Trade credits and bank loans for SMEs in the textile sector.
The understanding of financing decisions in emerging markets is far less developed than in developed economies. Nevertheless, access to external finance is of particular importance for emerging markets since it is one of the key factors that contributes to the healthy development of real sector firms in these countries. (1) For firms in emerging markets, particularly small- and medium-sized enterprises (SMEs), access to bank loans is limited compared to firms in developed countries. Therefore, SMEs operating in emerging markets rely on trade credit to fulfill their need for additional finance. Moreover, Demirguc-Kunt and Maksimovic (2001) emphasize that trade credit is an answer for markets where alternative sources of finance are mostly unavailable and the development of the stock and bond market is modest.
However, there is an ongoing debate in the current finance literature if trade credit is a complement or substitute to bank credit. (2) In other words, does trade credit facilitate the access to bank credit or does it replace bank credit as quenching the need for external finance?
The purpose of this paper is to demonstrate the relationship between trade credits and bank loans through investigating SMEs operating in the textile sector in Turkey. The contribution of our work to the debate in the literature is at least threefold. First of all, one side of the debate focuses on the asymmetric information concept. Specifically, suppliers are thought to have a cost advantage over banks in acquiring information about the financial health of buyers. Small firms are known to lack the long-term relationship with providers of external finance and they are opaque in terms of their investment projects compared with large firms (Scherr and Hulburt, 2001). After studying small US firms, Petersen and Rajan (1995) reason that small firms consider borrowing from suppliers as a financing "lender of last resort" due to their limited access to bank loans. Moreover, Arslan (2003) indicates that SMEs in Turkey are more likely to be credit rationed than large firms due to the asymmetric information problem. Thus, focusing on small firms provides a better insight on the role of asymmetric information in determining the relationship between trade credits and bank loans. Besides, given that SMEs are firms within a financial growth cycle, as expressed by Berger and Udell (1998), they need more financial resources to fund their growth. (3)
Fisman and Love (2003) emphasize the industry-specific element to trade credit incentives. Therefore in this study, we concentrate on the textile sector to investigate the bank loans-trade credits relationship. The reason we only investigate SMEs in the textile industry is due to the fact that the representative sector is of particular importance for Turkish industrial production. Specifically, the Turkish textile sector accounts for almost 20% of Turkish Gross Domestic Product (GDP), 21% of total domestic industrial employment and 38% of total Turkish export earnings. As of 2002, Turkey is the second biggest supplier of textiles to the European Union after China. (4) Bagdadioglu and Ulucan (2005) indicate that almost 96% of textile firms in Turkey are classified as an SME and their biggest corporate concern is obtaining investment capital. The Halkbank Report (2003) shows that bank loan usage by Turkish SME textile firms only reach 10% of the level of that of SME textile firms in developed countries. Similarly, it is shown by Fisman and Love (2003) that trade credit usage in the textile sector in the US is above the median usage of the entire industries in the country. To sum up, investigating the relationship between trade credits and bank loans for such a strategic industry of Turkey is a crucial issue to keep track of their healthy financial stability.
Furthermore, we also investigate the role of financial crises on the relationship between trade credits and bank loans. Meltzer (1960) found that during the mid-1950's money tightening in the US, firms substituted bank loans with trade credits. Aligning with these findings, empirical results of Nilsen (1994) show that small firms react to economic recession by resorting to trade credits. Emerging markets are characterized as having large economic fluctuations in short-time intervals. Love, Preve and Sarria-Allende (2007) study the effect of financial crises on trade credit for a sample of 890 firms in six emerging economies. The authors found that the provision of trade credit increases right after a crisis but contracts in a crisis period because of a decline in trade credit due to a bank credit crunch, which the authors call the "redistribution view." In contrast, Miwa and Ramseyer (2006) find that small firms react to economic recessions by increasing their usage of trade credit. As for Turkey, the textile sector was the one most adversely affected among the industries in Turkey by the 1997 Asia crisis and 1998 Russian debt default due to the "contagion effect" (5) and the 2000-01 internal financial crises of Turkey. The largest number of closed factories and lost jobs in Turkey belonged to the textile sector owing to the indicated external and domestic crises (Halkbank, 2003). Consequently, investigating how financial crises influence the relationship between trade credits and banks loans for SMEs in the Turkish textile sector can help to find out the pattern of the change in the interaction between these concepts during economic constriction and expansion periods.
Last but not least, we investigate the accounting data of on average 493 SME textile firms operating in the period beginning in 1997 and ending in 2005 with a total of 1973 observations in our analyses. Thus, we provide an outstandingly large number of firm level data for a single sector in an emerging market. The source of our data is the database of The Central Bank of Turkey (TCMB--Turkiye Cumhuriyet Merkez Bankasi). If we had been obliged to work with textile firms that both fit into the SME definition and were publicly traded on the Istanbul Stock Exchange (ISE), we would have ended up with only 12 firms on average within the period of our analyses. Since the sample is made up of small sized firms, which make up more than 96% of the total textile industry in Turkey, our study expands the horizon of investigation on emerging markets by not being restricted only to publicly traded firms on stock exchanges.
Our results show that there is a non-linear relationship between trade credits and bank loans for textile SMEs in Turkey. In other words, trade credits initially encourage bank credits by complementing them. However, after a certain level of trade credit usage, trade credits curb bank loans and act as a substitute to them. Moreover, we do not detect that outside and domestic financial crises alter the pattern of the non-linear relationship. Finally, we also study the impact of specific firm characteristics on obtaining bank loans. We find a positive relationship between bank loans and firm size, firm age and having foreign sales. However, there is a modest negative relationship between bank loans and profitability. Nevertheless, the impact of interest coverage ratios on bank loans is not statistically different from zero. Finally, we find a slightly fast speed of adjustment to target bank loans for the firms.
The remainder of the paper is organized as follows. In section 1 we introduce the background of our empirical hypotheses. Section 2 presents the construction of the sample and the methodology. Section 3 includes the results of our analysis with discussions. Finally, section 4 includes concluding remarks.
Trade credit arises when a customer is permitted by a supplier to delay payments for goods already delivered. As indicated in Cunat (2007), although its implicit interest rates are commonly very high as compared with the rates on bank loans, (6) trade credit is widely used in firms thanks to its specific advantages. Firstly, firms incur lower transaction costs from obtaining a trade credit than when they attempt to access institutional loans. Furthermore, it is less costly for firms to have their suppliers postpone trade credit payments than renegotiating bank loans. Finally, being in the same value chain, there is a mutual dependence between suppliers and firms. Suppliers may finance the growth of small customers to assure growth of their own sales and eventually capture future profitable business from the firms. Owing to the stronger communication between suppliers and the firms, trade credits allow the firms to match the timing of cash outlays and cash receipts from sales.
However, how do trade credits interact with bank loans? In other words, the question focuses on whether trade credit facilitates obtaining external finance from banks or alternatively hinders the access to bank loans by substituting for them. The findings in the literature are contradictory.
On the one hand, by approaching the matter from the perspective of the asymmetric information problem, a strand of the literature finds that the relationship is complementary. Moral hazard problems, which refer to the case when credit is diverted to non-value maximizing projects by borrowers, emanate from the asymmetric information between the creditors and borrowing firms. Trade credit is believed to decrease the moral hazard problem because, in case of a default, suppliers are entitled to cease the supply of goods to the firms. Besides, losing the current supplier is more costly in the presence of some kind of product specificity--a particularly common issue for textile firms--or when there is a certain link between the suppliers and customers of end products, which is an issue more common for emerging markets. In accord with this, Biais and Gollier (1997) show that trade credit increases the availability of bank credit, especially for small firms that are typically affected by asymmetric information problems. Cook (1999) also finds evidence for the facilitating role of trade credit, through diminishing the adverse selection problem. The author shows that trade credit provides a positive signal for firms, hence increasing the probability of acquiring bank credit. Love, Preve and Sarria-Allende (2007) confirm the complementary relationship for six emerging market countries from another aspect called the "redistribution view" through showing that a credit crunch affecting financial lenders also affects trade credit lenders. Consequently, on the one hand, we hypothesize that trade credit boosts the usage of bank credit for Turkish SMEs in the textile sector.
On the other hand, the prediction that trade credit is a substitute for insufficient bank financing is empirically confirmed by Atanasova and Wilson (2004) and Petersen and Rajan (1994, 1995) and Marotta (1997). The authors observe that credit-constrained firms make a larger use of trade credit when credit conditions are tighter. Moreover, Petersen and Rajan (1997) stress that excessive trade credit could give a negative signal to a bank that a firm cannot obtain credit at any bank. In accordance to this finding, Wilson and Summers (2002) provide evidence that to assure their financial health by maintaining a strong value chain, suppliers provide credit for firms that can no longer rely on banks for additional credit. Finally, another approval is reached by Huyghebaert, Van de Gucht and Van Hulle (2007)--in their study of business start-ups--who indicate that suppliers are more willing to renegotiate the outstanding debt or grant additional debt, whereas banks are more likely to liquidate borrowers upon default. Therefore, on the other hand it is also plausible to hypothesize a negative relationship between trade credit and bank credit.
We also consider the impacts of firm specific characteristics on bank loans as control variables in our analyses. Firstly, we consider the profitability of firms. According to the pecking order theory of Myers (1984), the more firms are profitable, the less external finance they resort to. Yet, the trade-off theory of capital structure refrains adopting an expectation as to the effect of profitability on obtaining an institutional loan. Furthermore, McConnell and Petit (1984) indicate for small firms that since the cost of debt decreases for firms as their size grow, smaller firms tend to use less bank debts. For this reason, size is the third firm specific characteristic that we include in our tests. Moreover, Milne (1991) and Guariglia (1999) express that if a firm has a large interest cover ratio then it is classified as credit constrained. Consequently, Mateut and Mizen (2002) highlight that a firm with higher interest cover ratio uses larger trade credits and less bank loans. Hence, our expectation is to reach a negative relationship between interest cover ratio and bank loans. Gonenc and Arslan (2003) study Turkish publicly traded firms and find that, when compared with firms having no foreign sales, those with foreign sales have a higher level of short term borrowing in their liabilities. In accord with this finding, we expect a positive relationship between banks loans and foreign sales. Finally, we control for the association between firm age and bank loans since Petersen and Rajan (1994) explain that young firms tend to use more external finance given that they are in the growth stage of their life cycle.
The Case for Turkey
Zhu and Xu (2007) and Demirguc-Kunt and Maksimovic (2001) highlight that a firm's usage of bank credit relative to trade credit is higher in countries that have efficient legal systems and large banks. Aligning with this, Fisman and Love (2003) find that firms in countries with less developed financial markets and weak legal enforcement appear to substitute bank loans with informal credit provided by their suppliers. A typical feature of an emerging market is poorly functioning financial markets with limited financial instruments. In accord with this, being an emerging market, in Turkey a private bond market is not available and company openness is far below 25% on average (Erol, 2005). Moreover, the legal infrastructure in Turkey relatively exacerbates the asymmetric information between creditors and borrowers compared with that of developed Anglo-Saxon states (La Porta, et al., 1998).
Turkey faced one of the most important events in its economic history within the period of our analysis, namely 1997-2005. The adverse influences of the Asian meltdown in 1997 and the Russian debt default in 1998 spread to real sector firms in Turkey and the textile sector was the worst affected among them. In 1999, Turkey signed up an economic stabilization agreement with the IMF. Therefore, Turkey benefited from a low level of inflation, interest and fixed exchange rates through the years 1999-2000. Henceforth, the years 1999-2000 are referred to as the pre-crisis period. However, due to poor public finance management and a fragile banking system, Turkey was struck by a financial crisis that has had a financial and economic impact in the years 2001-02. Henceforth, these years are referred to as the crisis period. These crises led the Turkish government to adopt new alternative economic policies under another IMF package and in turn during the following years, namely 2003, 2004 and 2005, the financial and economic indicators became normal again.
Data and Methodology
We use a dataset of Turkish textile SME firms for the period beginning in 1997 and ending in 2005. Our sample is made up of 1973 observations and on average 493 firms. The data for this paper is obtained from the TCMB, which annually collects audited balance sheet and income statement data from the entire Turkish real sector firms so as to prepare internal analyses on industries. Firms voluntarily submit their data to the TCMB. In order to ensure reliability, the data is internally filtered by the Department of Statistics in the TCMB.
There is no consensus for the institutional definition of SMEs in Turkey. We based ourselves on the definition in Hall, Hutchinson and Michaelas (2004). Therefore, for our analyses, we picked the textile firms that have at least 10 employees and not more than 200. We were aware that the exclusion of micro firms, which employ between one and nine employees, and other firms with 200-250 employees could lead to potential biases, so we made robustness checks. However, the results of our robustness checks including both the micro firms and the ones with over 200 employees are very similar to those obtained in this paper. This result is not contrary to our expectations since the average number of employees in the Turkish textile sector for the years 1997-2005 is 105 (Turkish Statistical Institute, 2006). The results on our robustness checks are not reported for brevity and are available upon request.
We measured bank loan as the ratio of short-term bank loan to total assets. We did not use total bank loans because Turkish SMEs predominantly use short-term bank loans. Moreover, the ratio of short-term bank loans to the total bank loans for Turkish SMEs is 95%. (7) This also aligns with the indication of Chittenden and Bragg (1997) in their study of firms in UK, Germany and France that long term loans constitute a very small percentage of the liabilities of small firms. Furthermore, trade credit was measured as the ratio of accounts payable to total assets. Size was one of the control variables and it was measured as the inflation-adjusted natural logarithm of total assets and age of firms represented the number of years a firm had been operating. (8) Moreover, profitability was calculated as the ratio of earnings before interests and taxes to total assets whereas interest coverage ratio was calculated as the ratio of financial expenses to net sales. Finally, foreign sales was measured as the ratio of foreign sales to total sales.
We used the Generalized Method of Moments (Arrelano and Bond, 1991) methodology to estimate the relationship between bank loans and trade credits. Within this methodology, which avoids potential endogeneity problems, it is essential to take the adjustment process into account while developing the dynamic model. Therefore our estimation model was established within the following process:
Equation (1) is the static model:
[[gamma].sub.it] = [beta][x.sub.it] + [[xi].sub.n] + [[gamma].sub.t] + [u.sub.it] (1)
Where [beta] is a kx1 vector of constants, [x.sub.it] is a kx1 vector of explanatory variables, [[xi].sub.i] is the firm effect, which is assumed to be constant for firm i over t, [[gamma].sub.t] is time effect assumed constant for given t over i, is error term, and t=1,.... T and i=1, ...., N.
Let [y.sup.*] it be the target bank loans estimated from the equation (1), then
[y.sub.it] [-y.sub.it-1] = [lambda]([y.sup.*.sub.it-yit-1]) (2)
The coefficient is [lambda] is negatively associated with adjustment costs and has a value between 0 and 1. When [lambda]=1 then [y.sub.it-1], therefore adjustment occurs without any frictions. In other words, Ozkan (2001) states that this value approaches to 1 as the costs of being in disequilibrium exceed the adjustment costs. However, when [lambda] = 0 then = [y.sub.it] = [y.sup.*.sub.it] and in this case firms suffer less from disequilibrium and hence their slower adjustment process. After considering these issues, equation (2) was transformed into the following equation, which was estimated within our dynamic analysis.
[y.sub.it] = (1-[lambda])[y.sub.it-1] + [beta][x.sub.it] + [[xi].sub.i] + [[gamma].sub.t] + [u.sub.it]
Table 1 provides descriptive statistics of the variables we used in our analyses. Panel A shows the descriptive of the whole period beginning in 1997 and ending in 2005. Panel B includes the 1997-98 period that reflects the outside shocks of the 1997 Asian crisis and the 1998 Russian crisis. Panel C exhibits the results for 1999-2000 pre-crisis period of Turkey, Panel D shows those for the 2001-02 financial crisis period and, finally, Panel E demonstrates the post-crisis period. The mean value of bank loans is 20.9% for the whole period and the variable displays a declining pattern when examining its mean value from Panel B to Panel E. It is remarkable that the level of bank loans of Turkish textile SMEs during the post-crisis period was less than that of the bank loans during the crisis period. The mean value of trade credit in the panels shows that its usage was higher than that of bank loans for the entire period except the pre-crisis period of 1999-2000, in which interest rates had abruptly and dramatically fell and loan terms became less strict. Nevertheless, the level of trade credits exceeded the level of bank loans during the post-crisis period. Our results show that the mean value of size does not show much difference during different periods. The most interesting result derived from the tables is that in the crisis period of 2001-02, the mean (median) value of profitability was 7.1% (4.4%) while the mean (median) value of the variable was 1.4% (1.8%) in the post-crisis period. Results in the table explain that the ratio of foreign sales to total sales was 37.7% on average. Another noticeable result in the panel is that the mean value of foreign sales was 40% during the crisis period while the mean value of the variable fell to 30.45% during the post-crisis period. These results on both foreign sales and profitability can be attributed to the devaluation of the domestic currency during the crisis period and the abrupt appreciation of the freely floating domestic currency during the post-crisis period. In contrast to the suggestion, the highest mean value of foreign sales ratio, which was 43.2% among the different periods, was reached during the pre-crisis period. During the pre-crisis period, Turkey adopted a fixed exchange rate regime, which enabled stability in prices of foreign sales, thus encouraging domestic exports. Finally, the firms in our sample were fairly young since the mean (median) age of our sample was 18.48 (18).
We report the Pearson correlation matrix of our variables in Table 2. Our results in the correlation matrix show that correlations between the variables in our analyses did not exceed 50%. It should be noted from the table that the correlation coefficient between trade credits and bank loans was positive yet slim.
Our objective in this part was to show the pattern of the relationship between trade credits and bank loans. Therefore Figure 1 demonstrates the mean values of the trade credits and banks loans beginning in 1997 and ending in 2005. The lines in the figure show that, except for the year 1997, the level of trade credit was always above that of bank loan. Aligning with our expectations, the gap between trade credit and bank loan was the biggest during the crisis period and it narrowed in the post-crisis period. However, the gap was narrower in 1997 and 1998, in which the Asian and Russian crises took place, than the pre-crisis period of 1999-2000. After 1998, trade credits and bank loans slightly moved in the same direction and this gives the first clue for the "complementing argument," which stipulates that usage of trade credit facilitates access to bank credit.
[FIGURE 1 OMITTED]
We also divided the observations of trade credits into four separate quartiles and report respective mean, median and standard deviation values for the usage of bank loans corresponding to the each of the quartiles. The objective of this task was to examine whether the usage of bank loans increase or decrease in accord with increase or decrease in usage of trade credits. The results for each of the years are reported separately in order to present the trend in the relationship throughout the different periods. We used t-statistics to verify the statistical significant difference of the mean values of the first and the last quartiles.
Our univariate results illustrate that the relationship between trade credits and bank loans is non-monotonic. Specifically, in the first quartile bank loans initially increased. Yet, after a certain level of trade credit usage, which corresponds either to the second or third quartiles, bank loans started to decrease. The differences between the mean value of the lowest and the highest quartiles are statistically significant in the years 1997, 1998, 2000, 2004 and 2005. The quartile analysis for the year 2005 is the sole exception to the general pattern since bank loans were found to increase monotonically with trade credits in that year. The univariate results obtained in Table 3 justify the need to make further analysis through multivariate analyses to control for the non-linear relationship between trade credits and bank loans.
Table 4 presents the results of our dynamic analyses. It should also be noted that we also took the squared term of the trade credit as an explanatory variable in order to test the non-monotonic relationship between bank loans and trade credits, which was captured during our univariate analysis.
Our results show for both models that while the trade credit is positively statistically significant, the squared term of trade credit, however, is negatively statistically significant. Moreover, both the economic and statistical significance levels of the variables are slightly close to each other. Our results suggest that at low levels of trade credits, banks are encouraged to supply loans to Turkish textile SMEs. However, after a certain level of trade credits, the supply of bank loans becomes adversely related with the trade credits.
Bearing in mind that the textile industry in Turkey is one of the worst affected by exogenous macroeconomic shocks, we focused on whether the relationship between trade credits and bank loans differed during the crisis periods of Turkey. Therefore, we interacted both the trade credit and the square of trade credit variable with the crisis dummy. Crisis dummy takes the value of unity if the observation year is 1997, 1998, 2001 and 2002 and zero otherwise. Similar to our previous findings, the interaction of trade credit variable with the crisis dummy is significantly and positively related to bank loans. Moreover, the interaction of squared term of trade credit variable with the crisis dummy is again significantly negatively associated with bank loans. Nevertheless, we observed a slight decline of economic and statistical significance of both of the variables during the crises years.
In Model 2, we incorporated control variables explaining the bank loan usage of Turkish textile SMEs. As expected, the size of firms is positively related with bank loans, but with a very low economic and statistical significance. Furthermore, we found a very modest negative relationship between the profitability of firms and the usage of bank loans given that both the economic and statistical significance of the profitable variable is at low levels. Nevertheless, our results demonstrate that the impact of interest coverage ratio on bank loans is not statistically significantly different from zero. Age of firms is found to be strongly positively associated with bank loans. Lastly, we found that foreign sales are positively related with bank loans despite a low economic significance.
Finally, the results on the lagged bank loan variable display the speed of adjustment of Turkish textile SMEs to their target bank loans. The speed of adjustment for the firms is slightly closer to one since it is around 0.60. Therefore, we deduce for the Turkish textile SMEs that the cost of being in disequilibrium is slightly greater than the costs of adjustment to the target bank loan level.
Our results provide a mixed support for the two distinct arguments regarding the relationship between bank loans and trade credits. In other words, at low levels of trade credit our results confirm our hypothesis on the complementary argument that was also supported by the findings of Biais and Gollier (1997) and Love, Preve and Sarria-Allende (2007). Given that trade credit signals that a firm is creditworthy, initially, the eagerness of banks to lend Turkish textile SMEs increases. At this stage the firms are relatively at the advantageous situation for the supply of external funds since both trade credits and bank loans go hand in hand. However, at the higher levels of usage of trade credits its relationship with bank loan is found to be confirming the substitution argument of Wilson and Summers (2002) and Atanasova and Wilson (2004). We understand from our results that at a higher level of trade credits, banks begin the credit rationing of textile SMEs. This is caused by the fear of banks that the firms become more likely to be unable to service their further financial obligations considering the burden caused by their higher level of trade credit usage. Interestingly, the non-linear pattern of the relationship between bank loans and trade credits is not affected by crises. Put differently, the attitude of suppliers of bank loans to the level of trade credits used by the firms does not change during the economic upswings or downturns. Therefore, our findings for crisis periods contradict those of Meltzer (1960) and Nilsen (1994).
Finally, our results lend a weak support to the prediction that larger textile SMEs are more eligible for bank loans due to the decreased asymmetric information between their financial quality and supplier of funds. For this reason our results fairly confirm those of McConnell and Petit (1984) on the impact of size on acquiring bank loans. Moreover, given that the profitability variable is only just negatively related with bank loans, our results provide a modest support to the pecking order hypothesis, which suggests that profitability of firms diminish their resorting to bank loans. However, our results regarding interest coverage variable do not go hand in hand with those regarding profitability, since the former is found to be statistically insignificant. The results on the impact of firm age on bank loan usage are the contrary to the findings of Petersen and Rajan (1994), which indicate that young firms tend to use more external finance given that they are in the growth stage of their life cycle. Instead, we reason that older Turkish textile SMEs are more attractive for the supply of bank loans since they are more transparent and, hence, better known by the financial institutions given that they have a longer track of financial records and activities to be judged upon. In other words, in the market, older firms have an established reputation, which facilitates their access to bank loans. Finally, our findings on the foreign sales of Turkish textile SMEs are in agreement with those of Gonenc and Arslan (2003) regardiing Turkish publicly traded firms. Specifically, textile SMEs with export activities have a higher ability to acquire bank loans.
Acquiring external funds for an SME is of particular importance to textile firms in Turkey. Therefore, we studied two alternatives of external finance: trade credits and bank loans. In other words, the objective of this study was to find the relationship between trade credits and bank loans of Turkish SMEs operating in the textile sector. Moreover, the objective of the study also encompassed observing if the association between trade credits and bank loans differed during crisis periods. Our data was obtained from TCMB, hence we were not limited to the few textile SMEs that are publicly traded on the ISE. Our analyses included 1973 observations from an average 493 firms covering the period beginning in 1997 and ending in 2005. We conducted our analyses through GMM, incorporating a dummy variable to control for crises.
Our univariate analyses indicate a non-linear relationship between trade credits and bank loans. Our regression analyses also confirm that bank loans initially increase with trade credits. However, after a certain level of trade credit usage, supply of bank loans starts to decrease. Moreover, this relationship pattern is not affected by economic downturns and upswings. Therefore, at low levels of trade credit usage we support the complementing argument for Turkish textile SMEs, but at higher levels of trade credit usage we confirm the substitution argument.
We also included control variables in our analyses. We find that, on the one hand, firm size, firm age and foreign sales have a positive impact on bank loans. On the other hand, profitability has a fairly adverse influence on bank loans of Turkish textile SMEs. Given that we conducted our analyses on a partial adjustment model we find a modest speed of adjustment to target bank loans for the Turkish textile SMEs.
The main limitation of this study was focusing on only a single sector in one country. The issue of substitution or complementarity of trade credits and bank loans is important and until now there is insufficient knowledge on the relationship, especially in emerging economies. Therefore, further research could follow the same path for other sectors, and even for other countries, especially emerging economies.
The earlier version of this paper was presented at the 14th annual conference of the Multinational Finance Society in July 2007 in Greece. We give our thanks to Serap Celen and Gulin Kucuay for their valuable help in managing the data in the Central Bank of Turkey. The authors are especially grateful for the insightful and detailed comments of Mehmet Baha Karan, Manuel Rocha Armada, Edgar Ortiz, Lakshman Alles, and Elisabeth O'Dowd. The usual disclaimer regarding errors applies.
For further information on this article, contact:
Ozgur Arslan, Department of Business Administration, Hacettepe University, Beytepe, Ankara 06800, Turkey
Tel.: 00 90 312 297 8700/Fax: 00 90 312 299 2055
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Ozgur Arslan, Department of Business Administration, Hacettepe University, Beytepe, Ankara, Turkey
Goknur Umutlu, Department of Business Administration, Hacettepe University, Beytepe, Ankara, Turkey
(1.) See; Arslan, Florackis and Ozkan (2006).
(2.) See among others; Burkart and Ellingsen (2004), De Blasio (2005), Love, Preve and Sarria-Allende (2007), Ge and Qui (2007), Fabbri and Klapper (2008).
(3.) Finally, it should be noted that Fisman and Love (2003) suggest the necessity of studying usage patterns of trade credits and bank loans of smaller firms since observation for the largest publicly traded firms in studies can hardly be generalized to the rest of the population.
(4.) For further information on the importance of the textile sector for the Turkish economy, see IFM & Partners (2004).
(5.) See; Eiteman, Stonehill and Moffett (2006).
(6.) The authors outline that the interest rate of trade credit is more expensive than that of bank credit due to its higher insurance premium and default premium.
(7.) Besides, due to the missing variables in the database, we would have lost 67% of our observations if we had attempted to include long term bank loans.
(8.) Previously we had also included the squared term of variable size and age in the regression. However, the squared terms were found to be statistically insignifant and, therefore, excluded from the original analyses. The results are available upon request.
Table 1. Descriptive Statistics for Key Variables Standard Mean Median Deviation Panel A: Whole period Bank Loan 0.209 0.161 0.205 Trade Credit 0.254 0.221 0.180 Size 14.402 14.795 2.143 Profitability 0.037 0.028 0.160 Interest Coverage -0.079 -0.036 0.188 Foreign Sales 0.377 0.254 0.367 Age 18.477 18.000 8.778 Panel B: 1997-98 (Outside Shocks) Bank Loan 0.248 0.219 0.213 Trade Credit 0.263 0.227 0.186 Size 13.136 13.122 1.051 Profitability 0.027 0.031 0.140 Interest Coverage -0.091 -0.065 0.104 Foreign Sales 0.410 0.303 0.383 Age 20.488 19.000 7.819 Panel C: 1999-2000 (The Pre-Crisis Period) Bank Loan 0.289 0.251 0.259 Trade Credit 0.269 0.242 0.176 Size 14.212 14.161 1.093 Profitability 0.037 0.036 0.167 Interest Coverage -0.099 -0.055 0.221 Foreign Sales 0.432 0.336 0.373 Age 20.223 19.000 8.375 Panel D: 2001-02 (The Crisis Period) Bank Loan 0.172 0.122 0.185 Trade Credit 0.291 0.250 0.198 Size 15.169 15.195 1.005 Profitability 0.071 0.044 0.191 Interest Coverage -0.105 -0.055 0.156 Foreign Sales 0.400 0.276 0.379 Age 18.777 18.000 8.923 Panel E: 2003-04-05 (The Post-Crisis Period) Bank Loan 0.168 0.142 0.153 Trade Credit 0.210 0.178 0.151 Size 14.654 15.876 3.198 Profitability 0.014 0.018 0.131 Interest Coverage -0.039 -0.018 0.219 Foreign Sales 0.304 0.167 0.330 Age 15.930 14.000 8.847 Minimum Maximum Panel A: Whole period Bank Loan 0.000 0.991 Trade Credit 0.000 0.889 Size 6.709 18.291 Profitability -2.406 0.877 Interest Coverage -5.509 0.000 Foreign Sales 0.000 1.149 Age 0.000 82.000 Panel B: 1997-98 (Outside Shocks) Bank Loan 0.000 0.877 Trade Credit 0.000 0.833 Size 10.190 17.545 Profitability -1.159 0.652 Interest Coverage -1.047 0.000 Foreign Sales 0.000 1.032 Age 0.000 61.000 Panel C: 1999-2000 (The Pre-Crisis Period) Bank Loan 0.000 0.991 Trade Credit 0.000 0.846 Size 10.497 17.967 Profitability -1.033 0.877 Interest Coverage -3.922 0.000 Foreign Sales 0.000 1.060 Age 0.000 82.000 Panel D: 2001-02 (The Crisis Period) Bank Loan 0.000 0.955 Trade Credit 0.000 0.889 Size 11.233 17.976 Profitability -0.904 0.785 Interest Coverage -1.856 0.000 Foreign Sales 0.000 1.149 Age 0.00082.000 Panel E: 2003-04-05 (The Post-Crisis Period) Bank Loan 0.000 0.965 Trade Credit 0.000 0.832 Size 6.709 18.291 Profitability -2.406 0.516 Interest Coverage -5.509 0.000 Foreign Sales 0.000 1.065 Age 0.000 82.000 Note: This table provides descriptive statistics for the main variables used in our analyses. Bank loan is measured as the ratio of short-term bank loan to total assets. Trade credit is measured as the ratio of accounts payable to total assets. Size is the inflation-adjusted natural logarithm of total assets. Profitability is the ratio of earnings before interest and taxes (EBIT) to total assets. Interest coverage is the ratio of financial expenses to net sales. Foreign sales is the ratio of foreign sales to net sales. Finally age represents the number of years the firm has been operating. Table 2. Pearson Correlation Matrix (VV=1973) Size Age Bank Loan Trade Profit Credit Size 1.00 Age 0.01 1.00 Bank Loan -0.08 0.00 1.00 Trade Credit -0.18 -0.01 0.05 1.00 Profit -0.01 0.00 -0.33 -0.05 1.00 Foreign Sales -0.03 0.06 0.23 0.07 0.07 Interest Coverage 0.00 0.00 0.00 0.02 0.03 Foreign Interest Sales Coverage Size Age Bank Loan Trade Credit Profit Foreign Sales 1.00 Interest Coverage 0.01 1.00 Note: This table presents the Pearson's Correlation matrix for the variables used in our analyses of 1973 observations. Size is the inflation adjusted natural logarithm of total assets. Age represents the number of years the firm has been operating. Bank loan is measured as the ratio of short-term bank loan to total assets. Trade credit is measured as the ratio of accounts payable to total assets. Profit is the ratio of earnings before interest and taxes (EBIT) to total assets. Interest Cov. is the ratio of financial expenses to net sales. Foreign sales is the ratio of foreign sales to net sales. Table 3. Trade Credit Quartiles Years Bank Loan 1st Quartile 2nd Quartile 1997 Mean 0.2750 0.3219 Median 0.2450 0.3311 Standard deviation 0.2623 0.2317 n 151 151 1998 Mean 0.2445 0.2608 Median 0.1934 0.2345 Standard deviation 0.2446 0.2265 n 128 127 1999 Mean 0.2053 0.2765 Median 0.1288 0.2521 Standard deviation 0.2291 0.2207 n 135 134 2000 Mean 0.2983 0.3189 Median 0.2127 0.2988 Standard deviation 0.2972 0.2587 n 137 137 2001 Mean 0.1454 0.1974 Median 0.0370 0.1153 Standard deviation 0.2163 0.2157 n 154 153 2002 Mean 0.1334 0.1489 Median 0.0173 0.1226 Standard deviation 0.2012 0.2113 n 211 211 2003 Mean 0.1670 0.2189 Median 0.1243 0.1840 Standard deviation 0.1858 0.2064 n 200 200 2004 Mean 0.1306 0.1677 Median 0.0708 0.1231 Standard deviation 0.1678 0.1636 n 186 185 2005 Mean 0.1275 0.1732 Median 0.0813 0.1503 Standard deviation 0.1474 0.1529 n 172 172 Years 3rd Quartile 4th Quartile t-test 1997 0.3105 0.1906 3.339 *** 0.3187 0.1624 0.2286 0.1670 151 151 1998 0.2619 0.1927 1.975 ** 0.2651 0.1714 0.2072 0.1681 127 128 1999 0.2405 0.2030 0.092 0.2304 0.1900 0.1906 0.1756 134 134 2000 0.2669 0.1819 3.868 *** 0.2649 0.1520 0.2259 0.1891 137 137 2001 0.1769 0.1210 1.133 0.1456 0.0601 0.1772 0.1571 153 154 2002 0.1686 0.1300 0.204 0.1357 0.1010 0.1632 0.1350 211 211 2003 0.2051 0.1730 -0.356 0.2060 0.1546 0.1672 0.1490 200 200 2004 0.1933 0.1675 -2.306 ** 0.1852 0.1580 0.1465 0.1391 185 185 2005 0.1796 0.1879 -3.72 *** 0.1530 0.1672 0.1500 0.1537 172 172 Note: This table provides univariate mean comparisons of bank loan for trade credit quartiles from lowest to highest. It also provides median comparisons, standard deviation comparisons and number of observations denoted as "n." Bank loan is measured as the ratio of short-term bank loan to total assets. Trade credit is measured as the ratio of short term accounts payable to total assets. ***, ** and * indicate significance levels at 1%, 5% and 10% respectively. Table 4. Relationship Between Bank Loans and Trade Credits Dependent Variable = Bank Loan Independent Variables Model 1 Model 2 [Bank Loan.sub.t-1] 0.375 *** 0.394 *** (3.02) (3.51) Trade Credit 0.523 *** 0.415 *** (4.01) (4.26) Trade Credit * (Crisis Dummy) 0.311 *** 0.206 *** -(2.99) (3.17) (Trade Credit)2 -0.642 *** -0.718 *** (-3.25) (-3.01) (Trade Credit)2 * -0.405 *** -0.462 *** (Crisis Dummy) (-2.74) (-3.22) Size 0.016 * (1.79) Profitability -0.009 * (1.82) Interest Coverage -0.122 (-0.92) Age 0.283 *** (3.11) Foreign Sales 0.065 ** (1.99) Wald 1 (22.05) *** (25.02) *** Wald 2 (17.24) ** (32.15) *** Sargan 115.3 65.14 [lambda] 0.625 0.606 AR 1 (-3.172) *** (-3.826) *** AR 2 (-2.624) *** 0.4371 Note: This table presents GMM estimations for the years 1997-2005. Asymptotic standard errors are robust to heteroscedasticity. Bank loan is measured as the ratio of short-term bank loan to total assets. Trade credit is measured as the ratio of accounts payable to total assets. Crisis is a dummy variable taking the value of unity if the observations are at the years 1997, 1998, 2001 and 2002 and zero otherwise. Size is the inflation adjusted natural logarithm of total assets. Profitability is the ratio of earnings before interest and taxes (EBIT) to total assets. Interest coverage is the ratio of financial expenses to net sales. Foreign sales is the ratio of foreign sales to net sales. Finally age represents the number of years the firm has been operating. Wald 1 tests statistical significance of estimated coefficients that are asymptotically distributed under the hypothesis of non-relationship. Wald 2 tests statistical significance of time related variables. Ho hypothesis of the Sargan test is the orthogonality condition in terms of errors and has a asymptotic distribution. AR1 and AR2 tests are respectively first and second order autocorrelations of the residuals. [lambda] is the adjustment factor and calculated through the estimated coefficient of the lagged dependant variable. ***, ** and * indicate significance levels at 1%, 5% and 10% respectively.
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|Title Annotation:||Small and medium enterprises|
|Author:||Arslan, Ozgur; Umutlu, Goknur|
|Publication:||Journal of Small Business and Entrepreneurship|
|Date:||Sep 22, 2009|
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