metadata: authors: 0: author: Beck, Thorsten
1: author: Demirguc-Kunt, Asli
2: author: Singer, Dorothe
count: World
docna: 0: docna: Is small beautiful ? financial structure,
            size and access to finance
docty: Policy Research Working Paper
subtopic: Legal Institutions of the Market Economy,Employment and Unemployment,Financial Economics,Finance and Development,Financial Sector Policy,Access to Finance
teratopic: Finance and Financial Sector Development
abstracts: cdata!: Combining two unique data sets, this
            paper explores the relationship between the relative
            importance of different financial institutions and their
            average size and firms' access to financial services.
            Specifically, the authors explore the relationship between
            the share in total financial assets and average asset size
            of banks, low-end financial institutions, and specialized
            lenders, on the one hand, and firms' access to and use
            of deposit and lending services, on the other hand. Two
            findings stand out. First, the dominance of banks in most
            developing and emerging markets is associated with lower use
            of financial services by firms of all sizes. Low-end
            financial institutions and specialized lenders seem
            particularly suited to ease access to finance in low-income
            countries. Second, there is no evidence that smaller
            institutions are better in providing access to finance. To
            the contrary, larger specialized lenders and larger banks
            might actually ease small firms' financing constraints,
            but only at low levels of gross domestic product per capita.
repnme: repnme: Is small beautiful ? financial structure,
            size and access to finance
display_title: Is small beautiful financial
            structure, size and access to finance
listing_relative_url: /research/2011/09/15111505/small-beautiful-financial-structure-size-access-finance
topicv3: Finance and Growth m1327404 1081,Access to Finance m1327464 1249,Financial Sector Development m1327681 1920,Enterprise Surveys m1504072 2762,Financial Institutions m1505015 3797
publishtoextweb_dt: 2011-09-01T00:00:00Z
historic_topic: Finance and Financial Sector Development
no_of_pages: 34
model_extractions: WPS5806 Policy Research Working Paper 5806 Is Small Beautiful? Financial Structure, Size and Access to Finance _Thorsten Beck Asli Demirgüç-Kunt Dorothe Singer_ The World Bank Development Research Group Finance and Private Sector Development Team September 2011
Policy Research Working Paper 5806

# **Abstract**

Combining two unique data sets, this paper explores the relationship between the relative importance of different financial institutions and their average size and firms’ access to financial services. Specifically, the authors explore the relationship between the share in total financial assets and average asset size of banks, lowend financial institutions, and specialized lenders, on the one hand, and firms’ access to and use of deposit and lending services, on the other hand. Two findings stand out. First, the dominance of banks in most developing and emerging markets is associated with lower use of financial services by firms of all sizes. Low-end financial institutions and specialized lenders seem particularly suited to ease access to finance in low-income countries. Second, there is no evidence that smaller institutions are better in providing access to finance. To the contrary, larger specialized lenders and larger banks might actually ease small firms’ financing constraints, but only at low levels of gross domestic product per capita.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at ademirguckunt@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Is Small Beautiful? Financial Structure, Size and**

# **Access to Finance**

**Thorsten Beck, Asli Demirgüç-Kunt and Dorothe Singer**<sup>**1**</sup>

## **JEL classification:** G10, G30, O16

**Keywords:** Financial development, structure of financial Sector, size of financial sector, access to finance, small and medium enterprises

> 1 Beck: CentER, Department of Economics, Tilburg University and CEPR; Demirgüç-Kunt and Singer: The World Bank. We are grateful for comments from participants at the World Bank conference on Financial Structure, especially our discussant Jung Wan. This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.
# **1. Introduction**

The structure of the financial system is again in the headlines. Moving beyond the questions of banks vs. markets, policy makers are looking for advice on which kind of financial institutions and which market structures serve best in pushing out the access frontier. Which institutions are best suited to expand financial services to low-end customers, including small and medium-sized enterprises? Are these banks which can exploit scale and technological capacity, or specialized lenders, such as leasing or factoring companies which can offer expertise in tailored lending products, or low-end financial institutions which are closest to customers? Similarly, are small or large financial institutions better in serving low-end customers? On the one hand, large institutions can exploit scale economies and better diversify risks; on the other hand, small institutions might have better local market knowledge and flatter hierarchies, both of which facilitate serving low-end customers.

Combining two unique data sets, this paper explores the relationship between the importance of different financial institutions, including low-end financial institutions, specialized lenders and banks, as well as the average size of these institutions and firms’ access to financial services, including account and lending services. In addition, we explore the potential heterogeneity of these relationships both across countries at different levels of economic development, across industries with different needs for external finance and across firms of different sizes, thus taking into account the different needs and capacities of countries in supporting different financial structure, different constraints of firms of different sizes and different needs for external finance across different industries.

The relationship between financial structure, the average size of different financial institutions and access to finance is a critical question for policy makers. Access to financial services, especially by small and medium-sized enterprises, has become critical in many developing countries. Small and medium-sized enterprises make up a large part of the emerging private sector in most countries, but are also more constrained in their access to financial services than large firms (Ayyagari, Beck and Demirguc-Kunt, 2007; Beck, Demirguc-Kunt and Maksimovic, 2005). While micro-finance has helped alleviate access to finance by the poor by adapting specific lending techniques such as group lending, it seems less conducive to easing financing constraints of more formal and larger enterprises. More recently, specific financing 2
forms such as leasing or factoring have been promoted as conducive to easing financing constraints of SMEs, as they are based on the underlying assets and cash flows rather than borrowers’ financial history (Berger and Udell, 2006). On the other hand, banks, particularly large banks, have also shown increased interest in SME financing, exploiting scale economies and technology (Beck, Demirguc-Kunt and Martinez Peria, 2011a). The question on the size of financial institutions – often intertwined with the ownership question – is directly related to entry barriers and minimum capital requirements imposed by policy makers in developing countries to foster a specific market structure (Beck et al., 2011b; Beck et al., 2011c and World Bank, 2011).

This paper uses a unique and confidential dataset to shed light on the relationship between the structure of the financial system and the size of its institutions, on the one hand, and access to financial services by enterprises, on the other hand. Specifically, using data from the World Bank and IMF’s Financial Sector Assessment Program (FSAP), we are able to compute both the relative importance of different segments of the financial system that cater to low-end customers, such as small and medium-size enterprises, as well as the average size of institutions within this segment. We then match these country-level indicators to firm-level indicators from the World Bank’s Enterprise Surveys on financing obstacles and actual use of deposit and loan services by enterprises in developing and emerging countries. In addition, we examine the relationship between financial structure and firms’ access to finance across countries at different levels of GDP per capita, across firms of different sizes, and across industries with different needs of external finance, to thus take explicitly into account the potential cross-county, crossfirm and cross-industry heterogeneity in the effect of financial structure on firms’ access to finance.

Our research speaks to several literatures. First, the financial structure literature has discussed the implications of bank- vs. market-based financial systems for firm, industry and GDP per capita growth<sup>2</sup> , but has not considered the importance of other segments of the financial system, including specialized lenders such as leasing, finance or factoring companies or low-end financial institutions such as cooperatives, credit unions and microfinance institutions. This paper is the first, to our knowledge, that explores the relationship between the importance of

> 2 For the relationship between the degree to which a country is bank- or market-based and firm, industry and GDP per capita growth, see Demirguc-Kunt and Maksimovic (2002), Beck and Levine (2002) and Levine (2002), respectively.

3
these two segments focused on SME lending, for access to finance by enterprises. Theory and literature offer different predictions on the effect of importance of these segments on firms’ access to finance. On the one hand, specialized lenders can exploit their expertise in specific lending products such as leasing and factoring to improve firms’ access to external finance. Similarly, low-end financial institutions might have an advantage in working with smaller and less formal enterprises than banks, as they are closer to the client and might have more adequate organizational structures, such as flat hierarchies, and lending techniques, such as group lending.<sup>3</sup> On the other hand, banks have a larger scale and technical capacity to cater to a large number of low-end clients (De la Torre, Martinez Peria and Schmukler, 2010). They might be therefore in a better position to invest in technology and risk management systems than other financial institutions.

Second, our research speaks to a large literature on the effects of the size of financial institutions on firms’ access to financial services (Berger, Hasan and Klapper, 2004). This literature has focused mostly on the size of banks, but has not come to an unambiguous result. On the one hand, smaller banks might be closer to the client and can use relationship lending to effectively serve small and medium-sized enterprises. On the other hand, larger banks might have an advantage in using transaction-based lending techniques such as leasing or factoring. While this literature has focused on banks, we expand it to consider the relationship between the average size of low-end financial institutions, specialized lenders and access to finance by enterprises. Similar arguments as for banks can be made for non-bank institutions. On the one hand, smaller institutions might be closer to the client; on the other hand, larger institutions might serve these clients more effectively by exploiting their scale.

Our results suggest that the dominance by banks in most financial systems of developing markets is associated with lower use of financial services by firms of all sizes. To the contrary, a larger share of low-end financial institutions and specialized lenders is associated with higher use of financial services in low-income, but not necessarily in middle-income countries. Large financial institutions, on the other hand, are not necessarily associated with lower use of financial services. To the contrary, larger specialized lenders and larger banks might actually ease small firms’ financing constraints, while large low-end financial institutions seem to impede access to

> 3 See Armendariz and Morduch (2005) for a survey.

4
financial institutions only for medium-sized and large enterprises. And larger low-end financial institutions might actually be better in easing access to finance in low-income countries.

Before proceeding, an important caveat is due. Our results derive from cross-sectional variation across countries and although we control for an array of firm and country characteristics, we can therefore not completely exclude the possibility of omitted variable bias. We mitigate this concern, however, by testing for the differential relationship between financial structure and average size of financial institutions, on the one hand, and access to external finance by firms in countries at different levels of GDP per capita, firms of different sizes and firms in industries with different financing needs. It is important to stress, however, that we do not interpret our findings as causal relationships.

The remainder of the paper is structured as follows. The next section discusses the data sources and variables we use. Section 3 presents methodology and section 4 our results. Section 5 concludes.

# **2. Data**

We use data from two main sources to construct our sample. We use the Financial Sector Assessment Program (FSAP) reports, which are jointly prepared by the IMF and World Bank<sup>4</sup> , to construct our measures of the importance and average size of different segments of the financial system and firm-level data from the World Bank’s Enterprise Surveys to measure firms’ access to and use of financial services. Since there is limited overlap between the two datasets, we end up with a total of 54 sample countries and up to 50 countries per regressions. All our countries are developing or emerging countries, with 19 countries in Europe and Central Asia, 10 countries in Latin America, 23 countries in Sub-Saharan Africa, and 2 countries in East Asia and Pacific. The level of economic development, as measured by GDP per capita (in constant 2000 USD), varies significantly across our sample countries, ranging from 134 USD in Malawi to 7,229 USD in Uruguay.

Established in 1999, the FSAP is a comprehensive and in-depth analysis of a country’s financial sector. Historically, full FSAP updates take place about every four to seven years in any

> 4 To be exact, FSAP is a joint undertaking of the World Bank and the IMF in developing and emerging market countries and of the IMF alone in advanced economies.

5
given country. Among other things, the reports generally include a table that reports on the country’s financial structure broken down into institutional categories such as banks or pension funds. The aggregation level of institutional categories varies across reports. There is no standardized categorization of institutions; while one report may have “banks” as one institutional category, another report may have “private banks” and “state-owned banks” as institutional categories instead, which combined would be equivalent to the category “banks” in the former report. The table typically provides the following information for each institutional category: number of institutions, assets in (mostly) local currency units, assets as a percentage of total financial sector assets and assets as percentage of GDP. Note that not all reports report data in all four categories and while reports generally include a couple of years of historic data they may record data in one category for one year but not the next and often data just for one or two years are reported.<sup>5</sup> Using this financial structure information, we build a database from all financial structure information reported in table form in FSAP reports from the beginning of the program until mid 2009.

For some countries, more than one FSAP report is available. Unfortunately, the reporting structure is almost never the same as in the previous report(s) for the same country and crosschecks of the data revealed that the reported information is not even necessarily consistent across reports for the same country. We therefore assume that the most recent report contains the most accurate information and only keep observations from the most recent report available. Our final database consists of an unbalanced panel for 89 countries over the years 1995-2008. We convert any variables in local currency units into 2000 constant US dollars using exchange rates from the IMF’s International Financial Statistics.

While we have data available for a broader array of institutions, we focus on three types. First, low-end financial institutions which include credit unions, building societies, community banks, cooperatives, microfinance institutions, cash lenders, mutual banks, postal banks, rural banks, savings and loans institutions, and thrift banks. This category is supposed to capture nonbank institutions that serve the low-end of the market, including small and medium-sized enterprises. Second, specialized non-bank financial institutions which comprise – among others – finance companies, factoring companies, banks specialized in housing, merchant banks, and

> 5 See Appendix Table 1 below for data availability across countries and categories.

6
special credit institutions. This category is supposed to capture non-bank financial institutions that specialize in certain lending activities that might be more attractive for small and mediumsized enterprises, such as leasing and factoring. The final category is deposit-taking or commercial banks.

We use the FSAP data to construct two indicators. The **asset share** is calculated as each type’s assets relative to the sum of low-end financial institutions, specialized non-bank financial institutions and commercial bank financial assets gauges the importance of each segment within the financial system by dividing the total assets of each category by total financial assets of these three segments in the country. The three asset shares add up to 100.<sup>6</sup> The **average size** is computed by dividing the total amount of assets per category by the number of institutions per category.

Both indicators vary widely across our sample countries. The share of banks varies from almost 99% in Ukraine to 61% in Colombia. The share of specialized lenders varies from 38% in Colombia to less than one percent in Senegal, Ukraine, Bolivia, and Madagascar. The share of low-end financial institutions varies from 21% in Burkina Faso to less than one-half percent in Chile and Latvia. The average size of banks in USD ranges from 3.5 billion in Turkey to 10 million in Guinea-Bissau. The average size of specialized lenders varies from 350 million USD in Chile to less than one million in Mongolia. The average size of low-end financial institutions varies from 800 million in Turkey to less than one million in Mongolia.

We combine the financial structure data with data from the World Bank/IFC Enterprise Surveys. The Enterprise Surveys collect firm level-data from key manufacturing and service sectors in over 120 countries since 2002.<sup>7</sup> Countries are surveyed every three to four years but not simultaneously. To ensure data consistency and inter-country comparability we only use data from countries in the standardized dataset 2006-2010 which contains data for 100 countries.<sup>8</sup> The number of firms surveyed in each country depends on the size of the economy with more firms

> 6 There are other categories such as insurance companies or pension funds that we do not include in our analysis. 7 Only private sector firms are surveyed; fully state-owned firms are excluded.

> 8 Due to changes in the questionnaire data from the earlier years cannot be easily compared to data collected in the more recent years. In the six instances where multiple years of data are available for a given country, we keep only the most recent year of data.

7
being surveyed in larger economies and is chosen to make each country’s sample representative of the non-agricultural private economy.

From the Enterprise Survey we construct the following four access to and use of financial services indicators: (i) _access to finance_ is an indicator variable ranging from 1-5 with 1 indicating access to finance is “no obstacle” to the operation of firm to 5 indicating a “very severe obstacle”; (ii) _account_ is a dummy variable equal to one if the firm has an account at the time of the survey and zero otherwise; (iii) _overdraft_ is a dummy variable equal to one if the firm has an overdraft facility at the time of the survey and zero otherwise; (iv) _loan_ is a dummy variable equal to one if the firm has a line of credit or loan from a financial institution at the time of survey and zero otherwise.

We match the two samples by building a cross-sectional dataset that matches the firm characteristics with the average of the available data from the FSAP reports. Maximum country overlap between the two data sources is 54 countries with over 25,000 firm level observations. Appendix Table A1 lists the countries in our sample, a breakdown of the firm distribution by country, and by-country summary statistics of the FSAP variables we will use in the subsequent analysis. Table 1 provides descriptive statistics and Table 2 correlations on the country-level.

The descriptive statistics in Table 1 show that over 90% of firms in our sample have an account. This percentage, however, varies significantly across countries. While in the Slovak Republic, 20.8% of firms have an account, 99.8% do so in Croatia. Almost 50% of firms have an overdraft facility and 45% have a loan. Behind this average, however, are again large crosscountry variations. While only 1.3% of firms have an overdraft facility and 3.1% a loan in Guinea-Bissau, 87.5% and 74.5%, respectively, do so in Chile.

We also use information from the Enterprise Surveys to control for firm-level characteristics that might affects a firm’s ease of access to financial products. In particular, we construct dummy variables for firm size (small, up to 19 employees; medium, 20-99 employees; large, 100 or more employees), being a subsidiary, and being publicly listed, and control for the percentage of the firm owned by private foreign owners and the percentage of a firm owned by the state, as well as the firm’s age. The descriptive statistics in Table 1 show that 47.4% of all firms are small, 34.3% are medium-sized and 18.3% large. 13% are subsidiaries of other firms, 8
and 6.2% are publicly listed. The foreign ownership share is, on average, 10.7%, while the average government ownership is 0.7%. On average, firms are 17.5 years old.

Finally, we control for industry-level variation in the need for external finance. Specifically, we use the Rajan and Zingales (1998) indicator on the fraction of investment that cannot be financed through internal cash flows, computed over the 1980s for listed firms in the U.S. The underlying assumption in Rajan and Zingales and our work is that for technological reasons some industries depend more heavily on external finance than others and that this industry variation does not differ across countries. We use the self-reported industry categorization by firms in the Enterprise Surveys to match with the Rajan and Zingales classification. Since this variable is only available for manufacturing industries, we lose about a half of our sample. The average fraction of external need for finance across our sample is 0.29, varying from -0.45 (tobacco) to 1.14 (plastic products).

The correlations in Table 2 suggest that there is no systematic relationship between the country-level metrics of financial segment size. Not surprisingly, however, the average asset size of some of the institutional categories is positively and significantly correlated. The log of GDP per capita is, as expected, positively and significantly correlated with the mean asset size of all institutional categories except low-end NBFIs. There are no significant correlations between the asset shares of the different segments of the financial system and access to finance. There are, however, significant correlations between the average size of financial institutions and access to finance. Countries with larger banks have a higher share of firms with loans and overdraft and firms that complain less about financing obstacles. Countries with larger specialized lenders also have more firms with overdraft facilities or loans. Many of the firm characteristics are also correlated with each other. Countries with more small firms, for instance, have fewer listed and younger firms. Our access indicators are also significantly correlated with our industry indicator of external dependence, with firms in industries more reliant on external finance reporting lower financing obstacles and a higher probability of having an account, a loan or an overdraft.

9
# **3. Methodology**

To estimate the effect of the mean asset size and assets as share of total assets of different types of financial institutions on obstacles to and the use of financial services we use the following empirical baseline specification: Financial Servicesij =  +  1 Medium Firmij +  2 Large Firmij +  3 Subsidiaryij +  7 Firm Ageij +  8 Firm Sectorij +  9 GDP per capitaj +  10 Financial Sector Indicatorj + eij where _Financal Services_ indicates one of our four dependent variables measuring access to and use of financial services of firm _i_ in country _j_ . Because our dependent variables have different data structures, we use different and data-structure appropriate econometric models to estimate the effect on each. We use ordered probit when the dependent variable is _access to finance_ and probit when it is _account_ , _overdraft,_ or _loan_ . _Financial Sector Indicator_ is our independent variable of interest that varies across regressions: average size or assets as share of financial sector assets per the institutional categories low-end financial institutions, specialized lenders, and banks. Standard errors are clustered at the country level in all specifications so that we allow for correlation of error terms across firms within a country but not across countries. It is important to note that our regressions imply empirical associations, but not necessarily causality.

In a second step, we want to assess whether the relationship between financial structure and access to financial services varies across countries with different levels of economic development, across firms of different sizes and across industries with different needs for external finance. We therefore interact, in separate regressions, the _Financial Sector Indicator_ with GDP per capita, with dummy variables indicating that the firm is small, medium or large size, or with the Rajan and Zingales (1998) indicator of external dependence. In the case of interactions with size dummies, we do not include the financial service indicator by itself, while in the case of interaction regressions with external dependence we include both external dependence and its interaction with the financial service indicator. Since Ai and Norton (2003) 10
have shown that it might be difficult to interpret the marginal effects of interaction terms in nonlinear models, we run these regressions with OLS.

# **4. Results**

Tables 3 and 5 report our main results using Asset Share and Average Size as financial sector indicators, respectively, while Tables 4 and 6 report the regressions with interaction terms. In the case of Tables 4 and 6, Panel A reports the coefficient estimates, while Panel B reports the partial effects at the 25<sup>th</sup> , 50<sup>th</sup> and 75<sup>th</sup> percentiles of GDP per capita and the external dependence ratio. In the interest of space and readability, we report regression coefficients of all variables in Table 3, while in all subsequent tables report just the coefficients of interest, namely the coefficients of the _Financial Sector Indicator_ and interaction terms. Due to data limitations on the average size variables the country sample and the number of firms do not stay constant across specifications in Tables 5 and 6.<sup>9</sup>

# **4.1 Asset Share across Different Segments**

The results in Table 3 suggest that there is a marginally positive relationship between the importance of low-end financial institutions or specialized lenders and firms’ access to financial services. Specifically, firms in countries with a larger share of low-end financial institutions are more likely to have an account or a loan and firms in countries with a higher share of specialized lenders are more likely to have an overdraft, though these relationships are significant only at the 10% level. We also find that a larger share of banks in total financial assets is associated with lower use of financial services by enterprises. The share of bank assets in total financial assets enters negatively and significantly at the 10% level in the regression of overdraft and negatively and significantly at the 5% level in the regression of loans. None of the financial sector shares is significantly associated with financing obstacles.

The coefficient estimates on our control variables are largely as expected and hold across the three categories of financial institutions. Firms in countries with higher GDP per capita as well as medium and large firms are more likely to have an account, overdraft facility, and loan and report fewer obstacles to access to finance. Firms that are subsidiaries are more likely to

> 9 The dependent variables in tables 3 and 5 allow for a balanced panel across countries by construction.

11
have an account and an overdraft facility, while there appears to be no significant relationship between a firm being publicly listed and its access to and use of financial services. As the percentage of foreign ownership in a firm increases firms are less likely to encounter obstacles to access to finance and are more likely to have an account. However, they are also less likely to have to have a loan. Firms are also less likely to have a loan as the percentage of state ownership in a firm increases suggesting that in both cases alternative financing options might be available to such firms. Finally, the older firms are the more likely they are to have an account and overdraft facility.

The results of Table 4 show that the relationship between the importance of low-end financial institutions, specialized lenders and access to finance varies significantly across countries. While the asset share of low-end financial institutions enters positively and significantly in the regressions of financing obstacles, account and overdraft, its interaction with GDP per capita enters negatively and significantly. When we calculate the partial effects (Panel B) for the share of low-end financial institutions at the 25<sup>th</sup> , 50<sup>th</sup> , and 75<sup>th</sup> percentile of GDP per capita in our sample we find that there is no statistically significant relation between the share of low-end financial institutions and financing obstacles for countries at the 25<sup>th</sup> percentile of GDP per capita (equivalent to the GDP per capita of Mongolia). However, there is a significantly negative relation at the 50<sup>th</sup> and 75<sup>th</sup> percentile of GDP per capita (equivalent to the GDP per capita of Guatemala and Brazil, respectively). When we look at the outcome of having an account or a loan only the partial effect for countries at the 25<sup>th</sup> percentile of GDP per capita is significant and positive, while the relation between the share of low-end financial institutions and the share of firms with overdraft is not significant at any level of GDP per capita. Firms in countries with a higher share of low-end financial institutions thus report lower financing obstacles only in lower-middle and middle-income countries, while they benefit – in terms of better access to financial services – only in low-income countries.

Neither the level of the share of specialized financial institutions nor its interaction with GDP per capita enters significantly. The partial effects calculations in Panel B suggest that the importance of specialized financial institutions has no statistically significant impact except in the case of overdrafts for countries at the 50<sup>th</sup> percentile of GDP per capita where the impact is significant and positive. Finally, the relationship between banks’ importance and firms’ use of 12
overdrafts and loans is negative and significant only in countries at the 25<sup>th</sup> and 50<sup>th</sup> percentile of GDP per capita. The negative effect of bank dominance is thus concentrated in low and lowermiddle income countries.

When interacting the relative importance of different segments of the financial system with the external dependence across different sectors, the interaction term suggests that a more prominent role of low-end financial institutions reduces financing obstacles for industries that rely more on external finance. The percentile calculations, however, indicate that combined with the level effect there is no significant relationship. None of the other interaction terms of asset share with external dependence, enters significantly at the 5% level, suggesting that the relationship between the relative size of different segments of the financial system and access to finance by enterprises does not vary across sectors with different needs for external finance.

When interacting the financial sector indicators with firm size dummies, we cannot find any significant relationship between the relative importance of low-end financial institutions or specialized lenders and access to finance and no differential effect across firms of different sizes, with one exception. Specifically, the likelihood of having an account increases with a higher share of low-end financial institutions for medium and large firms, while none of the other firmsize interactions enters significantly at the 5% level. In the case of specialized lenders, the likelihood of having an overdraft is significant only for small and medium, but not for large firms. Finally, we find that a more prominent role for banks is associated with a lower likelihood of obtaining an overdraft facility or loan for small and medium-sized firms, while the relationship is not significant at the 5% level for large firms.

# **4.2 Average Size of Financial Institutions**

The Table 5 regressions suggest that smaller low-end financial institutions are associated with higher financing obstacles as reported by firms, but also a higher probability of having an account and a loan. On the other hand, having larger specialized lenders is associated with a higher probability of having an overdraft facility and loan. The average size of banks, on the other hand, is not associated with access to finance.

13
The coefficient estimates in the regression reported in Table 6 show a non-linear relationship between the average size of different financial institutions and access to finance across countries at different levels of GDP per capita, across firms of different sizes, and across different external financing needs. Larger low-end financial institutions are associated with lower financing obstacles and a higher likelihood of use of an overdraft facility across all countries although the partial effects diminish as the GDP per capita increases. While the coefficient on average size enters negatively (positively) and significantly, its interaction enters positively (negatively) and significantly in the regressions of financing obstacles (loans and overdrafts). Assessing the partial effects, we find that the average size of low-end institutions has a negative (positive) relationship with financing obstacles (likelihood of having an overdraft) at all levels of GDP per capita, but decreasingly so as we move up the ladder of economic development. The negative effect of average size is significant only for low and lower-middle-income countries in the case of loans. We do not find any significant relationship between the average size of lowend institutions and the likelihood of having an account at any level of GPD per capita. The negative relationship of the average size of low-end financial institutions with financing obstacles and the use of accounts holds across firms of all sizes, though it is strongest for small enterprises. The negative relationship of the size of low-end financial institutions with the use of loans only holds for medium-sized and large enterprises. The interaction regressions with the external dependence variable suggest that the relationship between larger low-end financial institutions and the likelihood of receiving a loan is stronger in industries with a higher need for external financing. This relationship is significant at the 1% level for all three percentile calculations.

A larger average size of specialized lenders continues to be positively associated with the likelihood of having an overdraft or loan across all countries, while there is no significant relation with financing obstacles and the use of accounts. This positive relationship holds for firms of all sizes and is strongest for small firms, with the exception of being insignificant for large firms in the loan column. The coefficient of the interaction term with the external dependence ratio is never significant suggesting there is no differential effect of the average size of specialized lenders across industries with different external financing needs.

14
Larger banks are associated with lower financing obstacles in poorer countries (at the 25<sup>th</sup> percentile level of GDP per capita), while the relationship turns insignificant in middle-income countries. Similarly, we find a positive relation of average size of banks with the likelihood of having an account at the 25<sup>th</sup> and 50<sup>th</sup> percentiles of GDP per capita, but not at the 75<sup>th</sup> percentile. We also find evidence that larger banks are associated with a higher likelihood of overdrafts and loans for small firms, though the relationship with loans is significant only at the 10% level. The interaction with external finance is significant at the 5% level for likelihood of having an account and an overdraft facility. However, when combined with the level effect we see from the results in Table 6 Panel B that the overall effect of banks is insignificant across the different percentiles of the external dependence ratio.

# **4.3 Robustness Tests**

In unreported robustness tests, we gauge the sensitivity of the interaction regressions of Tables 4 and 6 to the estimation technique. Specifically, we find that our main findings hold when using non-linear estimation techniques as in Tables 3 and 5. We also re-ran our financing obstacles regressions including dummy variables indicating whether a firm has an account, a loan or an overdraft. The loan dummy enters positively and significantly, consistent with findings by Beck, Demirguc-Kunt and Maksimovic (2008), but the results do not change.

# **5. Conclusions**

Using unique data on financial structure and the average size of different financial institutions, this paper explores the implications of the relative importance and average size of institutions that cater specifically to SMEs compared to the importance of banks and their average size.

Our results indicate that the dominance of banks in the financial systems of most developing countries is rather detrimental for firms’ access to financial services. We do not find any evidence that smaller institutions – be they banks, specialized lenders or low-end financial institutions are better in providing access to finance for enterprises. Critically, however, we find that “one size does not fit all.” Low-end financial institutions and specialized lenders seem especially appropriate to ease access to finance in low-income countries. Similarly, larger lowend financial institutions and banks seem to ease access to finance only at low levels of GDP per 15
capita. We also find variation across firm sizes, not so much in the importance of different segments of the financial system, but rather in the relationship with the average size. We do not find that larger low-end financial institutions hurt small firms’ access to credit. Even more important, larger specialized lenders and banks are actually associated with a greater likelihood of loan and overdraft use by small firms. We also find that some of our effects are stronger for industries more reliant on external finance.

Our results, while tentative, send important policy messages. First, the dominance of banks in most financial systems across the developing world is indeed associated with the limited access to financial services by enterprises. This calls for diversification and more competition within the financial system, including from low-end financial institutions and specialized lenders. Second, smaller financial institutions are not necessarily better equipped to improve access to financial services by enterprises. While certainly not a call for consolidation, this again implies a diversified financial system with institutions of different sizes.

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