According to a survey conducted in November 2021 among locals of the ** prefectures in Japan, ** percent of residents in Iwate claimed to possess abundant knowledge in regards to money and financial matters. A closer look at the survey results revealed that the self-perceived financial literacy was the lowest in Yamanashi Prefecture, with ***** percent of the respondents stating that they possessed abundant knowledge with regards to money.

In 2021, ** percent of respondents in Poland believed that children's financial education should start from the age of three to nine.

In 2021, 60 percent of respondents who graduated secondary education or higher in Indonesia reported to own an account at a financial institution. This showed a decrease by two percent since the previous surveyed period.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Tanzania is 1001.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Data and variables description. Visit https://dataone.org/datasets/sha256%3Ab06c49c5192e6e7669ea1345ee457dc29faf4e5ad1b32d8103b882d22baf0b7e for complete metadata about this dataset.

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.

Graph and download economic data for Other Financial Information: Other Money Receipts by Highest Education: College Graduate: Master's, Professional, Doctoral Degree (CXUOTHRMONYLB1409M) from 2012 to 2021 about doctoral degree, receipts, information, professional, tertiary schooling, financial, education, and USA.

The countries of Australia, Denmark, Iceland, Norway, Sweden, Austria, France, Germany have the most gender inclusive financial systems, with *** percent of women owning their own accounts at a mobile-money-service provider in 2021. Such numbers reflect levels of financial and digital literacy, household decision-making power, and financial independence from partners and other household members.

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) are available under SN 8189, geographic indicators for Sweep 8 and 9 (2011 Census Boundaries) are available under SN 8190, and geographic indicators for Sweep 9 (2021 Census Boundaries) are available under SN 9337. The Sweep 1 geography file was previously held under SN 7104.
• Linked Health Administrative Datasets (Hospital Episode Statistics) for financial years 1997-2022 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.

When asked about financial knowledge that they seek, around ***** percent of surveyed Chinese consumers of financial products wanted to know more about the Chinese yuan. Around ***** percent of respondents were interested in learning about personal credit.

AE: Current Education Expenditure: Total: % of Total Expenditure in Public Institutions data was reported at 79.464 % in 2021. This records an increase from the previous number of 73.881 % for 2020. AE: Current Education Expenditure: Total: % of Total Expenditure in Public Institutions data is updated yearly, averaging 73.881 % from Dec 2019 (Median) to 2021, with 3 observations. The data reached an all-time high of 79.464 % in 2021 and a record low of 73.767 % in 2019. AE: Current Education Expenditure: Total: % of Total Expenditure in Public Institutions data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Arab Emirates – Table AE.World Bank.WDI: Social: Education Statistics. Current expenditure is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration).;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Median;

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

According to Cognitive Market Research, the global Personal Finance Management Tools Market size will be USD XX million in 2024 and will expand at a compound annual growth rate (CAGR) of XX% from 2024 to 2033. Market Dynamics Key Driver

Key Drivers for Personal finance management tools market

Increasing investments in the market: The key Driver of Personal Finance Management Tools 

Increasing investment in the market Is driving the growth of financial tools, enhancing accessibility and efficiency in financial planning. The increasing investment in the market especially after Covid-19 had a significant impact on the expansion of the PFM tools market. The pandemic had a positive impact on the increase in savings and investments in the market due to future uncertainties. For instance, the study conducted on U.S. investors who have personal experience with COVID-19, who are in a vulnerable health category, who tested positive, and who know someone in their close circle of friends or family who died because of COVID-19, increase their investments by 12%. The increase in investment in the market is leading to the rise in the demand for personal finance management tools. For instance, as of 2023 about 3% of the Indian population actively invest in the stock market. This number has gradually grown, prominent reason for growth is access to technology and, more people becoming financially aware. According to NSE, more than 120 million investors were registered between 2019 and 2023 indicating a significant rise in Indian Stock Market. In January 2024 alone over 5.4 million new investors joined.

Rising financial literacy fuels the Financial Management tools market 

Financial literacy empowers individuals to make informed financial choices. The financial literacy rate among its young and adult population has been growing due to various factors including the recent advancement in technology and media coverage. Additionally, the policies formed by the government globally are leading to improved literacy rates. • For instance, the expansion of digital financial services has helped decrease the number of adults without access to an account from 2.5 billion in 2011 to 1.4 billion in 2021, with 76% of the global adult population owning an account by 2021. Countries achieving significant progress have implemented large-scale policies, such as India's Aadhaar initiative, which has provided over 1.2 billion residents with universal digital identification, facilitating the opening of Jan Dhan Yojana (JDY) accounts. Leveraging government payments has also been instrumental; for instance, 35% of adults in low-income countries who received government payments opened their first financial account for this purpose.
• For instance, according to survey each person in China, on average, had 10 accounts and 7 cards at the end of 2023. The steps taken by the government had a significant impact on financial literacy leading to financial inclusion which has made people aware about the investment choices available in the market leading to the expansion in the PFM tools market.

Restraints

Security and compliance risks pose challenges for AI-powered financial tools, making data protection crucial to prevent cyber threats and frauds.

AI-powered financial tools can pose privacy and security risks. Personal financial information is sensitive data that can be vulnerable to cyberattacks and data breaches. It's important to use financial tools that have robust security features in place to protect your information and minimize the risk of unauthorized access. The most common scams in PFM tools include phishing, insider trading, money laundering and mortgage fraud. Phishing attacks are a significant threat to the financial sector, with attackers often targeting financial institutions and individuals to steal credentials or financial information. For instance, in 2024, India saw a 175% surge in phishing attacks targeting the financial sector, with over 135,000 incidents reported from January to June. According to SlashNext’s 2024 Phishing Intelligence Report, a substantial 703% surge in credential phishing attacks was also observed in the same period. AI in financial tools presents compliance challenges related to data privacy, security, algorithmic bias, transparency, and accountability, requir...

Uzbekistan Current Education Expenditure: Secondary: % of Total Expenditure in Secondary Public Institutions data was reported at 100.000 % in 2021. Uzbekistan Current Education Expenditure: Secondary: % of Total Expenditure in Secondary Public Institutions data is updated yearly, averaging 100.000 % from Dec 2021 (Median) to 2021, with 1 observations. The data reached an all-time high of 100.000 % in 2021 and a record low of 100.000 % in 2021. Uzbekistan Current Education Expenditure: Secondary: % of Total Expenditure in Secondary Public Institutions data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Uzbekistan – Table UZ.World Bank.WDI: Social: Education Statistics. Current expenditure is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration).;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Median;

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimates (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 colleges, universities, and technical and vocational institutions that participate in federal student financial aid programs under the Higher Education Act of 1965 (as amended). The NCES EDGE program uses address information reported in the annually updated IPEDS directory file to develop point locations for all institutions reported in IPEDS. The point locations in this data layer were developed from the 2021-2022 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.

First, 1,200 financial research reports were collected. Experts in the financial field analyzed these reports, extracted the plain text from the main body, and saved it in the TXT format as the basic unstructured text corpus. Then, the experts and the knowledge graph team studied these corpora together, designed the schema of the knowledge graph from the perspective of financial business, and performed iterative optimization according to the characteristics of the evaluation, and finally, determined that it contained 10 entity types, 6 entity attributes, and 19 relationships between the entities. Subsequently, these corpora were annotated with the help of the annotation system of the Yuanhai Knowledge Graph Platform, which is a product of DataGrand Inc. The annotation system is specifically used for the annotation of the knowledge graph, and supports the annotation of entities, entity attributes, and relationships between entities. Before annotating, all annotators were trained by financial experts to align their understanding of the schema. All annotated data were reviewed by experts, and then, divided into seed KG and evaluation KG, as described in the previous section. Reference：Wang, W.G., et al.: Data set and evaluation of automated construction of financial knowledge graph. Data Intelligence 3(3), (2021). doi: 10.1162/dint_a_00108

The Financial Information of Universities and Colleges Survey was developed to provide financial information (income and expenditures) on all universities and degree-granting colleges in Canada. This information provides for a better understanding of the financial position for that level of education. The survey classifies the type of income received (grants, fees, donations and investments), by funds such as: general operating, sponsored research and capital. Expenditures include general operating, special purpose and trust, sponsored research, ancillary, and capital. Operating expenditures include salaries and benefits, materials and supplies, utilities, externally contracted services, as well as scholarships, bursaries and prizes. These expenses can be further classified by function (instruction and non-sponsored research, library, physical plant and student services). The Canadian Association of University Business Officers (CAUBO) provides financial data on the major degree-granting institutions in Canada. The CAUBO data are an important part of the Statistics Canada's Financial Information of Universities and Colleges Survey. Data are available in MS Excel format, as well as tables and reports available in MS Access format. There are databases provided, these require installation (setup using the .zip file). For current FIUC data refer to Statistics Canada. Access data here