Abstract

This study provides an update on measures of educational attainment for a broad cross section of countries. In our previous work (Barro and Lee, 1993), we constructed estimates of educational attainment by sex for persons aged 25 and over. The values applied to 129 countries over a five-year intervals from 1960 to 1985.

The present study adds census information for 1985 and 1990 and updates the estimates of educational attainment to 1990. We also have been able to add a few countries, notably China, which were previously omitted because of missing data.

Dataset:

Educational attainment at various levels for the male and female population. The data set includes estimates of educational attainment for the population by age - over age 15 and over age 25 - for 126 countries in the world. (see Barro, Robert and J.W. Lee, "International Measures of Schooling Years and Schooling Quality, AER, Papers and Proceedings, 86(2), pp. 218-223 and also see "International Data on Education", manuscipt.) Data are presented quinquennially for the years 1960-1990;

Educational quality across countries. Table 1 presents data on measures of schooling inputs at five-year intervals from 1960 to 1990. Table 2 contains the data on average test scores for the students of the different age groups for the various subjects.Please see Jong-Wha Lee and Robert J. Barro, "Schooling Quality in a Cross-Section of Countries," (NBER Working Paper No.w6198, September 1997) for more detailed explanation and sources of data.

Geographic coverage

The data set cobvers the following countries: - Afghanistan - Albania - Algeria - Angola - Argentina - Australia - Austria - Bahamas, The - Bahrain - Bangladesh - Barbados - Belgium - Benin - Bolivia - Botswana - Brazil - Bulgaria - Burkina Faso - Burundi - Cameroon - Canada - Cape verde - Central African Rep. - Chad - Chile - China - Colombia - Comoros - Congo - Costa Rica - Cote d'Ivoire - Cuba - Cyprus - Czechoslovakia - Denmark - Dominica - Dominican Rep. - Ecuador - Egypt - El Salvador - Ethiopia - Fiji - Finland - France - Gabon - Gambia - Germany, East - Germany, West - Ghana - Greece - Grenada - Guatemala - Guinea - Guinea-Bissau - Guyana - Haiti - Honduras - Hong Kong - Hungary - Iceland - India - Indonesia - Iran, I.R. of - Iraq - Ireland - Israel - Italy - Jamaica - Japan - Jordan - Kenya - Korea - Kuwait - Lesotho - Liberia - Luxembourg - Madagascar - Malawi - Malaysia - Mali - Malta - Mauritania - Mauritius - Mexico - Morocco - Mozambique - Myanmar (Burma) - Nepal - Netherlands - New Zealand - Nicaragua - Niger - Nigeria - Norway - Oman - Pakistan - Panama - Papua New Guinea - Paraguay - Peru - Philippines - Poland - Portugal - Romania - Rwanda - Saudi Arabia - Senegal - Seychelles - Sierra Leone - Singapore - Solomon Islands - Somalia - South africa - Spain - Sri Lanka - St.Lucia - St.Vincent & Grens. - Sudan - Suriname - Swaziland - Sweden - Switzerland - Syria - Taiwan - Tanzania - Thailand - Togo - Tonga - Trinidad & Tobago - Tunisia - Turkey - U.S.S.R. - Uganda - United Arab Emirates - United Kingdom - United States - Uruguay - Vanuatu - Venezuela - Western Samoa - Yemen, N.Arab - Yugoslavia - Zaire - Zambia - Zimbabwe

This dataset tracks annual distribution of students across grade levels in Citizens Of The World Charter School West Valley

******* was the European country with the highest share of graduates in 2024, with almost **** of those aged between 15 and 64 having a degree. On the contrary, only ** percent of the population aged 15 to 64 in ********************** hold a tertiary education title.

Objective: The INVEDUC project analyses public attitudes and preferences of citizens regarding different aspects of education policy in eight Western European countries. It also studies to what extent and via which mechanisms public opinion influences processes of policy-making.

Method: The INVEDUC survey of public opinion on education policy was conducted in April in May 2014 in eight Western European countries: Denmark, France, Germany, Ireland, Italy, Spain, Sweden and the United Kingdom. The total number of observations is 8,905 (i.e. between 1,000 and 1,500 for the different countries), drawn from random samples of the respective populations. The interviews were conducted by native speakers via computer-assisted telephone interviewing (CATI) and implemented by TNS Infratest Sozialforschung, Munich.

Questionnaire content: The survey covers the following aspects related to education policy: support for education spending relative to spending for other social policies; preferences for the distribution of education spending across different sectors of the education system (early childhood education and care, general schools, vocational education and training, higher education); willingness to pay taxes for additional spending on education; support for education spending in the face of different fiscal and policy trade-offs (higher taxes, higher public debt, cutbacks in other parts of the welfare state); support for social investment policies vs. social transfers and workfare policies; attitudes and preferences regarding the governance of education (comprehensive education, decentralisation of education governance, division of labor between public and private provision of education, school competition, role of employers in VET). The survey contains a number of experimental components, in particular when measuring the effect of trade-offs on preferences.

Demography: national citizenship; other citizenship; city size; financial situation of household; age (year of birth); sex; highest educational attainment (country-specific); age at completion of full-time education; employment status or age at completion of full-time education; current situation; reasons for part-time employment; occupational status; occupation (ISCO 2008); public service employment; sector; likelihood of own unemployment; net household income (country specific, classified); net personal income; education-related debt; household size; number of children in household; number of children under 10 years of age in household; single parent; trade union membership; parents´ university degree.

K-12 Education Market Outlook

As per our latest research, the global K-12 Education market size in 2024 stands at USD 154.5 billion, reflecting the sector’s robust expansion in response to widespread digital transformation and growing investments in educational technology. The market is projected to grow at a CAGR of 9.7% from 2025 to 2033, reaching a forecasted value of USD 352.1 billion by 2033. This growth is primarily fueled by rapid digitalization, increased government spending on education infrastructure, and the rising adoption of e-learning solutions globally.

One of the most significant growth factors in the K-12 Education market is the accelerated integration of technology into classrooms. The COVID-19 pandemic acted as a catalyst, compelling educational institutions to adopt digital platforms for remote learning and virtual classrooms. This shift has continued post-pandemic, with schools increasingly leveraging learning management systems (LMS), digital content, and interactive tools to enhance the learning experience. The proliferation of affordable internet access and the widespread use of smart devices among students and educators have further enabled this transformation. As a result, schools are not only improving accessibility and engagement but are also laying the groundwork for more personalized and data-driven education.

Another driver of market growth is the expanding focus on student-centric and competency-based learning approaches. Educational stakeholders are prioritizing adaptive learning technologies, real-time assessment tools, and analytics-driven platforms to tailor instruction according to individual student needs. This trend is underpinned by growing awareness among policymakers and educators regarding the limitations of traditional, one-size-fits-all teaching methods. Investments in professional development for teachers, aimed at equipping them with digital skills and pedagogical strategies, are also contributing to the market’s momentum. Moreover, the emphasis on collaborative learning, critical thinking, and creativity is encouraging schools to adopt a diverse range of digital resources and platforms.

Government initiatives and public-private partnerships are playing a pivotal role in shaping the K-12 Education market landscape. Many countries are launching national programs to modernize school infrastructure, promote STEM (Science, Technology, Engineering, and Mathematics) education, and bridge the digital divide. These initiatives often include substantial funding for hardware procurement, software deployment, and teacher training. Additionally, the private sector’s involvement in developing innovative edtech solutions and providing managed services is accelerating the pace of transformation. As governments and organizations collaborate to address challenges such as accessibility, affordability, and inclusivity, the market is expected to witness sustained growth over the forecast period.

Regionally, Asia Pacific is emerging as the fastest-growing market, driven by extensive government investments, a large student population, and rapid technological adoption in countries like China and India. North America continues to hold a significant share, supported by mature digital infrastructure and high levels of edtech integration in schools. Europe is also making notable strides, particularly in Western European countries that are prioritizing digital literacy and inclusive education. Meanwhile, Latin America and the Middle East & Africa are experiencing steady growth, albeit from a smaller base, as governments focus on improving educational outcomes and expanding access to quality education. The global outlook for the K-12 Education market remains highly positive, with all regions contributing to its dynamic evolution.

Component Analysis

The K-12 Education market by component is segmented into hardware, software, and services, each playing a vital role in the digital transformation of the education sector. Hardware forms the

BackgroundQuality Physical Education (QPE) programs have received increasing attention as a means to address the significant decline in adolescents’ physical health. Since the introduction of the general concept of QPE by the United Nations Educational, Scientific and Cultural Organization in 2015, there has been limited research on QPE within non-Western areas, particularly in China. This research gap hinders the development of QPE strategies and practices tailored to China’s specific context, which is crucial for improving adolescents’ health. Therefore, this study aimed to develop a model of QPE in China.MethodThis qualitative study adopted a grounded theory approach to examine Chinese physical education (PE) teachers’ perspectives on quality PE. Twenty-two PE teachers from diverse regions were purposively sampled and in-depth interviews were conducted online. Interview transcripts were recorded, transcribed verbatim, and analysed following open, axial, and selective coding.ResultsThe QPE model consisted of five levels, 12 categories, 51 concepts, and 216 statement labels. The five levels are: (1) student level (comprising students’ development, and students’ engagement and experiences in PE), (2) family level (comprising parents’ engagement and attitude toward PE, and home-based sports resources), (3) school level (comprising PE teacher, sports facilities and equipment in the school, PE curriculum, school-based extracurricular PA programs, co-operation family-school-community in PE, and school leadership and school community support for PE), (4) community level (comprising community-based sports resources), and (5) government level (comprising government support for PE).ConclusionThis study broadens global knowledge of QPE, particularly in non-Western countries. It provides practical implications for policymakers and educators in secondary education to design and improve QPE programs. By fostering a sustainable educational framework. These contributions are essential for developing effective strategies that promote PA engagement and well-being among adolescents.

Immigrant selectivity describes the notion that migrants are not a random sample of the population at origin, but differ in certain traits such as educational attainment from individuals who stay behind. In this article, we move away from group-level descriptions of educational selectivity and measure it as an individual's relative position in the age- and gender-specific educational distribution of the country of origin. We describe the extent of educational selectivity for a selection of Western European destinations as well as a selection of origin groups ranging from recent refugee to labor migrant populations. By contrasting refugees to labor migrants, we address longstanding assumptions about typical differences in the degree of selectivity between different types of immigrants. According to our findings, there are few and only minor differences between refugee and labor migrants. However, these differences vary; and there are labor migrant groups that score similar or lower on selectivity than do the refugees covered in this study. Selectivity differences between refugees and labor migrants therefore seem less prominent than arguments in the literature suggest. Another key finding is that every origin group is composed of varying proportions of positively and negatively selected individuals. In most cases, the origin groups cover the whole spectrum of selectivity, so that characterizing them as either predominantly positively or negatively selected does not seem adequate. Furthermore, we show that using country-level educational distributions as opposed to sub-national regional-level distributions can lead to inaccurate measurements of educational selectivity. This problem does not occur universally, but only under certain conditions. That is, when high levels of outmigration from sub-national regions in which economic opportunities are considerably above or below the country average, measurement inaccuracy exceeds ignorable levels. In instances where researchers are not able to use sub-national regional measures, we provide them with practical guidance in the form of pre-trained machine-learning tools to assess the direction and the extent of the measurement inaccuracy that results from relying on country-level as opposed to sub-national regional-level educational distributions.

Out of the OECD countries, Luxembourg was the country that spent the most on educational institutions per full-time student in 2020. On average, 23,000 U.S dollars were spent on primary education, nearly 27,000 U.S dollars on secondary education, and around 53,000 U.S dollars on tertiary education. The United States followed behind, with Norway in third. Meanwhile, the lowest spending was in Mexico.

This table contains figures on the population aged 15 to 90 in the Netherlands, with the exception of persons in establishments, institutions and homes (institutional population). The figures show the highest level of education attained by individuals, broken down by gender, age and origin.

CBS is moving to a new classification of the population by origin. From now on, it is more decisive where someone was born, in addition to where someone's parents were born. The word "migration background" is no longer used. The main classification Western/non-Western is replaced by a classification based on continents and common immigration countries. This classification is gradually introduced in tables and publications with population by origin.

The figures come from the Labour Force Survey (EBB).

Data available from: 2013

Status of figures: The figures in this table are final.

Changes as of 15 May 2024: Figures for Q1 2024 have been added.

Changes as of 13 January 2023: None, this is a new table. This table is compiled on the basis of the Labour Force Survey (LFS). Due to changes in the research design and the questionnaire of the EBB, the figures for 2021 are not necessarily comparable with the figures up to 2020. The key figures in this table have therefore been made consistent with the (non-seasonally adjusted) figures in the table Labour participation, key figures seasonally adjusted (see paragraph 4), in which the results for the period 2013-2020 have been recalculated to reflect the results from 2021 onwards. If the results are further detailed by job and personal characteristics, there may nevertheless be differences between 2020 and 2021 as a result of the new method.

When will there be new figures? New figures will be published on 12 August 2024.

This study includes data on regional level for nine Western European countries: election returns, occupation categories, religion, population.

Abstract

This survey is part of a multi-country pilot study which combines surveys of primary schools with household and other micro surveys to assess service delivery systems in education, measure performance, and establish a baseline for examining the impact of policy and institutional reforms over time.

Work on the PESD project was launched in late 2001 as part of the World Bank’s analytical work on poverty in PNG. The project was launched in close consultation with the Government of PNG and AusAID.8 Work on the PESD survey started in early 2002.

The survey operation itself was implemented by the Education Department of the National Research Institute (NRI) in Port Moresby.

Geographic coverage

The PESD survey covered 214 schools in 19 districts across 8 provinces --Counting NCD as a province-- out of a total of 20 in the country, with two provinces selected in each of the four main regions.

The following provinces were covered: - Southern (Papua) region: Gulf; National Capital District (NCD) - Highlands region: Enga; Eastern Highlands - Momase region: West Sepik (Sandaun); Morobe - Islands region: West New Britain; East New Britain

These provinces cover a wide spectrum both in terms of poverty levels and educational development. They range from the relatively rich (NCD and Gulf with headcounts of 19 and 28%) to the poor Sandaun (headcount of over 60%), from the well-educated (NCD and East New Britain with adult literacy rates of 84 and 74%) to poorly-educated (Enga and Eastern Highlands with adult literacy rates of 26 and 38%), from those with high primary enrolment (NCD and ENB) to those with low enrolment (Enga, Gulf and Sandaun), from those with high grade 1-8 retention rates (NCD with 79%) to those with low retention rates (Eastern Highlands and Sandaun with just above 20%).

Kind of data

Sample survey data [ssd]

Sampling procedure

Three districts were randomly selected within provinces with probability proportional to the number of schools in the district. In two of the provinces, viz. Gulf and West New Britain, that only had two districts, both were selected. Ten schools were then selected randomly within each district. In NCD, which does not have districts but is organized by wards/census enumeration areas, 30 schools were randomly selected.

The original sample included 220 schools. Many of the schools in the original sample could not be covered for a variety of reasons. In these cases, replacement schools (randomly selected from the same district) were used. A special effort was made to ensure coverage of remote schools. In particular, some sites were revisited later to cover schools that could not be surveyed during the first attempt due to logistical difficulties. The schools are widely dispersed throughout the country.

The PESD schools are further classified by the level of poverty and remoteness. The level of poverty is measured by the estimated poverty rate for the LLG where the school is located, and the remoteness index is based on a composite measure of distance and travel time from the school to a range of facilities. The PESD sample of schools is well distributed across the remoteness and poverty spectrum. (For further details on the measures of poverty and remoteness, see Annexes 2 and 3 of the survey report.) Also, while poverty rate and the remoteness indices are significantly correlated across the PESD sample, these attributes are not collinear. The weighted correlation coefficient is 0.15, while the unweighted correlation is 0.27, both statistically significant at the 5% level or better.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey used a series of instruments for collecting data at different levels. These included:

Instruments at the school level: - School survey – the main instrument (S1) - Grade 5 teacher survey (S2) - Board of Management survey (S3) - Parent survey (S4)

Instruments at the district/provincial level: - District Education Administrator (DEA) survey (D2) - Provincial Education Adviser (PEA) survey (P1)

An instrument for health centers: - Health facility survey (H1)

These instruments were used to collect data on a range of topics including: characteristics of the head teacher, teachers, characteristics of schools, inspectors, BOM, parents, school finances, classroom environment, teacher activity, resources for teaching, community-school interaction, organization and structure of DEA/PEA offices, District and Provincial Education Boards, budget process, school fee subsidy and other sources of funding, and roles and responsibilities in education.

The health facility survey was not intended to be a full service delivery survey in order to keep the field operations and costs within manageable limits. It was added as a rider to the school survey. Health facilities that could be reached within 20 minutes from the sample schools were covered. Thus, as against a sample of 214 schools, the survey covered 117 health facilities. A short instrument collected information on how often the facilities were open, the presence of staff, and the availability of key medicines. Table 2.2 in the survey report gives details of PESD sample coverage by instrument, province and district.

According to a study from 2020, over 70 percent of the poorest children living in Mali, Niger, Nigeria, and Guinea dis not attend school. Overall, Sub-Saharan Africa has the highest illiteracy rate in the world. Countries in East and West Africa suffer from high levels of poverty, including health, malnutrition, lack of clean water and electricity, poor education, and other similar aspects.

All data are expressed as a percentage, except for GDP per capita, net wages, total population, life expectancy, expected years of education, average years of schooling, life and non-life premium, total premium, bank deposits, financial assets and deposits of insurance companies, which are expressed in absolute terms.

Source of data:

Data on Life and Non-life premium, Total (gross) premium, Premium reserve data, Financial assets and Deposits of insurance companies are collected from the official reports of insurance supervision agencies: Insurance Supervision Agency in Montenegro (http://www.ano.me/en/), Croatian Financial Services Supervisory Agency (https://www.hanfa.hr/en/), National Bank of Serbia (https://www.nbs.rs/internet/english/, Insurance Supervision Agency of North Macedonia (http://aso.mk/en/?lang=en) and Financial Supervisory Authority in Albania (https://amf.gov.al/).

The economic indicators for the observed Western Balkan countries (GDP per capita, unemployment rate, inflation rate, net earnings and average effective deposit interest rate) are taken from the website Eurostat (https://ec.europa.eu/eurostat) and Statista (https://www.statista.com/)

All demographic indicators, except for the expected and average years of schooling and education index, were collected from the Eurostat and UNDP database (https://ec.europa.eu/eurostat/data/database; http://hdr.undp.org/en/countries/profiles/ ).

Data on expected and average school years were taken from the UNESCO Institute for Statistics (http://uis.unesco.org) , while the education index was obtained as a result of a calculation based on a formula published on the UNDP website (http://hdr.undp.org/en/content/education-index).

Data on bond yield were collected from the website of European Commission (https://ec.europa.eu/), i.e. from EC reports - EU Candidate Countries’ & Potential Candidates’ Economic Quarterly (CCEQ), except two data for Serbia (2006 and 2007) which were estimated by Makima extrapolation.

Bank deposits data are taken from the official reports of banks' regulatory institutions: Central bank of Montenegro (https://www.cbcg.me/en), National bank of Serbia (https://www.nbs.rs/en/indeks/), Croatian National bank (https://www.hnb.hr/en/home), National bank of the Republic of North Macedonia (https://www.nbrm.mk/pocetna-en.nspx); Bank of Albania (https://www.bankofalbania.org/home/)

Description of columns:

f1-GDPper capita; f2- Unemployment (%); f3-Inflation rate (%); f4- Net Wages €; f5- Deposit rate (%); f6- Population; f7- Female (%); f8- Population <15 (%); f9- Population 15-64 (%); f10- Dep old (%); f11- Dep young (%); f12- Urban population (%); f13-Life exp. (years); f14- Preschool enroll rate (%); f15- Elem school enroll rate (%); f16-High school enroll rate (%); f17- University enroll rate (%); f18- Expected years of schooling; f19- Avg. years of schooling; f20- Education Index (%); f21- Fertility rate (number of children to a woman); f22- Birth rate (per 1000 inhabitants); f23- Health costs (% GDP); f24-premium reserve per GDP,

i1- life premium €; i2- non-life premium €; i3- total premium €; i4- bond yield (%); i5a- bank deposits ( national currency); i5b- bank deposits €; i6a-financial assets in insurance (national currency); i6b- financial assets in insurance €; i7a- deposits of insurers (national currency); i7b –deposit of insurers €

Diabetes mellitus is a chronic metabolic health condition affecting millions globally. Diabetes is a growing concern among aging societies, with its prevalence increasing among those aged 65 and above. Enabling disease self-management via relevant education is part of high-quality care to improve health outcomes and minimize complications for individuals living with diabetes. Successful diabetes self-management education (DSME) programs usually require tailoring for the intended audience; however, there is limited literature about the preferences of older persons in Western countries concerning DSME. As such, a broad overview of DSME for older persons was an identified need. To map the available evidence on DSME for persons aged 65 years and older in Western countries, the JBI methodology for conducting and reporting scoping reviews was used. In this scoping review, we considered all studies about DSME for older persons with T1D and T2D in Western countries where lifestyles, risks, prevention, treatment of diabetes, and approaches to self-management and DSME are similar (e.g., North America, Western and Northern Europe and Australasia). Systematic keyword and subject heading searches were conducted in 10 databases (e.g., MEDLINE, JBI EBP) to identify relevant English language papers published from 2000 to 2022. Titles and abstracts were screened to select eligible papers for full-text reading. Full-text screening was done by four independent reviewers to select studies for the final analysis. The review identified 2,397 studies, of which 1,250 full texts were screened for eligibility. Of the final 44 papers included in the review, only one included participants’ understanding of DSME. The education programs differed in their context, design, delivery mode, theoretical underpinnings, and duration. Type of research designs, outcome measures used to determine the effectiveness of DSME, and knowledge gaps were also detailed. Overall, most interventions were effective and improved clinical and behavioural outcomes. Many of the programs led to improvements in clinical outcomes and participants’ quality of life; however, the content needs to be adapted to older persons according to their culture, different degrees of health literacy, preference of education (e.g., individualized or group), preference of setting, degree of frailty and independence, and comorbidities. Few studies included the voices of older persons in the design, implementation, and evaluation of DSME programs. Such experiential knowledge is vital in developing educational programs to ensure alignment with this population’s preferred learning styles, literacy levels, culture, and needs—such an approach could manifest more substantive, sustained results.

The West Africa Coastal Vulnerability Mapping: Demographic and Health Survey Data Sets present grids of maternal education levels and household wealth based on Demographic and Health Survey (DHS) cluster level data for ten West African countries. While the maternal education levels are comparable across countries, owing to different underlying indicators, the household wealth index is not. Education can directly influence risk perception, skills and knowledge and indirectly reduce poverty, improve health, and promote access to information and resources. When facing natural hazards or climate risks, educated individuals, households, and societies are assumed to be more empowered and more adaptive in their response to, preparation for, and recovery from disasters. Education is a key background indicator that helps contextualize a country's health and development situation. The household wealth index is a composite measure of a household's cumulative living standard. The wealth index is calculated using easy-to-collect data on a household's ownership of selected assets, such as televisions and bicycles, materials used for housing construction, and types of water access and sanitation facilities. Bayesian spatial interpolation methods were employed to create country level grids based on DHS cluster point data for each country. Data are from the following dates by country: Benin (2006), Cameroon (2011), Cote d'Ivoire (2012), Ghana (2008), Guinea (2012), Liberia (2011), Nigeria (2010), Sierra Leone (2008), and Togo (1998).

The average for 2021 based on 3 countries was 94.81 percent. The highest value was in Costa Rica: 98.04 percent and the lowest value was in Puerto Rico: 92.4 percent. The indicator is available from 1970 to 2023. Below is a chart for all countries where data are available.

Distribution of variables according to drinking status in women (n =  2,833).

The statistic shows the 20 countries with the lowest fertility rates in 2024. All figures are estimates. In 2024, the fertility rate in Taiwan was estimated to be at 1.11 children per woman, making it the lowest fertility rate worldwide. Fertility rate The fertility rate is the average number of children born per woman of child-bearing age in a country. Usually, a woman aged between 15 and 45 is considered to be in her child-bearing years. The fertility rate of a country provides an insight into its economic state, as well as the level of health and education of its population. Developing countries usually have a higher fertility rate due to lack of access to birth control and contraception, and to women usually foregoing a higher education, or even any education at all, in favor of taking care of housework. Many families in poorer countries also need their children to help provide for the family by starting to work early and/or as caretakers for their parents in old age. In developed countries, fertility rates and birth rates are usually much lower, as birth control is easier to obtain and women often choose a career before becoming a mother. Additionally, if the number of women of child-bearing age declines, so does the fertility rate of a country. As can be seen above, countries like Hong Kong are a good example for women leaving the patriarchal structures and focusing on their own career instead of becoming a mother at a young age, causing a decline of the country’s fertility rate. A look at the fertility rate per woman worldwide by income group also shows that women with a low income tend to have more children than those with a high income. The United States are neither among the countries with the lowest, nor among those with the highest fertility rate, by the way. At 2.08 children per woman, the fertility rate in the US has been continuously slightly below the global average of about 2.4 children per woman over the last decade.

In 2018, students from 196 different countries and regions were studying in China. The highest number of students came from South Korea amounting to 50,600, while only 20,996 students came from the United States.

International students in China

The total number of foreign students in China increased steadily over recent years and reached more than 490,000 in 2018. That was roughly double as much as ten years ago and made China one of the leading host destinations for international students. Looking at their origins in terms of global regions reveals that by far the largest share of students come from Asia, while the Americas and Europe together accounted for only slightly more than 22 percent of all students in 2018. While the share of students from Western countries has been shrinking steadily in recent years, more and more students from Asia and Africa were attracted to study in China. Regarding the United States, the figures interestingly not only decreased in relation to other regions, but also in total numbers. In contrast, students particularly from Africa are increasingly able and willing to study in China, and numbers from countries participating in China's Belt and Road Initiative displayed the highest growth rates over recent years.

Student situation

Regarding the financial situation of international students in China, most of them were either self-funded or receiving a scholarship from foreign institutions. However, the number of students supported by the Chinese government increased considerably over the last ten years, with a growing number of scholarships granted to students from developing countries. Preferred universities for study were either located in the two most developed cities Beijing and Shanghai, or in the eastern and southern coastal regions of China.

Higher education around the globe is striving to develop rigor and productive doctoral studies that mainly evolve in fostering doctoral students’ research skills by furnishing the necessary socialization process which predicts their future professional and academic decisions. Although scholars investigated the socialization experiences of doctoral students from different perspectives and stages, a large body of evidence is concentrated in western countries that do not define or imply non-western countries like Pakistan. Therefore, the present qualitative study sought to be an icebreaker and stimulant investigation to unfold doctoral students’ socialization experience in research-intensive universities through the lens of Weidman’s socialization framework. After interviewing 24 doctoral students, the findings revealed that doctoral students have high expectations from research universities to enhance their research capabilities. Moreover, most students knew the research’s significance for personal and professional development. The study concluded the progressive and adverse research socialization experiences of doctoral students.