Tag: Ethnicity

  • Hiding in plain sight? A simple statistical effect may largely explain the ethnicity degree awarding gap

    Hiding in plain sight? A simple statistical effect may largely explain the ethnicity degree awarding gap

    • By Sean Brophy (@seanbrofee), Senior Lecturer at the Centre for Decent Work and Productivity, Manchester Metropolitan University.

    A persistent challenge in UK higher education is the ethnicity degree awarding gap – the difference between White and ethnic minority students receiving top degrees (firsts or 2:1s). The Office for Students (OfS) aims to entirely eliminate this gap by 2030/31, but what if most of this gap reflects success in widening participation rather than systemic barriers?

    Between 2005/6 and 2021/22, university participation grew 21% faster for Asian students and 17% faster for Black students compared to White students. This remarkable success in widening access might paradoxically explain one of the UK’s most persistent higher education challenges.

    Figure 1 presents ethnicity gaps over time compared to a White baseline (the grey line constant at zero). The data for 2021/22 shows significant gaps: 21 percentage points for Black students, 9 for Asian students, and 4 for Mixed ethnicity students compared to their White peers. Traditional explanations focus on structural barriers, cultural differences, and potential discrimination, and much of the awarding gap remains unexplained after adjusting for prior attainment and background characteristics. However, a simpler explanation might be hiding in plain sight: the gap may also reflect a statistical effect created by varying participation rates across ethnic groups.

    Ethnicity Degree Awarding Gap (2014/15 – 2021/22)

    Figure 1. Source: HESA

    Here is the key insight: ethnic minority groups now participate in higher education at remarkably higher rates than White students, which likely then drives some of the observed ethnicity awarding gaps. Figure 2 presents the over-representation of ethnic groups in UK higher education relative to the White reference group (again, the constant grey line). The participation gap has grown substantially – Asian students were 22 percentage points more likely to attend university than White students in 2021/22, with Black students 18 points higher.

    Over-representation of ethnic groups in HE compared to White baseline (2005/6-2021/22)

    Figure 2. Source: UCAS End Of Cycle Report 2022

    This difference in participation rates creates an important statistical effect, what economists call ‘compositional effects’. When a much larger proportion of any group enters university, that group may naturally include a broader range of academic ability. Think of it like this: if mainly the top third of White students attend university, but nearly half of ethnic minority students do, we would expect to see differences in degree outcomes – even with completely fair teaching and assessment.

    This principle can be illustrated using stylized ability-participation curves for representative ethnic groups in Figure 3. These curves show the theoretical distribution of academic ability for Asian, Black, and White groups, with the red shaded area representing the proportion of students from each group accepted into higher education in 2021/22. It would be surprising if there was no degree awarding gap under these conditions!

    Stylized ability-participation curves by ethnic group

    This hypothesis suggests the degree awarding gap might largely reflect the success of widening participation policies. Compositional effects like these are difficult to control for in studies, and it is noteworthy that, to date, no studies on the ethnicity awarding gap have adequately controlled for these effects (including one of my recent studies).

    While this theory may offer a compelling statistical explanation, future research pursuing this line of inquiry needs to go beyond simply controlling for prior achievement. We need to examine both how individual attainment evolves from early education to university, using richer measures than previous studies, and how the expansion of university participation has changed the composition of student ability over time. This analysis must also account for differences within broad ethnic categories (British Indian students, for example, show different patterns from other Asian groups) and consider how university and subject choices vary across groups.

    My argument is not that compositional effects explain everything — rather, understanding their magnitude is crucial for correctly attributing how much of the gap is driven by traditional explanations, such as prior attainment, background characteristics, structural barriers, or discrimination. Only with this fuller picture can we properly target resources and interventions where they’re most needed.

    If this hypothesis is proven correct, however, it underscores why the current policy focus on entirely eliminating gaps through teaching quality or support services, while well-intentioned, may be misguided. If gaps are the statistically inevitable result of differing participation patterns among ethnic groups, then institutional interventions cannot entirely eliminate them. This doesn’t mean universities shouldn’t strive to support all students effectively – but it does require us to fundamentally rethink how we measure and address educational disparities.

    Rather than treating all gaps as problems to be eliminated, we should:

    1. Fund research which better accounts for these compositional effects.
    2. Develop benchmarks that account for participation rates when measuring degree outcomes.
    3. Contextualize the success of widening participation with acknowledging awarding gaps as an inevitable statistical consequence.
    4. Focus resources on early academic support for students from all backgrounds who might need additional help, particularly in early childhood.
    5. Explore barriers in other post-16 or post-18 pathways that may be contributing to the over-representation of some groups in higher education.

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  • Comparative Data on Race & Ethnicity in Education Abroad by Percentage of Students [2025]

    Comparative Data on Race & Ethnicity in Education Abroad by Percentage of Students [2025]

    References

     

    American Association of Community Colleges. (2024). AACC Fast Facts 2024. https://www.aacc.nche.edu/researchtrends/fast-facts/

     

    Fund for Education Abroad (FEA). (2024, December). Comparative Data on Race & Ethnicity of FEA Awards 20222023 by Percentage of Students. Data obtained from Joelle Leinbach, Program Manager at the Fund for Education Abroad. https://fundforeducationabroad.org/  

     

    Institute of International Education. (2024). Profile of U.S. Study Abroad Students, 2024 Open Doors U.S. Student Data. https://opendoorsdata.org/data/us-study-abroad/student-profile/  

     

    Institute for International Education. (2024). Student Characteristics: U.S. Students Studying Abroad at Associate’s Colleges Data from the 2024 Open Doors Report. https://opendoorsdata.org/data/us-study-abroad/community-college-student-characteristics/

     

    Institute for International Education. (2022, May) A Legacy of Supporting Excellence and Opportunity in Study Abroad: 20-Year Impact Study, Comprehensive Report. Benjamin A. Gilman International Scholarship. https://www.gilmanscholarship.org/program/program-statistics/ 

     

    United States Census Bureau. (2020). DP1 | Profile of General Population and Housing Characteristics, 2020: DEC Demographic Profile. https://data.census.gov/table?g=010XX00US&d=DEC+Demographic+Profile  

     

    U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics. (2023, August). Characteristics of Postsecondary Students. https://nces.ed.gov/programs/coe/indicator/csb/postsecondarystudents

    Bibliography of Literature, Presentations & Curriculum Integration Projects Incorporating the Comparative Data Table on Race & Ethnicity in Education Abroad

    Comp, D. & Bakkum, N. (2025, January). Study Away/Abroad for All Students! – Who Studies Away/Abroad at Columbia College? Invited presentation for faculty at the Winter 2025 Faculty and Staff Development Days at Columbia College Chicago.

    Lorge, K. & Comp, D. (2024, April). A Case for Simple and Comparable Data to Assess Race and Ethnicity in Education Abroad. The Global Impact Exchange: Publication of Diversity Abroad. Spring 2024. https://www.diversityabroad.org/GlobalImpactExchange 

    Comp, D. (2019). Effective Utilization of Data for Strategic Planning and Reporting with Case Study: My Failed Advocacy Strategy. In. A.C. Ogden, L.M. Alexander, & Mackintosh, E. (Eds.). Education Abroad Operational Management: Strategies, Opportunities, and Innovations, A Report on ISA ThinkDen, 72-75. Austin, TX: International Studies Abroad. https://educationaltravel.worldstrides.com/rs/313-GJL-850/images/ISA%20ThinkDen%20Report%202018.pdf  

    Comp, D. (2018, July). Effective Utilization of Data for Strategic Planning and Reporting in Education Abroad. Invited presentation at the ISA ThinkDen at the 2018 ThinkDen meeting, Boulder CO.

    Comp, D. (2010). Comparative Data on Race and Ethnicity in Education Abroad. In Diversity in International Education Hands-On Workshop: Summary Report and Data from the Workshop held on September 21, 2010, National Press Club, Washington, D.C. (pp. 19-21). American Institute For Foreign Study. https://www.aifsabroad.com/publications/

    Stallman, E., Woodruff, G., Kasravi, J., & Comp, D. (2010, March). The Diversification of the Student Profile. In W.W. Hoffa & S. DePaul (Eds.). A History of US Study Abroad: 1965 to Present, 115-160. Carlisle, PA: The Forum on Education Abroad/Frontiers: The Interdisciplinary Journal of Study Abroad.

    Comp, D., & Woodruff, G.A. (2008, May). Data and Research on U.S. Multicultural Students in Study Abroad. Co-Chair and presentation at the 2008 NAFSA Annual Conference, Washington, D.C.

    Comp, D.  (2008, Spring). U.S. Heritage-Seeking Students Discover Minority Communities in Western Europe.  Journal of Studies in International Education, 12 (1), 29-37.

    Comp, D.  (2007). Tool for Institutions & Organizations to Assess Diversity of Participants in Education Abroad. Used by the University of Minnesota Curriculum Integration Project.

    Comp, D. (2006). Underrepresentation in Education Abroad – Comparative Data on Race and Ethnicity. Hosted on the NAFSA: Association of International Educators, “Year of Study Abroad” website.

    Comp, D. (2005, November). NAFSA: Association of International Educators Subcommittee on Underrepresentation in Education Abroad Newsletter, 1 (2), 6.

    Past IHEC Blog posts about the Comparative Data Table on Race & Ethnicity in Education Abroad

    Tool for Institutions & Organizations to Assess Diversity of Participants in Education Abroad [February 15, 2011]

    How Do We Diversify The U.S. Study Abroad Student Population? [September 21, 2010]

    How do we Diversify the U.S. Study Abroad Student Profile? [December 8, 2009]

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  • Student Debt by Ethnicity | HESA

    Student Debt by Ethnicity | HESA

    Hi all. Just a quick one today, this time on some data I recently got from StatsCan.

    We know a fair a bit about student debt in Canada, especially with respect to distribution by gender, type of institution, province, etc. (Chapter 6 of The State of Postsecondary Education in Canada is just chock full of this kind of data if you’re minded to take a deeper dive). But to my knowledge no one has ever pulled and published the data on debt by ethnicity, even though this data has been collected for quite some time through the National Graduates Survey (NGS). So I ordered the data, and here’s what I discovered.

    Figure 1 shows incidence of borrowing for the graduating class of 2020, combined for all graduates of universities and graduates, for the eight largest ethnicities covered by the NGS (and before anyone asks, “indigeneity” is not considered an ethnicity so anyone indicating an indigenous ethnicity is unfortunately excluded from this data… there’s more below on the challenges of getting additional data). And the picture it shows is…a bit complex.

    Figure 1: Incidence of Borrowing, College and University Graduates Combined, Class of 2020

    If you just look at the data on government loan programs (the orange bars), we see that only Arab students have borrowing rates in excess of 1 in 2. But for certain ethnicities, the borrowing rate is much lower. For Latin American and Chinese students, the borrowing rate is below 1 in 3, and among South Asian students the borrowing rate is barely 1 in 5. Evidence of big differences in attitudes towards borrowing!

    Except…well when you add in borrowing from private sources (e.g. from banks and family) so as to take a look at overall rates of borrowing incidence, the differences in borrowing rates are a lot narrower. Briefly, Asian and Latin American students borrow a lot more money from private sources (mainly family) than do Arab students, whites, and Blacks. These probably come with slightly easier repayment terms, but it’s hard to know for sure. An area almost certainly worthy of further research.

    There is a similarly nuanced picture when we look at median levels of indebtedness among graduates who had debt. This is shown below in Figure 2.

    Figure 2: Median Borrowing, College and University Graduates Combined, Class of 2020

    Now, there isn’t a huge amount of difference in exiting debt levels by ethnicity: the gap is only about $6,000 between the lowest total debt levels (Filipinos) and the highest (Chinese). But part of the problem here is that we can’t distinguish the reason for the various debt levels. Based on what we know about ethnic patterns of postsecondary education, we can probably guess that Filipino students have low debt levels not because they are especially wealthy and can afford to go to post-secondary without financial assistance. But rather because they are more likely to go to college and this spend less time, on average, in school paying fees and accumulating debt. Similarly, Chinese students don’t have the highest debt because they have low incomes; they have higher debt because they are the ethnic group the most likely to attend university and spend more time paying (higher) fees.

    (Could we get the data separately for universities and colleges to clear up the confound? Yes, we could. But it cost me $3K just to get this data. Drilling down a level adds costs, as would getting data based on indigenous identity, and this is a free email, and so for the moment what we have above will have to do. If anyone wants to pitch in a couple of grand to do more drilling-down, let me know and I would be happy to coordinate some data liberation).

    It is also possible to use NGS data to look at post-graduate income by debt. I obtained the data by in fairly large ranges (e.g. $0-20K, $20-60K, etc.), but it’s possible on the basis of that to estimate roughly what median incomes are (put it this way: the exact numbers are not exactly right, but the ordinal rank of income of the various ethnicities are probably accurate). My estimations of median 2023 income of 2020 graduates—which includes those graduates who are not in the labour market full-time, if you’re wondering why the numbers look a little low—are shown below in Figure 3.

    Figure 3: Estimate Median 2023 Income, College and University Graduates Combined, Class of 2020

    Are there differences in income here? Yes, but they aren’t huge. Most ethnic groups have median post-graduate incomes between $44 and $46,000. The two lowest-earning groups (Latin Americans and Filipinos) re both disproportionately enrolled in community colleges, which is part of what is going on in this data (if you want disaggregated data, see above).

    Now, the data from the previous graphs can be combined to look at debt-to-income ratios, both for students with debt, and all students (that is, including those that do not borrow). This is shown below in Figure 4.

    Figure 4: Estimated Median 2023 Debt-to-Income Ratios, College and University Graduates Combined, Class of 2020

    If you’re just dividing indebtedness by income (the blue bars), you get a picture that looks a lot like Figure 2 in debt, because differences in income are pretty small. But if you are looking at debt-to-income ratios across all students (including those that do not borrow) you get a very different picture because as we saw in Figure 1, there are some pretty significant differences in overall borrowing rates. So, for instance, Chinese students go from having the worst debt-to-income ratio on one measure to being middle of the pack on another because they have relatively low incidence of borrowing; similarly, students of Latin American origin go from being middle-of-the-pack to nearly the lowest debt-to-income ratios because they are a lot less likely to borrow than others. Black students end up having among the highest debt-to-income ratios not because they earn significantly less than other graduates, but because both the incidence and amount of their borrowing is relatively high.

    But I think the story to go with here is that while there are differences between ethnic groups in terms of borrowing, debt, and repayment ratios, and that it’s worth trying to do something to narrow them, the difference in these rates is not enormous. Overall, it appears that as a country we are achieving reasonably good things here, with the caveat that if this data were disaggregated by university/ college, the story might not be quite as promising.

    And so ends the first-ever analysis of student debt and repayment by ethnic background. Hope you found it moderately enlightening.

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