Hong Kong: Financial openness index: The latest value from 2022 is 2.29 index points, unchanged from 2.29 index points in 2021. In comparison, the world average is 0.307 index points, based on data from 174 countries. Historically, the average for Hong Kong from 1970 to 2022 is 2.254 index points. The minimum value, 1.092 index points, was reached in 1971 while the maximum of 2.29 index points was recorded in 1972.

Hong Kong PE Ratio: Hang Seng Index: Finance Sector data was reported at 9.660 Unit in Oct 2018. This records a decrease from the previous number of 10.680 Unit for Sep 2018. Hong Kong PE Ratio: Hang Seng Index: Finance Sector data is updated monthly, averaging 13.165 Unit from Jan 1986 (Median) to Oct 2018, with 394 observations. The data reached an all-time high of 27.330 Unit in Oct 2007 and a record low of 7.250 Unit in Feb 2016. Hong Kong PE Ratio: Hang Seng Index: Finance Sector data remains active status in CEIC and is reported by Hong Kong Exchanges and Clearing Limited. The data is categorized under Global Database’s Hong Kong SAR – Table HK.Z003: Main Board: PE Ratio.

Hong Kong Real Wage Index: Financial & Insurance Activities data was reported at 127.700 Sep1992=100 in Mar 2018. This records a decrease from the previous number of 129.000 Sep1992=100 for Sep 2017. Hong Kong Real Wage Index: Financial & Insurance Activities data is updated semiannually, averaging 127.100 Sep1992=100 from Mar 2004 (Median) to Mar 2018, with 29 observations. The data reached an all-time high of 137.900 Sep1992=100 in Sep 2011 and a record low of 122.600 Sep1992=100 in Mar 2015. Hong Kong Real Wage Index: Financial & Insurance Activities data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong – Table HK.G104: Nominal and Real Wage Index: by Industry: HSIC 2.0.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 2597 companies listed on the Hong Kong Stock Exchange (XHKG) in Hong Kong. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

Top 5 used data fields in the End-of-Day Pricing Dataset for Hong Kong:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Hong Kong:

Hang Seng Index: The main index that tracks the performance of major companies listed on the Hong Kong Stock Exchange. This index provides an overview of the overall market performance in Hong Kong.

Hang Seng China Enterprises Index (HSCEI): The index that tracks the performance of mainland Chinese companies listed on the Hong Kong Stock Exchange. This index reflects the performance of Chinese companies with significant operations in Hong Kong.

Company A: A prominent Hong Kong-based company with diversified operations across various sectors, such as finance, real estate, or retail. This company's stock is widely traded on the Hong Kong Stock Exchange.

Company B: A leading financial institution in Hong Kong, offering banking, insurance, or investment services. This company's stock is actively traded on the Hong Kong Stock Exchange.

Company C: A major player in the Hong Kong property development or other industries, involved in the construction and management of real estate projects. This company's stock is listed and actively traded on the Hong Kong Stock Exchange.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Hong Kong, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Hong Kong ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Hong Kong?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Hong Kong exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct tr...

Hong Kong Nominal Salary Index (A): Jun83=100: Financial Management data was reported at 385.700 Jun1983=100 in 1995. This records an increase from the previous number of 347.600 Jun1983=100 for 1994. Hong Kong Nominal Salary Index (A): Jun83=100: Financial Management data is updated yearly, averaging 214.750 Jun1983=100 from Jun 1984 (Median) to 1995, with 12 observations. The data reached an all-time high of 385.700 Jun1983=100 in 1995 and a record low of 111.000 Jun1983=100 in 1984. Hong Kong Nominal Salary Index (A): Jun83=100: Financial Management data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR – Table HK.G108: Nominal and Real Salary Index: Middle Level Managerial and Professional Employees: By Occupational Group.

Hong Kong SAR (China) Hong Kong Stock Exchange: Index: Hang Seng Finance Index data was reported at 38,825.970 NA in Mar 2025. This records an increase from the previous number of 38,468.660 NA for Feb 2025. Hong Kong SAR (China) Hong Kong Stock Exchange: Index: Hang Seng Finance Index data is updated monthly, averaging 33,333.035 NA from Jun 2013 (Median) to Mar 2025, with 142 observations. The data reached an all-time high of 45,681.410 NA in Jan 2018 and a record low of 24,424.380 NA in Oct 2022. Hong Kong SAR (China) Hong Kong Stock Exchange: Index: Hang Seng Finance Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s Hong Kong SAR (China) – Table HK.EDI.SE: Hong Kong Stock Exchange: Monthly.

Hong Kong: Financial markets development: The latest value from 2021 is 0.696 index points, a decline from 0.744 index points in 2020. In comparison, the world average is 0.239 index points, based on data from 158 countries. Historically, the average for Hong Kong from 1980 to 2021 is 0.584 index points. The minimum value, 0.149 index points, was reached in 1982 while the maximum of 0.822 index points was recorded in 1997.

Graph and download economic data for Consumer Price Index for Hong Kong SAR, China (DDOE02HKA086NWDB) from 1980 to 2017 about Hong Kong, CPI, price index, indexes, and price.

Graph and download economic data for Volatility of Stock Price Index for Hong Kong SAR, China (DDSM01HKA066NWDB) from 1984 to 2021 about Hong Kong, volatility, stocks, price index, indexes, and price.

Graph and download economic data for NASDAQ Hong Kong Financials Index (NASDAQNQHK30) from 2001-03-30 to 2025-11-07 about Hong Kong, NASDAQ, financial, indexes, and USA.

Hong Kong Nominal Wage Index: Qtr: Financial & Insurance Activities data was reported at 242.100 Sep1992=100 in Jun 2018. This records an increase from the previous number of 239.400 Sep1992=100 for Mar 2018. Hong Kong Nominal Wage Index: Qtr: Financial & Insurance Activities data is updated quarterly, averaging 188.000 Sep1992=100 from Mar 2004 (Median) to Jun 2018, with 58 observations. The data reached an all-time high of 242.100 Sep1992=100 in Jun 2018 and a record low of 156.000 Sep1992=100 in Mar 2004. Hong Kong Nominal Wage Index: Qtr: Financial & Insurance Activities data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong – Table HK.G104: Nominal and Real Wage Index: by Industry: HSIC 2.0.

Techsalerator's Corporate Actions Dataset in Hong Kong offers a comprehensive collection of data fields related to corporate actions, providing valuable insights for investors, traders, and financial institutions. This dataset includes crucial information about the various financial instruments of all 2597 companies traded on the Hong Kong Stock Exchange (XHKG).

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Top 5 used data fields in the Corporate Actions Dataset for Hong Kong:

• Dividend Declaration Date: The date on which a company's board of directors announces the dividend payout to its shareholders. This information is crucial for investors who rely on dividends as a source of income.

• Stock Split Ratio: The ratio by which a company's shares are split to increase liquidity and affordability. This field is essential for understanding changes in share structure.

• Merger Announcement Date: The date on which a company officially announces its intention to merge with another entity. This field is crucial for investors assessing the impact of potential mergers on their investments.

• Rights Issue Record Date: The date on which shareholders must be on the company's books to be eligible for participating in a rights issue. This data helps investors plan their participation in fundraising events.

• Bonus Issue Ex-Date: The date on which a company's shares start trading without the value of the bonus issue. This information is vital for investors to adjust their portfolios accordingly.

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Top 5 corporate actions in Hong Kong:

Initial Public Offerings (IPOs): Hong Kong is a major hub for IPOs, with numerous companies seeking to list on the Hong Kong Stock Exchange (HKEX). Corporate actions related to IPOs contribute to the city's status as a global financial center.

Global Investment and Finance: Hong Kong's role as a gateway to China and an international financial center leads to corporate actions involving global investment firms, banks, and financial services providers.

Real Estate and Property Development: Corporate actions related to real estate development, property investment, and construction projects are significant in Hong Kong, given its high property prices and active real estate market.

Technology and Innovation: Corporate actions in the technology sector, including investments in startups, venture capital funding, and collaborations, contribute to Hong Kong's efforts to become a regional tech hub.

Sustainable Finance Initiatives: Hong Kong's commitment to sustainable finance leads to corporate actions involving green bonds, sustainable investments, and initiatives to promote environmental, social, and governance (ESG) practices.

Top 5 financial instruments with corporate action Data in Hong Kong

Hong Kong Stock Exchange (HKEX) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Hong Kong Stock Exchange. This index would provide insights into the performance of the Hong Kong stock market.

Hong Kong Stock Exchange (HKEX) Foreign Company Index: The index that tracks the performance of foreign companies listed on the Hong Kong Stock Exchange, if foreign listings were present. This index would give an overview of foreign business involvement in Hong Kong.

HongMart: A Hong Kong-based supermarket chain with operations in multiple regions. HongMart focuses on providing essential products to local communities and contributing to the retail sector's growth.

FinServe Hong Kong: A financial services provider in Hong Kong with a focus on promoting financial inclusion and access to banking services, particularly among underserved communities.

AgriTech Hong Kong: A company dedicated to advancing agricultural technology in Hong Kong, focusing on optimizing crop yields and improving food security to support the city's agricultural sector.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Hong Kong, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

‍

Data fields included:

Dividend Declaration Date Stock Split Ratio Merger Announcement Date Rights Issue Record Date Bonus Issue Ex-Date Stock Buyback Date Spin-Off Announcement Date Dividend Record Date Merger Effective Date Rights Issue Subscription Price ‍

Q&A:

How much does the Corporate Actions Dataset cost in Hong Kong?

The cost of the Corporate Actions Dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

How complete is the Corporate Actions Dataset coverage in Hong Kong?

Techsalerator ...

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

National Coverage.

Analysis unit

Individual

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling 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. 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 by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in Hong Kong SAR, China was 1,028 individuals.

Mode of data collection

Landline and cellular telephone

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 over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Hang Seng Index: The Future of Hong Kong?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Abstract

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

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

Geographic coverage

National Coverage

Analysis unit

Individual

Universe

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

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or 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 by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid 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 Kish grid 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 in Hong Kong SAR, China was 1,007 individuals.

Mode of data collection

Other [oth]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, 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 Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

Graph and download economic data for Lerner Index in Banking Market for Hong Kong SAR, China (DDOI04HKA066NWDB) from 1996 to 2014 about lerner index, Hong Kong, banks, depository institutions, and indexes.

Graph and download economic data for NASDAQ Hong Kong Financials TR Index (NASDAQNQHK30T) from 2001-03-30 to 2025-11-07 about Hong Kong, NASDAQ, financial, indexes, and USA.

Hong Kong Nominal Salary Index (A): Financial Management data was reported at 168.500 Jun1995=100 in 2018. This records an increase from the previous number of 161.800 Jun1995=100 for 2017. Hong Kong Nominal Salary Index (A): Financial Management data is updated yearly, averaging 119.800 Jun1995=100 from Jun 1988 (Median) to 2018, with 31 observations. The data reached an all-time high of 168.500 Jun1995=100 in 2018 and a record low of 45.300 Jun1995=100 in 1988. Hong Kong Nominal Salary Index (A): Financial Management data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR – Table HK.G108: Nominal and Real Salary Index: Middle Level Managerial and Professional Employees: By Occupational Group.

Hong Kong: Financial institutions development, access: The latest value from 2021 is 0.458 index points, a decline from 0.467 index points in 2020. In comparison, the world average is 0.363 index points, based on data from 175 countries. Historically, the average for Hong Kong from 1980 to 2021 is 0.389 index points. The minimum value, 0.264 index points, was reached in 1992 while the maximum of 0.498 index points was recorded in 2011.

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 1007.

Mode of data collection

Landline and Cellular Telephone

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