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Firm level determinants to small and medium sized enterprises’ access to financing in indonesia

Firm Level Determinants to Small and Medium-Sized Enterprises’ Access to Financing in Indonesia by Rita Pidani and Ishak Balaka. Academy of Taiwan Business Management Review, April 2013, Volume 9, Number 1, pp. 117-126.

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Firm level determinants to small and medium sized enterprises’ access to financing in indonesia

  1. 1. 1 Firm Level Determinants to Small and Medium-Sized Enterprises’ Access to Financing in Indonesia Rita R. Pidani Newcastle Business School, Faculty of Business and Law The University of Newcastle, 1 University Drive, Callaghan NSW 2308, Australia Corresponding author’s email: Ishak Balaka Faculty of Economics Haluoleo University, Sulawesi Tenggara, Indonesia ABSTRACT This paper empirically investigates the extent to which firm-level characteristics of small and medium sized enterprises (SMEs) impact on the probability of their accessing external finance in Indonesia. A conceptual framework linking firm-level characteristics and access to external finance is developed and tested for statistical magnitude by utilising binomial logit regression. Results suggest significant differences exist between SMEs with and without access to finance in relation to their organisational size and legal status. The findings indicate that organisational size and legal status have significant effects on the probability of SMEs accessing external finance. The implication of the findings is that Indonesian public and private agencies should pursue policies aimed at alleviating dissemination bottlenecks by improving SMEs’ financial and institutional capabilities, and building strong networks to promote awareness of banking products and requirements. INTRODUCTION Small and Medium Enterprise (SME) development is recognised as a key avenue to economic growth, innovation, and market competition in most developed economies (Abor and Quartey, 2010; OECD, 2012). In developing countries, the role of SMEs is of no less importance. SMEs, in this context, are not only important for the role they play in stimulating job creation, they are also crucial in promoting diversification and thus, improving distribution of income within an economy (Tambunan 2011; Pidani et al., 2012). Many developing countries today continue to endure considerably high unemployment rates even after decades of opening up their economies and engaging with the free market system. In this respect, it is often claimed that meeting SMEs’ needs and creating a healthy business environment are vital for they increase the chances of job growth and wealth creation which then translates into a vibrant economy (Pidani et al., 2012). Considering the variations that exist between countries, the characteristics of SMEs in developing countries are not completely different from those in developed market economies. The main differences rest on the higher proportion of those establishments but lower contribution to GDP and export compared to those of developed countries (Dalberg, 2011). This is certainly true for Indonesia; although SMEs contribution to employment was three times larger than that of large enterprises (hereafter LEs) in 2010 (MOCSME, 2012), their contribution to the total GDP and total export was far smaller than that of LEs (25 per cent and 15.5 per cent compared to 75 and 80 per cent respectively) in the same year. Narrowing the gap between SMEs’ and LEs’ contributions to GDP and export shares is, therefore, important for the Indonesian government so as to help increase SMEs’ performance in both domestic and global markets. Various schemes and initiatives have been introduced to help their job growth and to overcome some of the challenges within these enterprises. Previous studies of SMEs in developing economies have identified a multitude of barriers affecting their business operations. One of the most important barriers faced by SMEs in Indonesia is lack of access to financial sources to develop their businesses (Thee, 2006, Tambunan, 2011). Discrimination of businesses based on their sizes by commercial banks has created obstacles for SMEs’ owners to obtain external sources of finance which are essential to reduce the impact of cash flow problems in their operation (see for example: Subandi, 2007; Rafianldy, 2004; and Deputi Bidang Pengkajian, 2006). In Indonesia, the government policy’s priority on large enterprises in the past and micro firms afterwards has led to a scarcity of ready lending sources for SMEs and exacerbates the access challenges faced by this ‘missing middle’ (Hayashi, 2002). Following the 1997 Asian financial crisis which saw LEs hit hard and SMEs resilient to the shocks, there has been on-going government pressure on commercial banks to extend their credit to smaller firms. In response to a high default rate and margin losses by the large corporate borrowers, banks are bound to cater for the stronger “medium’ and those even further down the ‘size’ spectrum in order to expand their revenue. A recent study by Struyk and
  2. 2. 2 Haddaway (2011) indicated that about half the commercial bank loans were allocated to micro, and small medium firms in Indonesia in the last quarter of 2008. The bottlenecks between supply and demand of banking and financial services, however, are consistently reported as a problem occurring primarily due to the inability of the system to provide finance in appropriate forms and limited entrepreneurs’ knowledge of financial alternatives (Thompson, 2011). The rather sluggish development of the non-bank financial institution (NBFI) sector in Indonesia, as compared to other East Asian countries, has limited the SMEs’ scope of access to relying merely on commercial banks as the traditional vantage point to obtain credit (Berry et al., 2001). SMEs that intend to access financing often do not have effective knowledge of other types of financial institutions, the various products and services available, or credit policies and procedures (ITC, 2012). Some entrepreneurs with conservative personal values tend to avoid the high cost of searching for information and other financing costs and hence, rely on internal-financing options (contributions from owners, family and friends and profit reinvestment) as alternative sources of funding. These internal-financing options, however, are often insufficient for SMEs to achieve sustained growth. Prior studies support the view that firm characteristics are equally important factors that can shape the capital structure and performance of SMEs (see for example Majed, Alsharayri and Dandan, 2010; Sorooshian, Norzima, Yusuf and Rosnah, 2010). A review of these empirical studies suggests that combination and interaction between internal firm characteristics such as the size of the firm; the age of the firm; and the location of the firm, play a complementary role in influencing firm access to finance and can lead to the direction of change of firm capital structure formation and performance over time. Gaining sufficient access to external finance, therefore, represents a critical success factor of a firm (Le et al., 2006), but the extent of the benefit of acquiring such a source depends on the interplay of organisational factors that determine its outcome. In view of these results, this paper adds to the existing empirical literature by examining firm physiognomy of Indonesian SMEs, so as to understand the difficulties and weaknesses in their endeavour to achieve a sustained degree of capitalisation and to equip corporate and public policy makers with strategic information in formulating and implementing effective support systems. This study contributes to the literature by identifying the influence of firm characteristics on access to finance in the Indonesian context where such determinants have not been widely investigated. It also examines a wide range of variables of firm characteristics that have not been incorporated by previous studies as determinants of financial access in the Indonesian context. Finally, this study is pertinent for there is a need to support Indonesian government initiatives, particularly after the Asian financial crisis, to enhance SMEs’ opportunities for growth and development. The rest of the paper is divided into three sections. The next section provides a review of the literature on prior research as well as a statement of the hypotheses tested in this study. Following this, we describe the methodology employed in the analyses and report on the empirical analysis of the impact of entrepreneurial and firm characteristics of Indonesian manufacturing SMEs on their access to external finance. Finally, we discuss the implications of the findings for policy formulation and implementation. ACCESS TO FINANCE AND FIRM CHARACTERISTICS: A DISCUSSION OF THEORY AND THE EMPIRICAL MODEL Capital structure is defined as the mix of debt and equity employed by a company to finance its operation (Brealey et al., 2008). The traditional approach to valuation and leverage suggests that through a reasonable use of these debt-equity proportions, a firm can increase its total value and thereby reduce the overall cost of its capital. Hence, a company with a given set of assets would have an optimal capital structure where the weighted average cost of capital is at a minimum and the total market value at a maximum. Since debt finance is tax deductible, the tax saving benefit makes it cheaper than equity finance and it can shift a firm’s overall cost of capital downwards. Modigliani-Miller’s theorem (Modigliani and Miller, 1958), in contrast, stated that the total market value of a company’s securities and the weighted average cost of capital are independent of the firm’s capital structure decision in a perfect market context. The proposition used by Modigliani-Miller is that the total value of investments in a company depends on profitability and risk and not on the mix of debt and equity funds. So, where a company finances its expansion by cheaper debt funds, the weighted average cost of capital will remain the same because the net benefit will be offset by the increase in the required return on the shares. This concept, however, has been found irrelevant in explaining the phenomenon in a real world context due to the exercise of taxes, agency costs, information asymmetry and the costs of financial distress that might influence capital structure decisions (Peirson et al., 2006). Studies of financial market imperfections by Jensen and Meckling (1976) resulted in the agency cost concept which explains the conflicts that arise between shareholders, managers and creditors collectively known as principal-agent problems. Shareholders and managers’ conflicts stem from the failure of managers to meet the shareholders’ value maximization goal due to divergent interests and information owned by both parties. Since the concept assumes the separation of ownership and control, shareholders incur costs to keep managers in
  3. 3. 3 check and ensure that they keep on acting in the best interest and for the prosperity of the firm. Shareholders and lenders’ conflicts arise when shareholders (through managers) take actions to maximize stock price by investing in projects with greater risk than creditors anticipated or by raising the debt level higher than was expected. These actions are detrimental to the creditors as such actions will raise the cost of debt and ultimately lower stock price in the long run. These conflicts, however, will never have an affect if shareholders and creditors share the same information in a timely manner (Kira and He, 2012). The presence of asymmetric information in the agency cost concept can lead to moral hazards and according to Stiglitz and Weiss (1981), can affect a firm’s access to external finance and thereby its capital structure. Based on the pecking order theory, firms that are excluded from financial systems due to information asymmetries and transaction costs would rely on their personal wealth and internal resources before opting to use debt and equity (Myers and Majluf, 1984; Seghers, Manigart and Vanacker, 2012) to achieve growth. In a context of SMEs in developing country, the presence of asymmetric information and adverse selection is mainly due to the lack of credit histories and connections that hinder competent SMEs from accessing external finance and, thus, restrict their development (Beck and Demirguc-Kunt, 2006). Additionally, since equity finance is rarely available in this context of finance, SMEs are dependent solely on debt finance as the only available alternative to maximize their firms’ values externally. Prior research indicates that small firms’ financial needs and alternatives change as they expand, become more experienced and knowledgeable about the formal credit channels (Berger and Udell, 2005). Based on this concept, it can also be assumed that a small firm’s external borrowing tends to increase with the level of internal resource characteristics of that firm. Extant literature, indeed, indicates that such characteristics as firm age, firm size, and business information have a statistically significant impact on smaller firms’ development and performance (Chandler, 2009; Hongyan, 2009; Bougheas et al, 2005; Kitindi et al, 2007; Cassar, 2004; Barbosa and Moraes, 2012). The conclusion that emanates from these studies is that a firm with a developed level of resources and business experience would find it easy to grow laterally and would be keen to invest for business expansion. From an organizational standpoint, firm age can explain the extent of accumulated knowledge from which a firm can build its capabilities and provide it with better leverage to weather turbulent economic conditions (Chandler, 2009). The positive relationship between age of a firm and access to finance has been identified by Fatoki and Asah (2011) and Kira and He (2012). These studies support the view that the age of a firm is an accumulation of knowledge and experience, and therefore established firms are more likely to cope with information asymmetry and transaction cost barriers better than their younger counterparts. Lack of credit history and networks, and cost concerns of accessing external finance make young firms dependent on internal sources to finance their business (Klapper et al., 2010) and less likely to expand their business operation. Based on the above discussion, it can be hypothesized that firm age has a positive effect on firm access to finance (H1). Firm size is one of the organizational variables most frequently referred to in explaining firms’ financial performance. Size, as usually indicated by the number of full time employees, annual sales volume, and the value of the assets, is expected to have an important influence on the debt ratio. The business cycle process, which often requires an increasing demand in resources, has been strongly correlated with size. It is assumed that the larger the real assets a firm has, the greater the firm’s ability to expand resources by acquiring long term debt (Burkart and Ellingsen, 2004). Such firms may also have diversified production portfolios and therefore are more likely to be stable in absorbing risks due to their higher substitutability for insolvency (Honyan, 2009) compared to smaller firms. As a result of the above discussion, this study hypothesizes that firm size has a positive effect on firm access to finance (H2). A firm’s location in the context of this study is defined as the place where the office or manufacturing plants are located, which can influence the firm’s access to finance. Based on Berger and Udell (2005), it is assumed that the geographic proximity of firms to city centres facilitates the firm’s access to finance due to the ease of showcasing their customer’s credit quality to banks and other creditors. Given that the degree of economic and infrastructure development in Indonesia is not evenly distributed, it is most likely that the location of Indonesian firms also has an influence on their access to finance. In the context of Indonesia, significant geographic variations and wide discrepancies in infrastructure and economic development have led to a stark contrast between urban and rural areas. Firms in the urban areas are presumed to benefit more from the advantage of proximity to centres of information, economical transportation, large pools of skilled labour and capital, and access to credit markets that contribute to high product quality as compared to those firms located in rural areas. Therefore, manufacturing SMEs in urban areas should be able to sell their products and gain success more easily than firms located in rural areas (Zhao and Zou, 2002; Fernhaber et al., 2008; Fatoki and Asah, 2011 Kira and He, 2012). These in turn, develop a positive feedback and improve their credit access from creditors. Based on the above discussion, it can be hypothesized that a firm’s location has a positive effect on firm access to finance (H3).
  4. 4. 4 Industry type in this study is based on the sampling sector classification used by the World Bank for survey purposes in Indonesia. The relationship between industry membership and capital structure has received considerable attention in the finance field. Myers (1984) in his study, for example, noted that industry type influenced firm capital structure indirectly through the nature and the composition of the assets financed by the firm. Barbosa and Moraes (2012) explained that the association between industry membership and firm capital structure is built on the assumption that industry type is a proxy measure of business risk (Barbosa and Moraes, 2012). Hence, firms competing in the same business sector and relatively identical settings are more likely to bear the same probability of risk in regards to their earnings and sales. The findings by Harris and Raviv (1991) and recently Viviani (2008), indeed, indicated that specific industries have a common leverage ratio which, over time, is relatively stable. The leverage level adopted by individual firms within each industry tends to bounce back to the industry average over the same period. Abor (2007) suggests that firms competing in the agricultural sector require raw materials, labour and equipment, but have low cost of liquidation. As a result, these firms are more likely to be financed with debt. While firms in the wholesale and retail industry demonstrated the weakest asset structure and debt ratio. Thus, it is logical to hypothesize that that industry type has a positive effect on firm access to finance (H4). The review of literature shows that there has been a number of research studies on the relationship between the legal status of a firm and its access to finance. In this study, legal status refers to the form of business ownership which is assumed to have some bearing on the type of financial structure adopted by the firm (Abor, 2008). Some studies have found that firms with limited liability (incorporated) have more development attributes than those firms with unlimited liability (Dietmar et al., 1998). A number of advantages, such as high-level commitment of managers to the firm’s goals due to the separation of ownership and administration as well as publication of business information that indicates future prospects and ability to service a loan (Fatoki and Asah, 2011), have made incorporation possess stronger ability to access finance than limited liabilities. Another study by Abor (2008) also indicated that incorporated firms prefer to use equity finance rather than debt finance due to the reasonable amount of their assets against losses generated by the corporation. Sole proprietorship and partnership, in contrast, tend to rely on debt finance due to their asset limitations to answer against losses. A recent study by Kira and He (2012) has also supported the positive relationship between financial leverage and incorporation and consequently it is logical to hypothesize that: firm legal status has a positive effect on access to finance (H5). DATA, METHOD AND VARIABLES Our analysis is based on the enterprise survey that was administered by the World Bank in Indonesia in 2009. After separating small and medium sized enterprises from large ones, the sample size was reduced from 1,444 to 1,114 respondents. Sampling criteria were consistent with the Indonesian government definition of small and medium-sized enterprises, that is, a firm with under Rp10 billion turn-over and full time employees between 5 to 99 full-time workers (Bank Indonesia and Indonesian Statistics in Pidani, 2010). The sample population is classified into three industries, namely, manufacturing, services, and other products. The survey provides information that helps to identify the firm level determinants of SMEs access to finance. Based on respondents responses to the survey instrument, firms were classified into two main groups regarding the dependent variable: (i) those which had used checking or savings accounts; overdraft facility; line of credit or a loan from a financial institution. The series of questions relating to these were deemed mutually inclusive, so affirmative responses to one or many of the statements were treated as a single affirmative response; (ii) firms which had not used and were currently considering using these financial services. Of the 1,114 respondents, 78.8 per cent (n = 436) indicated that they had not used such financial services but were currently considering to do so. Consistent with previous research (Pidani, 2010; Tambunan, 2011), the number of Indonesian firms which currently did not have access to financial services was substantially very high. In order to analyse the effect of a wide range of firm characteristics against access to the finance indicator, we used logit models. Binary logit models were used to test the way in which financial services use varies by firm age, firm size, firm location, industry membership and firm legal status. The logit model takes the form of: Yi = β1 + β2Zi + … + βkZk + ui where Yi = 1 =, if Yi * > 1 or Yi = 0 =, if Yi * < 1 where the Z’s represent the firm level characteristics given in Table 1 and Yi represents the probability of a firm using financial services. Univariate significance tests between group means (Chi-square or Mann-Whitney U tests) were conducted on the nominal and ordinal variables respectively. Table I provides a full description of the independent variables employed in the empirical analysis.
  5. 5. 5 Table 1: Variable definitions Variable Definitions Measurement ACCESS Access to financial services Binary: 1 = Yes; 0 = No AGE Age of a firm Continuous SIZE Size of a firm Nominal: 1 = small enterprises; 2 = medium enterprises LOCATION Location of a firm Nominal: 1 = capital city; 2 = city with population over 1 million – other than capital; 3 = over 250,000 to 1 million; 4 = 50,000 to 250,000; 5 = less than 50,000 INDUSTRY Membership of industry Nominal: 1 = manufacturing; 2 = services; 3 other LEGAL Legal status of a firm Nominal: 1 = shareholding company with shares traded in the stock market; 2 = shareholding company with non-traded shares or shares traded privately; 3 = sole proprietorship; 4 = partnership; 5 = limited partnership Source: Derived from survey data. EMPIRICAL RESULTS Table 2 provides summary statistics on the independent variables across the two sub-samples of financial access. Table 2 shows that both groups with and without access to financial services are in a relatively young age group. About 74.4 per cent of respondent firms are aged less than 20 years old. Some 2.6 per cent of respondent firms with access to financial services are aged over 40. The results from the standard t-test indicate the same pattern of an insignificant difference between SMEs with access and without access to finance in the means of their firm age, at 5 per cent significance level (t-value = 1.094 and p-value = 0.274). A standard t-test shows that the assumptions of homogeneity of variance are supported by Levene’s test (F-value = 0.030, p-value = 0.862). The two-tailed significance for firm age indicates that SMEs with access to finance have a relatively similar mean of age (mean = 16.7, s.d.= 10.87) to their non-access counterparts (mean = 15.8, s.d.= 10.62). The results on the firm size show that there are about five times as many small-sized firm and nearly twice as many medium-sized firm found in the group without access to finance (59.5 per cent and 18.7 per cent respectively) as their counterparts in the group with such access (12.2 per cent and 9.6 per cent respectively). This pattern is supported by Chi-square test which indicates significant difference between firms with access and those without access in regards to their firm size. The results from the test for independence showed the calculated value of 2 (1, N = 1,114) = 38.045 and p-value of 0.000 which is less than the critical value of 0.05. The findings also reveal that groups that have used and have not used financial services are distributed in relatively the same array along the five location categories. Most of the respondents, however, are clustered in LOCATION2 or the urban area other than capital city with more than 1 million population followed by LOCATION3 or areas between rural and urban of 250 thousand to 1 million of population, and then LOCATION1 or the capital city. The Chi-square test results show no significant difference between firms with and without financial access in terms of their firm location. The calculated value of the test shows insignificant result of 2 (4, N = 1,114) = 2.361 and p-value of 0.670 which is more than the critical value of 0.05. With regard to the type of industry, the majority of respondents, despite their type of industry, are in the group without access to finance. Only one third of manufacturing SMEs and quarter of SMEs from services and other industries, however, have access to such financial services. The findings from the Chi-square test indicate no significant difference between groups with and without access to finance in regards to their type of industry as the calculated value of 2 (2, N = 1,114) = 0.099 and p-value of 0.952 which is also more than the critical value of 0.05. The proportion of sole proprietorship without access to financial services is almost four times as many as their counterparts with such access. But the share of firms without access to finance decreases as SMEs expand their company’s control and ownership to become corporations. This is confirmed by the Chi-square test results which also show a significant difference between the two groups of dependent variables relative to their firm’s legal status. The calculated value of the test shows significant result of 2 (3, N = 1,114) = 18.460 and p- value of 0.000 which is less than the critical value of 0.05.
  6. 6. 6 Table 2: Summary statistics (proportions) Variable No accessa With access All firms AGEc 78.2 % (n = 871) 21.8 % (n =243) 100 % (n = 1,114) SIZE1b 59.5 % (n = 663) 12.2 % (n = 136) 71.7 % (n = 818) SIZE2 18.7 % (n = 208) 9.6 % (n = 107) 28.3 % (n = 315) LOCATION1b 10.8 % (n = 120) 2.5 % (n = 28) 13.3 % (n = 148) LOCATION2 43.2 % (n = 481) 13.2 % (n = 147) 56.4 % (n = 628) LOCATION3 21.4 % (n = 238) 5.5 % (n = 61) 26.8 % (n = 299) LOCATION4 2.4 % (n = 27) 0.5 % (n = 6) 3.0 % (n = 33) LOCATION5 0.4 % (n = 5) 0.1 % (n = 1) 0.5 % (n = 6) INDUSTRY1 61.5 % (n = 685) 17.1 % (n = 191) 78.6 % (n = 876) INDUSTRY2 8.4 % (n = 94) 2.2 % (n = 25) 10.7 % (n = 119) INDUSTRY3 8.3 % (n = 92) 2.4 % (n = 27) 11.2 % (n = 119) LEGAL1b 0.9 % (n = 10) 0.3 % (n = 3) 1.2 % (n = 13) LEGAL2 8.9 % (n = 99) 3.6 % (n = 40) 12.5 % (n = 139) LEGAL3 62.9 % (n = 701) 14.9 % (n = 166) 76.5 % (n = 867) LEGAL5 5.5 % (n = 61) 3.1 % (n = 34) 8.5 % (n = 95) a, b, c indicates a binary, nominal and continuous variable respectively where the proportion (%) represent the frequency of occurrences. Table 3 presents the results of logit regression analysis for firms with access to finance services relative to those without these (dependent variable coded 0 and 1 respectively). Overall, the estimated model is statistically significant because the p-value is less than the critical value of 0.01 (Prob > chi2 = 0.000). Independent variables were tested for multi-collinearity using variance inflation factor (VIF). These tests appeared satisfactory in that the mean VIF was 1.13 which less than 5 for any variable (Acock, 2012). All the variables attract the hypothesised signs except the variable measuring firm location (LOCATION), which is negative but statistically insignificant. . Consonant with its insignificant regression weight, this variable shows a very small marginal effect (-0.035) in predicting the probability of accessing finance. The result, in particular, provides support for previous empirical research findings (see Pidani, 2010), suggesting that although there are wide discrepancies in infrastructure and economic development between rural and urban areas in Indonesia, they may not be an obstruction to firm’s ability to have access to financial services. The proposed hypothesis on firm location, is therefore, rejected for showing insignificant negative effects on access to finance at 5 per cent significance level.
  7. 7. 7 Table 3: Probability of Access to Finance in Indonesia: Logit Estimation Variable Coefficient Std.err. p-value marginal effects AGE 0.0015 0.0071 0.836NS 0.00024 SIZE1 0.9767 0.1577 0.000*** 0.16 LOCATION -0.0058 0.1024 0.954NS -0.001 INDUSTRY 0.1099 0.1134 0.332NS 0.018 LEGAL Constant Number of observations Log-likelihood LR chi2 (5) Prob > chi2 0.2269 -3.4262 1,114 -563.181 42.30 0.000 0.0948 0.4991 0.017** 0.000*** 0.04 ***, ** denotes significance at 1% and 5% levels respectively (two tailed tests).  the marginal effects are calculated at the mean values of the independent variables. The regression weight of the firm age (AGE) is found insignificant in predicting the probability of a firm to have financial access at 5 per cent significance level. It is also noted that the estimated coefficient of firm age is very small in magnitude as shown by its marginal effect (0.00024). This finding is contrary to Wengel and Rodiguez’ (2006) study in Indonesia which found firm’s age to be negative and highly significant for small and large enterprises alike. Based on these findings, the first proposed hypothesis, that is a firm’s age has a positive effect on access to finance, is partially accepted for showing positive but insignificant effects on access to finance at 5 per cent significance level. Table 3 also reveals that, consistent with prior expectations, everything else being equal, the larger the size of the firm the more likely it is that it will have access to financial services. In addition to significant Chi- square test result, the regression weight and the magnitude of marginal effect of this endogenous variable shows positive and substantial results in predicting firm’s probability to access finance (2.271 and 0.71 respectively). The results support the tenet that larger firms do have more flexibility in managerial and production matters that lead to greater use of external finance than smaller firms. Firm size, which is associated with the economies of scale advantage, can help firms manage the costs associated with business expansion, making growing firms seek larger amounts of formal credit and resources. Hence, the proposed hypothesis on the relationship between firm size and access to finance is accepted at the 1 per cent significance level that firm size significantly affects firm’s probability to access finance. In respect to the type of industry to which a firm belongs, Table 3 shows that firms in the manufacturing industry appear to be making the least use of alternative sources of external finance relative to firms from services and other industry. This is evidenced by the positive regression weight but insignificant magnitude of marginal effect of industry type in predicting the probability of a firm to access financial services (-1.343 and -0.53 respectively). Hypothesis on firm industry membership is, then, partially accepted at the 5 per cent significance level as the variable has a positive but insignificant effect on firm’s probability to access to financial services. In addition, those firms which were currently having access to finance were characterised by significantly less company control and limited liability on the company legal structure. In other words, keeping all else equal, the lesser the control and liability of the owners in the company legal structure, the more likely they are to have external access to finance. Based on this, the hypothesis on firm industry membership is, then, accepted at 1 per cent significance level as the variable has a significant positive effect on firm’s probability to access to financial services. The significant difference between groups shown in the previous univariate Chi- square test is supported by the positive regression weight as well as by reasonable magnitude of marginal effect in predicting the probability of a firm to access finance (0.633 and 0.198 respectively).
  8. 8. 8 CONCLUSION The purpose of this study has been to extend extant research by focusing on those factors which might explain why some Indonesian SMEs have access to external financing and some others do not so. Following hypotheses development, our regression results provide support for two of five proposed hypotheses. Firstly, SMEs’ probability to access finance is affected positively by their firm size (H2). It is also affected positively by their firm legal structure (H5). The firm age effect was not significant in our models. We expected the changes that had taken place in the older SMEs’ business experience, accumulation of knowledge, and business network to have a significant impact on their ability to acquire external financing in comparison to the newly established ones. The contrasting vector results and a partial support to previous study’s findings (see Wengel and Rodriguez, 2006), however, may have resulted from the fact that younger Indonesian SMEs are more likely to have better information acquisition and therefore risk tolerance as well as network advantages which are all contingent on their willingness and ability to acquire external finance compared to their older counterparts. Availability of financial alternatives, per se, does not automatically translate into organisational change since risk and uncertainty need to compete with status quo to prove viable. We also expected firm location and industry membership to be significant factors in SMEs probability to access finance. But they were very weak and insignificant vectors in affecting firm’s access to finance. These unexpected results seem to suggest that although the role of location and industry membership in explaining the probability to access finance have been extensively discussed by the literature (Gilbert 2008; Fatoki and Asah, 2011; Kira and He, 2012), in the light of the above findings, it can be implied that since most firms, regardless of their industry types, are located within urban areas (LOCATION1, LOCATION3 and LOCATION4), the spiral effects of the localisation economies including financial service facilities are likely to be shared by both groups of the dependent variable. Therefore externalities from urbanisation may not be considered as important as other externalities that can specifically address and assist individual group’s needs based on their common organisational characteristics. Causality between firm size and firm legal status and access to finance in our regression model provide some new evidence in support of previous studies, in that SMEs which are having access to external finance are characterised by having developed a particular firm size and legal statusy. The indication from these findings is that SMEs need to pay attention to the firm’s capacity and competitiveness as priorities that can promote their growth. SMEs need to be aware that by strengthening their institutional performance, they can increase their potential to access external financing. Dissemination of information on the requirements of banks and other creditors can help SMEs to get their institutions and investments prepared and thus improve their access to debt. Public and private agencies’ efforts to alleviate this information barrier can operate by improving firms’ financial and institutional capabilities and building strong networks to promote awareness of both bank’s and non-bank financial institutions’ products and requirements. The above findings, however, should be interpreted with caution, as in the context of SMEs, the role and the characteristics of their owner-manager might be critical for the external financing behaviour of the firm. Since we do not control for these variables, our results simply establish the relationship between the specified dependent and independent variables. The consequences of these exclusions might have influenced the overall regression results. Thus, although our models provide explanatory value for our dependent variables, the generally low pseudo R2 values for our model indicate that we have not fully captured all the influences on the dependent variable.
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