This statistic presents the share of population in selected European countries holding a current account in a financial institution as of 2017. Data shows that in that year over 99 percent of population aged 15 and older in Finland, Norway and Denmark held a current account.

United States CSI: Personal: Expected Real HH Inc Change: Next Yr: Don’t Know data was reported at 1.000 % in May 2018. This stayed constant from the previous number of 1.000 % for Apr 2018. United States CSI: Personal: Expected Real HH Inc Change: Next Yr: Don’t Know data is updated monthly, averaging 2.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 7.000 % in Feb 1978 and a record low of 0.000 % in Nov 2017. United States CSI: Personal: Expected Real HH Inc Change: Next Yr: Don’t Know data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H024: Consumer Sentiment Index: Personal Finance. The question was: How about the next year or two -- do you expect that your (family) income will go up more than prices will go up, about the same, or less than prices will go up?

United States CSI: Personal: HH Fin'l Situation: 1Yr Ago: Worse: Assets are Lower data was reported at 2.000 % in May 2018. This stayed constant from the previous number of 2.000 % for Apr 2018. United States CSI: Personal: HH Fin'l Situation: 1Yr Ago: Worse: Assets are Lower data is updated monthly, averaging 2.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 20.000 % in Feb 2009 and a record low of 0.000 % in Jan 2017. United States CSI: Personal: HH Fin'l Situation: 1Yr Ago: Worse: Assets are Lower data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H024: Consumer Sentiment Index: Personal Finance. The question was: We are interested in how people are getting along financially these days. Would you say that you (and your family living there) are better off or worse off financially than you were a year ago? Responses to the query 'Why do you say so?'

The statistic shows the results of a survey about the share of accounts held at financial institutions in Japan in 2017, by type of account holder. In the period surveyed, the lowest share of people holding an account at a financial institution was among young adults between the age of 15 and 24 years, with around ** percent. However, a look at the statistic shows that the financial inclusion in Japan is very high reaching almost 100 percent across all other types of people.

United States CSI: Personal: HH Fin'l Situation: 5Yr Trend: Don’t Know data was reported at 3.000 % in May 2018. This stayed constant from the previous number of 3.000 % for Apr 2018. United States CSI: Personal: HH Fin'l Situation: 5Yr Trend: Don’t Know data is updated monthly, averaging 5.000 % from Feb 1979 (Median) to May 2018, with 119 observations. The data reached an all-time high of 13.000 % in Jan 1981 and a record low of 2.000 % in Sep 2017. United States CSI: Personal: HH Fin'l Situation: 5Yr Trend: Don’t Know data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H024: Consumer Sentiment Index: Personal Finance.

Exemption limit, number of rates, peak rate, entry rate and income at which peak rate applies for individual income tax from 1949-50 to 2017-18

United States CSI: Personal: Real Income Gains Probability: Next 5 Yrs: 100% data was reported at 7.000 % in May 2018. This records an increase from the previous number of 6.000 % for Apr 2018. United States CSI: Personal: Real Income Gains Probability: Next 5 Yrs: 100% data is updated monthly, averaging 5.000 % from Dec 1997 (Median) to May 2018, with 246 observations. The data reached an all-time high of 9.000 % in Mar 2017 and a record low of 1.000 % in Feb 2013. United States CSI: Personal: Real Income Gains Probability: Next 5 Yrs: 100% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H024: Consumer Sentiment Index: Personal Finance. The question was: What do you think the chances are that your (family) income will increase by more than the rate of inflation in the next five years or so?

United States CSI: Personal: Losing a Job Probability: Next 5 Yrs: 1-24% data was reported at 29.000 % in May 2018. This records a decrease from the previous number of 30.000 % for Apr 2018. United States CSI: Personal: Losing a Job Probability: Next 5 Yrs: 1-24% data is updated monthly, averaging 28.000 % from Dec 1997 (Median) to May 2018, with 246 observations. The data reached an all-time high of 37.000 % in Jan 2017 and a record low of 21.000 % in Apr 2013. United States CSI: Personal: Losing a Job Probability: Next 5 Yrs: 1-24% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H024: Consumer Sentiment Index: Personal Finance. The question was: During the next 5 years, what do you think the chances are that you (or your husband/wife) will lose a job you wanted to keep?

United States CSI: Personal: HH Fin'l Situation: 5Yr Trend: Continuous Increase data was reported at 40.000 % in May 2018. This records a decrease from the previous number of 41.000 % for Apr 2018. United States CSI: Personal: HH Fin'l Situation: 5Yr Trend: Continuous Increase data is updated monthly, averaging 31.000 % from Feb 1979 (Median) to May 2018, with 119 observations. The data reached an all-time high of 45.000 % in Oct 2017 and a record low of 17.000 % in Aug 2012. United States CSI: Personal: HH Fin'l Situation: 5Yr Trend: Continuous Increase data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H024: Consumer Sentiment Index: Personal Finance.

The Department of Taxation and Finance annually publishes statistical information on the New York State empire state child credit (ESCC). The data used to generate this report come from an annual study file based on the latest available data drawn from New York State personal income tax returns. The tables in this report summarize tax credit activity by filing status and place of residence. The totals in the summary tables may not match the detail tables due to rounding and disclosure requirements.

Australia Households: Personal Finance: sa: New Loan Commitments: Fixed Term Loans: External Refinancing data was reported at 157.800 AUD mn in Mar 2020. This records a decrease from the previous number of 160.600 AUD mn for Feb 2020. Australia Households: Personal Finance: sa: New Loan Commitments: Fixed Term Loans: External Refinancing data is updated monthly, averaging 127.200 AUD mn from Jul 2002 (Median) to Mar 2020, with 210 observations. The data reached an all-time high of 247.200 AUD mn in Apr 2013 and a record low of 66.600 AUD mn in Jun 2017. Australia Households: Personal Finance: sa: New Loan Commitments: Fixed Term Loans: External Refinancing data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.KB011: Lending Indicators: Economic and Financial Statistics (EFS) Collection: Personal Finance.

United States PCE: 2017p: saar: SE: FI: Financial Services (FIN) data was reported at 744.687 USD bn in Mar 2025. This records an increase from the previous number of 741.232 USD bn for Feb 2025. United States PCE: 2017p: saar: SE: FI: Financial Services (FIN) data is updated monthly, averaging 685.015 USD bn from Jan 2007 (Median) to Mar 2025, with 219 observations. The data reached an all-time high of 744.687 USD bn in Mar 2025 and a record low of 638.035 USD bn in Apr 2007. United States PCE: 2017p: saar: SE: FI: Financial Services (FIN) data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A089: NIPA 2023: Personal Consumption Expenditure: Chain Linked 2017 Price: saar.

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

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). 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 1005.

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 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

United States PCE: 2017p: saar: MB: SE: Financial & Insurance data was reported at 150.621 USD bn in Mar 2025. This records an increase from the previous number of 150.336 USD bn for Feb 2025. United States PCE: 2017p: saar: MB: SE: Financial & Insurance data is updated monthly, averaging 143.099 USD bn from Jan 2007 (Median) to Mar 2025, with 219 observations. The data reached an all-time high of 166.321 USD bn in Jan 2022 and a record low of 122.979 USD bn in May 2020. United States PCE: 2017p: saar: MB: SE: Financial & Insurance data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A090: NIPA 2023: Personal Consumption Expenditure: Chain Linked 2017 Price: Market Based: saar.

United States CPI U: AW: GS: PC: Misc Personal Services: Financial data was reported at 0.238 % in 2017. This records an increase from the previous number of 0.235 % for 2016. United States CPI U: AW: GS: PC: Misc Personal Services: Financial data is updated yearly, averaging 0.233 % from Dec 1997 (Median) to 2017, with 21 observations. The data reached an all-time high of 0.328 % in 2001 and a record low of 0.179 % in 2009. United States CPI U: AW: GS: PC: Misc Personal Services: Financial data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I011: Consumer Price Index: Urban: Weights (Annual).

Brazil Wholesale Trade: CNAE 2.0: Equipment & Personal Articles: Financial Expenditure data was reported at 8,381,138.000 BRL th in 2017. This records a decrease from the previous number of 9,827,254.000 BRL th for 2016. Brazil Wholesale Trade: CNAE 2.0: Equipment & Personal Articles: Financial Expenditure data is updated yearly, averaging 5,308,263.000 BRL th from Dec 2007 (Median) to 2017, with 11 observations. The data reached an all-time high of 13,150,836.000 BRL th in 2015 and a record low of 2,202,701.000 BRL th in 2007. Brazil Wholesale Trade: CNAE 2.0: Equipment & Personal Articles: Financial Expenditure data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Wholesale Trade, Retail Trade, Repair of Automotive and Motorcycles Sector – Table BR.RJB005: Wholesale Trade: Financial Data: CNAE 2.0: Equipment and Personal Articles.

This dataset presents aggregated values of Other Income as a category of the estimates of Personal Income for Small Areas ABS release. The data spans over the financial years of 2010-11 to 2014-15 …Show full descriptionThis dataset presents aggregated values of Other Income as a category of the estimates of Personal Income for Small Areas ABS release. The data spans over the financial years of 2010-11 to 2014-15 and is aggregated to the 2016 Statistical Area Level 4 (SA4) boundaries. This release presents regional data on the number of income earners, amounts they receive, and the distribution of income for the 2010-11 to 2014-15 financial years. An improved geocoding process has been introduced for this release. As such, previously released estimates for the 2010-11 and 2012-13 financial year have been superseded. The following personal income categories are provided in this census release: Employee Income Own Unincorporated Business Income Investment Income Superannuation Income Other Income (Income not allocatable to any other categories) Total Income (Sum of previous categories) These statistics provide insights into the nature of regional economies and the economic well-being of the people who live there. The data has been sourced from the Australian Taxation Office (ATO) and is presented with the updated 2016 editions of the Australian Statistical Geography Standards (ASGS): Statistical Area Level 2 (SA2); Statistical Area Level 3 (SA3); Statistical Area Level 4 (SA4); Greater Capital City Statistical Area (GCCSA) and Local Government Area (LGA). For more information on the release please visit the Australian Bureau of Statistics. Please note: When interpreting these results, it should be noted that some low income earners, for example those receiving Government pensions and allowances, or those who earned below the tax free threshold, may not be present in the data, as they may not be required to lodge personal tax forms. Other individuals may not lodge a tax return even if required, therefore care should be taken in interpreting the data as well as comparing the data in this publication with other income data produced by the ABS. To minimise the risk of identifying individuals in aggregate statistics, a confidentialisation process called perturbation has been applied to the data. Perturbation involves small random adjustment of the statistics and is considered the most satisfactory technique for avoiding the release of identifiable statistics while maximising the range of information that can be released. Where data is not available or not for publication, the record has been set to a null value. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2017): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

United States PCE: 2017p: saar: CC: SE: HE: Financial & Insurance data was reported at 0.150 % Point in Mar 2025. This records a decrease from the previous number of 0.250 % Point for Dec 2024. United States PCE: 2017p: saar: CC: SE: HE: Financial & Insurance data is updated quarterly, averaging 0.205 % Point from Jun 1959 (Median) to Mar 2025, with 264 observations. The data reached an all-time high of 1.720 % Point in Mar 2000 and a record low of -0.660 % Point in Mar 1981. United States PCE: 2017p: saar: CC: SE: HE: Financial & Insurance data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A091: NIPA 2023: Personal Consumption Expenditure: Contributions to Change: Chain Linked 2017 Price: saar.

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.

Lithuania Enterprises: FI: Repair of Computers, Personal & Household Goods data was reported at -79.000 EUR th in Mar 2018. This records a decrease from the previous number of -68.000 EUR th for Dec 2017. Lithuania Enterprises: FI: Repair of Computers, Personal & Household Goods data is updated quarterly, averaging -10.000 EUR th from Mar 2005 (Median) to Mar 2018, with 53 observations. The data reached an all-time high of 1,656.000 EUR th in Sep 2007 and a record low of -84.000 EUR th in Sep 2017. Lithuania Enterprises: FI: Repair of Computers, Personal & Household Goods data remains active status in CEIC and is reported by Statistics Lithuania. The data is categorized under Global Database’s Lithuania – Table LT.O011: Enterprises: Financial Statistics: NACE Rev. 2.