Global Financial Inclusion (Global Findex) Database 2011
The Global Financial Inclusion (Global Findex) Database is a project funded by the Bill & Melinda Gates Foundation to measure how people - including the poor, women, and rural residents -around the world save, borrow, make payments and manage risk.
The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering around 150,000 adults in more than 140 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.
Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.
The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
The Global Findex database includes indicators measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. More specifically, the database includes indicators on the use of formal accounts, the frequency of formal account use, mode of formal account use (ATM, bank, bank agent, etc.), purposes of formal account use (remittances, government payments, wage payments, etc.), self-reported reasons for not having a formal account, savings behavior, savings method (bank, informal savings club, etc.), sources of borrowing (bank, friends/family, informal lender, etc.), purposes of borrowing (home purchase, school fees, emergency/health, funerals/weddings), the use of mobile phones to make payments, and the purchase of health and agriculture insurance.
The target population is the civilian, non-institutionalized population 15 years and above.
Producers and sponsors
Development Research Group, Finance and Private Sector Development Unit
Carried out the survey in association with its annual Gallup World Poll.
Development Research Group, World Bank
The Bill and Melinda Gates Foundation
The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.
Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling 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. 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 by means of the Kish grid.
Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.
The sample size in Afghanistan was 1,000 individuals. Gender-matched sampling was used during the final stage of selection.
Data weighting is used to ensure a nationally representative sample for each economy. First, base sampling weights are constructed to account for oversamples and household size. If an oversample has been conducted, the data are weighted to correct the disproportionate sample. Weighting by household size (number of residents age 15 and above) is used to adjust for the probability of selection, as residents in large households will have a disproportionately lower probability of being selected for the sample. Second, poststratification weights are constructed. Population statistics are used to weight the data by gender, age, and, where reliable data are available, education or socioeconomic status.
Dates of Data Collection
Data Collection Mode
Data Collection Notes
Interviews were conducted in the following languages: Dari, Pashto.
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 over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.
Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.
Estimates of Sampling Error
Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.
The reference citation for the Global Findex data is as follows: Development Research Group, Finance and Private Sector Development Unit 2011. Measuring Financial Inclusion: The Global Findex Database 2011. Ref: AFG_2011_FINDEX_v02_M. World Bank, Washington, D.C.
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
DDI Document ID
Development Economics Data Group
The World Bank
Documentation of the DDI
Date of Metadata Production
DDI Document version
Version 02 (April 2015) - DDI updated to include the modifications to the inc_q data. This updated version also includes modifications to the Series information, Abstract, Scope and Sampling procedure metadata fields to match the number of economies included in the dataset.