Small and medium-sized enterprises and diversification of financing sources: Strategy or desperation

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1 Small and medium-sized enterprises and diversification of financing sources: Strategy or desperation Myint Moe Chit Nottingham University Business School The University of Nottingham Malaysia Campus Jalan Broga Semenyih, Selangor Malaysia Abstract Using a firm-level survey database covering 92 countries, we examine the financing behaviour and the determinants of financing source diversification of small and medium-sized enterprises (SMEs) to explore whether the diversification of financing sources is a strategic decision or a desperate activity. Our results show that the extent of financing source diversification differs according to the differences in firm-specific characteristics and country-specific financial and institutional environments in which SMEs operate. Although SMEs specialise their source of financing in particular, retained earnings for their investment, SMEs experiencing financial constraints rely on more diversified financing sources to fund their investment. After controlling for other firm-specific characteristics and country-specific variables, we find a quadratic relationship between financing constraints and source diversification such that SMEs experiencing a moderate level of financial constraints use the most diversified source of financing. Key words: Financial Development, Legal and regulatory environment, Financing Patterns, Capital Structure, SMEs JEL Classification: G32, L26, O16, K40

2 I. Introduction The role of SMEs has been considered increasingly important in developing and emerging countries due to their ability to promote private ownership, market competition, entrepreneurship and innovation (Hallberg 2000; Beck et al. 2005). A recent study of Ayyagari et al. (2011) confirms that SMEs generate the most new jobs and they are the major providers of employment in developing and emerging countries. However, it has been acknowledged that growth and survival of SMEs have been hampered by the lack of access to finance (Ayyagari et al. 2008). This problem of limited access to external finance is more pronounced for SMEs in emerging and developing countries due to market imperfections, weak institutional and macroeconomic environments and a less advanced banking sector (Beck et al. 2008; Krasniqi 2010). For those reasons, much of the literature on SME financing behaviour has been dedicated to the SMEs access to finance (Carpenter and Petersen 2002; Beck and Demirguc- Kunt 2006; Beck et al. 2008; Krasniqi 2010; Bartoli et al. 2013) and their capital structure (Hall et al. 2000; Bhaird and Lucey 2009; Psillaki and Daskalakis 2009; Degryse et al. 2012) According to the literature on SMEs capital structure, the majority of small firms rely exclusively on internal funds or retained earnings to finance their investment (Carpenter and Petersen 2002). The phenomena of specialisation on financing source could be due to a number of reasons. First of all, SMEs are unable to access external finance because they are subject to credit rationing resulting from information asymmetries (Stiglitz and Weiss 1981). Secondly, Carpenter and Petersen (2002) suggest that, compared to internal finance, the costs of external finance are higher for SMEs due to imperfections in capital markets. The third reason for SMEs specialisation in the use of internal finance is because of their preference of independence to growth and wealth maximisation (Vos et al. 2007; Bell and Vos 2009). Last but not the least, Colla et al. (2013) suggest that debt diversification is costly due to high information collection and monitoring costs. The costs of debt diversification are likely to be much higher for informationally opaque smaller firms (SMEs) with limited financial, managerial and human capital resources. According to these arguments, SMEs rely on a single source of financing either by choice or by force. In this paper, we focus on a related but less explored topic in financing behaviour of SMEs diversification of financing sources. Using firm-level data from surveys conducted in 92 countries and country-specific financial and institutional variables, we examine SMEs financial behaviour to demonstrate that a significant number of SMEs rely on diversified sources of finance to fund their investment. Then, we explore the role of firm-specific

3 characteristics and country-specific institutional environments on the diversification of SMEs financing sources. In particular, we attempt to identify the reason for SMEs reliance on diversified financing sources. For SMEs that are known to suffer from credit constraints due to limited access to external finance, having multiple sources of financing might be an appropriate financing strategy to improve their growth prospect or at least to enhance their chance of survival. On the other hand, since SMEs are also well known for their exclusive reliance on retaining earnings to fund their investment, using more expensive external sources of financing could be a sign of a desperate act to mitigate financial constraints that severely hinder their survival. The main objective of our paper is to explore whether the diversification of financing sources is a strategic decision or a desperate activity of SMEs in emerging and developing economies. If diversification of financing sources is a SME s strategic decision, the extent of diversification should be related to firm-specific characteristics and institutional environments under which it operates but independent from the degree of financial constraints experienced by the firm. On the other hand, if the extent of source diversification is influenced by the degree of financial constraints experienced by the firm, after controlling for firm-specific and countryspecific determinants, we may conclude that the diversification of financing sources could be a consequence of desperation rather than a strategic move. The study on the diversification of financing sources is limited and mostly focuses on large listed and credit rated firms (Rauh and Sufi 2010; Colla et al. 2013) or SMEs from developed economies (Vos et al. 2007). To the best or our knowledge, this is the first paper that looks at the determinants of financing source diversification among SMEs in developing and emerging economies. Our results show that the extent of financing source diversification differs according to differences in firm-specific characteristics and country-specific institutional environments in which SMEs operate. We find that larger SMEs have better access to external funds and rely more on diversified financing sources, which is consistent with the findings of Cassar (2004), Gregory et al. (2005) and Vos et al. (2007). We also find that the relationship between firm age and diversification of financing sources is non-linear suggesting the extent of diversification increases with firm age but very old SMEs specialise their source of finance. In line with the propositions of Packing Order Theory (Myers and Majluf 1984) and the empirical results of Hall et al. (2000), Carpenter and Petersen (2002) and Degryse et al. (2012), our findings indicate that more profitable SMEs rely on less diversified sources as they can use their profits to fund future investments. We also find a positive relationship between a firm s growth and

4 the diversification of financing sources. Furthermore, we find more evidence of SMEs appetite for financing source specialisation as SMEs operating in a country with more developed financial intermediary sector tend to rely more on bank financing as a primary source to fund their investments. In contrast, our measure of a country s legal and regulatory environment has a significant positive impact on the diversification of financing sources. Our findings also indicate that although SMEs specialise their source of financing in particular, exclusively on retained earnings for their investment, financially constrained SMEs rely more on diversified financing sources to fund their investment. After controlling for other firm-specific characteristics and country-specific variables, we find a quadratic relationship between the degree of financing constraints and the source diversification such that SMEs experiencing a moderate level of financial constraint tend to rely on the most diversified sources of financing. This particular finding, combined with the evidence of SMEs overwhelming reliance on internal funds, suggests that SMEs diversification of financing sources could be a consequence of desperation rather than a strategic move. The rest of the paper is organised as follows. Section 2 describes the overview of our data and the descriptive statistics of financing patterns. Section 3 provides evidence on the diversification of financing sources and identifies the patterns of diversification across different firm size categories and different degrees of financial constraints. In section 4, we review the literature on the determinants of firms financing behaviour and conduct an exploratory analysis to identify the determinants of source diversification. The estimation model and empirical results are presented and discussed in section 5. Finally, section 6 concludes. II. Data overview and summary statistics Descriptive statistics We use firm-level survey data from the World Bank s Business Environment and Enterprises Surveys (BEEPS) conducted between 2006 and 2014 (Data date: November 13, 2014) and country specific data from the World Governance Indicators (WGI 2014) and the World Development Indicators (WDI 2014) to investigate the determinants of SMEs financing behaviour. According to the classification of the World Bank, SMEs are defined as firms with

5 not more than 300 full-time employees (Ayyagari et al., 2007). 1 After removing the observations with missing values and inconsistent responses, our sample includes observations from 92 emerging and developing countries. 2 A summary of the firm size and the number of SMEs experiencing financial constraint across the different size categories are presented in Table 1. 3 Among the SMEs in our sample, just over 18% can be classified as micro SMEs with not more than 10 employees. About 44% are small firms (11 to 50 employees) and the remaining 38% are medium size firms (51 to 300 employees). The BEEPS survey also provides an indicator of the extent to which firms consider access to finance is an obstacle to their current operations. In our sample, over 72% of firms report that access to finance is an obstacle (to a certain degree) to their current operations. This proportion is considerably higher than the proportion of larger firms (employ more than 300 full-time employees) experiencing financial constraint. This finding confirms that access to finance and the cost of capital hinder the operation and performance of smaller firms more than the larger enterprises (Carpenter and Petersen 2002; Berger and Udell 1998). [INSERT TABLE 1 ABOUT HERE] BEEPS also surveys the proportion of a firm s investment in fixed assets funded by different sources of finance. These sources can be classified as retained earnings, banks, nonbank financial institutions, trade credit, new equities and informal sources. We use a sample of SMEs for which the proportions add up to 100%. Almost 32% of SMEs use at least two sources of financing and about 68% of SMEs rely on a single source of financing for their investment. A summary of SME financing patterns is presented in Table 2. Among the 68% of the observations that rely on a single source of finance, 52.7% use their retained earnings to fully finance their investment. Only 9.22% of SMEs in our sample rely on bank financing as the only source to fund their investment. 1 We also follow the European Union s definition of SMEs as firms with less than 250 employees. The results, not presented here for brevity, are almost identical. 2 List of countries are presented in Appendix A. The choice of country is solely depend on the availability of BEEPS survey data. 3 There are a few of countries with less than 10 observations. In order to check the sensitivity of our results, we also use a sample excluding those countries and firms. The results, available upon request, are almost identical.

6 Table 2 also shows that over 81% of SMEs used retained earnings to finance fully or partially over 67% of their investment in fixed assets. SMEs use bank loans to finance about 20% of their investment. It is important to note that only just over 30% of SME have access to the bank finance. It should also be noted that informal financing contributed only 3.38% to SME s investments in fixed assets. Among the remaining four sources of financing, about 12% of SMEs have access to trade credit to finance their investment which accounts for, on average, only 5.9% of total investment. Very few SMEs issue new equities to finance their investment, which might suggest the SMEs owner s unwillingness to sacrifice their control over the firm. About 7% of SMEs rely on the informal sources and the proportion of informal financing to total investment is about 3.4%. This evidence is consistent with the previous findings which suggest that SMEs rely exclusively on a single source of finance; specifically, their retained earnings (Carpenter and Petersen 2002; Vos et. al. 2007; Bhaird and Lucey 2009) and they have limited access to external sources to finance their investment (Holmes and Kent 1991; Hall et al. 2000). [INSERT TABLE 2 ABOUT HERE] Conditional source of financing In order to provide more evidence on the extent of the diversification of financing sources and its relationship with the degree of financial constraint experienced by a firm, we first examine the conditional financing structure of SMEs. Following the approach applied in Colla et al. (2013), we identify the significant user of a particular source of financing by imposing the condition that the usage of a particular source must exceed 30% of a firm s investment. Then, the conditional mean (median) ratio of each financing source of SMEs with and without financial constraint are calculated and presented in Table 3. As shown in Panel (A), the conditional means (medians) along the diagonal line, which shows the proportions of investment financed by the source upon which we condition, are between 61% and 90% for SMEs with no financial constraints. The conditional means (medians) off the main diagonal lines, which represent the proportions of investment financed by the remaining sources that we do not condition upon, are generally smaller. In contrast, Panel (B) shows that SMEs experiencing financial constraints have a smaller conditional means (medians) along the diagonal line compared to Panel (A). In addition, we observe a generally larger conditional

7 means (medians) off the diagonal line (except financing from banks and non-bank financial institutions) compared to SMEs without financial constraints. The evidence provided in Table 3 further confirms that SMEs rely predominantly on a single source of financing for their investment and the degree of source diversification is higher among the SMEs experiencing financial constraints. [INSERT TABLE 3 ABOUT HERE] III. SMEs and diversification of financing sources Measure of financing source diversification We calculate a normalised Herfindahl-Hirschman Index (HHI) of financing sources as a measure of source diversification. HHI 1 where 2 1 FS k 1 k FS k represent the proportions of financing from 6 different sources. A larger HHI value indicates more diversified sources of financing. If a SME employs all six sources of financing in equal proportions, the index equals one. In contrast, if a SME use a single source to finance to fund its investment in fixed assets, the index will be zero. The average of HHI index for our sample firms is 0.16, with a maximum value of For a robustness check, we also construct a dummy variable, Diversify, which equals to 1 if a SME s investment is financed by more than one source and zero otherwise, as an alternative measure of source diversification. Almost 32% of SMEs use at least two sources of financing and about 68% of SMEs rely on a single source of financing for their investment. 4 Source diversification, firm size and degree of financial constraints Table 4 presents the source diversification of SMEs across different size categories and different degrees of financial constraints. As present in Panel (A), we find that micro SMEs 4 Summary statistics and correlation matrix of all variables are presented in Appendix B and Appendix C, respectively.

8 employ less diversified sources and rely more on retained earnings and informal sources to finance their investment compared to their larger counterparts. The evidence also reveals that smaller SMEs rely more on informal sources compared to their larger and older counterparts. Table 4, Panel (B), presents the source diversification of SMEs across different degrees of financial constraints. We find evidence of the non-linear relationship between the extent of source diversification and the degree of financial constraints. The average value of HHI and the proportion of Diversify increase along with the degree of financial constraints. But the extent of diversification decreases again for SMEs experiencing severe financial constraints. SMEs with no financial constraints seem to specialise on financing source and they rely more on retained earnings to finance their investments. The proportion of retained earnings used to finance investments is the lowest for SMEs with moderate financial constraints. We also find that SMEs with severe financial constraints rely more on trade credits and informal sources and less on bank finance compared to other SMEs. The extent of source diversification is highest (most diversified) for SMEs, which report that access to finance is a moderate obstacle for their operations. Firms with severe financial constraints are the least diversified among the firms experiencing financial constraints. The results of F-test also confirm the significant differences in financing patterns among the SMEs of different size categories and SMEs experiencing different degrees of financial constraints. [INSERT TABLE 4 ABOUT HERE] IV. SMEs and source diversification: Which SMEs diversify? Determinants of source diversification In the previous section, we provide evidence of financing source diversification of SMEs across different size categories and different degrees of financial constraint. In this section, we examine the role of firm-specific and country-specific characteristics on SMEs financing source diversification. Most of the existing theoretical literature on capital structure and firm s financing decision are based on Trade-off theory proposed by (Modigliani and Miller 1958) and Pecking Order theory (Myers 1984; Myers and Majluf 1984). However, the existing theoretical literature on diversification of financing sources, especially for small firms, is rather limited. Recently, Colla et al. (2013) provide three possible explanations for debt specialisation

9 among the US listed firms. They propose that transaction costs due to information asymmetry, higher expected bankruptcy costs and constrained access to capital are the reasons why firms specialise in debt financing. It has been argued that smaller firms are more likely to experience greater information asymmetries (Stiglitz and Weiss 1981; Petersen and Rajan 1994; Hall et al. 2000). In addition, the transaction costs associated with information asymmetries are more likely to be higher for smaller firms (Titman and Wessels 1988; Wald 1999). As a result, smaller firm s access to external financing sources would be limited and/or the cost of financing could be higher than larger firms (Cassar and Holmes 2003; Cassar 2004). One way to overcome the transaction costs associated with information asymmetries could be to prepare a financial statement and audited by an independent body. As SMEs grow in size, they have better collateralisable assets (Gregory et al. 2005) and become more experienced and less informationally opaque (Berger and Udell 1998). Consequently, larger SMEs will have better access to external finance. In addition, as larger firms are likely to be more diversified, expected bankruptcy costs are likely to be lower for larger SMEs (Pettit and Singer 1985; Bhaird et al. 2007). Therefore, costs of using external financing sources are relatively lower for larger and more transparent firms and, as a results they are more likely to use diversified financing sources. According to the financial growth cycle of SMEs proposed by Berger and Udell (1998), the financial needs and financing options of a firm change as the business becomes older and more experienced. Gregory et al. (2005) also argue that older firms ought to have better access to external sources of financing since they are more likely to have technical expertise and managerial resources to reduce informational asymmetries. In addition, the expected bankruptcy cost of an older firm is lower compared to younger firms that are deficient in managerial knowledge and financial management abilities (Thornhill and Amit, 2003). On the other hand, Vos et al. (2007) argue that as SMEs become very old and matured, the owners level of loss aversion is more likely to be higher and they are more likely to specialise on the source of financing. These propositions and empirical evidence suggest that the relationship between firm age and source diversification could be non-linear. Pecking order hypothesis proposed by Myers (1984) suggest that, given the existence of information asymmetries between the firm and outsiders, a profitable firm, which has access to retained profits is likely to use internal funding rather than relying on outside sources to finance their investment opportunities. Lewis (2009) also provide evidence that potential costs of financial distress are likely to be higher for profitable firms with high working capital

10 balances. Since firms with higher expected bankruptcy costs are less likely to rely on diversified sources of finance in order to reduce renegotiation costs with multiple lenders (Colla et al. 2013), we expect a negative relationship between a firm s profitability and financing source diversification. Cesser and Holmes (2003) suggest that firms with relatively high growth opportunities will tend to use external sources to finance their growth. From the perspective of expected bankruptcy costs, Lewis (2009) also shows that the costs of financial distress are relatively lower for growing SMEs because of the attractiveness of their unexercised growth options. In the case of bankruptcy, there will be a liquid market for assets of the firms with high growth opportunities (Lewis 2009). Furthermore, Degryse et al. (2012) also suggest that growing SMEs need to borrow more from the external sources and they rely on diversified sources of finance to fund their investment projects. For these reasons, we conjecture that there is a positive relationship between growth opportunities and the diversification of financing sources. Another factor that could influence a SME s financing behaviour is access to finance or lack of it. SMEs are well known for experiencing credit constraints stemming from asymmetric information and market imperfections (Stiglitz and Weiss 1981; Hall et. al 2000; Carpenter and Petersen 2002). The extent of credit constraints is higher for smaller firms because of their `close nature and fewer disclosure requirements (Stiglitz and Weiss 1981; Petersen and Rajan 1994; Hall et al. 2000). Due to such market imperfections, Colla et al. (2013) argue that some firms may not have access to their desired financing sources or certain types of financing may be prohibitively expensive. Therefore, financial constraints stemming from market frictions may prevent some firms from reaching their desired optimal financing structure or end up relying on financing sources that are not optimal. In addition to the firm-specific characteristics discussed above, literature on firm s capital structure and financing behaviour also identify the importance of industry and countryspecific factors. Hall et al. (2004) suggest that, compared to large firms that have access to stock markets, the capital structures of SMEs will demonstrate greater inter-country variability. It is well established that there is a relationship between a firm s financial structure and the industry in which it operates (Holmes and Kent 1991; Jordan et al. 1998; Hall et al. 2000; Frank and Goyal 2009). For example, Holmes and Kent, (1991) argue that, compared to retail and service sectors, firms operating in the manufacturing sector are likely to have a different financial needs and structure due to the nature of the manufacturing industry that requires higher level of capital investment.

11 A country s financial and legal environments may also affect the availability of certain financing sources (La Porta et al. 1997; Demirgüç-Kunt and Maksimovic 1998; Bakker et al. 2004; Beck et al. 2008), and hence the extent of source diversity. For example, Demirgüç-Kunt and Maksimovic (1998) show that more firms are able to use long-term external financing in countries with a more developed legal system. Bakker et al. (2004) also confirms the role of legal environment and provide evidence that a country s legal and judicial environment is an important determinant of the mix of available financing products. The role of financial sector development on the availability and mix of financing sources is also confirmed by Beck et al. (2008) who show that firms rely on less external financing in countries with weak financial system. Based on the evidence provided in Section 3, the propositions of Colla et al. (2013) and theoretical and empirical literature on capital structure and financing behaviour of firms, we identify the firm-specific characteristics that influence SMEs financing behaviour. These characteristics are firm size, age, profitability, growth, financial transparency and access to finance. 5 We also include a dummy variable for SMEs, which have their annual financial statement checked and certified by an external auditor, as a proxy for transparency to reduce information asymmetries. These firm-specific variables combined with two country-specific institutional variables financial sector development measured by private credit to GDP and a country s legal and regulatory environment proxied by the first principle component of Rule of Law Index, Regulatory Quality Index and Government Effectiveness Index from WGI (2014) 6 are used for a formal analysis of the determinants of financing source diversification. Detailed definitions and sources of the variables are described in Table 5. [INSERT TABLE 5 ABOUT HERE] 5 Firm size is measured by the number of full time employees. We do not use total asset as a measure of firm size for two reasons. First, BEEPS survey provides only fixed assets which favour the manufacturing SMEs compared to the SMEs from service sector. Secondly, Bhaird and Lucey (2009) suggest that small firm owners are likely to use their personal assets as collateral to secure business loan. Therefore, firm s assets might not be an ideal measure of firm size to study the effect of size on SMEs financing behaviour. 6 The first component explains the 92.6% of the variations of the underlying variables.

12 Source diversification: Cluster analysis First we employ a cluster analysis, which can be viewed as an exploratory data analysis technique designed to discover unknown structure in data, to group SMEs with similar financing patterns. 7 This analysis allows us to identify whether firm-specific and country specific factors are different among these clusters or groups of SMEs. By using a stopping rule based on Calinski-Harabasaz pseudo F-statistic, we identify eight clusters of SMEs from our sample. Figure 1 exhibits the distribution of financing sources within each cluster. Among the eight clusters, two clusters include SMEs that use relatively diversified sources of finance, while the SMEs from the remaining six clusters specialise in a certain single source of finance for their investment (more than 70% of their investment). [INSERT FIGURE 1 ABOUT HERE] Table 6 presents the average proportions of financing sources, the degree of source diversification and firm-specific and country-specific characteristics across the eight clusters. We sorted the clusters in ascending order by the degree of source diversification. Cluster 1 and Cluster 2 include SMEs with most diversified sources of finance. In both clusters, about onehalf of their investments are financed by their retained earnings. However, SMEs from Cluster 1 are smaller but older and more financially constrained than those from Cluster 2 and they rely mostly on trade credits to finance their investment. In contrast, SMEs in Cluster 2, which are larger and operating in a relatively developed financial environment, mostly rely on bank finance and retained earnings for their investments. Cluster 3 includes the second least profitable SMEs and these SMEs predominantly use equity financing for their investments. SMEs in Cluster 4 are the most financially constrained compared to other clusters. They rely mainly on non-bank financial institutions to finance their investment. Cluster 5 includes the smallest and considerably the youngest SMEs and they operate under the least developed financial and legal environments. These SMEs use mainly informal sources to finance their investment. SMEs in Cluster 6 specialise on the use of bank financing for their investment, and they are the largest firm operating under the most developed financial and legal environment 7 The technique we employ in our cluster analysis is designed to discover unknown structure in data by minimising the Euclidean distance of an observation from the centroid of its own cluster and maximising the Euclidean distance of an observation from the centroid of other clusters.

13 in our sample. Cluster 7 includes the least growth SMEs in our sample and they rely mostly on trade credit to finance their investment. Finally, Cluster 8 includes over one-half of SMEs from our sample. These SMEs are the youngest and financially least constrained in our sample, and almost 98% of their investments are financed by retained earnings. [INSERT TABLE 6 ABOUT HERE] V. Diversification of SMEs financing sources: Empirical analysis Source diversification and determinants: bivariate analysis Table 7 presents the summary statistics, correlations between our measures of SMEs source diversification and different firm-specific characteristics and country-specific institutional environments. The correlation between HHI and each of the different firm-specific and country-specific variables are presented in Panel (A). The results suggest that firm size, firm age, firm s growth, financial constraint and having financial statements audited are positively correlated with the degree of financing source diversification. On the other hand, there is a significant negative correlation between the profitability of SMEs and HHI. In addition, financing source diversification is negatively correlated with the financial sector development of a country and positively correlated with the development of legal and regulatory environment. The results of the tests of differences in firm-specific and country-specific characteristics between the first quartile (least diversified) and the fourth quartile (most diversified) of HHI are reported in the last two columns. This bivariate analysis also shows that firm-specific and country-specific characteristics of the two groups of SMEs are significantly different. Panel (B) shows the similar analysis using our dichotomous measure of source specialisation and the results are almost identical to the results in Panel (A). Our findings indicate that the extent of financing source diversification differs according to the differences in firm-specific characteristics and country-specific institutional environments in which SMEs operate. [INSERT TABLE 7 ABOUT HERE]

14 Multivariate model and methods of estimation Our first measure of diversification, HHI, takes continuous fractional values ranged between 0 and 1. Most of the empirical analysis in the previous literature apply OLS or Tobit regression method to estimate a dependent variable with a fractional nature. However, recent literature on empirical econometrics suggest that such estimation methods are inadequate and inefficient as they fail to address two main characteristics of the fractional nature of the dependent variable. First, by definition our financing source diversification index (HHI) is bound to a closed interval of 0 and 1. Secondly, many SMEs rely on a single source of financing, i.e. the majority of the observations have the value of HHI equals zero. Therefore, we employ fractional logit regression model proposed by Papke and Wooldridge (1996) in order to deal with the fractional nature of our dependent variable, HHI. This estimation method guarantees all predicted values of HHI to lie in the unit interval and produces robust and relatively efficient results (Papke and Wooldridge 1996). In order to account for the role of industry in a firm s financing decision, we also include a set of dummy variables for 15 different industries. Diversification of SME financing sources (HHI) is estimated using the following fractional regression model: it E( HHI ) IND FSV i it CSV where is a logit function; IND s denotes set of dummy variables for the industry fixed effects; FSV is a vector of firm-specific variables; CSV represents country-specific macro and institutional variables. 8 We also use a Probit model to estimate our second measure of source diversification. it it Regression Results Table 8 presents the regression results of SMEs diversification of financing sources. The first 3 columns show the results of fractional regression using HHI as the measure of source diversification and Column (4) to (6) report the results of probit regression using Diversify, our dichotomous measure of source diversification, as the dependent variable. For interpretation purpose, we report only the marginal effects on SMEs source diversification. The first specification in column (1) and column (5) includes only firm-specific and country-specific 8 As a robustness check, we also estimate the model using Tobit regression method. The results presented in appendix D show similar results.

15 variables except the variable that represents the degree of financial constraints experienced by SMEs. We add the financial constraint variable in the model specification presented in column (2) and column (4). Finally, the model specification in Column (3) and Column (5) includes the squared term of financial constraint in order to test the possible non-linear relationship between the source diversification and financial constraint. [INSERT TABLE 8 ABOUT HERE] Joint significance of industry dummies confirms the significance of the relationship between a firm s financial structure and the industry in which it operates. The results show that the relationships between firm size and our measures of source diversification are positive and significant suggesting that larger firms use more diversified financing sources for investment. This finding is consistent with the results of Cassar (2004), Gregory et al. (2005) and Vos et al. (2007) which show that larger SMEs have better access to external funds and use more alternative financing sources because larger size reduces information asymmetries and represents better collateralisable assets. 9 Although the extent of the marginal effect of firm size looks small in the absolute term, the impact is economically significant. For example, holding other explanatory variables at their mean value, the largest firm in our sample is 7% more diversified compared to the smallest firm. Given the fact that the average diversification ratio is only 16%, an increase of 7% in the diversification ratio is very significant. 10 The relationship between firm age and diversification of financing sources is nonlinear. A possible explanation is that older firms have better access to external funds due to reduction information asymmetries (Gregory et al. 2005). However, as Vos et al. (2007) suggest, when SMEs become very old, the owners level of loss aversion is more likely to be higher and this will lead to a reduction in the use of alternative financing sources. In particular, the extent of source diversification starts to decline for SMEs older than 18 years, after controlling for other variables. Our results also indicate that there is a negative relationship between SMEs profitability and financing source diversification. This finding is in line with the proposition of 9 We also estimate a similar model specification using categorical variables as measures of firm size. The results, presented in appendix E, which use medium size as the reference category, show almost identical results confirming that smaller SMEs are less diversified compared to their larger counterparts. 10 Marginal Effect of Size at Means (MEMs) increase from 13.69% to 20.86%.

16 Pecking Order Theory which suggests that profitable firms with sufficient internal cash flows are less likely to seek alternative funds to finance their investment (Myers and Majluf 1984) and the empirical results of Hall et al. (2000), Carpenter and Petersen (2002) and Degryse et al. (2012) which show that more profitable SMEs rely on less diversified sources as they can use their profits as retained earnings to fund investments, and hence able to reduce their external borrowing. 11 We find a positive relationship between growth in sales revenue and diversification of financing sources. This is consistent with the notion of Lewis (2009) who proposes that firms with significant growth opportunities are expected to have relatively small losses in bankruptcy, thus use more external source and have a diversified sources of financing. Cassar and Holmes (2003) and Degryse et al. (2012) also find that growing SMEs borrow more from external sources and rely on diversified sources of finance because they need more funds to finance their projects. The results also show that there are positive and significant associations between our measures of financial transparency and source diversification suggesting the role of information asymmetries in a firm s access to external finance. The results in Column (2) and (5) indicate that SMEs with financial constraints are more likely to use diversified financing sources to fund their investments. These findings suggest that, in general, more financially constrained SMEs rely on more diversified sources. In a related literature, Beck et al. (2008) also provide evidence that firms experiencing severe financial constraints are more likely to use diversified sources of external finance. However, after controlling for other firm-specific characteristics and country-specific variables, the results in Column (3) and (6) indicate that the relationship between financing constraint and source diversification is non-linear such that SMEs experiencing moderate financial constraints use the most diversified sources of financing. Holding other explanatory variables at their mean values, the average diversification ration of financially unconstrained SME is 11.56%. For a SME experiencing a moderate level of financial constraints, the diversification ratio increases to 18.55%. However, the diversification ratio of a SME experiencing a severe financial constraint decreases back to 16.13%. Still, this diversification ratio is much higher than that of a financially unconstraint SME. Our cluster analysis reported in Table 6 also suggest that SMEs experiencing a greater degree of financial constraints (Cluster 1, 4 and 5) rely on more 11 The effect of our profitability, measured as ((Total Sales Total cost)/total Fixed Assets) needs to be interpreted with caution because the financial values we used to measure profitability are not taken from financial statements. They are based on managers response to survey questions.

17 diversified sources, but they use less retained earnings and bank finance and rely more on nonbank finance, trade credits and informal sources to fund their investment. Based on our regression results and cluster analysis, a possible explanation is that SMEs diversify the sources of finance to fund their investment because of a lack of access to their preferred sources retained earnings and bank finance. On the other hand, SMEs experiencing severe degree financial constraints have to rely on less variety of sources because certain types of financing may be prohibitively expensive although they use more diversified source of finance compared to financially unconstrained firms. Furthermore, there is a significant and negative association between our measure of the development of a country s financial environment, the ratio of private credit to GDP, and financing source diversification. A potential explanation is that SMEs prefer to specialise their financing sources. Therefore, when the financial system of a country provides more credits, SMEs tend to use more bank finance to fund their investment (as indicated by SMEs in Cluster 2 and 6). Beck et al. (2008) also show that higher levels of financial sector development, as measured by the ratio private credit to GDP, has a significant positive impact on SMEs usage of development bank finance. Holding other explanatory variables at their mean values, our results indicate that SMEs from South Africa, which has the highest average ratio of private credit to GDP, are 6.6% less diversified compared to their counterparts from financially least developed country, Guinea-Bissau. In contrast, our measure of a country s legal and regulatory environment has a significant positive impact on source diversification. SMEs from Chile, which has the most developed legal and regulatory environment among our sample countries, are 7.8% more diversified compared to otherwise similar SMEs from Democratic Republic of Congo where the index of legal and regulatory environment is the lowest. This finding supports the proposition that better legal and regulatory environment reduces the transaction costs such as monitoring and enforcement costs of using external financing sources. Hence, SMEs from a country with a more developed legal environment are able to use more diversified sources of financing to fund their investment. VI. Conclusion This paper provides the first large sample evidence on SMEs financing patterns and the firmspecific and country-specific determinants of the diversification of financing sources of SMEs in 92 developing and emerging economies. We find that the extent of financing source

18 diversification differs according to the differences in firm-specific characteristics and countryspecific institutional environments in which SMEs operate. SMEs specialise their source of financing in particular, retained earnings for their investment. However, SMEs experiencing financial constraints rely on diversified financing sources to fund their investment. We also provide evidence of a quadratic relationship between financing constraint and source diversification suggesting that SMEs experiencing a moderate level of financial constraints use the most diversified sources of financing. This particular finding, combined with the evidence that older and matured SMEs rely on less diversified sources of finance, suggest that diversification of financing sources among SMEs could be a consequence of desperation rather than a strategic move. The findings of our paper have two important implications. First of all, there is no onesize-fits-all solution to support SMEs financing requirements. SMEs financing needs and behaviour vary according to firm-specific characteristics and their evolution process. Secondly, development of a country s financial sector and legal and regulatory environment has significant impacts on SMEs financing decisions. These findings suggest a new direction of future research to explore the role of dynamic interaction between firm-specific characteristics and country-specific financial and institutional environments in which SMEs operate on SMEs financing behaviour.

19 References Ayyagari, M., Beck, T., & Demirguc-Kunt, A. (2007). Small and medium enterprises across the globe. Small Business Economics, 29(4), Ayyagari, M., Demirgüç-Kunt, A., & Maksimovic, V. (2011). Small vs. young firms across the world: Contribution to employment, job creation, and growth (World Bank Policy Research Working Paper No. No. 5631). The World Bank. Ayyagari, M., Demirgüç-Kunt, A., & Maksimovic, V. (2008). How important are financing constraints? The role of finance in the business environment. World Bank Economic Review, 22(3), Bakker, M., Klapper, L.F., & Udell, G.F. (2004). Financing small and medium-size enterprises with factoring: Global growth and its potential in Eastern Europe, Policy Research Working Papers. The World Bank. Bartoli, F., Ferri, G., Murro, P., & Rotondi, Z. (2013). SME financing and the choice of lending technology in Italy: Complementarity or substitutability? Journal of Banking and Finance, 37(12), Beck, T., & Demirguc-Kunt, A. (2006). Small and medium-size enterprises: Access to finance as a growth constraint. Journal of Banking and Finance, 30(11), Beck, T., Demirguc-Kunt, A., & Levine, R. (2005). SMEs, growth, and poverty: Cross-country evidence. Journal of Economic Growth, 10(3), Beck, T., Demirgüç-Kunt, A., & Maksimovic, V. (2008). Financing patterns around the world: Are small firms different? Journal of Financial Economics, 89(3), Bell, K., & Vos, E. (2009). SME capital structure: The dominance of demand factors, in: 22nd Australasian Finance and Banking Conference Social Science Research Network. Berger, A.N., & Udell, G.F. (1998). The economics of small business finance: The roles of private equity and debt markets in the financial growth cycle. Journal of Banking and Finance, 22(6-8), Bhaird, C., & mac an, Lucey, B. (2009). Determinants of capital structure in Irish SMEs. Small Business Economics, 35(3), Carpenter, R.E., & Petersen, B.C. (2002). Is the growth of small firms constrained by internal finance? Review of Economics and Statistics, 84(2), Cassar, G. (2004). The financing of business start-ups. Journal of Business Venturing, 19(2), Cassar, G., & Holmes, S. (2003). Capital structure and financing of SMEs: Australian evidence. Accounting and Finance, 43(2), Colla, P., Ippolito, F., & Li, K. (2013). Debt specialization. Journal of Finance, 68(5), Degryse, H., Goeij, P. de, & Kappert, P. (2012). The impact of firm and industry characteristics on small firms capital structure. Small Business Economics, 38(4), Demirgüç-Kunt, A., & Maksimovic, V. (1998). Law, finance, and firm growth. Journal of Finance, 53(6), Frank, M.Z., & Goyal, V.K. (2009). Capital structure decisions: Which factors are reliably important? Financial Management, 38(1), Gregory, B.T., Rutherford, M.W., Oswald, S., & Gardiner, L. (2005). An empirical investigation of the growth cycle theory of small firm financing. Journal of Small Business Management, 43(4), Hallberg, K., (2000). A market-oriented strategy for small and medium scale enterprises (No. IFD40). The World Bank.

20 Hall, G., Hutchinson, P., & Michaelas, N. (2000). Industry effects on the determinants of unquoted SMEs capital structure. International Journal of the Economics of Business, 7(3), Holmes, S., & Kent, P. (1991). An empirical analysis of the financial structure of small and large Australian manufacturing enterprises. Journal of Entrepreneurial Finance, 1(2), Jordan, J., Lowe, J., & Taylor, P. (1998). Strategy and financial policy in UK small firms. Journal of Business Finance and Accounting, 25(1-2), Krasniqi, B.A. (2010). Are small firms really credit constrained? Empirical evidence from Kosova. International Entrepreneurship and Management Journal, 6(4), La Porta, R., Lopez-De-Silanes, F., Shleifer, A., & Vishny, R.W. (1997). Legal determinants of external finance. Journal of Finance, 52(3), Lewis, C., Firm-specific estimates of the ex-ante bankruptcy discount. Available at SSRN: Modigliani, F., & Miller, M.H. (1958). The cost of capital, corporation finance and the theory of investment. American Economic Review, 48(3), Myers, S.C. (1984). The capital structure puzzle. Journal of Finance, 39(3), Myers, S.C., & Majluf, N.S. (1984). Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics, 13(2), Papke, L.E., & Wooldridge, J.M. (1996). Econometric methods for fractional response variables with an application to 401(k) plan participation rates. Journal of Applied Econometrics, 11(6), Petersen, M.A., & Rajan, R.G. (1994). The benefits of lending relationships: Evidence from small business data. Journal of Finance, 49(1), Pettit, R.R., & Singer, R.F. (1985). Small business finance: A research agenda. Financial Management (1972) 14(3), Psillaki, M., & Daskalakis, N. (2009). Are the determinants of capital structure country or firm specific? Small Business Economics, 33(3), Rauh, J.D., & Sufi, A. (2010). Capital structure and debt structure. Review of Financial Studies, 23(2), Sogorb-Mira, F. (2005). How SME uniqueness affects capital structure: Evidence from a Spanish data panel. Small Business Economics, 25(5), Stiglitz, J.E., & Weiss, A. (1981). Credit rationing in markets with imperfect information. American Economic Review, 71(3), Thornhill, S., & Amit, R. (2003). Learning about failure: Bankruptcy, firm age, and the resource-based view. Organization Science, 14(5), Titman, S., & Wessels, R. (1988). The determinants of capital structure choice. Journal of Finance, 43(1), Vos, E., Yeh, A.J.-Y., Carter, S., & Tagg, S. (2007). The happy story of small business financing. Journal of Banking and Finance, 31(9), Wald, J.K. (1999). How firm characteristics affect capital structure: An international comparison. Journal of Financial Research, 22(2), 161.

21 Table 1 Summary of firm size and financial constraints SMEs Degree of financial Large firms (>300) constraint Micro Small Medium Total (%) No. (%) (1~10) (11~50) (51~300) No constraint 401 1,165 1,251 2,817 (27.5%) 1,969 (38.0%) Minor ,031 (19.7%) 1,186 (22.9%) Moderate 424 1, ,496 (24.4%) 1,158 (22.3%) Major ,730 (16.9%) 604 (11.6%) Severe ,177 (11.5%) 269 (5.2%) Total 1,856 4,485 3,910 10,251 5,186 Source: BEEPS, compiled by the author Table 2 Summary statistics of SME financing patterns (Aggregate) Financing categories Mean (%) Sample percentage S=1 0<S<1 S=0 Retained earnings Bank finance Non-Bank finance Trade credits Equity finance Informal finance Source: BEEPS, compiled by the author

22 Table 3 Conditional sources of financing: by SMEs with different degree of financial constraints Condition Retained earnings Bank finance Nonbank finance Trade credit Equity financing Informal financing Panel (A) No Financial constraints Retained earnings>30% (1.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Bank finance>30% (0.1000) (0.8000) (0.0000) (0.0000) (0.0000) (0.0000) Non-bank finance>30% (0.1000) (0.0000) (0.5000) (0.0000) (0.0000) (0.0000) Trade credit >30% (0.0000) (0.0000) (0.0000) (0.8000) (0.0000) (0.0000) Equity financing >30% (0.0700) (0.0000) (0.0000) (0.0000) (0.6000) (0.0000) Informal financing >30% (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.8000) Panel (B) Financial constraints Retained earnings>30% (1.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Bank finance>30% (0.1000) (0.8000) (0.0000) (0.0000) (0.0000) (0.0000) Non-bank finance>30% (0.0000) (0.0000) (0.7000) (0.0000) (0.0000) (0.0000) Trade credit >30% (0.0400) (0.0000) (0.0000) (0.6000) (0.0000) (0.0000) Equity financing >30% (0.2000) (0.0000) (0.0000) (0.0000) (0.5000) (0.0000) Informal financing >30% (0.1000) (0.0000) (0.0000) (0.0000) (0.0000) (0.6000) Note: The values in parentheses are median.

23 Table 4 Source diversification: By size and degree of financial constraint of SMEs #Obs Percent HHI Diversify Panel A: Source of financing and firm size Micro (1~10) 1, Small (11~50) 4, Medium (51~300) 3, F-stat (ANOVA) (P-value) (0.000) (0.000) (0.000) (0.000) 3.43 (0.032) 2.27 (0.103) 2.18 (0.112) (0.000) Panel B: Source of financing and financial constraints No constraint 2, Minor constraint 2, Moderate 2, Major 1, Very severe 1, F-stat (ANOVA) (P-value) (0.000) (0.000) Retained earnings (0.000) Bank finance (0.000) Non-bank finance 8.35 (0.000) Trade credit (0.000) Equity financing 1.33 (0.255) Informal financing 7.65 (0.000)

24 Table 5 Definitions and the sources of variables Dependent variables HHI Normalized Herfindahl-Hirschman Index for diversification of financing sources Ranged between 0 and 1 where HHI=0: use a single source; HHI=1: Use equal proportion from 6 sources Diversify Dummy variable that takes the value of 1 if a SME uses at least two sources to finance its investment, 0 if it uses a single source. Sources of financing Retaining earnings Proportion of SME s investment on fixed assets financed from retaining earnings Bank finance Proportion of SME s investment on fixed assets financed by loan from commercial banks Non-Bank finance Proportion of SME s investment on fixed assets financed by loan from non-bank financial institutions Trade credit Proportion of SME s investment on fixed assets financed from supplier credit arrangements Equity finance Proportion of SME s investment on fixed assets Informal finance Firm-specific variables Firm size financed by issuing new equities Proportion of SME s investment on fixed assets financed by borrowings from moneylenders, friends, relatives, etc Source Author s calculation Author s calculation BEEPS BEEPS BEEPS BEEPS BEEPS BEEPS Natural log of the number of full-time BEEPS employees Firm age Natural log of the firm age BEEPS Profitability (Total sales total costs)/total fixed assets BEEPS (winsorised at 5%) Growth Annual growth rate of sales revenue BEEPS (Winsorised at 5%) Statement checked Dummy variable that takes on the value one if the firm prepares a financial statement and audited by an independent body. BEEPS Financial constraint Access to financing is no Obstacle (0), a minor obstacle (1), a moderate obstacle (2), a major obstacle (3), or a very severe obstacle (4) to the current operations of the firm. Country-specific variables Private credit 5-year average of Domestic credit to private sector (% of GDP) Legal & Regulatory environment 5-year average of the first principle component of Rule of Law Index, Regulatory Quality Index and Government Effectiveness Index from WGI (2014). BEEPS WDI (2014) WGI (2014)

25 Table 6 Cluster Analysis Sources of financing Firm characteristics Macroenvironment Cluster Retained earnings Bank finance Non-bank finance Trade credit Equity financing Informal financing HHI Diversify Total Labour Age Profit. Growth Finan const. Statement checked Private credit Legal & Regulatory #Obs.

26 Table 7 Source diversification and determinants: bivariate analysis Panel (A) HHI (1 st Quartile) HHI (4 th Quartile) Test of Differences Correlation Mean Median Mean Median t-test Wilcoxon test Ln(Labour) *** *** *** Ln(Firm Age) *** *** *** Profitability *** *** 2.526** Growth *** ** *** Statement Ch *** *** *** Finance const *** *** *** Private credit *** ** Legal & Reg: *** ** *** Panel (B) Diversify=0 Diversify=1 Test of Differences Correlation Mean Median Mean Median t-test Wilcoxon test Ln(Labour) *** *** *** Ln(Firm Age) *** *** *** Profitability *** *** 2.042** Growth *** *** *** Statement Ch *** *** *** Finance const *** *** *** Private credit *** ** * Legal & Reg: *** ** ***

27 Table 8 Marginal effects on diversification of financing sources (1) (2) (3) (4) (5) (6) VARIABLES HHI HHI HHI Diversify Diversify Diversify Ln(Labour) *** *** *** *** *** *** (0.0035) (0.0034) (0.0034) (0.0068) (0.0066) (0.0065) Ln(Firm age) * * * * * * (0.0217) (0.0219) (0.0217) (0.0393) (0.0403) (0.0403) Firm age square * * * * * * (0.0038) (0.0038) (0.0038) (0.0069) (0.0071) (0.0071) Profitability *** *** *** *** *** *** (0.0006) (0.0006) (0.0006) (0.0011) (0.0011) (0.0011) Growth *** *** *** *** *** *** (0.0049) (0.0049) (0.0048) (0.0094) (0.0093) (0.0091) Statement checked *** *** *** *** *** *** (0.0083) (0.0082) (0.0083) (0.0160) (0.0160) (0.0164) Financial constraints *** *** *** *** (0.0029) (0.0076) (0.0053) (0.0151) Financial constraints square *** *** (0.0019) (0.0039) Private credit ** ** * ** * * (0.0250) (0.0244) (0.0256) (0.0482) (0.0478) (0.0505) Legal & Regulatory ** ** ** ** ** ** (0.0035) (0.0036) (0.0038) (0.0072) (0.0074) (0.0079) Industry dummies (χ 2, 14) *** *** 63.28*** *** *** *** Observations 10,251 10,251 10,251 10,251 10,251 10,251 All regressions are run with industry fixed-effects and error terms clustered at the country level. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

28 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Cluster 8 Figure 1 The source of financing within a cluster