Learn more about where to find important digital financial inclusion data, how to use and interpret it appropriately.
These are typically numerical datasets compiled and published by official government agencies or prominent intergovernmental organisations like the World Bank. They are macro-level indicators like GDP per capita, the number of bank accounts, or the total value of mobile money transactions.
- Administrative data is useful for understanding big picture trends. They are also useful benchmarks when looking at smaller subsections of the market or specific theme. For example, if you’re looking at the change in how many people transact through a mobile phone, then it may be useful to understand how that compares with the change in value of mobile money transactions.
- Administrative data has other key benefits. It can be free to use and easy to access and compare across years, other countries or regions. It is also less subjective than survey data.
- However, different countries and agencies can use different methods or definitions for the same indicator which will hinder comparability and sometimes reliability of the data.
- There may also be limitations facing organisations collecting and reporting this data which can impinge the accuracy or frequency of data collection. Organisations may also use estimates where actual data is missing.
- Therefore, it is important to understand how this data is collected and, if estimates are used, what the methods and assumptions are that underpin those estimates.
The Global Findex Database was first launched in 2011 by the World Bank, with funding from the Bill and Melinda Gates Foundation. The database is the most prominent demand-side (customer) survey for financial inclusion that looks at how adults save, borrow, make payments and manage financial risk.
The Global Findex Database is primarily concerned with financial inclusion, with measures that hone in on the usage and access to formal and informal financial services. It also includes some indicators of fundamental digital infrastructure like mobile phone use.
The Findex database surveys individuals aged 15 years and above and the accompanying report is usually published every three years: 2011, 2014, 2017. The 2020 release was delayed due to the COVID-19 pandemic and was gathered in 2021 and published in July 2022. The coverage of the Global Findex survey varies with each release, covering approximately 150,000 people in over 140 countries. In 2021, approximately 128,000 people in 123 economies were surveyed.
Prior to the COVID-19 pandemic, the Global Findex conducted telephonic surveys. In localities where telephone coverage represents less than 80% of the population, face-to-face surveys were undertaken. Due to ongoing COVID-19–related restrictions and the limitations placed on face-to-face interviewing, phone based interviews were conducted in 67 economies that had been surveyed face-to-face in 2017.
Data weighting is used to ensure a nationally representative sample for each country. In some cases, regions are excluded due to security concerns or accessibility issues. For example, in the 2021 survey regions representing about 10% of the population were excluded in Mozambique for security reasons.
The Mobile Connectivity Index (MCI) was established in 2015 by the GSMA to propel digital inclusion and mobile internet connectivity. The MCI indicators measure and track mobile internet connectivity enablers and are updated annually and may be retrospectively adjusted. While some data points are sourced from GSMA Intelligence, others are taken from external sources such as the ITU and Tarfica.
The MCI covers 170 countries and consists of 46 indicators that are rolled up into a single index value for each country. These input indicators cover the key enablers of mobile internet adoption: infrastructure, affordability, consumer readiness and content and services. Each of these four enablers are given an equal weighting (25%) to derive the overall value. Within these enablers, multiple metrics carry different weights.
The use of indexes by the GSMA necessitates normalisation, this ensures that all units of measurement and ranges in the input data are accounted for. Following this, all indicators have the same orientation and are comparable, with a higher score representing a better rating. In order to normalise, GSMA employs the minimum-maximum method. The minimum-maximum method ensures that indices lie between 0 and 100 using the formula below:
Where Iq,c represents the indicator - which is a normalised minimum-maximum value. xq,c represents the value of the input data. The subscripts “q” and “c” in the equation represent indicator and country respectively.
The Index is constructed in accordance with best practice guidelines of the OECD and European Commission's Joint Research Centre.
AfricaNenda is an initiative dedicated to promoting digital payment systems across the African continent to drive inclusive growth and financial inclusion. The organization produces an annual report called the State of Inclusive Instant Payment Systems (SIIPS) in Africa, which provides crucial insights into the evolving landscape of digital payments in the region.
AfricaNenda conducts extensive research using both qualitative and quantitative methods, including stakeholder interviews, case studies, and data analysis from various sources across African countries.
The SIIPS report seeks to identify challenges and opportunities in the digital payments landscape, providing actionable insights for stakeholders. The report covers African countries, with a focus on regions with significant potential for digital payment adoption. It highlights key trends in mobile money, fintech innovations, regulatory frameworks, and infrastructure development necessary for scaling digital payments in Africa. The report is used by policy makers, financial service providers, and investors to inform strategies that support the expansion of digital payment ecosystems across the African continent.
The Finscope Survey is a comprehensive research initiative that provides valuable insights into financial behavior, access, and usage across various demographics in African countries. Launched by FinMark Trust in 2003, this survey has become a crucial tool for understanding and enhancing financial inclusion in the region.
Spearheaded by the Financial Sector Deepening (FSD) Network, Finscope Surveys have been conducted in numerous African countries, offering a broad perspective on national financial inclusion trends across the continent. Data is collected through nationally representative surveys involving face-to-face interviews with adults aged 16 and above, employing a multi-stage sampling technique to ensure diversity and accuracy in findings.
The surveys aim to identify barriers to access and usage of financial services at a national level, thereby informing policy and strategic interventions to improve financial inclusion in the countries in which the survey was conducted. The survey covers various aspects such as banking, savings, credit, insurance, and remittances, as well as exploring informal financial practices and digital financial services. These findings are utilized by African governments, financial institutions, and development organizations to design effective policies and products aimed at increasing financial inclusion in the region.
The World Bank's World Development Indicators (WDI) provide current global development data sourced from reputable entities. With over 1,500 time series indicators covering 217 economies and more than 40 country groups, the WDI database offers insights into a wide range of topics, including poverty, inequality, population dynamics, education, labor, health, gender, environment, and the economy. This extensive dataset, with many indicators dating back over 60 years, is an invaluable resource for policymakers, researchers, and analysts.
The WDI represents decades of collaborative effort, involving field workers conducting censuses and household surveys, as well as national and international statistical agencies developing essential nomenclature, classifications, and standards. This collective endeavor ensures the reliability and comparability of the data, making it a trusted source for monitoring development progress, identifying trends, and informing policy decisions.
Utilized in various publications, websites, and applications of the World Bank, including the Open Data site, the Atlas of Sustainable Development Goals, and the Corporate Scorecard, the WDI serves as a cornerstone for those seeking reliable development data.
The Global Competitiveness Index (GCI) by the World Economic Forum (WEF) is concerned with sustainable economic growth and prosperity. The GCI is the WEF’s contribution to assist policymakers, researchers and decision makers in understanding developmental challenges. The GCI is usually released annually - accompanying the Global Competitiveness Report of the WEF, however; due to COVID-19 restrictions the next iteration will be released in 2023.
The GCI covers approximately 140 countries covering 12 pillars from institutions and infrastructure to innovation. The pillars are informed by factors which are considered to be essential for improving productivity by empirical and theoretical research where the WEF considers productivity as the leading determinant for sustained economic growth. The index is compiled through two main sources: data from international organisations and surveys with executives leaders.
The Central Bank Digital Currency (CBDC) Tracker was first launched in 2020 and updated in 2021 by the Atlantic Council’s GeoEconomics Centre. The GeoEconomics Centre aims to shape a better global economic future, primarily through impactful visualisation and communication of data. The Centre works along three main pillars: the Future of Capitalism; the Future of Money and the Economic Statecraft Initiative.
The Tracker features data for over 100 countries on the state of CBDC development through six key stages from inactive to launched. Over and above tracking the level of CBDC activity, the database documents the specific design choices of CBDCs across different counties for considerations like underlying technology, governance and security. This information is collected from Central Banks, reputable third party sources like Bloomberg.com and supplemented by the Atlantic Council’s own research.
Launched in 2009, the International Monetary Fund’s Financial Access Survey (FAS) is the leading supply-side data source for financial inclusion, providing annual data on the access to and use of financial services and products. The FAS aims to support policy makers monitor financial inclusion and compare performance with other countries. Nine FAS indicators have been sanctioned by the G20 as their financial inclusion indicators, primarily relating to the prevalence of physical distribution points.
The FAS is an administrative data source. Each year central banks and other financial regulators in 189 countries collect and submit their data to the IMF. The data is disaggregated by financial service providers (e.g commercial banks and microfinance institutions) and contains 121 time-series data points. 70 of these are normalised by the FAS for easier cross-country comparability. Tha FAS uses data from the World Bank World Development Indicators database as denominators in these normalised metrics. For example, the World Bank Development Indicators adult population (15 years and older) is used to calculate ATMs per 100,000, and GDP is used to calculate the value of mobile money transactions as a % of GDP.
The Mobile Money Deployment Tracker by the GSMA was launched in 2010 as a global directory of live mobile money services. The GSMA Mobile Money programme makes use of primary and secondary sources and updates the Deployment Tracker monthly.
In order for a mobile service to be included in the Mobile Money Tracker, it needs to fulfil 4 criterion:
- The mobile money service must be available to the unbanked.
- The mobile money service must enable the user to transfer money and receive payments using their mobile phone.
- Mobile money services which utilise mobile phones as substitutes for traditional banking products are not included in the Deployment Tracker e.g., Apple Pay and Google Pay as substitutes for a traditional bank card.
- The mobile money service must provide a network of physical transaction points which increase the accessibility of the service. The network of physical transaction points must be greater than the service provider’s formal outlets. Mobile money agents are an example of physical transaction points.
The Identification for Development (ID4D) initiative was launched by the World Bank in 2015. ID4D engages in digital ID and civil registration ecosystems which facilitate increased access to superior services and enable all people to exercise their rights. ID4D reports are published annually. The purpose of the ID4D dataset is to estimate the number of individuals without proof of legal identity.
ID4D compiles quantitative data across 151 countries and based on official data from ID authorities, UNICEF birth registration data as well as voter registration in the ID4D Global Dataset. The ID4D Global Dataset estimates include all people aged 0 and above.
The team also collects qualitative data by conducting surveys in 99 countries in collaboration with the World Bank’s Findex team and compiles this data in the ID4D-Findex Survey Data. These surveys target individuals aged 15 and above.
The ID4D-Findex Survey Data is nationally representative and consists of one indicator - ID coverage. The ID4D Global Dataset consists of three indicators: “Under-5 birth registration” , “National/foundational ID registration data from ID authorities” as well as “Voter registration” which is used as a proxy for proof of identity where the previous indicator is not available.
In 2014, Standard and Poor (S&P) released the S&P Global FinLit Survey in collaboration with the World Bank Research Development Group, Gallup Inc. and the Global Financial Literacy Excellence. The S&P Global Finlit Survey was envisaged as “the most comprehensive global measurement of financial literacy.”
The S&P Global Finlit Survey conducted interviews with over 150,000 adults across 148 countries. Financial literacy was tested for four basic financial concepts: risk diversification; inflation; numeracy and compound interest across five survey questions with one correct answer per question.
Survey respondents were randomly selected and the surveyors made use of face-to-face interviews where telephone coverage was less than 80% of the population. Data weighting was undertaken with each country’s sample data to ensure national representativeness.
The World Bank's Global Jobs Indicators Database (JOIN) is a comprehensive resource for labour market data, covering 168 countries and 1,802 surveys. This database provides more than 100 nationally representative indicators on various aspects of employment, including labour force status, employment type, sector and occupation composition, education levels, hours worked, and earnings. JOIN offers disaggregated data for different worker categories, such as urban/rural, gender, age groups, and education levels.
JOIN serves as a vital tool for policymakers, researchers, and development organisations to analyse labour market trends and inform evidence-based decisions. By providing standardised and comparable indicators across countries, JOIN supports efforts to understand employment challenges, design effective job creation strategies, and monitor progress towards achieving employment-related development goals. The database's comprehensive coverage of labour market aspects enables in-depth analysis of workforce dynamics and contributes to the World Bank's mission of promoting sustainable economic growth and poverty reduction through improved employment opportunities.
The World Bank's Poverty and Inequality Platform (PIP) is a comprehensive interactive tool for accessing global poverty and inequality data. PIP is the result of a close collaboration between World Bank staff across the Development Data Group, the Development Reseacrh Group, and the Poverty and Equity Global Practice. PIP provides regional, global, and country-level poverty estimates dating back to 1981. The platform draws on income or detailed consumption data from nearly 2,400 household surveys across 170 countries, with the 2019 estimates based on surveys of more than 2 million randomly sampled households.
PIP serves as a vital resource for policymakers, researchers, and development organisations to analyse poverty trends and inform evidence-based decisions. Users can access poverty estimates for countries and regions at various poverty lines, view user-friendly dashboards with graphs illustrating trends in poverty and inequality, and explore country briefs. By providing this comprehensive poverty and inequality data, PIP supports efforts to monitor global poverty reduction progress and design effective strategies to improve living standards worldwide.
The Mobile Money Regulatory Index (MMRI) was established in 2018 by the GSMA to measure the effectiveness of national mobile money regulatory frameworks. The MMRI is concerned with the regulatory enablers of mobile money adoption and provides specific insights into policy areas.
The MMRI is released annually, covers 90 countries and includes 26 indicators. The MMRI regulations are categorised as either enabling or non-enabling and are categorised below six pillars: Authorisation, Consumer protection, Transaction limits, Know-Your-Customer (KYC), Agent Networks and Investment Infrastructure environment. Each of the pillars carry equal weighting at 15%, except Authorisation which is weighted 25%. In order to aggregate the data into an overall index score, an arithmetic aggregation across the index is applied.
The Index is constructed in accordance with best practice guidelines of the OECD and European Commission's Joint Research Centre.
The Global Fintech-Enabling Regulations Database was launched in 2021 by the World Bank. The Database is a curated catalogue of enabling laws, regulations and guidelines for digital financial services and fintech technology. The Global Fintech-Enabling Regulations Database is the World Bank’s contribution to policymakers, researchers and development organisations to enable them to identify, compare and contrast the merits of the legislatures which govern the fintech landscape.
The Global Fintech-Enabling Regulations Database covers approximately 200 countries. The data is collected through desk based interviews and supplemented by the in-country knowledge that the World Bank Group is able to provide through their regional branches. The database identifies 15 country level regulations; 6 fintech specific regulations and 6 foundational fintech regulations which are visualised as the number of countries where that regulation is present vs the number of countries where that regulation is not present.
Following the African Union Summit in 2012, The Africa Information Highway (AIH) was developed by the Statistics Department of the African Development Bank (AfDB) to significantly increase public access to official and other statistics across Africa, and support African countries with improving data quality, management and dissemination. The AIH is a mega network of live open data platforms, electronically linking all African countries and 16 regional organisations.
The AIH gives users access to a wide range of development data from multiple international and national official sources, making it a one single-stop portal for the capturing and sharing of development data across Africa. Since its launch, the AIH has expanded to include a variety of topically-specific portals, which includes among others:
- African Economic Outlook
- Socio-economic Indicators Database
- Africa Infrastructure Databases
- Agricultural Database
- National Account Database
- Selected Sustainable Development Goals Indicators
The AIH allows various users, such as policymakers, analysts, researchers, business leaders and investors around the world, to gain access to reliable and timely data on Africa. Users are able to perform comprehensive cross-country/regional analysis, visualise time series indicators over a period of time, use presentation-ready graphics or develop their own.
The Mobile Money Prevalence Index (MMPI) was established in 2021 by the GSMA to measure the level of mobile-led financial inclusion at the country-level. The MMPI is based on primary and secondary data, and collected annually to inform the GSMA flagship ‘State of Industry Report’.
As a composite index, the MMPI consists of three components: the Adult Penetration Rate (APR), the Activity Rate Index (ARI), and the Agent Distribution Index (ADI). The MMPI uses a geometric mean to ensure that one component cannot be compensated by movement in another, and is calculated as follows:
The MSME Finance Gap Database was initially developed in 2010, and then updated in 2018, to estimate the unmet demand for financing from micro, small, and medium enterprises in developing countries. It covers 128 countries and provides more accurate, country-level estimates of the finance gap. This data helps policymakers, financial institutions, and development organisations better understand and address the financing needs of MSMEs worldwide. The database uses an innovative methodology to assess both the supply of and demand for MSME finance on a global scale.
The methodology follows a 3 step approach:
- 1. Benchmarking: Benchmarks the prototypical financing environment where MSME credit markets function with minimal imperfections. This includes Australia, Canada, Denmark, Germany, the United Kingdom, the United States of America and others.
- 2. Potential demand for MSME finance: The benchmarks are then applied to the MSMEs in each operating category in emerging economies where the gap is to be calculated.
- 3. Existing supply of MSME finance: Existing lending to MSMEs by financial institutions data for 71 countries was sourced from the IMF’s Financial Access Survey, and the OECD’s SME Scorecard. The remaining data was estimated using a cross-sectional OLS regression framework.
The MSME finance gap is then calculated by subtracting the existing supply of finance in step 3 from the potential demand for finance calculated in step 2.
The World Bank's Education Statistics (EdStats) database is a comprehensive resource for global education data, covering over 200 countries and territories. Developed by the World Bank Education Group and the Development Economics Data Group, EdStats contains approximately 2,500 internationally comparable education indicators. These indicators span the entire education cycle from pre-primary to tertiary levels, focusing on key areas such as access, progression, completion, literacy, teachers, population demographics, and educational expenditures.
EdStats serves as a vital tool for policymakers, researchers, and development organisations to monitor educational progress and inform evidence-based decisions. The database incorporates learning outcome data from international assessments and equity information from household surveys. By providing this comprehensive education data, EdStats supports efforts to improve educational outcomes globally and track progress towards achieving education-related development goals.
The World Bank's Health, Nutrition and Population Statistics (HNP) database is a comprehensive resource for global health and demographic data developed by the World Bank's Development Economics Data Group (DECDG) and Health, Nutrition and Population Global Practice. The database includes about 470 indicators covering a wide range of topics such as demography, health financing, health system, immunisation, infectious diseases, nutrition, population andreproductive health.
The Health, Nutrition and Population Statistics (HNP) database serves as a vital tool for policymakers, researchers, and development organisations to monitor health trends, analyse health systems, and inform evidence-based decisions. The database provides crucial information on various aspects of global health, including HIV/AIDS, malaria, tuberculosis, non-communicable diseases, causes of death, surgery, and access to clean water and sanitation.
Reliable, timely, comprehensive and comparable data is key for evidence-based decision making. Yet, data across the continent is disparate, incomplete and often outdated. This compromises the ability to pinpoint barriers to digital financial inclusion and design targeted solutions. Reaching the most excluded with quality digital financial solutions requires stakeholders to invest in data gathering, and standardise what data is collected and how it is collected. We see three critical data challenges that require collective action to address:
- Availability: For some African countries, there is very limited data that is collected and available. Francophone Africa and smaller, low-income nations tend to have less data available.
- Availability of Data by Country:
- Timeliness: Often the available data is outdated, particularly where there are limited resources to run regular surveys.
Standardisation: There are a variety of funders and statistics organisations that commission data using different methodologies, causing a lack of standardisation.
These challenges limit how comparable digital financial inclusion data is across the continent. Comparability is a really important driver in decision making as it provides context. For example, if we want to understand the levels of uptake of financial services in a country, we might look at neighbouring countries, a regional average or countries of a similar income group. But if the time periods for two different countries are not the same or if the data is collected in a different way, then the comparison may not be robust or insightful.
An index number is a way of measuring the relative change of a composite group of related variables.
- Indexes can be really useful to decision makers as they can express a multi-dimensional issue in a single number.
- However, indices can also be misleading and require judgements, often subjective, on what indicators matter and how much they matter to a particular issue.
- Therefore, you should always interrogate the underlying indicators and weighting of indicators before you use the index.
These are typically numerical datasets compiled and published by official government agencies or prominent intergovernmental organisations like the World Bank. They are macro-level indicators like GDP per capita, the number of bank accounts, or the total value of mobile money transactions.
- Administrative data is useful for understanding big picture trends. They are also useful benchmarks when looking at smaller subsections of the market or specific themes. For example, if you’re looking at the change in how many people transact through a mobile phone, then it may be useful to understand how that compares with the change in value of mobile money transactions.
- Administrative data has other key benefits. It can be free to use and easy to access and compare across years, other countries or regions. It is also less subjective than survey data.
- Therefore, it is important to understand how the survey data was collected and what the sample groups looks like and if it is representative.