Generalized Socioeconomic Status of Family Origin: A One-Dimensional Index for Comparative Research on Educational Achievement Inequality

Comparative studies of educational inequality often face challenges in ensuring comparability. First, the marginal distribution of original data in each country sample can affect the estimation of the impact of children’s family socioeconomic status (SES). Second, while composite indices for SES are not new, their feasibility and validity in comparative studies remain underexplored.

This study develops a theoretical construct of Generalized Socioeconomic Status for Origin (GSES-O), aggregating multiple socioeconomic traits of children’s families. We aim to assess the dimensionality and comparability of the GSES-O measure to ensure its validity and facilitate its application in cross-national datasets.

Using data from 11 societies in the PISA 2012 survey, we examined the big four content dimensions of GSESO: parental education, parental occupational status (ISEI), household income, and household wealth. For parental education and occupational status, we used the highest value of either parent. Household wealth was measured through a series of possessions aggregated by principal component analysis (PCA). To ensure comparability, we applied percentile ranking transformations to these variables, eliminating the impact of marginal distribution differences across societies. Multiple imputation was used to address missing data problems. We used PCA to test dimensionality and found strong evidence for a one-dimensional composite measure.

The results suggest a unidimensional structure among the four GSES-O content variables across the 11 societies. PC1 explains 45%-68% of the variance, while PC2 accounts for 10%-23%. The absolute and relative dominance of PC1 supports the unidimensionality of the GSES-O measure. Preliminary robustness checks using PISA 2006 data also support these findings.

A unidimensional measure of GSES-O suggests that it is conceptually and methodologically feasible to apply in future comparative studies of SES achievement gaps. Further analysis will extend to additional waves of PISA and TIMSS to strengthen the evidence and conduct robustness of the evidence for this unidimensional measure.