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The Persistence of Persistent Inequality – A Review

The Persistence of Persistent Inequality – A Review. RC28 Spring meeting, Brmo May 2006. Yossi Shavit Meir Yaish Eyal Bar Haim. Mare ’ s model has served as an industry standard in research on educational stratification.

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The Persistence of Persistent Inequality – A Review

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  1. The Persistence of Persistent Inequality – A Review RC28 Spring meeting, Brmo May 2006 Yossi Shavit Meir Yaish Eyal Bar Haim 1 /14

  2. Mare’s model has served as an industry standard in research on educational stratification. Mare designed his model to yield margin-free estimates of educational stratification. Mare’s sought to disentangle two confounded aspects of educational stratification Dispersion. Stratification (social selection) Mare’s Model of Educational Transitions 2 /14

  3. A Reminder The model is written as: The association between X and the progression from educational level j-1 to j. 3 /14

  4. – The probability that person i will continue from educational level j-1 to level j The association between X and the progression from educational level j-1 to j. Mare shows that • In words, the additive linear effect of X on pij is a function of  and of educational expansion. • Note that bjs is at a maximum for p=0.5 • Therefore, an expansion of j can reduce or increase bjs even if  remains stable. 4 /14

  5. Following Mare • Despite the importance of educational expansion in the public and theoretical debate concerning IEO, it is usually ignored by empirical research. • Rather, it is treated as a nuisance to be adjusted for. • NOTE: Mare himself paid a great deal of attention to expansion. 5 /14

  6. Persistent Inequality? • Mare’s model has been replicated in a large number of studies (including Shavit & Blossfeld). • Main Findings: • Declining effects, over time, of social origins at lower educational transitions, • Stability/increasing effects of origins on the odds of higher transition points • I.E., Persistent Inequality (PI) 6 /14

  7. Challenges to Mare’s Model Main Challenge: PI is due to dynamic sample selection. • SES effects are stable (or even increase) over time because the risk-set of transitions grow ever more heterogeneous Secondary Challenges: • Myopia (C&H) • Educational choices are rarely binary (B&J) 7 /14

  8. Log Linear Alternatives to Mare’s Model • These models discard the notion of educational transitions. • Estimates of the association between origins and educational attainment is not biased by dynamic sample selection. 8 /14

  9. Log Linear Findings RE: PI • Jonsson, Mills and Müller (1996): • Sweden and Germany: • The association between class and education declined slightly over time (6% in the first half of 20th Century) • Britain - no change. 9 /14

  10. Log Linear Findings II • Breen, Luijkx, Müller and Pollak, (2005): • The origin-education association declined significantly across cohorts. • Importantly however, the decline was not linear • Pronounced in the post-WWII years; • Persistence since then. 10 /14

  11. Log Linear Findings III • Vallet (2004): • In France, too, the decline in the origin – education association is not linear in time. • It increased for cohorts born between 1908 and the late 1920s; • Declined sharply for those born between the late-1930s and the late 1940s, and • declined weakly thereafter. 11 /14

  12. Log Linear Findings IV • Barone (2006): • In Italy the origin-education association declined for cohorts born during the 1930s and 1940s and increased thereafter. 12 /14

  13. Persistent Inequality? • Yes! In a weak version: • Modest declines in IEO. • Much of the decline seems to have occurred during, or soon after WWII 13 /14

  14. Expansion and IEO • Expansion is a very common educational policy. • Can expansion reduce IEO? • The answer depends on how we define IEO. 14 /14

  15. Expansion (cont.) • As seen earlier, under the linear probability model, expansion can reduce IEO once the attainment probability has exceeded 0.5. • However, most RC28 research on IEO can not inform policy because it employs margin-free models (either transition or log-linear models). 15 /14

  16. Examples and Critique of Research on Expansion and IEO 16 /14

  17. Raftery and Hout (1993): A quasi-experimental design: Educational reform that expanded participation IEO Before IEO After • Findings: IEO decreased at the lower levels of education. • Critique: Changes in IEO, or lack thereof, may have resulted from factors other than educational expansion. 17 /14

  18. Ganzeboom, Treiman and Reijkin (2003) • Multi-level linear analysis • 30 countries • Units of analysis: cohort x country combinations. • Expansion is measured as average school years in each cohort x country combination. • Findings: negative effect of “expansion” on the correlation between socio-economic background and educational attainment. • Critique: Expansion should be measured as a process rather than in cross-section 18 /14

  19. Arum, Gamoran and Shavit (2007) • 15 Countries • Expansion measured longitudinally (process). • Saturation measured in cross-section. • AGS estimate effects of expansion and saturation on effects of origins on the log odds of transitions from secondary to higher education. • Findings: Saturation reduces IEO in transition to higher education, expansion neither enhances nor reduces IEO. • Critique: AGS’s analysis is an informal meta-analysis. No formal tests are employed 19 /14

  20. New Research: Bar-Haim, Shavit and Ayalon. Work in progress: “Educational Expansion and Inequality of Educational Opportunity.” Submitted to the Montreal Meeting of RC28. • ISSP Data • 24 countries • 1992, 1993, 1999 • Multi-level Logistic Regression 20 /14

  21. Individual-Level Variables • Father’s education • Gender • Birth Cohort (1940s, 1950s). Country-Level Variables • Expansion is defined as the proportionate change, between cohorts, in % attending higher education. • Saturation is a dummy representing 80+% higher education among children in the first cohort whose fathers were highly educated themselves. 21 /14

  22. Table1: Coefficients (standard errors), of multi-level logistic regression model on higher education * p<0.05 22 /14

  23. Summary of Results: IEO is defined as the effect of father’s education on the log odds of entering higher education: • Expansion enhances IEO between cohorts. • Saturation reduces IEO between cohorts. 23 /14

  24. Summary • Persistent Inequality persists, albeit in a weak version: • Declining IEO at lower levels (saturation effects) • Declining IEO during mid-century. • PI otherwise. 24 /14

  25. Question: Is there a distinct War (WWII) effect (e.g., Sorokin)? • Could the decline have been due to the resumption of normality after the War rather than to long term modernization processes? 25 /14

  26. Can Policy Affect IEO? • Effective egalitarian educational policy increases participation (expansion). • However, much of our collective work ignores expansion and thus, misses an important beneficial outcome. • Let’s bring expansion back into our models. 26 /14

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