Wealth-Based Inequalities in Higher Education Attendance: A Cross-National Analysis

• In Event: Equitable access to higher education

Proposal

To this day, it remains methodological difficult to conduct comparative international research on inequality in access to higher education. In fact, our understanding of issues pertaining to unequal access to higher education still predominantly relies on the extensive literature developed in North America, Europe, and other high-income and industrialized countries, where data infrastructures exist to gather demographic data at the individual level (Bernardi & Ballarino, 2014; Jerrim et al., 2015; Jerrim & Macmillan, 2015; Koucký et al., 2007; Shavit et al., 2007) (for exceptions, see: Ou and Hou (2019) on China; Simson (2021) on sub-Saharan Africa, and Ilie and Rose (2016) on 35 countries in South Asia and Sub-Saharan African countries). This means that what we know about how system-wide policies and reforms affect inequality has relied primarily on the Global North, which may differ from those in low- and middle-income countries where enrolment rates in higher education are lower, expansion is more recent (Simson, 2021), and the recent trend of mass participation has coincided with marketization and privatization (Buckner & Khoramshahi, 2021).

Numerous reasons still make it challenging for scholars to compare the overall extent of inequality in higher education across multiple countries. First, studies do not follow a standardized approach to measuring inequality; instead, they typically report the size of inequality as an odds ratio or marginal effect of family wealth on an individual’s likelihood of being enrolled in higher education relative to others within the country. This makes the reference category critically important to interpretations, as the size of inequality varies depending on which two groups are being compared. Another concern for cross-national comparability is that studies use different indicators to operationalize family wealth. Although most studies divide the population into wealth quintiles, some use wealth terciles or quartiles. This lack of consistency can affect interpretations of how large disparities in access are.

Given the lack of data available to educational researchers, the primary goal of our study is to calculate two inequality indicators, the dissimilarity index and human capital index, adapted from previous studies to quantify wealth-based inequalities in higher education attendance cross-nationally (See Ferreira et al., 2008; Krafft & Alawode, 2018). We also examine country-level factors that could reduce disparities in access to higher education. Few studies in higher education have developed such cross-national analysis due to limited data. We draw on our newly developed measures to examine whether countries' political egalitarian policies, economic inequality, and world cultural connections are associated with inequality and achieving universal access to higher education attainment on the global and national levels. We hope to contribute to CIES 2023 conference’s theme, “Improving Education for A more Equitable World,” by providing researchers with a tool kit for measuring inequality in higher education through cross-nationally comparable indicators, expanding our understanding of inequality issues in higher education beyond the Global North.

Data on educational outcomes and wealth were extracted from the World Inequality Database on Education (WIDE), a public dataset that pulls data from nationally representative demographic, health, and social surveys produced by the Global Education Monitoring Report. We constructed an original dataset using the inequality indicators using WIDE data, which was then merged with country-level factors extracted from various data sources. Our final panel dataset includes 131 countries between 2002 and 2019 and covers all world regions and country income levels, per World Bank country income classifications. We use three educational indicators available, Tertiary Attendance Rate, Upper Secondary Completion Rate, and two-year Tertiary Completion Rate, to calculate our inequality indicators. To conceptualize wealth, we disaggregate educational outcomes by young people’s family wealth background using WIDE’s five wealth income quintiles (each account for 20% of the population).

We calculate two inequality indicators. First, we calculate the Dissimilarity Index (D-Index), which measures the percentage of opportunities that would need to be redistributed from the most advantaged groups to the most disadvantaged groups to achieve equality between all groups (Ferreira et al., 2008; Krafft & Alawode, 2018). Second, we calculate The Human Opportunity Index (HOI), an indicator inspired by Sen’s (1976) capabilities approach. The HOI recognizes that a society can have perfect equality when no one has access to capabilities-enhancing services such as education.

As for our data analysis, we first conduct a descriptive analysis of inequality indicators and then perform a panel regression analysis of country-level factors associated with inequality and universal access. For our panel regression analysis, we estimate four different models that compare random and fixed effects models to examine global and national trends for inequality in higher education attainment over time. We use the Gini index as a measure of economic inequality, which quantifies the deviation of income distribution among individuals within a given economy from a perfectly equal income distribution. Data on the Gini index were extracted from the World Development Indicators database. We use the Egalitarian Component Index (ECI) as a measure of countries' political egalitarian policies. ECI assesses the extent to which egalitarian principle is achieved in a given country. Data for ECI is extracted from the Varieties of Democracy (V-DEM) database, which includes different indices that assess the democratization of countries across time (Coppedge et al., 2022). We use International non-governmental organizations (INGO) memberships as a measure of world cultural connections. INGO memberships are calculated using the total number of citizens who hold memberships in various INGOs and have been found to be associated with the expansion of education (Bromley & Meyer, 2017). Data on INGO membership is extracted from the Yearbook of International Associations by the Union of International Associations (Union of International Associations, 2016).

Our findings show large wealth-based inequalities in higher education attendance cross-nationally, which are: substantially larger in low- and middle-income countries than in high-income countries. We find that countries' secondary completion rates, national wealth, economic inequality, political egalitarian policies, and world cultural connections are associated with reducing disparities among wealth groups and achieving universal access to higher education. Our findings serve as a foundation for future studies on how country-level factors and policies exacerbate or reduce wealth-based inequalities in higher education.

Authors

• Yara Abdelaziz, O.I.S.E. University of Toronto

• Elizabeth Buckner, University of Toronto OISE