Redistribution effects of the childcare system for under 3’s in Hungary - Who is cared for?

1. Redistribution effects of the childcare system for under 3’s in Hungary - Who is cared for? Zsuzsa Blaskó Demographic Research Institute (Budapest), zsblasko@gmail.com András Gábos Senior researcher at TÁRKI Social Research Institute gabos@tarki.hu First draft, not to be quoted! 30-31 March 2012, Budapest Budapest Institute for Policy Analysis

2. 1. Motivations – why to investigate redistribution effects of early childcare? • Impact on employment • positive effect on mothers’… • … and also women’s employment • directly and also through social norms. • Reduced risk of poverty… • … and child poverty in particular • although family transfers compensate to a considerable extent in Hungary • Child-development • good quality, institutional child care can have a positive impact on child-development • cognitive and also non-cognitive skills • especially in the case of children from socially disadvantaged groups • In case of publicly funded institutions, where available substitutes are significantly more costly: is there an equal chance of access for all?

3. 2. The case of Hungary – Institutional setting • Parentalleave system supports 2+1 year of leave • Majorform of childcare: public nurseries – 2010: • number of nurseries: 668; available places: 32516 • actual number of children in nurseries: 35782 • coverage ratio (0-3): 8.2% • + family child care: number of institutions: 694 („családinapközik”)number of children aged 0-3: 3134 Fees • Costscoveredbythestate + local goverments • Parents pay for the meal only. (6700 HUFon average- 23 Euros) • Low-income parents receive discounts or are entitled for free service. • (A new legislation accepted in December 2011 allows local governments to charge parents for the service.) Geographic differences • Obligationtomaintaindaycare service forsettlementswith 10 000 + inhabitants: • Bigger settlements are better served. • Regional differences: Budapest versus Borsod-Abaúj-Zemplén, Szabolcs-Szatmár-Bereg

4. 2. The case of Hungary – Institutional setting (cont.) Indications of excessive demand • anecdotal evidence and media infos on long waiting lists, serious difficulties to ensure enrollment • number of applicants > number of available places by 12% in 2010 (NSO) • Overcrowded institutions:number of children enrolled > number of places (10% in 2010) • Nursery managers expressthe urging need for new places (Makay 2011). Selection of children into the nurseries. Who is given priority? • Central legislation: nurseries provide their services for children whose parents are unable to look after them during the day because of work, illness or other reason. • Local regulations add social disadvantages and/or having many children (3+) in the family. Regulations not strictly obligatory - no mechanism to monitor the selection process and its final outcomes.

5. 3. Research questions • To what extent are the (competing) criteria of selection met? Ie. are children of working parents and/or socially disadvantaged children given priority? • Are socially disadvantaged children underrepresented in the system? • If it is so: is it because…. • … their parents are under-employed… • … they have no access to nurseries for geographical reasons, • … for other reasons?

6. 4. Data • Labour Force Survey 2010 • with supplement on caring responsibilities • Hungary only at this stage • Mothers with children aged 0-3 selected • 1432 cases of which 125 mothers with a child in nursery

7. 5.1. Descriptive statistics (1.)

8. 5.1. Descriptive statistics (2.)

9. 5.2. Multivariateanalysis of main factors that may affect redistribution outcomes- Models - Base models All mothers with young children (aged 0-3) included BM1: all variables included, excepting employment status of the mother and the availability of child care in the settlement Variables related to the mother (age, family status, education, empl. status) Variables related to the child/hh (age of ch, younger sibling, nr of ch aged <16, non-parent inactive member) Daily child care availability BM2: employment status added to BM1 BM3: child care availability also included in BM2 Estimates for mothers with different social background Separate models for mothers with selected socio-demographic characteristics Condition put on age of the child: children aged 2-3 (ChA23) mother’s employment: working (WM) and inactive (IM) mother’s education: max primary (PE), lower secondary (LSE), upper secondary (USE), tertiary (TE)

10. 5.2. Multivariateanalysis of main factors that may affect redistribution outcomes- Main resultsforbasemodels - Three major observable factors play the most important role in predicting enrolment probabilities Mother’s employment status: if working, the probability of enrolment instead of other arrangements is about ten times higher compared to the situation when the mother is unemployed or inactive The age of the child: if the child is aged 2-3, the likelihood of enrolment is five times higher instead of not being enrolled compared to those aged 0-1 and 2.5 times higher compared to those aged 1-2 The availability of the local nursery: compared to when the local nursery is not available (this does not hold for Budapest), the estimated odds ratio is 3.4 for Budapest and 2.2 for other settlements Mother’s education (proxy for social status) looses significance when activity and availability of child care is controlled for.

11. 5.2. Multivariateanalysis of main factors that may affect redistribution outcomes- Main resultsforcontrolledmodels - Age of child (ChA23) Same as for the base model, but mothers’ employment status and availability of early child care are even stronger explanatory factors when only mothers with a child aged 2-3 are examined. (+ inactive hh member) Mother’s employment status (AM) Same as for the base model + age of mother. (IM) Few differentiating factors: living in Budapest and having a child aged 2-3 instead of under 2 increases the probability of enrolment. Mothers education (PE) Few differentiating factors: only inactivity has a significantly negative and strong effect on the probabilities of enrolment. (LSE) Similar to PE + nr of children (USE) All major factors plays an important role. R2 increases considerably (TE) Few differentiating factorsagain: inactivity + age of the child

12. 6. Conclusions Three major observable factors play the most important role in predicting enrolment probabilities: the employment status of the mother the age of the child the availability of nurseries Estimates for mother’s education (proxy for social status) did not prove to be significant when activity and availability of nurseries are controlled for According to these results, the socially disadvantaged children are underrepresented in the system mostly because their parents (mothers) are under-employed and because they have no access to nurseries within their close environment BUT the social gradient in the use of nurseries can be observed Among inactive mothers, neither of factors related to social status is significant and positively associated with enrolment When controlling for the attained level of mother’s education we found that only employment status differentiates at a considerable level among mothers with both low and high education, while availability does not BUT: all major factors, including availability, strongly differentiate among mothers with USE

13. 7. Open questions The low number of observations together with missing cases for child care enrolment strongly affect the accuracy of estimates, but other major data sources (like EU-SILC) include even fewer observations. Endogeneity: the main concern with this analysis, but not tackled yet. Is educational attainment the best proxy for social status in the absence of income data? Other countries?

14. 5.2. Multivariateanalysis of main factors that may affect redistribution outcomes- Main results -