The FR 3033p is the first part of a two-stage survey series, which has been conducted at regular five-year intervals since 1955. It is a census survey designed to identify the universe of finance companies eligible for potential inclusion in the FR 3033s. It gathers limited information including total assets, areas of specialization, and information on the corporate structure of such companies. The second part of these information collections, the FR 3033s, collects balance sheet data on major categories of consumer and business credit receivables and major liabilities, along with income and expenses, and is used to gather information on the scope of a company's operations and loan and lease servicing activities. In addition, additional questions were added to collect lending information related to the COVID-19 impacts.

1) Data Introduction • The Finance Data Dataset is a survey-based dataset collected via Google Forms during the COVID-19 lockdown. It includes various questions related to individuals' investment behavior, preferences, information sources, and expected returns.

2) Data Utilization (1) Characteristics of the Finance Data Dataset: • The dataset reflects behavioral finance attributes such as preferences for investment instruments (e.g., stocks, bonds, gold, public provident funds), investment purposes, investment horizons, and information acquisition channels.

(2) Applications of the Finance Data Dataset: • Development of AI-based investment profiling and recommendation models: The survey data can be used to build classification models for predicting investment behavior, as well as personalized financial product recommendation systems. • Financial education and consumer behavior research: Insights into investment objectives, risk tolerance, and time preferences can be utilized for designing financial literacy programs and customized financial consulting services.

Abstract

Financial services sector, like other economic sectors of Azerbaijan, has been characterized with fast development rate. Banking, insurance and post services hold leading positions among those services. Individuals are one of the major consumers of those services. Thus, more than 3.6 million people already use payment cards and about 500,000 people take consumer credits. Increase of financial literacy and better protection of consumer rights contribute to more efficient access of population to financial services. First of all, current status of financial literacy of population should be studied and problems revealed, to this end.

Increase of financial literacy and better protection of consumer rights became more urgent issues over the last decade. Fast integration of Azerbaijan into the world economy made it necessary to study those issues and implement appropriate measures in the country.

In view of the above mentioned facts, the Central Bank of the Republic of Azerbaijan, World Bank and SECO decided to carry out a financial literacy research of the population. The main objective of that project was to conduct a "Financial Literacy Survey", create a Single Database and prepare a Report reflecting outcomes of the survey.

Geographic coverage

The survey covered Baku (including 11 administrative districts), Ganja, Sumgait, Shirvan, Khirdalan, Sheki, Lankaran, Yevlakh, Nakhchivan, Guba, Gusar, Aghsu, Bilesuvar, Berde, Tovuz, Masalli cities, 2 settlements and 37 villages (see: table 1.1 of the survey report). 54% of survey participants live in urban (Baku- 23%) and 46% in rural areas. This is a similar pattern to the national demographic status.

Analysis unit

Household, individual

Universe

The survey was carried out among people above 18 years old (18 also included) (except for those not capable of being interviewed) with the latest birthday date within a year.

Kind of data

Sample survey data [ssd]

Sampling procedure

Definition of sampling frame and scale

1200 respondents were defined as a sample frame in 8 economic regions (2 economic regions of the country are under occupation) and Baku city. The main reason for conducting the survey among 1200 respondents is to ensure representativeness and financial feasibility of the project. Urban and rural ratio was set at 54% and 46% in line with statistic indicators. For detailed information see Table 1.1 of the survey report.

Preparation of the survey plan and implementation of survey sampling

Sampling was carried out at 2 stages: i) at the first stage, it was conducted while taking into account distribution of population by capital city, other urban and rural areas and economic regions with preliminary sampling units being street and villages (each preliminary sampling unit includes 15 respondents); ii) At the second stage, streets within the sampled cities and villages within economic regions were randomly selected. For example, according to results of the first stage of the sampling, a survey should be carried out among 45 respondents in Guba region and 15 respondents should be selected in urban areas and 30 respondents in rural areas. In view of the fact that primary sampling unit consists 15 respondents, 1 street within Guba town or its settlements and 2 villages among rural areas should be randomly selected.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was prepared based on the analogical questionnaire used in Russia and submitted by the Central Bank. The questionnaire was translated into Azerbaijani language, questions were adjusted to the country context, irrelevant questions were removed and new ones introduced. Meetings were arranged with representatives of the Central Bank and other relevant organizations, as well as their comments were discussed through e-mail during the preparation period of the questionnaire. The final version of the questionnaire was consisted of 65 questions and mainly covered such issues as registration of household's income and expenditures, financial awareness, financial literacy on basic calculations, violation of consumer rights during the use of financial services, access to financials services, payments cards and socio-demographic status of respondents. The questionnaire was prepared in Azerbaijani language and then, translated into English.

Cleaning operations

Entering and cleaning data, and creation of a Single Database

An operator entered and analyzed data through relevant software (SPSS). All questionnaires were coded during the entering process of data. An database specialist undertook additional control and regulation works to clean data. A Single Database was checked through preliminary analysis after major logic examination.

A Single Database was created at SPSS software based on questions of the questionnaire. Answers given by 1207 respondents were entered into the Single Database.

The purpose of the RHFS is to provide current and continuous measure of the financial health and property characteristics of single-family and multifamily rental housing properties in the United States. The survey provides information on the financing of single-family and multifamily rental housing properties with emphasis on new originations for purchase, refinancing, and loan terms associated with these originations. In addition, the survey includes information on property characteristics, such as number of units, amenities available, rental income and expenditure information. This survey was conducted in 2012 and will be conducted in 2015.

Abstract

The survey is the follow-up of the Diagnostic Review on Consumer Protection and Financial Literacy conducted by the World Bank in 2008-2009. The Diagnostic Review in Romania was the fourth in a World Bank-sponsored pilot program to assess consumer protection and financial literacy in developing and middle-income countries.1 The objectives of this Review were three-fold to: (1) refine a set of good practices for assessing consumer protection and financial literacy, including financial literacy; (2) conduct a review of the existing rules and practices in Romania compared to the good practices; and (3) provide recommendations on ways to improve consumer protection and financial literacy in Romania. The Diagnostic Review was prepared at the request of the National Authority for Consumers' Protection (ANPC), whose request was endorsed by the Ministry of Economy and Finance. Support was provided by the National Bank of Romania (BNR), which supervises banks and non-bank credit institutions. Further assistance was given by the supervisory commissions for securities (CNVM), insurance (CSA) and private pensions (CSSPP).

The Diagnostic Review found that the basic foundations needed for consumer protection and financial literacy are in place in Romania but they benefit from further strengthening support. The Review proposes improvements in six areas: consumer awareness, information and disclosure for consumers, professional competence, dispute resolution, financial education and financial literacy surveys.

Consequently, in 2010 the World Bank commissioned a nation-wide survey of the levels of financial literacy. A consultant (sociologist Manuela Sofia Stanculescu) developed the survey methodology (sampling methodology and questionnaire) in line with the Financial Literacy Survey in Russia (the World Bank, 2008) and the baseline survey Financial Capability in the UK (Financial Services Authority, 2005).2 The final form of the questionnaire was agreed with representatives of the National Bank of Romania (BNR), the Romanian Banking Institute (IBR), the National Authority for Consumers' Protection (ANPC), and the Financial Companies Association in Romania (ALB). The Institute for World Economy (Romanian Academy) collected the data in May 2010.

The main objective of this work is the establishment (and later the evaluation) of a well targeted national program of financial education.

Geographic coverage

National

Analysis unit

Household, individual

Universe

Non-institutionalized persons aged 18 or older

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of the survey is probabilistic, two-stage, stratified, representative at national level with an error of +/- 3% at a 95% confidence level.

The sample is based on two stratification criteria: (i) historical region (8 regions) and (ii) type of locality (7 types depending on the city size, in urban areas, and on the synthetic index of community development,4 in the rural ones).

The sample volume is 2048,5 out of which 148 cases represent a boost of persons aged 16, 17 or those had their 18th birthday after November 2009.6 Respondents were randomly selected from electoral registers corresponding to 185 voting sections (randomly selected), located in 141 localities (77 communes, 63 towns/cities and the capital Bucharest).

The sample includes a slight over-representation of men, rural respondents, and elderly particularly due to the boost of young but also to the fact that people left abroad concentrate among the 25-44 age category. Nevertheless, the sample fairly reproduces the structure (by gender, age categories and area of residence) of the country population 16+ years according to the data for 2009 provided by the National Institute for Statistics. Socio-demographic structure of the sample is presented in table 3 of the survey report.

Demographic data and data regarding the use of financial services were collected for all members of respondents? households. In the respondents? households live 5406 persons overall. This extended sample has also a slight over-representation of rural respondents and an under-representation of children (0-14 years) and persons 25-24 years (most probably young people who left abroad with children).

MORE INFORMATION ON THE SAMPLING METHODOLOGY

Sample volume: 2,200 non-institutionalized persons aged 18 or older. In addition, the sample will be boosted with 180 persons aged 16-18 years old. Overall, at least 2,000 valid questionnaires should be completed during fieldwork.

Type of the sample: Probabilistic, two-stage, stratified, representative at national level, with an error of +/- 2.8% at a 95% confidence level.

Stratification criteria: The sampling scheme is based on two stratification criteria

(a) Historical region (8 regions) (b) Type of locality, with 7 theoretical strata

i. Urban areas - 4 strata 1. very small towns under 30 thou inhabitants 2. small towns 30,001-100 thou inhabitants 3. medium cities 100,001-199 thou inhabitants 4. large cities 200 thou inhabitants or more

ii. Rural areas - 3 strata determined based on the synthetic index of community development 37 1. poor communes (the 30% communes with the lowest level of development within the country) 2. medium developed communes 3. developed communes (the 30% communes with the highest level of development within the country).

Sampling stages: The sampling scheme includes two stages.

Sampling units: There are two sampling units corresponding to the two sampling stages. In the first sampling stage, voting sections are selected and in the second stage, non-institutionalized persons aged 18 years or more.

Selection: Random selection in all sampling stages.

Sampling scheme: In the first stage the sample is distributed proportionally with the volume of population for each of the 56(= 8 x 7) theoretical strata different from zero.

The corresponding number of voting sections for each strata is determined taking into account on the one hand, the volume of each strata sub-sample (= sample size x share of total population in that strata) and, on the other hand, a minimum level of 10 questionnaires for each sampling point. The voting sections which will represent sampling points are then randomly selected based on the exhaustive national list of voting sections (the latest available from the Permanent Electoral Authority).

The sample has 188 sampling points (voting sections) of which 104 are in urban areas, and 84 are in rural localities, including the capital city.

For each sampling point is computed the number of corresponding questionnaires by dividing the strata sub-sample by the number of sampling points of that strata. In the second sampling stage, the electoral registers corresponding to the voting sections (selected as sampling points) are used as sampling frame. Non-institutionalized persons aged 18 or more are randomly selected from the electoral registers based on the mechanical step method.

In those localities where the electoral registers are not available (or the municipality do not grant access), the random route method will be used. All these cases will be specified and explained in the fieldwork report, except for Bucharest, where the random route method will be used for all voting sections, as the rate of replacement from electoral registers is high in all national representative surveys.

The electoral registers include only persons 18 years or more. Accordingly, the sample will include a boost of persons aged 16, 17 or persons that had their 18th birthday after November 2009.39 For each voting section, one person aged 16-18 years will be added. They will be selected based on the random route method.

Mode of data collection

Face-to-face [f2f]

Response rate

The overall response rate of the survey is 95.2%. More detailed information is provided in "Table 2 Response rates and quality of the sampling frame by sampling method (%) " of the survey report.

The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

Contains 180 time series and 65 indicators that are expressed as ratios to GDP, land area, or adult population to facilitate cross-economy comparisons. Provision of FAS data is voluntary.

Abstract

This report presents the main results of the 2017 Namibia Financial Inclusion Survey. The survey was conducted by the Namibia Statistics Agency, in all 14 regions of Namibia, with funding from the Bank of Namibia and the World Bank. By design, the NFIS surveys was intended to involve a range of stakeholders through syndicate membership to enrich the entire survey process through cross-cutting learning, sharing of information, and to facilitate the extended utilization of the final data. A nationally representative sample of Namibians 16 years and older was employed. During October and November 2017 1863 face-to-face interviews were conducted, one interview per selected household. The data was captured into a tablet-based questionnaire using the Survey-To-Go application. The data collected was weighted to reflect the adult/eligible population (i.e. aged 16 years or older) in Namibia, as this is the minimum age legally allowed for any individual to make use of formal financial products in their own capacity. It is also important to note that the results of 2017 are representative only at national and urban/rural areas levels, but not regional.

· To measure the levels of financial inclusion (inclusive of formal and informal usage) · To describe the landscape of access (type of products and services used by financially included individuals) · To identify the drivers of, and barriers to the usage of financial products and services · To track and compare results and provide an assessment of changes and reasons thereof (including possible impacts of interventions to enhance access) · To stimulate evidence-based dialogue that will ultimately lead to effective public/private sector interventions that will increase and deepen financial inclusion strategies · Provide information on new opportunities for increased financial inclusion and usage.

Geographic coverage

National sampling frame is a list of small geographical areas called Primary Sampling Units (PSUs). There are a total of 6453 PSUs in Namibia that were created using the enumeration areas (EA) of the 2011 Population and Housing Census. The measure of size in the frame is the number of households within the PSU as reflected in the 2011 Census. The frame units were stratified first by region, and then by urban/rural areas within each region.

The results are only representative at national level, but not at regional level.

Analysis unit

Individuals, households

Universe

The target population for the NFIS 2017 was all people aged 16 and above who live in private households in Namibia. The eligible population living in institutions, such as hospitals, hostels, police barracks and prisons were not covered in this survey. However, private households within institutional settings such as teachers' houses in school premises were covered.

Kind of data

Sample survey data [ssd]

Sampling procedure

The target population for the NFIS 2017 was eligible members of private households in Namibia. The eligible population living in institutions, such as hospitals, hostels, police barracks and prisons were not covered in this survey. However, private households within institutional settings such as teachers' houses in school premises were covered. The sample design was a stratified three-stage cluster sample, where the first stage units were the PSUs, the second stage units were the households and the third stage were the eligible members, that is individuals who, by the time of the survey were 16 years or older, available during the duration of survey, mentally/physically capable to be interviewed and have resided in the selected household for at least six month preceding the survey. The age limit for the eligibility criteria was based on the fact that only individuals aged 16 years or above are officially authorized to get personal formal financial products (such as open a personal bank account) from formal financial institutions in Namibia, which makes them the target population of the financial sector. Only one individual was interviewed per selected household

The national sampling frame was used to select the first stage units (PSUs). The national sampling frame is a list of small geographical areas called Primary Sampling Units (PSUs) created using the enumeration areas (EAs) of 2011 Population and Housing Census. There are a total of 6 453 PSUs in Namibia. A total of 151 PSUs were selected from all the 14 regions, and 2 114 households were drawn from them, constituting the sample size. Power allocation procedures were adopted to distribute the samples across the regions so that the smaller regions will get adequate samples.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2017 NFIS questionnaire was made up of 13 sections in total. The questionnaire was transmitted onto CAPI (Computer aided Personal Interview) using the Survey-To-Go application.

Cleaning operations

The data processing methodology that was adopted for this study was the Computer Assisted Personal Interview. Data management series of operations to collect, transmit, clean and store the survey data were designed using SurveyToGo computer system onto the Dubloo platform.

Data entry is very crucial, since the quality of data collected impact heavily on the output. The collection process was designed to ensure that the data gathered are both defined and accurate, so that subsequent decisions based on the findings are valid.

Response rate

After data processing, 1863 out of 2114 sampled households were successfully interviewed, resulting in 88.1 percent response rate which is highly satisfactory given that the NSA subscribes to a response rate of 80 percent for all data collection in the social statistics domain. Overall, the rural response is higher than the urban response.

It was not possible to interview all the selected households when the household sample was implemented, due to refusals or non-contacts.

Sampling error estimates

The most common measure of quality of the survey estimates reported from the sample surveys was the level of precision of the estimates. The quality indicators are meant to ascertain the analysis about the level of precision of the estimates at different domains. The statistical precision of the survey estimates were expressed using different types of statistics such as Standard errors (SE), the coefficient of variation (CV) and the Confidence Interval (CI). These statistics were used to indicate the level of precision of the survey estimates in estimating the population parameters of interest. There are a number of factors that can affect the precision of the survey estimates namely the size of the sample relative to the population size, the sample design and the variability of the characteristics of interest in the population. The data quality indicators were discussed in details in the following sub-section.

Abstract

The survey on financial literacy among the citizens of Bosnia and Herzegovina was conducted within a larger project that aims at creating the Action Plan for Consumer Protection in Financial Services.

The conclusion about the need for an Action Plan was reached by the representatives of the World Bank, the Federal Ministry of Finance, the Central Bank of Bosnia and Herzegovina, supervisory authorities for entity financial institutions and non-governmental organizations for the protection of consumer rights, based on the Diagnostic Review on Consumer Protection and Financial Literacy in Bosnia and Herzegovina conducted by the World Bank in 2009-2010. This diagnostic review was conducted at the request of the Federal Ministry of Finance, as part of a larger World Bank pilot program to assess consumer protection and financial literacy in developing countries and middle-income countries. The diagnostic review in Bosnia and Herzegovina was the eighth within this project.

The financial literacy survey, whose results are presented in this report, aims at establishing the basic situation with respect to financial literacy, serving on the one hand as a preparation for the educational activities plan, and on the other as a basis for measuring the efficiency of activities undertaken.

Geographic coverage

Data collection was based on a random, nation-wide sample of citizens of Bosnia and Herzegovina aged 18 or older (N = 1036).

Analysis unit

Household, individual

Universe

Population aged 18 or older

Kind of data

Sample survey data [ssd]

Sampling procedure

SUMMARY

In Bosnia and Herzegovina, as is well known, there is no completely reliable sample frame or information about universe. The main reasons for such a situation are migrations caused by war and lack of recent census data. The last census dates back to 1991, but since then the size and distribution of population has significantly changed. In such a situation, researchers have to combine all available sources of population data to estimate the present size and structure of the population: estimates by official statistical offices and international organizations, voters? lists, list of polling stations, registries of passport and ID holders, data from large random surveys etc.

The sample was three-stage stratified: in the first stage by entity, in the second by county/region and in the third by type of settlement (urban/rural). This means that, in the first stage, the total sample size was divided in two parts proportionally to number of inhabitants by entity, while in the second stage the subsample size for each entity was further divided by regions/counties. In the third stage, the subsample for each region/county was divided in two categories according to settlement type (rural/urban).

Taking into the account the lack of a reliable and complete list of citizens to be used as a sample frame, a multistage sampling method was applied. The list of polling stations was used as a frame for the selection of primary sampling units (PSU). Polling station territories are a good choice for such a procedure since they have been recently updated, for the general elections held in October 2010. The list of polling station territories contains a list of addresses of housing units that are certainly occupied.

In the second stage, households were used as a secondary sampling unit. Households were selected randomly by a random route technique. In total, 104 PSU were selected with an average of 10 respondents per PSU. The respondent from the selected household was selected randomly using the Trohdal-Bryant scheme.

In total, 1036 citizens were interviewed with a satisfactory response rate of around 60% (table 1). A higher refusal rate is recorded among middle-age groups (table 2). The theoretical margin of error for a random sample of this size is +/-3.0%.

Due to refusals, the sample structure deviated from the estimated population structure by gender, age and education level. Deviations were corrected by RIM weighting procedure.

MORE DETAILED INFORMATION

IPSOS designed a representative sample of approximately 1.000 residents age 18 and over, proportional to the adult populations of each region, based on age, sex, region and town (settlement) type.

For this research we designed three-stage stratified representative sample. First we stratify sample at entity level, regional level and then at settlement type level for each region.

Sample universe:

Population of B&H -18+; 1991 Census figures and estimated population dynamics, census figures of refugees and IDPs, 1996. Central Election Commision - 2008; CIPS - 2008;

Sampling frame:

Polling stations territory (approximate size of census units) within strata defined by regions and type of settlements (urban and rural) Polling stations territories are chosen to be used as primary units because it enables the most reliable sample selection, due to the fact that for these units the most complete data are available (dwelling register - addresses)

Type of sample:

Three stage random representative stratified sample

Definition and number of PSU, SSU, TSU, and sampling points

• PSU - Polling station territory Definition: Polling stations territories are defined by street(s) name(s) and dwelling numbers; each polling station territory comprises approximately 300 households, with exception of the settlements with less than 300 HH which are defined as one unite. Number of PSUs in sample universe: 4710
• SSU - Household Definition: One household comprises people living in the same apartment and sharing the expenditure for food
• TSU - Respondent Definition: Member of the HH , 18+ Number of TSUs in sample universe: = 2.966.766
• Sampling points Approximately 10 respondents per one PSU, total 104

Stratification, purpose and method

• First level strata: Federation of B&H Republika Srpska Brc ko District
• Second level strata: 10 cantons 2 regions -
• Third level strata: urban and rural settlements
• Purpose: Optimisation of the sample plan, and reducing the sampling error

• Method: The strata are defined by criteria of optimal geographical and cultural uniformity

• Selection procedure of PSU, SSU, and respondent Stratification, purpose and method

• PSU Type of sampling of the PSU: Polling station territory chosen with probability proportional to size (PPS) Method of selection: Cumulative (Lachirie method)

• SSU Type of sampling of the SSU: Sample random sampling without replacement Method of selection: Random walk - Random choice of the starting point

• TSU - Respondent Type of sampling of respondent: Sample random sampling without replacement Method of selection: TCB (Trohdal-Bryant scheme)

• Sample size N=1036 respondents

• Sampling error Marginal error +/-3.0%

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey was modelled after the identical survey conducted in Romania. The questionnaire used in the Financial Literacy Survey in Romania was localized for Bosnia and Herzegovina, including adaptations to match the Bosnian context and methodological improvements in wording of questions.

Cleaning operations

Before data entry, 100% logic and consistency controls are performed first by local supervisors and once later by staff in central office.

Verification of correct data entry is assured by using BLAISE system for data entry (commercial product of Netherlands statistics), where criteria for logical and consistency control are defined in advance.

Response rate

• Nobody at home: 2,8%
• Eligible person is not home: 2,8%
• Refusal : 32,79%
• Given up after a minimum of two visits: 0,82%
• Other (excluded after control): 0,29%
• Finished: 60,5%

Dataset holding responses made by households to the HFCS. The HFCS primarily measures wealth of households in Ireland.

SCE: Financial Situation: Year Ahead: Somewhat Better Off data was reported at 22.272 % in Apr 2025. This records a decrease from the previous number of 24.771 % for Mar 2025. SCE: Financial Situation: Year Ahead: Somewhat Better Off data is updated monthly, averaging 30.271 % from Jun 2013 (Median) to Apr 2025, with 143 observations. The data reached an all-time high of 38.179 % in Feb 2018 and a record low of 17.060 % in May 2022. SCE: Financial Situation: Year Ahead: Somewhat Better Off data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.H085: Survey of Consumer Expectations: Financial.

School District Finance Survey, 2012-13 (F-33 2012-13), is a study that is part of the Common Core of Data (CCD) program; program data available since 1990 at . F-33 2012-13 (https://nces.ed.gov/ccd/f33ageninfo.asp) is a universe survey that is designed to provide finance data for all local education agencies (LEAs) that provide free public elementary and secondary education in the United States. The data file for F-33 2012-13 contains records representing the public elementary and secondary education agencies in the 50 United States and the District of Columbia. Key statistics produced from F-33 2012-13 are expenditures by object and function, indebtedness, and revenues by source. The F-33 is collaboration by the National Center for Education Statistics (NCES) and the Census Bureau. Census is the primary collection agent. Census refers to the collection as the Annual Survey of Local Government Finances: School Systems and releases its own version of the data file and publication based on that file. The NCES and Census files differ in their inclusion of independent charter school districts, the classification of some revenue items, and the inclusion of some expenditure items.

SCE: Financial Situation: Year Ahead: Much Better Off data was reported at 4.730 % in Apr 2025. This records an increase from the previous number of 3.935 % for Mar 2025. SCE: Financial Situation: Year Ahead: Much Better Off data is updated monthly, averaging 4.899 % from Jun 2013 (Median) to Apr 2025, with 143 observations. The data reached an all-time high of 8.327 % in May 2019 and a record low of 1.735 % in Jun 2013. SCE: Financial Situation: Year Ahead: Much Better Off data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.H085: Survey of Consumer Expectations: Financial.

The survey covers micro (1 to 9 employees), small (10 to 49 employees), medium-sized (50 to 249 employees) and large firms (250 or more employees) and it provides evidence on the financing conditions faced by SMEs compared with those of large firms during the past six months. In addition to a breakdown into firm size classes, it provides evidence across branches of economic activity, euro area countries, firm age, financial autonomy of the firms, and ownership of the firms. Part of the survey is run by the ECB every six months to assess the latest developments of the financing conditions of firms in the euro area. The more comprehensive survey, run together with the European Commission, was initially conducted every two years, i.e. in 2009H1, 2011H1 and 2013H1. As from the wave 2014H1, the extended survey is run on the annual basis.

SCE: Credit Availability: Year Ago: Much Easier data was reported at 1.039 % in Apr 2025. This records an increase from the previous number of 0.646 % for Mar 2025. SCE: Credit Availability: Year Ago: Much Easier data is updated monthly, averaging 1.973 % from Jun 2013 (Median) to Apr 2025, with 143 observations. The data reached an all-time high of 4.650 % in Jan 2020 and a record low of 0.471 % in Jan 2014. SCE: Credit Availability: Year Ago: Much Easier data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.H085: Survey of Consumer Expectations: Financial.

School District Finance Survey, 2008-09 (F-33 2008-09), is a study that is part of the Common Core of Data (CCD) program; program data available since 1990 at . F-33 2008-09 (https://nces.ed.gov/ccd/f33ageninfo.asp) is a universe survey that is designed to provide finance data for all local education agencies (LEAs) that provide free public elementary and secondary education in the United States. The data file for F-33 2008-09 contains 16,563 records representing the public elementary and secondary education agencies in the 50 United States and the District of Columbia. Key statistics produced from F-33 2008-09 are expenditures by object and function, indebtedness, and revenues by source. The F-33 is collaboration by the National Center for Education Statistics (NCES) and the Census Bureau. Census is the primary collection agent. Census refers to the collection as the Annual Survey of Local Government Finances: School Systems and releases its own version of the data file and publication based on that file. The NCES and Census files differ in their inclusion of independent charter school districts, the classification of some revenue items, and the inclusion of some expenditure items.

Graph and download economic data for All Employees, Finance and Insurance (CES5552000001) from Jan 1990 to Jun 2025 about finance, insurance, financial, establishment survey, employment, and USA.

Financial Access Survey (FAS) indicators are expressed as ratios to GDP, land area, or adult population to facilitate cross-economy comparisons. Provision of FAS data is voluntary.

The Financial Access Survey draws on the IMF's Monetary and Financial Statistics Manual and Compilation Guide (http://data.imf.org/api/document/download?key=61061648)

National Survey on Financial Health (ENSAFI) 2023 aims to generate statistical information on the aspects that define the financial health of the

Composition of assets (including Employer Pension Plans valued on a termination basis) and debts held by all family units, by net worth quintiles, Canada and provinces.