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

South Ossetia and Abkhazia were not included for the safety of the interviewers. In addition, very remote mountainous villages or those with less than 100 inhabitants were also excluded. The excluded areas represent approximately 8 percent of the total population.

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 Georgia is 1000.

Mode of data collection

Face-to-face [f2f]

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.

Morocco Imports from Georgia was US$29.28 Million during 2023, according to the United Nations COMTRADE database on international trade. Morocco Imports from Georgia - data, historical chart and statistics - was last updated on July of 2025.

Tingkat Partisipasi Angkatan Kerja Georgia dilaporkan sebesar 53.3 % pada 2023. Rekor ini naik dibanding sebelumnya yaitu 51.9 % untuk 2022. Data Tingkat Partisipasi Angkatan Kerja Georgia diperbarui tahunan, dengan rata-rata 55.2 % dari 1998 sampai 2023, dengan 26 observasi. Data ini mencapai angka tertinggi sebesar 66.2 % pada 2001 dan rekor terendah sebesar 50.5 % pada 2020. Data Tingkat Partisipasi Angkatan Kerja Georgia tetap berstatus aktif di CEIC dan dilaporkan oleh National Statistics Office of Georgia. Data dikategorikan dalam Georgia Global Database – Table GE.G005: Economic Activity Rate.

Data The data for this Challenge are from multiple sources: CPSC Database and CPSC-Extra Database INCART Database PTB and PTB-XL Database The Georgia 12-lead ECG Challenge (G12EC) Database Undisclosed Database The first source is the public (CPSC Database) and unused data (CPSC-Extra Database) from the China Physiological Signal Challenge in 2018 (CPSC2018), held during the 7th International Conference on Biomedical Engineering and Biotechnology in Nanjing, China. The unused data from the CPSC2018 is NOT the test data from the CPSC2018. The test data of the CPSC2018 is included in the final private database that has been sequestered. This training set consists of two sets of 6,877 (male: 3,699; female: 3,178) and 3,453 (male: 1,843; female: 1,610) of 12-ECG recordings lasting from 6 seconds to 60 seconds. Each recording was sampled at 500 Hz.

The second source set is the public dataset from St Petersburg INCART 12-lead Arrhythmia Database. This database consists of 74 annotated recordings extracted from 32 Holter records. Each record is 30 minutes long and contains 12 standard leads, each sampled at 257 Hz.

The third source from the Physikalisch Technische Bundesanstalt (PTB) comprises two public databases: the PTB Diagnostic ECG Database and the PTB-XL, a large publicly available electrocardiography dataset. The first PTB database contains 516 records (male: 377, female: 139). Each recording was sampled at 1000 Hz. The PTB-XL contains 21,837 clinical 12-lead ECGs (male: 11,379 and female: 10,458) of 10 second length with a sampling frequency of 500 Hz.

The fourth source is a Georgia database which represents a unique demographic of the Southeastern United States. This training set contains 10,344 12-lead ECGs (male: 5,551, female: 4,793) of 10 second length with a sampling frequency of 500 Hz.

The fifth source is an undisclosed American database that is geographically distinct from the Georgia database. This source contains 10,000 ECGs (all retained as test data).

All data is provided in WFDB format. Each ECG recording has a binary MATLAB v4 file (see page 27) for the ECG signal data and a text file in WFDB header format describing the recording and patient attributes, including the diagnosis (the labels for the recording). The binary files can be read using the load function in MATLAB and the scipy.io.loadmat function in Python; please see our baseline models for examples of loading the data. The first line of the header provides information about the total number of leads and the total number of samples or points per lead. The following lines describe how each lead was saved, and the last lines provide information on demographics and diagnosis. Below is an example header file A0001.hea:

A0001 12 500 7500 05-Feb-2020 11:39:16
A0001.mat 16+24 1000/mV 16 0 28 -1716 0 I
A0001.mat 16+24 1000/mV 16 0 7 2029 0 II
A0001.mat 16+24 1000/mV 16 0 -21 3745 0 III
A0001.mat 16+24 1000/mV 16 0 -17 3680 0 aVR
A0001.mat 16+24 1000/mV 16 0 24 -2664 0 aVL
A0001.mat 16+24 1000/mV 16 0 -7 -1499 0 aVF
A0001.mat 16+24 1000/mV 16 0 -290 390 0 V1
A0001.mat 16+24 1000/mV 16 0 -204 157 0 V2
A0001.mat 16+24 1000/mV 16 0 -96 -2555 0 V3
A0001.mat 16+24 1000/mV 16 0 -112 49 0 V4
A0001.mat 16+24 1000/mV 16 0 -596 -321 0 V5
A0001.mat 16+24 1000/mV 16 0 -16 -3112 0 V6

Age: 74
Sex: Male
Dx: 426783006
Rx: Unknown
Hx: Unknown
Sx: Unknown

From the first line, we see that the recording number is A0001, and the recording file is A0001.mat. The recording has 12 leads, each recorded at 500 Hz sample frequency, and contains 7500 samples. From the next 12 lines, we see that each signal was written at 16 bits with an offset of 24 bits, the amplitude resolution is 1000 with units in mV, the resolution of the analog-to-digital converter (ADC) used to digitize the signal is 16 bits, and the baseline value corresponding to 0 physical units is 0. The first value of the signal, the checksum, and the lead name are included for each signal. From the final 6 lines, we see that the patient is a 74-year-old male with a diagnosis (Dx) of 426783006. The medical prescription (Rx), history (Hx), and symptom or surgery (Sx) are unknown.

Each ECG recording has one or more labels from different type of abnormalities in SNOMED-CT codes. The full list of diagnoses for the challenge has been posted here as a 3 column CSV file: Long-form description, corresponding SNOMED-CT code, abbreviation. Although these descriptions apply to all training data there may be fewer classes in the test data, and in different proportions. However, every class in the test data will be represented in the training data.

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission and is derived from GDOT's DLG-F street centerline database for the state of Georgia. The features included in this Layer were identified by the "FEATURE_TY" field and include all interstate highways and Georgia Highway 400. Please note this Layer is not intended for network analysis or large scale mapping as some small segments may have been ommitted during the feature selection process.Attributes:COUNTY_FIP = FIPS code of the county containing the featureROAD_NAME = The local name of the corresponding road segmentLABEL = Expressway values to be used with ESRI shield labels For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.comSource: Georgia Department of Transportation (GDOT), Atlanta Regional CommissionDate: 2003

Denmark Exports to Georgia was US$26.26 Million during 2024, according to the United Nations COMTRADE database on international trade. Denmark Exports to Georgia - data, historical chart and statistics - was last updated on June of 2025.

Finland Exports to Georgia was US$28.19 Million during 2024, according to the United Nations COMTRADE database on international trade. Finland Exports to Georgia - data, historical chart and statistics - was last updated on July of 2025.

Cuba Exports to Georgia was US$32.47 Thousand during 2022, according to the United Nations COMTRADE database on international trade. Cuba Exports to Georgia - data, historical chart and statistics - was last updated on July of 2025.

Senegal Imports from Georgia was US$1.19 Million during 2023, according to the United Nations COMTRADE database on international trade. Senegal Imports from Georgia - data, historical chart and statistics - was last updated on July of 2025.

Montenegro Imports from Georgia was US$490.82 Thousand during 2024, according to the United Nations COMTRADE database on international trade. Montenegro Imports from Georgia - data, historical chart and statistics - was last updated on July of 2025.

United States Exports to Georgia was US$1.74 Billion during 2024, according to the United Nations COMTRADE database on international trade. United States Exports to Georgia - data, historical chart and statistics - was last updated on July of 2025.

Angola Imports from Georgia was US$4.97 Million during 2023, according to the United Nations COMTRADE database on international trade. Angola Imports from Georgia - data, historical chart and statistics - was last updated on July of 2025.

Brazil Exports to Georgia was US$350.42 Million during 2024, according to the United Nations COMTRADE database on international trade. Brazil Exports to Georgia - data, historical chart and statistics - was last updated on July of 2025.

Algeria Exports to Georgia was US$503.29 Thousand during 2017, according to the United Nations COMTRADE database on international trade. Algeria Exports to Georgia - data, historical chart and statistics - was last updated on June of 2025.

Belarus Exports to Georgia was US$46.03 Million during 2021, according to the United Nations COMTRADE database on international trade. Belarus Exports to Georgia - data, historical chart and statistics - was last updated on July of 2025.

Turkey Exports to Georgia was US$2.75 Billion during 2024, according to the United Nations COMTRADE database on international trade. Turkey Exports to Georgia - data, historical chart and statistics - was last updated on June of 2025.

China Exports of miscellaneous edible preparations to Georgia was US$1.13 Million during 2022, according to the United Nations COMTRADE database on international trade. China Exports of miscellaneous edible preparations to Georgia - data, historical chart and statistics - was last updated on June of 2025.

Grenada Imports from Georgia was US$185 during 2024, according to the United Nations COMTRADE database on international trade. Grenada Imports from Georgia - data, historical chart and statistics - was last updated on June of 2025.

Russia Exports to Georgia was US$873.3 Million during 2021, according to the United Nations COMTRADE database on international trade. Russia Exports to Georgia - data, historical chart and statistics - was last updated on July of 2025.

Togo Imports from Georgia was US$10.69 Thousand during 2024, according to the United Nations COMTRADE database on international trade. Togo Imports from Georgia - data, historical chart and statistics - was last updated on July of 2025.