Findings ways to reduce achievement gaps between socially advantaged and disadvantaged groups has remained a stubborn problem, despite decades of research and school reforms. This is because efforts have largely failed to address one of the largest influences on educational opportunities and outcomes, namely the social composition of the school (Kahlenberg, 2001; Sirin, 2005). As shown in the 1960s in the ground-breaking Coleman Report (1966), the most effective way to improve the outcomes of socially disadvantaged students is to reduce social segregation between schools. This is because the quality of students’ educational opportunities and experiences is strongly associated with the social composition of the school, in multiple and complex ways (Benito, Alegre & Gonzalez-Balletbo, 2014; Chiu & Khoo, 2005; OECD, 2016; Willms, 2010).  For example, low SES schools (i.e., schools that primarily enrol students from lower socioeconomic status (SES) backgrounds) tend to have fewer resources (Chiu & Khoo, 2005), and have difficulty recruiting and retaining high quality teachers (Darling-Hammond, 2010). For this reason, Kahlenberg (2001) argues that the best way to reduce educational inequalities is to decrease social segregation between schools.

But how can social segregation between schools be reduced? To understand how to solve the problem, a good understanding of its causes is required. Research has uncovered a range of education policies and structures that are related to segregation, at least in some contexts. These include, for example, academic selection, privatization, school choice and school competition (Alegre & Ferrer, 2010; Lubienski, 2009; Lubienski et al., 2013). Nevertheless, it is not clear how these various factors interrelate with each other and other contextual features to shape school segregation. For example, data from the Programme for International Student Assessment (PISA) shows that the Netherlands has very high levels of private sector involvement, choice and academic selection, and yet has more socially integrated schools than Australia, a country with similarly high levels of private sector involvement but substantially less academic selection. Similarly, Australia and Canada both have comprehensive education systems and have similar levels of income inequality and poverty, and yet schools are much more socially integrated in the latter than in the former (Perry, 2018).

We are unable to address adequately the causes of school segregation because our current research understanding does not account for the complex interplay of education policies, structures and contexts. Despite the appeal of simple explanations, the extant evidence indicates that there is no single cause — or panacea — for school social segregation. Rather, it is likely that the education policies and systemic factors that are associated with social segregation are interrelated in particular ways in particular contexts. In other words, it is likely that there are multiple causal pathways that explain school social segregation. Second, it is plausible that there are other variables at work that have not been adequately addressed in the literature. Attending to these limitations in our theoretical and empirical understanding is essential if we want to find ways to reduce school social segregation.

The aim of this study is identify the causal pathways that explain school social segregation in OECD countries.  The specific research questions of this study are as follows:

1)     Which combinations of variables explain school social segregation?

2)     Are any particular variables necessary for explaining school social segregation? Are any particular variables sufficient for explaining school social segregation?

This study uses Qualitative Comparative Analysis (QCA) to identify the configurations of variables that explain school social segregation in OECD countries. QCA is an innovative approach that is ideal for cross-national comparisons. Developed by comparative sociologist Charles Ragin (1987, 1994, 2008), QCA draws on the strengths of both quantitative and qualitative methods by respecting the diversity and complexity of cases while also identifying generalizable cross-case patterns. While QCA is becoming widespread in the social sciences (Benoît Rihoux, 2006), it has not been extensively used in educational research. QCA is an ideal method for examining differences between countries and for generating knowledge that can be useful for policymakers, who need evidence about the particular conditions and configurations that are associated with the outcome they are seeking to achieve (Ragin, 2006; Benoît Rihoux & Grimm, 2006). Similar to inferential statistics, QCA examines the independent variables that predict the dependent variable. The techniques and principles of QCA analyses differ from standard statistical techniques, however. QCA is based on the principles of set theory, formal logic and Boolean algebra, whereas standard statistical techniques are based on correlations and linear algebra (Schneider & Wagemann, 2010). Rather than seeking to identify the unique contributions of independent variables that explain variation in the dependent variable, QCA seeks to identify the multiple configurations of variables that explain the dependent variable. Data from the latest round of the Programme for International Student Assessment (PISA) will be the main data source. A QCA analysis of all 34 OECD member countries will be conducted, with school social segregation as the dependent variable as calculated in the PISA dataset. The independent variables are derived from PISA and other secondary sources. Based on the recommendations of Marx (2006) regarding the appropriate ratio of cases to variables, six independent variables will be used in the analysis. These will capture the three main policies and structures related to school social segregation that have been identified in the literature, namely: school choice and competition; privatization; and school selectivity. It will also include two societal level indicators that have been identified with school socioeconomic segregation, namely income inequality (Gini) and ethno/linguistic heterogeneity. The sixth dependent variable is the presence of school fees, a variable that has not been extensively studied but that I believe has the potential to explain cross-national variations in school social segregation.

The analysis has not yet been conducted, so conclusions and findings are not known. Nevertheless, it is expected that the analysis will yield new insights about configurations of education policies/structures that explain school social segregation. Given the diversity of educational and societal contexts of OECD countries, it is expected that multiple configurations will be identified. Similarly, new knowledge about the explanatory power of particular policies/structures that have been identified in the literature - including school choice and competition, school selectivity, and privatization - will be identified. In particular, the analysis may be able to identify how individual variables combine to shape school segregation. For example, it may be found that a large private sector is related to segregation, but only when private schools are legally allowed to charge fees. To date, very little is known about how the various variables identified in the literature combine in unique ways. Finally, the analysis will include as a potential explanatory variable the degree to which fees are dominant in a country's system of schooling. The role of school fees has to date received very little attention in the literature about school socioeconomic segregation.

Alegre, M. À., & Ferrer, G. (2010). School regimes and education equity: Some insights based on PISA 2006. British Educational Research Journal, 36(3), 433-461. Benito, R., Alegre, M. À., & Gonzàlez-Balletbò, I. (2014). School segregation and its effects on educational equality and efficiency in 16 OECD comprehensive school systems. Comparative Education Review, 58(1), 104-134. doi: 10.1086/672011 Chiu, M. M., & Khoo, L. (2005). Effects of resources, inequality, and privilege bias on achievement: Country, school, and student level analyses. American Educational Research Journal, 42(4), 575-603. Coleman, J., Campbell, E., Hobson, C., McPartland, J., Mood, A., Weinfeld, F., & York, R. (1966). Equality of educational opportunity. Washington, DC: U.S. Government Printing Office. Darling-Hammond, L. (2010). The flat world and education: How America’s commitment to equity will determine our future. New York: Teachers College Press. Kahlenberg, R. (2001). All together now: Creating middle-class schools through public school choices. Washington DC: Brookings Institution. Lubienski, C. (2009). Do quasi-markets foster innovation in education? A comparative perspective. Paris: OECD. Lubienski, C., Lee, J., & Gordon, L. (2013). Self-managing schools and access for disadvantaged students: Organizational behaviour and school admissions. New Zealand Journal of Educational Studies, 48(1), 82-98. Marx, A. (2006). Towards a more robust model specification in QCA: Results from a methodological experiment: Compasss Working Paper, WP 2006–43. OECD. (2016). PISA 2015 results (volume I): Excellence and equity in education, volume I. Paris: OECD. Perry, L. B. (2018). Educational inequality in Australia. In J. Ball (Ed.), How unequal? Insights into inequality. Melbourne: Committee for Economic Development of Australia. Ragin, C. C. (1987). The comparative method: Moving beyond qualitative and quantitative strategies. Berkeley, CA: University of California Press. Rihoux, B. (2006). Qualitative comparative analysis (QCA) and related systematic comparative methods: Recent advances and remaining challenges for social science research. International Journal of Sociology, 21(5), 679-706. Rihoux, B., & Grimm, H. (2006). Introduction: Beyond the qualitative-quantitative divide. In B. Rihoux & H. Grimm (Eds.), Innovative comparative methods for policy analysis: Beyond the quantitative-qualitative divide (pp. 1-12). New York: Springer. Schneider, C. Q., & Wagemann, C. (2010). Standards of good practice in qualitative comparative analysis (QCA) and fuzzy-sets. Comparative Sociology, 9(3), 397-418. Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417-453. Willms, J. D. (2010). School composition and contextual effects on student outcomes. Teachers College Record, 112(4), 1008-1037.