slipping past the test · 2017-05-02 · slipping past the test: exploring an overlooked loophole...

Slipping Past the Test: Exploring an Overlooked Loophole to Social

Advantage in Russia’s Educational System

RC28 Conference, Tilburg, May 28-31, 2015

Yuliya Kosyakova (EUI, Florence)

Gordey Yastrebov (HSE, Moscow; EUI, Florence)

Dmitriy Kurakin (HSE, Moscow)

1. research question and the context of study

2. theory and hypotheses

3. data and method

4. some results

2

Structure

• Persistent inequalities: MMI and EMI arguments (Raftery,

Hout 1993; Shavit, Blossfeld (Eds.) 1993; Lucas 2001)

• Whether social inequality is also effectively maintained,

because more advantaged families are also more

susceptible to loopholes offered within institutional

(educational) systems?

→ bypass strategies

3

Research Question

Russia as a case study:

1. standardized test system:

• Unified State Exam (~ American SAT or German Abitur)

• obligatory to all students after 11 years of sec. gen. education

• major requirement to get in higher education

2. an institutional loophole to slip past the examination and

retain the opportunity to get into higher education through

secondary vocational track

• taken after 9 years of sec. gen. education

• after 3-4 years of studies allows enrolment in 2-3rd year of studies

in higher education

4

The Context of Russia’s

Educational System (1)

• Before USE (before 2009):

– admission exams administered by VUZs

– requirements above the level provided by school curriculum

– either: exceptional academic record

– or: additional training beyond school

• faculties of pre-higher education training (FDPs)

• informal training with VUZ tutors

– intimate knowledge about the content of exams

– direct facilitation of admission process → → →

5



→ → → inequality of opportunity:

• geographical inequality (proximity to VUZs)

• socioeconomic inequality (expenses, awareness)

• USE reform (2009):

– a remedy for the problem

– but: important maneuver overlooked!

• recognized by families and students

• FDPs → faculties for secondary vocational education

• SSUZs → agreements with VUZs to signal prospective enrolment

in higher education

6



7



USE introduced nationwide

General track preferences according

to social background:

– risk aversion theory (Breen, Goldthorpe 1997)

– rational choice arguments (Boudon 1974)

– primary effects vs secondary effects

H1: Children from more advantaged families are generally

more likely to choose academic track over vocational tracks

8

Theory & Hypotheses (1)

Adjustment in situation of lower ability (‘second chance’):

– bypass strategy → to avoid formal screening of performance

– advantaged families: avoid (!) sec. voc. track at fear of status

demotion, but:

– bypass strategy triggered if the risks of USE failure are

especially high ← e.g. compensatory advantage (Bernardi 2012)

H2.1(weak): The propensity to opt for the bypass strategy is more sensitive to

ability among children from more advantaged social backgrounds

H2.2(strong): Children from more advantaged social backgrounds are more

likely to opt for the bypass strategy, particularly, when their ability is low

9

Theory & Hypotheses (2)

Russian Panel Study of Trajectories

in Education and Careers (TrEC):

– Based on TIMSS 2011 stratified sample (Wave 0)

• 8th graders: 4,893 students in 210 schools in 42 regions

– Rounds:

• Wave 0 (TIMSS 2011, 8th gr.)

• Wave 1 (2012, 9th gr.)

• Wave 1.1 (PISA 2012, 9th gr.)

• Wave 2 (2013 10th gr., over with track choice)

10

Research Design: Data

• Track choice (dependent):

1. Primary vocational

2. True secondary vocational

3. Secondary vocational as bypass (pseudo-vocational)

• proxied with VUZ choice (more accurate data is pending)

4. Academic

5. Drop-outs (negligible and almost impossible)

• Social background (independent):

– maximum education of parents:

1. higher (and above)

2. below higher (i.e. secondary vocational and below)

11

Research Design: Variables (1)

• Prior ability (independent):

1. TIMSS scores in math and science (8th grade)

2. PISA scores in math, science and reading (9th grade)

• Controls:

– gender

– area of residence → to account for varying educational opportunities

1. rural areas

2. urban areas

3. major cities (Moscow and Saint-Petersburg)

12

Research Design: Variables (2)

• Modeling strategy

– multinomial logistic regressions (all analyses weighted)

– interaction between main variables of interest

• Model setups

– H1: P(track choice) = f (ability, SB, gender, residence)

– H2: P(track choice) = f (ability, SB, ability × SB, gender, residence)

– focus on behavior of predicted probabilities (not effect sizes, but

exploration of the general pattern)

13

Research Design: Models



14

Results: H1

15

Results: H1

• horizontal lines – unconditional (baseline) probabilities of corresponding track choice

• results for several measurements of ability plotted



SUPPORTED

16

Results: H1

H2.1(weak): The propensity to opt for the bypass

strategy is more sensitive to ability among children

from more advantaged social backgrounds

H2.2(strong): Children from more advantaged social

backgrounds are more likely to opt for the bypass

strategy, particularly, when their ability is low

17

Results: H2


• vertical lines – average ability score in corresponding track

• MIN and MAX values for ability score calculated within track for the more advantaged group 18

Results: H2 (math)



• MIN and MAX values for ability score calculated within track for the more advantaged group 19

Results: H2 (science)



• MIN and MAX values for ability score calculated within track for the more advantaged group

20

Results: H2 (reading)

H2.1(weak): The propensity to opt for the bypass

strategy is more sensitive to ability among children

from more advantaged social backgrounds

H2.2(strong): Children from more advantaged social

backgrounds are more likely to opt for the bypass

strategy, particularly, when their ability is low

STRONGER VERSION SUPPORTED

21

Results: H2

• Support for the stronger version of the ‘second chance’

hypothesis (H2.2):

– advantaged families recognize the opportunity of avoiding USE

through secondary vocational track

– and use it to provide their children an opportunity to retain

access to higher education even if their ability is low

– i.e. both families and institutions adapt so that reproduction of

inequality is always maintained to a certain degree

22

Preliminary Conclusions

• Future steps

– drawing a more accurate distinction between true secondary

vocational track and the bypass strategy (institutional adaptation)

– distinguishing between former FDPs and SSUZs with

agreements (correct coding using data on actual enrolment)

23

Directions of Further Research

24

Thank you for your attention!

Yuliya Kosyakova

Dmitriy Kurakin

Gordey Yastrebov*

* corresponding author:

[email protected]

25

Descriptive Statistics (cont.)

Variable Mean SD Min Max N

PISA math 493.7 82.2 194.7 798.2 4,271

PISA science 490.6 78.2 233.0 730.0 4,271

PISA reading 476.1 83.9 170.0 722.3 4,271

TIMSS math 542.9 77.7 308.7 804.0 4,657

TIMSS science 545.6 72.5 266.3 803.5 4,657

26

Descriptive Statistics (cat.)

Variable % N

Track choice

primary vocational 63.2 4,657

true secondary vocational 21.5 4,657

pseudo-vocational 7.3 4,657

academic 8.0 4,657

Education of parents

lower and middle 51.0 4,657

higher 49.0 4,657

Gender

boys 50.2 4,657

girls 49.8 4,657

Area of residence

rural areas 21.2 4,657

urban areas 69.7 4,657

Moscow & St. Petersburg 9.1 4,657

slipping past the test · 2017-05-02 · slipping past the test: exploring an overlooked loophole...

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