IFC Enterprise Finance Gap

IFC Enterprise Finance Gap

IFC Enterprise Finance Gap Database

The IFC Enterprise Finance Gap Database, using primarily data from World Bank Enterprise Surveys, estimates the number of micro, small, and medium enterprises (MSMEs) in the world, and the degree of access to credit and use of deposit accounts for formal and informal MSMEs. The database currently covers 177 countries.

400 million MSMEs in developing countries

15% have access to credit

40% unserved

10% underserved

Access to finance a major constraint for 25%

DATA SOURCES

Formal Sector:

Informal Sector:

  • Ayyagari, M., T. Beck, and A. Demirguc-Kunt (2007). "Small and Medium Enterprises across the Globe," Small Business Economics, 29, 415-434.
  • Informality Database by the International Institute for Labour Studies of the International Labour Organization: Bacchetta, M., E. Ernst, and J.P. Bustamante (2009).Globalization and Informal Jobs in Developing Countries. ILO and WTO: Switzerland.
  • ILO (2008). Global Employment Trends 2008: Recovering from a Second Jobs Dip. ILO, Geneva.
  • UNDP (2007). Human Development Report 2007/2008 Fighting Climate Change: Human Solidarity in a Divided World, United Nations: New York. (Pages 299-301.) (hdr.undp.org)
  • ILO, Key indicators of the Labor Market, 2009. (kilm.ilo.org/kilmnet)
  • ILO (2002). Women and men in the informal economy: a statistical picture. ILO, Geneva.
  • Schneider, Friedrich (2002). 'Size and Measurement of the Informal Economy in 110 Countries Around the World,' Paper presented at the Workshop of Australian National Tax Centre, ANU, Canberra, Australia, July 17, 2002.
  • World Bank Enterprise Surveys (www.enterprisesurveys.org)

DEFINITIONS

  • Types of firms included in the World Bank Enterprise Surveys:
    • The survey primarily covers manufacturing and services sectors, which corresponds to firms classified with ISIC codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev. 3.1). Please see the Enterprise Surveys web site for further details.
  • Deposit accounts: checking, saving and time deposits
  • Formal sector enterprises:
    Type Number of employees
    Micro 1-4
    Very Small 5-9
    Small 10-49
    Medium 50-250
  • Informal sector: All enterprises that are not registered with the municipality or tax authority and all the non-employer firms (independent of registration)
  • Categorization of enterprises based on credit usage and need:
    Type Definition
    Unserved Need credit but do not have access to any credit
    Underserved Have a loan and/or line of credit but find financing as a constraint
    Well-served Need credit and needs are met
    No need Neither have nor want credit
  • Women-owned MSMEs: World Bank Enterprise Surveys have the following information at firm level:
    1. Female ownership (at least one woman owner)
    2. Female sole proprietor
    3. Female decision maker (i.e., manager)
    • IFC MSME Finance Gap Database considers firms of type (1) and (2) as women-owned firms, and limits (3) to firms with women as the top manager and at least one women owner.
    • Note: For South Asia, the question used was "Are any of the principal owners female?" where principal owners are defined as those with >5% ownership. For other regions, the question was "Are any of the owners female?"


ESTIMATION METHODOLOGY 1

Estimating the number of formal enterprises:

  1. Create initial values using the MSME Country Indicators by IFC and World Bank.
  2. Conduct country deep-dives to refine the data for 25 countries, using local government sources, literature review, and additional research and insights from McKinsey and client experts.
  3. Adjust country-level data to manage varying definitions of SMEs. Whenever national definitions deviate from the MSME definitions given above, adjust the data to fit the common SME definition given above.
  4. Estimate the very small segment which is not usually defined by national data sources. The size of the very small segment—based on the number of enterprises with less than 10 employees—is calculated using data from Brazil, India, Kenya and Mexico, as a percentage of the total. This is then used as an estimate for the rest of the countries.
  5. Extrapolate data for those countries for which data were not available in the MSME Country Indicators by using regional averages for the ratios of micro enterprises to total population, very small enterprises to total population, small enterprises to total population, and medium enterprises to total population.

Estimating the number of formal SMEs with no credit or insufficient credit:

  1. Focus is on loans and overdrafts from financial institutions and other forms of financing such as trade finance, leasing, and factoring were excluded.
  2. Use data from World Bank Enterprise Surveysto calculate the percentage of firms of each size type that are well-served, underserved, unserved, and that do not need credit.
  3. Use the regional averages for countries without data according to level of credit usage and need, and enterprise size.
  1. Estimate average revenues of formal SMEs by using the average revenue data from the World Bank Enterprise Surveys for the countries where that information was available. For other countries, estimate average revenue of the "small" segment by using a linear regression of revenues to GDP per capita. Average revenues for the other segments (i.e., micro, very small, and medium, informal) are based on the regional averages from Enterprise Surveys as GDP per capita was not statistically significant in explaining the variation in revenues in these sectors.
  2. Establish a baseline of the value of formal credit currently used by using “Global Banking Profit Pools” database by McKinsey & Co., which has estimates of the amount of formal financing provided to formal SMEs in 78 countries and for each region. This dataset includes bank loans, overdrafts, formal trade finance, leasing, and factoring.Combining this database and the average revenue estimates, we estimated average credit usage among SMEs using formal credit as a percentage of their revenues. Note that Enterprise Surveys data does not include trade finance, leasing, and factoring, and this may lead to a potential bias in the estimates due to measurement error. However, the share of these three in overall firm finance is very low, and hence this error is expected to be very small.
  3. Assume the total potential need for formal financing is 20% of revenues for all institutions that were well-served, unserved and under-served. This assumption is based on interviews with IFC experts, and is verified by an analysis of McKinsey & Co. proprietary data on credit usage of 500 leading emerging-market listed companies.
  4. Estimate the credit gap on a country-by-country basis as:
    • For unserved enterprises, revenues are multiplied by 20%
    • For under-served enterprises, the amount of outstanding credit is multiplied by 50% based on feedback by IFC experts.
  5. Extrapolate the credit gap for countries where data are incomplete using regional averages for each segment.

Estimating the number of MSMEs without access to bank accounts and the value of their un-intermediated cash balances:

  1. All MSMEs are assumed to possibly benefit from a deposit account, allowing for the possibility that some MSMEs would not have any need.
  2. Calculate deposits as a percentage of revenue for those enterprises that already have deposit accounts using the Global Banking Pools database of McKinsey & Co. on deposit volumes for formal SMEs to calculate the average size of deposits for a formal SME as a percentage of average revenue. This is then used for all MSMEs to estimate the total size of current MSME (formal and informal) deposits. Where there was no country-level data, regional averages are used.
  3. Estimate the total value of un-intermediated cash balances by using the ratio of deposit balances to average revenues for those enterprises with deposit accounts as a proxy for un-intermediated cash balances for enterprises without accounts on a country-by-country basis. Regional averages were then used for countries with no or incomplete data.

Estimating the number of informal MSMEs:

  1. Estimate size of the informal workforce:
    • Estimate size of the informal sector as a percentage of the formal labor force, based on four different sources2 with a total coverage of 71 countries and ~80% of population providing the percentage of the formal labor force participating in the informal sector.
    • Collect labor-force and unemployment data from the ILO Key Indicators of Labour Market.
    • Estimate the number of adults working in the informal economy by multiplying the labor force by the percent of the formal labor force that participated in the informal economy, and then by multiplying this number by 1 minus the unemployment rate to account for the unemployed in the informal sector
  2. Estimate the percentage of the informal workforce that works in informal enterprises using a 2002 ILO report, “Women and men in the informal economy: a statistical picture,” which contains estimates for India (30%), Mexico (38%) and South Africa (49%) on the percent of the total informal workers employed in informal enterprises. (While Mexico and South Africa data include only employment in informal, non-agricultural enterprises, India data do not.)
    • South Asia and East Asia: India estimate of 30% is used
    • Sub-Saharan Africa: South Africa estimate of 49% was adjusted to 33% based on feedback by IFC experts suggesting the improbability that half of all informal-sector persons worked in informal enterprises outside of agriculture across the continent
    • Latin America: Mexico estimate of 38% was adjusted to 50% based on estimates from an Inter-American Development Bank’s 2005 paper, "El Grupo BID y La Microempresa."
    • High-income OECD countries: We used 85% to take into account the much smaller percentage of the informal working in agriculture
    • Other regions: Based on feedback by IFC experts and syndication and barring better data, we used 50% estimate for the remaining regions.
  3. Link informal workforce in informal enterprises to the number of informal MSMEs:
    • Assume all informal sector workers in informal enterprises work in informal MSMEs (as opposed to large informal enterprises)
    • Using local data from India, the average number of employees of an informal enterprise is estimated as 2 people in South Asia.
    • All informal employees of informal enterprises are then divided by 2 to arrive at the number of informal MSMEs in South Asia.
    • In other regions, the estimate of how many people work in each informal enterprise is adjusted from 2 to 1.7 based on the number of people who are self-employed (using data from the 2002 ILO paper cited above). For example, in India, 52% of people outside of the agricultural sector are self-employed, while the Latin America figure is estimated at 60%. We created a ratio of the percent of self-employment in India over the amount in Latin America, and we multiplied this ratio by 2 (the number of people per informal enterprise in India) to estimate that there are 1.7 people per informal enterprise in Latin America. We repeated this for the other regions, using the India data as our base.
  4. When informal employment data are not available, use the regional average of number of SMEs per 1,000 people.

Methodology for estimating the finance gap for microenterprises and informal MSMEs

  1. Data availability for credit usage and need by microenterprises, informal MSMEs and nonemployer firms : Data on microenterprises with 1-5 employees are available for around 80 countries. Data on informal enterprises at the time of the study were available for only 14 countries: 10 in SSA (Angola, Botswana, Burkina Faso, Cameroon, Cape Verde, Cote d’Ioivre, DRC, Madagascar, Mali, and Mauritius); 3 in Latin America (Argentina, Guatemala, Peru), and one in South Asia (Nepal).
  2. Extrapolate data on credit usage and need by taking the regional average for countries with data and using it as an estimate for countries in the same region without data.
  3. Estimate the value of credit gap for micro and informal MSMEs
    • Estimate formal micro and informal MSMEs’ average revenue: We estimated the average revenue on a regional basis for formal microenterprises and informal MSMEs:
      • Formal microenterprises: Using data on the 14 countries for which data were available, the ratio of average revenues of microenterprises to the average revenue of very small enterprises was calculated for each country. This was then used together with average revenues for very small enterprises in each of the remaining countries to estimate the average revenues of formal microenterprises in each country.
      • Informal MSMEs: Again using data on the same 14 countries, the ratio average revenues of informal MSMEs to formal MSMEs was calculated for each country. This was then used together with average revenues of formal MSMEs in each of the remaining countries to estimate average revenues of informal MSMEs in each country.
    • Establish a baseline of the value of formal credit currently used by extrapolating from data on formal SMEs with 5 or more employees: Using the ratio of formal MSMEs credit to formal MSMEs revenues, we multiplied average revenues for enterprises with credit in both segments (i.e. formal micro and all informal) by this ratio to estimate their total current usage of credit. The result gives a baseline assessment of the value of formal credit being used for each segment of enterprises in each region.
    • Estimate the total potential need for formal financing for countries where data are available using a similar method used for formal SMEs.
    • Extrapolate to the whole region: For countries with incomplete data, we used regional averages for each segment as estimates.

Methodology for estimating gender-based differences

  1. Use Enterprise Survey Data set
    • The latest comprehensive data set available in mid-April 2011
    • The India manufacturing and retail surveys were appended, as they were not included in the dataset
    • China is not included as the survey did not ask a question on gender of ownership
    • For the MENA region, most of the data cuts include only Yemen in the original data set as the questionnaire administered in Yemen used the same set of questions as the rest of the countries outside MENA. This was appended with a separate indicator database from Enterprise Surveys to calculate regional averages for MENA of percent of enterprises with >1 women owners, percent of women-owned enterprises with access to deposits, percent of women-owned enterprises with access to loan or line of credit.
  2. Define type of women owned MSMEs:
    • Enterprise Surveys have information on the following at firm level:
      1. Female ownership: At least one women owner
      • Female sole proprietor
      • Female decision maker (i.e., manager)
    • IFC MSME Finance Gap Database considers firms of type (i) and (ii) as women-owned firms, and limits (iii) to firms with women as the top manager and at least one women owner. There are ~16,000 firms in the sample that are of type (i), ~4,000 of type (ii), and ~3,000 that are of the limited version of type (iii).
    • Due to sample size limitations, female ownership was used as the key variable for the majority of analyses. Other definitions were also used when sample size was big enough.
    • There are a large number of firms that have not responded to the gender-related questions. The country-level aggregates for these firms are marked as “n.a.” in the “female ownership” column in the data set.
  3. Estimate the number of MSMEs with >1 women owner
    • Starting point: number of micro, small and medium enterprises
    • For each country where gender disaggregated Enterprise Surveys data are available, the percentage of firms within each size bucket that had >1 women owner is used. Regional averages of this percentage are calculated for each size bucket, using number of enterprises of each size in each country as weights.
    • For countries where gender disaggregated Enterprise Surveys data were not available, regional averages were substituted.
  4. Notes on estimating the number of women-owned MSMEs with no or insufficient credit
    • If a country had <20 firms for calculating the country-level variable (e.g. percentage of women-owned small enterprises that are underserved), then that variable is not calculated, and the regional average is substituted instead.
    • For MENA, the sample size for “>1 women owners” was very small since the data set only included Yemen and MENA has relatively low woman ownership. As such, extrapolations were imperfect, and MENA was excluded.

Caveats

  1. The purpose of the analysis and the reports, including the 2010 "Two Trillion and Counting" publication, was to have a practical approach to estimate the gap for credit in developing countries in the MSME sector at the global and regional level using a limited set of resources and data which were available at the time. The main data sources include: (1) National statistical agencies (used to derive the number of MSMEs in developing countries); (2) the World Bank Enterprise Surveys (used to estimate the number of firms expressing that access to finance is an operational constraint for their business, as well as the % of firms that are well served, underserved, and that do not need credit); and (3) the "Global Banking Profit Pools" database by McKinsey & Co. (used to estimate the amount of formal financing provided to formal SMEs in 78 countries across regions).
  2. The study does not imply that the credit gap can be or should be closed immediately, but instead points out to the necessary improvements, including those in the enabling environment, for this to happen.
  3. The methodology was specifically developed to estimate the global and regional MSME credit gap (formal and informal) to the extent possible, and hence, the predictions at country level should be interpreted with caution.
  4. an ideal scenario with better data at both the firm and financial institution level that are comparable across countries and over time, detailed information on the growth opportunities and productivity measures of each of the surveyed enterprises as well as the quantity of credit demanded compared to the availability of credit at different prices would have been useful to conduct a demand estimation. However, it is common that MSMEs do not have proper bookkeeping, or no proper accounting records, due to a variety of reasons. Therefore, it may not be feasible to quantify their unmet credit demand based on their commercially viable projects.
  5. Given the data limitations, IFC decided to undertake a 'best estimate' analysis using a set of assumptions which included: (i) the use of fixed ratios to estimate the total potential need for formal financing which were conservative in nature; (ii) static firm size distributions and populations (given the already limited availability of such data at the country level); and (iii) taking the credit supply as fixed.
  6. The fixed ratios mentioned above to determine the total potential need for formal financing were discussed with various industry experts and were verified by McKinsey &Co. using proprietary data on credit usage for 500 leading Emerging Market listed companies.

  • 1 The estimation methodology was specifically developed to predict the global and regional MSME credit gap (formal and informal) to the extent possible, and hence, the predictions at country level should be interpreted with caution.
  • 2 Beck et al. (2007), Informality Database by the International Institute of Labour Studies of ILO, ILO (2009), and UNDP (2007).
Closing the Credit Gap for Formal and Informal Micro, Small, and Medium Enterprises

Closing the Credit Gap for Formal and Informal Micro, Small, and Medium Enterprises reports on the state of the MSME finance gap based on data from the IFC Enterprise Finance Gap Database, with a focus on informal enterprises based on the research findings presented to the IFC SME and Jobs Committee.

Access To Credit Among Micro, Small, And Medium Enterprises (Factsheet)

This factsheet provides a summary of the data from the IFC Enterprise Finance Gap Database, and highlights the key data points available for the formal micro, formal SME, and the aggregated formal and informal MSME sectors.

DATA

In 2010, IFC conducted a study to estimate the number of micro, small, and medium enterprises (MSMEs) in the world, and to determine the degree of access to credit and use of deposit accounts for formal and informal MSMEs. The study used primarily data from the World Bank Enterprise Surveys (ES). In 2011 the data was revisited as new enterprise surveys became available. The resulting database, IFC Enterprise Finance Gap Database, covers 177 countries.

Explore data and create your own summaries and visualizations

Financing Gap for Small Firms in Developing Economies Remains Close to $2 Trillion, New IFC Study Says

Washington, D.C., October 16, 2013—IFC, a member of the World Bank Group, released today a new study, Closing the Credit Gap for Formal and Informal Micro, Small, and Medium Enterprises that reports on the state of the credit gap for micro, small and medium enterprises (MSMEs) in developing economies. The report draws on additional data on the sizable informal enterprise sector, building on previous research in 2010 where estimates on the MSME financing gap were made for the first time.

Study findings indicate that the financing gap for MSMEs in developing economies remains around $2 trillion, which is about one-third of outstanding MSME credit in these countries. Over 200 million formal and informal MSMEs in developing countries are estimated to be either unserved—do not have a loan or overdraft—or underserved—have a loan or overdraft, but still find access to finance as a constraint. An estimated 80 percent of all enterprises in developing economies—about 300 million of them—are informal MSMEs or nonemployer firms. These firms, together with formal microenterprises, account for more than 90 percent of all unserved MSMEs in developing economies.

"MSMEs face many obstacles in developing economies, however, access to finance remains by far the biggest obstacle to their growth to date,” said Peer Stein, Director of IFC’s Access to Finance Advisory Services. “On average, about two-thirds of full-time jobs in developing economies are provided by such firms, therefore, urgent action is essential in meeting their financing needs."

The study also identifies a number of potential improvements in the financial sector infrastructure that can significantly improve access to finance by ensuring accessibility of credit information, enabling movable collateral, and strengthening creditor rights, thus making it less costly and more efficient for financial institutions to lend to MSMEs clients. In this regard, better information and data are key to understand the demand and better segment the informal market, enabling the design of custom-tailored interventions depending on the capacity and willingness of the firms to formalize.

One such key source of information is the IFC Enterprise Finance Gap Database, launched today and publicly available on SME Finance Forum web site (www.smefinanceforum.org). The database is the most comprehensive source of data on number of formal and informal MSMEs along with the size of the credit gap and deposit gap, for 177 countries, segmented by firm size and ownership.

“The web site for the database also offers a new data visualization tool that can help financial institutions, policy-makers and researchers quickly understand and analyze the unmet financing needs of MSMEs,” said Matt Gamser, Head of the SME Finance Forum housed at IFC. “This will facilitate a more comparative analysis of the financing gap by region and country.”

The IFC Enterprise Finance Gap Database can be accessed at: http://financegap.smefinanceforum.org

About IFC

IFC, a member of the World Bank Group, is the largest global development institution focused exclusively on the private sector. Working with private enterprises in more than 100 countries, we use our capital, expertise, and influence to help eliminate extreme poverty and promote shared prosperity. In FY13, our investments climbed to an all-time high of nearly $25 billion, leveraging the power of the private sector to create jobs and tackle the world’s most pressing development challenges. For more information, visit www.ifc.org