This dataset is about books. It has 1 row and is filtered where the book is The economic impacts of UK labour productivity : enhancing industrial policies and their spillover effects on the energy system. It features 7 columns including author, publication date, language, and book publisher.

The Business Structure Database (BSD) contains a small number of variables for almost all business organisations in the UK. The BSD is derived primarily from the Inter-Departmental Business Register (IDBR), which is a live register of data collected by HM Revenue and Customs via VAT and Pay As You Earn (PAYE) records. The IDBR data are complimented with data from ONS business surveys. If a business is liable for VAT (turnover exceeds the VAT threshold) and/or has at least one member of staff registered for the PAYE tax collection system, then the business will appear on the IDBR (and hence in the BSD). In 2004 it was estimated that the businesses listed on the IDBR accounted for almost 99 per cent of economic activity in the UK. Only very small businesses, such as the self-employed were not found on the IDBR.

The IDBR is frequently updated, and contains confidential information that cannot be accessed by non-civil servants without special permission. However, the ONS Virtual Micro-data Laboratory (VML) created and developed the BSD, which is a 'snapshot' in time of the IDBR, in order to provide a version of the IDBR for research use, taking full account of changes in ownership and restructuring of businesses. The 'snapshot' is taken around April, and the captured point-in-time data are supplied to the VML by the following September. The reporting period is generally the financial year. For example, the 2000 BSD file is produced in September 2000, using data captured from the IDBR in April 2000. The data will reflect the financial year of April 1999 to March 2000. However, the ONS may, during this time, update the IDBR with data on companies from its own business surveys, such as the Annual Business Survey (SN 7451).

The data are divided into 'enterprises' and 'local units'. An enterprise is the overall business organisation. A local unit is a 'plant', such as a factory, shop, branch, etc. In some cases, an enterprise will only have one local unit, and in other cases (such as a bank or supermarket), an enterprise will own many local units.

For each company, data are available on employment, turnover, foreign ownership, and industrial activity based on Standard Industrial Classification (SIC)92, SIC 2003 or SIC 2007. Year of 'birth' (company start-up date) and 'death' (termination date) are also included, as well as postcodes for both enterprises and their local units. Previously only pseudo-anonymised postcodes were available but now all postcodes are real.

The ONS is continually developing the BSD, and so researchers are strongly recommended to read all documentation pertaining to this dataset before using the data.

Linking to Other Business Studies
These data contain IDBR reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

Latest Edition Information
For the sixteenth edition (March 2024), data files and a variable catalogue document for 2023 have been added.

The dataset gathers historical series on the funding and enrolment in the UK public education system from 1833 to 2019. Funding and enrolment are distributed by level of education, funders and economic categories. It is based on the method of quantitative history which follows the principles of national accounting and provides a stable frame to integrate financial and other data, and allow comparisons across time and space

Abstract copyright UK Data Service and data collection copyright owner.

The Great Britain Historical Database has been assembled as part of the ongoing Great Britain Historical GIS Project. The project aims to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain at sub-county scales. Further information about the project is available on A Vision of Britain webpages, where users can browse the database's documentation system online.

This study assembles historical data from the National Insurance system, plus some data from trade union welfare systems gathered and published by the Board of Trade Labour Department. The data were computerised by the Great Britain Historical GIS Project. They form part of the Great Britain Historical Database, which contains a wide range of geographically-located statistics, selected to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain, generally at sub-county scales.

Most of the data here was originally published by the Ministry of Labour, either in the Labour Gazette, later the Employment Gazette, or in the specialised Local Unemployment Index (LUI), published between 1927 and 1939. The largest dataset here is a complete transcription of the LUI data for each January, April, July and October from January 1927 to July 1939 inclusive, the most detailed information that exists on the geography of the inter-war depression, other than the 1931 census.

Unlike census data, these data concern a wide range of regions, "divisions", "districts", towns and sometimes areas within towns, seldom defined (the LUI data do list counties). The study therefore also includes two specially constructed gazetteers which attempt to provide towns and areas within towns with point coordinates. Another limitation is that these data generally provide counts of the unemployed, but not counts of the insured, or numbers in work, so calculation of rates often requires data from other sources such as the census. The study also includes two transcriptions from unpublished tabulations in the National Archives, relating to unemployment in 1928 and 1933.

Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research.

For the second edition (February 2024), the data was updated; data running up to 1974 has been added and the former study 3711 has been incorporated.

- Trade Union Unemployment Percentages for four sectors (capenters and joiners, engineers, printing, and shipbuilding), 1902-14.

- Annual unemployment rates 1923-38 for 8 "divisions".

- Local Unemployment Index, 1927-39 arranged by age and sex.

- Unemployment statistics 1945-74 for towns and development areas from the Labour Gazette.

- Unpublished data on casuals, temporarily stopped, etc in 1928.

- Unpublished data on composition of the unemployed in January,1933 arranged by sex.

- Local government districts which were Special Areas in 1934.

Daily data showing SAP of gas, and rolling seven-day average, traded in Great Britain over the On-the-Day Commodity Market (OCM). These are official statistics in development. Source: National Gas Transmission.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Nick Fewings on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This dataset contains the code, input sheets, set-up guide and documentation for the EVOLVE research project (https://evolveenergy.eu/) economic dispatch model of Great Britain. Within this research project, a novel modelling framework has been developed to quantify the potential benefit of including higher proportions of ocean energy within large-scale electricity systems. Economic dispatch modelling is utilised to model hourly supply-demand matching for a range of sensitivity runs, adjusting the proportion of ocean energy within the generation mix. The framework is applied to a 2030 case study of the power system of Great Britain, testing installed wave or tidal stream capacities ranging from 100 MW to 10 GW. This dataset contains all of the data, code and documentation required to run this economic dispatch model. The project results found that for all sensitivity runs, ocean energy increases renewable dispatch, reduces dispatch costs, reduces generation required from fossil fuels, reduces system carbon emissions, reduces price volatility, and captures higher market prices. The development of this model, and analysis of the model results, is described in detail in a journal paper (currently in press). A preprint of this paper is included within the folder. It can be referenced as: S. Pennock, D.R. Noble, Y. Verdanyan, T. Delahaye and H. Jeffrey (2023). 'A modelling framework to quantify the power system benefits from ocean energy deployments'. Applied Energy, Volume 347, 1 October 2023, 121413 ( https://doi.org/10.1016/j.apenergy.2023.121413 ).

Abstract copyright UK Data Service and data collection copyright owner.

Understanding Society, (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

The Understanding Society: Calendar Year Dataset, 2021, is designed to enable cross-sectional analysis of individuals and households relating specifically to their annual interviews conducted in the year 2021, and, therefore, combine data collected in three waves (Waves 11, 12 and 13). It has been produced from the same data collected in the main Understanding Society study and released in the longitudinal datasets SN 6614 (End User Licence) and SN 6931 (Special Licence). Such cross-sectional analysis can, however, only involve variables that are collected in every wave in order to have data for the full sample panel. The 2021 dataset is the second of a series of planned Calendar Year Datasets to facilitate cross-sectional analysis of specific years. Full details of the Calendar Year Dataset sample structure (including why some individual interviews from 2022 are included), data structure and additional supporting information can be found in the document '9193_calendar_year_dataset_2021_user_guide'.

As multi-topic studies, the purpose of Understanding Society is to understand the short- and long-term effects of social and economic change in the UK at the household and individual levels. The study has a strong emphasis on domains of family and social ties, employment, education, financial resources, and health. Understanding Society is an annual survey of each adult member of a nationally representative sample. The same individuals are re-interviewed in each wave approximately 12 months apart. When individuals move, they are followed within the UK, and anyone joining their households is also interviewed as long as they are living with them. The fieldwork period for a single wave is 24 months. Data collection uses computer-assisted personal interviewing (CAPI) and web interviews (from wave 7) and includes a telephone mop-up. From March 2020 (the end of wave 10 and 2nd year of wave 11), due to the coronavirus pandemic, face-to-face interviews were suspended, and the survey has been conducted by web and telephone only but otherwise has continued as before. One person completes the household questionnaire. Each person aged 16 or older participates in the individual adult interview and self-completed questionnaire. Youths aged 10 to 15 are asked to respond to a paper self-completion questionnaire. In 2020, an additional frequent web survey was separately issued to sample members to capture data on the rapid changes in people’s lives due to the COVID-19 pandemic (see SN 8644). The COVID-19 Survey data are not included in this dataset.

Further information may be found on the Understanding Society main stage webpage and links to publications based on the study can be found on the Understanding Society Latest Research webpage.

Co-funders

In addition to the Economic and Social Research Council, co-funders for the study included the Department of Work and Pensions, the Department for Education, the Department for Transport, the Department of Culture, Media and Sport, the Department for Community and Local Government, the Department of Health, the Scottish Government, the Welsh Assembly Government, the Northern Ireland Executive, the Department of Environment and Rural Affairs, and the Food Standards Agency.

End User Licence and Special Licence versions:

There are two versions of the Calendar Year 2021 data. One is available under the standard End User Licence (EUL) agreement, and the other is a Special Licence (SL) version. The SL version contains month and year of birth variables instead of just age, more detailed country and occupation coding for a number of variables and various income variables have not been top-coded (see the document '9194_eul_vs_sl_variable_differences' for more details). Users are advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The SL data have more restrictive access conditions; prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. The main longitudinal versions of the Understanding Society study may be found under SNs 6614 (EUL) and 6931 (SL).

Low- and Medium-level geographical identifiers produced for the mainstage longitudinal dataset can be used with this Calendar Year 2021 dataset, subject to SL access conditions. See...

The Great Britain Historical Database has been assembled as part of the ongoing Great Britain Historical GIS Project. The project aims to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain at sub-county scales. Further information about the project is available on A Vision of Britain webpages, where users can browse the database's documentation system online.

These data were originally collected by the Censuses of Population for England and Wales, and for Scotland. They were computerised by the Great Britain Historical GIS Project and its collaborators. They form part of the Great Britain Historical Database, which contains a wide range of geographically-located statistics, selected to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain, generally at sub-county scales.

The first census report to tabulate social class was 1951, but this collection also includes a table from the Registrar-General's 1931 Decennial Supplement which drew on census occupational data to tabulate social class by region. In 1961 and 1971 the census used a more detailed classification of Socio-Economic Groups, from which the five Social Classes are a simplification.

This is a new edition. Data from the Census of Scotland have been added for 1951, 1961 and 1971. Wherever possible, ID numbers have been added for counties and districts which match those used in the digital boundary data created by the GBH GIS, greatly simplifying mapping.

The Business Structure Database (BSD) contains a small number of variables for almost all business organisations in the UK. The BSD is derived primarily from the Inter-Departmental Business Register (IDBR), which is a live register of data collected by HM Revenue and Customs via VAT and Pay As You Earn (PAYE) records. The IDBR data are complimented with data from ONS business surveys. If a business is liable for VAT (turnover exceeds the VAT threshold) and/or has at least one member of staff registered for the PAYE tax collection system, then the business will appear on the IDBR (and hence in the BSD). In 2004 it was estimated that the businesses listed on the IDBR accounted for almost 99 per cent of economic activity in the UK. Only very small businesses, such as the self-employed were not found on the IDBR.

The IDBR is frequently updated, and contains confidential information that cannot be accessed by non-civil servants without special permission. However, the ONS Virtual Micro-data Laboratory (VML) created and developed the BSD, which is a 'snapshot' in time of the IDBR, in order to provide a version of the IDBR for research use, taking full account of changes in ownership and restructuring of businesses. The 'snapshot' is taken around April, and the captured point-in-time data are supplied to the VML by the following September. The reporting period is generally the financial year. For example, the 2000 BSD file is produced in September 2000, using data captured from the IDBR in April 2000. The data will reflect the financial year of April 1999 to March 2000. However, the ONS may, during this time, update the IDBR with data on companies from its own business surveys, such as the Annual Business Survey (SN 7451).

The Business Structure Database Longitudinal, 1997-2013 was compiled by Michael Anyadike-Danes, Aston Business School, with support from Economic and Social Research Council funding.

Researchers are advised to read the documentation accompanying the main BSD collection held by the UK Data Archive under SN 6697 before applying for or using the longitudinal data.

Linking to other business studies
These data contain IDBR reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

For the second edition (April 2019), the full postcodes have been replaced with only the first part of the postcode (e.g., SW1V rather than SW1V 2QQ) in the two geography data files. A look up file that includes postcode districts has been added so that users can still aggregate to higher geographies.

The data contained in this file is derived from the study published in https://doi.org/10.3390/en13153939.

For each of the studied countries, hourly data of the selected heating season (2018-2019) is presented, which includes: Primary energy, Cost and GHG emissions of a Heat pump and the most common non-electric heating system of the country.

One file per country is provided as a xlsx file with the following hourly features:

Date

Hour

Heat demand: Building heating demand in kWh

COPR32: Instant COP of the HP with R32 working fluid

COPR410a: Instant COP of the HP with R410a working fluid

ConElec: Electric consumption of the heat pump in kWh

ConNE: Non-electric systems consumption

€.elec: Cost of the electric consumption in €

€.NE: Cost of the Non-electric systems consumption in €

Price difference: Price difference between both systems in €

PE.elec: Primary energy of the electricity consumption in KWh

PE.NE: Primary energy of the non-electric systems consumption in KWh

GHG.elec: GHG emissions of the electricity consumption in kg

GHG.NE: GHG emissions of the electricity consumption in kg

QUEST projects both used and produced an immense variety of global data sets that needed to be shared efficiently between the project teams. These global synthesis data sets are also a key part of QUEST's legacy, providing a powerful way of communicating the results of QUEST among and beyond the UK Earth System research community. This dataset contains socio-economic scenarios from the IPCC SRES report.

Country Risk Assessment helps businesses to confidently evaluate global markets by incorporating country evaluation into strategic planning. Analysing trends over time to forecast and proactively plan for potential market shifts.

Country Risk Assessment is an estimate of the average credit risk of a country’s businesses. It is drawn up based on macroeconomic, financial and political data. It offers: - An indication of a country’s potential influence on businesses’ financial commitments. - Insight into the economic and political environment that could impact credit risk.

Dataset Structure and Content: Assessment Coverage: 20 sample companies with country risk evaluations Geographic Diversity: Multiple countries represented via ISO-3166 alpha2 country codes.

Risk Classification System: The dataset employs a standardized A-E rating scale to categorize country risk levels: A1: Very good macroeconomic outlook with stable political context and quality business climate (lowest default probability) A2: Good macroeconomic outlook with generally stable political environment A3: Satisfactory outlook with some potential shortcomings A4: Reasonable default probability with potential economic weaknesses B: Uncertain economic outlook with potential political tensions C: Very uncertain outlook with potential political instability D: Highly uncertain outlook with very unstable political context E: Extremely uncertain outlook with extremely difficult business conditions (highest default probability)

Application Context: This sample demonstrates how country risk assessments can be systematically documented and tracked over time. Each assessment includes comprehensive evaluations of the macroeconomic environment, political stability, and business climate factors that directly influence payment behavior and default probabilities. The dataset structure allows for both current and historical tracking, enabling trend analysis and comparative risk evaluation across different national markets. It serves as a representative example of how comprehensive country risk data can be organized and utilized for strategic business decision-making. Note: This is sample data intended to demonstrate the structure and capabilities of a country risk assessment system.

Learn More For a complete demonstration of our Country Risk Assessment capabilities or to discuss how our system can be integrated with your existing processes, please visit https://business-information.coface.com/economic-insights to request additional information.

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.

United Kingdom UK: Average Transaction Cost of Sending Remittances from a Specific Country data was reported at 7.009 % in 2017. This records a decrease from the previous number of 7.349 % for 2016. United Kingdom UK: Average Transaction Cost of Sending Remittances from a Specific Country data is updated yearly, averaging 7.562 % from Dec 2011 (Median) to 2017, with 7 observations. The data reached an all-time high of 8.400 % in 2013 and a record low of 7.009 % in 2017. United Kingdom UK: Average Transaction Cost of Sending Remittances from a Specific Country data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Payment System. Average transaction cost of sending remittance from a specific country is the average of the total transaction cost in percentage of the amount sent for sending USD 200 charged by each single remittance service provider (RSP) included in the Remittance Prices Worldwide (RPW) database from a specific country.; ; World Bank, Remittance Prices Worldwide, available at http://remittanceprices.worldbank.org; Unweighted average;

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

National Coverage

Analysis unit

Individual

Universe

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

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or 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 by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid 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 Kish grid 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 in United Kingdom was 1,000 individuals.

Mode of data collection

Other [oth]

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 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, 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 Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

In a resource-constrained world with growing population and demand for energy, goods, and services with commensurate environmental impacts, we need to understand how these trends relate to various aspects of economic activity. 7see-GB is a computational model that links energy demand through to final economic consumption, and is used to explore decadal scenarios for the UK macroeconomy. This dataset includes the published model (*.vpm) from the source model 7see-GB, version 5-30 (14Nov17). They show how results were created for the paper 'Consequences of selecting technology pathways on cumulative CO2 emissions for the UK'. The source model was developed in Vensim(r) (5.8b) and these published models can be viewed with the Vensim Reader, as provided with this dataset. There are instructions on how to navigate the published models and inspect variables shown in the paper. The .exe and .dmg files are free 'Model Reader' executables for Windows/OSX which allow a user to run the model without buying the Vensim simulator.

The Census@Leicester datasets include socio-demographic data from the 2001, 2011, and 2021 Leicester censuses to enable the exploration of recent historical trends. It also includes data from the 2021 census for both Nottingham and Coventry to enable comparisons with other cities.

This online resource that can be used for teaching and research purposes by staff and students and to create a legacy for the Census@Leicester Project.

Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.

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

National coverage.

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 1000.

Mode of data collection

Landline and Cellular Telephone

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