research in population education

Global Education

By: Hannah Ritchie , Veronika Samborska , Natasha Ahuja , Esteban Ortiz-Ospina and Max Roser

A good education offers individuals the opportunity to lead richer, more interesting lives. At a societal level, it creates opportunities for humanity to solve its pressing problems.

The world has gone through a dramatic transition over the last few centuries, from one where very few had any basic education to one where most people do. This is not only reflected in the inputs to education – enrollment and attendance – but also in outcomes, where literacy rates have greatly improved.

Getting children into school is also not enough. What they learn matters. There are large differences in educational outcomes : in low-income countries, most children cannot read by the end of primary school. These inequalities in education exacerbate poverty and existing inequalities in global incomes .

On this page, you can find all of our writing and data on global education.

Key insights on Global Education

The world has made substantial progress in increasing basic levels of education.

Access to education is now seen as a fundamental right – in many cases, it’s the government’s duty to provide it.

But formal education is a very recent phenomenon. In the chart, we see the share of the adult population – those older than 15 – that has received some basic education and those who haven’t.

In the early 1800s, fewer than 1 in 5 adults had some basic education. Education was a luxury; in all places, it was only available to a small elite.

But you can see that this share has grown dramatically, such that this ratio is now reversed. Less than 1 in 5 adults has not received any formal education.

This is reflected in literacy data , too: 200 years ago, very few could read and write. Now most adults have basic literacy skills.

What you should know about this data

• Basic education is defined as receiving some kind of formal primary, secondary, or tertiary (post-secondary) education.
• This indicator does not tell us how long a person received formal education. They could have received a full program of schooling, or may only have been in attendance for a short period. To account for such differences, researchers measure the mean years of schooling or the expected years of schooling .

Despite being in school, many children learn very little

International statistics often focus on attendance as the marker of educational progress.

However, being in school does not guarantee that a child receives high-quality education. In fact, in many countries, the data shows that children learn very little.

Just half – 48% – of the world’s children can read with comprehension by the end of primary school. It’s based on data collected over a 9-year period, with 2016 as the average year of collection.

This is shown in the chart, where we plot averages across countries with different income levels. 1

The situation in low-income countries is incredibly worrying, with 90% of children unable to read by that age.

This can be improved – even among high-income countries. The best-performing countries have rates as low as 2%. That’s more than four times lower than the average across high-income countries.

Making sure that every child gets to go to school is essential. But the world also needs to focus on what children learn once they’re in the classroom.

Millions of children learn only very little. How can the world provide a better education to the next generation?

Research suggests that many children – especially in the world’s poorest countries – learn only very little in school. What can we do to improve this?

• This data does not capture total literacy over someone’s lifetime. Many children will learn to read eventually, even if they cannot read by the end of primary school. However, this means they are in a constant state of “catching up” and will leave formal education far behind where they could be.

Children across the world receive very different amounts of quality learning

There are still significant inequalities in the amount of education children get across the world.

This can be measured as the total number of years that children spend in school. However, researchers can also adjust for the quality of education to estimate how many years of quality learning they receive. This is done using an indicator called “learning-adjusted years of schooling”.

On the map, you see vast differences across the world.

In many of the world’s poorest countries, children receive less than three years of learning-adjusted schooling. In most rich countries, this is more than 10 years.

Across most countries in South Asia and Sub-Saharan Africa – where the largest share of children live – the average years of quality schooling are less than 7.

• Learning-adjusted years of schooling merge the quantity and quality of education into one metric, accounting for the fact that similar durations of schooling can yield different learning outcomes.
• Learning-adjusted years is computed by adjusting the expected years of school based on the quality of learning, as measured by the harmonized test scores from various international student achievement testing programs. The adjustment involves multiplying the expected years of school by the ratio of the most recent harmonized test score to 625. Here, 625 signifies advanced attainment on the TIMSS (Trends in International Mathematics and Science Study) test, with 300 representing minimal attainment. These scores are measured in TIMSS-equivalent units.

Hundreds of millions of children worldwide do not go to school

While most children worldwide get the opportunity to go to school, hundreds of millions still don’t.

In the chart, we see the number of children who aren’t in school across primary and secondary education.

This number was around 260 million in 2019.

Many children who attend primary school drop out and do not attend secondary school. That means many more children or adolescents are missing from secondary school than primary education.

Access to basic education: almost 60 million children of primary school age are not in school

The world has made a lot of progress in recent generations, but millions of children are still not in school.

The gender gap in school attendance has closed across most of the world

Globally, until recently, boys were more likely to attend school than girls. The world has focused on closing this gap to ensure every child gets the opportunity to go to school.

Today, these gender gaps have largely disappeared. In the chart, we see the difference in the global enrollment rates for primary, secondary, and tertiary (post-secondary) education. The share of children who complete primary school is also shown.

We see these lines converging over time, and recently they met: rates between boys and girls are the same.

For tertiary education, young women are now more likely than young men to be enrolled.

While the differences are small globally, there are some countries where the differences are still large: girls in Afghanistan, for example, are much less likely to go to school than boys.

Research & Writing

Talent is everywhere, opportunity is not. We are all losing out because of this.

Access to basic education: almost 60 million children of primary school age are not in school, interactive charts on global education.

This data comes from a paper by João Pedro Azevedo et al.

João Pedro Azevedo, Diana Goldemberg, Silvia Montoya, Reema Nayar, Halsey Rogers, Jaime Saavedra, Brian William Stacy (2021) – “ Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It .” World Bank Policy Research Working Paper 9588, March 2021.

Cite this work

Our articles and data visualizations rely on work from many different people and organizations. When citing this topic page, please also cite the underlying data sources. This topic page can be cited as:

BibTeX citation

Reuse this work freely

All visualizations, data, and code produced by Our World in Data are completely open access under the Creative Commons BY license . You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

The data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.

All of our charts can be embedded in any site.

Our World in Data is free and accessible for everyone.

Help us do this work by making a donation.

The World Bank Group is the largest financier of education in the developing world, working in 94 countries and committed to helping them reach SDG4: access to inclusive and equitable quality education and lifelong learning opportunities for all by 2030.

Education is a human right, a powerful driver of development, and one of the strongest instruments for reducing poverty and improving health, gender equality, peace, and stability. It delivers large, consistent returns in terms of income, and is the most important factor to ensure equity and inclusion.

For individuals, education promotes employment, earnings, health, and poverty reduction. Globally, there is a  9% increase in hourly earnings for every extra year of schooling . For societies, it drives long-term economic growth, spurs innovation, strengthens institutions, and fosters social cohesion.  Education is further a powerful catalyst to climate action through widespread behavior change and skilling for green transitions.

Developing countries have made tremendous progress in getting children into the classroom and more children worldwide are now in school. But learning is not guaranteed, as the  2018 World Development Report  (WDR) stressed.

Making smart and effective investments in people’s education is critical for developing the human capital that will end extreme poverty. At the core of this strategy is the need to tackle the learning crisis, put an end to  Learning Poverty , and help youth acquire the advanced cognitive, socioemotional, technical and digital skills they need to succeed in today’s world. 

In low- and middle-income countries, the share of children living in  Learning Poverty  (that is, the proportion of 10-year-old children that are unable to read and understand a short age-appropriate text) increased from 57% before the pandemic to an estimated  70%  in 2022.

However, learning is in crisis. More than 70 million more people were pushed into poverty during the COVID pandemic, a billion children lost a year of school , and three years later the learning losses suffered have not been recouped .  If a child cannot read with comprehension by age 10, they are unlikely to become fluent readers. They will fail to thrive later in school and will be unable to power their careers and economies once they leave school.

The effects of the pandemic are expected to be long-lasting. Analysis has already revealed deep losses, with international reading scores declining from 2016 to 2021 by more than a year of schooling.  These losses may translate to a 0.68 percentage point in global GDP growth.  The staggering effects of school closures reach beyond learning. This generation of children could lose a combined total of  US$21 trillion in lifetime earnings  in present value or the equivalent of 17% of today’s global GDP – a sharp rise from the 2021 estimate of a US$17 trillion loss. 

Action is urgently needed now – business as usual will not suffice to heal the scars of the pandemic and will not accelerate progress enough to meet the ambitions of SDG 4. We are urging governments to implement ambitious and aggressive Learning Acceleration Programs to get children back to school, recover lost learning, and advance progress by building better, more equitable and resilient education systems.

Last Updated: Mar 25, 2024

The World Bank’s global education strategy is centered on ensuring learning happens – for everyone, everywhere. Our vision is to ensure that everyone can achieve her or his full potential with access to a quality education and lifelong learning. To reach this, we are helping countries build foundational skills like literacy, numeracy, and socioemotional skills – the building blocks for all other learning. From early childhood to tertiary education and beyond – we help children and youth acquire the skills they need to thrive in school, the labor market and throughout their lives.

Investing in the world’s most precious resource – people – is paramount to ending poverty on a livable planet.  Our experience across more than 100 countries bears out this robust connection between human capital, quality of life, and economic growth: when countries strategically invest in people and the systems designed to protect and build human capital at scale, they unlock the wealth of nations and the potential of everyone.

Building on this, the World Bank supports resilient, equitable, and inclusive education systems that ensure learning happens for everyone. We do this by generating and disseminating evidence, ensuring alignment with policymaking processes, and bridging the gap between research and practice.

The World Bank is the largest source of external financing for education in developing countries, with a portfolio of about $26 billion in 94 countries including IBRD, IDA and Recipient-Executed Trust Funds. IDA operations comprise 62% of the education portfolio.

The investment in FCV settings has increased dramatically and now accounts for 26% of our portfolio.

World Bank projects reach at least 425 million students -one-third of students in low- and middle-income countries.

The World Bank’s Approach to Education

Five interrelated pillars of a well-functioning education system underpin the World Bank’s education policy approach:

• Learners are prepared and motivated to learn;
• Teachers are prepared, skilled, and motivated to facilitate learning and skills acquisition;
• Learning resources (including education technology) are available, relevant, and used to improve teaching and learning;
• Schools are safe and inclusive; and
• Education Systems are well-managed, with good implementation capacity and adequate financing.

The Bank is already helping governments design and implement cost-effective programs and tools to build these pillars.

Our Principles:

• We pursue systemic reform supported by political commitment to learning for all children. 
• We focus on equity and inclusion through a progressive path toward achieving universal access to quality education, including children and young adults in fragile or conflict affected areas , those in marginalized and rural communities,  girls and women , displaced populations,  students with disabilities , and other vulnerable groups.
• We focus on results and use evidence to keep improving policy by using metrics to guide improvements.   
• We want to ensure financial commitment commensurate with what is needed to provide basic services to all. 
• We invest wisely in technology so that education systems embrace and learn to harness technology to support their learning objectives.   

Laying the groundwork for the future

Country challenges vary, but there is a menu of options to build forward better, more resilient, and equitable education systems.

Countries are facing an education crisis that requires a two-pronged approach: first, supporting actions to recover lost time through remedial and accelerated learning; and, second, building on these investments for a more equitable, resilient, and effective system.

Recovering from the learning crisis must be a political priority, backed with adequate financing and the resolve to implement needed reforms.  Domestic financing for education over the last two years has not kept pace with the need to recover and accelerate learning. Across low- and lower-middle-income countries, the  average share of education in government budgets fell during the pandemic , and in 2022 it remained below 2019 levels.

The best chance for a better future is to invest in education and make sure each dollar is put toward improving learning.  In a time of fiscal pressure, protecting spending that yields long-run gains – like spending on education – will maximize impact.  We still need more and better funding for education.  Closing the learning gap will require increasing the level, efficiency, and equity of education spending—spending smarter is an imperative.

• Education technology  can be a powerful tool to implement these actions by supporting teachers, children, principals, and parents; expanding accessible digital learning platforms, including radio/ TV / Online learning resources; and using data to identify and help at-risk children, personalize learning, and improve service delivery.

Looking ahead

We must seize this opportunity  to reimagine education in bold ways. Together, we can build forward better more equitable, effective, and resilient education systems for the world’s children and youth.

Accelerating Improvements

Supporting countries in establishing time-bound learning targets and a focused education investment plan, outlining actions and investments geared to achieve these goals.

Launched in 2020, the  Accelerator Program  works with a set of countries to channel investments in education and to learn from each other. The program coordinates efforts across partners to ensure that the countries in the program show improvements in foundational skills at scale over the next three to five years. These investment plans build on the collective work of multiple partners, and leverage the latest evidence on what works, and how best to plan for implementation.  Countries such as Brazil (the state of Ceará) and Kenya have achieved dramatic reductions in learning poverty over the past decade at scale, providing useful lessons, even as they seek to build on their successes and address remaining and new challenges.  

Universalizing Foundational Literacy

Readying children for the future by supporting acquisition of foundational skills – which are the gateway to other skills and subjects.

The  Literacy Policy Package (LPP)   consists of interventions focused specifically on promoting acquisition of reading proficiency in primary school. These include assuring political and technical commitment to making all children literate; ensuring effective literacy instruction by supporting teachers; providing quality, age-appropriate books; teaching children first in the language they speak and understand best; and fostering children’s oral language abilities and love of books and reading.

Advancing skills through TVET and Tertiary

Ensuring that individuals have access to quality education and training opportunities and supporting links to employment.

Tertiary education and skills systems are a driver of major development agendas, including human capital, climate change, youth and women’s empowerment, and jobs and economic transformation. A comprehensive skill set to succeed in the 21st century labor market consists of foundational and higher order skills, socio-emotional skills, specialized skills, and digital skills. Yet most countries continue to struggle in delivering on the promise of skills development. 

The World Bank is supporting countries through efforts that address key challenges including improving access and completion, adaptability, quality, relevance, and efficiency of skills development programs. Our approach is via multiple channels including projects, global goods, as well as the Tertiary Education and Skills Program . Our recent reports including Building Better Formal TVET Systems and STEERing Tertiary Education provide a way forward for how to improve these critical systems.

Addressing Climate Change

Mainstreaming climate education and investing in green skills, research and innovation, and green infrastructure to spur climate action and foster better preparedness and resilience to climate shocks.

Our approach recognizes that education is critical for achieving effective, sustained climate action. At the same time, climate change is adversely impacting education outcomes. Investments in education can play a huge role in building climate resilience and advancing climate mitigation and adaptation. Climate change education gives young people greater awareness of climate risks and more access to tools and solutions for addressing these risks and managing related shocks. Technical and vocational education and training can also accelerate a green economic transformation by fostering green skills and innovation. Greening education infrastructure can help mitigate the impact of heat, pollution, and extreme weather on learning, while helping address climate change. 

Examples of this work are projects in Nigeria (life skills training for adolescent girls), Vietnam (fostering relevant scientific research) , and Bangladesh (constructing and retrofitting schools to serve as cyclone shelters).

Strengthening Measurement Systems

Enabling countries to gather and evaluate information on learning and its drivers more efficiently and effectively.

The World Bank supports initiatives to help countries effectively build and strengthen their measurement systems to facilitate evidence-based decision-making. Examples of this work include:

(1) The  Global Education Policy Dashboard (GEPD) : This tool offers a strong basis for identifying priorities for investment and policy reforms that are suited to each country context by focusing on the three dimensions of practices, policies, and politics.

• Highlights gaps between what the evidence suggests is effective in promoting learning and what is happening in practice in each system; and
• Allows governments to track progress as they act to close the gaps.

The GEPD has been implemented in 13 education systems already – Peru, Rwanda, Jordan, Ethiopia, Madagascar, Mozambique, Islamabad, Khyber Pakhtunkhwa, Sierra Leone, Niger, Gabon, Jordan and Chad – with more expected by the end of 2024.

(2)  Learning Assessment Platform (LeAP) : LeAP is a one-stop shop for knowledge, capacity-building tools, support for policy dialogue, and technical staff expertise to support student achievement measurement and national assessments for better learning.

Supporting Successful Teachers

Helping systems develop the right selection, incentives, and support to the professional development of teachers.

Currently, the World Bank Education Global Practice has over 160 active projects supporting over 18 million teachers worldwide, about a third of the teacher population in low- and middle-income countries. In 12 countries alone, these projects cover 16 million teachers, including all primary school teachers in Ethiopia and Turkey, and over 80% in Bangladesh, Pakistan, and Vietnam.

A World Bank-developed classroom observation tool, Teach, was designed to capture the quality of teaching in low- and middle-income countries. It is now 3.6 million students.

While Teach helps identify patterns in teacher performance, Coach leverages these insights to support teachers to improve their teaching practice through hands-on in-service teacher professional development (TPD).

Our recent report on Making Teacher Policy Work proposes a practical framework to uncover the black box of effective teacher policy and discusses the factors that enable their scalability and sustainability.

 Supporting Education Finance Systems

Strengthening country financing systems to mobilize resources for education and make better use of their investments in education.

Our approach is to bring together multi-sectoral expertise to engage with ministries of education and finance and other stakeholders to develop and implement effective and efficient public financial management systems; build capacity to monitor and evaluate education spending, identify financing bottlenecks, and develop interventions to strengthen financing systems; build the evidence base on global spending patterns and the magnitude and causes of spending inefficiencies; and develop diagnostic tools as public goods to support country efforts.

Working in Fragile, Conflict, and Violent (FCV) Contexts

The massive and growing global challenge of having so many children living in conflict and violent situations requires a response at the same scale and scope. Our education engagement in the Fragility, Conflict and Violence (FCV) context, which stands at US$5.35 billion, has grown rapidly in recent years, reflecting the ever-increasing importance of the FCV agenda in education. Indeed, these projects now account for more than 25% of the World Bank education portfolio.

Education is crucial to minimizing the effects of fragility and displacement on the welfare of youth and children in the short-term and preventing the emergence of violent conflict in the long-term. 

Support to Countries Throughout the Education Cycle

Our support to countries covers the entire learning cycle, to help shape resilient, equitable, and inclusive education systems that ensure learning happens for everyone. 

The ongoing  Supporting  Egypt  Education Reform project , 2018-2025, supports transformational reforms of the Egyptian education system, by improving teaching and learning conditions in public schools. The World Bank has invested $500 million in the project focused on increasing access to quality kindergarten, enhancing the capacity of teachers and education leaders, developing a reliable student assessment system, and introducing the use of modern technology for teaching and learning. Specifically, the share of Egyptian 10-year-old students, who could read and comprehend at the global minimum proficiency level, increased to 45 percent in 2021.

In  Nigeria , the $75 million  Edo  Basic Education Sector and Skills Transformation (EdoBESST)  project, running from 2020-2024, is focused on improving teaching and learning in basic education. Under the project, which covers 97 percent of schools in the state, there is a strong focus on incorporating digital technologies for teachers. They were equipped with handheld tablets with structured lesson plans for their classes. Their coaches use classroom observation tools to provide individualized feedback. Teacher absence has reduced drastically because of the initiative. Over 16,000 teachers were trained through the project, and the introduction of technology has also benefited students.

Through the $235 million  School Sector Development Program  in  Nepal  (2017-2022), the number of children staying in school until Grade 12 nearly tripled, and the number of out-of-school children fell by almost seven percent. During the pandemic, innovative approaches were needed to continue education. Mobile phone penetration is high in the country. More than four in five households in Nepal have mobile phones. The project supported an educational service that made it possible for children with phones to connect to local radio that broadcast learning programs.

From 2017-2023, the $50 million  Strengthening of State Universities  in  Chile  project has made strides to improve quality and equity at state universities. The project helped reduce dropout: the third-year dropout rate fell by almost 10 percent from 2018-2022, keeping more students in school.

The World Bank’s first  Program-for-Results financing in education  was through a $202 million project in  Tanzania , that ran from 2013-2021. The project linked funding to results and aimed to improve education quality. It helped build capacity, and enhanced effectiveness and efficiency in the education sector. Through the project, learning outcomes significantly improved alongside an unprecedented expansion of access to education for children in Tanzania. From 2013-2019, an additional 1.8 million students enrolled in primary schools. In 2019, the average reading speed for Grade 2 students rose to 22.3 words per minute, up from 17.3 in 2017. The project laid the foundation for the ongoing $500 million  BOOST project , which supports over 12 million children to enroll early, develop strong foundational skills, and complete a quality education.

The $40 million  Cambodia  Secondary Education Improvement project , which ran from 2017-2022, focused on strengthening school-based management, upgrading teacher qualifications, and building classrooms in Cambodia, to improve learning outcomes, and reduce student dropout at the secondary school level. The project has directly benefited almost 70,000 students in 100 target schools, and approximately 2,000 teachers and 600 school administrators received training.

The World Bank is co-financing the $152.80 million  Yemen  Restoring Education and Learning Emergency project , running from 2020-2024, which is implemented through UNICEF, WFP, and Save the Children. It is helping to maintain access to basic education for many students, improve learning conditions in schools, and is working to strengthen overall education sector capacity. In the time of crisis, the project is supporting teacher payments and teacher training, school meals, school infrastructure development, and the distribution of learning materials and school supplies. To date, almost 600,000 students have benefited from these interventions.

The $87 million  Providing an Education of Quality in  Haiti  project supported approximately 380 schools in the Southern region of Haiti from 2016-2023. Despite a highly challenging context of political instability and recurrent natural disasters, the project successfully supported access to education for students. The project provided textbooks, fresh meals, and teacher training support to 70,000 students, 3,000 teachers, and 300 school directors. It gave tuition waivers to 35,000 students in 118 non-public schools. The project also repaired 19 national schools damaged by the 2021 earthquake, which gave 5,500 students safe access to their schools again.

In 2013, just 5% of the poorest households in  Uzbekistan  had children enrolled in preschools. Thanks to the  Improving Pre-Primary and General Secondary Education Project , by July 2019, around 100,000 children will have benefitted from the half-day program in 2,420 rural kindergartens, comprising around 49% of all preschool educational institutions, or over 90% of rural kindergartens in the country.

In addition to working closely with governments in our client countries, the World Bank also works at the global, regional, and local levels with a range of technical partners, including foundations, non-profit organizations, bilaterals, and other multilateral organizations. Some examples of our most recent global partnerships include:

UNICEF, UNESCO, FCDO, USAID, Bill & Melinda Gates Foundation:  Coalition for Foundational Learning

The World Bank is working closely with UNICEF, UNESCO, FCDO, USAID, and the Bill & Melinda Gates Foundation as the  Coalition for Foundational Learning  to advocate and provide technical support to ensure foundational learning.  The World Bank works with these partners to promote and endorse the  Commitment to Action on Foundational Learning , a global network of countries committed to halving the global share of children unable to read and understand a simple text by age 10 by 2030.

Australian Aid, Bernard van Leer Foundation, Bill & Melinda Gates Foundation, Canada, Echida Giving, FCDO, German Cooperation, William & Flora Hewlett Foundation, Conrad Hilton Foundation, LEGO Foundation, Porticus, USAID: Early Learning Partnership

The Early Learning Partnership (ELP) is a multi-donor trust fund, housed at the World Bank.  ELP leverages World Bank strengths—a global presence, access to policymakers and strong technical analysis—to improve early learning opportunities and outcomes for young children around the world.

We help World Bank teams and countries get the information they need to make the case to invest in Early Childhood Development (ECD), design effective policies and deliver impactful programs. At the country level, ELP grants provide teams with resources for early seed investments that can generate large financial commitments through World Bank finance and government resources. At the global level, ELP research and special initiatives work to fill knowledge gaps, build capacity and generate public goods.

UNESCO, UNICEF:  Learning Data Compact

UNESCO, UNICEF, and the World Bank have joined forces to close the learning data gaps that still exist and that preclude many countries from monitoring the quality of their education systems and assessing if their students are learning. The three organizations have agreed to a  Learning Data Compact , a commitment to ensure that all countries, especially low-income countries, have at least one quality measure of learning by 2025, supporting coordinated efforts to strengthen national assessment systems.

UNESCO Institute for Statistics (UIS):   Learning Poverty Indicator

Aimed at measuring and urging attention to foundational literacy as a prerequisite to achieve SDG4, this partnership was launched in 2019 to help countries strengthen their learning assessment systems, better monitor what students are learning in internationally comparable ways and improve the breadth and quality of global data on education.

FCDO, Bill & Melinda Gates Foundation:  EdTech Hub

Supported by the UK government’s Foreign, Commonwealth & Development Office (FCDO), in partnership with the Bill & Melinda Gates Foundation, the EdTech Hub is aimed at improving the quality of ed-tech investments. The Hub launched a rapid response Helpdesk service to provide just-in-time advisory support to 70 low- and middle-income countries planning education technology and remote learning initiatives.

MasterCard Foundation

Our Tertiary Education and Skills  global program, launched with support from the Mastercard Foundation, aims to prepare youth and adults for the future of work and society by improving access to relevant, quality, equitable reskilling and post-secondary education opportunities.  It is designed to reframe, reform, and rebuild tertiary education and skills systems for the digital and green transformation.

Bridging the AI divide: Breaking down barriers to ensure women’s leadership and participation in the Fifth Industrial Revolution

Common challenges and tailored solutions: How policymakers are strengthening early learning systems across the world

Compulsory education boosts learning outcomes and climate action

Areas of focus.

Data & Measurement

Early Childhood Development

Financing Education

Foundational Learning

Fragile, Conflict & Violent Contexts

Girls’ Education

Inclusive Education

Skills Development

Technology (EdTech)  

Tertiary Education

Initiatives

• Show More +
• Tertiary Education and Skills Program
• Service Delivery Indicators
• Evoke: Transforming education to empower youth
• Global Education Policy Dashboard
• Global Education Evidence Advisory Panel
• Show Less -

Collapse and Recovery: How the COVID-19 Pandemic Eroded Human Capital and What to Do About It

BROCHURES & FACT SHEETS

Flyer: Education Factsheet - May 2024

Publication: Realizing Education's Promise: A World Bank Retrospective – August 2023

Flyer: Education and Climate Change - November 2022

Brochure: Learning Losses - October 2022

STAY CONNECTED

Human Development Topics

Around the bank group.

Find out what the Bank Group's branches are doing in education

Global Education Newsletter - April 2024

What's happening in the World Bank Education Global Practice? Read to learn more.

Human Capital Project

The Human Capital Project is a global effort to accelerate more and better investments in people for greater equity and economic growth.

Impact Evaluations

Research that measures the impact of education policies to improve education in low and middle income countries.

Education Videos

Watch our latest videos featuring our projects across the world

Additional Resources

Education Finance

Higher Education

Digital Technologies

Education Data & Measurement

Education in Fragile, Conflict & Violence Contexts

This site uses cookies to optimize functionality and give you the best possible experience. If you continue to navigate this website beyond this page, cookies will be placed on your browser. To learn more about cookies, click here .

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Published: 13 April 2020

The effect of education on determinants of climate change risks

• Brian C. O’Neill   ORCID: orcid.org/0000-0001-7505-8897 1 ,
• Leiwen Jiang   ORCID: orcid.org/0000-0002-2073-6440 2 , 3 ,
• Samir KC 2 , 4 ,
• Regina Fuchs 5 ,
• Shonali Pachauri   ORCID: orcid.org/0000-0001-8138-3178 4 ,
• Emily K. Laidlaw 4 ,
• Tiantian Zhang 6 ,
• Wei Zhou 6 &
• Xiaolin Ren 7  

Nature Sustainability volume  3 ,  pages 520–528 ( 2020 ) Cite this article

3251 Accesses

41 Citations

94 Altmetric

Metrics details

• Socioeconomic scenarios
• Sustainability

Increased educational attainment is a sustainable development priority and has been posited to have benefits for other social and environmental issues, including climate change. However, links between education and climate change risks can involve both synergies and trade-offs, and the balance of these effects remains ambiguous. Increases in educational attainment could lead to faster economic growth and therefore higher emissions, more climate change and higher risks. At the same time, improved attainment would be associated with faster fertility decline in many countries, slower population growth and therefore lower emissions, and would also be likely to reduce vulnerability to climate impacts. We employ a multiregion, multisector model of the world economy, driven with country-specific projections of future population by level of education, to test the net effect of education on emissions and on the Human Development Index (HDI), an indicator that correlates with adaptive capacity to climate impacts. We find that improved educational attainment is associated with a modest net increase in emissions but substantial improvement in the HDI values in developing country regions. Avoiding stalled progress in educational attainment and achieving gains at least consistent with historical trends is especially important in reducing future vulnerability.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 12 digital issues and online access to articles

111,21 € per year

only 9,27 € per issue

Buy this article

• Purchase on Springer Link
• Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

Effectiveness of carbon dioxide emission target is linked to country ambition and education level

Education outcomes in the era of global climate change

Right-wing ideology reduces the effects of education on climate change beliefs in more developed countries

Data availability.

The samples of census datasets analysed during the current study are publicly available from IPUMS International at https://international.ipums.org/international/ . The national sample household survey data analysed for this study are publicly available for Brazil ( https://www.ibge.gov.br/estatisticas/sociais/habitacao/9050-pesquisa-de-orcamentos-familiares.html?=&t=downloads ), China ( https://opendata.pku.edu.cn/dataverse/CFPS?language=en ), India (Human Development Survey, https://www.icpsr.umich.edu/icpsrweb/DSDR/studies/36151 ), Mexico ( http://en.www.inegi.org.mx/programas/enigh/tradicional/2005/ ), South Africa ( https://www.datafirst.uct.ac.za/dataportal/index.php/catalog/316 ) and Uganda ( http://microdata.worldbank.org/index.php/catalog/2059 ), after registering and submitting requests. The national survey data for some countries are available but restrictions apply to the availability of these data, which were used under licence for the current study and so are not publicly available. These include India (National Sample Survey 2004–2005 and 2011–2012, http://www.icssrdataservice.in/datarepository/index.php/ ) and Indonesia ( https://microdata.bps.go.id/mikrodata/index.php/catalog/SUSENAS ). While these original full datasets have restrictions on availability, tables of derived results from the original datasets can be provided upon request.

Code availability

The code for the version of the iPETS model used to produce economic and emissions projections for this analysis is available upon request. It will eventually be publicly available at ipetsmodel.com.

Smith, W. C., Anderson, E., Salinas, D., Horvatek, R. & Baker, D. P. A meta-analysis of education effects on chronic disease: the causal dynamics of the population education transition curve. Soc. Sci. Med. 127 , 29–40 (2015).

Article   Google Scholar  

Coady, D. & Dizioli, A. Income inequality and education revisited: persistence, endogeneity and heterogeneity. Appl. Econ. 50 , 2747–2761 (2018).

Hanmer, L. & Klugman, J. Exploring women’s agency and empowerment in developing countries: where do we stand? Fem. Econ. 22 , 237–263 (2016).

Transforming Our World: The 2030 Agenda for Sustainable Development (United Nations, 2015).

A Guide to SDG Interactions: From Science to Implementation (International Council for Science, 2017).

McCollum, D. L. et al. Connecting the sustainable development goals by their energy inter-linkages. Environ. Res. Lett. 13 , 033006 (2018).

Pradhan, P., Costa, L., Rybski, D., Lucht, W. & Kropp, J. P. A systematic study of sustainable development goal (SDG) interactions. Earth’s Future 5 , 1169–1179 (2017).

Moyer, J. D. & Bohl, D. K. Alternative pathways to human development: assessing trade-offs and synergies in achieving the Sustainable Development Goals. Futures 105 , 199–210 (2019).

Gomez-Echeverri, L. Climate and development: enhancing impact through stronger linkages in the implementation of the Paris Agreement and the Sustainable Development Goals (SDGs). Phil. Trans. R. Soc. A 376 , 20160444 (2018).

Oppenheimer, M. et al. in IPCC Climate Change 2014: Impacts, Adaptation and Vulnerability (eds Field, C. B. et al.) 1039–1100 (Cambridge Univ. Press, 2014) .

O’Neill, B. C. et al. Global demographic trends and future carbon emissions. Proc. Natl Acad. Sci. USA 107 , 17521–17526 (2010).

Crespo Cuaresma, J., Lutz, W. & Sanderson, W. Is the demographic dividend an education dividend? Demography 51 , 299–315 (2014).

Lutz, W., Muttarak, R. & Striessnig, E. Universal education is key to enhanced climate adaptation. Science 346 , 1061–1062 (2014).

Article   CAS   Google Scholar  

Gall, M. Indices of Social Vulnerability to Natural Hazards: A Comparative Evaluation (Univ. South Carolina, 2007).

Füssel, H.-M. Review and Quantitative Analysis of Indices of Climate Change Exposure, Adaptive Capacity, Sensitivity, and Impacts (World Bank, 2010); https://openknowledge.worldbank.org/handle/10986/9193

KC, S. & Lutz, W. The human core of the shared socioeconomic pathways: population scenarios by age, sex and level of education for all countries to 2100. Glob. Environ. Change 42 , 181–192 (2017).

O’Neill, B. C. et al. The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob. Environ. Change 42 , 169–180 (2017).

Riahi, K. et al. The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42 , 153–168 (2017).

Ren, X. et al. Avoided economic impacts of climate change on agriculture: integrating a land surface model (CLM) with a global economic model (iPETS). Clim. Change 146 , 517–531 (2018).

Ren, X., Lu, Y., O'Neill, B. C. & Weitzel, M. Economic and biophysical impacts on agriculture under 1.5 °C and 2 °C warming. Environ. Res. Lett. 13 , 115006 (2018).

Böhringer, C. & Löschel, A. Computable general equilibrium models for sustainability impact assessment: status quo and prospects. Ecol. Econ. 60 , 49–64 (2006).

Scrieciu, S. S. The inherent dangers of using computable general equilibrium models as a single integrated modelling framework for sustainability impact assessment. A critical note on Böhringer and Löschel (2006). Ecol. Econ. 60 , 678–684 (2007).

Lutz, W. & Skirbekk, V. in World Population & Human Capital in the Twenty-First Century: An Overview (eds Lutz, W. et al.) 14–38 (Oxford Univ. Press, 2014).

IPCC: Summary for Policymakers. In Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

Fuso Nerini, F. et al. Mapping synergies and trade-offs between energy and the sustainable development goals. Nat. Energy 3 , 10–15 (2018).

Wiedenhofer, D., Smetschka, B., Akenji, L., Jalas, M. & Haberl, H. Household time use, carbon footprints, and urban form: a review of the potential contributions of everyday living to the 1.5 °C climate target. Curr. Opin. Environ. Sustain. 30 , 7–17 (2018).

Duarte, R. et al. Modeling the carbon consequences of pro-environmental consumer behavior. Appl. Energy 184 , 1207–1216 (2016).

Dickson, J. R., Hughes, B. B. & Irfan, M. T. Advancing Global Education (Routledge, 2010).

Burke, M., Davis, W. M. & Diffenbaugh, N. S. Large potential reduction in economic damages under UN mitigation targets. Nature 557 , 549–553 (2018).

IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer, L. A.) (IPCC, 2014).

Casey, G. & Galor, O. Is faster economic growth compatible with reductions in carbon emissions? The role of diminished population growth. Environ. Res. Lett. 12 , 014003 (2017).

Bongaarts, J. & O’Neill, B. C. Global warming policy: is population left out in the cold? Science 361 , 650–652 (2018).

Jiang, L. & O’Neill, B. C. Global urbanization projections for the shared socioeconomic pathways. Glob. Environ. Change 42 , 193–199 (2017).

Dellink, R., Chateau, J., Lanzi, E. & Magné, B. Long-term economic growth projections in the shared socioeconomic pathways. Glob. Environ. Change 42 , 200–214 (2017).

Lutz, W. & Kc, S. Global human capital: integrating education and population. Science 333 , 587–592 (2011).

KC, S. et al. Projection of populations by level of educational attainment, age, and sex for 120 countries for 2005–2050. Demogr. Res. 22 , 383–472 (2010).

KC, S., Potancokova, M., Bauer, R., Goujon, A. & Striessnig, E. in World Population and Human Capital in the Twenty-First Century (eds Lutz, W. et al.) 434–518 (Oxford Univ. Press, 2014).

Cutler, D. & Lleras-Muney, A. in Encyclopedia of Health Economics (ed. Culyer, A. J.) 232–245 (Elsevier, 2014).

Baker, D. P., Leon, J., Smith Greenaway, E. G., Collins, J. & Movit, M. The education effect on population health: a reassessment. Popul. Dev. Rev. 37 , 307–332 (2011).

KC, S. & Lentzner, H. The effect of education on adult mortality and disability: a global perspective. Vienna Yearb. Popul. Res. 8 , 201–235 (2010).

Rindfuss, R. R., St. John, C. & Bumpass, L. L. Education and the timing of motherhood: disentangling causation. J. Marriage Fam. 46 , 981–984 (1984).

Gerster, M., Ejrnæs, M. & Keiding, N. The causal effect of educational attainment on completed fertility for a cohort of Danish women—does feedback play a role? Stat. Biosci. 6 , 204–222 (2014).

Kravdal, Ø. Effects of current education on second- and third-birth rates among Norwegian women and men born in 1964: substantive interpretations and methodological issues. Demogr. Res. 17 , 211–246 (2007).

Forced Out: Mandatory Pregnancy Testing and the Expulsion of Pregnant Students in Tanzanian Schools (CRR, 2013); https://go.nature.com/2WJOg9J

Bongaarts, J. The causes of educational differences in fertility in Sub-Saharan Africa. Vienna Yearb. Popul. Res. 8 , 31–50 (2010).

Clarke, D. Children and their parents: a review of fertility and causality. J. Econ. Surv. 32 , 518–540 (2018).

Hoem, J. M. & Kreyenfeld, M. Anticipatory analysis and its alternatives in life-course research. Part 1: the role of education in the study of first childbearing. Demogr. Res. 15 , 461–484 (2006).

Calvin, K. et al. The SSP4: a world of deepening inequality. Glob. Environ. Change 42 , 284–296 (2017).

Rogelj, J. et al. Scenarios towards limiting global mean temperature increase below 1.5 °C. Nat. Clim. Change 8 , 325–332 (2018).

Download references

Acknowledgements

We thank the Asian Demographic Research Institute at Shanghai University for hosting research stays for B.C.O. during which parts of this work were carried out. A substantial amount of the work for this study was completed while B.C.O., L.J., E.K.L. and X.R. were at the National Center for Atmospheric Research, Boulder, CO.

Author information

Authors and affiliations.

Josef Korbel School of International Studies and Pardee Center for International Futures, University of Denver, Denver, CO, USA

Brian C. O’Neill

Asian Demographic Research Institute, Shanghai University, Shanghai, China

Leiwen Jiang & Samir KC

Population Council, New York, NY, USA

• Leiwen Jiang

International Institute for Applied Systems Analysis, Laxenburg, Austria

Samir KC, Shonali Pachauri & Emily K. Laidlaw

Statistics Austria, Vienna, Austria

Regina Fuchs

Institute of Population and Development Studies, Zhejiang University, Hangzhou, China

Tiantian Zhang & Wei Zhou

National Center for Atmospheric Research, Boulder, CO, USA

Xiaolin Ren

You can also search for this author in PubMed   Google Scholar

Contributions

B.C.O. led, and L.J., S.KC and S.P. contributed to, the design of the study. B.C.O. coordinated the paper and led the writing, with contributions from L.J., S.KC, S.P. and E.K.L. L.J., R.F., S.P. and E.K.L. led the analysis of household survey data, with contributions from T.Z. and W.Z. S.KC carried out the population–education projections. L.J. carried out the household projections. X.R. carried out the iPETS model projections, with contributions from B.C.O. B.C.O., L.J., S.KC, S.P., E.K.L. and X.R. interpreted results.

Corresponding author

Correspondence to Brian C. O’Neill .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information.

Supplementary Methods 1–3, Tables 1–4, Figs. 1–4 and references.

Reporting Summary

Rights and permissions.

Reprints and permissions

About this article

Cite this article.

O’Neill, B.C., Jiang, L., KC, S. et al. The effect of education on determinants of climate change risks. Nat Sustain 3 , 520–528 (2020). https://doi.org/10.1038/s41893-020-0512-y

Download citation

Received : 03 August 2018

Accepted : 11 March 2020

Published : 13 April 2020

Issue Date : July 2020

DOI : https://doi.org/10.1038/s41893-020-0512-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Spatial and temporal changes in social vulnerability to natural hazards: a case study for china counties.

Natural Hazards (2024)

Achieving carbon neutrality in Africa is possible: the impact of education, employment, and renewable energy consumption on carbon emissions

• Chinyere Ori Elom
• Robert Ugochukwu Onyeneke
• Chidebe Chijioke Uwaleke

Carbon Research (2024)

Climate change unequally affects nitrogen use and losses in global croplands

• Chenchen Ren
• Xiuming Zhang

Nature Food (2023)

Assessing populations exposed to climate change: a focus on Africa in a global context

• Daniela Ghio
• Anne Goujon
• Thomas Petroliagkis

Population and Environment (2023)

Digitalization and its impact on labour market and education. Selected aspects

• Piotr Hetmańczyk

Education and Information Technologies (2023)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

< Back to my filtered results

Population education in non-formal education and development programmes: a manual for field-workers

This manual provides practical examples of strategies, approaches and materials designed to integrate population education into various development programmes. It is a useful reference for field workers who are involved in planning, implementing and evaluating out-of-school population education programmes.

• History & Overview
• Meet Our Team
• Program FAQs
• Social Studies
• Language Arts
• Distance Learning
• Population Pyramids
• World Population “dot” Video
• Student Video Contest
• Lower Elementary (K-2)
• Upper Elementary (3-5)
• Middle School (6-8)
• High School (9-12)
• Browse all Resources
• Content Focus by Grade
• Standards Matches by State
• Infographics
• Articles, Factsheets & Book Lists
• Population Background Info
• Upcoming Online Workshops
• On-Demand Webinar Library
• Online Graduate Course
• Request an Online or In-person Workshop
• About Teachers Workshops
• About Online Teacher Workshops
• Pre-Service Workshops for University Classes
• In-Service Workshops for Teachers
• Workshops for Nonformal Educators
• Where We’ve Worked
• About the Network
• Trainer Spotlight
• Trainers Network FAQ
• Becoming a Trainer
• Annual Leadership Institutes
• Application

Filter Resources

By grade level, by resource type.

• Lesson Plan ?
• Lesson Packet ?
• Curriculum ?

Classroom Resources

Lesson plan:.

Students articulate their thoughts about the ethical issues related to population reaching seven billion and...

Through an article, video, simulation game, and small group research, students explore factors that influence...

Students test different pH solutions on radish seeds to determine the optimal level for seed...

Students discuss the concept of biotic potential and use equations to determine how a family...

Students use beans to model population growth in several mystery countries while varying four key...

Students participate in a game that mimics the relationship between population growth and aquifer depletion....

Students examine their own perceptions of gender roles through two short mental exercises, then research...

Students determine a list of criteria to use when deciding the fate of endangered species,...

Students play a game that simulates a cap and trade system, and analyze its successes...

Health case study reading: Discover Rwanda's success in improving child and maternal mortality over the...

Secondary-level reading surveys past and present food and diet trends in the United States –...

Secondary- level reading provides a journey through the history of schooling (K-12 and higher education)...

Secondary-level reading traces the history of transportation in the United States from horse-drawn carriages to...

Secondary-level reading provides an overview of the history of love and marriage in the U.S.,...

Secondary-level reading looks at the history of work in the United States for different segments...

Secondary-level reading explores the relationship between U.S. people and the natural environment from early settlements...

Personal consumption background reading: What is an ecological footprint and what are the impacts of...

Rich and poor case study reading: There are already great inventions for reducing global poverty....

Lesson Packet:

U.S. population takes center stage in this downloadable packet of classroom lessons, 330 Million in...

Thematic unit for the middle school classroom covers issues related to garbage and solid waste...

Thematic unit for the high school classroom on outdoor and indoor air pollution. Includes teaching...

Thematic unit for the high school classroom on biodiversity issues, endangered animals, and extinction rates....

Thematic unit for the high school classroom covers a wide range topics related to climate...

Packet of 2024 Earth Day lesson plans for K - 5th grades is free to...

Packet of 2024 Earth Day lesson plans for 9th - 12th grades. Free to download!...

Packet of 2024 Earth Day lesson plans for 6th, 7th and 8th grades is free...

Thematic unit for the high school classroom on energy issues in more developed and less...

Curriculum:

Fully online, elementary curriculum connects students to the world around them while building their math,...

A must for high school students to explore some of the most pressing environmental, social...

Fully online, interdisciplinary curriculum helps middle school students understand the connections between human population growth,...

You've found the ultimate multi-disciplinary tool to introduce students of all ages to population studies...

Students work in small groups to determine the main environmental concerns during given periods of...

Acting as countries in a simulation game, students discuss how resources are inequitably distributed throughout...

A simulation and gardening lab that gives students hands-on experience with the effects of increasing...

In small groups, students explore changes in regional fertility rates and life expectancy trends over...

A visual demonstration of the limited farmland available on Earth (instructor cuts an apple to...

Acting as store owners, students conduct a mini-census to identify their potential market and then...

Acting as the residents of five major regions of the world, students compare various statistics...

In two simulation games, students determine individual short-term consumption strategies that will maximize resources for...

Join Population Education on Twitter! We love connecting with teachers and educators in the twitterverse....

Bar graph shows the populations of the 10 most populous countries. The top 10 most...

Colorful, informative poster makes the perfect classroom wall decoration - it inspires conversations and questions....

Bar charts shows the percentage of people using safely managed drinking water and safely managed...

Line graph shows the average number of children per woman in the United States over...

Infographic shows that the average size of houses in the United States has increased from...

Bar graph shows the amount of carbon dioxide per capita, in metric tons, is emitted...

Six cards that compare resource use in the United States and worldwide.

Infographic shows the how household net worth in the U.S. is distributed among the population....

Privacy Overview

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Population Education: A General Introduction

2021, IJSES

Population education is basically the education that deals about population and its different aspects like fertility, mortality, migration, infiltration, emigration etc. that means we can say that population education is a branch of education that entails the individual to gain some knowledge about population and its impact on our society as well as the whole world. It is a new area of study.

Related Papers

Journal of Educational and Social Research

MUHAMMAD YOUSAF

muhammad yousaf

This research aimed to evaluate the implementation of Population Education in senior high school in terms of (1) learning process, (2) learning materials, (3) evaluation process, (4) course outcome, (5) teachers' role, (6) perception of Population Education, and (7) factors supporting and inhabitting Population Education. The research subjects were one teachers' supervisor, three teachers, and 65 students. The data were collected through questionnaires, interviews, and documentation and analyzed quantitatively using descriptive statistics. The qualitative data collected through interviews were used for deeper explanation. The research findings were: (1) the teaching process was not quite appropriate, (2) materials for Population Education were available and efficient , (3) the evaluation process was not appropriate, (4) the students were satisfied with the teachers' role, (5) the students' perception of Population Education was very positive, and (6) the constraints in Population Education included (a) limitation in time, (b) too many extracurricular activities, (c) rapid change of data, and (d) the validity of materials.

Steve Viederman

Dimitar Simeonov

The role of education and its relation to well-being is a topic of growing public interest. The level of development of the population education is of the utmost importance for the economic and social perspective of each country. This report aims to place education as a key element of human well-being, using a geographical approach.

Abstract: This working paper is an overview of the status of population education in early 1972. It is defined as a program which provides for a study of the population situation of the family, community, nation and world, with the purpose of developing in the students ...

RELATED PAPERS

Osservatorio Indipendente Concorsi Universitari (OICU)

Virtual Archaeology Review

Jaime Molina

Journal of Solution Chemistry

Bart Spiegeleer

European eating disorders review : the journal of the Eating Disorders Association

Maria La Via

Proceedings of the London Mathematical Society

jean-michel Vallin

Ciudad Y Territorio Estudios Territoriales Issn 1133 4762 2014 Vol Xlvi N 181

Agustín Ajá

Recasting Culture and Space in …

Shawn Parkhurst

Materials Today: Proceedings

Mauro Zarrelli

学校原版英国伦敦艺术大学毕业证 ual文凭证书设计录取通知书原版一模一样

Futuros que están siendo https://futuros-que-estan-siendo-548080.webflow.io/

Gaya Makaran

Journal of Social Issues

Craig Haney

Reproduction

jkjhygffg bhhnhfgf

Sophia Kompotiati

The European Journal of Social and Behavioural Sciences

Pärje Ülavere

International Journal of Psychophysiology

Ray Edward Johnson

Mario Boaratti

George Nolas

American Journal of Medical Genetics Part C: Seminars in Medical Genetics

Inge De Wandele

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• HHS Author Manuscripts

The relationship between education and health: reducing disparities through a contextual approach

Anna zajacova.

Western University

Elizabeth M. Lawrence

University of North Carolina

Adults with higher educational attainment live healthier and longer lives compared to their less educated peers. The disparities are large and widening. We posit that understanding the educational and macro-level contexts in which this association occurs is key to reducing health disparities and improving population health. In this paper, we briefly review and critically assess the current state of research on the relationship between education and health in the United States. We then outline three directions for further research: We extend the conceptualization of education beyond attainment and demonstrate the centrality of the schooling process to health; We highlight the dual role of education a driver of opportunity but also a reproducer of inequality; We explain the central role of specific historical socio-political contexts in which the education-health association is embedded. This research agenda can inform policies and effective interventions to reduce health disparities and improve health of all Americans.

URGENT NEED FOR NEW DIRECTIONS IN EDUCATION-HEALTH RESEARCH

Americans have worse health than people in other high-income countries, and have been falling further behind in recent decades ( 137 ). This is partially due to the large health inequalities and poor health of adults with low education ( 84 ). Understanding the health benefits of education is thus integral to reducing health disparities and improving the well-being of 21 st century populations. Despite tremendous prior research, critical questions about the education-health relationship remain unanswered, in part because education and health are intertwined over the lifespans within and across generations and are inextricably embedded in the broader social context.

We posit that to effectively inform future educational and heath policy, we need to capture education ‘in action’ as it generates and constrains opportunity during the early lifespans of today’s cohorts. First, we need to expand our operationalization of education beyond attainment to consider the long-term educational process that precedes the attainment and its effect on health. Second, we need to re-conceptualize education as not only a vehicle for social success, valuable resources, and good health, but also as an institution that reproduces inequality across generations. And third, we argue that investigators need to bring historical, social and policy contexts into the heart of analyses: how does the education-health association vary across place and time, and how do political forces influence that variation?

During the past several generations, education has become the principal pathway to financial security, stable employment, and social success ( 8 ). At the same time, American youth have experienced increasingly unequal educational opportunities that depend on the schools they attend, the neighborhoods they live in, the color of their skin, and the financial resources of their family. The decline in manufacturing and rise of globalization have eroded the middle class, while the increasing returns to higher education magnified the economic gaps among working adults and families ( 107 ). In addition to these dramatic structural changes, policies that protected the welfare of vulnerable groups have been gradually eroded or dismantled ( 129 ). Together, these changes triggered a precipitous growth of economic and social inequalities in the American society ( 17 ; 106 ).

Unsurprisingly, health disparities grew hand in hand with the socio-economic inequalities. Although the average health of the US population improved over the past decades ( 67 ; 85 ), the gains largely went to the most educated groups. Inequalities in health ( 53 ; 77 ; 99 ) and mortality ( 86 ; 115 ) increased steadily, to a point where we now see an unprecedented pattern: health and longevity are deteriorating among those with less education ( 92 ; 99 ; 121 ; 143 ). With the current focus of the media, policymakers, and the public on the worrisome health patterns among less-educated Americans ( 28 ; 29 ), as well as the growing recognition of the importance of education for health ( 84 ), research on the health returns to education is at a critical juncture. A comprehensive research program is needed to understand how education and health are related, in order to identify effective points of intervention to improve population health and reduce disparities.

The article is organized in two parts. First, we review the current state of research on the relationship between education and health. In broad strokes, we summarize the theoretical and empirical foundations of the education-health relationship and critically assess the literature on the mechanisms and causal influence of education on health. In the second part, we highlight gaps in extant research and propose new directions for innovative research that will fill these gaps. The enormous breadth of the literature on education and health necessarily limits the scope of the review in terms of place and time; we focus on the United States and on findings generated during the rapid expansion of the education-health research in the past 10–15 years. The terms “education” and “schooling” are used interchangeably. Unless we state otherwise, both refer to attained education, whether measured in completed years or credentials. For references, we include prior review articles where available, seminal papers, and recent studies as the best starting points for further reading.

THE ASSOCIATION BETWEEN EDUCATION AND HEALTH

Conceptual toolbox for examining the association.

Researchers have generally drawn from three broad theoretical perspectives to hypothesize the relationship between education and health. Much of the education-health research over the past two decades has been grounded in the Fundamental Cause Theory ( 75 ). The FCT posits that social factors such as education are ‘fundamental’ causes of health and disease because they determine access to a multitude of material and non-material resources such as income, safe neighborhoods, or healthier lifestyles, all of which protect or enhance health. The multiplicity of pathways means that even as some mechanisms change or become less important, other mechanisms will continue to channel the fundamental dis/advantages into differential health ( 48 ). The Human Capital Theory (HCT), borrowed from econometrics, conceptualizes education as an investment that yields returns via increased productivity ( 12 ). Education improves individuals’ knowledge, skills, reasoning, effectiveness, and a broad range of other abilities, which can be utilized to produce health ( 93 ). The third approach, the Signaling or Credentialing perspective ( 34 ; 125 ) has been used to explain the observed large discontinuities in health at 12 or 16 years of schooling, typically associated with the receipt of a high school and college degrees, respectively. This perspective views earned credentials as a potent signal about one’s skills and abilities, and emphasizes the economic and social returns to such signals. Thus all three perspectives postulate a causal relationship between education and health and identify numerous mechanisms through which education influences health. The HCT specifies the mechanisms as embodied skills and abilities, FCT emphasizes the dynamism and flexibility of mechanisms, and credentialism identifies social responses to educational attainment. All three theoretical approaches, however, operationalize the complex process of schooling solely in terms of attainment and thus do not focus on differences in educational quality, type, or other institutional factors that might independently influence health. They also focus on individual-level factors: individual attainment, attainment effects, and mechanisms, and leave out the social context in which the education and health processes are embedded.

Observed associations between education and health

Empirically, hundreds of studies have documented “the gradient” whereby more schooling is linked with better health and longer life. A seminal 1973 book by Kitagawa and Hauser powerfully described large differences in mortality by education in the United States ( 71 ), a finding that has since been corroborated in numerous studies ( 31 ; 42 ; 46 ; 109 ; 124 ). In the following decades, nearly all health outcomes were also found strongly patterned by education. Less educated adults report worse general health ( 94 ; 141 ), more chronic conditions ( 68 ; 108 ), and more functional limitations and disability ( 118 ; 119 ; 130 ; 143 ). Objective measures of health, such as biological risk levels, are similarly correlated with educational attainment ( 35 ; 90 ; 140 ), showing that the gradient is not a function of differential reporting or knowledge.

The gradient is evident in men and women ( 139 ) and among all race/ethnic groups ( 36 ). However, meaningful group differences exist ( 60 ; 62 ; 91 ). In particular, education appears to have stronger health effects for women than men ( 111 ) and stronger effects for non-Hispanic whites than minority adults ( 134 ; 135 ) even if the differences are modest for some health outcomes ( 36 ). The observed variations may reflect systematic social differences in the educational process such as quality of schooling, content, or institutional type, as well as different returns to educational attainment in the labor market across population groups ( 26 ). At the same time, the groups share a common macro-level social context, which may underlie the gradient observed for all.

To illustrate the gradient, we analyzed 2002–2016 waves of the National Health Interview Survey (NHIS) data from adults aged 25–64. Figure 1 shows the levels of three health outcomes across educational attainment levels in six major demographic groups predicted at age 45. Three observations are noteworthy. First, the gradient is evident for all outcomes and in all race/ethnic/gender groups. Self-rated health exemplifies the staggering magnitude of the inequalities: White men and women without a high school diploma have about 57% chance of reporting fair or poor health, compared to just 9% for college graduates. Second, there are major group differences as well, both in the predicted levels of health problems, as well as in the education effects. The latter are not necessarily visible in the figures but the education effects are stronger for women and weaker for non-white adults as prior studies showed (table with regression model results underlying the prior statement is available from the authors). Third, an intriguing exception pertains to adults with “some college,” whose health is similar to high school graduates’ in health outcomes other than general health, despite their investment in and exposure to postsecondary education. We discuss this anomaly below.

Predicted Probability of Health Problems

Source: 2002–2016 NHIS Survey, Adults Age 25–64

Pathways through which education impacts health

What explains the improved health and longevity of more educated adults? The most prominent mediating mechanisms can be grouped into four categories: economic, health-behavioral, social-psychological, and access to health care. Education leads to better, more stable jobs that pay higher income and allow families to accumulate wealth that can be used to improve health ( 93 ). The economic factors are an important link between schooling and health, estimated to account for about 30% of the correlation ( 36 ). Health behaviors are undoubtedly an important proximal determinant of health but they only explain a part of the effect of schooling on health: adults with less education are more likely to smoke, have an unhealthy diet, and lack exercise ( 37 ; 73 ; 105 ; 117 ). Social-psychological pathways include successful long-term marriages and other sources of social support to help cope with stressors and daily hassles ( 128 ; 131 ). Interestingly, access to health care, while important to individual and population health overall, has a modest role in explaining health inequalities by education ( 61 ; 112 ; 133 ), highlighting the need to look upstream beyond the health care system toward social factors that underlie social disparities in health. Beyond these four groups of mechanisms that have received the most attention by investigators, many others have been examined, such as stress, cognitive and noncognitive skills, or environmental exposures ( 11 ; 43 ). Several excellent reviews further discuss mechanisms ( 2 ; 36 ; 66 ; 70 ; 93 ).

Causal interpretation of the education-health association

A burgeoning number of studies used innovative approaches such as natural experiments and twin design to test whether and how education causally affects health. These analyses are essential because recommendations for educational policies, programs, and interventions seeking to improve population health hinge on the causal impact of schooling on health outcomes. Overall, this literature shows that attainment, measured mostly in completed years of schooling, has a causal impact on health across numerous (though not all) contexts and outcomes.

Natural experiments take advantage of external changes that affect attainment but are unrelated to health, such as compulsory education reforms that raise the minimum years of schooling within a given population. A seminal 2005 study focused on increases in compulsory education between 1915 and 1939 across US states and found that a year of schooling reduced mortality by 3.6% ( 78 ). A re-analysis of the data indicated that taking into account state-level mortality trends rendered the mortality effects null but it also identified a significant and large causal effect on general health ( 88 ). A recent study of a large sample of older Americans reported a similar pattern: a substantial causal effect of education for self-rated health but not for mortality ( 47 ). School reform studies outside the US have reported compelling ( 122 ) or modest but significant ( 32 ) effects of schooling on health, although some studies have found nonsignificant ( 4 ), or even negative effects ( 7 ) for a range of health outcomes.

Twin design studies compare the health of twins with different levels of education. This design minimizes the influence of family resources and genetic differences in skills and health, especially for monozygotic twins, and thus serves to isolate the effect of schooling. In the US, studies using this design generated robust evidence of a causal effect of education on self-rated health ( 79 ), although some research has identified only modest ( 49 ) or not significant ( 3 ; 55 ) effects for other physical and mental health outcomes. Studies drawing on the large twin samples outside of the US have similarly found strong causal effects for mortality ( 80 ) and health ( 14 ; 16 ; 51 ) but again some analyses yielded no causal effects on health ( 13 ; 83 ) or health behaviors ( 14 ). Beyond our brief overview, readers may wish consult additional comprehensive reviews of the causal studies ( 40 ; 45 ; 89 ).

The causal studies add valuable evidence that educational attainment impacts adult health and mortality, even considering some limitations to their internal validity ( 15 ; 88 ). To improve population health and reduce health disparities, however, they should be viewed as a starting point to further research. First, the findings do not show how to improve the quality of schooling or its quantity for in the aggregate population, or how to overcome systematic intergenerational and social differences in educational opportunities. Second, their findings do take into account contexts and conditions in which educational attainment might be particularly important for health. In fact, the variability in the findings may be attributable to the stark differences in contexts across the studies, which include countries characterized by different political systems, different population groups, and birth cohorts ranging from the late 19 th to late 20 th centuries that were exposed to education at very different stages of the educational expansion process ( 9 ).

TOWARD A SOCIALLY-EMBEDDED UNDERSTANDING OF THE EDUCATION-HEALTH RELATIONSHIP

To date, the extensive research we briefly reviewed above has identified substantial health benefits of educational attainment in most contexts in today’s high-income countries. Still, many important questions remain unanswered. We outline three critical directions to gain a deeper understanding of the education-health relationship with particular relevance for policy development. All three directions shift the education-health paradigm to consider how education and health are embedded in life course and social contexts.

First, nearly universally, the education-health literature conceptualizes and operationalizes education in terms of attainment, as years of schooling or completed credentials. However, attainment is only the endpoint, although undoubtedly important, of an extended and extensive process of formal schooling, where institutional quality, type, content, peers, teachers, and many other individual, institutional, and interpersonal factors shape lifecourse trajectories of schooling and health. Understanding the role of the schooling process in health outcome is relevant for policy because it can show whether interventions should be aimed at increasing attainment, or whether it is more important to increase quality, change content, or otherwise improve the educational process at earlier stages for maximum health returns. Second, most studies have implicitly or explicitly treated educational attainment as an exogenous starting point, a driver of opportunities in adulthood. However, education also functions to reproduce inequality across generations. The explicit recognition of the dual function of education is critical to developing education policies that would avoid unintended consequence of increasing inequalities. And third, the review above indicates substantial variation in the education-health association across different historical and social contexts. Education and health are inextricably embedded in these contexts and analyses should therefore include them as fundamental influences on the education-health association. Research on contextual variation has the potential to identify contextual characteristics and even specific policies that exacerbate or reduce educational disparities in health.

We illustrate the key conceptual components of future research into the education-health relationship in Figure 2 . Important intergenerational and individual socio-demographic factors shape educational opportunities and educational trajectories, which are directly related to and captured in measures of educational attainment. This longitudinal and life course process culminates in educational disparities in adult health and mortality. Importantly, the macro-level context underlies every step of this process, shaping each of the concepts and their relationships.

Enriching the conceptualization of educational attainment

In most studies of the education-health associations, educational attainment is modeled using years of schooling, typically specified as a continuous covariate, effectively constraining each additional year to have the same impact. A growing body of research has substituted earned credentials for years. Few studies, however, have considered how the impact of additional schooling is likely to differ across the educational attainment spectrum. For example, one additional year of education compared to zero years may be life-changing by imparting basic literacy and numeracy skills. The completion of 14 rather than 13 years (without the completion of associated degree) could be associated with better health through the accumulation of additional knowledge and skills as well, or perhaps could be without health returns, if it is associated with poor grades, stigma linked to dropping out of college, or accumulated debt ( 63 ; 76 ). Examining the functional form of the education-health association can shed light on how and why education is beneficial for health ( 70 ). For instance, studies found that mortality gradually declines with years of schooling at low levels of educational attainment, with large discontinuities at high school and college degree attainment ( 56 ; 98 ). Such findings can point to the importance of completing a degree, not just increasing the quantity (years) of education. Examining mortality, however, implicitly focused on cohorts who went to school 50–60 years ago, within very different educational and social contexts. For findings relevant to current education policies, we need to focus on examining more recent birth cohorts.

A particularly provocative and noteworthy aspect of the functional form is the attainment group often identified as “some college:” adults who attended college but did not graduate with a four-year degree. Postsecondary educational experiences are increasingly central to the lives of American adults ( 27 ) and college completion has become the minimum requirement for entry into middle class ( 65 ; 87 ). Among high school graduates, over 70% enroll in college ( 22 ) but the majority never earn a four-year degree ( 113 ). In fact,, the largest education-attainment group among non-elderly US adults comprises the 54 million adults (29% of total) with some college or associate’s degree ( 113 ). However, as in Figure 1 , this group often defies the standard gradient in health. Several recent studies have found that the health returns to their postsecondary investments are marginal at best ( 110 ; 123 ; 142 ; 144 ). This finding should spur new research to understand the outcomes of this large population group, and to glean insights into the health returns to the postsecondary schooling process. For instance, in the absence of earning a degree, is greater exposure to college education in terms of semesters or earned credits associated with better health or not? How do the returns to postsecondary schooling differ across the heterogeneous institutions ranging from selective 4-year to for-profit community colleges? How does accumulated college debt influence both dropout and later health? Can we identify circumstances under which some college education is beneficial for health? Understanding the health outcomes for this attainment group can shed light on the aspects of education that are most important for improving health.

A related point pertains to the reliability and validity of self-reported educational attainment. If a respondent reports 16 completed years of education, for example, are they carefully counting the number of years of enrollment, or is 16 shorthand for “completed college”? And, is 16 years the best indicator of college completion in the current context when the median time to earn a four-year degree exceeds 5 years ( 30 )? And, is longer time in college given a degree beneficial for health or does it signify delayed or disrupted educational pathways linked to weaker health benefits ( 132 )? How should we measure part-time enrollment? As studies begin to adjudicate between the health effects of years versus credentials ( 74 ) in the changing landscape of increasingly ‘nontraditional’ pathways through college ( 132 ), this measurement work will be necessary for unbiased and meaningful analyses. An in-depth understanding may necessitate primary data collection and qualitative studies. A feasible direction available with existing data such as the National Longitudinal Survey of Youth 1997 (NLSY97) is to assess earned college credits and grades rather than years of education beyond high school.

As indicated in Figure 2 , beyond a more in-depth usage of the attainment information, we argue that more effective conceptualization of the education-health relationship as a developmental life course process will lead to important findings. For instance, two studies published in 2016 used the NLSY97 data to model how gradual increases in education predict within-individual changes in health ( 39 ; 81 ). Both research teams found that gradual accumulation of schooling quantity over time was not associated with gradual improvements in health. The investigators interpreted the null findings as an absence of causal effects of education on health, especially once they included important confounders (defined as cognitive and noncognitive skills and social background). Alternatively, perhaps the within-individual models did not register health because education is a long-term, developing trajectory that cannot be reduced to point-in-time changes in exposure. Criticisms about the technical aspects of theses studies notwithstanding ( 59 ), we believe that these studies and others like them, which wrestle with the question of how to capture education as a long-term process grounded in the broader social context, and how this process is linked to adult health, are desirable and necessary.

Education as (re)producer of inequality

The predominant theoretical framework for studying education and health focuses on how education increases skills, improves problem-solving, enhances employment prospects, and thus opens access to other resources. In sociology, however, education is viewed not (only) as increasing human capital but as a “sieve more than a ladder” ( 126 ), an institution that reproduces inequality across generations ( 54 ; 65 ; 103 ; 114 ). The mechanisms of the reproduction of inequality are multifarious, encompassing systematic differences in school resources, quality of instruction, academic opportunities, peer influences, or teacher expectations ( 54 ; 114 ; 132 ). The dual role of education, both engendering and constraining social opportunities, has been recognized from the discipline’s inception ( 52 ) and has remained the dominant perspective in sociology of education ( 18 ; 126 ). Health disparities research, which has largely dismissed the this perspective as “specious” ( 93 ), could benefit from pivoting toward this complex sociological paradigm.

As demonstrated in Figure 2 , parental SES and other background characteristics are key social determinants that set the stage for one’s educational experiences ( 20 ; 120 ). These characteristics, however, shape not just attainment, but the entire educational and social trajectories that drive and result in particular attainment ( 21 ; 69 ). Their effects range from the differential quality and experiences in daycare or preschool settings ( 6 ), K-12 education ( 24 ; 136 ), as well as postsecondary schooling ( 5 ; 127 ). As a result of systematically different experiences of schooling over the early life course stratified by parental SES, children of low educated parents are unlikely to complete higher education: over half of individuals with college degrees by age 24 came from families in the top quartile of family income compared to just 10% in the bottom quartile ( 23 ).

Unfortunately, prior research has generally operationalized the differences in educational opportunities as confounders of the education-health association or as “selection bias” to be statistically controlled, or best as a moderating influence ( 10 ; 19 ). Rather than remove the important life course effects from the equation, studies that seek to understand how educational and health differences unfold over the life course, and even across generations could yield greater insight ( 50 ; 70 ). A life course, multigenerational approach can provide important recommendations for interventions seeking to avoid the unintended consequence of increasing disparities. Insofar as socially advantaged individuals are generally better positioned to take advantage of interventions, research findings can be used to ensure that policies and programs result in decreasing, rather than unintentionally widening, educational and health disparities.

Education and health in social context

Finally, perhaps the most important and policy-relevant emerging direction to improving our understanding of the education-health relationship is to view both as inextricably embedded within the broad social context. As we highlight in Figure 2 , this context underlies every feature of the development of educational disparities in health. In contrast to the voluminous literature focusing on individual-level schooling and health, there has been a “startling lack of attention to the social/political/economic context” in which the relationships are grounded ( 33 ). By context, we mean the structure of a society that varies across time and place, encompassing all major institutions, policy environments, as well as gender, race/ethnicity, age, and socioeconomic stratification. Under what circumstances, conditions, and policies are the associations between education and health stronger or weaker?

Within the United States, the most relevant units of geo-political boundaries generating distinct policy contexts are states, although smaller geographic units are also pertinent ( 44 ; 100 ). Since the 1980s, the federal government has devolved an increasing range of key socioeconomic, political, and health-care decisions to states. This decentralization has resulted in increasing diversity across states in conditions for a healthy life ( 96 ; 101 ). A recent study demonstrates how different environments across US states yield vastly different health returns to education ( 100 ). State-level characteristics had little impact on adults with high education, whose disability levels were similarly low regardless of their state of residence. In contrast, disability levels of low-educated adults were not only high but also varied substantially across states: disability was particularly high in states that have invested less in the social welfare of its residents, such as Mississippi, Kentucky, and West Virginia. Highly-educated adults, particularly white adults and men who can convert education into other resources most readily, use personal resources to protect their health like a ‘personal firewall’ ( 97 ). Their less-educated peers, meanwhile, are vulnerable without social safety nets. Demonstrating the potential for informing policy in this area, the findings directly identify state policies that influence the extent to which educational attainment matters for health and longevity. These include economic policies including state income tax structures and education expenditures per capita, as well as policies influencing social cohesion in a state, such as income inequality and unemployment rates. Beyond the US, investigators can leverage differences in political systems across countries to assess the impact of different welfare regimes on the education-health associations, as some European researchers began generating ( 41 ; 82 ).

Similar to variation across geo-political boundaries, research on variation across time can highlight policies and conditions that mitigate or inflate health disparities. How has the education-health association changed over time? In recent decades, the association has become increasingly strong, with widening disparities in health outcomes across education ( 53 ; 77 ; 86 ; 116 ; 143 ). These increases started in the 1980s ( 17 ) at the same time that social inequality began rising with the political embrace of pro-market neoliberal policies ( 33 ). Since then, the United States has been increasingly marked by plummeting economic wellbeing (except for the wealthiest Americans), growing economic segregation, emerging mass incarceration, downward social mobility, and despair in many working-class communities ( 17 ; 95 ; 129 ). Conversely, in the two decades prior (1960s and 70s), social disparities in health were decreasing ( 1 ; 72 ). During those decades, many pro-social policies such as Civil rights legislation, War on Poverty programs, and racial desegregation were improving social inequalities. Macro-level political forces, clearly, can influence not only social but also health inequalities ( 104 ). Two facts follow: growing disparities are not inevitable and changes in the education-health relationship may be strongly linked to social policies. While some of the growth in educational inequalities may be attributable to changes in educational composition of the population with increasingly negatively select groups of adults at the lowest levels of schooling, these compositional changes likely play only a minor role in the overall trends ( 38 ; 58 ). Linking education and health to the broader social context brings to the forefront the ways in which we, as individuals and a collective society, produce and maintain health disparities.

Implications for Policy and Practice

Reducing macro-level inequalities in health will require macro-level interventions. Technological progress and educational expansion over the past several decades have not decreased disparities; on the contrary, educational disparities in health and mortality have grown in the US. Moreover, the consistent, durable relationship between education and health and the multitude of mechanisms linking them suggests that programs targeting individual behaviors will have limited impact to counteract disparities. Thus, we argue that future findings from the new research directions proposed here can be used to intervene at the level of social contexts to alter educational trajectories from an early age, with the ultimate goal of reducing health disparities. We note two promising avenues for policy development.

One potential solution may focus on universal federal and state-level investment in the education and well-being of children early in the life course to disrupt the reproduction of social inequalities and change subsequent educational trajectories. Several experimental early-education programs such as the Perry Preschool Project and Carolina Abecedarian Project have demonstrated substantial, lasting, and wide-ranging benefits, including improved adult health ( 25 ; 57 ; 102 ). These programs provided intensive, exceptionally high-quality, and diverse services to children, and it is these characteristics that appear central to their success ( 138 ). Further research on the qualitative and social dimensions of education and their effects on health can inform future model educational programs and interventions across all ages.

Another important issue for both researchers and policymakers pertains to postsecondary enrollment and attrition, and their effects on health. Educational expansion in the college-for-all era has yielded high post-secondary enrollment, but also unacceptable dropout rates with multiple detrimental consequences, including high rates of student debt ( 64 ) and stigma ( 76 ), which may negatively affect health. Emerging studies found that college dropouts fail to benefit from their postsecondary investments. Next we need to understand under what circumstances college goers do reap health benefits, or how their postsecondary experience can be modified to improve their health.

For both of these avenues, effective implementation will need further research on the specific institutional characteristics and social contexts that shape the schooling effects. However, in designing interventions and policies, we need to be aware of the dual role of education as a drive and reproducer of inequality. Individuals from advantaged backgrounds may be better positioned to take advantage of new educational opportunities, and thus any interventions and programs need to ensure that marginalized populations have equal or greater access in order to avoid the unintended consequence of further intensifying disparities. Finally, researchers and policymakers should engage in a dialogue such that researchers effectively communicate their insights and recommendations to policymakers, and policymakers convey the needs and challenges of their practices to researchers.

Education and health are central to individual and population well-being. They are also inextricably embedded in the social context and structure. Future research needs to expand beyond the individual-focused analyses and hypothesize upstream ( 96 ), taking a contextual approach to understanding education and health. Such an approach will require interdisciplinary collaborations, innovations in conceptual models, and rich data sources. The three directions for further research on health returns to education we outlined above can help generate findings that will inform effective educational and health policies and interventions to reduce disparities. During this critical time when health differences are widening and less educated Americans are experiencing social and health declines, research and policy has the opportunity to make a difference and improve the health and well-being of our population.

Contributor Information

Anna Zajacova, Western University.

Elizabeth M. Lawrence, University of North Carolina.

Education Policies and Intergenerational Educational Mobility in China: New Evidence for the 1986–95 Birth Cohort

• Research Briefs
• Open access
• Published: 22 May 2024
• Volume 43 , article number  43 , ( 2024 )

Cite this article

You have full access to this open access article

• Jiawei Wu   ORCID: orcid.org/0000-0002-3702-292X 1 &
• Guillaume Marois   ORCID: orcid.org/0000-0002-2701-6286 1 , 2  

187 Accesses

2 Altmetric

Explore all metrics

Research on educational mobility for Chinese born in or before 1976–85 abounds. Although the Compulsory Education Law implemented in 1986 and the expansion of higher education introduced in 1999 changed Chinese millennials’ educational achievements, little is known about the educational mobility for the 1986–95 birth cohort and where it stands in the long-term trends. In this study, we calculated population-level educational percentile ranks by birth cohort and gender using data from the 1982 to 2020 China Censuses before linking these ranks to respondents in Chinese General Social Survey (CGSS) or China Family Panel Studies (CFPS) to document 1986–95 birth cohort's educational mobility and its historical position. We also explored the role played by offspring's hukou origin (urban or rural) and ethnicity (Han or ethnic minorities). In the 1986–95 birth cohort, women's educational percentile ranks for secondary and tertiary levels fell below men's for the first time in China, suggesting that the proportion of women in higher education overtook men's. From 1976–85 to 1986–95 birth cohorts, while educational rank-rank correlations remained stable in all parent–child dyads and were constantly higher for offspring with urban hukou origin, there is suggestive evidence on increased educational mobility for women with rural hukou origin. Ethnicity differences were not found. Our findings imply that China's Compulsory Education Law and higher education expansion may have contributed to greater educational mobility for women with rural hukou origin in the 1986–95 birth cohort and their diminished disadvantage in education.

Similar content being viewed by others

The Relation Between Family Socioeconomic Status and Academic Achievement in China: A Meta-analysis

Education and Parenting in the Philippines

The influence of education on health: an empirical assessment of OECD countries for the period 1995–2015

Avoid common mistakes on your manuscript.

Introduction

Intergenerational educational mobility indicates to what extent an individual's educational achievement (destination) is correlated with his/her parent's education (origin). Greater educational mobility reflects weaker contributions from family background, which is of vital importance in tackling inequality, fostering fairness, encouraging aspirations, and promoting sustainable development (Narayan et al., 2018 ). However, empirical studies revealed varying levels of educational mobility across countries and continents, suggesting regional and global inequalities in educational opportunities (Azam & Bhatt, 2015 ; Breen et al., 2019 ; Causa & Johansson, 2010 ; Hertz et al., 2008 ; Hu & Qian, 2023 ; Kye, 2011 ; Skopek & Leopold, 2020 ; Torche, 2021 ; Wittemann, 2023 ).

For decades education policies increasing elementary education and expanding higher education implemented to promote equal educational opportunities (Torche, 2021 ) have not necessarily achieved intended effects. For example, Demirel-Derebasoglu and Okten ( 2022 ) noted that after Turkey's compulsory schooling reform, gender gap decreased in completing new compulsory schooling but remained unchanged at the post-compulsory schooling level. Urbina ( 2018 ) found that Mexico’s “11-year plan” education reform led to greater mobility in primary and lower-secondary schooling but lower mobility at higher levels of education. These findings are consistent with the “maximally maintained inequality (MMI)” hypothesis formulated from empirical evidence in Ireland (Raftery & Hout, 1993 ) that educational expansion will translate to less inequalities in educational opportunity only when the advantaged groups have reached saturation at a particular level of education.

In China, educational mobility continue to spark public debates and academic interests (Gruijters et al., 2019 ; Hannum et al., 2019 ). Following its founding, China implemented various egalitarian education policies such as the waiver of tuition fees, rapid roll-out of primary and secondary education in rural areas, and favoring political criterion over academic ability for admission to universities (Deng & Treiman, 1997 ). As a result, there was exceptionally high educational mobility for the 1950s and 1960s birth cohorts, but the educational mobility declined for the 1966–75 and 1976–85 birth cohorts (Xie et al., 2022 ; Zhou & Xie, 2019 ). Ample research repeatedly found huge inequality in educational mobility tied with an individual's ascribed characteristics such as urban/rural hukou (household registration) status (Fang & Feng, 2020 ; Wu, 2019 ; Wu & Treiman, 2004 ), place/province of birth (Hannum & Wang, 2006 ), gender (Huo & Golley, 2022 ), and ethnicity (There are 55 ethnic minorities in China who account for less than 10% of China's population) (Hannum, 2002 ).

China implemented the Compulsory Education Law in 1986 to promote universal 9-year schooling and introduced the expansion of higher education in 1999 to boost human capital accumulation (Golley & Kong, 2018 ). Higher education expansion was found to increase educational mobility in general (Liu & Wan, 2019 ) and benefit women from rural areas or women with intermediately educated parents in receiving higher education (Zhang & Chen, 2014 ). However, Guo et al., ( 2019 ) showed that these two policies led to greater educational mobility for the urban population but lower mobility for the rural population. Gruijters, ( 2022 ) found that educational expansion in China diminished disparities in obtaining basic education, but inequality persisted or even increased at the more advanced levels for the 1985–89 and 1990–94 birth cohorts. These studies used years of schooling or probabilities of transitions between adjacent educational levels which, compared with rank-based measures, are less comparable across birth cohorts and gender (Hannum et al., 2019 ; Xie et al., 2022 ). Although a few studies used sample-wide or population-level educational percentile ranks (Emran et al., 2020 , 2023 ), less is known about educational mobility, its variation by gender, hukou status, and ethnicity, and where it stands in the long-term trends for China’s 1986–95 birth cohort who were exposed to major education policy changes and had reached the last leg of their educational advancement (master or PhD) by 2020.

Data and Methods

Data and sample.

We primarily drew on data from Censuses and surveys. First, we used the 1982 China Census microdata (1% of the population) from the Integrated Public Use Microdata Series (IPUMS) (Minnesota Population Center, 2020), as well as the 1990, 2000, 2010, and 2020 China Censuses aggregated statistics compiled by China's National Bureau of Statistics. The purpose was to capture the population-level distribution of educational attainment for men and women from various birth cohorts to generate educational percentile rank for each educational level. Then, as parent–child dyads could not be identified in Censuses, we turned to survey data from Chinese General Social Survey (CGSS) and China Family Panel Studies (CFPS). Both nationally representative, CGSS was launched in 2003 and adopts a repeated cross-sectional design (Bian & Li, 2012 ) while CFPS is a longitudinal survey fielded biannually from 2010 (Xie & Hu, 2014 ). Although CFPS does not suffer from sample truncation due to coresidency restrictions on nonresident family members (Emran et al., 2020 ), information on nonresident family members' hukou origin and ethnicity is missing and they do not have individual-level sampling weights. Despite sample truncation which only applies to respondents' nonresident children, CGSS data were used for our main analysis following prior work (Xie et al., 2022 ) and we performed sensitivity analysis using CFPS data (details are given in the Supplementary Material). We pooled 11 waves (2003, 2005, 2006, 2008, 2010, 2011, 2013, 2015, 2017, 2018, 2021) of the CGSS and selected respondents aged 25 or above in the interview year who fell in birth cohorts 1946–55, 1956–65, 1966–75, 1976–85, and 1986–95. Our study sample included 83,569 respondents (as expected, the number of respondents in the 1986–95 birth cohort was relatively small, n = 5079) whose fathers’ and mothers’ birth cohorts varied from 1896–1905 to 1976–85.

Educational Percentile Rank

Margin-free (Hannum et al., 2019 ) and less prone to bias (Emran & Shilpi, 2018 ; Emran et al., 2018 ; Nybom & Stuhler, 2017 ), rank-based measures of intergenerational mobility become increasingly popular in economics literature (Chetty et al., 2014 ). Less sensitive to education expansion (Torche, 2021 ), educational percentile rank tells a person's position in educational achievement relative to peers of the same gender in the same birth cohort (Xie et al., 2022 ). We used China Censuses to calculate educational percentile ranks for seven educational levels ((semi-)illiterate, primary school, middle school, high school, junior college, bachelor, and master/PhD) which run from 0 (lowest) to 100 (highest). Informed by Xie et al., ( 2022 ), wherever possible we calculated a birth cohort's educational percentile rank when they were 25–34 years old in a specific wave of Census to handle survival selection by education.

Rank-Rank Correlation

We linked educational percentile ranks to pooled CGSS data matching respondents’ and their parents’ birth cohort and gender. We estimated weighted ordinary least-squares (OLS) regressions to examine parent–child rank-rank correlation in education to explore relative educational mobility in father-son, father-daughter, mother-son, and mother-daughter dyads (Hu & Qian, 2023 ) by respondents’ (i.e., offspring’s) birth cohort and gender. The weights were constructed from the original sampling weight in each wave of CGSS to account for variation in sample size by birth cohort across multiple waves (Song et al., 2020 ) and we reported standardized coefficients.

Trends in Educational Percentile Rank

Table 1 presents the trends in educational percentile rank by gender in China for ten 10-year birth cohorts from 1896–1905 to 1986–95. The educational percentile rank for each educational level persistently trended downward for both genders with few exceptions (e.g., the educational percentile rank of junior college and bachelor degree for men and women from the 1946–55 birth cohort). Until the 1976–85 birth cohort, women's rank for each educational level had always been higher than men's, implying a persistent higher proportion of women in lower educational levels. However, the gap continued to narrow and among the 1986–95 birth cohort, for the first time for each educational level from middle school to master/PhD, women's ranks fell below men’s.

Trends in Rank-Rank Correlation in Education

We calculated rank-rank correlation in education for father-son, father-daughter, mother-son, and mother-daughter dyads by offspring’s birth cohort for all respondents in CGSS and by respondents’ hukou origin and ethnicity from birth cohort 1946–55 to 1976–85. We visualized the results in Figs.  1 , 2 , and 3 and presented the estimates in the Supplementary Material (Table S1). Figure  1 shows that from birth cohort 1946–55 to 1976–85, parent–child educational rank-rank correlation trended upward for all four dyads and parent-daughter correlation was generally higher than parent-son correlation. Between 1976–85 and 1986–95 birth cohorts, educational mobility remained stable in all parent–child dyads. We used seemingly unrelated estimation to test if the coefficients varied between groups and found that the slight rise in father-son rank-rank correlation (from 0.41 to 0.43) and the little dip in mother-daughter rank-rank correlation (from 0.47 to 0.45) were not statistically significant.

Parent–child rank-rank correlation in education by offspring’s birth cohort in China (results based on CGSS data)

Parent–child rank-rank correlation in education by offspring’s birth cohort and hukou origin in China (results based on CGSS data)

Parent–child rank-rank correlation in education by offspring’s birth cohort and ethnicity in China (results based on CGSS data)

Figure  2 shows that from birth cohort 1946–55 to 1986–95, the rank-rank correlation in education trended upward for Chinese with urban hukou origin. In comparison, it ascended less markedly for Chinese with rural hukou origin and decreased slightly in the 1986–95 birth cohort for parent-daughter dyads. As noted previously, we formally tested the differences and found that the small increases in educational mobility for women with rural hukou origin in father-daughter and mother-daughter dyads in the 1986–95 birth cohort were not significant (p > 0.1 in both cases). Figure  2 also shows that the rank-rank correlation in education had been generally higher for Chinese with urban hukou origin in all four dyads and the gap continued to widen in the 1986–95 birth cohort (e.g., standing at 0.51 (urban hukou origin) versus 0.27 (rural hukou origin) in father-daughter dyads). As shown in Fig.  3 , the rank-rank correlation in education trended upward for both Han Chinese and ethnic minorities, but we did not find ethnicity differences in educational mobility for any parent–child dyad in any birth cohort.

The low response rates in CGSS 2005 or 2006 may cause sample selection problems so we removed respondents in these waves (n = 15,625), repeated the analyses, and found similar results (Figures S1, S2, and S3 in Supplementary Material). Sensitivity analysis using CFPS data implied greater decreases in rank-rank correlation (i.e., greater increases in educational mobility) for women with rural hukou origin in father-daughter dyad (p > 0.05) and mother-daughter dyad (p < 0.01) in the 1986–95 birth cohort (Figure S5). However, there were discrepancies in regards to other findings (e.g., in Figure S4 the drop in rank-rank correlation for women from the mother-daughter dyad in the 1986–95 birth cohort was also significant, but it was insignificant in Fig.  1 ). These discrepancies are possibly driven by the very high proportion of respondents with rural hukou origin in CFPS (around 84%, compared with about 64% in CGSS). Taken together, evidence on the increased educational mobility for women with rural hukou origin in the 1986–95 birth cohort may be best interpreted as suggestive.

Discussion and Conclusion

In this study, against the backdrop of China's Compulsory Education Law implemented in 1986 and the expansion of higher education introduced in 1999, we documented China’s 1986–95 birth cohort’s educational attainment and education mobility and situated them in long-term trends. The educational percentile rank for each educational level dropped continuously among men and women in China, reflecting steady absolute upward educational mobility. The few exceptions found in the 1946–55 birth cohort were caused by the tumultuous Cultural Revolution (1966–76) during which the hiatus of tertiary education left multiple cohorts of young adults seeking a limited number of openings (Deng & Treiman, 1997 ). The reversal of women's disadvantage in education was observed in the 1986–95 birth cohort where women's educational percentile rank from high school to master/PhD fell below men's for the first time, suggesting a higher proportion of women in secondary and tertiary education. This remarkable progress testifies reduced gender inequality in education in China after higher education expansion (Treiman, 2013 ; Wu & Zhang, 2010 ; Zeng et al., 2014 ) and follows the trend in high-income countries (DiPrete & Buchmann, 2013 ).

We found an upward long-term trend in parent–child rank-rank correlation in education from 1946–55 to 1976–85 birth cohorts in China, implying reduced educational mobility and echoing Huo and Golley ( 2022 ) and Xie et al. ( 2022 ). However, this upward trend stagnated in all parent–child dyads from 1976–85 to 1986–95 birth cohorts. Future work is required to investigate whether this stagnation truly holds in China and resembles the long-term stability found in the United States (Xie et al., 2022 ).

For Chinese with urban hukou origin, the rank-rank correlations continued to climb in all parent–child dyads from the 1976–85 to 1986–95 birth cohorts. This indicates the reinforced effect of family background on urbanites' education regardless of gender, confirming that hukou is a critical ascriptive factor in social mobility in China (Li, 2021 ). Both daughters and sons with urban hukou origin were beneficiaries of exclusive parental investment after the one-child policy was implemented in the late 1970s (Fong, 2002 ). Consequently, they were more likely to acquire an advantageous position in education their parents once held. In contrast, analyses based on CGSS and CFPS data hinted at suggestive evidence that women with rural hukou origin in the 1986–95 birth cohort may have had greater educational mobility. These women, historically disadvantaged in education, may benefit noticeably from the Compulsory Education Law and higher education expansion that offered more educational opportunities (Guo et al., 2019 ; Liu & Wan, 2019 ; Zhang & Chen, 2014 ). However, there may be inequality in the ranking of higher education institutes respondents attended (e.g., elite versus ordinary universities) (Charles & Bradley, 2009 ) as predicted by the "effectively maintained inequality (EMI)" hypothesis (Lucas, 2001 ), which cannot be examined here due to lack of information.

We did not find differences in educational mobility in China between Han Chinese and ethnic minorities. Although mobility and inequality are two different dimensions of social stratification (Xie et al., 2022 ), our finding echoes Golley and Kong ( 2018 ) who showed the persistent minor role played by ethnicity, compared with hukou status at young age, in the inequality of opportunities in education. However, this finding shall be interpreted with caution due to small sample size (reflected in wider confidence intervals) and the heterogeneity in socio-economic status among various ethnic minority groups in China.

We acknowledge some limitations in this study such as not considering the nonlinear (e.g., convex or concave) relationships (Emran et al., 2020 ) in rank-rank correlations, not examining absolute mobility at the 25th/75th percentile of the parental educational rank distribution (Emran & Shilpi, 2018 ), or not further investigating if educational mobility varied between parental farm/nonfarm occupations in the rural sample (Emran et al., 2023 ). Overall, our results imply that the long-term decline in educational mobility in China may have plateaued for China’s 1986–95 birth cohort at the population level. Our short report documents the reversal of female disadvantage in education and provides time-sensitive suggestive evidence on increased educational mobility for women with rural hukou origin in the 1986–95 birth cohort following China’s Compulsory Education Law and higher education expansion introduced before 2000. These findings based on CGSS and CFPS data invite more research into evaluating the effects of education policies and call for policy-makers to take continued actions to tackle inequality and promote fairness.

Azam, M., & Bhatt, V. (2015). Like father, like son? Intergenerational educational mobility in India. Demography, 52 (6), 1929–1959. https://doi.org/10.1007/s13524-015-0428-8

Article   Google Scholar  

Bian, Y., & Li, L. (2012). The Chinese general social survey (2003–8). Chinese Sociological Review, 45 (1), 70–97. https://doi.org/10.2753/CSA2162-0555450104

Breen, R., Ermisch, J., & Helske, S. (2019). Educational reproduction in Europe: a descriptive account. Demographic Research, 41 (49), 1373–1400. https://doi.org/10.4054/DemRes.2019.41.49

Causa, O., & Johansson, Å. (2010). Intergenerational social mobility in OECD countries. OECD Journal: Economic Studies . https://doi.org/10.1787/eco_studies-2010-5km33scz5rjj

Charles, M., & Bradley, K. (2009). Indulging our gendered selves? Sex segregation by field of study in 44 countries. American Journal of Sociology, 114 (4), 924–976. https://doi.org/10.1086/595942

Chetty, R., Hendren, N., Kline, P., & Saez, E. (2014). Where is the land of opportunity? The geography of intergenerational mobility in the United States. The Quarterly Journal of Economics, 129 (4), 1553–1623. https://doi.org/10.1093/qje/qju022

Demirel-Derebasoglu, M., & Okten, C. (2022). Gender gap in intergenerational educational persistence: can compulsory schooling reduce it? Population Research and Policy Review, 41 (5), 2037–2083. https://doi.org/10.1007/s11113-022-09741-3

Deng, Z., & Treiman, D. J. (1997). The impact of the cultural revolution on trends in educational attainment in the People’s Republic of China. American Journal of Sociology, 103 (2), 391–428. https://doi.org/10.1086/231212

DiPrete, T. A., & Buchmann, C. (2013). Rise of women: the growing gender gap in education and what it means for american schools . New York: Russell Sage Foundation.

Google Scholar  

Emran, M. S., Ferreira, F. H. G., Jiang, Y., & Sun, Y. (2023). Occupational dualism and intergenerational educational mobility in the rural economy: evidence from China and India. The Journal of Economic Inequality, 21 (3), 743–773. https://doi.org/10.1007/s10888-023-09599-1

Emran, M. S., Jiang, H., & Shilpi, F. (2020). Gender bias and intergenerational educational mobility: theory and evidence from China and India . Washington, DC: World Bank.

Emran, M. S., & Shilpi, F. (2018). Estimating intergenerational mobility with incomplete data: coresidency and truncation bias in rank-based relative and absolute mobility measures . Washington, DC: World Bank.

Emran, S., William, G., & Forhad, S. (2018). When measure matters. Journal of Human Resources, 53 (3), 589. https://doi.org/10.3368/jhr.53.3.0216-7737R1

Fang, C., & Feng, X. (2020). Social origin and educational attainment in China: a historical analysis of 70 birth cohorts. The Journal of Chinese Sociology, 7 (1), 19. https://doi.org/10.1186/s40711-020-00131-w

Fong, V. L. (2002). China’s one-child policy and the empowerment of urban daughters. American Anthropologist, 104 (4), 1098–1109. https://doi.org/10.1525/aa.2002.104.4.1098

Golley, J., & Kong, S. T. (2018). Inequality of opportunity in China’s educational outcomes. China Economic Review, 51 , 116–128. https://doi.org/10.1016/j.chieco.2016.07.002

Gruijters, R. J. (2022). Trends in educational stratification during China’s great transformation. Oxford Review of Education, 48 (3), 320–340. https://doi.org/10.1080/03054985.2021.1987207

Gruijters, R. J., Chan, T. W., & Ermisch, J. (2019). Trends in educational mobility: how does China compare to Europe and the United States? Chinese Journal of Sociology, 5 (2), 214–240. https://doi.org/10.1177/2057150x19835145

Guo, Y., Song, Y., & Chen, Q. (2019). Impacts of education policies on intergenerational education mobility in China. China Economic Review, 55 , 124–142. https://doi.org/10.1016/j.chieco.2019.03.011

Hannum, E. (2002). Educational stratification by ethnicity in China: enrollment and attainment in the early reform years. Demography, 39 (1), 95–117. https://doi.org/10.2307/3088366

Hannum, E., Ishida, H., Park, H., & Tam, T. (2019). Education in East Asian societies: postwar expansion and the evolution of inequality. Annual Review of Sociology, 45 (1), 625–647. https://doi.org/10.1146/annurev-soc-073018-022507

Hannum, E., & Wang, M. (2006). Geography and educational inequality in China. China Economic Review, 17 (3), 253–265. https://doi.org/10.1016/j.chieco.2006.04.003

Hertz, T., Jayasundera, T., Piraino, P., Selcuk, S., Smith, N., & Verashchagina, A. (2008). The inheritance of educational inequality: international comparisons and fifty-year trends. The B.E. Journal of Economic Analysis & Policy . https://doi.org/10.2202/1935-1682.1775

Hu, Y., & Qian, Y. (2023). Gender, education expansion and intergenerational educational mobility around the world. Nature Human Behaviour, 7 (4), 583–595. https://doi.org/10.1038/s41562-023-01545-5

Huo, Y., & Golley, J. (2022). Intergenerational education transmission in China: the gender dimension. China Economic Review, 71 , 101710. https://doi.org/10.1016/j.chieco.2021.101710

Kye, B. (2011). Intergenerational transmission of women’s educational attainment in South Korea: an application of a multi-group population projection model. Demographic Research, S10 (3), 79–112. https://doi.org/10.4054/DemRes.2011.24.3

Li, Y. (2021). Social mobility in China: a case study of social mobility research in the global south. In V. Iversen, A. Krishna, & K. Sen (Eds.), Social mobility in developing countries: concepts, methods, and determinants (pp. 221–246). Oxford: Oxford University Press.

Chapter   Google Scholar  

Liu, L., & Wan, Q. (2019). The effect of education expansion on intergenerational transmission of education: evidence from China. China Economic Review, 57 , 101327. https://doi.org/10.1016/j.chieco.2019.101327

Lucas, S. R. (2001). Effectively maintained inequality: education transitions, track mobility, and social background effects. American Journal of Sociology, 106 (6), 1642–1690. https://doi.org/10.1086/321300

Minnesota Population Center. (2020). Integrated Public Use Microdata Series, International: Version 7.3 . Minneapolis, MN: IPUMS. https://doi.org/10.18128/D020.V7.3

Narayan, A., Van der Weide, R., Cojocaru, A., Lakner, C., Redaelli, S., Gerszon Mahler, D., Ramasubbaiah, R. G. N., & Thewissen, S. (2018). Fair progress?: Economic mobility across generations around the world. The World Bank . https://doi.org/10.1596/978-1-4648-1210-1

Nybom, M., & Stuhler, J. (2017). Biases in standard measures of intergenerational income dependence. Journal of Human Resources, 52 (3), 800–825. https://doi.org/10.3368/jhr.52.3.0715-7290R

Raftery, A. E., & Hout, M. (1993). Maximally maintained inequality: expansion, reform, and opportunity in Irish education, 1921–75. Sociology of Education, 66 (1), 41–62. https://doi.org/10.2307/2112784

Skopek, J., & Leopold, T. (2020). Educational reproduction in germany: a prospective study based on retrospective data. Demography, 57 (4), 1241–1270. https://doi.org/10.1007/s13524-020-00896-2

Song, X., Massey, C. G., Rolf, K. A., Ferrie, J. P., Rothbaum, J. L., & Xie, Y. (2020). Long-term decline in intergenerational mobility in the United States since the 1850s. Proceedings of the National Academy of Sciences, 117 (1), 251–258. https://doi.org/10.1073/pnas.1905094116

Torche, F. (2021). Educational mobility in the developing world. In V. Iversen, A. Krishna, & K. Sen (Eds.), Social mobility in developing countries: concepts, methods, and determinants. Oxford: Oxford University Press.

Treiman, D. (2013). Trends in educational attainment in China. Chinese Sociological Review, 45 , 3–25. https://doi.org/10.2753/CSA2162-0555450301

Urbina, D. R. (2018). Intergenerational educational mobility during expansion reform: evidence from Mexico. Population Research and Policy Review, 37 (3), 367–417. https://doi.org/10.1007/s11113-018-9466-4

Wittemann, V. (2023). Educational reproduction in Sweden: a replication of Skopek and Leopold 2020 using Swedish data. Demographic Research, 48 (25), 733–774. https://doi.org/10.4054/DemRes.2023.48.25

Wu, X. (2019). Inequality and social stratification in postsocialist China. Annual Review of Sociology, 45 (1), 363–382. https://doi.org/10.1146/annurev-soc-073018-022516

Wu, X., & Treiman, D. J. (2004). The household registration system and social stratification in China: 1955–1996. Demography, 41 (2), 363–384. https://doi.org/10.1353/dem.2004.0010

Wu, X., & Zhang, Z. (2010). Changes in educational inequality in China, 1990–2005: evidence from the population census data. In E. Hannum, H. Park, & Y. Goto Butler (Eds.), Globalization, changing demographics, and educational challenges in East Asia. Leeds: Emerald Group Publishing Limited.

Xie, Y., Dong, H., Zhou, X., & Song, X. (2022). Trends in social mobility in postrevolution China. Proceedings of the National Academy of Sciences, 119 (7), e2117471119. https://doi.org/10.1073/pnas.2117471119

Xie, Y., & Hu, J. (2014). An introduction to the China family panel studies (CFPS). Chinese Sociological Review, 47 (1), 3–29. https://doi.org/10.2753/CSA2162-0555470101.2014.11082908

Zeng, J., Pang, X., Zhang, L., Medina, A., & Rozelle, S. (2014). Gender inequality in education in China: a meta-regression analysis. Contemporary Economic Policy, 32 (2), 474–491. https://doi.org/10.1111/coep.12006

Zhang, Z., & Chen, Q. (2014). The expansion of higher education admissions and the gender equalization of higher education opportunity: an empirical study based on Chinese general social survey (CGSS2008) data. The Journal of Chinese Sociology, 1 (1), 1. https://doi.org/10.1186/s40711-014-0001-7

Zhou, X., & Xie, Y. (2019). Market transition, industrialization, and social mobility trends in postrevolution China. American Journal of Sociology, 124 (6), 1810–1847. https://doi.org/10.1086/703346

Download references

Open access funding provided by International Institute for Applied Systems Analysis (IIASA). This work was supported by Research Fund for International Scientists of the National Natural Science Foundation of China [Grant number 72250610226]. The authors wish to acknowledge the statistical office that provided the underlying data making this research possible: National Bureau of Statistics, China.

Author information

Authors and affiliations.

Asian Demographic Research Institute, School of Sociology and Political Sciences, Shanghai University, Shanghai, China

Jiawei Wu & Guillaume Marois

International Institute for Applied Systems Analysis, Wittgenstein Centre for Demography and Global Human Capital (IIASA, OeAW, University of Vienna), Laxenburg, Austria

Guillaume Marois

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Guillaume Marois .

Ethics declarations

Competing interests.

No potential competing interests was reported by the author(s).

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 855 KB)

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Wu, J., Marois, G. Education Policies and Intergenerational Educational Mobility in China: New Evidence for the 1986–95 Birth Cohort. Popul Res Policy Rev 43 , 43 (2024). https://doi.org/10.1007/s11113-024-09887-2

Download citation

Received : 13 December 2023

Accepted : 02 May 2024

Published : 22 May 2024

DOI : https://doi.org/10.1007/s11113-024-09887-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Educational mobility
• Educational trends
• Intergenerational mobility
• Educational percentile rank
• Find a journal
• Publish with us
• Track your research

Map Options

Education Rankings by Country 2024

There is a correlation between a country's educational system quality and its economic status, with developed nations offering higher quality education.

The U.S., despite ranking high in educational system surveys, falls behind in math and science scores compared to many other countries.

Educational system adequacy varies globally, with some countries struggling due to internal conflicts, economic challenges, or underfunded programs.

While education levels vary from country to country, there is a clear correlation between the quality of a country's educational system and its general economic status and overall well-being. In general, developing nations tend to offer their citizens a higher quality of education than the least developed nations do, and fully developed nations offer the best quality of education of all. Education is clearly a vital contributor to any country's overall health.

According to the Global Partnership for Education , education is considered to be a human right and plays a crucial role in human, social, and economic development . Education promotes gender equality, fosters peace, and increases a person's chances of having more and better life and career opportunities.

"Education is the most powerful weapon which you can use to change the world." — Nelson Mandela

The annual Best Countries Report , conducted by US News and World Report, BAV Group, and the Wharton School of the University of Pennsylvania , reserves an entire section for education. The report surveys thousands of people across 78 countries, then ranks those countries based upon the survey's responses. The education portion of the survey compiles scores from three equally-weighted attributes: a well-developed public education system, would consider attending university there, and provides top-quality education. As of 2023, the top ten countries based on education rankings are:

Countries with the Best Educational Systems - 2021 Best Countries Report*

Ironically, despite the United States having the best-surveyed education system on the globe, U.S students consistently score lower in math and science than students from many other countries. According to a Business Insider report in 2018, the U.S. ranked 38th in math scores and 24th in science. Discussions about why the United States' education rankings have fallen by international standards over the past three decades frequently point out that government spending on education has failed to keep up with inflation.

It's also worthwhile to note that while the Best Countries study is certainly respectable, other studies use different methodologies or emphasize different criteria, which often leads to different results. For example, the Global Citizens for Human Rights' annual study measures ten levels of education from early childhood enrollment rates to adult literacy. Its final 2020 rankings look a bit different:

Education Rates of Children Around the World

Most findings and ranking regarding education worldwide involve adult literacy rates and levels of education completed. However, some studies look at current students and their abilities in different subjects.

One of the most-reviewed studies regarding education around the world involved 470,000 fifteen-year-old students. Each student was administered tests in math, science, and reading similar to the SAT or ACT exams (standardized tests used for college admissions in the U.S.) These exam scores were later compiled to determine each country's average score for each of the three subjects. Based on this study, China received the highest scores , followed by Korea, Finland , Hong Kong , Singapore , Canada , New Zealand , Japan , Australia and the Netherlands .

On the down side, there are many nations whose educational systems are considered inadequate. This could be due to internal conflict, economic problems, or underfunded programs. The United Nations Educational, Scientific, and Cultural Organization's Education for All Global Monitoring Report ranks the following countries as having the world's worst educational systems:

Countries with the Lowest Adult Literacy Rates

• Education rankings are sourced from both the annual UN News Best Countries report and the nonprofit organization World Top 20

Download Table Data

Enter your email below, and you'll receive this table's data in your inbox momentarily.

Which country ranks first in education?

Which country ranks last in education, frequently asked questions.

• Best Countries for Education - 2023 - US News
• Literacy rate, adult total (% of people ages 15 and above) - World Bank
• World Best Education Systems - Global Citizens for Human Rights
• UNESCO - Global Education Monitoring Reports
• World’s 10 Worst Countries for Education - Global Citizen
• International Education Database - World Top 20

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

Glossary of research terms.

• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

This glossary is intended to assist you in understanding commonly used terms and concepts when reading, interpreting, and evaluating scholarly research. Also included are common words and phrases defined within the context of how they apply to research in the social and behavioral sciences.

• Acculturation -- refers to the process of adapting to another culture, particularly in reference to blending in with the majority population [e.g., an immigrant adopting American customs]. However, acculturation also implies that both cultures add something to one another, but still remain distinct groups unto themselves.
• Accuracy -- a term used in survey research to refer to the match between the target population and the sample.
• Affective Measures -- procedures or devices used to obtain quantified descriptions of an individual's feelings, emotional states, or dispositions.
• Aggregate -- a total created from smaller units. For instance, the population of a county is an aggregate of the populations of the cities, rural areas, etc. that comprise the county. As a verb, it refers to total data from smaller units into a large unit.
• Anonymity -- a research condition in which no one, including the researcher, knows the identities of research participants.
• Baseline -- a control measurement carried out before an experimental treatment.
• Behaviorism -- school of psychological thought concerned with the observable, tangible, objective facts of behavior, rather than with subjective phenomena such as thoughts, emotions, or impulses. Contemporary behaviorism also emphasizes the study of mental states such as feelings and fantasies to the extent that they can be directly observed and measured.
• Beliefs -- ideas, doctrines, tenets, etc. that are accepted as true on grounds which are not immediately susceptible to rigorous proof.
• Benchmarking -- systematically measuring and comparing the operations and outcomes of organizations, systems, processes, etc., against agreed upon "best-in-class" frames of reference.
• Bias -- a loss of balance and accuracy in the use of research methods. It can appear in research via the sampling frame, random sampling, or non-response. It can also occur at other stages in research, such as while interviewing, in the design of questions, or in the way data are analyzed and presented. Bias means that the research findings will not be representative of, or generalizable to, a wider population.
• Case Study -- the collection and presentation of detailed information about a particular participant or small group, frequently including data derived from the subjects themselves.
• Causal Hypothesis -- a statement hypothesizing that the independent variable affects the dependent variable in some way.
• Causal Relationship -- the relationship established that shows that an independent variable, and nothing else, causes a change in a dependent variable. It also establishes how much of a change is shown in the dependent variable.
• Causality -- the relation between cause and effect.
• Central Tendency -- any way of describing or characterizing typical, average, or common values in some distribution.
• Chi-square Analysis -- a common non-parametric statistical test which compares an expected proportion or ratio to an actual proportion or ratio.
• Claim -- a statement, similar to a hypothesis, which is made in response to the research question and that is affirmed with evidence based on research.
• Classification -- ordering of related phenomena into categories, groups, or systems according to characteristics or attributes.
• Cluster Analysis -- a method of statistical analysis where data that share a common trait are grouped together. The data is collected in a way that allows the data collector to group data according to certain characteristics.
• Cohort Analysis -- group by group analytic treatment of individuals having a statistical factor in common to each group. Group members share a particular characteristic [e.g., born in a given year] or a common experience [e.g., entering a college at a given time].
• Confidentiality -- a research condition in which no one except the researcher(s) knows the identities of the participants in a study. It refers to the treatment of information that a participant has disclosed to the researcher in a relationship of trust and with the expectation that it will not be revealed to others in ways that violate the original consent agreement, unless permission is granted by the participant.
• Confirmability Objectivity -- the findings of the study could be confirmed by another person conducting the same study.
• Construct -- refers to any of the following: something that exists theoretically but is not directly observable; a concept developed [constructed] for describing relations among phenomena or for other research purposes; or, a theoretical definition in which concepts are defined in terms of other concepts. For example, intelligence cannot be directly observed or measured; it is a construct.
• Construct Validity -- seeks an agreement between a theoretical concept and a specific measuring device, such as observation.
• Constructivism -- the idea that reality is socially constructed. It is the view that reality cannot be understood outside of the way humans interact and that the idea that knowledge is constructed, not discovered. Constructivists believe that learning is more active and self-directed than either behaviorism or cognitive theory would postulate.
• Content Analysis -- the systematic, objective, and quantitative description of the manifest or latent content of print or nonprint communications.
• Context Sensitivity -- awareness by a qualitative researcher of factors such as values and beliefs that influence cultural behaviors.
• Control Group -- the group in an experimental design that receives either no treatment or a different treatment from the experimental group. This group can thus be compared to the experimental group.
• Controlled Experiment -- an experimental design with two or more randomly selected groups [an experimental group and control group] in which the researcher controls or introduces the independent variable and measures the dependent variable at least two times [pre- and post-test measurements].
• Correlation -- a common statistical analysis, usually abbreviated as r, that measures the degree of relationship between pairs of interval variables in a sample. The range of correlation is from -1.00 to zero to +1.00. Also, a non-cause and effect relationship between two variables.
• Covariate -- a product of the correlation of two related variables times their standard deviations. Used in true experiments to measure the difference of treatment between them.
• Credibility -- a researcher's ability to demonstrate that the object of a study is accurately identified and described based on the way in which the study was conducted.
• Critical Theory -- an evaluative approach to social science research, associated with Germany's neo-Marxist “Frankfurt School,” that aims to criticize as well as analyze society, opposing the political orthodoxy of modern communism. Its goal is to promote human emancipatory forces and to expose ideas and systems that impede them.
• Data -- factual information [as measurements or statistics] used as a basis for reasoning, discussion, or calculation.
• Data Mining -- the process of analyzing data from different perspectives and summarizing it into useful information, often to discover patterns and/or systematic relationships among variables.
• Data Quality -- this is the degree to which the collected data [results of measurement or observation] meet the standards of quality to be considered valid [trustworthy] and  reliable [dependable].
• Deductive -- a form of reasoning in which conclusions are formulated about particulars from general or universal premises.
• Dependability -- being able to account for changes in the design of the study and the changing conditions surrounding what was studied.
• Dependent Variable -- a variable that varies due, at least in part, to the impact of the independent variable. In other words, its value “depends” on the value of the independent variable. For example, in the variables “gender” and “academic major,” academic major is the dependent variable, meaning that your major cannot determine whether you are male or female, but your gender might indirectly lead you to favor one major over another.
• Deviation -- the distance between the mean and a particular data point in a given distribution.
• Discourse Community -- a community of scholars and researchers in a given field who respond to and communicate to each other through published articles in the community's journals and presentations at conventions. All members of the discourse community adhere to certain conventions for the presentation of their theories and research.
• Discrete Variable -- a variable that is measured solely in whole units, such as, gender and number of siblings.
• Distribution -- the range of values of a particular variable.
• Effect Size -- the amount of change in a dependent variable that can be attributed to manipulations of the independent variable. A large effect size exists when the value of the dependent variable is strongly influenced by the independent variable. It is the mean difference on a variable between experimental and control groups divided by the standard deviation on that variable of the pooled groups or of the control group alone.
• Emancipatory Research -- research is conducted on and with people from marginalized groups or communities. It is led by a researcher or research team who is either an indigenous or external insider; is interpreted within intellectual frameworks of that group; and, is conducted largely for the purpose of empowering members of that community and improving services for them. It also engages members of the community as co-constructors or validators of knowledge.
• Empirical Research -- the process of developing systematized knowledge gained from observations that are formulated to support insights and generalizations about the phenomena being researched.
• Epistemology -- concerns knowledge construction; asks what constitutes knowledge and how knowledge is validated.
• Ethnography -- method to study groups and/or cultures over a period of time. The goal of this type of research is to comprehend the particular group/culture through immersion into the culture or group. Research is completed through various methods but, since the researcher is immersed within the group for an extended period of time, more detailed information is usually collected during the research.
• Expectancy Effect -- any unconscious or conscious cues that convey to the participant in a study how the researcher wants them to respond. Expecting someone to behave in a particular way has been shown to promote the expected behavior. Expectancy effects can be minimized by using standardized interactions with subjects, automated data-gathering methods, and double blind protocols.
• External Validity -- the extent to which the results of a study are generalizable or transferable.
• Factor Analysis -- a statistical test that explores relationships among data. The test explores which variables in a data set are most related to each other. In a carefully constructed survey, for example, factor analysis can yield information on patterns of responses, not simply data on a single response. Larger tendencies may then be interpreted, indicating behavior trends rather than simply responses to specific questions.
• Field Studies -- academic or other investigative studies undertaken in a natural setting, rather than in laboratories, classrooms, or other structured environments.
• Focus Groups -- small, roundtable discussion groups charged with examining specific topics or problems, including possible options or solutions. Focus groups usually consist of 4-12 participants, guided by moderators to keep the discussion flowing and to collect and report the results.
• Framework -- the structure and support that may be used as both the launching point and the on-going guidelines for investigating a research problem.
• Generalizability -- the extent to which research findings and conclusions conducted on a specific study to groups or situations can be applied to the population at large.
• Grey Literature -- research produced by organizations outside of commercial and academic publishing that publish materials, such as, working papers, research reports, and briefing papers.
• Grounded Theory -- practice of developing other theories that emerge from observing a group. Theories are grounded in the group's observable experiences, but researchers add their own insight into why those experiences exist.
• Group Behavior -- behaviors of a group as a whole, as well as the behavior of an individual as influenced by his or her membership in a group.
• Hypothesis -- a tentative explanation based on theory to predict a causal relationship between variables.
• Independent Variable -- the conditions of an experiment that are systematically manipulated by the researcher. A variable that is not impacted by the dependent variable, and that itself impacts the dependent variable. In the earlier example of "gender" and "academic major," (see Dependent Variable) gender is the independent variable.
• Individualism -- a theory or policy having primary regard for the liberty, rights, or independent actions of individuals.
• Inductive -- a form of reasoning in which a generalized conclusion is formulated from particular instances.
• Inductive Analysis -- a form of analysis based on inductive reasoning; a researcher using inductive analysis starts with answers, but formulates questions throughout the research process.
• Insiderness -- a concept in qualitative research that refers to the degree to which a researcher has access to and an understanding of persons, places, or things within a group or community based on being a member of that group or community.
• Internal Consistency -- the extent to which all questions or items assess the same characteristic, skill, or quality.
• Internal Validity -- the rigor with which the study was conducted [e.g., the study's design, the care taken to conduct measurements, and decisions concerning what was and was not measured]. It is also the extent to which the designers of a study have taken into account alternative explanations for any causal relationships they explore. In studies that do not explore causal relationships, only the first of these definitions should be considered when assessing internal validity.
• Life History -- a record of an event/events in a respondent's life told [written down, but increasingly audio or video recorded] by the respondent from his/her own perspective in his/her own words. A life history is different from a "research story" in that it covers a longer time span, perhaps a complete life, or a significant period in a life.
• Margin of Error -- the permittable or acceptable deviation from the target or a specific value. The allowance for slight error or miscalculation or changing circumstances in a study.
• Measurement -- process of obtaining a numerical description of the extent to which persons, organizations, or things possess specified characteristics.
• Meta-Analysis -- an analysis combining the results of several studies that address a set of related hypotheses.
• Methodology -- a theory or analysis of how research does and should proceed.
• Methods -- systematic approaches to the conduct of an operation or process. It includes steps of procedure, application of techniques, systems of reasoning or analysis, and the modes of inquiry employed by a discipline.
• Mixed-Methods -- a research approach that uses two or more methods from both the quantitative and qualitative research categories. It is also referred to as blended methods, combined methods, or methodological triangulation.
• Modeling -- the creation of a physical or computer analogy to understand a particular phenomenon. Modeling helps in estimating the relative magnitude of various factors involved in a phenomenon. A successful model can be shown to account for unexpected behavior that has been observed, to predict certain behaviors, which can then be tested experimentally, and to demonstrate that a given theory cannot account for certain phenomenon.
• Models -- representations of objects, principles, processes, or ideas often used for imitation or emulation.
• Naturalistic Observation -- observation of behaviors and events in natural settings without experimental manipulation or other forms of interference.
• Norm -- the norm in statistics is the average or usual performance. For example, students usually complete their high school graduation requirements when they are 18 years old. Even though some students graduate when they are younger or older, the norm is that any given student will graduate when he or she is 18 years old.
• Null Hypothesis -- the proposition, to be tested statistically, that the experimental intervention has "no effect," meaning that the treatment and control groups will not differ as a result of the intervention. Investigators usually hope that the data will demonstrate some effect from the intervention, thus allowing the investigator to reject the null hypothesis.
• Ontology -- a discipline of philosophy that explores the science of what is, the kinds and structures of objects, properties, events, processes, and relations in every area of reality.
• Panel Study -- a longitudinal study in which a group of individuals is interviewed at intervals over a period of time.
• Participant -- individuals whose physiological and/or behavioral characteristics and responses are the object of study in a research project.
• Peer-Review -- the process in which the author of a book, article, or other type of publication submits his or her work to experts in the field for critical evaluation, usually prior to publication. This is standard procedure in publishing scholarly research.
• Phenomenology -- a qualitative research approach concerned with understanding certain group behaviors from that group's point of view.
• Philosophy -- critical examination of the grounds for fundamental beliefs and analysis of the basic concepts, doctrines, or practices that express such beliefs.
• Phonology -- the study of the ways in which speech sounds form systems and patterns in language.
• Policy -- governing principles that serve as guidelines or rules for decision making and action in a given area.
• Policy Analysis -- systematic study of the nature, rationale, cost, impact, effectiveness, implications, etc., of existing or alternative policies, using the theories and methodologies of relevant social science disciplines.
• Population -- the target group under investigation. The population is the entire set under consideration. Samples are drawn from populations.
• Position Papers -- statements of official or organizational viewpoints, often recommending a particular course of action or response to a situation.
• Positivism -- a doctrine in the philosophy of science, positivism argues that science can only deal with observable entities known directly to experience. The positivist aims to construct general laws, or theories, which express relationships between phenomena. Observation and experiment is used to show whether the phenomena fit the theory.
• Predictive Measurement -- use of tests, inventories, or other measures to determine or estimate future events, conditions, outcomes, or trends.
• Principal Investigator -- the scientist or scholar with primary responsibility for the design and conduct of a research project.
• Probability -- the chance that a phenomenon will occur randomly. As a statistical measure, it is shown as p [the "p" factor].
• Questionnaire -- structured sets of questions on specified subjects that are used to gather information, attitudes, or opinions.
• Random Sampling -- a process used in research to draw a sample of a population strictly by chance, yielding no discernible pattern beyond chance. Random sampling can be accomplished by first numbering the population, then selecting the sample according to a table of random numbers or using a random-number computer generator. The sample is said to be random because there is no regular or discernible pattern or order. Random sample selection is used under the assumption that sufficiently large samples assigned randomly will exhibit a distribution comparable to that of the population from which the sample is drawn. The random assignment of participants increases the probability that differences observed between participant groups are the result of the experimental intervention.
• Reliability -- the degree to which a measure yields consistent results. If the measuring instrument [e.g., survey] is reliable, then administering it to similar groups would yield similar results. Reliability is a prerequisite for validity. An unreliable indicator cannot produce trustworthy results.
• Representative Sample -- sample in which the participants closely match the characteristics of the population, and thus, all segments of the population are represented in the sample. A representative sample allows results to be generalized from the sample to the population.
• Rigor -- degree to which research methods are scrupulously and meticulously carried out in order to recognize important influences occurring in an experimental study.
• Sample -- the population researched in a particular study. Usually, attempts are made to select a "sample population" that is considered representative of groups of people to whom results will be generalized or transferred. In studies that use inferential statistics to analyze results or which are designed to be generalizable, sample size is critical, generally the larger the number in the sample, the higher the likelihood of a representative distribution of the population.
• Sampling Error -- the degree to which the results from the sample deviate from those that would be obtained from the entire population, because of random error in the selection of respondent and the corresponding reduction in reliability.
• Saturation -- a situation in which data analysis begins to reveal repetition and redundancy and when new data tend to confirm existing findings rather than expand upon them.
• Semantics -- the relationship between symbols and meaning in a linguistic system. Also, the cuing system that connects what is written in the text to what is stored in the reader's prior knowledge.
• Social Theories -- theories about the structure, organization, and functioning of human societies.
• Sociolinguistics -- the study of language in society and, more specifically, the study of language varieties, their functions, and their speakers.
• Standard Deviation -- a measure of variation that indicates the typical distance between the scores of a distribution and the mean; it is determined by taking the square root of the average of the squared deviations in a given distribution. It can be used to indicate the proportion of data within certain ranges of scale values when the distribution conforms closely to the normal curve.
• Statistical Analysis -- application of statistical processes and theory to the compilation, presentation, discussion, and interpretation of numerical data.
• Statistical Bias -- characteristics of an experimental or sampling design, or the mathematical treatment of data, that systematically affects the results of a study so as to produce incorrect, unjustified, or inappropriate inferences or conclusions.
• Statistical Significance -- the probability that the difference between the outcomes of the control and experimental group are great enough that it is unlikely due solely to chance. The probability that the null hypothesis can be rejected at a predetermined significance level [0.05 or 0.01].
• Statistical Tests -- researchers use statistical tests to make quantitative decisions about whether a study's data indicate a significant effect from the intervention and allow the researcher to reject the null hypothesis. That is, statistical tests show whether the differences between the outcomes of the control and experimental groups are great enough to be statistically significant. If differences are found to be statistically significant, it means that the probability [likelihood] that these differences occurred solely due to chance is relatively low. Most researchers agree that a significance value of .05 or less [i.e., there is a 95% probability that the differences are real] sufficiently determines significance.
• Subcultures -- ethnic, regional, economic, or social groups exhibiting characteristic patterns of behavior sufficient to distinguish them from the larger society to which they belong.
• Testing -- the act of gathering and processing information about individuals' ability, skill, understanding, or knowledge under controlled conditions.
• Theory -- a general explanation about a specific behavior or set of events that is based on known principles and serves to organize related events in a meaningful way. A theory is not as specific as a hypothesis.
• Treatment -- the stimulus given to a dependent variable.
• Trend Samples -- method of sampling different groups of people at different points in time from the same population.
• Triangulation -- a multi-method or pluralistic approach, using different methods in order to focus on the research topic from different viewpoints and to produce a multi-faceted set of data. Also used to check the validity of findings from any one method.
• Unit of Analysis -- the basic observable entity or phenomenon being analyzed by a study and for which data are collected in the form of variables.
• Validity -- the degree to which a study accurately reflects or assesses the specific concept that the researcher is attempting to measure. A method can be reliable, consistently measuring the same thing, but not valid.
• Variable -- any characteristic or trait that can vary from one person to another [race, gender, academic major] or for one person over time [age, political beliefs].
• Weighted Scores -- scores in which the components are modified by different multipliers to reflect their relative importance.
• White Paper -- an authoritative report that often states the position or philosophy about a social, political, or other subject, or a general explanation of an architecture, framework, or product technology written by a group of researchers. A white paper seeks to contain unbiased information and analysis regarding a business or policy problem that the researchers may be facing.

Elliot, Mark, Fairweather, Ian, Olsen, Wendy Kay, and Pampaka, Maria. A Dictionary of Social Research Methods. Oxford, UK: Oxford University Press, 2016; Free Social Science Dictionary. Socialsciencedictionary.com [2008]. Glossary. Institutional Review Board. Colorado College; Glossary of Key Terms. Writing@CSU. Colorado State University; Glossary A-Z. Education.com; Glossary of Research Terms. Research Mindedness Virtual Learning Resource. Centre for Human Servive Technology. University of Southampton; Miller, Robert L. and Brewer, John D. The A-Z of Social Research: A Dictionary of Key Social Science Research Concepts London: SAGE, 2003; Jupp, Victor. The SAGE Dictionary of Social and Cultural Research Methods . London: Sage, 2006.

• << Previous: Independent and Dependent Variables
• Next: 1. Choosing a Research Problem >>
• Last Updated: May 30, 2024 9:38 AM
• URL: https://libguides.usc.edu/writingguide

• Search Center for Research on Disability

• Frequently Asked Questions
• Collection Archive
• February 2023 Event
• October 2022 Event Archive
• March 2022 Event
• 2011-2014 Events
• Webinar FAQ
• Module 1 Accessibility, Disparities, and Program Participation
• Module 2 Disability Measurement and Eligibility Criteria
• Module 3 Models and Concepts of Disability
• Module 4 Available Data Sources
• Module 5 Accessing Publicly Available Data Sources

Annual Disability Statistics Collection

The Annual Disability Statistics Compendium, Annual Report on People With Disabilities, the Build Your Own Statistics tool, and infographics gather disability statistics published by many federal agencies into one place. They’re helpful for lawmakers and others who are working on laws or policies about disabilities, and for those writing grants for programs for people with disabilities.

2024 Release

New this year

There are many new changes to the Annual Disability Statistics Collection this year. The HTML version of the Compendium is now accessible and downloadable. Both online and downloaded versions of the Compendium can be read by screen readers. We updated the tables in the Compendium to include more measures of accuracy, and rounded estimates to the nearest thousandths to make the tables easier to read. Instead of publishing the Supplement, this year we made an online tool where you can Build Your Own Statistics.

To receive the Annual Disability Statistics Collection by mail

• Build Your Own Statistics

Find statistics previously published in the Supplement, including intersectional stats on race, age, gender, and disability.

Look through frequently asked questions about the Annual Disability Statistics Collection.

Annual Conference and Archive

View recordings and presentations from past Research on Disability events.

Collection Materials

Statistics in the Compendium are described for people ages 18-64, the working age. It also includes new measures of accuracy, such as margins of error and relative ratio, which are defined in the glossary.

Viewing and Download Options:

Online Compendium HTML Download (39.5 MB) Compendium PDF (for Print) CSV/XLS tables can be provided upon request

The Annual Report on People with Disabilities in America tracks the progress of people with disabilities using key social and economic indicators, and highlights increases or decreases for each indicator. The PDF is accessible.

Annual Report PDF

2024 Annual Disability Infographics

Social Inequities Experienced by African Americans (PDF)

Experiences of People with Disabilities in Rural America (PDF)

Disability Experiences of Veterans Ages 18-64 (PDF)

Center for Research on Disability

• ARRT on Employment
• Kessler Surveys
• Health Disparities and Intellectual Disabilities Projects
• Individual Characteristics RRTC
• Introduction
• Section 1: Population and Prevalence
• Section 2: Functioning
• Section 3: Employment
• Section 4: Industry and Occupation
• Section 5: Earnings
• Section 6: Poverty
• Section 7: Home Environments
• Section 8: Veterans
• Section 9: Health Insurance Coverage
• Section 10: Rural
• Section 11: Health
• Section 12: Social Security Administration Programs
• Section 13: Education
• Section 14: Vocational Rehabilitation
• Section 15: Voting and Registration

Phone Numbers

Routine and emergency care.

Companion Animal Hospital in Ithaca, NY for cats, dogs, exotics, and wildlife

Equine and Nemo Farm Animal Hospitals in Ithaca, NY for horses and farm animals

Cornell Ruffian Equine Specialists, on Long Island for every horse

Ambulatory and Production Medicine for service on farms within 30 miles of Ithaca, NY

Animal Health Diagnostic Center New York State Veterinary Diagnostic Laboratory

General Information

Cornell University College of Veterinary Medicine Ithaca, New York 14853-6401

Andrew Miller, DVM, DACVP

Department of Population Medicine and Diagnostic Sciences

Associate professor section chief, anatomic pathology.

Department of Population Medicine and Diagnostic Sciences Cornell University College of Veterinary Medicine T5 006A Veterinary Research Tower, Box 18 Ithaca, NY 14853-6401 Office: 607-253-3559 Fax: 607-253-3541 Email: [email protected]

Research Interest

My laboratory is focused primarily on studying the mechanisms underlying neurologic disease in animals utilizing both spontaneously arising disease and experimental models in a variety of laboratory animal species. One current focus of my lab is in utilizing the dog as a model for human brain cancer, primarily meningeal and glial tumors. This is being done through a variety of histologic, immunohistochemical, and molecular studies in both the dog and cat. We are especially interested in determining the roles that novel gene mutations have in the genesis of canine and feline meningioma and are utilizing RNAseq to better understand the transcriptome of this tumor. We are also actively involved in studying the immune cell microenvironment in canine brain tumors in order to determine if a relationship exists between immune cell infiltration and postsurgical outcome. Other active areas of research in my laboratory include analyzing the RNA transcriptome of canine soft tissue sarcomas, a common, but understudied tumor, in the dog. Due to my expertise in nonhuman primate pathology, I maintain numerous collaborations with researchers throughout the country aiding in various research studies.

• Residency, Veterinary Anatomic Pathology, Cornell University, 2008
• DVM, Cornell University, 2005
• BS in Animal Science, Cum Laude with Distinction in Research, Cornell University, 2001

Biography/Professional Experience

I am an Associate Professor in the College of Veterinary Medicine and Section Chief for Anatomic Pathology. I am a diplomate of the American College of Veterinary Pathologists (ACVP). I received my doctorate in veterinary medicine from Cornell University in 2005 and entered a residency in veterinary anatomic pathology at Cornell University, completed in 2008. I joined the faculty at the New England Primate Research Center, a division of Harvard Medical School as a research associate in 2008. I had achieved assistant professor by 2013 and held an appointment in the Pathology Department of the Massachusetts General Hospital during my tenure at the primate center. I joined the faculty in the Section of Anatomic Pathology as an Assistant Professor at Cornell University in fall of 2013. I have a strong interest in training veterinary students and pathology residents and preparing them for a career in comparative veterinary pathology. I believe that this is best done through hands on exposure to clinical cases and basic research techniques. I am involved nationally in the ACVP and other pathology groups including the Canine Brain Tumor Consortium, a multidisciplinary group organized through the National Cancer Institute. I strongly believe in the integration of teaching and collaborative research as a way to foster continued growth in the field of comparative veterinary pathology. 

Publications

Reyes VAA, Donovan TA, Miller AD , Porter BF, Frank CB, Rissi DR. Doublecortin immunolabeling in canine gliomas with distinct degrees of tumor infiltration. J Vet Diagn Invest. 2022, epub Dec. 15

Nelissen S, Miller AD . Comparison of anti-Mullerian hormone and inhibin immunolabeling in canine and equine granulosa cell tumors. J Vet Diagn Invest. 2022 34(6):1027-1031.

Riker J, Clarke LL, Demeter EA, Miller AD , Howerth EW, Miller DM, Rissi DR. Histiocytic sarcoma with central nervous system involvement in 6 cats. J Vet Diagn Invest. 2023 35(1):87-91. (pathology and data interpretation)

Buote NJ, Loftus JP, Miller AD . Laparoscopic twist technique has the best overall artifact profile when comparing three laparoscopic hepatic cup biopsy techniques for dogs. Am J Vet Res. 2022, epub Nov. 4

Latifkar A, Wang F, Mullman JJ, Panizza E, Fernandez IR, Ling L, Miller AD , Fischbach C, Weiss RS, Lin H, Cerione RA, Antonyak MA. IGF2BP2 promotes cancer progression by degrading the RNA transcript encoding a v-ATPase subunit. Proc Natl Acad Sci USA. 2022, 119(45):e2200477119. (pathology and data interpretation)

Martins M, do Nascimento GM, Nooruzzaman M, Yuan F, Chen C, Caserta LC, Miller AD , Whittaker GR, Fang Y, Diel DG. The omicon variant BA.1.1 presents a lower pathogenicity than B.1 D614G and delta variants in a feline model of SARS-CoV-2 infection. J Virol. 2022, 96(17): e0096122.

Holdsworth JP, Marino DJ, Loughin, Miller AD , Sackman JJ, Lesser ML, O’Donnell M. Biopsy and histologic findings of the dura mater at the level of the foramen magnum in 121 CKCS with Chiari-like malformation. Front Vet Sci 2022, 9:954092. (pathology and data interpretation; manuscript editing)

Cruz Otero JD, Jager M, Armien AG, Miller A , Stokol T. Granular variant of a histiocytic tumor on the toe of a cat: Case rep[ort and literature review. Vet Clin Pathol 2022, epub Jun 24. (pathology and data interpretation; manuscript editing)

Herberger A, Hammond J, Miller A , Olsen B. MRI of a middle ear cholesteatoma in a cat. Vet Radiol Ultrasound. 2022, 63(5):E16-E20. (pathology and data interpretation; manuscript editing)

Tanaka Y, Watanabe K, Miller AD , Matsumoto K, Kobayashi Y. Cholesterol granuloma associated with degenerative neuropathy in the cauda equina of a dog. J Vet Diagn Invest. 2022, epub Aug. 22. (pathology and data interpretation; manuscript editing)

Faust AE, Soletti L, Cwalina NA, Miller AD , Wood MD, Mahan MA, Cheetham J, Brown BN. Development of an acellular nerve cap xenograft for neuroma prevention. J Biomed Mater Res A. 2022 110(11):1738-1748. (pathology and data interpretation; manuscript editing)

Krane GA, Shockley KR, Malarkey DE, Miller AD , Miller CR, Tokarz DA, Jensen HL, Janardhan KS, Breen M, Mariani CL. Inter-pathologist agreement on diagnosis, classification, and grading of canine glioma. Vet Comp Oncol. 2022, 20(4):881-889. (pathology and data interpretation; manuscript editing)

Demeter E, Kent M, Glass E, Rissi D, Edwards J, Miller AD . OLIG2 immunolabeling in feline ependymoma. J Vet Diagn Invest. 2022, 34(5):898-901.

Lin M, Kent M, Glass EN, Jones AM, Miller AD , Demeter EA. Pathology in practice. J Am Vet Med Assoc. 2022, May 15;259(S2):1-5. (pathology and data interpretation; manuscript editing)

Johnson PJ, Rivard BC, Wood JH, DiRubio ML, Henry JG, Miller AD . Relationship between histological tumor margins and magnetic resonance imaging signal intensities in brain neoplasia in dogs. J Vet Inter Med. 2022, 36(3):1039-1048. (pathology and data interpretation; manuscript editing)

Tanaka Y, Watanabe K, Nakagun S, Miller AD , Sasaki M, Kobayashi Y. Bilateral plaque-like white matter degeneration in cerebral septal regions of a Chapman’s zebra (Equus quagga chapmani). J Comp Path. 2022, 194:54-57. (pathology and data interpretation; manuscript editing)

Johnson PJ, Todd-Donato AB, Miller AD , Wang Y, Holm C, Panisello-Manterelo Cl, Colon Acevedo CS, Wood JH. Association of hydromyelia and acute compressive myelopathy caused by intervertebral disk extrusion in dogs. J Vet Intern Med. 2022, 36(3):1049-1056. (pathology and data interpretation; manuscript editing)

Rissi DR, McHale BJ, Miller AD . Primary nervous system lymphoma in cats. J Vet Diagn Invest. 2022, 34(4):712-717.

Whitty JA, Miller AV, Miller AD , Carney PC, Scrivani PV. Associations between styloid process sheath CT sizes and age, body weight, and breed in horses.  Vet Radiol Ultrasound. 2022, 63(4):436-449. (pathology and data interpretation; manuscript editing)

Healey EA, Andre NM, Miller AD , Whitaker GR, Berliner EA. Outbreak of feline infectious peritonitis (FIP) in shelter housed cats: molecular analysis of the feline coronavirus S1/S2 cleavage site consistent with a  "circulating virulent-avirulent theory" of FIP pathogenesis. JFMS Open Rep. 2022, Feb 11;8(1):20551169221074226. (pathology and data interpretation; manuscript editing)

Seibert LM, Center SA, Randolph JF, Miller ML, Miller AD , Choi E, Flanders JA, Harvey HJ. Relationships between congenital peritoneopericardial diaphragmatic hernia or congenital central diaphragmatic hernia and ductal plate malformations in dogs and cats. J Am Vet Med Assoc. 2021, 259(9):1009-1024. (pathology and data interpretation; manuscript editing)

Colman K, Andrews RN, Atkins H, Boulineau T, Bradley A, Braendli-Baiocco A, Capobianco R, Caudell D, Cline M, Doi T, Ernst R, van Esch E, Everitt J, Fant P, Gruebbel MM, Mecklenburg L, Miller AD , Nikula KJ, Satake S, Schwartz J, Sharma A, Shimoi A, Sobry C, Taylor I, Vemireddi V, Vidal J, Wood C, Vahle JL. International harmonization of nomenclature and diagnostic criteria (INHAND): Non-proliferative and proliferative lesions of the non-human primate (M. fascicularis). J Toxicol Pathol. 2021, 34(3 Suppl):1S-182S. (pathology and data interpretation; manuscript editing)

Peralta S, Duhamel GE, Katt WP, Heikinheimo K, Miller AD , Ahmed F, McCleary-Wheeler A, Grenier JK. Comparative transcriptional profiling of canine acanthomatous ameloblastoma and homology with human ameloblastoma. Sci Rep. 2021, 11:17792. (pathology and data interpretation; manuscript editing)

Cercone M, Chevalier J, Kennedy JG, Miller AD , Fortier LA. Early failure of a polyvinyl alcohol hydrogel implant with osteolysis and foreign body reactions in an ovine model of cartilage repair. Am J Sports Med. 2021, 49(12):3395-3403. (pathology and data interpretation; manuscript editing)

Tse MY, Porter IR, Demeter E, Behling-Kelly E, Wakshlag JJ, Miller AD . Osteogenesis imperfecta in two Finnish Lapphund puppies. Vet Med (Auck). 2021, 12:177-185.

Burns E, Habib H, Favro G, Katzman S, Sakai RR, Marquardt S, Bordbari M, Ming-Whitfield B, Peterson J, Dahlgren A, Rivas V, Ramirez Alanis C, Peng S, Donnelly C, Dizmang B-S, Kallenberg A, Grahn R, Miller A , Woolard K, Moeller B, Puschner B, Finno C. Increased α-tocopherol metabolism in horses with equine neuroaxonal dystrophy. J Vet Intern Med. 2021, 35(5):2473-2485. (pathology and data interpretation; manuscript editing)

Hales EN, Aleman M, Marquardt SA, Katzman SA, Woolard KD, Miller AD , Finno CJ. Postmortem diagnose of spinal ataxia in 316 horses in California. J Am Vet Med Assoc. 2021, 258(12):1386-1393. (pathology and data interpretation; manuscript editing) 

Crouch EEV, McAloose D, McEntire MS, Morrisey JK, Miller AD . Pathology of the bearded dragon (Pogona vitticeps): a retrospective analysis of 36 cases. J Comp Path. 2021, 186:51-61.

Krane GA, Malarkey DE, Miller AD , Miller CR, Tokarz DA, O’Dea CA, Jensen H, Janardhan KS, Flagler N, Shockley KR, Rainess BA, Mariani CL. Immunohistochemical evaluation of immune cell infiltration in canine glioma. Vet Pathol, 2021, 58(5):952-963. (pathology and data interpretation; manuscript editing)

Loehr AR, Pierpoint TM, Gelsleichter E, Galang AMD, Fernandez IR, Moore ES, Guo MZ, Miller AD , Weiss RS. Targeting cancer stem cells with differentiation agents as an alternative to genotoxic chemotherapy for the treatment of malignant testicular germ cell tumors. Cancers (Basel). 2021, 13(9):2045. (pathology and data interpretation; manuscript editing)

Meuten D, Moore F, Donovan T, Bertram C, Klopfleisch R, Foster R, Smedley R, Dark M, Milovancev M, Stromberg P, Williams B, Aubreville M, Avallone G, Bolfa P, Cullen J, Dennis M, Goldschmidt M, Luong R, Miller A , Miller M, Munday J, Roccabianca P, Salas E, Schulman F, Laufer-Amorin R, Asakawa M, Craig L, Dervisis N, Esplin DG, Hauck M, Kagawa Y, Kiupel M, Linder K, Meichner C, Marconato L, Oblak M, Santos R, Simpson M, Whitley D, Tvedten H. International Guidelines for Veterinary Tumor Pathology: A Call to Action. Vet Pathol. 2021, 58(5):766-794. (pathology and data interpretation; manuscript editing)

O’Sullivan LE, Kent M, Glass EN, Song RB, Warren JD, de Lahunta A, Miller AD . Pathology in practice. J Am Vet Med Assoc. 2021 258(8):857-860.

Avallone G, Rasotto R, Chambers JK, Miller AD , Behling-Kelly E, Monti P, Berlato D, Valenti P, Roccabianca P. Review of Histological Grading Systems in Veterinary Medicine. Vet Pathol. 2021; 58(5):809-828. (pathology and data interpretation; manuscript editing)

Miller AJ, Center SA, Randolph JF, Friesen CH, Miller AD , Warner KW. Disparities in hepatic copper concentrations determined by atomic absorption spectroscopy, inductively coupled plasma mass spectroscopy, and digital image analysis of rhodanine-stained sections in dogs. J Am Vet Med Assoc. 2021, 258(4): 395-406. (pathology and data interpretation; manuscript editing)

Awards and Honors

• C.L. Davis Foundation Samuel W. Thompson Lecture Award, 2018
• Brittonkill Education Foundation Alumni Achievement Award, 2012

Professional/Academic Affiliations

• American Society for Investigative Pathology
• American College of Veterinary Pathologists
• American Association of Veterinary Laboratory Diagnosticians
• American Association of Neuropathologists