BLOG

10 Useful Data Sources for Measuring Financial Inclusion

In recent years, data sources for financial inclusion have become richer yet more complicated to navigate. On the demand side, there are numerous sources: The Global Findex was released in 2012; there are various country-level surveys like FinScope, FinAccess and the Financial Inclusion Tracker Survey (FITS) just to name a few. Similarly, the supply-side data sources have grown and deepened to include more focus on sub-national data and to cover more indicators.

How do we navigate this busy data landscape? And what do the data sources tell us? Which data source do we use for what purpose? Ultimately, there is no single “best source” for data on financial inclusion. To know what data you need, you must start with asking yourself what you want to know.

A brick worker carries bricks in a stack.
A brick worker carries bricks in a stack. Photo by Moksumul Haque.

Demand-side data sources

1. The Global Findex is the only global demand-side data source allowing for global and regional cross-country analysis It includes data from 148 countries and collects information on 506 indicators from at least 1,000 individuals over 15 years old within each country. The sample is nationally representative and randomly selected. Since the survey is a module added to the Gallup World Poll, it combines information about socio-demographic conditions and access to or usage of financial services. The Global Findex is mainly used for global trend analysis and cross-country comparison to highlight headline financial inclusion indicators such as the number of adults with access to formal bank accounts. The drawback is that the data is not sub-nationally representative, which means that it is less useful for in-country policymakers and their decision-making as there is just not sufficient granularity. Also, the definition of formal financial services is based on people’s perception of whether their provider is a formal financial institution, which is not necessarily aligned with the regulatory and supervisory framework of a country. The sample is randomized at the individual level, which allows users to aggregate the data by individual characteristics, such as income and gender, but this also makes the data incompatible with household-level surveys.

2. The FinScope Survey was the first globally recognized demand-side data source allowing for measurement of financial inclusion indicators at a sub-national level. It originated in 2002 and is trademarked by FinMark Trust, which means that it is the only organization with permission to use the methodology. FinScope is a nationally representative survey explaining how individuals manage their financial lives. It also provides insight into attitudes and perceptions regarding financial products and services. The sample size varies widely across countries, and to date, surveys have included responses from anywhere between 1,000 and 21,000 individuals. The unit of sampling is at the individual level but the survey does enable some conclusions from the household level. To date, approximately 17 countries have conducted FinScope surveys – or are in the process of doing so. Often industry players will contribute to the cost of the survey and help tailor the questionnaire to meet multiple stakeholders’ needs. FinScope data is not comparable across countries on all indicators.

3. FinAccess/Access to Financial Services Surveys are similar to FinScope but are not conducted by FinMark Trust. To a large extent, the FinAccess in Kenya and Access to Financial Services in Nigeria (and the like) follow the same principles as FinScope, but because they were not conducted by FinMark Trust they have a different label. Similar to the FinScope, these surveys are designed through industry consultation which means they have the potential to meet many needs and answer many questions. They suffer from the same drawback as Finscope; that is, they are not designed for cross-country comparison. Additionally, as these surveys are commissioned and carried out by various entities, there may be inconsistencies in data quality. In addition, across both FinScope and FinAccess surveys, there is no standard definition of “financially included”, so the meaning behind this term varies according to the definitions of local stakeholders. For instance, some countries might include payment cards or mobile wallets that are not linked to an account, while others may not.

4. Financial Inclusion Tracker Surveys (FITS) are a nationally representative panel survey designed to collect trend data about households’ financial behavior over time. The Bill and Melinda Gates Foundation’s Financial Services for the Poor team in partnership with Intermedia designed these surveys to run over a three-year period in three countries. The sample size is 3,000 households in Uganda and Tanzania and 5,000 households in Pakistan, and the survey will measure the same households throughout the entire period. Data from the survey represent collective behavior and usage patterns for all members of a particular household. The data has been and are used to estimate trends in poverty levels of mobile money users. This focus on households, while a useful perspective, can also be a shortcoming because it is not as helpful for analysis at the individual level. Furthermore, the survey has only been carried out for three countries.

5. Financial Inclusion Insight Surveys (FII). The Bill and Melinda Gates Foundation in partnership with Intermedia has also recently launched the data collection effort for the FII survey. The data will be available during 2014. Contrary to FITS, the FII surveys are not panel surveys as they do not track the same household over time. They focus more on measuring individual perception and behavior, making them comparable to the FinAccess and the like. However, their strong focus on mobile money and digital financial services sets these surveys apart. The strategic objective of the surveys and the methodologies and frequencies for data collection vary between the eight countries for which the data will be collected (Kenya, Tanzania, Uganda, Nigeria, India, Pakistan, Bangladesh and Indonesia). The sample size is typically high in order to allow for sub-national representation. The surveys include welfare measures based on the Grameen Progress out of Poverty index (PPI) which is unique to the FII. Because FII surveys only focus on insights into digital financial services, they do not capture many indicators around access and usage for non-digital financial services. Furthermore, they are only carried out for eight countries.

Supply-side data sources

6. The IMF Financial Access Survey (FAS) is the most comprehensive global supply-side data on financial inclusion. In addition to providing policy makers and researchers with annual geographic and demographic data on access to basic consumer financial services worldwide, the FAS is one of the main data sources for the G20 Basic Set of Financial Inclusion Indicators endorsed by the G20 Leaders at the Los Cabos Summit in June 2012 as well as the more comprehensive G20 Financial Inclusion Indicators. The FAS database currently contains annual data for 189 jurisdictions, including all G20 economies, covering a nine-year period (2004-2012). Countries are responsible for managing their data and metadata. Similar to the Global Findex’s cross-country comparison advantage for demand-side data, the IMF FAS provides the same functionality but for supply-side data. The IMF FAS is not sub-nationally representative and the data is dependent on countries’ ability to capture data from financial service providers. Moreover, IMF FAS only includes data on prudentially regulated financial service providers.

7. GSMA Mobile Money Adoption Survey. In 2011, Mobile Money for the Unbanked (MMU) initiated a global adoption survey to give managers of mobile money deployments better insights into the performance of their service relative to each other. In October 2013, the MMU released the initial findings of the 2013 Mobile Money Adoption Survey. The full results from the survey will be published as part of MMU 2013 State of the Industry report on the 24th of February at Mobile World Congress. The 2013 survey represents 114 service providers from 57 countries, with 100 submitting information on mobile money, 18 on mobile insurance, and 12 on mobile credit and savings. While the database itself is not public, MMU publishes the analysis of the aggregated results. The survey offers a snapshot of the mobile money industry every year and also gives mobile money service providers a source of benchmark data.

8. Word Bank’s Global Payment Survey. The Global Payments survey is a comprehensive survey carried out in 139 countries and provides information on the status of national payment and securities settlement systems worldwide. This is expected to guide reform efforts in the payment system arena both nationally and globally. The 2008 and 2010 surveys (only two conducted to date) provide a snapshot of the payment and securities settlement systems in both advanced and emerging economies.

9. The MIX’s Geospatial Maps. The MIX Market is the premier source of public information on microfinance institutions (MFIs) and their financial and social performance. MIX offers a suite of popular analysis reports at the global, regional, and country levels, including global analyses of key issues for the sector. The MIX has within the last year expanded its focus through geospatial mapping efforts to aggregate and visualize data for financial service providers including and beyond MFIs. To date, nine countries have subnational data visualized through the portal. The MIX’s move to visualize geo-spatial sub-national supply-side data through publicly available geo-spatial maps will enrich the supply-side data landscape. This will be a challenging undertaking as frequent data collection can be expensive and/or ad hoc depending on when data may become available.

10. Fspmaps.com is a website funded by the Bill and Melinda Gates Foundation in partnership with Spatial Development International. Fspmaps.com provides analytical tools to answer several financial access questions. Similar to the MIX, the website is leveraging geospatial information for financial inclusion tracking and analysis. The website hosts comprehensive geospatially referenced financial access point data, as well as high-resolution population data including poverty densities and other demographic attributes. Through the analytical tools you can obtain detailed information about where people – including poor people – live in relation to financial service access points. Underserved areas can be better detected this way. Another analytical tool allows you to drop a pin on the map and calculate population served with mobile coverage but lacking adequate financial access. The website also allows users to import private datasets as a drag and drop function. Fspmaps.com currently hosts data for Tanzania, Uganda, Nigeria and Bangladesh and will soon also host geospatial data from Kenya and parts of Indonesia and India. You can read more about additional features and tools in the paper here.

Comments

10 January 2014 Submitted by Hanh (not verified)

Thank you very much for putting together. What about FinStats that has information on # accounts, branches...?

11 January 2014 Submitted by Y P Issar (not verified)

The blog provides a very comprehensive review of various data sources which shall prove useful to many FI researchers.
In India, Census maps households having bank accounts (with banks and post offices only, not MFIs) and thus exact such data upto each households is available every ten years. May be other countries are also collecting similar data as part of census efforts which can be explored. Due to its usefulness, a campaign to cover account holding status under Census need be launched in all countries.
Interesting part is that Indian Census can provide villagewise lists of households not having a bank account thus facilitating coverage efforts with better targeting and lower costs.
We need to recognise that for poor a bank account in a family is first major achievement rather than covering of all adults immediately.
Thus household coverage data granularity can help FI efforts in a significant new ways.
In India, the central bank publishes data on inclusion by collecting the same from banks which can be compared with Census data which may have more authenticity. Thus having two independant sources can be a better approach, particularly a source which has no incentive to exagerate..( ex GM FI Punjab national Bank New Delhi)

13 January 2014 Submitted by Jose Luis Aguela (not verified)

Thnaks for putting together all the list of sources to measure Finacial Inclusion, both, from the demand and the offer sides. It would be great if some institutions would take this indexes as a way of measure themselves in getting financial inclusion. Even goverments must be so interested to pay attention to kind of measures.

20 January 2014 Submitted by Raul Santos (not verified)

Great resource. Another good information source is the Bank for International Settlements. It is not specific to financial inclusion but has useful statistics about payment systems and banking.

23 January 2014 Submitted by Daniel Douglass (not verified)

Excellent post! Thanks for sharing MIX’s work here. Stay tuned this April for the launch of MIX’s new platform dedicated to enabling financial inclusion for policy makers and FSPs. It will help actors better visualize markets (included and excluded) to better optimize strategies, measure progress and coordinate as a community. 

03 March 2014 Submitted by Miranda Beshara (not verified)

Thanks for the great post!

I also find Mapping the Invisible Market by Accion's Center for Financial Inclusion (http://www.centerforfinancialinclusion.org/fi2020/mapping-the-invisible…) a good resource to easily navigate the Global Findex data. It provides data, analysis, and tools to look at how major forces such as demographic change, income growth, urbanization, and technology influence financial inclusion.

04 March 2014 Submitted by Mauricio Pinzon (not verified)

Excellent post! Thanks for this useful information! I am wondering if you guys have a similar list of data sources on financial inclusion to firm level. I know WB's Enterprise Survey has a specif section on Finance. Do you guys have other ideas regarding data on firms? Many thanks!

29 April 2014 Submitted by timothy (not verified)

Avery insightful article.i was just wondering whether data on inclusion of rural areas worldwide,Africa and Kenya in particular exists

07 May 2014 Submitted by Karina Nielsen (not verified)

Timothy, Kenya is one of the countries with the highest number of both demand- and supply-side data sources on financial inclusion. Depending on what you want to know, different data sources might be of relevance to you. feel free to write me on: knielsen1@worldbank.org with a specific question and I'll be happy to think through relevant data sources with you.

17 June 2014 Submitted by Mohsin Termezy (not verified)

Just what the doctor ordered. Thankyou Karina Broens Nielsen for penning this. I was wondering if there is an assessment of the regulatory readiness i.e. Enablement Index both for Banking regulator and Cellular operator regulator. Does AFI support any of such efforts?

21 September 2014 Submitted by robert Haudry d... (not verified)

Many thanks Karina
very professional summary of the state of the art

23 September 2014 Submitted by Global Findex (not verified)

Thank you, Karina for such an insightful article! Please feel free to follow us on Twitter for the latest updates on the Global Findex database: @globalfindex

A new round of data will be released early next year!

31 October 2014 Submitted by Veronique Faber (not verified)

If your are looking for data on insurance coverage, you can go to http://www.microinsurancenetwork.org. A fully searchable online platform will be launched in Q1 2015 with new data coming in. Insurance coverage as an alternative risk management tool and safety net is such important piece of the financial inclusion puzzle on a household level. 

Add new comment

CAPTCHA