DEEL I:Participatie aan formele

volwasseneneducatie in de EU

P. Róbert et al. (2011)I. Nicaise

Definities

Arbeidskrachtentellingvoltijds (alternerend) diplomagericht volwassenenonderwijs

Adult Education SurveyElke

kwalificatiegerichte opleiding

Opm: enkel 25-64 jarigen

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Participatie aan volwasseneneducatie(%, AES 2009)

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Formele volwasseneneducatie marginaal ?

• Volume uren in F is veelvoud van NF• F is kwalificatiegericht• 12% op jaarbasis kan theoretisch betekenen dat (bij

gemiddelde duur van 3 jaar) de gemiddelde volwasssene 1,6 bijkomende kwalificatie haalt in de loop van zijn loopbaan !

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Naar initieel opleidingsniveau

0

5

10

15

20

25

30

FR GR HU BG CY CZ AT IT HR EE DE LV PL ES SK LT PT NL SI NO FI BE SE UK

ISCED12 ISCED34 ISCED56

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Naar anciënniteit op arbeidsmarkt

0

5

10

15

20

25

30

35

40

45

FR HU BG CY CZ AT HR EE DE LV SK LT PT NL SI NO BE SE Totalof

pooleddata

since 0-5 years since 6-10 years since 11-20 year

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Naar arbeidsmarktstatuut

0

10

20

30

40

50

60

FR GR HU BG CY CZ AT IT HR EE DE LV PL ES SK LT PT NL SI NO FI BE SE UK

works unemployed finally out of labour force (retired, disabled) temporary out of labour force

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Naar type contract

0

2

4

6

8

10

12

14

16

18

20

FR GR HU BG CY CZ AT IT HR EE DE LV PL ES SK LT PT NL SI NO FI BE SE UK

self-employed

weakly integrated (temporary/limited contract or part time employee)

strongly integrated (unlimited contract and full-time employee)

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Hinderpalen

Socio-demografisch• Gender (V)• Jonge kinderen• Leeftijd (45+)• Ruraal

Socio-economisch• Lage scholing

• Lang op arbeidsmarkt• Zwakke arbeidsmarktpositie

• arbeider

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Socio-demografische hinderpalenHinderpaal BG CY FR HU HR CZ AT DE ES PT EE LT SK LV BE FI SI NO SE

Geslacht (V) -- -- -- -- - - - - Ø Ø Ø Ø Ø Ø + + + + ++

45 + -- -- -- -- - - - Ø Ø - - - - Ø ++ + - + ++

Kinderen 0-3 -- - -- -- - - - - - Ø Ø Ø Ø Ø + + ++ + ++

Kinderen 4-5 -- -- -- -- - -- - - - - Ø Ø Ø Ø + + ++ + ++

Landelijk -- -- -- -- - - Ø -- - Ø + + + + -- Ø ++ ++ ++

Legende: (steeds in vergelijking met het ‘gemiddelde’ patroon)- - sterke ondervertegenwoordiging; - ondervertegenwoordigingØ gemiddeld patroon+ relatief goede vertegenwoordiging; ++ goede vertegenwoordiging

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Socio-economische hinderpalen

Legende: (steeds in vergelijking met het ‘gemiddelde’ patroon)- - sterke ondervertegenwoordiging; - ondervertegenwoordigingØ gemiddeld patroon+ relatief goede vertegenwoordiging; ++ goede vertegenwoordiging

Hinderpaal CY CZ FR SK BG LV EE HU HR AT DE LT SI ES NO PT FI BE SE

Max. diploma LSO

-- -- -- -- -- -- -- -- - -- - -- - Ø + ++ ++ + +

Niet-werkend - - -- - - -- - -- - Ø Ø - Ø Ø + Ø + + ++

Tijdelijk werk -- - - - -- - -- -- - -- - -- ++ + Ø + ++ Ø ++

Deeltijds werk -- -- -- -- -- - - -- -- Ø Ø Ø -- Ø + -- -- ++ ++

Arbeider -- - - -- - - - - Ø - Ø + ++ + + ++ ++ ++ +

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Clustering van landen

Omvang van socio-economische en demografische hinderpalen

Sterkte van impact van socio-economische en demografische hinderpalen

Sterk Relatief sterk Relatief zwak zwak

Groot Bulgarije, Frankrijk, Cyprus

Hongarije

Groter dan gemiddeld Kroatië Oostenrijk, Tsjechië, Letland

Duitsland

Kleiner dan gemiddeld Estland, Litouwen, Slovakije

Klein Zweden, Noorwegen, België, Slovenië

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Besluit van EU-vergelijking• Matteüseffecten alom tegenwoordig• Duidelijke correlatie tussen gemiddeld niveau van participatie en mate

van gelijkheid• Verband tussen welvaartsregime en mate van ongelijkheid is beperkt:

sociaal-democratische regimes (Noord-Europa) = meest egalitair (comprehensief initieel onderwijs, genereuze steun aan LLL, grotere mobiliteit) andere landen (bv. Frankrijk / VK) ?

• België sluit aan bij Noord-Europese kopgroep voor wat formele volwasseneneducatie betreft => wordt inegalitair profiel dan bepaald door non-formele educatie ? Zie deel 2….

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DEEL II: Equal opportunities in formal versus non-formal adult education: an empirical analysis of participation patterns in Belgium

Ides Nicaise & Zhipeng ChenKU Leuven

Introduction

• Much empirical work on unequal participation in AE, but limited theoretical foundation

• Research questions:– (How) does SEB affect the distribution of AE ?– (Why) is formal AE more / less equally distributed

than non-formal AE ?(note: this paper relates to employed individuals only)

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Simple odds ratios (-1)

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Theoretical framework

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Long arm of familyLong arm of family

Long arm of job ? Long arm of job ?

Participation in formal and/or non-formal AE(multinomial logit regression)

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Formal Non-formal bothMale (female = ref) 0.87 1.14 1.26 Education (ref.low) upper sec educ 1.15 1.31 * 1.30 higher education 2.43 ** 2.96 ** 3.15 **Age (ref.56-65) 24-35 3.29 ** 1.86 ** 3.25 ** 36-45 2.51 * 1.67 ** 2.14 * 46-55 1.25 1.13 1.07 Occupation (ref. blue-collar) low-skilled white-collar 1.68 * 2.06 ** 5.93 ** high-skilled white-collar 1.78 * 2.25 ** 5.95 **Country of Birth (ref. Belgium) EU 0.85 0.78 0.85 non-EU 1.42 0.74 1.00 hours worked (ref.part-time) Full-time 1.32 1.25 1.40 Parents' Education (ref. low educ) intermediate educ 0.85 1.06 1.03 high education 0.74 0.93 0.86 Family situation (ref. Single, no dependants) Couple, no dependants 1.23 0.88 0.75 Couple with dependants 0.82 0.81 0.45 ** Single with dependants 0.38 * 0.59 * 0.58

Comments on multinomial regressions

1. Well-known inequalities by age, level of education, occupational status, family situation (no effect of gender, type of contract and citizenship)

2. Inequalities are more pronounced in non-formal than in formal education => redistributive effect of publicly funded / organised provision

3. Effect of initial education: ‘skill begets skill’ (human capital), SES or peer group effect (social capital)?

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Comments on multinomial regressions (ctd)

Long arm of the family ?• Indirect effect (via achieved initial education and

occupational status)• Parameter for parental education in regression is not

significant => no direct influenceLong arm of the job ?• Occupational status affects opportunities for AE• # hours worked has no (net) effect (two opposite effects?)• Employer support ? => next model

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Employer support

• Statistical problem: endogeneity of employer support => can be observed only among actual participants => ordinary logistic regression would yield biased results

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Employer support (ctd)

Solution: bivariate probit analysis: simultaneous estimation of – a ‘selection equation’ (participation) and – an ‘outcome equation’ (employer support)

covariance between error terms indicates (direction of) the relation between employer support and participation

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Employer support (bivariate probit model)

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Participationemployer support

Male (female = ref) 0.06 0.17 Education (ref.low) upper sec educ 0.15 0.46 ** higher education 0.66 ** 0.97 **Age (ref.56-65) 24-35 0.49 ** 0.30 * 36-45 0.38 ** 0.30 * 46-55 0.09 0.08 Occupation (ref. blue-collar) low-skilled white-collar 0.48 ** 0.55 ** high-skilled white-collar 0.53 ** 0.65 **Country of Birth (ref. Belgium) EU -0.12 -0.19 non-EU -0.08 -0.14 hours worked (ref.part-time) Full-time 0.14 * 0.28 **Parents' Education (ref. low educ) intermediate educ -0.06 0.00 high education 0.01 0.06 Family situation (ref. Single, no dependants) Couple Without Dependants -0.10 - Couple with Dependants -0.24 ** - Single With Dependants -0.30 ** -rho 0.99 **

Comments on employer support

• Employers tend to invest more in younger, more educated, full-time workers in higher occupations => Mathew effect

• Positive ‘rho’ (= covariance between error terms of particpation and employer support)=> employer support tends to reinforce inequalities in participation

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To conclude: (in Belgium)

• Persistent inequalities in adult education• Formal AE has some redistributive effect• Long arm of the family may have indirect effect on

access to AE, but has no direct effect• Long arm of the job has several explanations:– Higher-level occupations require more training– Occupational status / social capital effect– Employer support

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