Annex A. Methodology for producing the national Scoreboards

Financing SMEs and Entrepreneurs: An OECD Scoreboard provides a framework to monitor trends in SMEs’ and entrepreneurs’ access to finance – at the country level and internationally – and supports the formulation and evaluation of policies in this domain.

The individual country profiles present data for a number of core indicators, which measure trends in SME debt and equity financing, credit conditions, solvency and policy measures. The set of indicators and policy information provide governments and other stakeholders with a consistent framework to evaluate whether SME financing needs are being met, to support the design and evaluation of policy measures, and to monitor the implications of financial reforms on SME access to finance. Consistent time series for country data permit an analysis of national trends in participating countries. It is mainly by comparing trends that insights are drawn from the varying conditions in SME financing across countries. The focus on analysis of changes in variables, rather than on absolute levels, helps overcome existing limitations to cross-country comparability of the core indicators, due to differences in definitions and reporting practices.

This Annex describes the methodology for producing the national country profiles, discusses the use of proxies in case of data limitations or deviation from preferred definitions, and addresses the limits in cross-country comparability. It also provides recommendations for improving the collection of data on SME finance.

Trends in financing SMEs and entrepreneurs are monitored through 17 core indicators, which assess specific questions related to access to finance. These core indicators meet the following criteria:

  • Usefulness: the indicators must be an appropriate instrument to measure how easy or difficult it is for SMEs and entrepreneurs to access finance and to help policy makers formulate or adjust their policies and programmes.

  • Availability: the data for constructing the indicators should be readily available in order not to impose new burdens on governments or firms.

  • Feasibility: if the information for constructing the indicator is not publicly available, it should be feasible to make it available at a modest cost, or to collect it during routine data exercises or surveys.

  • Timeliness: the information should be collected in a timely manner so that the evolving conditions of SME access to finance can be monitored. Annual data may be more easily available, but should be complemented by quarterly data, when possible, to better capture variability in financing indicators and describe turning points.

  • Comparability: the indicators should be relatively uniform across countries in terms of the population surveyed, content, method of data collection and periodicity or timeliness.

The data in the national Scoreboards are supplied by country experts with access to the information needed from a variety of supply-side and demand-side sources.

Most of the Scoreboard indicators are built on supply-side data, that is, data provided by financial institutions and other government agencies. There are several indicators which are based on demand-side surveys of SMEs. However, not all countries undertake such surveys. Use is made of quantitative demand-side data, as collected by SME surveys, to complement the picture and improve the interpretative power of the OECD Scoreboard. Whereas a plethora of qualitative SME surveys (i.e. opinion surveys) exist, quantitative demand-side surveys are less common. Experience shows that qualitative information based on opinion survey responses must be used cautiously. The broader perception of entrepreneurs about access to finance and credit conditions, emanating from such opinion surveys, has its own value though and complements the hard data provided in the quantitative analysis. Furthermore, the cross-country comparability of national surveys remains limited, as survey methodologies and the target population differs from country to country. Comparable demand-side surveys are undertaken on a regular basis by the European Central Bank and the European Commission, which provide an example of the benefits that can come from standardised definitions and methodology across countries when conducting demand-side surveys.

In order to monitor the core indicators, data are collected for 22 variables. Each variable has a preferred definition (see Table A A.1.), intended to facilitate time consistency and comparability. In a number of cases, however, it is not possible for countries to adhere to the “preferred definition” of an indicator, due to data limitations or differences in reporting practices, and a proxy is used instead. For this reason, in each country profile the data are accompanied by a detailed table of definitions and sources for each indicator.

Share of SME loans in total business loans: This ratio captures the allocation of credit by firm size, that is, the relative importance of SME lending in the national credit market. The business loan data, which are used in the construction of several indicators in the Scoreboard, include overdrafts, lines of credit, short-term and long-term loans, regardless of whether they are performing or non-performing loans. In principle, this data does not include personal credit card debt and residential mortgages.

Share of SME new lending in total new business lending: This ratio equally captures the allocation of credit by firm size, but for new loans (flows). Flows, which are measured over an accounting period (i.e. one year), are expected to reflect short-term events and are therefore more volatile than stocks, which measure the value of an asset at a given point in time, and thus reflect latest flows, as well as values that may have cumulated over time, net of depreciation.

Share of short-term loans in SME loans: This ratio shows the debt structure of SMEs or whether loans are being used to fund current operations or investment and growth needs. However, caution has to be used in interpreting this indicator, because it is affected by the composition of short-term loans versus long-term loans in the SME loan portfolio of banks. Indeed, the share of long-term loans could actually increase during a financial crisis, because it is easier for the banks to shut off short-term credit.

SME government loan guarantees, SME government guaranteed loans, SME direct government loans: These indicators show the extent of public support for the financing of SMEs in the form of direct funding or credit guarantees. By comparing government loan guarantees with guaranteed loans, information can be drawn on the take up of government programmes and on their leverage effect.

SME interest rates and interest rate spreads: These indicators describe the tightness of the market and the (positive or negative) correlation of interest rates with firm size.

Collateral required: This indicator also shows tightness of credit conditions. It is based on demand-side surveys where SMEs report if they have been explicitly required to provide collateral for their last loan. It is not available from supply-side sources, as banks do not generally divulge this information.

SME rejection rate: This indicator shows the degree to which SME credit demand is met. An increase in the ratio indicates a tightening in the credit market as more credit applications have been turned down. A limitation in this indicator is that it omits the impact of “discouraged” borrowers. However, discouragement and rejection seem to be closely correlated, as the number of discouraged borrowers tends to increase when credit conditions become tighter and a higher proportion of credit applications are refused.

SME utilisation rate: This ratio also captures credit conditions, more precisely the willingness of banks to provide credit, and is therefore sometimes used in addition to or instead of the rejection rate. An increase of this ratio indicates that a higher proportion of authorised credit is being used by entrepreneurs and SMEs, which usually occurs when credit conditions are tightening.

Venture capital and growth capital investments: This indicator shows the ability to access external equity in the form of seed, start-up, early stage venture capital as well as expansion capital and is ideally broken down by the investment stage. It excludes buyouts, turnarounds and replacement capital, as these are directed at restructuring and generally concern larger enterprises.

Leasing and hire purchases: This indicator contains information on the use of leasing and hire purchases. New production of leasing includes finance leases and operating leases of all asset types (automotive, equipment and real estate) as well as the rental of cars, vans and trucks.

Factoring and invoice discounting provides information on factoring turnover volumes, including invoice discounting, recourse factoring, non-recourse factoring, collections (domestic factoring), export factoring, import factoring and export invoice discounting (international factoring).

SME non-performing loans/SME loans: This indicator provides information about the relative performance of SME loans in banks’ portfolio, that is, the riskiness implied by exposure to SME loans. It can be compared with the overall ratio of non-performing loans to all business loans to determine whether SMEs are more risky.

Payment delays: This indicator contributes to assess SME cash flow problems. Business-to-business (B2B) payment delays show supplier credit delays and how SMEs are coping with cash flow problems by delaying their payments and are more relevant to assess cash flow problems compared with business-to-consumer or business-to-government data.

SME bankruptcies or bankruptcies per 10 000 or per 1 000 SMEs: This indicator is a proxy for SME survival prospects. Abrupt changes in bankruptcy rates demonstrate how severely SMEs are affected by economic crises. However, the indicator likely underestimates the number of SME exits, as some SMEs close their business even when not being in financial difficulties. Bankruptcies per 10 000 or per 1 000 SMEs are the preferred measures, because this indicator is not affected by the increase or decrease in the total number of enterprises in the economy.

Inflation-adjusted data

Differences in inflation levels across countries hamper comparability of trends over time. Considering this and since 2016, indicators in the trends chapter therefore have been adjusted for inflation when appropriate. For this purpose, the GDP deflator from the OECD Economic Outlook publication, deflating nominal values into real values, is used. The base year used is 2007 considering that the time series graphs found predominantly in Chapter 1 compare the median growth rate since 2008. This deflator is derived by dividing an index of GDP (measured in current prices) by a chain volume index of GDP. It is therefore a weighted average of the price indices of goods and services consumed by households; expenditure by government on goods, services and salaries; fixed capital assets; changes in inventories; and exports of goods and services minus imports of goods and services.1 It is a very broad indicator of inflation and, given its comprehensiveness, it is thus suitable to deflate current price nominal data into a real terms prices basis for measures of national income, public expenditure and other economic variables with a focus beyond consumer items.

Inclusion of median values

In order to facilitate interpretation of the data, median values of core indicators are included when appropriate in Chapter 1 of this publication. This enables a better assessment of how participating countries are positioned in terms of the assessed core indicators on SME financing. Given the limited comparability of some indicators, this relative position needs to be interpreted carefully and within the country-specific context, however. Median values rather than average values are displayed because they are less sensitive to outliers in the data.

The SME target population of the Scoreboard consists of non-financial “employer” firms, that is, firms with at least one employee besides the owner/ manager, which operate a non-financial business. This is consistent with the methodology adopted by the OECD-Eurostat Entrepreneurship Indicators Programme to collect data about business demography. The target group excludes firms with no employees or self-employed individuals, which considerably reduces the number of firms that can be considered SMEs. For most of the countries in the report, data are available for this target population. However, not all countries collect data at the source and compile them in accordance with these criteria. Therefore, in a few cases data include financial firms and/or self-employed individuals. This is mostly the case in countries reporting financial indicators based on loan size, rather than the target population, or when sole proprietorships/ self-entrepreneurs cannot be distinguished from the SME population at the supply-side level of reporting.

The data in the present report cover the period 2007 to 2020, covering the assessment of trends over the medium term, both in the pre-crisis period (2007), the financial crisis (2008 and 2009) and the period afterwards. Specific attention is placed on developments occurring in 2019, 2020 and the first half of 2021, in order to identify the most recent trends in SME finance during the COVID-19 pandemic andrelated policy response.

Data limitations and country-level specific reporting practices imply that the national Scoreboards may deviate from the preferred definitions of some core indicator. Some of the main deviations in definition of variables and data coverage are discussed below.

The OECD Scoreboard aims to collect business loan data that include overdrafts, lines of credit, short-term loans, and long-term loans, regardless of whether they are performing or non-performing loans. Additionally, it aims to exclude personal credit card debt and residential mortgages. However, for some countries, significant deviations exist from this preferred SME loan definition. For instance, in some cases, credit card debt is included in SME loans, and it cannot be determined which part corresponds to consumer credit card debt and which part is business credit card debt. In other cases, lines of credit and overdrafts are excluded, while a number of other products are indeed included in SME loans, such as securitised loans, leasing and factoring.

In some countries, central banks do not require any reporting on SME lending. In these cases the SME loans are estimated from SME financial statements available from tax authorities.

The indicators on SME loans authorised and SME loans requested, which are used to calculate the rejection rate, are obtained from demand-side surveys. However, not all countries undertake such surveys, or, if they do, the results are not necessarily comparable. This also constitutes an area, where substantial data improvements could be made, such as enriching the analysis by the inclusion of an indicator on the level of discouragement to apply for a bank loan. To capture discouragement, this indicator should ideally be analysed in tandem with the number of loan applications. If both, loan applications and rejection rates decrease over the same period, this would suggest a higher level of discouragement. As presumably the least credit-worthy firms are deterred from applying for a loan, this could also be indicative of the average riskiness of SME lending.

Another potential improvement concerns the granularity and level of detail of the data; it might be possible to distinguish the rejection rate according to the type of loan (e.g. specific rejection rates on overdrafts, term loans, credit card loans and so on), to separate partial rejections from full rejections, including more analysis on the (likely) reason(s).

A similar problem holds true for the utilisation rate; which consists of SME loans used divided by SME loans authorised. A decline in this ratio suggests that the credit market is easing, or that banks have been providing more credit than has been used. Again, not every country has reliable survey data on the SME loans used and caution is warranted when making comparisons across countries.

The report includes data on government loan guarantees and on the value of loans backed by government guarantees. Supply-side data are the best source of information on loan guarantees. There are many sources for such guarantees: local, regional or central governments. In some countries, an important volume of guarantees is also provided by mutual guarantee schemes. These are private schemes that typically benefit from public support, in the form of direct funding or counter-guarantees. However, the various loan guarantees schemes, public, private and mixed, are not always consolidated to obtain national figures. Therefore, the OECD Scoreboard reports mostly on government loan guarantees which are readily available at central government level. This is also a way to avoid the double-counting of guarantees that have multiple layers, given the existence of counter-guarantees at other levels (regional or supra-national). Still, cross-country differences exist in the degree to which the reported data include all government guarantee programmes, or only large ones.

In some cases, lack of awareness and reporting make it difficult to collect data on guaranteed SME loans. In fact, SMEs are not always aware that their loan is backed by a government guarantee and banks do not usually report this information. When these guaranteed SME loans are reported, they usually represent the full value of the loan and not the portion of the loan that is actually backed by a public institution guarantee. Nevertheless, this figure has a value of its own when compared to the total amount of SME loans outstanding. Also, it allows the calculation of the leverage effect of government guarantees to SMEs (ratio of guaranteed SME loans to corresponding government guarantees).

Significant differences exist across countries in the calculation for SME interest rates. While there is agreement that “fees” should be included in the “cost” of the SME loans, it appears to be particularly difficult to determine which “fees”, among the various charges applied to firms, to include in the interest rates. In most cases, the interest rate charged on SME loans, net of any fee, is reported. The additional fees, however, represent a rather significant cost for SMEs that is not being captured by the current indicators built on supply-side data, particularly in the case of small SME loans. In this regard, demand-side surveys could be used to collect information on the total cost of funding.

Central banks usually do not collect key pieces of information on SME access to finance, such as the collateral required for SME loans. Banks consider this to be confidential information. A rough approximation can be obtained from demand-side information, that is, the percentage of SMEs required to provide collateral on new loans. This measure is currently used in the OECD Scoreboard, and more transparent reporting by banks on the terms of their SME lending is recommended to improve information on SME credit conditions.

The present report monitors external equity, that is, venture and growth capital. Venture capital is usually reported by stage of development: seed, start-up and early expansion capital. Later stage expansion capital, referred to as growth capital, is also reported. Buyouts, turnarounds and replacement capital are excluded from venture and growth capital. Country classification systems do not always break down private equity data into these categories and most do not break it down by firm size. Indeed, at present, the lack of a standard international definition of venture capital limits cross-country comparability. Also, venture capital data are sometimes collected by private venture capital associations, which rely on voluntary reporting and whose membership may be incomplete. There is a need for greater standardisation of venture capital data reporting, in terms of both the definition used for the different stages of investment, and the methodology employed to collect data.2

Most of the indicators of the Scoreboard relate to bank finance, although in practice SMEs and entrepreneurs also rely on other financing options. Including statistics on the use of asset-based finance allows for a more complete overview of trends of access to finance for SMEs and entrepreneurs. Asset-based financing covers a variety of instruments whereby a firm obtains cash based on the value of a particular asset, rather than on credit standing. These instruments include asset-based lending, factoring, hire purchases and factoring.

Asset-based lending is any sort of lending secured by an asset (such as accounts receivable, inventory, real estate, equipment). As these loans are usually issued by banks, information on asset-based loans is already covered in the indicator on SME loans, and a separate indicator is not required. More detailed information on the composition of bank loans would, however, shed light on the importance of asset-based lending and what assets are most often used as a security.

The indicator on leasing covers either the new production (i.e. a flow indicator) of finance leases and operating leases of all asset types (automotive, equipment and real estate) and also includes the rental of cars, vans and trucks. Leasing is an agreement whereby the owner of an asset provides the right to use the asset for a specified period of time in exchange for a series of payments. Information on hire purchases, which are agreements where the purchaser agrees to pay for the goods in parts or percentages over a number of months and which is very similar to leasing is also covered.. Factoring is a type of supplier financing where firms sell their credit-worthy accounts receivable at a discount and receive immediate cash. Data on factoring turnover volumes includes all turnover that is covered by invoice discounting, recourse factoring, non-recourse factoring, collections (domestic factoring), export factoring, import factoring and export invoice discounting (international factoring).

It is important to note that these data usually do not distinguish between SMEs and large corporations, and a breakdown of data according to the size of the lessees does not exist in most countries, although research indicates that leasing and other forms of asset-based finance are very often used by SMEs. Increasing the number of countries providing data and deriving information on the take-up of asset-based finance by firm size, either directly or through a proxy, constitutes an important avenue for future research.

There is also a great deal of latitude in how banks define non-performing loans. The generally accepted threshold of 90-day arrears, i.e. payments of interest and principal past due by 90 days or more, is indeed used by many of the Scoreboard countries, but not all. Even when this same threshold is adopted, there is a great deal of variation across countries in the measurement of SME non-performing loans. In some cases, these are measured as a percentage of the entire SME loan portfolio and in other cases they are not. In addition, it is common practice to classify loans that are unlikely to be repaid in full as non-performing, even when the threshold of 90-day arrears is not met. The circumstances under which loans are considered unlikely to be repaid, and hence deemed non-performing, vary substantially across countries and financial institutions. Caution is therefore warranted when interpreting this data.

When compared to the non-performing loans ratio of large firms, this indicator provides a good description of the performance of SME loans on a national level, irrespective of the particularity of the national definition. In addition, if the changes in the non-performing ratio are analysed over time, the indicator has value for cross-country comparisons.

Payment delays and bankruptcy data are usually collected for all enterprises and not broken down by firm size. Since SMEs account for more than 97% of the enterprises in the participating countries, the national figures for payment delays and bankruptcy rates were used in this report. However, bankruptcies are hard to compare across countries because of different bankruptcy costs, legislation and behaviour in the face of bankruptcy. In some cases, bankruptcy procedures take a long time and so bankruptcies only show up in later periods rather than during the crisis period.

Payment delays are reported as delays beyond the contractual date on a B2B or on a broader B2B and B2C basis. Reporting of payment delays is important, given that it captures an additional source of cash flow constraints for SMEs. The reporting of both indicators and the comparison of B2B with B2C delays can also be used to uncover whether and how SMEs make use of such payment delays to resolve short-term cash flow issues in lieu of working capital credit facilities.

One of the biggest challenges to comparability is represented by existing differences in the statistical definition of an SME by banks and national organisations across countries. Greater harmonisation continues to prove difficult due to the different economic, social and political concerns of individual countries. In addition, within-country differences exist: some banks and financial institutions do not use their national statistical definitions for an SME but a different definition to collect data on SME financing.

In many cases, the national authorities collect loan data using the national or EU definition for an SME, based on firm size, usually the number of employees or the annual turnover (see Box A.A.1).

In other cases, the SME loan data are based not on firm size but rather on a proxy, that is, loan size.3 However, the size of the SME loan can differ among countries and sometimes even among banks within the same country.

Several reasons are advanced for not compiling financial statistics based on firm size including:

  • Banks do not collect data by firm size;

  • It is too expensive to collect such data;

  • Breaking down loan data by firm size would jeopardise confidentiality and are not gathered or communicated as a consequence.

Experience gained from the OECD Scoreboard suggests that loan data broken down by firm size are already in the financial system but are not extracted unless banks are under a regulatory obligation to provide them. Experience also suggests that the challenges mentioned above could be addressed quite easily. For instance, confidentiality requirements in theory could be met through the use of judicious sub-grouping. In this case, resolution of this issue could be found if national regulatory authorities were to make the provision of this information mandatory for banks.

The many limitations in data collection above outlined limit the possibility to make cross-country comparisons using the raw data. However, it is possible to observe general trends for the indicators, both within and across countries, using growth rates. When analysing trends, the differences in the exact composition of the indicators are muted by the fact that the changes in the indicators over time are being examined instead of levels. Additionally, if the indicators are analysed as a set, it is possible to form an overview of the country trends in SME financing. It is precisely comparing trends that the Scoreboard sheds light on changing market conditions and policies for financing SMEs and entrepreneurs.

However, again, caution is required in cross-country comparisons, especially as concerns the use of flow variables and stock measures. Flows, which are measured over an accounting period (i.e. one year), capture changes of a given variables and are therefore more volatile than stocks, which measure levels, i.e. the value of an asset at a given point in time, and thus reflect latest flows, as well as values that may have cumulated over time, net of depreciation. The comparison of flows and stock measures can be particularly problematic when growth rates are considered. In fact, a negative growth rate of a flow variable can be compatible with a positive growth rate of the same variable measured in stocks. This would be the case if the stock variables increases over time but the absolute increase by which the stock variables grows becomes smaller. Similarly, a negative growth rate of a loan stock does not necessarily mean a decline in SME lending, but could be attributed to maturing loans exceeding the value of new loans granted. Such difficulties underline the importance of complementing stock data with flows of new loans.

To enable more timely collection of data and better cross-country comparison in the future, it is necessary for countries to advance in the harmonisation of data content and in the standardisation of methods of data collection. The adoption of a standardised table for data collection and submission on SME finance has contributed to improve the process of data collection for the Scoreboard, while allowing for some customisation at the country level, and should thus be further pursued, as country coverage increases. The systematic use of the template is furthermore intended to facilitate the timely publication of the data on core indicators on the OECD.Stat website, from which it can then be customised, manipulated and downloaded.

The long-term objectives of timeliness, comparability, transparency and harmonisation of data should continue to be pursued actively by national authorities. To that end, national authorities should work with financial institutions to improve the collection of data on SME and entrepreneurship finance, by:

  • Requiring financial institutions to use the national definition for an SME based on firm size.

  • Requiring financial institutions to report on a timely basis to their regulatory authorities SME loans, interest rates, collateral requirements, by firm size and broken down into the appropriate size subcategories, as well as those SME loans which have government support.

  • Working towards international harmonisation of data on non-performing loans.

  • Encouraging international, regional and national authorities as well as business associations to work together to harmonise quantitative demand-side surveys in terms of survey population, questions asked and timeframes; encourage the competent organisations to undertake yearly surveys.

  • Promoting the harmonisation of the definition of venture capital in terms of stages of development.

Since the Scoreboard pilot exercise was launched in 2009-10, important progress has been made in terms of standardisation and comparability of information. As country coverage continues to increase, it is important for good practices in data collection and reporting to be shared among countries, but also for further advancement to be made in the harmonisation of core indicators. A number of areas can be identified to improve the monitoring over time of trends at the country level and across countries.

First, it is of paramount importance to improve reporting of SME loan variables. Key areas for refinement include:

  • Separate reporting of financial information for non-employer and employer-firms, so as to harmonise the financial data with the SME definition employed in national statistics. The separation would also allow for a more in-depth evaluation of financing trends at the country level, distinguishing between funding that is directed to businesses that generate employment from that directed to self-employers, which may however represent an important share of the country’s business activity.

  • Collection of stock and flow data for SME loans. These two indicators are complementary and should be jointly analysed in order to draw a comprehensive picture of the evolution of the SME lending portfolio.

  • Information on the composition of lending portfolios, broken down by different products (overdrafts/ lines of credit/ leases/ business mortgages or credit cards/ securitised loans). Greater granularity in the reporting of business loans would allow for the identification of the underlying elements of the SME business loan portfolio. This represents a necessary first step towards pursuing greater harmonisation in the definition of SME loans across countries, or, at least identifying a common “base composition” for more meaningful cross-country comparisons.

Second, it is also necessary to fill the gaps in available data and work towards more comprehensive information for other core indicators in the Scoreboard:

  • Government guarantees: Provide consolidated figures, which take into account the entire range of public guarantee programmes, while excluding double counting related, for instance, to the counter-guarantee of the same lending portfolio. Include additional information on the scope and coverage of public guarantee schemes, in particular information on the volume of outstanding guarantees, the public contribution to the fund’s capitalisation, and the value of the loans supported by public guarantees. The Scoreboard data should be complemented, in the policy section of country profiles, by the monitoring of the take-ups and phasing out of these guarantee schemes.

  • Government guaranteed loans: Provide the corresponding loans backed by the reported government guarantees so as to allow for the calculation of a leverage ratio. Optimally, the guaranteed portions of these loans should be also reported.

  • Non-performing loans (NPLs): Provide the NPL ratio for SME loans, together with the overall NPL ratio of the business loan portfolio or the NPL ratio for large firms. The latter would be used as a benchmark against which the performance and quality of the SME loan portfolio is measured.

  • Asset-based finance: Obtain data broken down by firm size or a functioning proxy of firm size. Currently, business associations usually do not make the distinction according to the use of these instruments by firm size, which limits the understanding of the importance of these non-bank financial instruments for SMEs.

  • SME loan fees: Provide information on the standard practice of the commercial banking sector with respect to loan fees charged to SME loans in addition to the interest rate, at a national level. If possible, use demand-side surveys to collect information on this indirect cost on SME lending.

  • Collateral: Improve the description of what constitutes collateral and use demand-side survey information to compensate for lack of supply-side data on collateral.

Efforts are underway to include more disaggregated data on SME and entrepreneurship financing in future editions of the Scoreboard publication, given the significant heterogeneity of the SME population and the impact that these underlying characteristics have on access to finance and financing conditions.

In order to obtain a better picture of the availability of more granular data in the Scoreboard countries, a survey was conducted as part of a stocktaking exercise and as an input for the longer-term objective of including more detailed information in the Scoreboard report. In total, 25 countries participated in the survey. Based on the survey results, four levels of disaggregation are being explored:

  • The geographical location of the company, where this refers to TL 2 regions (based on the OECD nomenclature), which mostly corresponds to NUTS 2 regions in the EU.

  • The gender of the principal owner, making a distinction between firms that are primarily owned (not necessarily managed) by women and firms that are primarily owned by men; “dual-ownership” is a third category.

  • The main sector of operation, using NACE Level 1 sectors as the reference.

  • Firm size, i.e. going beyond the classic dichotomy between SMEs and large companies to look into data disaggregated by smaller size bands (e.g. micro vs. small vs. medium).

A pilot exercise is currently underway, focusing on the subnational dimension in access to finance, exploiting synergies with another ongoing project of CFE, with support from the European Commission, on regional drivers and barriers to enterprise growth. The rest of this section explains the relevance of including these four levels of disaggregation in the Scoreboard.

Subnational perspective

Enterprise financing conditions at the local level reflect local economic conditions. SMEs in lagging regions typically find it more difficult to receive a loan and, when they receive one, are charged higher interest rates than SMEs in better-off regions. This does not necessarily imply geographical discrimination, but rather reflects financial performance of the borrower (internal factors) and/or higher perceived credit risk by the lenders due to a less favourable local business environment (external factors), as shown for example by higher rates of nonperforming loans in lagging regions. Equity finance is also geographically concentrated, depriving growth-oriented SMEs and start-ups in more peripheral regions from much needed growth capital. While technology could in principle allow for a greater distance between investors and entrepreneurs, a recent report by the British Business Bank finds that in 82% of equity investment stakes, the investor had an office within two hours travel time of the company that they were backing (British Business Bank, 2021[1]).

Gender perspective

Women entrepreneurs have long-faced barriers in financial markets, and these barriers have been persistent over time and across contexts. For example, women entrepreneurs in the EU are about 25% less likely than their male counterparts to use bank loans to fund their business. Even when women receive external finance, they typically receive smaller amounts, pay higher interest rates and are required to secure more collateral. Moreover, only about 13% of governmental start-up funding (e.g. grants, loans) goes to female founders. Even among growth-oriented businesses seeking venture capital, only about 2% of European equity investments go to all-female founding teams, and when women do receive venture capital investments, it is about 70% of the funding that male founders receive (Halabisky and Basille, forthcoming[2]). While the availability of enterprise financing data by the gender of the business owner is scarce, it would be important in the near future to work in this direction, for example by central banks asking commercial banks to collect and share aggregate information on the distribution of business loans by the gender of the borrower.

Sector perspective

Financing needs and access to finance opportunities change depending on the main activity and industry in which a business operates. SMEs in sectors that rely more intensively on physical assets, such as manufacturing, can be expected to receive credit more easily, as capital assets can be pledged as collateral. Asset-based financing is also more easily available to these enterprises. Companies whose business model hinges on intangible assets (IA) (e.g. patents and trademarks), on the other hand, are at a disadvantage in credit markets, as these assets are firm-specific and difficult to use as collateral in traditional debt relations. The availability of alternative sources of finance, from equity finance to private debt, is particularly important for IA-intensive companies, which are often major drivers of growth. Equity finance, in addition to being geographically concentrated, is also sector-concentrated in knowledge-intensive industries, such as ICT, biotechnologies and medical services. Interestingly, however, the sector distribution of high-growth firms is much less concentrated than the sector distribution of equity investments, suggesting the existence of an industry mismatch between the allocation of equity finance and the distribution of high-growth firms.

Firm size perspective

The rationale behind the OECD SME and Entrepreneurship Financing Scoreboard is that SMEs are disadvantaged compared to larger companies in access to external finance, making it relevant to assess trends in the SME finance gap and policies that can bridge this gap. However, a closer look would show that micro and small enterprises face the most constraints, whereas access to finance and financing conditions for mid-sized companies are closer to those available for larger companies. Typical problems of credit markets, such as information asymmetries or lack of collateral, are much more prominent among smaller SMEs. To the extent possible, efforts should seek to go beyond the classic dichotomy of SMEs and larger companies by collecting more granular information on credit and equity finance by smaller firm-size classes.


← 1. OECD (2009), OECD Factbook 2009: Economic, Environmental and Social Statistics, OECD Publishing, Paris. DOI:

← 2. See Annex C in OECD (2013), Entrepreneurship at a Glance 2013, OECD Publishing, Paris, for a detailed discussion on the international comparability of venture capital data.

← 3. Recent studies by the World Bank provide evidence that loan size is an adequate proxy for size of the firm accessing the loan. See for instance Ardic O.P., Mylenko N., Saltane V. (2012), “Small and medium enterprises: a cross-country analysis with a new data set”, Pacific Economic Review, Vol. 17, Issues 4, pp. 491-513.

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