Colleges and universities in the United States are still a popular study destination for Chinese students, with around 277 thousand choosing to take courses there in the 2023/24 academic year. Although numbers were heavily affected by the coronavirus pandemic, China is still the leading source of international students in the U.S. education market, accounting for 24.6 percent of all incoming students. The education exodus Mathematics and computer science courses led the field in terms of what Chinese students were studying in the United States, followed by engineering and business & management programs. The vast majority of Chinese students were self-funded, wth the remainder receiving state-funding to complete their overseas studies. Tuition fees can run into the tens of thousands of U.S. dollars, as foreign students usually pay out-of-state tuition fees. What about the local situation? Although studying abroad attracts many Chinese students, the country itself boasts the largest state-run education system in the world. With modernization of the national tertiary education system being a top priority for the Chinese government, the country has seen a significant increase in the number of local universities over the last decade. Enrolments in these universities exceeded 37 million in 2023, and a record of more than ten million students graduated in the same year, indicating that China's education market is still expanding.

The Trends in International Mathematics and Science Study, 2015 (TIMSS 2015) is a data collection that is part of the Trends in International Mathematics and Science Study (TIMSS) program; program data are available since 1999 at . TIMSS 2015 (https://nces.ed.gov/timss/) is a cross-sectional study that provides international comparative information of the mathematics and science literacy of fourth-, eighth-, and twelfth-grade students and examines factors that may be associated with the acquisition of math and science literacy in students. The study was conducted using direct assessments of students and questionnaires for students, teachers, and school administrators. Fourth-, eighth-, and twelfth-graders in the 2014-15 school year were sampled. Key statistics produced from TIMSS 2015 provide reliable and timely data on the mathematics and science achievement of U.S. students compared to that of students in other countries. Data are expected to be released in 2018.

Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset combines key education statistics from a variety of sources to provide a look at global literacy, spending, and access.

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:world_bank_health_population

http://data.worldbank.org/data-catalog/ed-stats

https://cloud.google.com/bigquery/public-data/world-bank-education

Citation: The World Bank: Education Statistics

Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @till_indeman from Unplash.

Inspiration

Of total government spending, what percentage is spent on education?

This dataset is about universities and is filtered where the country includes Qatar, featuring 5 columns: city, country, foundation year, graduate students, and international students. The preview is ordered by total students (descending).

Forecast: International Students in Tertiary Education in ICT in Australia 2023 - 2027 Discover more data with ReportLinker!

Australia Number of Enrolments: Year to Date: Higher Education: International Students: Thailand: National data was reported at 0.000 Person in Nov 2024. This stayed constant from the previous number of 0.000 Person for Oct 2024. Australia Number of Enrolments: Year to Date: Higher Education: International Students: Thailand: National data is updated monthly, averaging 0.000 Person from Jan 2002 (Median) to Nov 2024, with 275 observations. The data reached an all-time high of 1.000 Person in Dec 2014 and a record low of 0.000 Person in Nov 2024. Australia Number of Enrolments: Year to Date: Higher Education: International Students: Thailand: National data remains active status in CEIC and is reported by Department of Education. The data is categorized under Global Database’s Australia – Table AU.G120: Education Statistics: Number of Enrolments.

This contains the do file and dataset to replicate all output for Paul Clist & Ying-yi Hong's Do International Students Learn Foreign Preferences? The Interplay of Language, Identity and Assimilation, forthcoming at the Journal of Economic Psychology. It also contains the experimental script. Abstract: Every year millions of students study at foreign universities, swapping one set of cultural surroundings for another. This may reveal whether measured preferences are fixed or flexible, whether they can be altered in the short-run by moving country, or learning a new language. We disentangle these influences by measuring international students' preferences. For Chinese students in the UK (who arrived up to five years previously) we randomise a survey's language. We add reference groups in each country, doing the survey in the relevant language. Simple comparisons provide a causal estimate of language's effect, and observational estimates of differences by country, location and nationality. We find language has a large causal effect on a range of survey responses. The effect size is similar to differences by country or nationality (at 0.4 standard deviations), and larger than differences by location (at 0.1 standard deviations). Assimilation theories predict any movement in measured preferences for Chinese students in the UK would be towards those of UK students, even if they may be small. We do not find this. In Mandarin, Chinese students hardly differ from those in Beijing. Yet in English, they are not close to either Chinese students in Beijing nor British students in the UK. This can be explained by an identity-priming model with monocultural subjects. For Chinese students in the UK, speaking English reduces the pull of a Chinese frame without increasing the pull of a British one. International students do not so much learn foreign preferences as learn to ignore old ones. Our reliance on mostly stated preferences enables a rich dataset covering many domains; future work is needed to see if such large effects are also found across a wide range of revealed preferences.

Forecast: International Students in Tertiary Education in ICT in Switzerland 2023 - 2027 Discover more data with ReportLinker!

This dataset contains all anonymous data generated and analysed for publication.

The Census data API provides access to the most comprehensive set of data on current month and cumulative year-to-date exports using the End-use classification system. The End-use endpoint in the Census data API also provides value, shipping weight, and method of transportation totals at the district level for all U.S. trading partners. The Census data API will help users research new markets for their products, establish pricing structures for potential export markets, and conduct economic planning. If you have any questions regarding U.S. international trade data, please call us at 1(800)549-0595 option #4 or email us at eid.international.trade.data@census.gov.

United States Gross Purchases by Foreigners: Intl Organizations: Foreign Stocks data was reported at 28.000 USD mn in May 2018. This records a decrease from the previous number of 41.000 USD mn for Apr 2018. United States Gross Purchases by Foreigners: Intl Organizations: Foreign Stocks data is updated monthly, averaging 52.000 USD mn from Jan 1977 (Median) to May 2018, with 497 observations. The data reached an all-time high of 688.000 USD mn in Jan 2000 and a record low of 0.000 USD mn in Mar 1984. United States Gross Purchases by Foreigners: Intl Organizations: Foreign Stocks data remains active status in CEIC and is reported by US Department of Treasury. The data is categorized under Global Database’s USA – Table US.Z037: Foreign Purchases and Sales in Long Term Securities.

Data underlying comparisons of UK productivity against that of the remaining G7 countries.

United States Unemployment Rate: Foreign Born: Male data was reported at 4.600 % in Feb 2025. This stayed constant from the previous number of 4.600 % for Jan 2025. United States Unemployment Rate: Foreign Born: Male data is updated monthly, averaging 4.550 % from Jan 2007 (Median) to Feb 2025, with 218 observations. The data reached an all-time high of 15.300 % in Apr 2020 and a record low of 2.100 % in Jun 2019. United States Unemployment Rate: Foreign Born: Male data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G037: Current Population Survey: Unemployment Rate.

From 2012-2017, the International Development Innovation Network (IDIN) was a program led by the Massachusetts Institute of Technology’s D-Lab; implemented by a global consortium of academic, institutional, and innovation center partners; and supported by USAID’s Higher Education Solutions Network in the U.S. Global Development Lab. Today, IDIN Network members and partners continue to support innovators and entrepreneurs around the globe to design, develop, and disseminate technologies to improve the lives of people living in poverty. The IDIN Network is made up of more than 1,000 dynamic innovators from around the world who all share a common experience: attending an International Development Design Summit to create technologies with communities in developing countries. After a design summit, Network members pursued innovative projects, some from a summit and some of their own creation. With access to funding, training, mentorship, and workshop space, these innovators’ prototypes became products designed to make a difference. What We Do The IDIN program supported local innovation to create impact in five ways: Design Summits – Hands-on design experiences co-creating low-cost, practical technologies for people living in poverty IDIN Network Resources – Funding, training, and educational opportunities to support our 1,000+ Network members around the world Innovation Centers – Maker spaces connecting innovators to resources and training to develop technologies that make a social impact Research – A team generating new knowledge on local innovation and the role it plays in sustainable and community development Student Participation – Classes, projects, research, field visits, and technology development to train the next generation of innovators

Main data files comprise 22 variables in three subcategories of risk (political, financial, and economic) for 146 countries for 1984-2021. Data are annual averages of the components of the ICRG Risk Ratings (Tables 3B, 4B, and 5B) published in the International Country Risk Guide. Indices include: political: government stability, socioeconomic conditions, investment profile, internal conflict, external conflict, corruption, military in politics, religion in politics, law and order, ethnic tensions, democratic accountability, and bureaucratic quality; financial: foreign debt, exchange rate stability, debt service, current account, international liquidity; and economic: inflation, GDP per head, GDP growth, budget balance, current account as % of GDP. Table 2B provides annual averages of the composite risk rating. Table 3Ba provides historical political risk subcomponents on a monthly basis from May 2001-February 2022. Also includes the IRIS-3 dataset by Steve Knack and Philip Keefer, which covers the period of 1982-1997 and computed scores for six additional political risk variables: corruption in government, rule of law, bureaucratic quality, ethnic tensions, repudiation of contracts by government, and risk of expropriation. Additional data files provide country risk ratings and databanks (economic and social indicators) for new emerging markets for 2000-2009.

Progress in International Reading Literacy Study (PIRLS) student results and responses data. Includes PIRLS for 2011. Data is publicly available. Commonwealth has licence to provide PIRLS 2011 data. Progress in International Reading Literacy Study (PIRLS) student results and responses data. Includes PIRLS for 2011. Data is publicly available. Commonwealth has licence to provide PIRLS 2011 data.

Graph and download economic data for Labor Force Participation Rate - Foreign Born (LNU01373395) from Jan 2007 to Feb 2025 about foreign, participation, civilian, 16 years +, labor force, labor, household survey, rate, and USA.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of International Falls by race. It includes the distribution of the Non-Hispanic population of International Falls across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of International Falls across relevant racial categories.

Key observations

Of the Non-Hispanic population in International Falls, the largest racial group is White alone with a population of 5,198 (92.71% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the International Falls
• Population: The population of the racial category (for Non-Hispanic) in the International Falls is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of International Falls total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for International Falls Population by Race & Ethnicity. You can refer the same here

Abstract

“What is important for citizens to know and be able to do?” That is the question that underlies the triennial survey of 15-year-old students around the world known as the Programme for International Student Assessment (PISA). PISA assesses the extent to which students near the end of compulsory education have acquired key knowledge and skills that are essential for full participation in modern societies. The assessment, which focuses on reading, mathematics, science and problem solving, does not just ascertain whether students can reproduce knowledge; it also examines how well students can extrapolate from what they have learned and apply that knowledge in unfamiliar settings, both in and outside of school. This approach reflects the fact that modern economies reward individuals not for what they know, but for what they can do with what they know. All 34 OECD member countries and 31 partner countries and economies participated in PISA 2012, representing more than 80% of the world economy.

With mathematics as its primary focus, the PISA 2012 assessment measured 15-year-olds’ capacity to reason mathematically and use mathematical concepts, procedures, facts and tools to describe, explain and predict phenomena, and to make the wellfounded judgements and decisions needed by constructive, engaged and reflective citizens. Literacy in mathematics defined this way is not an attribute that an individual has or does not have; rather, it is a skill that can be acquired and used, to a greater or lesser extent, throughout a lifetime.

The PISA assessment provides three main types of outcomes: - basic indicators that provide a baseline profile of students’ knowledge and skills; - indicators that show how skills relate to important demographic, social, economic and educational variables; and - indicators on trends that show changes in student performance and in the relationships between student-level and school-level variables and outcomes.

Geographic coverage

PISA 2012 covered 34 OECD countries and 31 partner countries and economies. All countries attempted to maximise the coverage of 15-year-olds enrolled in education in their national samples, including students enrolled in special educational institutions.

Analysis unit

To better compare student performance internationally, PISA targets a specific age of students. PISA students are aged between 15 years 3 months and 16 years 2 months at the time of the assessment, and have completed at least 6 years of formal schooling. They can be enrolled in any type of institution, participate in full-time or part-time education, in academic or vocational programmes, and attend public or private schools or foreign schools within the country. Using this age across countries and over time allows PISA to compare consistently the knowledge and skills of individuals born in the same year who are still in school at age 15, despite the diversity of their education histories in and outside of school.

Kind of data

Sample survey data [ssd]

Sampling procedure

The accuracy of any survey results depends on the quality of the information on which national samples are based as well as on the sampling procedures. Quality standards, procedures, instruments and verification mechanisms were developed for PISA that ensured that national samples yielded comparable data and that the results could be compared with confidence.

Most PISA samples were designed as two-stage stratified samples (where countries applied different sampling designs. The first stage consisted of sampling individual schools in which 15-year-old students could be enrolled. Schools were sampled systematically with probabilities proportional to size, the measure of size being a function of the estimated number of eligible (15-year-old) students enrolled. A minimum of 150 schools were selected in each country (where this number existed), although the requirements for national analyses often required a somewhat larger sample. As the schools were sampled, replacement schools were simultaneously identified, in case a sampled school chose not to participate in PISA 2012.

Experts from the PISA Consortium performed the sample selection process for most participating countries and monitored it closely in those countries that selected their own samples. The second stage of the selection process sampled students within sampled schools. Once schools were selected, a list of each sampled school's 15-year-old students was prepared. From this list, 35 students were then selected with equal probability (all 15-year-old students were selected if fewer than 35 were enrolled). The number of students to be sampled per school could deviate from 35, but could not be less than 20.

Around 510 000 students between the ages of 15 years 3 months and 16 years 2 months completed the assessment in 2012, representing about 28 million 15-year-olds in the schools of the 65 participating countries and economies.

Mode of data collection

Face-to-face [f2f]

Research instrument

Paper-based tests were used, with assessments lasting two hours. In a range of countries and economies, an additional 40 minutes were devoted to the computer-based assessment of mathematics, reading and problem solving.

Test items were a mixture of questions requiring students to construct their own responses and multiple-choice items. The items were organised in groups based on a passage setting out a real-life situation. A total of about 390 minutes of test items were covered, with different students taking different combinations of test items.

Students answered a background questionnaire, which took 30 minutes to complete, that sought information about themselves, their homes and their school and learning experiences. School principals were given a questionnaire, to complete in 30 minutes, that covered the school system and the learning environment. In some countries and economies, optional questionnaires were distributed to parents, who were asked to provide information on their perceptions of and involvement in their child’s school, their support for learning in the home, and their child’s career expectations, particularly in mathematics. Countries could choose two other optional questionnaires for students: one asked students about their familiarity with and use of information and communication technologies, and the second sought information about their education to date, including any interruptions in their schooling and whether and how they are preparing for a future career.

Cleaning operations

Software specially designed for PISA facilitated data entry, detected common errors during data entry, and facilitated the process of data cleaning. Training sessions familiarised National Project Managers with these procedures.

Response rate

Data-quality standards in PISA required minimum participation rates for schools as well as for students. These standards were established to minimise the potential for response biases. In the case of countries meeting these standards, it was likely that any bias resulting from non-response would be negligible, i.e. typically smaller than the sampling error.

A minimum response rate of 85% was required for the schools initially selected. Where the initial response rate of schools was between 65% and 85%, however, an acceptable school response rate could still be achieved through the use of replacement schools. This procedure brought with it a risk of increased response bias. Participating countries were, therefore, encouraged to persuade as many of the schools in the original sample as possible to participate. Schools with a student participation rate between 25% and 50% were not regarded as participating schools, but data from these schools were included in the database and contributed to the various estimations. Data from schools with a student participation rate of less than 25% were excluded from the database.

PISA 2012 also required a minimum participation rate of 80% of students within participating schools. This minimum participation rate had to be met at the national level, not necessarily by each participating school. Follow-up sessions were required in schools in which too few students had participated in the original assessment sessions. Student participation rates were calculated over all original schools, and also over all schools, whether original sample or replacement schools, and from the participation of students in both the original assessment and any follow-up sessions. A student who participated in the original or follow-up cognitive sessions was regarded as a participant. Those who attended only the questionnaire session were included in the international database and contributed to the statistics presented in this publication if they provided at least a description of their father’s or mother’s occupation.

Palau has not produced a definitive list of endangered species although a number of species have been accorded legal protection. All endemics are vulnerable due to their sole residence being a single remote archipelago. This dataset host the available records of red list for Palau as recorded by IUCN.