This statistic presents the level of self assessed financial literacy in the United States in 2017, by age-group. During the survey period, 50 percent of respondents, aged between 18 and 29 years, admitted that they were somewhat financially literate.

The 2017 National Financial Well-Being in America Survey, conducted for the CFPB Offices of Financial Education and Financial Protection for Older Americans, was an online survey conducted to measure the financial well-being of adults in the United States. These data were created as a foundation for internal and external research into financial well-being and are relevant to work being done by researchers in the Office of Research who have access to the (deidentified) data.

About ** percent of Indians surveyed who had a secondary education or more owned an account in a financial institution in the country. This had increased significantly among primary or lesser educated Indians during the same time period, from ** percent in 2011 to ** percent in 2017.

This layer shows education levels. Counts are broken down by sex. Data is from US Census American Community Survey (ACS) 5-year estimates.Data shown in this layer is a percentage of total households.This layer is symbolized by the percentage of adults (25+) who were not high school graduates. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). To view only the census tracts that are predominantly in Tempe, add the expression City is Tempe in the map filter settings.A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2017-2021ACS Table(s): B15002 (Not all lines of these ACS tables are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Data Preparation: Data curated from Esri Living Atlas clipped to Census Tract boundaries that are within or adjacent to the City of Tempe boundaryDate of Census update: December 8, 2022National Figures: data.census.govAdditional Census data notes and data processing notes are available at the Esri Living Atlas Layer:https://tempegov.maps.arcgis.com/home/item.html?id=84e3022a376e41feb4dd8addf25835a3

In a survey on financial knowledge and education conducted in 2017, 79 percent of Italians answered that they would find it interesting being able to improve their knowledge about saving, investments, banking and payment protection. Moreover, 42 percent said they would be interested in knowing more about mortgage and personal loans. Finally, only three percent of the respondents declared that they were not interested in improving their financial knowledge.

Financial knowledge needs improving

The results of the survey also revealed that Italians seemed to be lacking an acceptable level of general financial knowledge. In fact, 53 percent of the respondents admitted that they were not very well informed when talking about banking and financial products, while only seven percent of the people interviewed said they were very well informed on the topics. Moreover, among those aware of their lack of financial knowledge, 55 percent said that they would like to know more about the topic but wouldn’t be willing to pay to learn more.

Latest financial products are even more obscure

Due to innovation and technological development, new products and players have entered the banking and financial sector. When talking about the latest developments in the sector, Italians seem to be even more unaware. For example, another survey showed that 66 percent of Italians seemed to have never heard of cryptocurrencies. Moreover, 51 percent of the respondents had no idea what crowdfunding was.

Analysis of ‘State Education Resource Center Checkbook Financial Data FY 2017 – ARCHIVE’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/07613b6b-a607-44e2-a944-b411ece494ab on 26 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset will be removed from data.ct.gov on 11/3/2021 as part of efforts to declutter and improve the quality of the Open Data Portal. Users that need access to this asset should download the asset or contact site administrators. More information about the data retirement process can be found here: https://data.ct.gov/dataset/Assets-retired-from-data-ct-gov/i9r5-65tv

--- Original source retains full ownership of the source dataset ---

The FY 2017 System-Wide Report is the report of final DOE expenses. FY 2017 began on July 1, 2016 and ended on June 30, 2017. The financial data used in the FY 2017 System-Wide Report represents the DOE’s 2017 year-end audited spending condition. In addition to using the audited school registers as of October 31, 2016 for pupil counts, pupil enrollment data has been refined to count students with disabilities with Individual Education Programs (IEPs) for specialized classroom instruction based on their program recommendations as of December 31, 2016.

Lithuania Enterprises: CG: Education data was reported at 12,097.000 EUR th in Mar 2018. This records a decrease from the previous number of 18,239.000 EUR th for Dec 2017. Lithuania Enterprises: CG: Education data is updated quarterly, averaging 7,856.000 EUR th from Mar 2005 (Median) to Mar 2018, with 53 observations. The data reached an all-time high of 18,239.000 EUR th in Dec 2017 and a record low of 2,116.000 EUR th in Mar 2005. Lithuania Enterprises: CG: Education 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.

The National Center for Education Statistics' (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated point locations (latitude and longitude) for postsecondary institutions included in the NCES Integrated Postsecondary Education Data System (IPEDS). The IPEDS program annually collects information about enrollments, program completions, graduation rates, faculty and staff, finances, institutional prices, and student financial aid from every college, university, and technical and vocational institution that participates in federal student financial aid programs under the Higher Education Act of 1965 (as amended). IPEDS school point locations are derived from reported information about the physical location of schools. The NCES EDGE program collaborates with the U.S. Census Bureau's Education Demographic, Geographic, and Economic Statistics (EDGE) Branch to develop point locations for schools reported in the annual IPEDS file. The point locations in this data layer were developed from the 2017-2018 IPEDS collection. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations. All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

Abstract copyright UK Data Service and data collection copyright owner.

Next Steps (also known as the Longitudinal Study of Young People in England (LSYPE1)) is a major longitudinal cohort study following a nationally representative group of around 16,000 who were in Year 9 attending state and independent schools in England in 2004, a cohort born in 1989-90.

The first seven sweeps of the study were conducted annually (2004-2010) when the study was funded and managed by the Department for Education (DfE). The study mainly focused on the educational and early labour market experiences of young people.

In 2015 Next Steps was restarted, under the management of the Centre for Longitudinal Studies (CLS) at the UCL Faculty of Education and Society (IOE) and funded by the Economic and Social Research Council. The Next Steps Age 25 survey was aimed at increasing the understanding of the lives of young adults growing up today and the transitions out of education and into early adult life.

The Next Steps Age 32 Survey took place between April 2022 and September 2023 and is the ninth sweep of the study. The Age 32 Survey aimed to provide data for research and policy on the lives of this generation of adults in their early 30s. This sweep also collected information on many wider aspects of cohort members' lives including health and wellbeing, politics and social participation, identity and attitudes as well as capturing personality, resilience, working memory and financial literacy.

Next Steps survey data is also linked to the National Pupil Database (NPD), the Hospital Episode Statistics (HES), the Individualised Learner Records (ILR) and the Student Loans Company (SLC).

There are now two separate studies that began under the LSYPE programme. The second study, Our Future (LSYPE2) (available at the UK Data Service under GN 2000110), began in 2013 and will track a sample of over 13,000 young people annually from ages 13/14 through to age 20.

Further information about Next Steps may be found on the CLS website.

Secure Access datasets:

Secure Access versions of Next Steps have more restrictive access conditions than Safeguarded versions available under the standard End User Licence (see 'Access' section).

Secure Access versions of the Next Steps include:

• sensitive variables from the questionnaire data for Sweeps 1-9. These are available under Secure Access SN 8656.
  
• National Pupil Database (NPD) linked data at Key Stages 2, 3, 4 and 5, England. These are available under SN 7104.
• Linked Individualised Learner Records learner and learning aims datasets for academic years 2005 to 2014, England. These are available under SN 8577.
  
• detailed geographic indicators for Sweep 1 and Sweep 8 (2001 Census Boundaries) - available under SN 8189 and geographic indicators for Sweep 8 (2011 Census Boundaries) - available under SN 8190. The Sweep 1 geography file was previously held under SN 7104.
• Linked Health Administrative Datasets (Hospital Episode Statistics) for years 1998-2017 held under SN 8681.
• Linked Student Loans Company Records for years 2007-2021 held under SN 8848.

When researchers are approved/accredited to access a Secure Access version of Next Steps, the Safeguarded (EUL) version of the study - Next Steps: Sweeps 1-9, 2004-2023 (SN 5545) - will be automatically provided alongside.

The Student Loans Company (SLC) is a non-profit making government-owned organisation that administers loans and grants to students in colleges and universities in the UK. The Next Steps: Linked Administrative Datasets (Student Loans Company Records), 2007 - 2021: Secure Access includes data on higher education loans for those Next Steps participant who provided consent to SLC linkage in the age 25 sweep. The matched SLC data contains information about participant's applications for student finance, payment transactions posted to participant's accounts, repayment details and overseas assessment details.

The study includes four datasets:

Applicant: SLC data on cohort member’s application for student finance between academic years 2007 and 2020

Payments: SLC data on payment transactions made to cohort member between financial years 2007 and 2021.

Repayments: SLC data on cohort member’s repayment transactions between financial years 2009 and 2021.

Overseas: SLC data on overseas assessment for cohort member between 2007 and 2020

In financial year 2024, the financial inclusion index of India was ****, according to the Reserve Bank of India. It rose from **** in 2017 to its current state, indicating improved financial inclusion. The financial inclusion index measures the extent of access to and usage of formal financial services, including banking, insurance, investments, pensions, and postal sectors.

The financial reports for the department assist assessments of forecast financial performance, and its use of the parliamentary authority for resources.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

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

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world's population(see Table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 1000.

Mode of data collection

Computer Assisted Personal Interview [capi]

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

CN: Cultural, Education, Art, Craft, Sport & Recreational Product: State Holding: Total Asset: Current data was reported at 18,873.000 RMB mn in 2017. This records a decrease from the previous number of 23,253.000 RMB mn for 2016. CN: Cultural, Education, Art, Craft, Sport & Recreational Product: State Holding: Total Asset: Current data is updated yearly, averaging 18,934.500 RMB mn from Dec 2012 (Median) to 2017, with 6 observations. The data reached an all-time high of 23,253.000 RMB mn in 2016 and a record low of 15,316.000 RMB mn in 2012. CN: Cultural, Education, Art, Craft, Sport & Recreational Product: State Holding: Total Asset: Current data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BEA: Industrial Financial Data: State Holding Enterprise: Manufacturing: Cultural, Education, Art, Craft, Sport and Recreational Product.

Lithuania Enterprises: Equity: Education data was reported at 56,420.000 EUR th in Mar 2018. This records a decrease from the previous number of 69,395.000 EUR th for Dec 2017. Lithuania Enterprises: Equity: Education data is updated quarterly, averaging 52,020.000 EUR th from Mar 2005 (Median) to Mar 2018, with 53 observations. The data reached an all-time high of 74,024.000 EUR th in Mar 2017 and a record low of 13,273.000 EUR th in Jun 2006. Lithuania Enterprises: Equity: Education 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.

Russia Financial Investment: OKVED2: Year to Date: Education data was reported at 124.300 RUB bn in Dec 2018. This records an increase from the previous number of 97.600 RUB bn for Sep 2018. Russia Financial Investment: OKVED2: Year to Date: Education data is updated quarterly, averaging 82.750 RUB bn from Mar 2017 (Median) to Dec 2018, with 8 observations. The data reached an all-time high of 166.100 RUB bn in Dec 2017 and a record low of 1.200 RUB bn in Mar 2017. Russia Financial Investment: OKVED2: Year to Date: Education data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.OA002: Financial Investment: ytd: by Economic Activity.

Lithuania Enterprises: Operating Profit or Loss: Education data was reported at 3,236.000 EUR th in Mar 2018. This records an increase from the previous number of -3,975.000 EUR th for Dec 2017. Lithuania Enterprises: Operating Profit or Loss: Education data is updated quarterly, averaging 1,117.000 EUR th from Mar 2005 (Median) to Mar 2018, with 53 observations. The data reached an all-time high of 7,737.000 EUR th in Mar 2010 and a record low of -7,547.000 EUR th in Sep 2017. Lithuania Enterprises: Operating Profit or Loss: Education 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.

Lithuania Enterprises: FI: Education data was reported at -221.000 EUR th in Mar 2018. This records a decrease from the previous number of 8.000 EUR th for Dec 2017. Lithuania Enterprises: FI: Education data is updated quarterly, averaging 19.000 EUR th from Mar 2005 (Median) to Mar 2018, with 53 observations. The data reached an all-time high of 1,070.000 EUR th in Mar 2008 and a record low of -2,207.000 EUR th in Dec 2008. Lithuania Enterprises: FI: Education 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.