Thailand Borrowing Rate: Finance Companies: 12 Months: Maximum data was reported at 1.850 % pa in Nov 2018. This stayed constant from the previous number of 1.850 % pa for Oct 2018. Thailand Borrowing Rate: Finance Companies: 12 Months: Maximum data is updated monthly, averaging 5.750 % pa from Jan 1982 (Median) to Nov 2018, with 443 observations. The data reached an all-time high of 16.000 % pa in Jun 1998 and a record low of 1.500 % pa in Jul 2009. Thailand Borrowing Rate: Finance Companies: 12 Months: Maximum data remains active status in CEIC and is reported by Bank of Thailand. The data is categorized under Global Database’s Thailand – Table TH.M002: Interest Rates: Finance Companies.

Data analytics maintained its position as the leading AI application among financial services firms in 2024. A 2024 industry survey indicated that ** percent of companies leveraged AI for data analytics, showing modest growth from the previous year. Generative AI experienced the strongest year-over-year adoption increase, becoming the second most widely used AI technology, with more than half of firms either implementing or evaluating the technology. Reflecting this growing embrace of AI solutions, the financial sector's investment in AI technologies continues to surge, with spending projected to reach over ** billion U.S. dollars in 2025 and more than double to *** billion U.S. dollars by 2028. The main benefits of AI in the financial services sector Financial services firms reported that AI delivered the greatest value through operational efficiencies, according to a 2024 industry survey. The technology also provided significant competitive advantages, cited by ** percent of respondents as a key benefit. Enhanced customer experience emerged as the third most important advantage of AI adoption in the sector. Adoption across business segments The integration of AI varies across different areas of financial services. In 2023, operations lead the way with a ** percent adoption rate, closely followed by risk and compliance at ** percent. In customer experience and marketing, voice assistants, chatbots, and conversational AI are the most common AI applications. Meanwhile, financial reporting and accounting dominate AI use in operations and finance.

United States - Delinquency Rate on Loans to Finance Agricultural Production, Banks Ranked 1st to 100th Largest in Size by Assets was 1.68% in January of 2025, according to the United States Federal Reserve. Historically, United States - Delinquency Rate on Loans to Finance Agricultural Production, Banks Ranked 1st to 100th Largest in Size by Assets reached a record high of 14.93 in January of 1987 and a record low of 0.97 in July of 2015. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Delinquency Rate on Loans to Finance Agricultural Production, Banks Ranked 1st to 100th Largest in Size by Assets - last updated from the United States Federal Reserve on June of 2025.

Historical Dataset of High School Of Economics & Finance is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2007-2023),Total Classroom Teachers Trends Over Years (2007-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2007-2023),American Indian Student Percentage Comparison Over Years (2019-2023),Asian Student Percentage Comparison Over Years (2007-2023),Hispanic Student Percentage Comparison Over Years (2007-2023),Black Student Percentage Comparison Over Years (2007-2023),White Student Percentage Comparison Over Years (2007-2023),Two or More Races Student Percentage Comparison Over Years (2019-2023),Diversity Score Comparison Over Years (2007-2023),Free Lunch Eligibility Comparison Over Years (2006-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2006-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2011-2022),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2011-2022),Graduation Rate Comparison Over Years (2012-2022)

Graph and download economic data for Delinquency Rate on Lease Financing Receivables, Banks Ranked 1st to 100th Largest in Size by Assets (DRLFRT100S) from Q1 1987 to Q1 2025 about delinquencies, leases, finance, assets, banks, depository institutions, rate, and USA.

Thailand Borrowing Rate: Finance Companies: Call: Maximum data was reported at 0.700 % pa in Jun 2018. This stayed constant from the previous number of 0.700 % pa for May 2018. Thailand Borrowing Rate: Finance Companies: Call: Maximum data is updated monthly, averaging 3.750 % pa from Jan 1985 (Median) to Jun 2018, with 402 observations. The data reached an all-time high of 13.500 % pa in Jun 1998 and a record low of 0.700 % pa in Jun 2018. Thailand Borrowing Rate: Finance Companies: Call: Maximum data remains active status in CEIC and is reported by Bank of Thailand. The data is categorized under Global Database’s Thailand – Table TH.M002: Interest Rates: Finance Companies.

Graph and download economic data for Charge-Off Rate on Lease Financing Receivables, Banks Not Among the 100 Largest in Size by Assets (CORLFROBN) from Q1 1985 to Q1 2025 about charge-offs, leases, finance, assets, banks, depository institutions, rate, and USA.

This dataset tracks annual asian student percentage from 2007 to 2023 for High School Of Economics & Finance vs. New York and New York City Geographic District # 2 School District

Personal Financial Services Market Outlook

The global personal financial services market size is on a robust growth trajectory, projected to expand from USD 8.5 trillion in 2023 to USD 14.2 trillion by 2032, growing at a CAGR of approximately 5.6% during the forecast period. This market's expansion is primarily driven by the increasing demand for personalized financial advice and management services, amplified by the growing complexity of financial decisions individuals and families face today. The rising awareness about financial planning and the benefits of early investment strategies among younger generations further fuel market growth. As digitalization reshapes the financial landscape, the adoption of technology-driven financial solutions is setting a new benchmark in customer service excellence, enhancing the market's appeal and potential.

One of the significant growth factors in the personal financial services market is the rapidly increasing awareness and importance of financial literacy. Financial literacy empowers individuals to make informed decisions, optimize their savings, and maximize their investment returns. Governments and private institutions worldwide are increasingly investing in financial education programs, which are proving instrumental in driving market expansion. Furthermore, as life expectancy continues to rise, there is a heightened demand for comprehensive retirement planning services. Individuals are seeking more robust financial solutions to ensure they maintain their lifestyle post-retirement, leading to increased uptake of personal financial advisory services.

Technological advancements are another critical driver propelling the personal financial services market. The evolution of fintech has revolutionized how consumers interact with financial service providers, offering enhanced accessibility and convenience. From AI-driven investment advisors to blockchain-based secure transactions, technology is not only streamlining operations but also offering innovative solutions that meet the dynamic needs of modern consumers. The integration of big data analytics in financial services has further enabled personalized financial planning, allowing service providers to offer tailored advice based on individual financial behavior and preferences. This trend is expected to continue, shaping the future of personal financial services with more customized and efficient offerings.

In this evolving landscape, the concept of Financial Escort Service is gaining traction as a unique offering within the personal financial services sector. These services are designed to provide clients with a dedicated financial advisor who acts as a guide through complex financial decisions, much like a personal concierge for one's financial life. The Financial Escort Service aims to enhance client experience by offering personalized attention and tailored advice, ensuring that clients are well-informed and confident in their financial choices. As the demand for bespoke financial solutions grows, this service is becoming increasingly popular among individuals seeking a more intimate and customized approach to managing their finances. By integrating this service, financial institutions can differentiate themselves in a competitive market, offering clients a level of service that goes beyond traditional advisory roles.

Additionally, demographic shifts, such as the growing middle class in emerging economies and the increasing number of high net worth individuals (HNWIs), are contributing significantly to market growth. With more disposable income and a greater need for sophisticated financial management, these demographic groups are seeking out personal financial services tailored to their unique needs. The younger generation, tech-savvy and investment-oriented, is also driving the demand for digital financial platforms that offer comprehensive financial solutions at their fingertips. These demographic trends are not only expanding the customer base for financial services but are also pushing firms to innovate and diversify their service offerings to cater to a broader range of client needs.

Regionally, North America currently leads the personal financial services market, owing to its advanced financial infrastructure and high concentration of financial service providers. However, the Asia Pacific region is anticipated to exhibit the highest growth rate during the forecast period. The rapid development of digital banking solutions and the increasing awareness of financial planning in countries such as China and India are

As of March 2024, JPMorgan Chase Bank was the largest bank in the United States by the number of branches, with ***** branches nationwide. It was followed by Wells Fargo Bank, which operated ***** branches, and Bank of America, with ***** branches. For context, Wells Fargo had approximately three times the number of branches as Lloyds Bank, the leading British bank by branch count. Is the U.S. banking sector stable? The stability of the U.S. banking sector has improved steadily since the aftermath of the 2008 financial crisis. The share of non-performing loans held by U.S. banks has consistently decreased over time. As of the first quarter of 2024, all four of the largest U.S. banks—Wells Fargo, JPMorgan Chase, Bank of America, and Citigroup—maintained a Common Equity Tier 1 (CET1) capital ratio well above the Basel-III minimum requirement of *** percent. The CET1 capital ratio, which measures a bank’s core capital against its risk-weighted assets, is a key indicator of a bank's financial strength and resilience. Digital banking in the U.S. With the rise of digital services, many traditional banking functions can now be performed online, reducing the need for a physical presence. Since 2009, the number of bank branches in the United States has steadily declined as consumers increasingly rely on digital banking solutions. This trend accelerated during the COVID-19 pandemic, with more Americans turning to online banking for convenience and cost-effectiveness.

Inclusive Finance Market Outlook

The global inclusive finance market size was valued at $1.2 trillion in 2023 and is expected to reach $3.6 trillion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. This substantial growth is driven by the increasing need to provide financial services to underserved populations, particularly in developing regions where traditional banking services are scarce.

The growth of the inclusive finance market is largely attributed to the rising penetration of mobile phones and internet connectivity, which have made digital financial services more accessible to remote and underserved areas. With the proliferation of smartphones, even individuals in rural regions can now access financial services directly from their devices, bypassing traditional banking infrastructures. This has created unprecedented opportunities for financial inclusion, enabling millions to participate in the global economy.

Another significant growth factor is the increasing awareness and governmental support for financial inclusion initiatives. Many governments and international organizations are recognizing the importance of inclusive finance in fostering economic growth and reducing poverty. Policies and regulations are being tailored to encourage financial institutions to extend their services to the unbanked population. Moreover, financial literacy programs are being implemented to educate consumers about the benefits and usage of financial products and services.

Technological advancements in fintech have also played a crucial role in the expansion of inclusive finance. Blockchain technology, artificial intelligence, and machine learning are being utilized to develop innovative financial products that cater to the unique needs of underserved populations. These technologies enable more efficient and secure transactions, personalized financial services, and better risk management, thereby increasing the trust and confidence of users in digital financial solutions.

In terms of regional outlook, Asia Pacific is anticipated to witness the highest growth rate in the inclusive finance market, driven by large unbanked populations in countries like India, Indonesia, and the Philippines. North America and Europe are also expected to see significant growth, due to the high adoption rate of digital financial services and supportive regulatory frameworks. Latin America and the Middle East & Africa regions are poised to grow as well, but at a comparatively slower pace due to existing economic challenges and infrastructural limitations.

Product Type Analysis

The product type segment of the inclusive finance market encompasses various financial products such as microloans, savings accounts, insurance, payment systems, and others. Microloans are particularly significant in this segment, as they provide small amounts of capital to individuals and small businesses that lack access to traditional banking services. Microloans have been instrumental in empowering entrepreneurs in developing countries by providing the necessary funds to start or expand their businesses, ultimately contributing to economic development and poverty alleviation.

Savings accounts are another critical product type in the inclusive finance market. These accounts offer a safe and secure place for individuals to store their money, earn interest, and manage their finances more effectively. For many people in underserved regions, savings accounts provide a crucial entry point into the formal financial system, helping them to build financial stability and plan for the future.

Insurance products within the inclusive finance market help to mitigate risks and provide financial protection to low-income individuals and small businesses. Microinsurance, for example, offers affordable coverage for health, life, and property, helping to safeguard against unforeseen circumstances that could otherwise result in financial ruin. By offering accessible insurance solutions, the inclusive finance market plays a vital role in enhancing the resilience of vulnerable populations.

Payment systems are also a key component of the inclusive finance market. Digital payment platforms, mobile wallets, and other electronic payment solutions facilitate seamless and secure transactions, making it easier for individuals and businesses to conduct financial activities. By reducing the reliance on cash and increasing the efficiency of transactions, payment systems contribute to greater financial inclusion and economic

The industry is composed of non-depository institutions that conduct primary and secondary market lending. Operators in this industry include government agencies in addition to non-agency issuers of mortgage-related securities. Through 2025, rising per capita disposable income and low levels of unemployment helped fuel the increase in primary and secondary market sales of collateralized debt. Nonetheless, due to the pandemic and the sharp contraction in economic activity in 2020, revenue gains were limited, but have climbed as the economy has normalized and interest rates shot up to tackle rampant inflation. However, in 2024 the Federal Reserve cut interest rates as inflationary pressures eased and is expected to be cut further in 2025. Overall, these trends, along with volatility in the real estate market, have caused revenue to slump at a CAGR of 1.5% to $485.0 billion over the past five years, including an expected decline of 1.1% in 2025 alone. The high interest rate environment has hindered real estate loan demand and caused industry profit to shrink to 11.6% of revenue in 2025. Higher access to credit and higher disposable income have fueled primary market lending over much of the past five years, increasing the variety and volume of loans to be securitized and sold in secondary markets. An additional boon for institutions has been an increase in interest rates in the latter part of the period, which raised interest income as the spread between short- and long-term interest rates increased. These macroeconomic factors, combined with changing risk appetite and regulation in the secondary markets, have resurrected collateralized debt trading since the middle of the period. Although the FED cut interest rates in 2024, this will reduce interest income for the industry but increase loan demand. Although institutions are poised to benefit from a strong economic recovery as inflationary pressures ease, relatively steady rates of homeownership, coupled with declines in the 30-year mortgage rate, are expected to damage the primary market through 2030. Shaky demand from commercial banking and uncertainty surrounding inflationary pressures will influence institutions' decisions on whether or not to sell mortgage-backed securities and commercial loans to secondary markets. These trends are expected to cause revenue to decline at a CAGR of 0.8% to $466.9 billion over the five years to 2030.

An important indicator of the financial strength of governmental entity is its bond rating. The bond rating is similar in nature to the credit score of an individual – the higher the score, the better the ability to borrow money to finance purchases at a lower interest rate. Similarly, the higher the bond rating for a governmental entity, the more opportunities to borrow money for capital needs at lower interest rates. A high bond rating is in excellent indicator of the overall financial health of a government.This measure is obtained each year when the city seeks to issue bonds to finance its’ projects. As part of this process, bond ratings are always obtained from the rating agencies: Standard & Poor’s. Fitch Ratings and Moody's Investor Service.This page provides data for the Bond Rating performance measure.Bond ratings are a reflection of the financial strength of an entity. A high rating means an entity can issue bonds to finance capital projects at lower interest rates; lower rates result in less interest to be paid on the repayment of the bonds. Ultimately, this lowers the costs of our capital projects to our taxpayers.The performance measure dashboard is available at 5.04 Bond Rating.Additional InformationSource: Standard & Poors, Moody's Investor Service, and Fitch Ratings are the major bond rating agencies in the United States and are widely used by governmental and non-governmental entities throughout the country.Contact: Jerry HartContact E-Mail: Jerry_Hart@tempe.govData Source Type: ExcelPreparation Method: ManualPublish Frequency: AnnuallyPublish Method: ManualData Dictionary

Brazil Lending Rate: per Annum: Pre-Fixed: Corporate Entities: Overdraft: HSBC Finance Brasil S.A. Banco Multiplo data was reported at 0.000 % pa in 03 Jul 2019. This stayed constant from the previous number of 0.000 % pa for 02 Jul 2019. Brazil Lending Rate: per Annum: Pre-Fixed: Corporate Entities: Overdraft: HSBC Finance Brasil S.A. Banco Multiplo data is updated daily, averaging 0.000 % pa from Jan 2012 (Median) to 03 Jul 2019, with 1866 observations. The data reached an all-time high of 0.000 % pa in 03 Jul 2019 and a record low of 0.000 % pa in 03 Jul 2019. Brazil Lending Rate: per Annum: Pre-Fixed: Corporate Entities: Overdraft: HSBC Finance Brasil S.A. Banco Multiplo data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Interest and Foreign Exchange Rates – Table BR.MB041: Lending Rate: per Annum: by Banks: Pre-Fixed: Corporate Entities: Overdraft. Lending Rate: Daily: Interest rates disclosed represent the total cost of the transaction to the client, also including taxes and operating. These rates correspond to the average fees in the period indicated in the tables. There are presented only institutions that had granted during the period determined. In general, institutions practicing different rates within the same type of credit. Thus, the rate charged to a customer may differ from the average. Several factors such as the time and volume of the transaction, as well as the guarantees offered, explain the differences between interest rates. Certain institutions grant allowance of the use of the term overdraft. However, this is not considered in the calculation of rates of this type. It should be noted that the overdraft is a modality that has high interest rates. Thus, its use should be restricted to short periods. If the customer needs resources for a longer period, should find ways to offer lower rates. The Brazilian Central Bank publishes these data with a delay about 20 days with relation to the reference period, thus allowing sufficient time for all Financial Institutions to deliver the relevant information. Interest rates presented in this set of tables correspond to averages weighted by the values of transactions conducted in the five working days specified in each table. These rates represent the average effective cost of loans to customers, consisting of the interest rates actually charged by financial institutions in their lending operations, increased tax burdens and operational incidents on the operations. The interest rates shown are the average of the rates charged in the various operations performed by financial institutions, in each modality. In one discipline, interest rates may differ between customers of the same financial institution. Interest rates vary according to several factors, such as the value and quality of collateral provided in the operation, the proportion of down payment operation, the history and the registration status of each client, the term of the transaction, among others . Institutions with “zero” did not operate on modalities for those periods or did not provide information to the Central Bank of Brazil. The Central Bank of Brazil assumes no responsibility for delay, error or other deficiency of information provided for purposes of calculating average rates presented in this

This data collection contains information on finances for a sample of postsecondary institutions in the United States. Data on financial characteristics of postsecondary institutions are taken from Finance and Consolidated surveys, collected annually. The finance data are used for reporting and projecting capital outlays of two-year and four-year colleges and universities, trends in replacements of plant assets, and performance of endowment funds. Part 1, Institutional Characteristics, includes variables on control and level of institution, religious affiliation, highest level of offering, Carnegie classification, and state FIPS codes and abbreviations. Part 2, Current Funds Revenues by Source (Part A of the survey), provides each institution's current fund revenues by source (e.g., tuition and fees, government, gifts). Part 3, Current Funds Expenditures by Function (Part B), covers expenditures for instruction, research, and plant maintenance. Part 4, Clarifying Questions (Part C), contains information on total E&G revenues and expenditures to determine what is included/excluded from reported current fund expenditures. Part 5, Clarifying Question 5 (Part C5), lists excluded financial activities by subentities. Part 6, Utility Expenditures (Part D), reports all expenditures for utilities in the operation and maintenance of the plant, auxiliary enterprises, and independent operations, excluding expenditures for hospitals. Part 7, Scholarships and Fellowship Expenditures (Part E), covers scholarships, defined as grant-in-aid, trainee stipends, tuition and fee waivers, prizes to undergraduate students, and fellowships given to graduate students. Part 8, Expenditures for Library Acquisitions (Part F), covers costs involved in acquisition of library materials. Part 9, Indebtedness on Physical Plant (Part G), reports data on indebtedness liability against the physical plant, including auxiliary enterprises facilities as well as educational and general facilities, and excluding debt issued and backed by the state government. Part 10, Details of Endowment Assets (Part H), provides information on the amounts of gross investments of endowment, term endowment, and funds functioning as endowment for the institution, and any of its foundations and other affiliated organizations. Part 11, Selected Funds Balances (Part I), includes both unrestricted and restricted funds balances. Part 12, Hospital Revenues (Part J), reports the revenues for, or generated by, major public service hospitals over which the institution has fiscal control (excluding medical schools). Part 13, Physical Plant Assets (Part K), reports the values of land, buildings, and equipment owned, rented, or used by the institution. Part 14, Consolidated Form (CN) data (Part CN), includes revenues from tuition and fees, federal, state, and local grants, contracts, and sales of educational services. It also includes instructional expenditures, scholarships, and fellowships by source of financial aid.

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 Ecuador is 1000.

Mode of data collection

Landline and mobile telephone

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.

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

Some communities in the East and Sahel regions were excluded for security reasons. The areas represent 4 percent of the total population.

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 Burkina Faso is 1000.

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.

This dataset tracks annual graduation rate from 2012 to 2022 for Food And Finance High School vs. New York and New York City Geographic District # 2 School District

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

Northwest Territories, Yukon, and Nunavut (representing approximately 0.3 percent of the Canadian population) were excluded.

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 Canada is 1007.

Mode of data collection

Landline and mobile telephone

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.

Regardless of their operational focus, almost all industry players have recorded consistent sales growth since 2019, albeit with high costs that are reflected in a negative profit margin. The coronavirus pandemic in particular helped the industry to increase confidence, as consumers refrained from making cash payments for hygiene reasons and increasingly used electronic means of payment instead. Industry turnover has risen by an average of 11.7% annually since 2019 and is expected to amount to 1.9 billion euros in the current year.In the current year, industry players operating in the financing product segment continue to benefit from the high base rate, as traditional financing options at credit institutions, savings banks and cooperative banks are associated with high interest rates for consumers. At the same time, growth in the asset management product segment is likely to be lower than during the period of low interest rates, as many consumers prefer the combination of high interest rates and personal advice on site, are sceptical about technological innovations and, with rising interest rates on traditional savings products, are more likely to use the services of their main bank than the products and services of fintechs with slightly higher interest rates on average, but without the personal component. At 2.5%, industry turnover is likely to grow less strongly this year than last year.For the period from 2024 to 2029, IBISWorld forecasts an average increase in turnover of 7.9% per year to 2.8 billion euros. In addition to the degree of digitalisation, the future development of the industry is primarily dependent on the development of the business climate, as companies form the most important customer market. Demographic change is likely to slow down sales growth, as many older people lack the technical affinity for dealing with online payment and financing methods. Internationalisation and the expansion of business activities to the European domestic market offer opportunities for growth.