Advancing financial well-being through research Understanding factors that support consumer financial well-being can help practitioners and policymakers empower more families to lead better financial lives to serve their own goals.

A person’s financial well-being comes from their sense of financial security and freedom of choice—both in the present and when considering the future. We measured it using our 10-item Financial Well-Being Scale.

The survey dataset includes respondents’ scores on that scale, as well as measures of individual and household characteristics that research suggests may influence adults’ financial well-being, including:

Income and employment Savings and safety nets Past financial experiences Financial behaviors, skills, and attitudes

Abstract

The objectives of the Smallholder Household Survey in Cote d'Ivoire were to: - Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, and poverty status and market relationships; - Segment smallholder households in Cote d'Ivoire according to the most compelling variables that emerge; - Characterize the demand for financial services in each segment, focusing on customer needs, attitudes and perceptions related to both agricultural and financial services; and, - Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value.

Geographic coverage

National coverage

Analysis unit

Households and individual household members

Universe

The universe for the survey consists of smallholder households defined as households with the following criteria: 1) Household with up to 5 hectares OR farmers who have less than 50 heads of cattle, 100 goats/sheep/pigs, or 1,000 chickens; AND 2) Agriculture provides a meaningful contribution to the household livelihood, income, or consumption.

Kind of data

Sample survey data [ssd]

Sampling procedure

The smallholder household survey in Cote d’Ivoire is a nationally-representative survey, with a target sample size of 3,000 smallholder households. The sample was designed to provide reliable survey estimates at the national level.

Sampling Frame In preparation for the 2014 population census, the country was divided into 22,600 census enumeration areas (EAs). For the ongoing 2015 agricultural census, the National Statistical Office (INS) has identified 18,321 EAs that contain agricultural households. The sampling frame for the smallholder survey is the list of these enumeration areas (EAs) containing agricultural households.

Sample allocation and selection In order to take nonresponse into account, the target sample size was increased to 3,333 households assuming a nonresponse rate of 10%. The total sample size was first allocated to the zones based on their population counts using the power allocation method. Within each zone, the resulting sample was then distributed to urban and rural areas in proportion to their population. Given that EAs were the primary sampling units and 15 households were selected in each EA, a total of 223 EAs were selected. The sample for the smallholder survey is a stratified multistage sample. Stratification was achieved by separating each zone into urban and rural areas. The urban/rural classification is based on the 2014 population census. Therefore, 6 strata were created, and the sample was selected independently in each stratum.

In the first stage, EAs were selected as primary sampling units with probability proportional to size, the size being the population count in the EAs. A household listing operation was conducted in all selected EAs to identify smallholder households and to provide a frame for selecting smallholder households to be included in the sample. In the second stage, 15 smallholders were sampled in each EA with equal probability.

In each sampled household, the household questionnaire was administered to the head of the household, the spouse, or any knowledgeable adult household member to collect information about household characteristics. The multiple respondent questionnaire was administered to all adult members in each sampled household to collect information on their agricultural activities, financial behaviors, and mobile money use. In addition, in each sampled household only one household member was selected using the Kish grid and was administered the single respondent questionnaire.

The full description of the sample design can be found in the user guide for this data set.

Sampling deviation

After the selection of the EAs and the printing of the EA maps, it was necessary to reduce the number of EAs to be listed to 212 for budgetary reasons. Therefore, 212 EAs were randomly selected among the previously 223 sampled EAs and were finally included in the survey sample.

The smallholder survey in Cote d’Ivoire is the fifth survey in the series, following the surveys in Mozambique, Uganda, Tanzania and Bangladesh. Fieldwork in the first countries experienced a lot of failed call backs where identified eligible households and household members could not be interviewed during the time allocated to fieldwork in each country. As a result, the final sample size fell slightly short of the target. For this reason, in Cote d’Ivoire the number of households selected in each EA was increased from 15 to 17 following the household listing operation in all sampled EAs.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

To capture the complexity of smallholder households, the smallholder household survey was divided into three questionnaires: 1) The Household questionnaire; 2) the Multiple Respondent questionnaire; and 3) the Single respondent questionnaire. It was designed in this way to capture the complete portrait of the smallholder household, as some members of the household may work on other agricultural activities independently and without the knowledge of others.

The household questionnaire collected information on the following: • Basic household members’ individual characteristics (age, gender, education attainment, schooling status, relationship with the household head). • Whether each household member contributes to the household income or participates in the household’s agricultural activities. This information was later used to identify all household members eligible for the other two questionnaires. • Household assets and dwelling characteristics.

Both the Multiple and Single Respondent questionnaires collected different information on the following: • Agricultural practices—farm information such as size, crop types, livestock, decision-making, farming association, and markets. • Household economics—employment, income, expenses, shocks, borrowing and saving habits, and investments.

The Single respondent questionnaire also collected the following information: • Mobile phones—attitudes toward phones, use, access, ownership, desire, and importance. • Financial services—attitudes toward financial products and services such as banking and mobile money, including ownership, usage, access and importance.

The questionnaires were translated into French and then pretested. After the pretest, debriefing sessions were held with the pretest field staff and the questionnaires were modified based on the observations from the pretest. After the questionnaires were finalized, a script was developed to support data collection on mobile phones. The script was tested and validated before it was use in the field.

Cleaning operations

The data files were checked for completeness, inconsistencies and errors by InterMedia and corrections were made as necessary and where possible.

Response rate

The user guide to the data set provides detailed tables on household and household member response rates for the Cote d’Ivoire smallholder household survey. A total of 3,415 households were selected for the survey, of which 3,109 were found to be occupied during data collection. Of these, 3,019 were successfully interviewed, yielding a household response rate of 97.1 percent.

In the interviewed households, 6,659 eligible household members were identified for the Multiple Respondent questionnaire. Interviews were completed with 5,706 eligible household members, yielding a response rate of 85.7 percent for the Multiple Respondent questionnaire.

Among the 3,019 eligible household members selected for the Single Respondent questionnaire, 2,949 were successfully interviewed yielding a response rate of 97.7 percent.

Sampling error estimates

The sample design for the smallholder household survey was a complex sample design featuring clustering, stratification and unequal probabilities of selection. For key survey estimates, sampling errors taking into account the design features were produced using either the SPSS Complex Sample module or STATA based on the Taylor series approximation method.

Abstract

The objectives of the Smallholder Household Survey in Nigeria were to: • Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, and poverty status and market relationships; • Segment smallholder households in Nigeria according to the most compelling variables that emerge; • Characterize the demand for financial services in each segment, focusing on customer needs, attitudes and perceptions related to both agricultural and financial services; and, • Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value.

Geographic coverage

National coverage

Analysis unit

Households and individual household members

Universe

The universe for the survey consists of smallholder households defined as households with the following criteria: 1) Household with up to 5 hectares OR farmers who have less than 50 heads of cattle, 100 goats/sheep/pigs, or 1,000 chickens; AND 2) Agriculture provides a meaningful contribution to the household livelihood, income, or consumption.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Procedure

The smallholder household survey in Nigeria is a nationally-representative survey with a target sample size of 3,000 smallholder households. In order to take nonresponse into account, the target sample size was increased to 3,225 households assuming a response rate of 93%. The sample was designed to produce national level estimates as well as estimates for each of the six geo-political zones of

Nigeria is comprised of the following states: - North Central: Benue, Federal Capital Territory (FCT), Kogi, Kwara, Nasarawa, Niger, and Plateau - North East: Adamawa, Bauci, Borno, Gombe, Taraba and Yobe - North West: Jigawa, Kaduna, Kano, katsina, Kebbi, Sokoto and Zamfara - South East: Abia, Anambra, Ebonyi, Enugu, and Imo - South South: Akwa Ibom, Bayelsa, Cross River, Delta, Edo, and River - South West: Ekiti, Lagos, Ogun, Ondo, Osun, and Oyo

Sampling Frame

Nigeria is divided into 774 local governments (LGAs) and its last housing and population census took place in 2006. In preparation for this last census, the National Population Commission (NPopC) demarcated over 662,000 enumeration areas (EAs) for the country. From these EAs, two hierarchical master sample frames were developed by the Nigeria Bureau of Statistics (NBS): the LGA master frame and the National Integrated Survey of Households (NISH). The smallholder survey used the NISH as sampling frame but retained only the EAs containing agricultural households.

Sample allocation and selection

The total sample size was first allocated to the geo-political zones in proportion to their number of agricultural EAs in the sampling frame. Within each zone, the resulting sample was then further distributed to states proportionally to their number of agricultural EAs. Given that EAs were the primary sampling units and 15 households were selected in each EA, a total number of 215 EAs were selected The sample for the smallholder survey is a stratified multistage sample. A stratum corresponds to a state and the sample was selected independently in each stratum.

In the first stage, EAs were selected as primary sampling units with equal probability. A household listing operation was carried out in all selected EAs to identify smallholder households and to provide a frame for the selection of smallholder households to be included in the sample. In the second stage, 15 smallholders were selected in each EA with equal probability.

In each selected household, a household questionnaire was administered to the head of the household, the spouse or any knowledgeable adult household member to collect information about household characteristics. A multiple respondent questionnaire was administered to all adult members in each selected household to collect information on their agricultural activities, financial behaviors and mobile money usage. In addition, in each selected household only one household member was selected using the Kish grid and was administered the single respondent questionnaire.

The full description of the sample design can be found in the user guide for this data set.

Sampling deviation

The household listing operation identified fewer than 15 smallholder households in many sampled EAs. As a result, the sample take of 15 households per EA couldn’t be implemented in those EAs. To avoid a situation where a sample falls short, the sample take was increased to 17 smallholder households where possible while retaining in the sample all smallholder households in EAs with fewer than 17 smallholder households. This yielded 3,457 sampled households.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

To capture the complexity of smallholder households, the smallholder household survey was divided into three questionnaires: the Household questionnaire, the Multiple Respondent questionnaire, and the Single respondent questionnaire. It was designed in this way to capture the complete portrait of the smallholder household, as some members of the household may work on other agricultural activities independently and without the knowledge of others.

The household questionnaire collected information on the following: • Basic household members’ individual characteristics (age, gender, education attainment, schooling status, relationship with the household head). • Whether each household member contributes to the household income or participates in the household’s agricultural activities. This information was later used to identify all household members eligible for the other two questionnaires. • Household assets and dwelling characteristics.

Both the Multiple and Single Respondent questionnaires collected different information on the following: • Agricultural practices—farm information such as size, crop types, livestock, decision-making, farming association, and markets. • Household economics—employment, income, expenses, shocks, borrowing and saving habits, and investments.

The Single respondent questionnaire collected the following information: • Mobile phones—attitudes toward phones, use, access, ownership, desire, and importance. • Financial services—attitudes toward financial products and services such as banking and mobile money, including ownership, usage, access and importance.

The questionnaires were translated into Igbo, Hausa, Yoruba and Pidgin and were pretested on 5 -7 November 2016. After the pretest, debriefing sessions were held with the pretest field staff and the questionnaires were modified based on the observations from the pretest. After the questionnaires were finalized, a script was developed to support data collection on smartphones. The script was tested and validated before it was used in the field.

Cleaning operations

The data files were checked for completeness, inconsistencies and errors by InterMedia and corrections were made as necessary and where possible.

Response rate

The tables in the User Guide show household and household member response rates for the Nigeria smallholder household survey. A total of 3,457 households was selected for the survey, of which 3,310 were found to be occupied during data collection. Of these occupied households, 3,026 were successfully interviewed, yielding a household response rate of 91 percent.

In the interviewed households 6,643 eligible household members were identified for the Multiple Respondent questionnaire. Interviews were completed with 5,128 eligible household members, yielding a response rate of 77 percent for the Multiple Respondent questionnaire.

Among the 3,206 eligible household members selected for the Single Respondent questionnaire, 2,773 were successfully interviewed, yielding a response rate of 92 percent.

Sampling error estimates

The sample design for the smallholder household survey was a complex sample design featuring clustering, stratification and unequal probabilities of selection. For key survey estimates, sampling errors taking into account the design features can be produced using either the SPSS Complex Sample module or STATA based on the Taylor series approximation method.

This data collection is one in a series of financial surveys of consumers conducted annually since 1946. In a nationally representative sample, the head of each spending unit (usually the husband, the main earner, or the owner of the home) was interviewed. The basic unit of reference in the study was the spending unit, but some family data are also available. The questions in the 1957 survey covered the respondent's attitudes toward national economic conditions and price activity, as well as the respondent's own financial situation. Other questions examined the spending unit head's occupation, and the nature and amount of the spending unit's income, debts, liquid assets, changes in liquid assets, savings, investment preferences, and actual and expected purchases of cars and other major durables. The survey also elicited respondent's attitudes about different methods of using income remaining after their expenses were met, e.g., investing in stocks or putting money in savings. In addition, the survey explored in detail the subject of housing, e.g., previous and present home ownership, value of respondent's dwelling, and mortgage information. Regarding financial assets, the respondent was asked questions on attitudes toward financial assets, minimum balance in checking accounts, and common stock ownership and changes. Also included were questions on life insurance coverage and premiums, and whether the spouse had a full-time job and how much of the year he or she worked. Personal data include number of people in the spending unit, age, sex, and education of the head, and the race and sex of the respondent. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR03616.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Abstract

The objectives of the Smallholder Household Survey in Tanzania were to: • Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, and poverty status and market relationships; • Segment smallholder households in Tanzania according to the most compelling variables that emerge; • Characterize the demand for financial services in each segment, focusing on customer needs, attitudes and perceptions related to both agricultural and financial services; and, • Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value.

Geographic coverage

National coverage

Analysis unit

Households and individual household members

Universe

The universe for the survey consists of smallholder households defined as households with the following criteria: 1) Household with up to 5 hectares OR farmers who have less than 50 heads of cattle, 100 goats/sheep/pigs, or 1,000 chickens; AND 2) Agriculture provides a meaningful contribution to the household livelihood, income, or consumption.

Kind of data

Sample survey data [ssd]

Sampling procedure

The smallholder household survey in Tanzania is a nationally representative survey with a target sample size of 3,000 smallholder households. The sample was designed to provide reliable survey estimates at the national level.

Sampling Frame. The sampling frame is the list of enumeration areas (EAs) containing agricultural households. These EAs were created in preparation for the 2012 population and housing census. The census questionnaire included a question on whether any household member operated any land for agricultural purposes during the 2011–2012 agricultural year. The information collected helped to identify agricultural households during the census.

Sample allocation and selection. For the sample allocation, regions were combined into the following zones: • Border: Ruvuma, Iringa, Mbeya, Rukwa, and Kigoma • Coastal: Tanga, Pwani, Dar es Salaam, Lindi, and Mtwara • Inland: Dodoma, Arusha, Kilimanjaro, Morogoro, Singida, Tabora, Manyara, Njombe, and Katavi • Lake: Shinyanga, Kagera, Mwanza, Mara, Simiyu, and Geita • Zanzibar: all regions

To take nonresponse into account, the target sample size was increased to 3,158 households assuming a nonresponse rate of 5 percent observed in similar national household surveys. The total sample size was first allocated to the zones in proportion to the number of agricultural households in the sampling frame. Within each zone, the resulting sample was then distributed to urban and rural areas in proportion to number of agricultural households.

Given that EAs were the primary sampling units and 15 households were selected in each EA, a total of 212 EAs were selected.

The sample for the smallholder survey is a stratified multistage sample. Stratification was achieved by separating each zone into urban and rural areas. The urban/rural classification is based on the 2012 population census. Therefore, 10 strata were created, and the sample was selected independently in each stratum.

In the first stage, EAs were selected as primary sampling units with probability proportional to size, the size being the number of agricultural households in the EAs. A household listing operation was conducted in all selected EAs to identify smallholder households and to provide a frame for selecting smallholder households to be included in the sample. In the second stage, 15 smallholders were sampled in each EA with equal probability.

In each sampled household, the household questionnaire was administered to the head of the household, the spouse, or any knowledgeable adult household member to collect information about household characteristics. The multiple respondent questionnaire was administered to all adult members in each sampled household to collect information on their agricultural activities, financial behaviors, and mobile money use. In addition, in each sampled household only one household member was selected using the Kish grid and was administered the single respondent questionnaire.

The full description of the sample design can be found in the user guide for this data set.

Sampling deviation

The smallholder survey in Tanzania is the third survey in the series, following the surveys in Mozambique and Uganda. Fieldwork in those two countries experienced a lot of failed call backs where identified eligible households and household members could not be interviewed during the time allocated to fieldwork in each country. As a result, the final sample size fell slightly short of the target. For this reason, in Tanzania the number of households selected in each EA was increased from 15 to 17 following the household listing operation in all sampled EAs.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

To capture the complexity of smallholder households, the smallholder household survey was divided into three questionnaires: the Household questionnaire, the Multiple Respondent questionnaire, and the Single respondent questionnaire. It was designed in this way to capture the complete portrait of the smallholder household, as some members of the household may work on other agricultural activities independently and without the knowledge of others.

The household questionnaire collected information on the following:
• Basic household members’ individual characteristics (age, gender, education attainment, schooling status, relationship with the household head). • Whether each household member contributes to the household income or participates in the household’s agricultural activities. This information was later used to identify all household members eligible for the other two questionnaires. • Household assets and dwelling characteristics.

Both the Multiple and Single Respondent questionnaires collected different information on the following: • Agricultural practices—farm information such as size, crop types, livestock, decision-making, farming association, and markets. • Household economics—employment, income, expenses, shocks, borrowing and saving habits, and investments.

The Single respondent questionnaire collected the following information: • Mobile phones—attitudes toward phones, use, access, ownership, desire, and importance. • Financial services—attitudes toward financial products and services such as banking and mobile money, including ownership, usage, access and importance.

The questionnaires were translated into Kiswahili and then pretested. After the pretest, debriefing sessions were held with the pretest field staff and the questionnaires were modified based on the observations from the pretest. After the questionnaires were finalized, a script was developed to support data collection on mobile phones. The script was tested and validated before it was use in the field.

Cleaning operations

The data files were checked for completeness, inconsistencies and errors by InterMedia and corrections were made as necessary and where possible.

Response rate

The tables in the user guide to this data set show household and individual response rates for the Tanzania smallholder household survey. A total of 3,503 households was selected for the survey, of which 3,020 were found to be occupied during data collection. Of these, 2,993 were successfully interviewed, yielding a household response rate of 99.1 percent.

In the interviewed households 5,935 eligible household members were identified for the Multiple Respondent questionnaire. Interviews were completed with 5,034 eligible household members, yielding a response rate of 84.8 percent for the Multiple Respondent questionnaire.

Among the 2,993 eligible household members selected for the Single Respondent questionnaire, 2,795 were successfully interviewed a response rate of 93.4 percent.

Sampling error estimates

The sample design for the smallholder household survey was a complex sample design featuring clustering, stratification and unequal probabilities of selection. For key survey estimates, sampling errors taking into account the design features were produced using either the SPSS Complex Sample module or STATA based on the Taylor series approximation method.

Abstract

The FinScope survey tool was developed by FinMark Trust as a nationally representative survey of consumer perceptions about financial services and issues. FinScope provides insights into how people source their income and manage their financial lives. It looks at the use of, and demand for, financial services as well as attitudes, vulnerability, coping behaviour and consumption patterns. By exploring the use of informal as well as formal financial products, FinScope aims to create a picture of the role that the formal and informal sectors play in a country’s financial market.

Geographic coverage

National Coverage

Analysis unit

Individuals

Universe

The survey covered all usual household residents age 16 and above in Zambia.

Kind of data

Sample survey data [ssd]

Sampling procedure

Much like the previous survey in the series, the Finscope 2005 used a stratified, multi-stage cluster sampling approach. Stratification was done at the province and urban/rural level before cluster sampling was performed using enumerator areas as PSUs. The sampling frame for the survey was developed by the Zambian Central Statistical Office (CSO) based on the 2000 Zambian population census. The survey used three-stage stratified cluster sampling to create the sample population. Stratification was done at the province and urban/rural levels before cluster sampling was performed using enumerator areas as PSUs. 400 EAs were selected with probability proportional to size. Within each EA, 10 households were randomly selected. In the third stage, one eligible (aged 16 and above) member of the household was randomly selected using a Kish grid. Further details of the sampling methodology are provided in Annex 1 of the final report for the 2009 Zambian Finscope.

Mode of data collection

Face-to-face [f2f]

Research instrument

A single questionnaire was used for the Zambian Finscope 2009. The questionnaire design phase included 14 focus group discussions and the facilitation of a stakeholder workshop in August 2009, which was attended by a broad cross-section of stakeholders within the financial sector.

These activities assisted in adapting the FinScope Zambia 2005 questionnaire to include notable developments that had taken place in Zambia’s financial sector and economy as a whole since the first survey was conducted, while at the same time allowing for comparisons with the 2005 data.

The revised questionnaire was translated into Zambia’s seven vernacular languages and included questions on the following topics: • Household information and demographics • Farming and fishing • Income and expenditure • Access to infrastructure • Financial literacy and awareness • Attitudes and perceptions towards finance • Savings • Borrowing • Product penetration and banking • Insurance • Informal finance • Remittances • Psychographics

Cleaning operations

Prior to data capture, a data entry programme was developed, tested and refined using the Census and Survey Processing (CSPro) software package. This process was carried out in close consultation with FinMark Trust to ensure that the survey indicator values could be calculated. Nine data operators and two data supervisors were trained to familiarise them with the data entry programme and also to facilitate their understanding of the questionnaire.

The completed questionnaires were checked before being captured. Data inputting was carried out from November to December 2009, after which the data was cleaned, weighted, validated and converted into Statistical Package for the Social Sciences (SPSS) software. Following the submission of preliminary findings to the FSDP Secretariat by FinMark Trust, a second validation procedure was performed in January using additional population projections and other survey data. This resulted in slight adjustments to the weighting. Further information on the cleaning, weighting and validation process is available in Annex 2 of the 2009 Report.

DataFirst did some editing of the data before release. The original version of the Zambian Finscope 2009 had a large number of geographic variables, many of which ended up being excluded. Some were excluded because they weren't actually usable without a private codebook, others because they could potentially lead to identification of individuals in the dataset. The final set of geographic variables included were province, district, and an urban/rural binary variable.

The original datafile also contained a number of variables that detailed surveyor, supervisor, and visitation information. These were also excluded from the distributed datafile. Also excluded were variables used only for informing placement in the Kish grid.

The Community Credit research project explores pathways for trusted collaboration between credit unions and the communities they serve. To understand the experiences of people historically underserved by the consumer financial services industry, we focused in particular on the lived experience of low-income residents in Southern California. As part of a larger, mixed-methods study, in 2022 we conducted an online survey investigating people’s everyday financial practices, evolving perceptions of trust and risk, and their unmet financial needs. The general population survey data was collected between April 15 and April 22, 2022. The credit union data was collected between May 3 and July 18, 2022. This data set contains the responses of the survey participants after excluding any personally identifying data. All study materials and procedures were approved by the University of California, Irvine Office of Human Research Protections and the Institutional Review Board (protocol ID 20216839)...., Survey data was collected via the Qualtrics platform. The survey contains 52 questions. It was distributed to the general population in zip codes within the counties of Los Angeles and Orange. It was also distributed directly to members of a large credit union headquartered in Orange County (“large†according to NCUA asset classes). Participants were eligible to complete the survey if they live in Orange County or Los Angeles County, are older than 18, and have a combined household income of less than $100,000. Incomplete responses have been removed. The survey yielded 1,370 complete responses (1,213 from the general population participants and 157 from members of the large credit union)., Note that the files do not contain all the responses from the survey questions. Responses that provided potentially identifying information were removed. Survey participants’ gender, education status, employment status, and marital status were removed; data on these elements are provided in aggregate in the readme file. Responses are segmented into two files reflecting participants from the general population (“Gen Pop†) and from the credit union (“CU†).

This data collection is one in a series of financial surveys of consumers conducted annually since 1946. In a nationally representative sample, the head of each family unit was interviewed. Starting in 1966, in order to examine the effect that increased car ownership was having on American families, the data collected in this series were organized so that they could be analyzed by both family unit and car unit. The 1970 data are based on car unit. Survey questions regarding automobiles included number of drivers and car owners in the family, make and model of each car, purchase method, car financing and installment debt, and expectations of car purchases in the coming year. Other questions in the 1970 survey covered the respondent's attitudes toward national economic conditions (e.g., the effect of income tax, Vietnam War involvement, and relations with other communist countries on United States business) and price activity, as well as the respondent's own financial situation. Other questions examined the family unit head's occupation, and the nature and amount of the family's income, debts, liquid assets, changes in liquid assets, savings, investment preferences, and actual and expected purchases of major durables. In addition, the survey explored in detail the subject of housing, e.g., previous and present home ownership, value of respondent's dwelling, and mortgage information. Questions in this survey also focused on life insurance coverage, mutual funds, and credit card use. Personal data include age and education of head, household composition, and occupation.

This survey studied the financial awareness and knowledge of people living in Finland, and their views on financial issues. The survey is based on the OECD financial literacy questionnaire and methodological guidance developed by the International Network on Financial Education (INFE). Data collection was conducted by TNS Gallup Finland. The survey was financed by Academy of Finland (269130), OP Group Research Foundation (OP-Pohjola ryhmän tutkimussäätiö), Foundation for Economic Education (Liikesivistysrahasto), Vaasan Aktiasäätiö, and Finnish Foundation for Share Promotion (Pörssisäätiö). Main themes included management of personal finances, consumption and saving behaviour, insurances, preparedness for retirement, financial knowledge in general, over-indebtness, own financial abilities and trust in financial institutions. The respondents were also asked to evaluate their financial decisions and the influences behind their decisions. The respondents' management of daily finances was charted with questions on whether the respondents planned their personal or household's consumption beforehand (e.g. by making a budget) and what methods they used for planning and monitoring their finances (e.g. online banking). Some questions focused on the financial products (e.g. credit cards, bank accounts, debts, insurances, investments) that the respondents used and where they got information on these products. General attitudes towards personal finance management were studied through statements relating to, for example, paying bills on time, taking risks in life, monitoring of finances and worrying about the future. Ways of dealing with insufficient income to cover costs were also charted (e.g. whether the respondents borrowed food or money from family or friends). Saving behaviour was investigated by asking the respondents, for example, whether they saved money on their disposal account or invested in shares, and for how long they could live on their savings if their main source of income was lost. Attitudes towards and awareness about insurances were studied through statements concerning, for example, the necessity of insurance for families and the understandability of contents and conditions of different insurances. Next, the survey charted whether the respondents were self-employed and how they prepared for retirement (for example, how the respondents used pension insurance for the self-employed (YEL), whether they knew which pension insurances they were entitled to, and whether they thought that they were saving enough money for retirement). The respondents' financial knowledge was assessed by asking questions about the economy and different financial issues (e.g. interest and inflation). Knowledge and experiences about over-indebtness were charted through questions concerning, for example, defaults on debts, payments or repayments and the respondents' own debt situation. The respondents' awareness was examined on whether they knew where they should appeal a decision made by a bank or insurance company if they were not satisfied with it. Finally, the survey charted abilities, satisfaction and trust in financial issues and institutions (e.g. how the respondents would describe their own abilities in making good financial decisions, whether they were satisfied with their life overall, and whether they thought that banks, insurance companies or the justice system could be trusted). Background variables included, among others, gender, age, marital status, household composition, gross annual income of the respondent and household, highest level of education, as well as NUTS2 and NUTS3 regions of residence.

This NeuroVERSE record contains two files: ALSFocus_Wave1_Public.csv – This data file contains responses from both the Demographics survey and the survey on Insurance Needs and Financial Burdens, which were fielded in Wave 1 of ALS Focus. This file includes responses collected from February 13, 2020 through April 3, 2020, the launch date and close date of Wave 1, respectively. The file uses a ‘flattened’ format with one record per subject. Variables are organized across the columns with variable names corresponding to the data dictionary. Subject_ID 199 is a test case and has been removed from the data file as of January 5, 2022. Wave 1_Data Dictionary_Public.xlsx – This file is the data dictionary for surveys fielded in ALS Focus Wave 1, including the Demographics survey and the survey on Insurance Needs and Financial Burdens.

COVID-19-related regulations have impacted the economy and people’s well-being, highlighting the long-standing problem of inequality. This research explored how COVID-19-related restrictive policies, such as a lockdown or social distancing, affected people’s well-being. In Study 1, a cross-sectional online survey (N = 685), we examined the associations between socio-economic characteristics, the number of resources, their relative change, people’s stress levels, and their support of restrictive policies. We found that financial loss due to COVID-19, the number of children at home, and the intensity of restrictive measures were associated with higher stress by restrictive measures. The lower support for restrictive measures was observed among those who experienced financial loss due to COVID-19, had more children at home, less frequently accessed COVID-19-related information in the media, and did not perform self-isolation. Men were generally less supportive of restrictions than women, and the number of new COVID-19 cases was negatively related to the support. Lower stress and higher support for restrictive measures were positively associated with life satisfaction. In Study 2, an experience-sampling survey (Nparticipants = 46, Nresponses = 1112), the participants rated their well-being and level of available resources daily for one month. We observed that daily increases in well-being, characterized by higher life satisfaction and lower levels of stress and boredom, were positively associated with more social communication and being outdoors. In summary, the findings support the resource and demand framework, which states that people with access to resources can better cope with the demands of restrictive policies. Implications for policies and interventions to improve well-being are discussed.

Average: AH: Apartment: Financial Assets data was reported at 126,840.000 KRW th in 2017. This records an increase from the previous number of 120,350.000 KRW th for 2016. Average: AH: Apartment: Financial Assets data is updated yearly, averaging 119,555.000 KRW th from Mar 2010 (Median) to 2017, with 8 observations. The data reached an all-time high of 126,840.000 KRW th in 2017 and a record low of 82,330.000 KRW th in 2010. Average: AH: Apartment: Financial Assets data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.H074: Survey of Household Finances & Living Conditions (SHFLC): Household Assets, Liabilities & Income By The Kind of House.

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. Information is also included from related surveys of pension providers and the earlier such surveys conducted by the Federal Reserve Board. No other study for the country collects comparable information. Data from the SCF are widely used, from analysis at the Federal Reserve and other branches of government to scholarly work at the major economic research centers.The survey has contained a panel element over two periods. Respondents to the 1983 survey were re-interviewed in 1986 and 1989. Respondents to the 2007 survey were re-interviewed in 2009.The study is sponsored by the Federal Reserve Board in cooperation with the Department of the Treasury. Since 1992, data have been collected by the National Opinion Research Center (NORC) at the University of Chicago.

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.

Researchers at Innovations for Poverty Action partnered with the Competition Authority of Kenya (CAK) to conduct a phone-based survey among users of digital financial services (DFS), including mobile money, mobile banking, and mobile loans. The survey covered consumers' experiences with DFS, preferences over service providers and levels of trust, price awareness and transparency, fraud and dispute resolution, and questions measuring financial security during the pandemic. The findings will help CAK develop strategies to protect consumers from fraud and to address and redress complaints. Additionally, results will guide researchers on which consumer protection topics need further investigation.

Abstract

The FinScope survey provides a holistic understanding of how individuals generate an income and how they manage their financial lives. It identifies the factors that drive financial behaviour and those that prevent individuals from using financial products and services. Implementing the FinScope survey over time provides the opportunity to assess whether, and how, a country's situation changes.

The main objective of the FinScope Rwanda 2020 survey was to: • Describe the levels of financial inclusion (i.e. levels of access to financial products and services – both formal and informal) • Describe the landscape of access (i.e. the type of products and services used by financially included individuals) • Identify the drivers of, and barriers to, financial access • Assess trends/changes over time (from 2016) • Stimulate evidence-based dialogue that will lead to effective public and private sector interventions in order to increase and deepen financial inclusion • Provide information on new opportunities for increased financial inclusion and the extent to which financial services are meeting Rwandans’ needs.

Geographic coverage

FinScope data are at the national level coverage

Analysis unit

Basic units of analyisis were individuals and households.

Universe

The target population eligible for FinScope survey is every 16 years old and above resident in selected households

Kind of data

Sample survey data [ssd]

Sampling procedure

FinScope Rwanda 2020 is representative at national, urban/rural and district levels. The sampling frame was provided by National Institute of Statistics. The sample was designed according to the latest Census through the listing information conducted in the selected Enumeration Areas (EA). All households in the selected EAs were listed. As such about 158 386 households were listed. Within each selected EAs, sixteen households were randomly selected from the listed households. Within the selected households, individual respondents were randomly selected using the automated Kish Grid. A total of 12,480 interviews were conducted during September to November 2019.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was drafted in English and translated into Kinyarwanda. An instruction manual was developed in order to guide the enumerators how to use better the questionnaire and conduct interviews.

The modules of the questionnaire to be collected in the FinScope Customer Survey 2020 are as follows: Module A Localization and Identification of the Household Module B Household Register Module C Household Information and Demographics Module D Access to Infrastructure Module E Financial Capacity Module F E-Payments and Mobile Money Module G Money Management – Saving/Investment Module H Money Management – Borrowing Module I Money Management – Risk and Risk Management Module J Money Management – Remittances Module K Banking Module L Informal Products Module M Farming Module N Income and Expenditures Module O General Information

SCE: Credit Availability: Year Ago: Somewhat Easier data was reported at 8.882 % in Apr 2025. This records an increase from the previous number of 7.468 % for Mar 2025. SCE: Credit Availability: Year Ago: Somewhat Easier data is updated monthly, averaging 17.195 % from Jun 2013 (Median) to Apr 2025, with 143 observations. The data reached an all-time high of 24.215 % in Jan 2018 and a record low of 5.976 % in Aug 2022. SCE: Credit Availability: Year Ago: Somewhat 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.

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.

The FinScope 2005 was a nationally representative survey measuring access, usage, perceptions and demand patterns on financial services and issues to create insights into how consumers source their income and manage their financial lives. The survey aimed to establish credible benchmarks and indicators of access, provide insights into regulatory and market obstacles to growth and innovation, and highlight opportunities for policy reform and innovation in product development and delivery.