Abstract copyright UK Data Service and data collection copyright owner.

The European State Finance Database (ESFD) is an international collaborative research project for the collection of data in European fiscal history. There are no strict geographical or chronological boundaries to the collection, although data for this collection comprise the period between c.1200 to c.1815. The purpose of the ESFD was to establish a significant database of European financial and fiscal records. The data are drawn from the main extant sources of a number of European countries, as the evidence and the state of scholarship permit. The aim was to collect the data made available by scholars, whether drawing upon their published or unpublished archival research, or from other published material.
The ESFD project at the University of Leicester serves also to assist scholars working with the data by providing statistical manipulations of data and high quality graphical outputs for publication. The broad aim of the project was to act as a facilitator for a general methodological and statistical advance in the area of European fiscal history, with data capture and the interpretation of data in key publications as the measurable indicators of that advance. The data were originally deposited at the UK Data Archive in SAS transport format and as ASCII files; however, data files in this new edition have been saved as tab delimited files. Furthermore, this new edition features documentation in the form of a single file containing essential data file metadata, source details and notes of interest for particular files.

Main Topics:

The files in this dataset relate to the datafiles held in the Leicester database in the directory /korner/*.*.
File Information
g123eud1.* Total expenditure of Prussia, Sweden, Denmark and Russia, 1720-1808, expressed in metric tonnes of silver

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

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

National coverage

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 United Kingdom is 1000.

Mode of data collection

Landline and mobile telephone

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.

The Local Government Budget and Financial Report Database provides the public access to current and official versions of budgets for Counties, County Ag Extensions, County/City Assessors, County Hospitals, & Townships, Benefited Fire, Lighting, Water Districts, and Misc Budgets (DART, EMC, E911, Rural Improvement Zones, Recreational Water, Sanitary Sewer Districts). The public has access to multiple forms that are part of the budget. The database also provides the public access to annual financial reports for Counties. The site provides both GAAP and Cash Financial Reports for multiple fiscal years.

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

National coverage

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 Nepal 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.

This data contains the latest State and Local Government Finance data from the U.S. Census. A detailed description of the project can be found in: Pierson K., Hand M., and Thompson F. (2015). The Government Finance Database: A Common Resource for Quantitative Research in Public Financial Analysis. PLoS ONE doi: 10.1371/journal.pone.0130119

Abstract copyright UK Data Service and data collection copyright owner.

The European State Finance Database (ESFD) is an international collaborative research project for the collection of data in European fiscal history. There are no strict geographical or chronological boundaries to the collection, although data for this collection comprise the period between c.1200 to c.1815. The purpose of the ESFD was to establish a significant database of European financial and fiscal records. The data are drawn from the main extant sources of a number of European countries, as the evidence and the state of scholarship permit. The aim was to collect the data made available by scholars, whether drawing upon their published or unpublished archival research, or from other published material.
The ESFD project at the University of Leicester serves also to assist scholars working with the data by providing statistical manipulations of data and high quality graphical outputs for publication. The broad aim of the project was to act as a facilitator for a general methodological and statistical advance in the area of European fiscal history, with data capture and the interpretation of data in key publications as the measurable indicators of that advance. The data were originally deposited at the UK Data Archive in SAS transport format and as ASCII files; however, data files in this new edition have been saved as tab delimited files. Furthermore, this new edition features documentation in the form of a single file containing essential data file metadata, source details and notes of interest for particular files.

Main Topics:

The files in this dataset relate to the datafiles held in the Leicester database in the directory /armies/*.*. The interest of this data in the longer term is to build up a run of statistics for the period of the so-called military revolution'. This was a decisive factor in the increase of state expenditure on war and the creation of the so-calledfiscal military state'. It may also be possible to build up, in the longer term, calculations of a relative state efficiency (expenditure in terms of army size), relative state mobilization (army size in terms of overall population levels) and an index of state expenditure in real terms (via the cost of payment of armies).
File Information
g101arm1.* Sizes of armies of European states at various dates between 1660 and 1861

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

Abstract copyright UK Data Service and data collection copyright owner.

The European State Finance Database (ESFD) is an international collaborative research project for the collection of data in European fiscal history. There are no strict geographical or chronological boundaries to the collection, although data for this collection comprise the period between c.1200 to c.1815. The purpose of the ESFD was to establish a significant database of European financial and fiscal records. The data are drawn from the main extant sources of a number of European countries, as the evidence and the state of scholarship permit. The aim was to collect the data made available by scholars, whether drawing upon their published or unpublished archival research, or from other published material.
The ESFD project at the University of Leicester serves also to assist scholars working with the data by providing statistical manipulations of data and high quality graphical outputs for publication. The broad aim of the project was to act as a facilitator for a general methodological and statistical advance in the area of European fiscal history, with data capture and the interpretation of data in key publications as the measurable indicators of that advance. The data were originally deposited at the UK Data Archive in SAS transport format and as ASCII files; however, data files in this new edition have been saved as tab delimited files. Furthermore, this new edition features documentation in the form of a single file containing essential data file metadata, source details and notes of interest for particular files.

Main Topics:

This dataset contains data on seventeenth-century French revenues and expenditure, drawing upon J-R Malet. The data are the same as those found in SN 3068 but are presented in the form of the original tables and as published in M.M. and R.J. Bonney Jean-Roland Malet: premier historien des finances de la monarchie francaise (Comite pour l'histoire economique et financiere de la France, Paris, 1993).
File Information
g167md01.* Malet's figures for royal expenditure in France, 1600-10
g167md02.* Malet's figures for royal expenditure in France, 1611-42
g167md03.* Malet's figures for royal expenditure in France, 1643-56
g167md04.* Malet's figures for royal expenditure in France, 1661-88
g167md05.* Malet's figures for royal expenditure in France, 1689-95
g167md06.* Malet's figures for receipts from the pays d'elections, 1600-10
g167md07.* Malet's figures for receipts from the pays d'elections, 1611-42
g167md08.* Malet's figures for receipts from the pays d'elections, 1643-56
g167md09.* Malet's figures for receipts from the pays d'elections, 1661-88
g167md10.* Malet's figures for receipts from the pays d'elections, 1661-88 (charges)
g167md11.* Malet's figures for receipts from the pays d'elections, 1661-88 (net to Treasury)
g167md12.* Malet's figures for receipts from the pays d'elections, 1689-95
g167md13.* Malet's figures for receipts from the pays d'elections, 1689-95 (charges)
g167md14.* Malet's figures for receipts from the pays d'elections, 1689-95 (net to Treasury)
g167md15.* Malet's figures for receipts from the pays d'etats, 1600-10
g167md16.* Malet's figures for receipts from the pays d'etats, 1611-42
g167md17.* Malet's figures for receipts from the pays d'etats, 1643-60
g167md18.* Malet's figures for receipts from the pays d'etats, 1661-88
g167md19.* Malet's figures for receipts from the pays d'etats, 1661-88 (charges)
g167md20.* Malet's figures for receipts from the pays d'etats, 1661-88 (net to Treasury)
g167md21.* Malet's figures for receipts from the pays d'etats, 1689-95
g167md22.* Malet's figures for receipts from the pays d'etats, 1689-95 (charges)
g167md23.* Malet's figures for receipts from the pays d'etats, 1689-95 (net to Treasury)
g167md24.* Malet's figures for dons gratuits from the pays d'etats, 1661-88
g167md25.* Malet's figures for dons gratuits from the pays d'etats, 1661-88 (charges)
g167md26.* Malet's figures for dons gratuits from the pays d'etats, 1661-88 (net to Treasury)
g167md27.* Malet's figures for dons gratuits from the pays d'etats, 1689-95
g167md28.* Malet's figures for dons gratuits from the pays d'etats, 1689-95 (charges)
g167md29.* Malet's figures for dons gratuits from the pays d'etats, 1689-95 (net to Treasury)
g167md30.* Malet's figures for receipts from the revenue farms, 1600-10
g167md31.* Malet's figures for receipts from the revenue farms, 1611-42
g167md32.* Malet's figures for receipts from the revenue farms, 1643-56
g167md33.* Malet's figures for receipts from the revenue farms, 1661-88
g167md34.* Malet's figures for receipts from the revenue farms, 1661-88 (charges)
g167md35.* Malet's figures for receipts from the revenue farms, 1661-88 (net to...

Abstract

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.

Geographic coverage

National Coverage.

Analysis unit

Individual

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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 Japan was 1,000 individuals.

Mode of data collection

Landline 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 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.

Sampling error estimates

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.

Abstract copyright UK Data Service and data collection copyright owner.

The European State Finance Database (ESFD) is an international collaborative research project for the collection of data in European fiscal history. There are no strict geographical or chronological boundaries to the collection, although data for this collection comprise the period between c.1200 to c.1815. The purpose of the ESFD was to establish a significant database of European financial and fiscal records. The data are drawn from the main extant sources of a number of European countries, as the evidence and the state of scholarship permit. The aim was to collect the data made available by scholars, whether drawing upon their published or unpublished archival research, or from other published material.
The ESFD project at the University of Leicester serves also to assist scholars working with the data by providing statistical manipulations of data and high quality graphical outputs for publication. The broad aim of the project was to act as a facilitator for a general methodological and statistical advance in the area of European fiscal history, with data capture and the interpretation of data in key publications as the measurable indicators of that advance. The data were originally deposited at the UK Data Archive in SAS transport format and as ASCII files; however, data files in this new edition have been saved as tab delimited files. Furthermore, this new edition features documentation in the form of a single file containing essential data file metadata, source details and notes of interest for particular files.

Main Topics:

The files in this dataset relate to the datafiles held in the Leicester database in the directory /orm/*.*.
File Information
g127frd1.* French treasury receipts from ordinary revenue, 1322-40
g127frd2.* Ordinary and extraordinary receipts as percentage of French treasury receipts, 1322-40
g127frd3.* Categories of extraordinary revenue to the French treasury, 1322-30
g127frd4.* Notional revenue of the French crown, 1460-83
g127frd5.* Selected revenues of the French crown, 1179-1221 (in livres tournois)

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

Abstract copyright UK Data Service and data collection copyright owner.The European State Finance Database (ESFD) is an international collaborative research project for the collection of data in European fiscal history. There are no strict geographical or chronological boundaries to the collection, although data for this collection comprise the period between c.1200 to c.1815. The purpose of the ESFD was to establish a significant database of European financial and fiscal records. The data are drawn from the main extant sources of a number of European countries, as the evidence and the state of scholarship permit. The aim was to collect the data made available by scholars, whether drawing upon their published or unpublished archival research, or from other published material. The ESFD project at the University of Leicester serves also to assist scholars working with the data by providing statistical manipulations of data and high quality graphical outputs for publication. The broad aim of the project was to act as a facilitator for a general methodological and statistical advance in the area of European fiscal history, with data capture and the interpretation of data in key publications as the measurable indicators of that advance. The data were originally deposited at the UK Data Archive in SAS transport format and as ASCII files; however, data files in this new edition have been saved as tab delimited files. Furthermore, this new edition features documentation in the form of a single file containing essential data file metadata, source details and notes of interest for particular files. Main Topics: The files in this dataset relate to the datafiles held in the Leicester database in the directory /rjb/.. File Information g135sld1.* Average annual estimates in tonnes of the movement of silver and silver equivalent, 1501-1800 Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research. No sampling (total universe) Compilation or synthesis of existing material

Explore global financial development data including remittance inflows, bank assets, loans, insurance premiums, stock market indicators, and more. Analyze trends in India, Qatar, Saudi Arabia, and other countries with the World Bank dataset.

Remittance inflows to GDP, Foreign bank assets, Global leasing volume, Private debt securities, Bank Z-score, Loans requiring collateral, Stock price volatility, Bank cost to income ratio

Bahrain, China, India, Kuwait, Oman, Qatar, Saudi Arabia

Follow data.kapsarc.org for timely data to advance energy economics research.

Abstract copyright UK Data Service and data collection copyright owner.

The European State Finance Database (ESFD) is an international collaborative research project for the collection of data in European fiscal history. There are no strict geographical or chronological boundaries to the collection, although data for this collection comprise the period between c.1200 to c.1815. The purpose of the ESFD was to establish a significant database of European financial and fiscal records. The data are drawn from the main extant sources of a number of European countries, as the evidence and the state of scholarship permit. The aim was to collect the data made available by scholars, whether drawing upon their published or unpublished archival research, or from other published material.
The ESFD project at the University of Leicester serves also to assist scholars working with the data by providing statistical manipulations of data and high quality graphical outputs for publication. The broad aim of the project was to act as a facilitator for a general methodological and statistical advance in the area of European fiscal history, with data capture and the interpretation of data in key publications as the measurable indicators of that advance. The data were originally deposited at the UK Data Archive in SAS transport format and as ASCII files; however, data files in this new edition have been saved as tab delimited files. Furthermore, this new edition features documentation in the form of a single file containing essential data file metadata, source details and notes of interest for particular files.

Main Topics:

The files in this dataset relate to the datafiles held in the Leicester database in the directory /rjb/italy/*.*.
File Information
g120fld1.* Florentine forced loans, 1390-1480
g120npd1.* Sheep and revenue from sheep to the Foggia sheep customs house, 1443-1805

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

This table describes the coding of the miscellaneous revenue variables and provides a short description of each. NEC stands for not elsewhere classified. The indentation of the variables in the first column indicates how subcategories of data collapse into larger categories. More detailed descriptions of each variable can be found in the Census’ 2006 classification manual (http://www.census.gov/govs/classification/) included with the database download.Miscellaneous General Revenue Variables.

This table displays the growth of special districts by comparing the 1977 data with the 2007 data, and is ordered by the number of districts existing in the 2007 data. The absolute number of special districts of each type is displayed, along with the percentage change between the two years, and the proportion of all special districts that each type comprises. The code column is the special district function code used by the census.Special District Growth.

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

National coverage

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 Senegal 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.

The Maryland Department of Commerce collects and publishes its Commerce Consolidated Finance Tracker data to provide information on how the agency's grants, tax credits, equity investments and loan enhancements were distributed between FY 2016 and the most recent past fiscal year. Users can search and sort by company, amount, location and program.

The COFI database includes power-generation projects in Belt and Road Initiative (BRI) countries financed by Chinese corporations and banks that reached financial closure from 2000 to 2021. Types of financing include debt and equity investment, with the latter including greenfield foreign direct investments (FDI) and cross-border mergers and acquisitions (M&As).

COFI is consolidated using nine source databases using both automated join method in R Studio, and manual joining by analysts. The database includes power plant characteristics data and investment detail data. It captures 509 power plants in 82 BRI countries, including 265 equity investment transactions and 319 debt investment transactions made by Chinese investors. Key data points for financial transactions in COFI include the financial instrument (equity or debt), investor name, amount, and financial close year. Key technical characteristics tracked for projects in COFI include name, installed capacity, commissioning year, country, and primary fuel type.

This project is a collaboration among the Boston University Global Development Policy Center, the Inter-American Dialogue, the China-Africa Research Initiative at the Johns Hopkins University (CARI), and the World Resources Institute (WRI).

The detailed methodology is given in the World Resources Institute publication “China Overseas Finance Inventory”.

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

National coverage

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 Tunisia 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.

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

National coverage

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 Ecuador is 1000.

Mode of data collection

Landline and mobile telephone

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.

United States Finance Co: Rec: Real Estate: Owned Assets: Other data was reported at 27.776 USD bn in Apr 2018. This records a decrease from the previous number of 28.641 USD bn for Mar 2018. United States Finance Co: Rec: Real Estate: Owned Assets: Other data is updated monthly, averaging 26.926 USD bn from Jun 1970 (Median) to Apr 2018, with 575 observations. The data reached an all-time high of 74.014 USD bn in Dec 2010 and a record low of 0.114 USD bn in Jun 1970. United States Finance Co: Rec: Real Estate: Owned Assets: Other data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.KB011: Finance Companies: Owned and Managed Receivables. On June 29, 2012, the Finance Companies (G.20) data has been revised back up to 2006 to reflect improvements in methodology and the incorporation of revisions from source data.