MSME FINANCE GAP ASSESSMENT OF THE SHORTFALLS AND OPPORTUNITIES IN FINANCING MICRO, SMALL AND MEDIUM ENTERPRISES IN EMERGING MARKETS © INTERNATIONAL FINANCE CORPORATION 2017. All rights reserved. 2121 Pennsylvania Avenue, N.W. Washington, D.C. 20433 www.ifc.org The material in this work is copyrighted. Copying and/or transmitting portions or all of this work without permission may be a violation of applicable law. IFC encourages dissemination of its work and will normally grant permission to repro- duce portions of the work promptly, and when the reproduction is for educational and non-commercial purposes, without a fee, subject to such attributions and notices as we may reasonably require. 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Additionally, “International Finance Corporation” and “IFC” are registered trademarks of IFC and are protected under international law. Photo Credits: World Bank Photo Library and Shutterstock Contents Acknowledgements..........................................................................................................................................III Acronyms.............................................................................................................................................................V Foreword............................................................................................................................................................ VII Executive Summary..........................................................................................................................................IX I. Introduction...................................................................................................................................................... 1 II. Other Finance Gap Studies.........................................................................................................................3 III. Current Methodology.................................................................................................................................. 7 IV. Quantifying the Finance Gap.................................................................................................................. 21 Number of Enterprises............................................................................................................................... 21 Formal Finance Gap...................................................................................................................................27 Gender Finance Gap.................................................................................................................................. 36 Potential Demand in the Informal Sector............................................................................................ 40 V. Implications of the MSME Finance Gap................................................................................................ 43 The Role of the Public Sector.................................................................................................................. 43 The Role of the Private Sector................................................................................................................ 46 Conclusion.......................................................................................................................................................... 51 Bibliography..................................................................................................................................................... 53 Annex...................................................................................................................................................................61 CONTENTS i Acknowledgements This report, MSME FINANCE GAP: Assessment of the Shortfalls and Opportunities in Financing Micro, Small and Medium Enterprises in Emerging Markets, was authored by Miriam Bruhn, Martin Hommes, Mahima Khanna, Sandeep Singh, Aksinya Sorokina and Joshua Seth Wimpey. Analytical support was provided by Yangyang Zhou. The authors would like to acknowledge guidance from an Advisory Board consisting of Deepa Chakrapani, Julie Fawn Earne, Matt Gamser, Dan Goldblum, Sharmila Hardi, Bill Haworth, Anushe Khan, Jorge Rodriguez-Meza, Maria Soledad Martinez Peria, Peer Stein, and Wendy Teleki, as well as valuable contributions from Raymond Anderson, Minerva Adei Kotei, Kaylene Alvarez, Nuria Alino Perez, Melina Mirmulstein and Leila Search. The team received useful insights from consultations with Luke Haggarty, Hans Peter Lankes, Toshiya Masuoka and others in the leadership team of IFC’s Economics & Private Sector Development Vice-Presidency. The authors also benefited from discussions with Mary Hallward-Driemeier and Navin Girishankar, members of the Country Private Sector Diagnostics team within the World Bank Group’s Trade and Competitiveness Global Practice. We thank Nadia Afrin, Barbara Balaj, and Li Wen Quach for the coordination in editing, design, and production. The authors would also like to acknowledge the major data providers: Bureau Van Dijk (Orbis), the IMF’s Financial Access Survey, the OECD’s Financing Small and Medium Enterprise and Entrepreneurs Scoreboard, and the World Bank’s Enterprise Surveys. ACKNOWLEDGEMENTS iii Acronyms AFI Alliance for Financial Inclusion BOW Banking on Women CAGR Cumulative Adjusted Growth Rate CENFRI Centre for Financial Regulation and Inclusion CGAP Consultative Group to Assist the Poor DTF Distance to frontier EAP East Asia and Pacific ECA Europe and Central Asia ECB European Central Bank EIB European Investment Bank EIF European Investment Fund ES Enterprise Survey EU European Union FI Financial institution FCC Fully credit-constrained FAS Financial Access Survey FMO Netherlands Development Finance Company GDP Gross domestic product GSMA Groupe Spéciale Mobile Association (Mobile Network Operators) IFC International Finance Corporation IFRS International Financial Reporting Standards IMF International Monetary Fund IRR Internal rate of return ISIC International Standard Industrial Classification IT Information technology KPI Key performance indicator ACRONYMS v KYC Know Your Customer LAC Latin America and the Caribbean LIFT Livelihoods and Food Securities Trust Fund LTDB Long-Term Debt MPOS Mobile point-of-sale MIX Microfinance Information Exchange NCC Not credit-constrained NFS Non-financial services NPL Non-performing loan OECD Organisation for Economic Co-operation and Development OLS Ordinary least squares PCC Partially credit-constrained PO Purchase order MAPE Mean absolute percentage error MENA Middle East and North Africa MFI Microfinance institution MSME Micro, Small and Medium Enterprises RoA Return on assets RoE Return on equity SA South Asia SAFE Survey on the Access to Finance of Enterprises SCF Supply chain financing SME Small and Medium Enterprises SSA Sub-Saharan Africa UNDP United Nations Development Programme UNIDO United Nations Industrial Development Organization WBG World Bank Group WDI World Development Indicators vi MSME FINANCE GAP Foreword At the International Finance Corporation, we pay special attention to small businesses because they are the engines of job creation and economic growth. Nine out of ten new jobs worldwide are created by small businesses, and we need nearly 3,3 million jobs every month in emerging markets by 2030 to absorb the growing workforce. Lack of access to finance is one of the biggest hurdles small businesses face that prevent them from growing and creating jobs. The private and public sector can better address this problem if they have better insights about the magnitude and nature of the finance gap. With this in mind, IFC and McKinsey & Company conducted the first comprehensive assessment of the global MSME finance gap in 2010. At that time, the magnitude of the gap – over $2 trillion annually – caused quite a stir. However, looking back now we find that with the scarce data available then along with the limitations of the methodology, we were, if anything, too conservative in our estimation before. To address this, a group of staff from across the World Bank Group has developed a new methodology that utilizes better and more diverse data from both supply and demand sides to assess the finance gap in developing countries. The results this time are even more staggering: 65 million enterprises, or 40 percent of formal micro, small and medium businesses in developing countries, have an unmet financing need of $5.2 trillion every year. So how are we addressing this financing gap and helping small businesses thrive? • We are providing investments and advisory services to financial intermediaries catering to small businesses. In 2016, 304 of our financial institution clients made 62 million loans to micro, small and medium enterprises valued at $412 billion. • We are strengthening financial markets by supporting collateral registries and credit bureaus that facilitated more than $250.6 billion in financing in 2016. More than 679,900 micro, small and medium enterprises were able to receive loans secured with movable property. • We are investing and working with the multiple FinTech companies such as Ant Financial, Welabs, Afluenta, Moni, Kreditech, and Confio, which use cutting edge innovations to revolutionize MSME finance. Through these fintechs, IFC reaches hundreds of thousands of MSMEs. • We also are promoting knowledge-sharing. The SME Finance Forum, which is managed by IFC, helps banks, fintechs and development banks learn from each other, link to new business and partnership opportunities, and lead in the industry-policymaker dialogue. FOREWORD vii The World Bank Group has taken on an ambitious goal of universal financial inclusion by 2020. The UN Sustainable Development Goals adopted by 193 member states calls for ensuring access to finance for small businesses, and the G20 leaders have also recognized the importance of financing SMEs as a critical piece of economic development. There is no doubt that closing the financing gap for small businesses has become a policy priority around the world. With co-operation and action by governments and the private sector, we believe that closing this gap for small businesses is achievable. Philippe Le Houérou Chief Executive Officer of IFC viii MSME FINANCE GAP Executive Summary M icro, Small and Medium Enterprises (MSMEs) represent a significant part of the world economy, and are one of the strongest drivers of economic development, innovation and employment. Access to finance is frequently identified as a critical barrier to growth for MSMEs.1 A growing body of literature has highlighted the extent to which MSMEs are credit constrained across developing countries — including the importance of relieving this constraint to achieve higher growth.2 Creating opportunities for MSMEs in emerging markets is a key way to advance economic development and reduce poverty. In this regard, it is also one of the major priorities of the World Bank Group and other development institutions around the globe. In recognition of the need to quantify the extent of the MSME finance gap, the International Finance Corporation (IFC) partnered with McKinsey & Company in 2010 to produce an estimate of the gap.3 As the first study of its kind, the aim was to produce approximate figures that could, at an aggregate level, highlight this critical issue and the scale of the challenge. However, the assumption and methodology of the study raised concerns about its use at a more granular level. For example, cross-country comparisons, crucial for strategic policy decisions by international organizations and others, were not possible. In response, a collaboration between various units at the IFC and the World Bank’s research unit developed an innovative methodology that reassesses the gap and significantly moves this analytical work forward. The team has estimated the systemic finance gap by utilizing more data from both the demand and supply sides. As a result, it has produced more accurate, actionable country-level estimates of the gap. In the developing economies studied,4 the potential demand for MSME finance is estimated at US $ 8.9 trillion, compared to the current credit supply of $3.7 trillion.5 The finance gap from formal MSMEs in these developing countries is valued at $5.2 trillion, which is equivalent to 19 percent of the gross domestic product (GDP) of countries covered in this analysis. This in turn amounts to 1.4 times the current level of MSME lending in these countries. In addition, there is an estimated $2.9 trillion potential demand for finance from informal enterprises in developing countries, which is 1. For example, see World Bank Enterprise Surveys: http://www.enterprisesurveys.org/research/enterprisenotes/topic/finance. 2. World Bank, Global Financial Development Report 2014: Financial Inclusion. (Washington, DC: World Bank Group, 2013). 3. Stein, Peer, Tony Goland and Robert Schiff. Two Trillion and Counting: Assessing the Credit Gap for Micro, Small, and Medium-size Enterprises in the Developing World. (2010). 4. This study covers 128 countries, of which 112 are low- and-middle income countries. The remaining low- and middle-income countries for which the analysis was not carried out due to data unavailability together comprise only about 1 percentage of the overall GDP of the emerging (low- and middle-income) economies. 5. The data source for the supply of MSME finance is the IMF Financial Access Survey — actual or extrapolated (if missing). EXECUTIVE SUMMARY ix equivalent to 10 percent of the GDP in these countries. This research estimates that there are 65 million formal micro, small and medium enterprises that are credit constrained,6 representing 40 percent of all enterprises in the 128 reviewed countries.7 Ostensibly, in comparison to the previous IFC estimate of the MSME finance gap, the level of the overall gap is larger. However, the increase in the estimate of the gap is primarily driven by changes in the methodology. It should not be necessarily interpreted as an increase in the gap, but rather as a more accurate re-calculation of the gap. Also, this robust methodology has the added benefit of being easier to update in future years. Thus, for the first time, the evolution of the gap will be captured, and the dynamic changes to the gap can be more accurately assessed. Data availability is the main hindrance to providing more granular estimates of the gap than can be provided here. Even with the currently proposed methodology, the lack of data imposed the need to make stronger assumptions than would be necessary if data availability was not an issue. As access to financing for MSMEs continues to be an issue of critical importance, there is an ongoing need to improve data collection efforts for MSME financing in developing countries. 6. The credit-constrained MSMEs may be either partially or fully credit constrained. 7. There are also a large number of informal enterprises lacking finance. However, due to the data limitations, this study does not estimate the number of informal businesses. x MSME FINANCE GAP I. Introduction A s in most economies, MSMEs in emerging markets are widely believed to be the engine of growth across. MSMEs employ a majority of the population and contribute significantly to economic growth. Yet, one of the main constraints to MSME growth has been access to finance. Given the importance that MSMEs play in economic development and job creation, financing for MSMEs has emerged as a popular topic of discussion and research (Hallberg 2001). Over the last decade, many researchers and academics have tried to analyze the issue of MSME access to finance, emphasizing their dependence on credit and cash flows. Beck and others (2014) concluded that MSMEs appear to be severely underfunded. Ayadi and Gadi (2013) found that SMEs face numerous obstacles in borrowing funds because they are small, less diversified, and have weaker financial structures. This is implied by evidence pertaining to payment delays on receivables, declining liquidity, and an increase in MSME insolvencies and bankruptcies. In addition, MSMEs find it difficult to provide high- quality collateral at all times. They also experience difficulties in ensuring transparency with respect to their creditworthiness. Some studies show that MSMEs are more likely to face more credit constraints than larger firms. They also rely more heavily on trade credit and informal sources of credit. Indeed, “throughout the developing world access to credit is inversely related to firm size but positively related to productivity and financial deepening in the country” (Kuntchev, Ramalho, Rodriguez-Meza, and Yang 2014). Within-country evidence also points to credit constraints for MSMEs. For example, an impact evaluation from India exploits variation in access to a targeted lending program. It finds that many SMEs are credit constrained, and that providing additional credit to SMEs can accelerate their sales and profit growth (Banerjee and Duflo 2012). In addition, research from Pakistan shows that a drop in subsidized credit led to a significant decline in exports for small firms, but not for large firms. Large firms were able to replace subsidized credit with credit at market interest rates. However, this was not true for small firms, thereby indicating that small firms are credit constrained (Zia 2008).6 This study presents a new approach to the estimation of the unmet demand for financing from MSMEs in developing countries. Importantly, it also describes the potential implications for the public-sector bodies, private sector financial institutions and technology providers. The present research adds significant value to the repository of data in the MSME space, and opens new opportunities for further investigation. It estimates both supply of and demand for MSME finance on a global scale, which has never been done in 8. Another potential explanation for the findings is that small firms are less productive. Therefore, they cannot pay market interest rates. However, over 95 percent of small firms have at least some credit at market interest rates. Moreover, Zia (2008) does not find that more productive firms were less affected by the drop in subsidized credit. I. INTRODUCTION 1 a comprehensive way. Although there are multiple regional and country-level studies, none have looked holistically at the entire universe of developing economies. With the primary motivation of developing a robust and replicable methodology for measuring the finance gap, the authors of this study have benefited from various data sources, such as the Bureau Van Dijk – Orbis data, the International Monetary Fund (IMF) Financial Access Survey data, the Organisation for Economic Co-operation and Development (OECD) Financing SME and Entrepreneurs Scoreboard, and the World Bank Enterprise Survey data, among others.7 However, challenges remain with regard to the availability and reliability of good data (especially in the informal sector) necessary to make strategic decisions for servicing MSMEs. This highlights the need for further improvements in MSME-related data globally through investments in primary data collection (surveys) and secondary data aggregation (maintaining private and public data depositories). In addition, further investments are needed to facilitate the comprehensive standardization of improved data. The potential demand approach used in this research assumes that firms in developing countries have the same willingness and ability to borrow as their counterparts in developed markets. This approach estimates MSME equilibrium lending in developed economies according to the industry, age and size categories, and applies this benchmark to MSMEs in developing countries. It estimates the MSME finance gap as the difference between current supply and potential demand which can potentially be addressed by financial institutions. The study also estimates the potential demand for MSME finance in the informal sector by investigating the size of the shadow economy. This report is divided into five sections. The first section reviews the literature about financing MSMEs. The second section describes the methodology of the present research, data sources, and the model specification. The third section analyzes the results of the finance gap estimation, including regional comparisons, formal and informal MSME sector results, and gender disaggregated statistics. The fourth section elaborates on the implications of the finance gap for the public sector, including government agencies and multilateral organizations and lending institutions. Finally, the fifth section highlights implications for the private sector, including banks and non-bank financial institutions, as well as financial technology companies. 9. There are other data sources of the supply and demand of MSME finance, such as originated by Microfinance Information Exchange (MIX), GSM Association (GSMA), Alliance for Financial Inclusion (AFI), the Consultative Group to Assist the Poor (CGAP), Finscope, and the United Nations Capital Development Fund (UNCDF). However, they lack standardization and comprehensive country coverage, thereby making the data difficult to use in a global study. 2 MSME FINANCE GAP II. Other Finance Gap Studies T he challenge of access to finance as a constraint for MSMEs has been thoroughly established through research efforts. However, little research has been conducted about the difference between the supply and demand of financing to MSMEs to determine if a financing gap exists for MSMEs, and, if so, what the size of such a gap would be. In recent years, researchers have tried to explore this question for emerging markets in general, or for a smaller group of developing countries. In 2010, for the first time, the IFC and McKinsey & Company tried to estimate the size of the MSME financing gap. The results were released through the IFC Enterprise Finance Gap database. This study was updated again in 2013 (IFC 2013) and eventually covered 177 economies. The study concluded that the size of the gap in developing economies was around $ 2.1–$ 2.6 trillion, or about one-third of the total outstanding MSME credit in these countries. Of the 85-100 million formal MSMEs in developing countries, close to 60 percent are estimated to be either unserved, that is, they do not have a loan or overdraft —or underserved, that is, they have a loan or overdraft, but still experience access to finance as a constraint. Regional studies have mostly focused on Europe because the data quality at the firm level is much better then in developing economies. As such, the OECD has been looking at this issue since 2006. They refer to the SME finance gap as the “financing gap”. The first study the OECD (2006) undertook was a qualitative assessment of how prevalent such a gap is in both OECD and non-OECD countries. The study concluded that emerging economies have a more pervasive gap than in OECD countries. Subsequently, the OECD (2016) started publishing an annual scorecard on SME financing, and explored options for alternative sources of financing for SMEs to bridge both their financing and information gaps (OECD 2015). Using the scorecard, the OECD now annually tracks core indicators for 37 OECD countries. In 2013, the European Investment Bank (EIB 2013) conducted a series of studies to measure the financing needs of its Eastern Partnership Programme Countries, including Armenia, Azerbaijan, Georgia, Moldova, and Ukraine. In its synthesis report containing the results from all five countries, the EIB tried to measure the demand and supply of credit based on publicly available data. The measure for demand was the average loan demanded by firms that received a loan. The measure for supply was based on outstanding loans to SMEs in a given country. The study concluded that although the financial sectors in these countries are doing an adequate job of providing financing to SMEs, there are sizeable gaps in rural areas, as well as in the agricultural sector. There are also financing gaps for SMEs lacking collateral, for longer tenure credits, and for SMEs whose owners have lower literacy levels. The European Investment Fund (EIF) (2014) tried to quantify suboptimal investment situations and the investment needs of SMEs through a pragmatic approach that incorporates a forward-looking element into the market assessment. As such, the EIF complemented the comparison of supply and potential demand for financing for SMEs with an analysis of SME finance market weaknesses. For each financial instrument, II. OTHER FINANCE GAP STUDIES 3 the EIF tried to assess a mismatch between potential demand and supply. The resulting mismatch is their measure of the SME finance gap.8 Supply was measured based on publicly available data, and expected demand was calculated based on reasonable estimates of average loan amounts multiplied by the number of expected applications. As the latter measures potential demand, it is also expected to take into consideration the fact that some SMEs may not apply for financing because they expect their applications to be rejected. This practical approach has been tested in multiple countries. Similar to the findings of Kuntchev and others (2013), the EIF also concluded that smaller and younger companies have bigger financing gaps. Based on the work of the EIB and the EIF, the European Union (EU) Commission estimated the SME financing gap for its member countries in 2013. The study concluded that for the years 2009-2012, the average SME financing gap for the EU was within the range of €20 to €112 billion per country. The study multiplied the average SME loan size by the proportion of financially viable SMEs that faced problems accessing financing between 2009 and 2012. This included SMEs that were refused loans, SMEs that had turned down bank loans, and those that were discouraged from applying for loans. Lopez de Silanes and others (2015) conducted a pan-European study estimating the difference between supply and demand in SME financing. In particular, they focused on five European countries: France, Germany, the Netherlands, Poland, and Romania. They concluded that the SME financing gap (as a share of GDP) in these countries is three to five times larger than that of the United States (US). This study used 10. The European Court of Auditors (2012b, p. 18) sees “a full analysis of nationwide demand and supply of SME finance by type of financial instrument” as best practice for an assessment of a financing gap. 4 MSME FINANCE GAP publicly available data on outstanding loans and equities issued to SMEs in order to estimate the supply of SME financing. The demand for loans and equity among SMEs was computed using the Survey on the Access to Finance of Enterprises (SAFE) of the European Central Bank (ECB) and publicly available data. The authors built on the EIB methodology (2013) by using additional sources of data and a broader measure of loan demand. The loan demand was measured by triangulating loans obtained versus loans desired by SMEs. They also measured the loan demand of those firms that had applied for loans, but had been rejected. A lack of cross-country statistics led multiple researchers to focus their work on country-level analysis of the financing gap. Most of these studies relied on publicly available measures of the supply of credit. However, some tracked different measures for actual demand, while others tried to measure potential demand. Singh and others (2016) concluded that in 2014-15, a $0.77 billion financing gap existed for women-owned SMEs in Bangladesh. This amount corresponds to an unmet financing demand for 60.2 percent of women-owned SMEs. Similarly, IFC (2014) estimated that there was $5 billion in additional loans demanded by Mongolian SMEs in 2014, of which 24 percent corresponds to demand by women-owned SMEs. In Indonesia, the IFC (2016) estimated that 54 percent of SMEs were interested in obtaining a bank loan. Of these SMEs, the potential demand for credit from women-owned SMEs in 2014 amounted to $6 billion. A similar study by the IFC in 2012 estimated the potential demand gap by MSMEs in India to be $418 billion. This study makes a unique contribution to the existing literature by providing estimates of the size of the MSME financing gap across developing economies from both the demand and supply sides. As noted, previous literature focused on regional- or country-level estimates due the paucity of data, and the only other cross-regional estimates analyzed the gap only from the supply side. This report estimates the size of the MSME finance gap using a potential demand approach, which is outlined in Section III. Essentially, it models the potential demand for credit by MSMEs, and tries to match it with the supply of credit. While arriving at this unique approach (Section III), the team also tried to extend the work of Beck and others (2013) to devise another measure. Beck and others (2013) outline multiple frameworks to measure potential demand using a financial possibility frontier, or a constrained optimum to categorize different problems of shallow financial markets that result in a mismatch between supply and demand for financial services. A simple regression equation was used to estimate the relationship between MSME outstanding finance volumes and other country-level macroeconomic and institutional characteristics (for example, population, GDP per capital, lending rates, existence of credit bureaus, the stability of the banking sector, and the number of MSMEs). Assumptions about potential values of some of the dependent variables were used to estimate the frontier volume of potential demand for MSME finance. The difference between this predicted value of potential demand and the current volume of outstanding MSME finance could have been another measure of the MSME finance gap. This measure II. OTHER FINANCE GAP STUDIES 5 would represent the additional MSME finance demanded by firms if the countries’ macroeconomic and institutional conditions improved. This approach concluded that the MSME financing gap in emerging economies exists and is sizeable. However, the size of the gap was sensitive to data limitations, including missing data and outliers. For example, as estimates of all countries depend on each other, changes in the data for one country sometimes led to big changes in estimated demand for another country. Furthermore, in order to use this macroeconomic model across multiple countries, the list of dependent variables that could be used was limited. In this regard, the team decided to only present the results of the potential demand approach (as outlined in Section III of this report). 6 MSME FINANCE GAP III. Current Methodology A ny proposed methodology to estimate the MSME finance gap faces a number of conceptual and data availability challenges. This section, guided by the leading question of why a finance gap might exist, discusses the proposed methodology in detail. The first issue in developing an empirical methodology aimed at estimating the gap is conceptualizing what a MSME finance gap actually means. According to the tenets of basic economic theory, under market- clearing equilibrium interest rates, the amount of financing demanded equals the credit being supplied. Thus, a “financing gap” is not a meaningful concept. However, subsequent economic literature has moved to identifying peculiarities of finance that may lead to the existence of a financing gap without price distortions because financiers may not supply loanable funds for a variety of reasons. For instance, some issues of primary importance include the complications arising from asymmetric information, such as moral hazard and adverse selection (Stiglitz and Weiss 1981). Therefore, the core of an empirical strategy in estimating the financing gap is to frame the effect of these challenges which leave firms unable to access external financing. The concept of a MSME financing gap proposed here relies on estimating how much financing MSMEs in a country would have sought (willingness) and been able to obtain (ability) if they operated in a better institutional, regulatory and macroeconomic environment. On the supply side, this environment would allow financiers to make available more financing as challenges, such as asymmetric information, would be mitigated. The second, more practical issue relating to estimating the finance gap is the scarcity of broadly available cross-country data on both the supply and the demand sides. Several institutions collect data about the supply of finance. For example, IFC surveys approximately 400 financial institutions annually (the Reach Survey), and collects data on the loan portfolio to MSMEs, retail and corporate customers. The data collection also includes deposit volumes, channels, and demographic information of the client base of the financial institution (FI). Another example is an annual survey conducted by MIX, which partners with 1,033 microfinance institutions to collect data about their loan portfolios, deposits, gender financing, channels, margins and other outreach and profitability indicators. Great progress has been achieved in improving these data sources. However, the data remains fragmented and is not entirely representative of each developing country.9 The Financial Access Survey (FAS) of the 11. Other institutions conduct country-level diagnostics to collect supply-side data. For example, as a result of a partnership between the UNCDF, the United Nations Development Programme (UNDP), the Livelihoods and Food Securities Trust Fund (LIFT) and the Centre for Financial Regulation and Inclusion (Cenfri), the Making Access Possible initiative collects supply-side data through the in-country research and interviews with key players (for example, Making Access Possible (for example, Myanmar Financial Inclusion Roadmap 2014-2020) III. CURRENT METHODOLOGY 7 International Monetary Fund (IMF) is a global data depository of statistics about outstanding loan portfolios of almost all of the financial institutions around the world. Although this database is not complete for all countries, it is a serious attempt to harmonize the data collection. Continued progress is expanding the coverage as well as the depth of MSME supply-side data, which will bolster further research on the topic. FAS data was used as a primary source of supply-side data for the purposes of the present study. It was supplemented by the data from the SME Scorecard of the Organisation for Economic Co-operation and Development (OECD), as described in more detail in Step 3 below. Estimations relating to the demand for financing require detailed firm-level data that is comparable across countries. Some institutions conducted in-depth, firm-level surveys and studies to identify demand for finance and constraints regarding access to finance at the country level, such as initiatives supported by development financial institutions, including the Consultative Group to Assist the Poor (CGAP), IFC,10 the World Bank, the Netherlands Development Finance Company (FMO), the UNCDF, FinScope,11and national statistical bureaus, among others. However, these surveys and studies lack cross-country harmonization. Thus, detailed firm-level data with comprehensive information about current financial standing and financing needs are unavailable at the global level. This restriction implies that any estimation of the financing gap has to rely on less complex, firm-level data sources, for example, data collected by the World Bank Enterprise Surveys. The lack of data also imposes the need to make stronger assumptions than would be necessary if data availability was not an issue. The lack of uniform data about the informal MSME market segment represents an especially serious constraint. Multiple agencies are working on collecting data from microfinance institutions, including MIX and the Groupe Speciale Mobile Association (GSMA) or mobile network operators, where many informal enterprises might be traced. However, there is no governing body or unified data aggregator which can be confidently used as a source of informality data across all developing countries. This study refers to the only known global research about the shadow economy by Schneider and others (2010), and an extension of this research by Schneider (2012) as a proxy for the informal MSME segment. Overview of Methodology The methodology proposed here for calculating the MSME finance gap relies on estimating the “potential demand” for financing by MSMEs in emerging economies, and then comparing it with the current supply of financing. The notion of potential demand expresses the amount of financing that MSMEs would need, and financial institutions would be able to supply if they operated in an improved institutional, regulatory and macroeconomic environment. Conceptually, this methodology concretely bases the calculation of the financing gap on underlying issues that give rise to it in the first place. For this purpose, and as a first step, the methodology entails benchmarking the prototypical financing environment where MSME credit markets function with minimal imperfections. How much do MSMEs of a certain size and a certain maturity level (age) operating in a certain industry/sector typically borrow under “ideal” conditions? The second step is to apply these benchmarks to MSMEs operating in the emerging 12. Multiple studies, including Bangladesh, Indonesia, Mongolia, and Vietnam. 13. Multiple country-level studies, including Cambodia, India, Myanmar, Pakistan, and Tanzania. 8 MSME FINANCE GAP economies where the gap is to be calculated. This results in the estimated “potential demand”. Finally, the third step is to compare the potential demand with the existing supply within these countries to quantify the MSME finance gap. From a data availability standpoint, this approach also has the practical benefit of requiring more detailed firm-level financial information only for MSMEs in the benchmarked developed economies. The underlying thrust of this methodology arises from assumptions first proposed by Rajan and Zingales (1998). In particular, the methodology here relies on three assumptions set forward in their influential paper, namely that: (1) “there are technological reasons for variability in dependence on external finance across industries”; (2) “technological differences persist across countries”; and, therefore (3) “we can use an industry’s dependence on external funds as identified in the United States as a measure of its dependence in other countries”. Although Rajan and Zingales use these assumptions for a very different purpose, they can peripherally guide the starting point of the methodology proposed here. This proposed approach has a number of advantages, as well as limitations, over the other finance gap estimation methods. Advantages of the Methodology By its very nature, the estimation of the finance gap requires contemplating a counterfactual scenario. As discussed, computing the actual demand for the countries is not helpful, as it would equal supply under market-clearing equilibrium conditions. The thrust behind the existence of a gap lies with those MSMEs that would/could borrow more given certain improvements in the financing environment. This can also be thought of as the higher willingness of financial institutions to finance credit-worthy MSMEs. IFC’s previous study (2010) about the MSME finance gap used firm-level datasets to identify enterprises that were credit constrained. It also made assumptions regarding how much these enterprises would want to borrow. III. CURRENT METHODOLOGY 9 The problems with the previous approach are primarily twofold: (1) the assumptions about how much credit constrained firms would borrow was highly arbitrary; and (2) the counterfactual under which the gap exists was not well defined. The problem stemming from the counterfactual definition was that it was difficult to comprehend the total increase in the demand for finance. The changes in the enabling environment would not only allow an expansion of access to those MSMEs currently without sufficient financing, but would also trigger even more borrowing by those MSMEs that currently had financing. On the supply side, the lack of a definition of the counterfactual also raised uncertainty about the bankability of those currently unserved or underserved MSMEs. In fact, the previous methodology did not consider how much financial institutions would want to finance. Hence, the bankability consideration was entirely absent. The methodology proposed here defines the counterfactual more concretely. By relying on a benchmarking approach, the regulatory and macroeconomic changes required for the gap to manifest are clearly defined. Limitations of the Methodology This methodological approach has several limitations. For example, the benchmarking exercise assumes that a MSME finance gap and market distortions in MSME lending do not exist in the benchmarked countries. In addition, the benchmarking concentrates simply on the debt-to-sales ratio. There is a strong assumption that debt levels are primarily a function of sales. The most important limitation, perhaps, is in terms of interpretation and usability. The MSME finance gap estimated utilizing this methodology captures the latent demand that is only realized over the long-term when these economies approach financial development and regulatory sophistication similar to that of the benchmarked countries. This may not be the most useful measure of the gap for some scenarios and countries. For example, in a low-income country with very little financial development and an inadequate enabling regulatory environment, the gap — when its level of development approaches that of an advanced economy — may not be the appropriate comparator. For this country, a much more actionable data feature could be the gap when benchmarked against a regional comparator. The methodology proposed here is fluid enough to be adapted for such a comparison. Data permitting, the ratios of debt-to-sales for a regional comparator can be utilized as the appropriate benchmark. For the purpose of this report, however, the benchmarked countries are defined globally so that the resulting gap is comparable across countries. Each of the three computation steps of the methodology are now described in more detail. Step 1: Benchmarking As outlined above, the first step of the methodology entails estimating the financing needs of MSMEs in benchmarked countries where credit markets function relatively smoothly. Rajan and Zingales (1998) use the United States as their sole benchmark. However, they acknowledge that any country with a well- functioning credit market can, in principle, be used to measure the industry’s dependence on external financing. A wider selection of benchmark countries will also broaden coverage to a diverse number of industries. Ten countries serve as benchmarks: Australia, Canada, Denmark, Germany, Ireland, Israel, New Zealand, Switzerland, the United Kingdom, and the United States. These countries are selected based on the criteria that they are high-income and rank highest on the “Getting Credit” module of the World 10 MSME FINANCE GAP Bank’s Doing Business Index. The Getting Credit module explores two sets of issues—the strength of credit reporting systems and the effectiveness of collateral and bankruptcy laws in facilitating lending. In addition, income-level proxies are used for a host of characteristics related to regulatory efficiency. Together, these two criteria drive the selection of countries in which the regulatory and institutional environment favors well-functioning credit markets. In the spirit of Rajan and Zingales’ original assumption, three broad industry groupings – Manufacturing, Services and Retail – are chosen as the first category to benchmark MSME financing profiles. In addition, departing from their assumption, two additional layers of disaggregation are introduced, namely the size and age of MSMEs. The additional granularity introduced by these two categories within an industry is based on guidance from the existing literature. An extensive literature review has shown that smaller firms tend to be more financially constrained than their larger counterparts (Beck and others 2005, 2006, and 2008; Cressy 2002; IADB 2004; and Schiffer and Weder 2001). Meanwhile, younger firms are more likely to struggle in a credit environment that lacks a strong regulatory environment because they have shorter credit histories and typically do not have established relationships with lenders (Berger and Udell 1995; Chakrobarty and others 2006; Cole 1998; Ezeoha and Botha 2012; and Steijvers and others 2009). The firm-level information regarding the amount of borrowing by typical firms within these categories in the ten benchmark countries is provided by Bureau van Dijk’s ORBIS database. It is a commercial dataset, which contains administrative data on balance sheets and income statements for over 130 million firms worldwide. The ORBIS database harmonizes the collected data into a standard “global” format that facilitates within and cross-country comparisons of firms. Work done by Kalemli-Ozcan and others (2015) to determine the representativeness of the ORBIS database on firms in select European countries finds that ORBIS covers 75-80 percent of the economic activity reported in Eurostat. It also matches the official size distribution of firms provided by Eurostat. III. CURRENT METHODOLOGY 11 For each of the three categories described above, the mean debt-to-sales ratio is computed across firms in the ten countries. Debt is the sum of short-term loans12 and long-term debt.13 Other non-current liabilities, such as trade debts, are not included. Sales refers to the operating revenues of the company. To limit the effect of outliers, the top and bottom 5th percentile of the distribution of the variables is omitted from the analysis. The assumption inherent in the benchmarking relies on an unconstrained business environment that allows for a true financial equilibrium to emerge. Therefore, the post-global financial crisis years from 2011-2015 are selected. The final dataset contains over 800,000 observations. Table 1 summarizes the computed mean debt-to-sales ratio for the intersection of each of the three categories. The summarized table 1 conforms to prevalent understandings of MSME financing needs in various categories. Young firms, defined as firms that commenced operations within five years, require more credit than their more mature counterparts within the same size and industry categories. For young firms, an increase in size is generally correlated with higher financing needs, whereas the opposite holds true for more mature firms. On average, holding other variables constant, MSMEs in the retail sector obtain the least amount of financing. An implicit assumption in the benchmarking exercise is that the observed use of financing by firms in these economies represents the actual demand. Furthermore, for the benchmarked countries, an additional supposition is that there is no potential demand beyond the actual demand. In other words, there is no MSME finance gap in these countries. Table 1: Mean Debt-to-Sales Ratios  Size of MSME (employees)a 0 to 9 10 to 19 20 to 49 50 to 99 100 to 249  Age of MSME Young Mature Young Mature Young Mature Young Mature Young Mature Manufacturing 0.34 0.28 0.32 0.22 0.33 0.21 0.31 0.20 0.34 0.19 Retail 0.25 0.21 0.22 0.17 0.22 0.16 0.25 0.14 0.31 0.14 Services 0.25 0.28 0.24 0.23 0.31 0.24 0.52 0.28 0.52 0.32 Source: MSME Finance Gap study calculations (based on the Orbis dataset). a. There is significant variation in the definition of MSME size categories and often relies on a combination of employees, assets and reve- nues. Even for a categorization based on the number of employees, there is substantial variation in definitions across countries. This study defines micro enterprises as those with less than 10 employees, and MSMEs as those with less than 250 employees. This is the most widely used definition in the publications, and according to research by IFC’s MSME Country Indicators (2014), the most widely used definition by individual countries. 14. The variables name in ORBIS is LOAN, and is defined as short-term financial debts (for example, to credit institutions), plus part of long-term financial debts payable within the year. 15. The variables name in ORBIS is LTDB, and is defined as long-term financial debts with maturities longer than a year (for example, to credit institutions) in the form of loans and credits. LTDB stands for Long-Term Debt. 12 MSME FINANCE GAP Step 2: Potential demand for MSME finance The second step of the methodology entails applying the ratios obtained in the first step to the universe of MSMEs in each category for all emerging economies. The World Bank Enterprise Surveys furnish this data in a consistent and comparable manner across countries. The Enterprise Surveys use a common questionnaire and a uniform sampling methodology to produce survey data about manufacturing and service sector firms that are comparable across countries. In total, 133 emerging economies are covered by the Enterprise Surveys. Stratification of the sample is based on three criteria: sector, firm size (the number of employees), and geographic location. The stratified random sampling methodology is used to generate a sample large enough to be representative of the non-agricultural, formal private economy, as well as key sectors and firm size classifications. For the purpose of this methodology, it is crucial that the Enterprise Surveys provide estimates of the universe of MSMEs within each category using the survey weights. In essence, for each of the 30 categories shown in table 1, the Enterprise Surveys provide estimates for both the average sales and the total number of firms. When applying the benchmark ratio of each category to the average sales and total number of firms estimated by the Enterprise Surveys, summing up across the economy produces the potential MSME demand for financing in each country. An estimation issue was identified when comparing total sales calculated for the universe of firms through the Enterprise Survey with known total aggregates from other sources. For example, for the manufacturing sector, the United Nations Industrial Development Organization (UNIDO) provides total sales (disaggregated by the International Standard Industrial Classification [ISIC] industry classification) for a large array of countries. Similarly, for the service sector, the World Bank’s World Development Indicators (WDI) provide the total value added by the service sector (a lower-bound on sales). Using these comparisons, the total sales for each of the categories under the respective industries was scaled up to compensate for non-universal coverage of the Enterprise Surveys.16 The resulting potential demand for each country is interpreted as the hypothetical equilibrium amount of financing for MSMEs in the country as a result of higher firm demand, as well as the higher propensity by financial institutions to lend given their operations in an institutional, regulatory and macroeconomic environment similar to that of the benchmarked countries. Step 3: Existing supply of MSME finance Existing lending to MSMEs by financial institutions is available from two data sources, namely the IMF’s Financial Access Survey (FAS), and the OECD’s SME Scorecard.17 Both FAS and OECD instruct monetary authorities to provide MSME data using their own local definition that reflects the local banking context. 16. In many countries, under-sampling by the Enterprise Survey is a known issue because, for example, of the sizes of the economies and available sampling resources. The population of firms covered by the Enterprise Surveys does not include firms with fewer than 5 employees, as well as agriculture, extractive industries, personal services, financial services, education, healthcare, and utilities, among others. 17. Other data sources reporting supply side data for microenterprises in particular were also considered. These included, for example, data collected as part of the MIX Market Partnership and the GSM Association (GSMA). III. CURRENT METHODOLOGY 13 The FAS is a cross-country, aggregated, supply-side database pertaining to access to, and the use of, financing and financial services by resident households and nonfinancial corporations, including by MSMEs. The FAS is administered annually and data is collected from national regulators and supervisors based on the IMF’s guidelines and survey formats. The FAS covers commercial banks, credit unions and financial cooperatives, deposit-taking microfinance institutions, as well as other non-deposit-taking financial corporations. The OECD’s Financing SMEs and Entrepreneurs report (2017) provides information on debt, equity, asset- based finance, and framework conditions for SME and entrepreneurship finance in 39 countries. When available, the FAS is the primary data source, and data from the OECD’s SME Scorecard is used to augment any missing data. Relevant to the analysis at hand, both datasets provide lending information specifically related to MSMEs. Although almost all countries report total lending activities, only 52 countries report disaggregated MSME lending through the FAS. The MSME lending volume for another 15 countries not covered by FAS is available through the OECD’s SME Scorecard. The reported MSME lending volumes were compared to total lending as well as private sector credit provided by the financial sector (IMF) to identify outliers. For countries where the ratios were too high or too low,18 the MSME lending data was substantiated through their central banks or through public information from statistical agencies. Furthermore, for another 3 countries not reporting to FAS or the OECD, MSME lending data was ascertained from credible country sources. The total MSME lending volume data is available for 71 countries.19 For the remaining countries, a regression framework is proposed to predict the missing MSME volume. The following cross-sectional ordinary least squares (OLS) regression is estimated using country-level data: MSME Lending= α{MSME} + β{Macro} + γ{Banking} + η (1) The dependent variable is the log of the current MSME lending in the country. MSME refers to a vector of country characteristics relating to MSMEs, specifically the number of MSMEs as a percentage of the total, the share of MSMEs with access to external financing, and the MSME lending volume as a percentage of the total. All of these variables come from the Enterprise Surveys. Countries where there are more MSMEs in the economy, and where there is more access to finance, are expected to have higher MSME lending volume. Macro refers to a vector of variables relating to the general macroeconomic environment, including population, GDP, and a dummy variable to indicate whether the country is fragile or conflict affected. All these variables are sourced from the World Bank’s World Development Indicators. The first two macro variables relating to the size of the economy are general, positive predictors of MSME lending. The dummy variable reflecting fragility and conflict is expected to have a negative effect. 18. In particular, the top and bottom 3 countries ranked by the ratios were considered for further substantiation. A few additional countries were chosen for further research when the ratios were flagged as an outlier for the country’s income group. 19. These include both developed and emerging economies. 14 MSME FINANCE GAP Finally, banking refers to a collection of variables relating to the banking, regulatory and institutional environment, including the lending interest rate (WDI); the Z score; the Lerner Index;20 credit bureaus; movable collateral registry dummies; contract enforcement, and distance to frontier (DTF).21 The lending interest rate conveys information about the price of financing directly, and the Lerner index captures the market competition. A more competitive market is expected to serve MSMEs better, and have higher MSME lending volumes. Establishing a credit bureau or collateral registry has been shown to increase access to financing for MSMEs. The two remaining regulatory variables point to the general, enabling regulatory environment that may be conducive to lending overall. The η refers to robust standard errors. The use of logs helps deal with outliers and prevents negative predicted values. In addition, to reduce noise and increase observations, three-year averages of all variables are used. The primary motivation for the regression and the choice of variables lies in their predictive power. As such, in a cross-sectional regression using aggregate country-level data, the volume of variables considered means that multicollinearity is potentially an issue. Thus, an interpretation of signs and estimate sizes is not prudent. However, the computation of the within-sample, mean absolute percentage error (MAPE) is used to confirm good fit and predictive power. In addition, a series of deliberately curtailed sub-sample regressions followed by “out-of-sample” predictions for countries omitted were conducted, and the MAPE was found to be satisfactory. As before, all predicted MSME lending volume ratios were compared to total lending, private sector credit provided by financial institutions, and GDP to check for the reasonableness of the ratios. MSME Finance Gap Bringing together the potential demand calculated in step 2 with the current supply collated/computed in step 3 produces the MSME finance gap for each country. MSME finance gap = Potential demand – Existing supply (2) This is the MSME finance gap, assuming firms in a developing country have the same willingness and ability to borrow as their counterparts in well-developed credit markets and operate in comparable institutional environments — and that financial institutions lend at similar intensities as their benchmarked counterparts. Disaggregating by Firm Size and Gender Ownership The nature of the calculation lends itself readily to calculating the potential demand for microenterprises and SMEs separately in step 2. Following the World Bank Group’s definition of classifying firms employing less than 10 permanent workers as microenterprises, the aggregations of potential demand are done 20. These two variables are banking sector stability and competition variables from the World Bank’s Global Financial Development Database. 21. These three variables are provided by the IFC’s Doing Business dataset. DTF refers to the “Distance to Frontier” rating mechanism of the Doing Business dataset. “The distance to frontier score aids in assessing the absolute level of regulatory performance and how it improves over time. This measure shows the distance of each economy to the “frontier,” which represents the best performance observed on each of the indicators across all economies in the Doing Business sample since 2005.” http:// www.doingbusiness.org/data/exploretopics/starting-a-business/frontier III. CURRENT METHODOLOGY 15 separately for firms with less or greater than 10 employees. Separating out current volumes of micro- enterprise and SME lending is trickier. As no cross-country data with broad coverage of this disaggregation is available, the share of lending to microenterprises as compared to SMEs from the Enterprise Surveys was used to extrapolate separate current lending volumes. Finally, the microenterprise and SME finance gaps are computed, as in equation (2) above. The disaggregation of the finance gap for female- and male-owned firms is not as straightforward. A stronger assumption is used in disaggregating the microenterprise and SME finance gaps respectively into female- and male-owned enterprises, respectively. It uses the gender-owner firm’s share of overall sales as computed using data from the Enterprise Surveys. An important consideration is the classification of female- and male-owned firms. The IFC uses a definition that partly relies on a majority ownership stake by women to classify MSMEs as female-owned. In recent surveys, the Enterprise Survey has started collecting information about ownership percentages. However, the older surveys do not contain this information, and only indicate if any of the owners are female. This presents two possible options for the definition of female ownership: n Option 1: At least 50 percent female ownership, OR Sole Proprietorships that are female-owned, OR female participation in ownership and management (top manager). n Option 2: Sole Proprietorships that are female-owned, OR female participation in ownership and management (top manager). Gender disaggregation using both of these definitions can be calculated. For cross-country comparisons across all emerging economies, the second option is suitable. The first option conforms to IFC’s definition even though the first criteria based on ownership percentages can only be applied to about 70 percent of the countries. The analysis presented in this report is based on Option 1, however the data for both options are available for download from http://www.smefinanceforum.org/data-sites. . 16 MSME FINANCE GAP Informal Finance Gap Cross-country data with broad coverage about the universe of informal firms, their economic activity and their financing sources is not available.22 Both demand-side and supply-side data are missing. As such, estimating the finance gap for the informal MSME sector is extremely difficult. Schneider (2012) is an oft-cited paper that estimates the size of the informal economy. The author defines the “shadow” economy as part of the economy that “includes all market-based legal production of goods and services that are deliberately concealed from public authorities for a variety of reasons.” (Schneider 2012, 6). Armed with assessments of the size of the informal economy, it is still far from a straightforward exercise to arrive at an estimate of the informal firm finance gap. A stronger assumption regarding the demand for financing by informal firms compared to their formal counterparts has to be made. Under the more idealized institutional and regulatory environment that underlies all computations of the formal firm finance gap, it is perhaps reasonable to assume that informal firms of similar sizes and sales as their formal counterparts would have similar financing needs. Thus, the potential demand for the formal sector is used to proportionally extrapolate the potential demand for the informal sector. Using this extrapolation, a stronger implicit assumption is made regarding the structure of the informal economy in terms of similarity of industry distribution to the formal economy. The final step of computing the current volume and estimating the gap is neither feasible nor relevant. Presumably, the amount of formal lending to informal firms is close to zero. Thus, the potential demand is the more relevant metric to articulate the financing gap that may potentially arise if and when these firms formalize and become serviceable by formal financial institutions. Number of Credit-Constrained Enterprises This report also complements the MSME finance gap by computing the number and percentage of credit-constrained MSMEs. There are a number of approaches that can be used to potentially identify whether a firm is credit constrained. In the World Bank’s Enterprise Surveys, for example, the firms are asked to self-rate the perceived scale at which financing presents an obstacle. As a subjective measure, this identification is problematic. Another approach is to look at firms that do not currently have a loan, a line of credit or overdraft protection. Identification solely based on usage is problematic because firms without current financing may not require external financing. Thus, a more robust and multidimensional identification strategy is required. The estimation of the number of credit-constrained enterprises in this report relies on a proposed measure by Kuntchev and others (2014). Based on a variety of questions (see box 1) regarding both usage of and the ability to obtain new credit, enterprises are categorized as fully credit-constrained (FCC), partially credit- constrained (PCC), and not credit-constrained (NCC) firms. Credit-constrained firms are defined as those that are fully constrained (FCC) or partially constrained (PCC).23 22. The Enterprise Surveys have included a few surveys of the informal sector, but do not provide broad coverage across countries. 23. The data regarding categorization is based on current conditions faced by enterprises. For example, the categorization of enterprises as not credit constrained (NCC) is only valid over the short run. Given macroeconomics and regulatory changes, or changes in product offerings, these enterprises may demand more credit and potentially cease to be NCC. As noted, the Enterprise Surveys only sample firms with 5 or more employees. Thus, the computation of the fraction of microenterprises belonging in each credit constraint category is based on this sample. Under the assumption that smaller microenterprises face similar constraints, the computed percentages are applied to the overall microenterprise population in the country. III. CURRENT METHODOLOGY 17 Box 1. Credit-Constrained Enterprises: Methodology The figure below provides a schematic representation of the approach to define credit-constrained enterprises. Definitions of the various categories are included below the figure. Fully credit-constrained (FCC) firms are defined as those that find it challenging to obtain credit. These are firms that have no source of external financing. They typically fall into two categories: those that applied for a loan and were rejected; and those that were discouraged from applying either because of unfavorable terms and conditions, or because they did not think the application would be approved. The terms and conditions that discourage firms include complex application procedures, unfavorable interest rates, high collateral requirements, and insufficient loan size and maturity. Partially credit-constrained (PCC) firms are defined as those that have been somewhat successful in obtaining external financing. PCC firms include those that have external financing, but were discouraged from applying for a loan from a financial institution. They also include firms that have an external source of financing, and firms that applied for a loan that was then partially approved or rejected. Non-credit-constrained (NCC) firms are those that do not appear to have any difficulties accessing credit or do not need credit. Firms in this category encompass those that did not apply for a loan as they have sufficient capital either on their own or from other sources. It also includes firms that applied for loans that were approved in full. There are limitations to the credit constraint indicator. The indicator does not incorporate any information about the creditworthiness of the firm. Therefore, among the credit-constrained firms, there may be some that were rationed for good reasons, such as insufficiently productive projects or a poor repayment history. Correspondence betweens red constrained groups and questions in Enterprise Surveys Did the firm have any source of external finance? Yes No Did the firm apply for a loan or line of credit? Did the firm apply for a loan or line of credit? No Yes No Yes Why not? Why not? Has enough Terms and Approved Approved Rejected Has enough Terms and Rejected capital conditions in full in part capital conditions Not Credit Partially Credit Fully Credit Constrained Constrained Constrained (NCC) (PCC) (FCC) Source: Kuntchev and others (2014) 18 MSME FINANCE GAP This report relies on the consolidated statistics provided by the MSME Country Indicators (IFC 2014) for ascertaining the number of enterprises in each country. This information is available for the number of microenterprises in 66 countries and for SMEs in 59 countries. For an additional 15 countries, the data has been collected directly from government sources. For the remaining countries, it has been estimated based on the World Bank’s Enterprise Surveys (see box 2 below). Box 2. Estimation of the Number of MSMEs based on the World Bank Enterprise Surveys The number of MSMEs is extrapolated from Enterprise Survey (ES) data when Country Indicator (CI) data are not available, and an Enterprise Survey has been conducted recently. ES data alone understate the number of MSMEs because the ES only covers a subset of the population of existing enterprises. Specifically, the ES covers the formal, registered private sector of manufacturing and services firms. It does not include firms with fewer than 5 employees, as well as those pertaining to mining, oil and extractives, financial intermediation, utilities, healthcare, or education. This report compares ES to CI data for 65 countries where both exist. It finds that the ES to CI ratio averages 0.34 to one for SMEs and 0.065 to one for microenterprises. For the subset of countries where CI data is not available (but ES data is), the ES numbers are scaled by these ratios to estimate the number of establishments in each. For example, if the ES estimates that 500 SMEs exist in country X, 500 is scaled by 0.34 to extrapolate that there are 1471 (500/0.34=1471) SMEs in country X. Source: MSME Finance Gap study calculations, World Bank Enterprise Surveys. III. CURRENT METHODOLOGY 19 IV. Quantifying the Finance Gap Number of Enterprises There are close to 162 million formal micro, small and medium enterprises (MSMEs) in developing countries, of which 141 million are microenterprises, and 21 million are SMEs (figure 1).24 Three countries — Brazil, China and Nigeria — contribute 67 percent to the total number of MSMEs, which is equivalent to 109 million enterprises. There are close to 12 million SMEs in China alone, which represents 56 percent of all SMEs in developing countries. China also has 44 million microenterprises, which represents 31 percent of all microenterprises in developing countries. Figure 1. Number of MSMEs in Developing Countries, millions MICROENTERPRISES 141.44 SMEs 20.75 TOTAL MSMEs 162.19 Source: IFC data and analysis. 24. The number of enterprises is larger than reported in the IFC Enterprise Finance Gap (2011) due to the following: (1) country coverage has changed; (2) some countries (such as Brazil, China, Colombia, Nigeria, and Thailand, among others) improved data collection and the data quality in the MSME space, which yielded the larger officially reported number of MSMEs. IV. QUANTIFYING THE FINANCE GAP 21 There is a large concentration of enterprises in the East Asia region (64 million), followed by Sub- Saharan Africa, which has 44 million MSMEs, the majority of which (97 percent) are microenterprises (see figure 2). Nigeria, which is a large contributor to the enterprise count in Sub-Saharan Africa, has 37 million MSMEs. Latin America and the Caribbean, which is the third largest region by number of MSMEs, has 28 million MSMEs, 26 million of which are microenterprises. This regional position is mainly driven by the large MSME segment in Brazil, which has 16 million MSMEs. In this regard, it is important to note that of the 132 countries for which the authors counted the number of businesses, this data has been determined by using the official country-level statistics for 96 and 74 countries, respectively. The data for the remaining countries has been extrapolated using the World Bank Enterprise Surveys.25 Figure 2. Number of Micro, Small and Medium Enterprises by Region 80 64.1 70 60 15.5 44.2 50 40 27.5 1.5 1.3 30 12.4 20 1.1 8.4 5.5 10 1.1 0.1 48.6 11.3 26.2 42.7 5.4 7.3 0 EAP ECA LAC MENA SA SSA Micro SME MSME Source: IFC data and analysis. Note: EAP = East Asia and Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; SA = South Asia; and SSA = Sub-Saharan Africa. 25. Refer to the Methodology Section of this report. 22 MSME FINANCE GAP The number of MSMEs is the largest in Upper-middle-income countries, which includes Brazil and China – the two largest contributors (see figure 3). Lower-middle-income countries are the second largest category, which includes Nigeria – the third largest contributor. Figure 3. Number of Micro, Small and Medium Enterprises by Country Income Group Numb r of MSMEs, millions 93.9 100 16.4 80 58.6 2.8 60 40 20 4.5 55.9 77.5 5.2 3.4 1.1 4.7 0 Low-incom Low r-middl -incom Upp r-middl -incom Hi h-incom Micro SME MSME Source: IFC data and analysis. Based on the approach explained in the methodology section of this report (box 1), it is estimated that in developing countries, 21 percent (29.6 million) of microenterprises are fully-constrained, and 19 percent (26.6 million) are partially constrained. However, 60 percent (85.2 million) remain financially unconstrained. A similar picture can be observed in the SME segment in developing countries. In this context, 30 percent (6.2 million) of SMEs are fully constrained, 14 percent (2.8 million) of SMEs are partially constrained, and 56 percent (11.7 million) are financially unconstrained. See figure 4. Figure 4. Number of Financially-Constrained MSMEs Numb r of Micro nt rpris s Numb r of SMEs Unconstr in d 85.2 Unconstr in d 11.7 P rtl Constr in d 26.6 P rtl Constr in d 2.8 Full Constr in d 29.6 Full Constr in d 6.2 0 20 40 60 80 100 0 2 4 6 8 10 12 Millions Millions Source: IFC data and analysis. IV. QUANTIFYING THE FINANCE GAP 23 Map 1 shows the regional differences in the number of financially-constrained enterprises among developing countries included in this report. Map 1. Number of Financially-Constrained MSMEs Worldwide Source: IFC data and analysis. 24 MSME FINANCE GAP Microenterprises On average, 21 percent of microenterprises in developing countries are fully constrained, 19 percent are partially constrained and 60 percent are unconstrained. South Asia has the largest proportion of financially constrained microenterprises – both fully and partially constrained (54 percent), followed by Sub-Saharan Africa (52 percent). Latin America has the lowest proportion of financially constrained microenterprise firms (21 percent). Europe and Central Asian region has the second lowest proportion of financially constrained microenterprises (27 percent). See table 2 and figure 5. Table 2. Distribution of Microenterprises by Financial Constraint Level (%) Region Fully Constrained Partly Constrained Unconstrained EAP 34 7 59 ECA 14 13 73 LAC 10 11 79 MENA 19 14 67 SA 37 16 46 SSA 12 40 48 Total 21 19 60 Source: IFC data and analysis. Note: EAP = East Asia and Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; SA = South Asia; and SSA = Sub-Saharan Africa Figure 5. Distribution of Microenterprises by Constraint Level Micro nt rpris s, millions EAP 16.53 3.34 28.73 ECA 1.57 1.50 8.27 LAC 2.54 2.87 20.77 MENA 1.03 0.76 3.59 SA 2.73 1.18 3.36 SSA 5.24 16.98 20.45 Full Constr in d P rt Constr in d Unconstr in d Source: IFC data and analysis. Note: EAP = East Asia and Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; SA = South Asia; and SSA = Sub-Saharan Africa. IV. QUANTIFYING THE FINANCE GAP 25 Small and Medium Enterprises On average, 30 percent of SMEs in all developing countries are fully constrained, 14 percent are partially constrained and 56 percent are unconstrained. Sub-Saharan Africa has the largest proportion of financially constrained SMEs – both fully and partially constrained (54 percent), followed by South Asia (50 percent). Europe and Central Asia has the highest share of unconstrained SMEs (69 percent), with only 31 percent of firms fully or partially constrained. This is followed by the Latin America and the Caribbean region, with 68 percent of financially unconstrained enterprises and only 32 percent of fully or partially constrained enterprises. See table 3 and figure 6. Table 3. Distribution of SMEs by Financial Constraint Level (%) Region Fully Constrained Partly Constrained Unconstrained EAP 33 11 56 ECA 17 14 69 LAC 9 22 68 MENA 14 20 66 SA 24 26 50 SSA 28 25 46 Total 30 14 56 Source: IFC data and analysis. Note: EAP = East Asia and Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; SA = South Asia; and SSA = Sub-Saharan Africa. Figure 6. Distribution of SMEs by Constraint Level SMEs, millions EAP 5.18 1.68 8.63 ECA 0.19 0.15 0.77 LAC 0.12 0.30 0.92 MENA 0.02 0.03 0.10 SA 0.27 0.30 0.57 SSA 0.43 0.39 0.71 Fully Constrained Partly Constrained Unconstrained Source: IFC data and analysis. Note: EAP = East Asia and Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; SA = South Asia; and SSA = Sub-Saharan Africa. 26 MSME FINANCE GAP Micro, Small and Medium Enterprises – Distribution by Income Groups Further analysis demonstrates that countries in the high-income group have the highest proportion of unconstrained micro, small and medium enterprises, that is, 81 percent (4.2 million MSMEs), with only 19 percent of financially constrained enterprises (one million). By contrast, countries in the low-income group have the largest proportion of fully or partially constrained MSMEs, that is, 67 percent (3 million MSMEs). Twenty-six percent of MSMEs in the Upper-middle-income countries are fully constrained (24.7 million), and 9 percent (8.2 million) partially constrained. Sixty-five percent of enterprises in this group are financially unconstrained (61.0 million). Finally, countries in the Lower-middle-income group have 15 percent (8.9 million) fully-constrained MSMEs, 33 percent (19.5 million) partially-constrained MSMEs and 52 percent (30.2 million) unconstrained MSMEs. These figures demonstrate greater market opportunities for financial institutions in the low and Lower-middle-income countries. See table 4. Table 4. Distribution of MSMEs by Financial Constraint Level (%) Country Income Group Fully Constrained Partly Constrained Unconstrained Low income 42 25 33 Lower-middle income 15 33 52 Upper-middle income 26 9 65 High income 7 13 81 Total 22 18 60 Source: IFC data and analysis. Formal Finance Gap Although the MSME segment is important for the global economy, data remains scarce, incomplete and fragmented. The present research attempts to complement existing data in the MSME space by estimating the potential demand for and current supply of MSME finance in order to determine the finance gap in 128 developing countries. This study finds that of a total of $8.9 trillion in potential demand for MSME finance, only $3.7 trillion is currently being supplied. See figure 7. Figure 7. MSME Finance Gap MSME Finance Gap, US$ trillions Current Finance Gap, Volume, 3.7 5.2 Potential Demand, 8.9 Source: IFC data and analysis. IV. QUANTIFYING THE FINANCE GAP 27 The unmet demand for financing in the MSME segment in developing countries is valued at $5.2 trillion, which represents 19 percent of these countries’ cumulative GDP. This finance gap suggests that 59 percent of potential demand for MSME finance is unmet. Potential demand represents a long-term indicator of the financing needs of MSMEs in developing countries. In this context, these needs can potentially be met only if public sector institutions create favorable conditions for business development, and if private sector financiers find appropriate approaches to serve MSMEs within constantly changing macroeconomic environments.26 The microenterprise finance gap is estimated at $718.8 billion, and the SME finance gap at $4.5 trillion. This unmet demand represents 81 percent of the potential demand from microenterprises, for a total of $882 billion. The unmet demand from SMEs is 56 percent of the potential demand for this segment, valued at $8.1 trillion. The total volume of current MSME financing is unevenly distributed between microenterprises and SMEs — with 96 percent attributed to SME finance, and only 4 percent to microenterprise finance. Interestingly, the total MSME finance gap has a very different distribution, with a 14 percent share attributed to the microenterprise finance gap and 86 percent to the SME finance gap. Such imbalances indicate that microenterprises have relatively higher unmet needs from formal sources, which might be replaced with alternative sources, such as funding from friends and family, business partners, peer-to-peer markets or informal financing arrangements. Figure 8 illustrates the distribution of the potential demand by enterprise size and compares the current finance volume with the finance gap. Figure 8. Potential MSME Finance Demand Distribution Micro nt rpris s Sm ll nd M dium Ent rpris s Curr nt Volum 19% $882 $8.1 Curr nt billion trillion Volum Fin nc 44% G p 56% Fin nc G p 81% Source: IFC data and analysis. 26. For one country in this analysis, Mauritania, the current volume of MSME finance is estimated at $611 million, and the estimated potential demand is $336 million. This results in a negative MSME finance gap of $275 million (the difference between potential and current needs). This may either reflect data issues or that MSMEs in the country are truly over-indebted. 28 MSME FINANCE GAP Map 2 demonstrates the regional distribution of the MSME finance gap across developing countries. Map 2. Formal MSME Finance Gap in Developing Countries Source: IFC data and analysis. IV. QUANTIFYING THE FINANCE GAP 29 Regional analysis of potential MSME demand demonstrates that it is highest in the East Asia and Pacific region – with almost 58 percent of the total global potential demand. This is mainly driven by the large demand and supply in China ($4.4 trillion and $2.5 trillion, respectively). The finance gap in Latin America and the Caribbean is the second largest after the East Asia region, and is mainly driven by Brazil ($0.6 trillion). India is another big contributor country, with a finance gap of $230 billion, representing 68 percent of the total gap in the South Asia region (figure 9). Figure 9. Regional Distribution of MSME Potential Demand and Finance Gap (%) Pot nti l D m nd Fin nc G p SSA SSA 5% 6% EAP EAP 46% SA 58% 6% SA 6% MENA 2% MENA 4% LAC 16% LAC 23% ECA ECA 15% 14% Source: IFC data and analysis. Note: EAP = East Asia and the Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; SA = South Asia; SSA = Sub-Saharan Africa. 30 MSME FINANCE GAP The country coverage in this study was driven by data availability in the World Bank Enterprise Surveys, which mostly cover developing countries. Sub-Saharan Africa has the largest number of countries (with 37 of the 128 countries), followed by Latin America and the Caribbean (with 30 of the 128 countries). The Middle East and North Africa and South Asia regions had the smallest number of countries: 8 and 7 countries, respectively. (See tables 5 and 6). However, the estimation model developed here does not depend on the country representation in the region. Table 5. Country Coverage of Present Research Region Number of Countries in this Study Number of Countries Coverage (%) EAP 17 38 45 ECA 29 58 50 LAC 30 42 71 MENA 8 21 38 SA 7 8 88 SSA 37 48 77 TOTAL 128 215 60 Source: IFC data and analysis. Note: EAP = East Asia and the Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; SA = South Asia; SSA = Sub-Saharan Africa; WBG = World Bank Group. Table 6. Regional Distribution of MSME Potential Demand and Finance Gap Number of Number of MSMEs, Potential Demand, Current Volume, Finance Gap, Region Countries millions US$ billions US$ billions US$ billions EAP 17 64 5,142 2,755 2,387 ECA 29 12 1,279 503 776 LAC 30 28 1,395 185 1,209 MENA 8 5 221 26 195 SA 7 8 501 164 337 SSA 37 44 404 70 331 TOTAL 128 162 8,942 3,642 5,235 Source: IFC data and analysis. Note: EAP = East Asia and the Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; SA = South Asia; SSA = Sub-Saharan Africa. IV. QUANTIFYING THE FINANCE GAP 31 There is a wide dispersion with regard to the total MSME finance gap volume among regions. The highest proportion of the finance gap compared to potential demand can be found in both the Latin America and the Caribbean and the Middle East and North Africa regions – with 87 and 88 percent, respectively. The smallest proportion can be found in East Asia and Pacific – 46 percent (see figure 10). On average, the total MSME finance gap accounts for 59 percent of potential demand, with the remaining 41 percent of financing currently supplied by financial institutions. Figure 10. MSME Finance Gap as a Proportion of Potential Demand (%) Pot nti l D m nd for MSME Fin nc 100 80 46% 61% 87% 88% 67% 82% 59% 60 40 54% 39% 20 41% 33% 18% 13% 12% 0 LAP LCA LAC MENA SA SSA Tot l Curr nt Volum Fin nc G p Source: IFC data and analysis Note: EAP = East Asia and the Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; SA = South Asia; SSA= Sub-Saharan Africa. 32 MSME FINANCE GAP As shown in figure 11, SMEs represent 14 percent of credit-constrained MSMEs in developing countries, and account for 86 percent of the MSME finance gap. Microenterprises represent 86 percent of credit- constrained MSMEs in developing countries, accounting for only 14 percent of the MSME finance gap. The highest microenterprise finance gap is in the Middle East and Africa regions (over 20 percent), with the lowest in the Latin America and the Caribbean region (9 percent). When examining the proportion of the SME finance gap as compared to potential demand, the highest figures were found in Latin America and the Caribbean and South Asia regions (with 91 percent and 86 percent, respectively). Figure 11. Finance Gap and Population of Enterprises (%) MSME Fin nc G p P rc nt of Cr dit-Constr in d MSMEs 100 100 7% 3% 4% 10% 13% 14% 26% 80 80 71% 74% 60 82% 86% 86% 60 88% 91% 98% 96% 74% 90% 87% 86% 40 40 93% 20 20 29% 26% 12% 18% 14% 14% 9% 0 0 EAP ECA LAC MENA SA SSA Tot l EAP ECA LAC MENA SA SSA Tot l Micro nt rpris SME Micro SME Source: IFC data and analysis. Note: EAP = East Asia and the Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; SA = South Asia; SSA = Sub-Saharan Africa. IV. QUANTIFYING THE FINANCE GAP 33 The finance gap in Upper-middle-income countries constitutes close to 71 percent of the finance gap in the developing countries in this review. This can partially be attributed to the fact that over 30 percent of the countries (44 of 128 countries) are in this category, and partially to the fact that China (which has a very high potential finance demand and gap) is one of the countries in this category. The Lower-middle-income countries, which have the largest country coverage (with 47 of 128 countries), have a total MSME finance gap of $1.17 trillion, with 24 percent attributed to microenterprises and 76 percent attributed to the SME finance gap (see figure 12). Figure 12. Finance Gap according to Income Group Finance Gap per Income Group, US$ trillions Potential Demand per Income Group, US$ trillions 3.72 6.94 1.17 1.47 0.30 0.48 0.05 0.06 Low-income Low-middle- Upper-middle- High-income Low-income Low-middle- Upper-middle- High-income income income income income Microenterprise SME Total Microenterprise SME Total Source: IFC data and analysis. Upper-middle-income countries account for 78 percent of potential demand and 71 percent of the finance gap. Fifty-eight percent of all MSMEs are in this category. Lower-middle-income countries have 36 percent of enterprises, and account for 16 percent of potential demand. They comprise 22 percent of the finance gap. High-income countries have 3 percent of MSMEs, and account for 5 percent of potential demand. They comprise 6 percent of the finance gap. Finally, low-income countries account for 3 percent of MSMEs, and 1 percent of both potential demand and the finance gap. The finance gap as a proportion of potential demand is the highest in the low-income and lower-middle- income countries, with 80 percent in comparison with a total of 59 percent for all developing countries included in this study (see figure 13). The microenterprise finance gap as a proportion of the microenterprise potential demand is the highest in the lower-middle-income countries (94 percent), and lowest in the high- income countries (63 percent). The SME finance gap as a proportion of potential SME demand is highest in low-income countries (78 percent), as compared to 56 percent in all developing countries. The higher the proportion, the smaller the current lending volume. Thus, there is a larger opportunity for financial institutions to serve these enterprises in need. However, appropriate models must be established to tap into the potential returns and effectively manage the risks. 34 MSME FINANCE GAP Figure 13. Finance Gap as Percentage of Potential Demand 100 94% 89% 81% 78% 80% 80% 77% 80 76% 63% 64% 64% 56% 59% 60 52% 54% 40 20 0 Low-incom Low-middl -incom Upp r-middl -incom Hi h-incom Gr nd Tot l Micro nt rpris SME Tot l Source: IFC data and analysis. In order to understand the scale of the estimated finance gap, it can be compared to the GDP of the countries under review. On average, the MSME finance gap represents 19 percent of individual countries’ GDP. In lower-middle-income and high-income countries, this indicator is 20-21 percent. In upper-middle-income countries, it is 18 percent, and in low-income countries, it is 15 percent. Regionally, the dispersion of this indicator is more evident, with the highest being in the Middle East and North Africa (26 percent) and the lowest in South Asia and Sub-Saharan Africa (16% in each). Generally, the higher the ratio, the higher the need for financing in relation to the size of the economy. This, in turn, provides further incentives for financial institutions to tap into this market opportunity with the right tools and approaches. Table 7a. Average Finance Gap as a Percentage of GDP (by region) EAP ECA LAC EAP ECA LAC MENA SA SSA Total Finance Gap / GDP 22 20 18 26 16 16 19 Table 7b. Average Finance Gap as a Percentage of GDP (by income group) Low income Low-middle income Upper-middle income High income Total Finance Gap / GDP 15 21 18 20 19 Source: IFC data and analysis. Note: EAP = East Asia and the Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; SA = South Asia; SSA = Sub-Saharan Africa. IV. QUANTIFYING THE FINANCE GAP 35 Gender Finance Gap Women-owned businesses comprise 28 percent of business establishments and account for 32 percent of the MSME finance gap. Female-owned MSMEs are generally smaller than their male-owned counterparts and thus employ fewer workers: 18-19 on average versus 21-22 at male-owned MSMEs. The total MSME finance gap for women27 is estimated to be valued at $1.7 trillion, which is over 6 percent of total GDP. Despite their smaller average size, female-owned businesses account for an outsized share of the finance gap — with 24 percent of the total microenterprise finance gap ($173 billion) and 33 percent of the total SME finance gap ($1.5 trillion) attributed to these female-owned firms. (See figures 14 and 15, map 3, and tables 8 and 9). Figure 14. Gender Composition of the Finance Gap Microenterprise Finance Gap, US$ billions SME Finance Gap, US$ billions 719 3,034 4,516 473 1,482 173 Women Men Total Women Men Total Source: IFC data and analysis. Figure 15. Women MSME Finance Gap as a Percentage of Current Volume of Finance Women Microenterprise Finance Gap Women SME Finance Gap Low-income 13% Low-income 51% Lower-middle-income 10% Lower-middle-income 58% Upper-middle-income 4% Upper-middle-income 40% High-income 11% High-income 17% Total 5% Total 40% Source: IFC data and analysis. 27. For the purposes of this analysis, a women-owned enterprise is defined as an enterprise that meets either of the following criteria: (1) at least 50 percent female ownership; (2) sole proprietorships that are female owned; and/or (3) female participation in ownership and management (top manager). Please, refer to the Option 1 in the Methodology section of this paper. The World Bank Enterprise Survey in three countries (Gambia, Mozambique, South Africa) has been conducted in 2006-2007 and did not contain extended questionnaire about the female participation in ownership and management. Therefore for these countries the authors have used only one indicator to define Women-owned enterprise, i.e. “Percent of firms with female participation in ownership” 36 MSME FINANCE GAP Map 3. Formal MSME Finance Gap in Developing Countries attributed to Female Enterprises (US$ billions) Source: IFC data and analysis. IV. QUANTIFYING THE FINANCE GAP 37 Table 8. Top Five Countries: Microenterprise Finance Gap for Women as a Share of the Total Microenterprise Finance Gap Microenterprise Women Finance Gap, Total Finance Gap, Women Finance Gap, Finance Gap US$ million US$ million percentage Morocco 12,672 14,138 90 Thailand 45,126 53,893 84 Benin 22 27 82 Kenya 823 1,086 76 Guinea 481 665 72 Source: IFC data and analysis. Table 9. Top Five Countries by Share of SME Finance Gap for Women (to the total SME finance gap) Women Finance Gap, Total Finance Gap, Women Finance Gap, SME Finance Gap US$ million US$ million percentage Yemen, Republic of 13,972 18,406 76 Timor-Leste 302 408 74 Micronesia, Federated States of 48 68 70 China 1,135,055 1,804,963 63 Mongolia 765 1.240 62 Source: IFC data and analysis. 38 MSME FINANCE GAP East Asia has the highest proportion of the microenterprise finance gap attributed to women-owned businesses (37 percent, $103 billion). The Middle East and North Africa region has the second highest proportion of the female microenterprise finance gap (29 percent, $16 billion). The smallest proportion is in Latin America and the Caribbean (5 percent, $5 billion). (See figure 16). Figure 16. Microenterprise Finance Gap: Women-Owned Enterprises 120 37% 100 29% 80 26% 60 40 10% 20 9% 5% 8 103 36 5 16 4 0 EAP ECA LAC MENA SA SSA GAP, US$ billions Wom n Fin nc G p s % of Tot l Fin nc G p Source: IFC data and analysis. Note: EAP = East Asia and the Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; SA = South Asia; SSA = Sub-Saharan Africa. East Asia has the highest proportion of the SME finance gap attributed to women-owned businesses (59 percent, $1.2 trillion). Sub-Saharan Africa region has the second highest proportion of the female SME finance gap (17 percent, $42 billion), while the smallest proportion is in Latin America and the Caribbean (8 percent, $93 billion), and South Asian regions (8 percent, $23 billion). See figure 17. Figure 17. SME Finance Gap: Women-Owned Enterprises (US$ billions) 1,400 1,200 59% 1,000 800 600 400 16% 17% 200 10% 67 93 8% 8% 1,236 22 23 42 0 EAP ECA LAC MENA SA SSA GAP, US$ billions Wom n Fin nc G p s % of Tot l Fin nc G p Source: IFC data and analysis. Note: EAP = East Asia and the Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; SA = South Asia; SSA = Sub-Saharan Africa. IV. QUANTIFYING THE FINANCE GAP 39 Potential Demand in the Informal Sector Based on Schneider, Buehn and Montenegro’s (2010) research, it has been established that the employment structure and other macroeconomic factors (such as taxation, the regulatory burden, social security, and income level) influence the shadow economy. Using their estimates of the shadow economy in 107 countries, the present research estimates that there is a $2.9 trillion potential demand for MSME finance in the informal sector in developing countries. This represents 11 percent of the GDP of these countries. Thus, the combined total formal and informal potential demand for MSME finance is estimated at $11.9 trillion in developing countries (see figure 18). Figure 18. Potential Total Demand for MSME Finance Formal and Informal Demand for MSME Finance, US$ trillions 2.9 11.9 8.9 Formal Informal Total Source: IFC data and analysis. The potential demand for MSME finance in the informal sector is the largest in East Asia and the Pacific region ($995 billion), followed by the Latin America and the Caribbean region ($756 billion). It is lowest in the Middle East and North Africa region ($69 billion). However, in absolute terms, the numbers might not be strictly comparable across regions because the informality data is available for only 110 countries of the 128 countries under review.28 Comparing the average informal potential demand for MSME finance, it is largest in East Asia and the Pacific ($55.3 billion) and smallest in the Middle East and North Africa ($7.7 billion). (See figure 19 and map 4). 28. The number of regions with data about the informal MSME segment are as follows: 12 in EAP; 25 in ECA; 23 in LAC; 6 in MENA; 6 in SA; and 35 in SSA. 40 MSME FINANCE GAP Figure 19. Informal Potential Demand for MSME Finance (total and average) Total Informal Potential Demand, US$ billions Average Informal Potential Demand, US$ billions ECA, 625 LAC, 25 SA, 24 SA, 166 SSA, MENA MENA, EAP, 995 LAC, 756 SSA, 312 EAP, 55 ECA, 22 8 8 69 Source: IFC data and analysis. Note: EAP = East Asia and the Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; SA = South Asia; SSA = Sub-Saharan Africa. Map 4. Potential Demand in Informal Sector in Developing Countries Source: IFC data and analysis. IV. QUANTIFYING THE FINANCE GAP 41 Further comparison of the informal potential demand for MSME finance by country income groups demonstrates that it is highest in Upper-middle-income countries (which includes China) — $1998.0 billion, and lowest in the low-income countries — $44.9 billion, as shown in table 10. Table 10. Informal Potential Demand for MSME Finance by Country Income Group Informal Potential Demand, Income Group Number of countries US$ billion Low income 21 44.9 Lower-middle income 36 729.6 Upper-middle income 37 1998.0 High income 13 151.0 Total 107 2923.6 Source: IFC data and analysis. The informal potential demand for MSME finance as a percentage of the formal potential demand for MSME finance in developing countries varies greatly across country groups and regions. It averages 33 percent in the developing countries included in this review (110 countries). It is highest in lower-income countries (80 percent), which highlights the higher informality of markets in this category. It is lowest in the high-income countries (32 percent). Sub-Saharan Africa and the Latin America and the Caribbean regions have the highest informality. Indeed, informal potential demand represents 78 and 54 percent of formal potential demand in these regions, respectively (see figure 20). Figure 20. Informal Potential Demand for MSME Finance (as a percentage of formal potential demand) Av r of Inform l s P rc nt of Form l Av r of Inform l s P rc nt of Form l 80 80 80% 78% 70 70 60 60 50 50 54% 51% 50% 40 40 46% 30 33% 30 33% 33% 32% 29% 20 20 19% 10 10 0 0 Low- Low-middl Upp r-middl Hi h- Tot l EAP ECA LAC MENA SA SSA Tot l incom incom incom incom Source: IFC data and analysis. 42 MSME FINANCE GAP V. Implications of the MSME Finance Gap The Role of the Public Sector A fundamental role of the government is to provide efficient regulation and supervision of the financial sector by creating an efficient regulatory framework. With respect to closing the MSME finance gap, two features are particularly important: the financial structure and competition. Recent studies indicate that more financially diverse markets are associated with improved access to finance.29 Policy recommendations to support a more diverse financial landscape encompass improving competition within the financial system, thereby allowing for a variety of financial institutions to operate.30 The broader regulatory environment, and in particular tax administration and governance, may also influence access to finance.31 Sometimes governments see direct intervention in the finance markets as a potentially useful tool. Commonly used direct government interventions include state-owned bank lending to MSMEs or directed credit. Success through these programs tends to be rare, but there are exceptions.32 Providing credit guarantees is another common form of direct intervention. Policymakers encourage banks to lend to MSMEs by taking on some of the credit risk, either through guarantees for a portfolio of loans or for individual loans. Risk-sharing arrangements can increase lending by lowering the amount of collateral that a MSME needs to pledge to receive a loan because the guarantor provides part of the collateral. Similarly, for a given amount of collateral, a credit guarantee can allow higher risk borrowers to receive a loan. However, a concern with risk-sharing arrangements is that they may not in fact lead to additional lending. Instead, banks may use guarantees to lower risk on loans that they would have issued anyway. A recent study found that 76 countries around the world had some form of interest rate caps on loans (Munzele and Henriquez Gallegos 2014). Common reasons for imposing caps are to protect consumers from excessive interest rates, to make loans more affordable, and to increase access to finance. However, 29. Beck, Demirgüç-Kunt, and Singer (2013) find that dominance of the financial system by banks is associated with a lower use of financial services by firms of all sizes. Other types of financial institutions, such as cooperatives and microfinance institutions (MFIs), seem particularly suited to easing access to finance in low-income countries. 30. Love and Martínez Pería (2015) find low bank competition together with diminished access to finance by firms. 31. Firms may choose informal finance over formal financing options when the regulatory environment is weak (Safavian and Wimpey 2007). The level of overall financial development also plays an important role for these SMEs by disproportionately increasing credit access for small and young firms (Chavis, Klapper, and Love 2010). 32. See the 2013 Global Financial Development Report. The bulk of empirical evidence suggests that government ownership of banks in developing economies has had negative consequences for a country’s long-run financial and economic development (World Bank 2012). V. IMPLICATIONS OF THE MSME FINANCE GAP 43 studies have concluded that interest rate caps tend to decrease rather than increase access to finance, whereas market-oriented policies are more likely to be effective at improving access to finance.33 Policymakers can take additional, more market-oriented actions to close the MSME finance gap. These actions include: (i) fostering the availability of credit information by improving corporate accounting and supporting information sharing between parties, including lenders and utility companies; (ii) passing movable collateral laws and supporting collateral registries; (iii) improving insolvency regimes; (iv) strengthening the legal, regulatory, and institutional infrastructure for factoring and leasing; and (v) creating an enabling environment for fostering innovation. The availability of detailed credit information with broad coverage is crucial for closing the SME finance gap. Firm financial statements and official documentation are essential parts of loan applications for many banks. However, the quality and reliability of these statements varies across countries and firms. MSMEs often lack the necessary technical knowledge for preparing the kind of sound financial statements needed for loan applications. Business development services may help them to build capacity in this area.34 Regulatory reforms that encourage informal firms to formally register with the authorities may also lead to better information and documentation about businesses.35 Credit registries or bureaus provide records of firms’ current and past loans. In an effort to provide more information about firms that have not previously had a loan, some credit bureaus also collect payment histories for utility bills or other services in addition to information from commercial banks and non-bank institutions. Credit registry and bureau records can help lenders observe whether loans have been repaid successfully in the past, and whether firms have existing liabilities that may make them risky borrowers. Cross-country research shows that the presence of credit bureaus is associated with lower financing constraints and a higher share of bank financing for MSMEs.36 Credit information can be used to generate credit scores predicting repayment on the basis of borrower characteristics. Regarding the women-owned MSME segment, since women often do not have formal financial transaction histories, they disproportionately have no records. As a result, there is frequently no information by which to rate them—which further exacerbates their inability to obtain formal financing. Data from the World Bank Enterprise Surveys shows that about 79 percent of loans or lines of credit require collateral. This number is similarly high in most regions of the world. Movable assets such as machinery, equipment or receivables — as opposed to fixed assets, such as land or buildings — often account for most of firms’ capital stock, particularly for MSMEs.37 Banks are often reluctant to accept movable assets as collateral due to non-existent or outdated secured transactions laws and collateral registries. Many 33. For more information, see the recent papers by: Helms and Reille 2004; Porteous, Collins, and Abrams (2010); Laeven (2003); and Munzele and Henriquez Gallegos (2014). 34. Bruhn, Karlan, and Schoar (forthcoming) finds that management consulting services improve firms’ accounting and record keeping. However, the authors do not examine whether management services lead to better access to finance. 35. For example, simplifying business registration procedures can encourage firms to register (Bruhn 2013; and Campos, Goldstein, and McKenzie 2015). 36. See Love and Mylenko 2003 and Martinez Peria and Singh 2014. The 2013 Global Financial Development Report (World Bank 2012) and IFC’s Credit Reporting Knowledge Guide (IFC 2012c) provide information on credit reporting institutions, as well as actions that governments can take to foster these institutions. 37. In developing economies, 78 percent of the capital stock of businesses is typically in movable assets, and only 22 percent is in immovable property (Alvarez de la Campa 2011). 44 MSME FINANCE GAP legal systems place unnecessary restrictions on creating collateral, leaving lenders unsure whether a loan agreement will be enforced by the courts.38 Reforming movable collateral frameworks may enable firms to leverage their assets to obtain credit. Some developing countries have successfully reformed these systems, including Afghanistan, Albania, Bosnia and Herzegovina, China, Colombia, Ghana, Mexico, Romania and Vietnam, among others.39 Overall, sound collateral laws and registries can enable firms to use their own assets to guarantee loans. In addition, such laws and registries may also reduce the need for publicly-sponsored guarantee schemes. Insolvency regimes can help to close the MSME finance gap by supporting predictability and efficiency in credit markets. An effective insolvency framework protects creditor rights, and specifies a mandatory and orderly mechanism for the reallocation of assets of insolvent firms among stakeholders.40 Many countries have significant legal gaps such that insolvency frameworks are unable to deal with MSMEs effectively. For MSMEs that do not possess a distinct legal identity from their shareholders, it may be necessary to create an entirely new legal framework for personal insolvency.41 Factoring is a financial transaction in which a firm sells its accounts receivable to a third party, called the factor, at a discount (equal to interest plus service fees) and receives immediate cash. Since a more creditworthy actor (the large buyer) is the liable party, the factor can issue credit at better terms than it would grant if the riskier MSME were the direct borrower. Factoring may be particularly useful in countries with weak judicial systems because factoring involves the outright purchase of accounts receivable by the factor, rather than collateralization of debt. However, factoring requires an appropriate legal framework.42 Another financial product that can help close the MSME finance gap is leasing (Berger and Udell 2006).43 Leasing focuses on the firm’s ability to generate cash flow from business operations to service leasing payments, rather than on its credit history or ability to pledge collateral. Leasing can generate business and financing opportunities for both lessors and lessees.44 Leasing can allow firms to: (i) overcome technological challenges through access to specialized equipment; (ii) access equipment or facilities when ownership is not feasible; (iii) utilize assets in a flexible manner; (iv) manage cash-flow; and (v) benefit from a lessors’ exploitation of economies of scale in purchasing and servicing. 38. For example, about 90 percent of movable property that could serve as collateral for a loan in the United States would likely be unacceptable to a lender in Nigeria (Fleisig, Safavian, and de la Peña 2006) 39. See Fleisig, Safavian, and de la Peña 2006. See also UNCITRAL 2010 for a guidebook on efficient and effective secured transactions laws; Fleisig and others 2006 on the benefits of a single registry; and Love, Martínez Pería, and Singh 2016 for a recent study covering movable collateral. 40. See Cirmizi, Klapper, and Uttamchandani 2012. Araujo, Ferreira, and Funchal 2012 examine the effects of a reform that increased creditor protections and improved the efficiency of the bankruptcy system. 41. The SME Finance Policy Guide provides specific recommendations for the elements that such a framework should include (IFC 2011b). 42. The UNCITRAL Legislative Guide on Secured Transactions (2010) includes detailed recommendations regarding the establishment of a legal framework that is amenable to factoring transactions. 43. Brown, Chavis, and Klapper (2010) show that close to 34 percent of firms in high-income countries use leasing to finance new investment, as compared to only 6 percent in low-income countries. 44. Fletcher and others (2005) provide a manual on leasing legislation, regulation, and supervision based on international best practices and IFC’s technical assistance experience (see also IFC 2011a). V. IMPLICATIONS OF THE MSME FINANCE GAP 45 Technology is the key differentiator in the access to finance space. Governments can create innovative initiatives supporting technological progress and knowledge exchange. A number of sandbox efforts have emerged to enable and promote the interaction between financial institutions and technology firms. Such regulatory sandboxes usually entail “the temporary relaxations or adjustments of regulatory requirements to provide a “safe space” for startups or established companies to test new technology-based financial services in a live environment for a limited time, without having to undergo a full authorization and licensing process.”45 For example, the Monetary Authority of Singapore has created a “Regulatory Sandbox” for Fintech Experiments, which will enable financial institutions as well as non-financial players to experiment with financial technology solutions. It is expected to encourage experimentation with innovative fintech solutions, while the overall safety and soundness of the financial system is maintained (Monetary Authority of Singapore 2017). Another example of the regulatory sandboxes has been implemented in the U.K., where companies willing to test their innovative products and services in the sandbox should apply to and be approved by the U.K. Financial Conduct Authority (FCA). Participants of such sandbox are granted an access to knowledge, business assistance and potential waivers to certain regulations. They can test their products and services with the real customers, if qualify and approved by FCA (Faden 2016). The Role of the Private Sector Previous studies have enumerated the significant Return on Equity (RoE) that banks can make by having dedicated SME functions and a structured approach to servicing the SME segment. For example, a survey by more than 10 emerging market banks showed that a best-in-class SME bank could aim for over 23 percent RoE performance, with a 15-18 percent differential in RoE compared to standard peers that did not have a structured approach in place (IFC 2012b). Moreover, IFC’s global SME banking global benchmarks have estimated that a best-in-class Return on Assets (RoA) for the SME portfolio is around 5 percent, compared to the total bank’s RoA of 4 percent. This includes compounded average annual growth rates on SME assets and liabilities of approximately 25-30 percent for a best-in-class bank, compared to 20 percent for the total bank’s assets. The microfinance industry has proven to be profitable as well. For example, MIX has estimated an average RoE of microfinance banks at 21 percent, and an even higher return of 23.3 percent for rural microfinance banks in 2015. Their return on asset figures were 3.4 and 3.6 percent, respectively (MIX 2015). Despite these positive findings, financial institutions in developing markets, including banks and MFIs, often find it hard to enter and operate in the MSME market. Some FIs use either a corporate banking or consumer banking model, that without adaptation and customization, has proven not to work well in targeting MSMEs. Typical challenges include: having high levels of informal businesses; a lack of reliable data; and lack of collateral coverage to hedge the perceived high risks. In addition, FIs in developing markets often have inappropriate processes, products and services, risk frameworks, and sales and servicing models to serve the segment profitably. As identified by the present research, the unmet demand — that is, the finance gap —in developing countries presents a significant business opportunity for financial institutions. However, since this segment is drastically different from both retail and corporate banking, FIs need to utilize the appropriate models and approaches to effectively tap into the revenue opportunity, while at the same time mitigating the potential risks. 45. Regulatory Sandboxes Provide “Safe Spaces” for Fintech Payment Services Innovation, Faden Mike, https://www. americanexpress.com/us/content/foreign-exchange/articles/regulatory-sandboxes-for-innovative-payment-solutions/ 46 MSME FINANCE GAP In order to stay profitable and competitive in the MSME market, financial institutions (FIs) should undertake a number of initiatives, including: (i) designing a business model; (ii) segmenting the customers; (iii) tailoring the products and services toward customer needs; (iv) developing credit assessment techniques and risk management capabilities; (v) establishing effective sales and delivery channels; and (vi) ensuring strong technology infrastructure. FIs that adhere to best practice standards tend to diversify their income sources among both lending and non-lending products. One of the strategies is to diversify the product offering and deepen the relationship by offering product bundles. Banks such as ICICI bank in India and Santander Bank in Brazil have proven to be successful in utilizing this strategy (IFC 2012b). Other institutions have placed a lot of emphasis on developing non-financial services (NFS) to increase customer loyalty, improve client retention rates, differentiate product offerings to the MSME market, increase the growth of their portfolios, and/or improve their customer service levels (IFC 2012d). Two of the leading banks in this area, including Türk Ekonomi Bankasi (TEB) Bank in Turkey and Standard Chartered Bank (in developing markets), have recognized the importance of NFS as an additional revenue enhancer. Another bank in Lebanon – BLC, with support of IFC, developed the “We Initiative” (www.we-initiatve. com ) as a cross-bank platform for supporting women with financial services in Lebanon. The program includes learning and development programs for women and unique financial products based on the specific environment for women in Lebanon. Services include collateral-free loans and mother’s fiduciary accounts for their children. The program has yielded impressive results. For example, from 2012-2015, loans to women grew by 8.0 percent as compared to 7.0 percent for the bank as a whole. Deposits grew by 8.8 percent for women as compared to 4.0 percent for the total bank. Finally, gross income grew by 8.4 percent for women as compared to 4.5 percent for the bank as a whole. Women’s non-performing loans (NPLs) stood at 2.5 percent in 2015 as compared to 5.7 percent for the bank as a whole. Moreover, women’s SME NPLs were 5.5 percent as compared to the total bank SME NPL rate at 7.4 percent (IFC 2016). V. IMPLICATIONS OF THE MSME FINANCE GAP 47 Technology and digital financial services are also playing an increasingly larger role in the provision of finance and payment services to the MSME market segment. There has been a proliferation of companies operating in this space, which can be grouped into the following categories: (i) marketplace lending; (ii) supply chain financing (SCF); (iii) non-cash merchant payments; and (iv) alternate data, advanced analytics, and underwriting process automation. Given that all these companies either provide direct financing or enable financing by other financial institutions, they are often referred to as fintechs or technology platforms. Marketplace lending provides credit to individuals or MSMEs through online platforms that match lenders and investors with borrowers. In some instances, the platforms provide direct lending to the ultimate beneficiaries and take balance sheet risks, whereas in other cases they simply connect businesses that need financing with investors who have a higher risk appetite. These types of platforms provide individuals or MSMEs with an alternative way to access credit, and provide investors a way to lend directly (World Bank 2017). The innovation of these platforms is mainly that financing takes places on an unsecured basis. Credit modeling and assessment is done using innovative credit-scoring models, and the underwriting process is very efficient — often outcompeting traditional banking loans in terms of both speed and time (World Economic Forum 2015). An example of these marketplace lending platforms in emerging markets includes Cumplo in Chile, which offers receivable financing and direct financing to SMEs. Another example, LendingKart Group in India, is a direct financier providing working capital loans to small businesses using big data and proprietary scoring models to assess creditworthiness. Supply chain finance technology platforms can facilitate access to finance to both suppliers that sell products to corporates, and to the distributors that purchase goods from the corporates. Most of the suppliers and distributors are also MSMEs. According to Saleem, Hommes, and Sorokina (2017), a bank considering launching or scaling up its supply chain finance business would typically have the following two options to enable its SCF operations. It could use a bank-led platform, or it could contract a bank-independent platform. The latter might be done through developing an internal IT infrastructure or adopting another bank’s platform. The former might be done by licensing the external technology solution, outsourcing the needed functionality as “Software as a Service” or participating in SCF marketplaces. Banks can select platforms that best match their needs and fill the gaps in their own technology infrastructure. Examples of SCF-focused platforms include: Ariba, Demica, GT Nexus, Kyriba, Misys, Orbian, Premium Technology, Prime Revenue, and Taulia, among others. Each platform has different levels of maturity, complexity, product offering, and geographic coverage. Supply chain finance solutions can take various forms to address different challenges (Saleem, Hommes and Sorokina 2017). Electronic payment solutions have also contributed to expanding access to credit for MSMEs because of the creation of the digital footprint created by their transaction history. Cash transactions conducted by merchants are not visible to financial service providers, but the situation changes dramatically once transactions become electronic. Indeed, this transaction history can be used to assess the creditworthiness of a particular business (World Bank 2017). Mobile point-of-sale (MPOS) technologies are now playing an important role in many markets. Square (US), Geo Pagos (Argentina), Kopo Kopo (Kenya) are examples of MPOS solutions, offering a variety of services, including payment processing, cash advances, targeted short message service (SMS) marketing solutions and business intelligence services. 48 MSME FINANCE GAP Advanced analytics based on alternative data from mobile phone usage patterns, social media presence, merchant/purchase habits and historical transactional behavior can be used to make better and more efficient sales and credit decisions. With worldwide operations, Lenddo is an example of such a platform, offering credit scoring services through application and social data verification using non-traditional data. Tala, which launched its services in Kenya in 2014, uses a combination of demographic, geographic, financial, and social information from mobile phones, utility contracts, and other sources to create risk scores and credit recommendations in real-time. FarmDrive in Kenya, focused on small farmers, is also collecting and aggregating alternate datasets from multiple sources that are then used to build credit scores for the farmers. Partnerships between financial institutions and fintechs can create a synergy by combining the scale and resources of traditional financial institutions and the innovative knowledge and advanced algorithms of the fintech companies. Some examples of partnerships that are currently taking place include Tiaxa partnerships with Diamond Bank in Ghana, with Finca in Tanzania, and with IBA in Congo. M-Shwari, which launched operations in November 2012, established a strategic partnership between the Commercial Bank of Africa (CBA) and Safaricom. CBA in Tanzania is now offering products through M-Pawa, which provides an opportunity for consumers to save or borrow money through their mobile phones. This offering was introduced in May 2014, and attracted many customers who could only initially borrow a small amount (a couple of dollars). As they build their credit history, they can increase their credit borrowing limits. V. IMPLICATIONS OF THE MSME FINANCE GAP 49 Conclusion T his report appraised empirical research regarding the existence and size of the micro, small and medium (MSME) enterprise finance gap in developing countries. A theoretical and empirical framework is presented to articulate and measure this gap at the country level, based on the interaction between supply and demand of finance for MSMEs. This research estimates that there are 65 million formal micro, small and medium enterprises that are credit constrained, representing 40 percent of all enterprises in the 128 reviewed countries. Of these developing economies surveyed, the potential demand for MSME finance is estimated at US$ 8.9 trillion, as compared to the current credit supply of $3.7 trillion. The finance gap attributed to formal MSMEs in developing countries is valued at $ 5.2 trillion, which is equivalent to 19 percent of the gross domestic product (GDP) of the 128 countries. This in turn amounts to 1.4 times the current level of MSME lending to these countries. The finance gap in the informal MSME market is another important aspect of this study. In this context, there is an estimated $ 2.9 trillion in potential demand for finance from informal enterprises in developing countries. This figure is indeed sizeable, and is equivalent to 10 percent of the GDP in these countries. In addition to contributing to the limited, but growing literature which tries to measure the size of the enterprise finance gap for emerging markets, this study introduces a new and a more systemic methodology to measure the gap. This revised methodology examines the gap from both a demand and supply constraint perspective. Many MSMEs may have a higher “potential” demand for financing. However, this demand often goes unacknowledged because the owner of the enterprise knows that is not likely to be met. Similarly, the supply of credit in these markets is a constraint. Financial institutions prefer to lend money to enterprises with better documentation, and an established track record. In other words, financial institutions prefer to supply credit to low-risk enterprises. The results of this report also raise some pertinent questions: Has the enterprise finance gap increased in recent years? Can there be a dynamic measure of this gap which can be regularly updated so that interventions toward reducing the gap can be measured? Throughout this study, the authors have tried to pre-empt and address these concerns. First, the findings indicate that the increase in the estimate of the finance gap from the 2011 measure is primarily driven by changes in the methodology. This should be interpreted as a holistic recalculation of the gap from both the supply and demand perspectives. Second, this robust methodology has the added benefit of being easier to update in future years. Thus, for the first time, the evolution of the gap can be captured, and the dynamic changes to the gap over time can be more accurately assessed. This study highlighted the key market-enabling policies that governments might pursue to close the MSME finance gap. The public sector has an important role in reforming the institutional environment, providing regulatory frameworks, and fostering competition and other market-oriented policy actions. Policy recommendations to support a more diverse financial landscape encompass improving competition within the financial system, as well as allowing a variety of financial institutions to operate. In addition, both CONCLUSION 51 directed lending programs and risk-sharing arrangements can have positive effects on MSME access to finance and growth. However, it can be a challenge to effectively design and manage them. Lastly, mounting evidence suggests that solid credit information systems, movable collateral frameworks and registries, and efficient insolvency regimes can increase lending to MSMEs. Governments are encouraged to continue developing and improving the financial infrastructure to enable greater MSME lending. The private sector benefits from market-enabling policies set by the public sector, and is able to directly intervene and promote financial inclusion. Private sector initiatives focusing on building the capacity of traditional financial institutions — such as banks, non-bank FIs, credit unions, savings and loan associations among others — can help them to better serve the MSME segment. The implementation of a targeted MSME strategy, coupled with capacity building of staff and management, are crucial for successful penetration to these underserved markets. A targeted MSME strategy could include the design of directed business models, sales and customer management policies, specialized credit risk models, and tailored products and services. In addition to the traditional financial institutions, technology and digital financial services providers can play a significant role in providing finance and payment services to the MSME market segment. A variety of fintech players, such as marketplace lenders, payment and supply chain finance platforms, among others, can significantly contribute to closing the finance gap either by operating on their own or by partnering with the larger, traditional financial institutions. There are a number of ways in which this study can be improved and expanded as part of a comprehensive research agenda to better understand the financing needs of MSMEs in developing and emerging economies. First, it is important to update the estimations at regular intervals. Most initiatives to reduce the finance gap would require assessment using an accurate country-level measure that is not only comparable with other countries, but also consistent across time. Second, there is value in disaggregating the MSME finance gap estimates by industries and sectors. For instance, the financing need for MSMEs in the manufacturing sector may be different from those of the services sector. The current methodology is flexible enough to accomplish this goal, provided that more granular data is collected at the sectoral level. Third, the proposed methodology can be adapted for better usability and interpretation by changing the benchmarked country. For example, data permitting, the debt-to-sales ratio for a regional comparator can be utilized as the appropriate benchmark. Fourth, the precision of the proposed methodology can be improved by extending the benchmarking to a more robust matching algorithm that goes beyond summarizing the results in the three categories, namely industry, size and age. An area of further research could include the proposed regression framework to estimate the supply of MSME finance. 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BIBLIOGRAPHY 59 Annex: MSME Finance Gap 2017 Country Region Incomelevel Number of Current MSME MSME Finance MSMEs Supply Finance gap gap / GDP Afghani- South Asia Low income 75,864 31,962,467 4,690,624,693 24% stan Albania Europe & Upper mid- 78,107 1,678,947,542 1,077,970,254 9% Central Asia dle income Angola Sub-Saharan Upper mid- 27,603 2,707,014,766 34,178,102,486 33% Africa dle income Antigua Latin America High 3,030 97,837,209 287,585,857 22% and Bar- & Caribbean income buda Argentina Latin America Upper mid- 589,781 13,240,770,257 85,883,903,135 15% & Caribbean dle income Armenia Europe & Lower mid- 26,166 1,266,114,349 1,145,072,303 11% Central Asia dle income Azerbaijan Europe & Upper mid- 261,950 6,894,776,091 6,805,414,229 13% Central Asia dle income Bahamas, Latin America High 6,258 2,282,670,600 60,474,014 1% The & Caribbean income Bangla- South Asia Lower mid- 2,761,932 18,937,042,371 38,972,713,376 20% desh dle income Barbados Latin America High 15,164 245,721,426 852,791,724 19% & Caribbean income Belarus Europe & Upper mid- 80,209 4,492,537,962 18,424,867,354 34% Central Asia dle income Belize Latin America Upper mid- 7,058 137,114,912 462,955,462 26% & Caribbean dle income Benin Sub-Saharan Low income 9,150 113,662,320 689,205,366 8% Africa Bhutan South Asia Lower mid- 24,464 192,401,293 91,389,034 5% dle income Bolivia Latin America Lower mid- 225,451 2,224,300,904 1,703,075,687 5% & Caribbean dle income Bosnia and Europe & Upper mid- 161,295 5,332,374,105 774,689,474 5% Herzegov- Central Asia dle income ina Botswana Sub-Saharan Upper mid- 13,137 1,425,602,197 2,669,630,855 19% Africa dle income Brazil Latin America Upper mid- 15,738,452 57,048,523,506 626,023,562,478 35% & Caribbean dle income ANNEX 61 Country Region Incomeleval Number of Current MSME MSME Finance MSMEs Supply Finance gap gap / GDP Bulgaria Europe & Upper mid- 371,299 7,495,751,836 6,478,198,896 13% Central Asia dle income Burkina Sub-Saharan Low income 41,718 382,454,699 1,609,940,885 15% Faso Africa Burundi Sub-Saharan Low income 3,799 227,941,840 490,969,888 16% Africa Cambodia East Asia & Lower mid- 376,069 571,765,294 3,709,338,045 21% Pacific dle income Cameroon Sub-Saharan Lower mid- 93,030 1,661,946,877 8,714,894,256 30% Africa dle income Cape Verde Sub-Saharan Lower mid- 9,719 232,061,311 290,118,728 18% Africa dle income Central Sub-Saharan Low income 22,326 30,623,390 242,920,736 16% African Africa Republic Chad Sub-Saharan Low income 5,170 282,238,635 1,134,072,276 10% Africa Chile Latin America High income 834,085 21,856,804,104 8,433,423,295 4% & Caribbean China East Asia & Upper mid- 56,061,600 2,483,952,766,729 1,890,328,123,161 17% Pacific dle income Colombia Latin America Upper mid- 2,311,539 4,573,057,029 56,207,522,736 19% & Caribbean dle income Congo, Sub-Saharan Low income 319,090 446,934,153 9,304,515,830 26% Dem. Rep. Africa Costa Rica Latin America Upper mid- 41,068 5,050,556,846 4,765,025,589 9% & Caribbean dle income Côte Sub-Saharan Lower mid- 203,491 1,426,843,718 2,355,285,515 7% d’Ivoire Africa dle income Croatia Europe & High income 153,262 7,256,471,842 9,496,554,331 19% Central Asia Czech Europe & High income 939,049 29,935,445,460 71,491,146,931 39% Republic Central Asia Djibouti Middle East & Lower mid- 2,805 65,413,570 146,558,734 9% North Africa dle income Dominica Latin America Upper mid- 2,433 57,579,279 69,096,974 13% & Caribbean dle income Dominican Latin America Upper mid- 791,236 3,474,739,423 12,959,360,152 19% Republic & Caribbean dle income Ecuador Latin America Upper mid- 700,999 4,049,685,700 17,937,808,957 18% & Caribbean dle income Egypt, Middle East & Lower mid- 2,453,567 2,819,748,677 46,722,358,190 14% Arab Rep. North Africa dle income Estonia Europe & High income 65,907 2,253,754,880 5,273,410,808 23% Central Asia Ethiopia Sub-Saharan Low income 136,633 1,687,733,587 4,290,163,843 7% Africa 62 MSME FINANCE GAP Country Region Incomeleval Number of Current MSME MSME Finance MSMEs Supply Finance gap gap / GDP Fiji East Asia & Upper mid- 10,011 251,675,667 1,084,830,273 25% Pacific dle income Gambia, Sub-Saharan Low income 9,558 50,651,573 97,953,281 12% The Africa Georgia Europe & Upper mid- 106,858 1,169,986,126 2,486,794,402 18% Central Asia dle income Ghana Sub-Saharan Lower mid- 26,190 2,738,047,528 4,992,806,125 13% Africa dle income Grenada Latin America Upper mid- 1,951 89,347,054 175,912,721 18% & Caribbean dle income Guatemala Latin America Lower mid- 184,468 670,610,775 15,850,041,239 25% & Caribbean dle income Guinea Sub-Saharan Low income 12,684 79,019,051 1,184,565,076 18% Africa Guinea- Sub-Saharan Low income 10,402 33,211,702 130,050,139 12% Bissau Africa Guyana Latin America Upper mid- 22,765 619,118,537 117,394,765 4% & Caribbean dle income Honduras Latin America Lower mid- 127,330 1,136,203,890 2,986,194,753 15% & Caribbean dle income Hungary Europe & High income 689,510 17,264,339,344 36,712,035,622 30% Central Asia India South Asia Lower mid- 1,563,999 139,455,882,221 230,062,869,817 11% dle income Indonesia East Asia & Lower mid- 2,480,152 56,612,630,954 165,852,545,872 19% Pacific dle income Iraq Middle East & Upper mid- 224,610 1,501,801,029 69,849,704,659 41% North Africa dle income Jamaica Latin America Upper mid- 10,438 432,143,613 2,717,638,556 19% & Caribbean dle income Jordan Middle East & Upper mid- 156,060 2,308,450,774 6,582,119,054 18% North Africa dle income Kazakh- Europe & Upper mid- 1,290,000 9,509,760,067 47,071,024,239 26% stan Central Asia dle income Kenya Sub-Saharan Lower mid- 1,560,500 3,854,957,054 19,326,332,625 30% Africa dle income Kosovo Europe & Lower mid- 103,697 1,653,642,974 342,253,144 5% Central Asia dle income Kyrgyz Europe & Lower mid- 298,500 91,889,281 1,403,743,130 21% Republic Central Asia dle income Lao PDR East Asia & Lower mid- 126,695 439,038,255 2,608,571,859 21% Pacific dle income Latvia Europe & High income 79,053 8,376,864,416 1,237,839,309 5% Central Asia Lebanon Middle East & Upper mid- 170,504 5,656,696,819 8,855,459,275 19% North Africa dle income ANNEX 63 Country Region Incomeleval Number of Current MSME MSME Finance MSMEs Supply Finance gap gap / GDP Lesotho Sub-Saharan Lower mid- 7,827 130,556,822 165,869,803 8% Africa dle income Lithuania Europe & High income 127,227 5,739,945,537 25,640,325,863 62% Central Asia Macedonia, Europe & Upper mid- 75,140 1,926,626,388 24,262,574 0% FYR Central Asia dle income Madagas- Sub-Saharan Low income 210,918 305,447,031 2,678,170,824 27% car Africa Malawi Sub-Saharan Low income 21,098 9,422,754 477,042,915 7% Africa Malaysia East Asia & Upper mid- 645,136 69,935,901,865 21,454,214,934 7% Pacific dle income Mali Sub-Saharan Low income 4,582 860,934,578 371,543,928 3% Africa Mauritania Sub-Saharan Lower mid- 2,305 611,111,327 (275,459,789) -5% Africa dle income Mauritius Sub-Saharan Upper mid- 40,112 2,435,207,831 428,581,666 4% Africa dle income Mexico Latin America Upper mid- 4,048,543 27,045,681,152 163,917,536,619 14% & Caribbean dle income Micronesia, East Asia & Lower mid- 1,139 33,000,000 77,922,441 24% Fed. Sts. Pacific dle income Moldova Europe & Lower mid- 49,444 671,503,966 894,338,409 14% Central Asia dle income Mongolia East Asia & Lower mid- 72,473 698,933,740 1,293,202,307 11% Pacific dle income Montene- Europe & Upper mid- 19,869 530,128,322 631,854,361 16% gro Central Asia dle income Morocco Middle East & Lower mid- 1,410,000 7,305,641,193 36,673,779,968 37% North Africa dle income Mozam- Sub-Saharan Low income 28,474 205,296,601 1,345,068,141 9% bique Africa Myanmar East Asia & Lower mid- 128,094 2,740,317,090 13,838,600,855 21% Pacific dle income Namibia Sub-Saharan Upper mid- 71,262 139,597,172 1,788,611,879 15% Africa dle income Nepal South Asia Low income 99,411 730,830,641 3,601,276,163 17% Nicaragua Latin America Lower mid- 173,742 242,772,450 3,111,643,152 25% & Caribbean dle income Niger Sub-Saharan Low income 8,084 329,239,323 3,123,437,438 44% Africa Nigeria Sub-Saharan Lower mid- 36,994,578 101,349,729 158,131,971,746 33% Africa dle income Pakistan South Asia Lower mid- 2,958,129 2,843,781,068 42,169,608,424 16% dle income Panama Latin America Upper mid- 34,883 6,053,916,662 21,269,386,679 41% & Caribbean dle income 64 MSME FINANCE GAP Country Region Incomeleval Number of Current MSME MSME Finance MSMEs Supply Finance gap gap / GDP Paraguay Latin America Upper mid- 14,616 2,507,273,201 3,970,951,794 14% & Caribbean dle income Peru Latin America Upper mid- 1,197,963 22,501,282,121 10,179,430,798 5% & Caribbean dle income Philippines East Asia & Lower mid- 816,759 15,248,794,855 221,793,419,218 76% Pacific dle income Poland Europe & High income 1,520,404 55,072,943,816 107,851,883,087 23% Central Asia Romania Europe & Upper mid- 407,410 18,232,839,393 45,871,481,609 26% Central Asia dle income Russian Europe & Upper mid- 1,669,439 134,058,734,022 222,020,514,626 17% Federation Central Asia dle income Rwanda Sub-Saharan Low income 123,390 217,157,882 1,273,776,437 16% Africa Samoa East Asia & Lower mid- 1,945 136,455,641 35,668,064 5% Pacific dle income Senegal Sub-Saharan Low income 22,270 493,738,437 915,447,621 7% Africa Serbia Europe & Upper mid- 84,082 5,136,836,096 10,089,573,405 28% Central Asia dle income Slovak Europe & High income 446,409 8,822,770,352 18,264,992,545 21% Republic Central Asia Slovenia Europe & High income 137,460 7,225,596,416 7,980,425,474 19% Central Asia Solomon East Asia & Lower mid- 3,050 43,213,645 173,839,087 15% Islands Pacific dle income South Sub-Saharan Upper mid- 667,432 41,462,741,608 30,342,558,100 10% Africa Africa dle income South Sub-Saharan Low income 7,313 139,925,153 291,354,886 3% Sudan Africa Sri Lanka South Asia Lower mid- 935,736 2,282,135,557 17,119,256,169 21% dle income St. Kitts Latin America High income 2,738 136,508,645 96,395,974 10% and Nevis & Caribbean St. Lucia Latin America Upper mid- 4,870 154,355,714 191,512,736 13% & Caribbean dle income St. Vincent Latin America Upper mid- 4,819 27,165,450 231,198,647 31% and the & Caribbean dle income Grenadines Sudan Sub-Saharan Lower mid- 13,088 1,087,084,350 21,690,686,257 26% Africa dle income Suriname Latin America Upper mid- 1,598 256,808,343 969,522,749 20% & Caribbean dle income Swaziland Sub-Saharan Lower mid- 162,853 119,893,187 1,822,841,863 45% Africa dle income Tajikistan Europe & Lower mid- 155,291 239,528,518 1,451,766,421 18% Central Asia dle income ANNEX 65 Country Region Incomeleval Number of Current MSME MSME Finance MSMEs Supply Finance gap gap / GDP Tanzania Sub-Saharan Low income 3,162,885 1,327,618,892 5,787,227,422 13% Africa Thailand East Asia & Upper mid- 2,872,026 112,777,964,028 40,743,237,597 10% Pacific dle income Timor- East Asia & Lower mid- 4,138 11,699,086 449,108,541 32% Leste Pacific dle income Togo Sub-Saharan Low income 14,892 232,454,157 389,955,574 10% Africa Tonga East Asia & Lower mid- 9,355 76,317,957 164,816,153 38% Pacific dle income Trinidad Latin America High income 19,186 1,522,268,219 4,522,897,594 16% and Tobago & Caribbean Tunisia Middle East & Lower mid- 601,416 6,005,002,488 6,873,526,885 16% North Africa dle income Turkey Europe & Upper mid- 2,587,319 152,283,092,698 80,249,986,670 11% Central Asia dle income Uganda Sub-Saharan Low income 25,133 531,364,911 4,869,014,554 18% Africa Ukraine Europe & Lower mid- 364,237 6,806,902,953 33,052,156,041 36% Central Asia dle income Uruguay Latin America High income 150,165 3,490,723,240 5,859,001,746 11% & Caribbean Uzbekistan Europe & Lower mid- 95,231 1,732,099,219 11,789,541,678 18% Central Asia dle income Vanuatu East Asia & Lower mid- 1,578 97,341,953 135,124,860 17% Pacific dle income Venezuela, Latin America Upper mid- 251,033 4,204,524,489 157,314,192,661 42% RB & Caribbean dle income Vietnam East Asia & Lower mid- 447,091 11,204,738,662 23,609,833,957 12% Pacific dle income Yemen, Middle East & Lower mid- 400,235 698,632,009 18,969,214,616 53% Rep. North Africa dle income Zambia Sub-Saharan Lower mid- 21,416 1,552,991,438 3,687,604,402 17% Africa dle income 66 MSME FINANCE GAP