205. Nadiri, M.I. (1969), The determinants of trade credit in the US total manufacturing sector. Econometrica: Journal of the Econometric Society, 37(3), 408-423. Niskanen, J., Niskanen, M. (2006), The determinants of corporate trade credit policies in a bank-dominated financial environment: The case of Finnish small firms. European Financial Management, 12, 81-102. Petersen, M., Rajan, R. (1997), Trade credit: Theories and evidence. Review of Financial Studies, 10, 661-691. Shin, H.H., Soenen, L. (1998), Efficiency of working capital management and corporate profitability. Financial Practice and Education, 8, 37-45. Wilson, N., Summers, B. (2002), Trade credit terms offered by small firms: Survey evidence and empirical analysis. Journal of Business Finance & Accounting, 29(34), 317-351.