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

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Presentation on theme: "1 /14 The Persistence of Persistent Inequality – A Review RC28 Spring meeting, Brmo May 2006 Yossi Shavit Meir Yaish Eyal Bar Haim."— Presentation transcript:

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

2 2 /14 Mare ’ s Model of Educational Transitions  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)

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

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

5 5 /14 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.

6 6 /14 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)

7 7 /14 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)

8 8 /14 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.

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

10 10 /14 Log Linear Findings II  Breen, Luijkx, M ü ller 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.

11 11 /14 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.

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

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

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

15 15 /14 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).

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

17 17 /14 Raftery and Hout (1993): A quasi-experimental design: IEO BeforeIEO After Educational reform that expanded participation  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.

18 18 /14 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

19 19 /14 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

20 20 /14 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

21 21 /14 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.

22 22 / * (0.22) Intercept 0.01 (0.16) Mean Father Education X Cohort (0.20) Expansion 0.89* (0.26) Saturation 0.18* (0.05) Male 0.28* (0.10) Cohort * (0.03) Father Education 0.01 (0.02) InterceptFather Education X Cohort * (0.03) Expansion -0.05* (0.01) Saturation -0.16* (0.05) Male X Cohort Table1: Coefficients (standard errors), of multi-level logistic regression model on higher education * p<0.05

23 23 /14 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.

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

25 25 /14  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?

26 26 /14  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.


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