Financial Access Survey (FAS) indicators are expressed as ratios to GDP, land area, or adult population to facilitate cross-economy comparisons. Provision of FAS data is voluntary.

The Financial Access Survey draws on the IMF's Monetary and Financial Statistics Manual and Compilation Guide (http://data.imf.org/api/document/download?key=61061648)

Contains 180 time series and 65 indicators that are expressed as ratios to GDP, land area, or adult population to facilitate cross-economy comparisons. Provision of FAS data is voluntary.

This selection includes data related to SPC member countries and territories for some of the indicators available in the original database published by the IMF.

Find more Pacific data on PDH.stat.

Cameroon CM: Loan Accounts: per 1000 Adults: Commercial Banks data was reported at 37.180 Number in 2020. This records an increase from the previous number of 33.520 Number for 2019. Cameroon CM: Loan Accounts: per 1000 Adults: Commercial Banks data is updated yearly, averaging 22.965 Number from Dec 2009 (Median) to 2020, with 12 observations. The data reached an all-time high of 37.180 Number in 2020 and a record low of 9.900 Number in 2009. Cameroon CM: Loan Accounts: per 1000 Adults: Commercial Banks data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cameroon – Table CM.World Bank.WDI: Banking Indicators. Borrowers from commercial banks are the reported number of resident customers that are nonfinancial corporations (public and private) and households who obtained loans from commercial banks and other banks functioning as commercial banks. For many countries data cover the total number of loan accounts due to lack of information on loan account holders.;International Monetary Fund, Financial Access Survey.;Median;Country-specific metadata can be found on the IMF’s FAS website (data.imf.org).

Financial inclusion data set obtained from International Monetary Fund’s annual Financial Access Survey, institutional data obtained from Worldwide Governance Indicators and control variables data obtained from WDI all corresponding to 125 countries for the period 2004-2015

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

Sample excludes Northeast states and remote islands, representing less than 10% of the population.

Analysis unit

Individuals

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world’s population (see table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer’s gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 3000.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

United States US: Branches: per 100,000 Adults: Commercial Banks data was reported at 32.666 Number in 2016. This records a decrease from the previous number of 33.033 Number for 2015. United States US: Branches: per 100,000 Adults: Commercial Banks data is updated yearly, averaging 33.941 Number from Dec 2004 (Median) to 2016, with 13 observations. The data reached an all-time high of 35.898 Number in 2009 and a record low of 32.386 Number in 2014. United States US: Branches: per 100,000 Adults: Commercial Banks data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Banking Indicators. Commercial bank branches are retail locations of resident commercial banks and other resident banks that function as commercial banks that provide financial services to customers and are physically separated from the main office but not organized as legally separated subsidiaries.; ; International Monetary Fund, Financial Access Survey.; Median; Country-specific metadata can be found on the IMF’s FAS website at http://fas.imf.org.

Abstract

The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.

Geographic coverage

National coverage

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The universe of inference includes all formal (i.e., registered) private sector businesses (with at least 1% private ownership) and with at least five employees. In terms of sectoral criteria, all manufacturing businesses (ISIC Rev 4. codes 10-33) are eligible; for services businesses, those corresponding to the ISIC Rev 4 codes 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95 are included in the Enterprise Surveys. Cooperatives and collectives are excluded from the Enterprise Surveys. All eligible establishments must be registered with the registration agency. In the case of Gambia, registration was with the Gambia Revenue Authority.

Kind of data

Sample survey data [ssd]

Sampling procedure

The WBES use stratified random sampling, where the population of establishments is first separated into non-overlapping groups, called strata, and then respondents are selected through simple random sampling from each stratum. The detailed methodology is provided in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling has several advantages over simple random sampling. In particular, it:

• produces unbiased estimates of the whole population or universe of inference, as well as at the levels of stratification
• ensures representativeness by including observations in all of those categories
• produces more precise estimates for a given sample size or budget allocation, and
• may reduce implementation costs by splitting the population into convenient subdivisions.

The WBES typically use three levels of stratification: industry classification, establishment size, and subnational region (used in combination). Starting in 2022, the WBES bases the industry classification on ISIC Rev. 4 (with earlier surveys using ISIC Rev. 3.1). For regional coverage within a country, the WBES has national coverage.

Note: Refer to Sampling Structure section in "The Gambia 2023 World Bank Enterprise Survey Implementation Report" for detailed methodology on sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The standard WBES questionnaire covers several topics regarding the business environment and business performance. These topics include general firm characteristics, infrastructure, sales and supplies, management practices, competition, innovation, capacity, land and permits, finance, business-government relations, exposure to bribery, labor, and performance. Information about the general structure of the questionnaire is available in the Enterprise Surveys Manual and Guide (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf).

The questionnaire implemented in the Gambia 2023 WBES included additional questions tailored for the Business Ready Report covering infrastructure, trade, government regulations, finance, labor, and other topics.

Response rate

Overall survey response rate was 67.8%.

Mozambique MZ: Deposit Accounts: per 1000 Adults: Commercial Banks data was reported at 331.995 Number in 2016. This records an increase from the previous number of 286.978 Number for 2015. Mozambique MZ: Deposit Accounts: per 1000 Adults: Commercial Banks data is updated yearly, averaging 147.128 Number from Dec 2005 (Median) to 2016, with 12 observations. The data reached an all-time high of 331.995 Number in 2016 and a record low of 60.033 Number in 2005. Mozambique MZ: Deposit Accounts: per 1000 Adults: Commercial Banks data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mozambique – Table MZ.World Bank.WDI: Banking Indicators. Depositors with commercial banks are the reported number of deposit account holders at commercial banks and other resident banks functioning as commercial banks that are resident nonfinancial corporations (public and private) and households. For many countries data cover the total number of deposit accounts due to lack of information on account holders. The major types of deposits are checking accounts, savings accounts, and time deposits.; ; International Monetary Fund, Financial Access Survey.; Median; Country-specific metadata can be found on the IMF’s FAS website at http://fas.imf.org.

The A2F Survey, which stands for “Access to Financial Services,” is a nationally representative survey conducted in Nigeria. It assesses the access to and use of financial services by Nigerian adults aged 18 and above across all 36 states and the Federal Capital Territory (FCT) of Abuja. The primary purpose of the A2F Survey was to gather credible data on financial inclusion in Nigeria. This data helps to identify opportunities for policy reform and market opportunities for financial service providers. The A2F Survey provides credible data that is essential for understanding the landscape of financial inclusion in Nigeria. It highlights opportunities for policy reform and market opportunities for financial service providers. The A2F Survey provides information about how Nigerians access and use financial services. It also offers insights into national trends in financial inclusion, financial health, the impact of events like Covid-19, Naira re-design, customer trust in financial institutions, and the adoption of digital financial services. The survey assesses all Nigerians’ access to and use of financial services, describes the access landscape, and identifies opportunities to promote financial inclusion in the country. It covers various aspects, including financial health, customer trust in financial institutions, and digital financial services. The A2F Survey is universally recognized by financial sector stakeholders in Nigeria. It serves as an established and leading source of information on national trends in financial inclusion, aiding industry professionals and policymakers in making informed decisions.

Sao Tome and Principe ST: Branches: per 100,000 Adults: Commercial Banks data was reported at 25.531 Number in 2017. This stayed constant from the previous number of 25.531 Number for 2016. Sao Tome and Principe ST: Branches: per 100,000 Adults: Commercial Banks data is updated yearly, averaging 29.837 Number from Dec 2008 (Median) to 2017, with 10 observations. The data reached an all-time high of 33.315 Number in 2013 and a record low of 20.361 Number in 2008. Sao Tome and Principe ST: Branches: per 100,000 Adults: Commercial Banks data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sao Tome and Principe – Table ST.World Bank.WDI: Banking Indicators. Commercial bank branches are retail locations of resident commercial banks and other resident banks that function as commercial banks that provide financial services to customers and are physically separated from the main office but not organized as legally separated subsidiaries.; ; International Monetary Fund, Financial Access Survey.; Median; Country-specific metadata can be found on the IMF’s FAS website at http://fas.imf.org.

Abstract

This report presents the main results of the 2017 Namibia Financial Inclusion Survey. The survey was conducted by the Namibia Statistics Agency, in all 14 regions of Namibia, with funding from the Bank of Namibia and the World Bank. By design, the NFIS surveys was intended to involve a range of stakeholders through syndicate membership to enrich the entire survey process through cross-cutting learning, sharing of information, and to facilitate the extended utilization of the final data. A nationally representative sample of Namibians 16 years and older was employed. During October and November 2017 1863 face-to-face interviews were conducted, one interview per selected household. The data was captured into a tablet-based questionnaire using the Survey-To-Go application. The data collected was weighted to reflect the adult/eligible population (i.e. aged 16 years or older) in Namibia, as this is the minimum age legally allowed for any individual to make use of formal financial products in their own capacity. It is also important to note that the results of 2017 are representative only at national and urban/rural areas levels, but not regional.

· To measure the levels of financial inclusion (inclusive of formal and informal usage) · To describe the landscape of access (type of products and services used by financially included individuals) · To identify the drivers of, and barriers to the usage of financial products and services · To track and compare results and provide an assessment of changes and reasons thereof (including possible impacts of interventions to enhance access) · To stimulate evidence-based dialogue that will ultimately lead to effective public/private sector interventions that will increase and deepen financial inclusion strategies · Provide information on new opportunities for increased financial inclusion and usage.

Geographic coverage

National sampling frame is a list of small geographical areas called Primary Sampling Units (PSUs). There are a total of 6453 PSUs in Namibia that were created using the enumeration areas (EA) of the 2011 Population and Housing Census. The measure of size in the frame is the number of households within the PSU as reflected in the 2011 Census. The frame units were stratified first by region, and then by urban/rural areas within each region.

The results are only representative at national level, but not at regional level.

Analysis unit

Individuals, households

Universe

The target population for the NFIS 2017 was all people aged 16 and above who live in private households in Namibia. The eligible population living in institutions, such as hospitals, hostels, police barracks and prisons were not covered in this survey. However, private households within institutional settings such as teachers' houses in school premises were covered.

Kind of data

Sample survey data [ssd]

Sampling procedure

The target population for the NFIS 2017 was eligible members of private households in Namibia. The eligible population living in institutions, such as hospitals, hostels, police barracks and prisons were not covered in this survey. However, private households within institutional settings such as teachers' houses in school premises were covered. The sample design was a stratified three-stage cluster sample, where the first stage units were the PSUs, the second stage units were the households and the third stage were the eligible members, that is individuals who, by the time of the survey were 16 years or older, available during the duration of survey, mentally/physically capable to be interviewed and have resided in the selected household for at least six month preceding the survey. The age limit for the eligibility criteria was based on the fact that only individuals aged 16 years or above are officially authorized to get personal formal financial products (such as open a personal bank account) from formal financial institutions in Namibia, which makes them the target population of the financial sector. Only one individual was interviewed per selected household

The national sampling frame was used to select the first stage units (PSUs). The national sampling frame is a list of small geographical areas called Primary Sampling Units (PSUs) created using the enumeration areas (EAs) of 2011 Population and Housing Census. There are a total of 6 453 PSUs in Namibia. A total of 151 PSUs were selected from all the 14 regions, and 2 114 households were drawn from them, constituting the sample size. Power allocation procedures were adopted to distribute the samples across the regions so that the smaller regions will get adequate samples.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2017 NFIS questionnaire was made up of 13 sections in total. The questionnaire was transmitted onto CAPI (Computer aided Personal Interview) using the Survey-To-Go application.

Cleaning operations

The data processing methodology that was adopted for this study was the Computer Assisted Personal Interview. Data management series of operations to collect, transmit, clean and store the survey data were designed using SurveyToGo computer system onto the Dubloo platform.

Data entry is very crucial, since the quality of data collected impact heavily on the output. The collection process was designed to ensure that the data gathered are both defined and accurate, so that subsequent decisions based on the findings are valid.

Response rate

After data processing, 1863 out of 2114 sampled households were successfully interviewed, resulting in 88.1 percent response rate which is highly satisfactory given that the NSA subscribes to a response rate of 80 percent for all data collection in the social statistics domain. Overall, the rural response is higher than the urban response.

It was not possible to interview all the selected households when the household sample was implemented, due to refusals or non-contacts.

Sampling error estimates

The most common measure of quality of the survey estimates reported from the sample surveys was the level of precision of the estimates. The quality indicators are meant to ascertain the analysis about the level of precision of the estimates at different domains. The statistical precision of the survey estimates were expressed using different types of statistics such as Standard errors (SE), the coefficient of variation (CV) and the Confidence Interval (CI). These statistics were used to indicate the level of precision of the survey estimates in estimating the population parameters of interest. There are a number of factors that can affect the precision of the survey estimates namely the size of the sample relative to the population size, the sample design and the variability of the characteristics of interest in the population. The data quality indicators were discussed in details in the following sub-section.

Serbia RS: Automated Teller Machines (ATMs): per 100,000 Adults data was reported at 45.603 Number in 2017. This records a decrease from the previous number of 50.664 Number for 2016. Serbia RS: Automated Teller Machines (ATMs): per 100,000 Adults data is updated yearly, averaging 45.079 Number from Dec 2005 (Median) to 2017, with 13 observations. The data reached an all-time high of 50.664 Number in 2016 and a record low of 13.825 Number in 2005. Serbia RS: Automated Teller Machines (ATMs): per 100,000 Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Serbia – Table RS.World Bank: Payment System. Automated teller machines are computerized telecommunications devices that provide clients of a financial institution with access to financial transactions in a public place.; ; International Monetary Fund, Financial Access Survey.; Median; Country-specific metadata can be found on the IMF’s FAS website at http://fas.imf.org.

France FR: Automated Teller Machines (ATMs): per 100,000 Adults data was reported at 104.380 Number in 2016. This records a decrease from the previous number of 106.993 Number for 2015. France FR: Automated Teller Machines (ATMs): per 100,000 Adults data is updated yearly, averaging 104.380 Number from Dec 2004 (Median) to 2016, with 13 observations. The data reached an all-time high of 109.251 Number in 2012 and a record low of 85.493 Number in 2004. France FR: Automated Teller Machines (ATMs): per 100,000 Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s France – Table FR.World Bank.WDI: Payment System. Automated teller machines are computerized telecommunications devices that provide clients of a financial institution with access to financial transactions in a public place.; ; International Monetary Fund, Financial Access Survey.; Median; Country-specific metadata can be found on the IMF’s FAS website at http://fas.imf.org.

US: Automated Teller Machines (ATMs): per 100,000 Adults data was reported at 173.903 Number in 2009. This records an increase from the previous number of 168.010 Number for 2008. US: Automated Teller Machines (ATMs): per 100,000 Adults data is updated yearly, averaging 168.688 Number from Dec 2004 (Median) to 2009, with 6 observations. The data reached an all-time high of 173.903 Number in 2009 and a record low of 165.783 Number in 2004. US: Automated Teller Machines (ATMs): per 100,000 Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Payment System. Automated teller machines are computerized telecommunications devices that provide clients of a financial institution with access to financial transactions in a public place.; ; International Monetary Fund, Financial Access Survey.; Median; Country-specific metadata can be found on the IMF’s FAS website at http://fas.imf.org.

The Quarterly Survey of Financial Assets and Liabilities (FALS) collects information on the financial assets and liabilities held by companies, with the exception of financial institutions. (Financial Institutions are organisations such as banks and building societies and these provide data directly to the Bank of England). The data are mainly used in constructing the financial accounts for the non-financial corporations’ sector of the UK National Accounts and as an input into the compilation of the capital account of the UK Balance of Payments (the difference between imports and exports).

Latest edition information
For the fourth edition (September 2022), quarter 1 data for 2022 have been added to the study.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Colombia is 1000.

Mode of data collection

Landline and mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

The following aspects are covered in the survey: -The state of SMEs: their financial situation, their growth and development. -The use of financial instruments: financial institutions and types of financing.Access to finance through banks: the use of banks, ease of access now and compared to the past, attitudes towards banks. - The use of small loans: whether SMEs have done this, their views about it and reasons that would encourage them to do so. - The use of venture capital: to what extent it is currently being used, do SMEs foresee this to be a form of financing in the future and for what reasons? - Financial management: how this is done and to whom SMEs turn for information or advice on financing.

The results by volumes are distributed as follows:

• Volume A: Countries
• Volume AA: Groups of countries
• Volume A' (AP): Trends
• Volume AA' (AAP): Trends of groups of countries
• Volume B: EU/socio-demographics
• Volume B' (BP) : Trends of EU/ socio-demographics
• Volume C: Country/socio-demographics ---- Researchers may also contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

The survey covers micro (1 to 9 employees), small (10 to 49 employees), medium-sized (50 to 249 employees) and large firms (250 or more employees) and it provides evidence on the financing conditions faced by SMEs compared with those of large firms during the past six months. In addition to a breakdown into firm size classes, it provides evidence across branches of economic activity, euro area countries, firm age, financial autonomy of the firms, and ownership of the firms.

Part of the survey is run by the ECB every six months to assess the latest developments of the financing conditions of firms in the euro area. The more comprehensive survey, run together with the European Commission, was initially conducted every two years, i.e. in 2009H1, 2011H1 and 2013H1. As from the wave 2014H1, the extended survey is run on the annual basis.

The survey covers micro (1 to 9 employees), small (10 to 49 employees), medium-sized (50 to 249 employees) and large firms (250 or more employees) and it provides evidence on the financing conditions faced by SMEs compared with those of large firms during the past six months. In addition to a breakdown into firm size classes, it provides evidence across branches of economic activity, euro area countries, firm age, financial autonomy of the firms, and ownership of the firms. Part of the survey is run by the ECB every six months to assess the latest developments of the financing conditions of firms in the euro area. The more comprehensive survey, run together with the European Commission, was initially conducted every two years, i.e. in 2009H1, 2011H1 and 2013H1. As from the wave 2014H1, the extended survey is run on the annual basis.