Time series data for the statistic School age population, secondary education, male (number) and country Hong Kong SAR, China. Indicator Definition:Male population of the age-group theoretically corresponding to secondary education as indicated by theoretical entrance age and duration.The indicator "School age population, secondary education, male (number)" stands at 164.35 Thousand as of 12/31/2020. Regarding the One-Year-Change of the series, the current value constitutes an increase of 0.1719 percent compared to the value the year prior.The 1 year change in percent is 0.1719.The 3 year change in percent is -6.29.The 5 year change in percent is -18.13.The 10 year change in percent is -44.78.The Serie's long term average value is 305.71 Thousand. It's latest available value, on 12/31/2020, is 46.24 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2019, to it's latest available value, on 12/31/2020, is +0.172%.The Serie's change in percent from it's maximum value, on 12/31/1978, to it's latest available value, on 12/31/2020, is -58.79%.

Time series data for the statistic GNI (current US$) and country Hong Kong SAR, China. Indicator Definition:GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in current U.S. dollars.The indicator "GNI (current US$)" stands at 445.65 Billion usd as of 12/31/2024, the highest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 7.82 percent compared to the value the year prior.The 1 year change in percent is 7.82.The 3 year change in percent is 12.96.The 5 year change in percent is 16.84.The 10 year change in percent is 49.81.The Serie's long term average value is 136.54 Billion usd. It's latest available value, on 12/31/2024, is 226.39 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1960, to it's latest available value, on 12/31/2024, is +33,489.11%.The Serie's change in percent from it's maximum value, on 12/31/2024, to it's latest available value, on 12/31/2024, is 0.0%.

Hong Kong's main stock market index, the HK50, rose to 26095 points on December 2, 2025, gaining 0.24% from the previous session. Over the past month, the index has declined 0.24%, though it remains 32.15% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Hong Kong. Hong Kong Stock Market Index (HK50) - values, historical data, forecasts and news - updated on December of 2025.

Time series data for the statistic Unemployment_Rate and country Hong Kong SAR, China. Indicator Definition:Unemployment refers to the share of the labor force that is without work but available for and seeking employment.The statistic "Unemployment Rate" stands at 2.79 percent as of 12/31/2024, the lowest value since 12/31/1998. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -0.156 percentage points compared to the value the year prior.The 1 year change in percentage points is -0.156.The 3 year change in percentage points is -2.38.The 5 year change in percentage points is -0.124.The 10 year change in percentage points is -0.503.The Serie's long term average value is 4.00 percent. It's latest available value, on 12/31/2024, is 1.21 percentage points lower, compared to it's long term average value.The Serie's change in percentage points from it's minimum value, on 12/31/1991, to it's latest available value, on 12/31/2024, is +0.993.The Serie's change in percentage points from it's maximum value, on 12/31/2003, to it's latest available value, on 12/31/2024, is -5.07.

Time series data for the statistic Vulnerable employment, male (% of male employment) (modeled ILO estimate) and country Hong Kong SAR, China. Indicator Definition:Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment.The indicator "Vulnerable employment, male (% of male employment) (modeled ILO estimate)" stands at 8.26 as of 12/31/2023, the lowest value since 12/31/2020. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -2.82 percent compared to the value the year prior.The 1 year change in percent is -2.82.The 3 year change in percent is -1.63.The 5 year change in percent is 4.10.The 10 year change in percent is -12.11.The Serie's long term average value is 8.31. It's latest available value, on 12/31/2023, is 0.673 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1997, to it's latest available value, on 12/31/2023, is +32.58%.The Serie's change in percent from it's maximum value, on 12/31/2002, to it's latest available value, on 12/31/2023, is -21.56%.

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.

Context

More international students are flocking to China than ever before. According to a report, over 540,000 foreigners studied in China in 2018 – marking a 40 percent increase from 2012. China attracts more international students than any other Asian power and ranks third globally, behind the United States and the United Kingdom.

Content

In 2018 there were a total of 492,185 international students from 196 countries/areas pursuing their studies in 1,004 higher education institutions in China’s 31 provinces/autonomous regions/provincial-level municipalities, marking an increase of 3,013 students or 0.62% compared to 2017. International students in Hong Kong, Macau and Taiwan are not included in the datasets. The datasets contain three CSV files (Continent, Country, Province) with different data about international students in China.

Columns Description

@Continent (Number/percent of international students by continent) Continent- The name of continent Number - The number of total international students Deaths- The percentage of total international students

@Country (Number of international students by country of origin) Rank- The rank of the country based on total students in China Country- The name of the country Number- The number of total international students

@Province (The top provinces/cities with the largest number of international students) Province- The name of the city/province Number- The number of total international students

Acknowledgements

This data collected from moe.gov.cn.

Inspiration

Currently, I'm studying at a Chinese university. Every year many international students come to China for their higher study, and the ratio of international students is growing steadily. This data will help us to understand the ratio of international students in China.

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 Hong Kong SAR, China is 1003.

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

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.

Time series data for the statistic Government expenditure on tertiary education as % of GDP (%) and country Hong Kong SAR, China. Indicator Definition:Total general (local, regional and central) government expenditure on tertiary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/The indicator "Government expenditure on tertiary education as % of GDP (%)" stands at 1.33 as of 12/31/2019, the highest value since 12/31/2014. Regarding the One-Year-Change of the series, the current value constitutes an increase of 35.83 percent compared to the value the year prior.The 1 year change in percent is 35.83.The 3 year change in percent is 32.49.The 5 year change in percent is 27.15.The 10 year change in percent is -33.56.The Serie's long term average value is 0.891. It's latest available value, on 12/31/2019, is 48.95 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1982, to it's latest available value, on 12/31/2019, is +2,482.34%.The Serie's change in percent from it's maximum value, on 12/31/2009, to it's latest available value, on 12/31/2019, is -33.56%.

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

Time series data for the statistic CO2_Emissions_(kt) and country Hong Kong SAR, China. Indicator Definition:Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring.The statistic "CO2 Emissions (kt)" stands at 33,871.00 kiloton as of 12/31/2023, the highest value since 12/31/2020. Regarding the One-Year-Change of the series, the current value constitutes an increase of 7.42 percent compared to the value the year prior.The 1 year change in percent is 7.42.The 3 year change in percent is 1.12.The 5 year change in percent is -20.49.The 10 year change in percent is -23.55.The Serie's long term average value is 38,339.18 kiloton. It's latest available value, on 12/31/2023, is 11.65 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1990, to it's latest available value, on 12/31/2023, is +24.76%.The Serie's change in percent from it's maximum value, on 12/31/2014, to it's latest available value, on 12/31/2023, is -25.55%.

Time series data for the statistic Consolidated foreign claims of BIS reporting banks to GDP (%) and country Hong Kong SAR, China. Indicator Definition:The ratio of consolidated foreign claims to GDP of the banks that are reporting to BIS. Foreign claims are defined as the sum of cross-border claims plus foreign offices’ local claims in all currencies. In the consolidated banking statistics claims that are granted or extended to nonresidents are referred to as either cross-border claims. In the context of the consolidated banking statistics, local claims refer to claims of domestic banks’ foreign affiliates (branches/subsidiaries) on the residents of the host country (i.e. country of residence of affiliates). Items (A+L from BIS Table 9A). End-of-year data (i.e. December data) are considered for banks claims. GDP is from World Development Indicators.The indicator "Consolidated foreign claims of BIS reporting banks to GDP (%)" stands at 307.16 as of 12/31/2020, the highest value at least since 12/31/1984, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 9.49 percent compared to the value the year prior.The 1 year change in percent is 9.49.The 3 year change in percent is 8.15.The 5 year change in percent is 15.35.The 10 year change in percent is 25.45.The Serie's long term average value is 219.06. It's latest available value, on 12/31/2020, is 40.22 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2002, to it's latest available value, on 12/31/2020, is +109.99%.The Serie's change in percent from it's maximum value, on 12/31/2020, to it's latest available value, on 12/31/2020, is 0.0%.

Time series data for the statistic Imports_Jordan_from_Hong_Kong_SAR,_China. Indicator Definition:Goods, Value of Imports, Cost, Insurance, Freight (CIF), US DollarsThe indicator "Goods, Value of Imports, Cost, Insurance, Freight (CIF), US Dollars" stands at 0.3146 Million as of 5/31/2025, the highest value since 11/30/2024. Regarding the One-Year-Change of the series, the current value constitutes an increase of 141.25 percent compared to the value the year prior.The 1 year change in percent is 141.25.The 3 year change in percent is -34.00.The 5 year change in percent is -3.42.The 10 year change in percent is -59.19.The Serie's long term average value is 1.51 Million. It's latest available value, on 5/31/2025, is 79.23 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 10/31/2016, to it's latest available value, on 5/31/2025, is +473.13%.The Serie's change in percent from it's maximum value, on 5/31/2006, to it's latest available value, on 5/31/2025, is -96.41%.

Hong Kong SAR (China) HK: Prevalence of Severe Food Insecurity in the Population: % of population data was reported at 0.500 % in 2017. This records a decrease from the previous number of 0.600 % for 2016. Hong Kong SAR (China) HK: Prevalence of Severe Food Insecurity in the Population: % of population data is updated yearly, averaging 0.600 % from Dec 2015 (Median) to 2017, with 3 observations. The data reached an all-time high of 0.800 % in 2015 and a record low of 0.500 % in 2017. Hong Kong SAR (China) HK: Prevalence of Severe Food Insecurity in the Population: % of population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Hong Kong SAR (China) – Table HK.World Bank.WDI: Social: Health Statistics. The percentage of people in the population who live in households classified as severely food insecure. A household is classified as severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to several of the most severe experiences described in the FIES questions, such as to have been forced to reduce the quantity of the food, to have skipped meals, having gone hungry, or having to go for a whole day without eating because of a lack of money or other resources.;Food and Agriculture Organization of the United Nations (FAO);;

Time series data for the statistic Imports_Dominican_Republic_from_Hong_Kong_SAR,_China. Indicator Definition:Goods, Value of Imports, Cost, Insurance, Freight (CIF), US DollarsThe indicator "Goods, Value of Imports, Cost, Insurance, Freight (CIF), US Dollars" stands at 4.78 Million as of 5/31/2025. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -9.97 percent compared to the value the year prior.The 1 year change in percent is -9.97.The 3 year change in percent is -48.60.The 5 year change in percent is -4.59.The Serie's long term average value is 4.47 Million. It's latest available value, on 5/31/2025, is 6.94 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 2/28/1990, to it's latest available value, on 5/31/2025, is +1,428.75%.The Serie's change in percent from it's maximum value, on 3/31/2018, to it's latest available value, on 5/31/2025, is -65.12%.

Time series data for the statistic Imports_Iraq_from_Hong_Kong_SAR,_China. Indicator Definition:Goods, Value of Imports, Cost, Insurance, Freight (CIF), US DollarsThe indicator "Goods, Value of Imports, Cost, Insurance, Freight (CIF), US Dollars" stands at 5.75 Million as of 5/31/2025. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -24.99 percent compared to the value the year prior.The 1 year change in percent is -24.99.The 3 year change in percent is 369.30.The 5 year change in percent is 129.41.The 10 year change in percent is -41.31.The Serie's long term average value is 2.84 Million. It's latest available value, on 5/31/2025, is 102.44 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 8/31/2001, to it's latest available value, on 5/31/2025, is +124,365.86%.The Serie's change in percent from it's maximum value, on 3/31/2015, to it's latest available value, on 5/31/2025, is -68.68%.

Hong Kong SAR (China) Percentage of Population Exposed to More Than 10 Micrograms per Cub m data was reported at 100.000 % in 2020. This stayed constant from the previous number of 100.000 % for 2019. Hong Kong SAR (China) Percentage of Population Exposed to More Than 10 Micrograms per Cub m data is updated yearly, averaging 100.000 % from Dec 1990 (Median) to 2020, with 23 observations. The data reached an all-time high of 100.000 % in 2020 and a record low of 100.000 % in 2020. Hong Kong SAR (China) Percentage of Population Exposed to More Than 10 Micrograms per Cub m data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Hong Kong SAR (China) – Table HK.OECD.GGI: Social: Air Quality and Health: Non OECD Member: Annual.

Hong Kong HK: Imports: % of Goods Imports: Food data was reported at 5.001 % in 2017. This records a decrease from the previous number of 5.258 % for 2016. Hong Kong HK: Imports: % of Goods Imports: Food data is updated yearly, averaging 7.889 % from Dec 1962 (Median) to 2017, with 56 observations. The data reached an all-time high of 28.535 % in 1962 and a record low of 2.825 % in 2006. Hong Kong HK: Imports: % of Goods Imports: Food data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Hong Kong SAR – Table HK.World Bank.WDI: Imports. Food comprises the commodities in SITC sections 0 (food and live animals), 1 (beverages and tobacco), and 4 (animal and vegetable oils and fats) and SITC division 22 (oil seeds, oil nuts, and oil kernels).; ; World Bank staff estimates through the WITS platform from the Comtrade database maintained by the United Nations Statistics Division.; Weighted average; Merchandise import shares may not sum to 100 percent because of unclassified trade.

Hong Kong HK: Labour Force With Advanced Education: Female: % of Female Working-age Population data was reported at 78.540 % in 2016. This records a decrease from the previous number of 79.110 % for 2015. Hong Kong HK: Labour Force With Advanced Education: Female: % of Female Working-age Population data is updated yearly, averaging 78.825 % from Dec 2009 (Median) to 2016, with 8 observations. The data reached an all-time high of 80.590 % in 2011 and a record low of 77.570 % in 2010. Hong Kong HK: Labour Force With Advanced Education: Female: % of Female Working-age Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Hong Kong – Table HK.World Bank: Labour Force. The percentage of the working age population with an advanced level of education who are in the labor force. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011).; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted Average;