New Directions in Welfare Congress OECD HQ (06-07/07/2011, Paris) Gálvez Muñoz, Lina; Rodríguez Modroño, Paula; Domínguez-Serrano, Mónica; Matus López, Mauricio Universidad Pablo de Olavide (Seville, Spain) TIME USE AND CHILDREN’S WELL- BEING: IMPLICATIONS FOR PUBLIC POLICIES

Objectives To analyze gender differences in child well-being, using a capability approach. Different functionings achievement of children and young people by gender. The development of activities by children can influence their behavior as adults and the collective well-being of a society in the future. To identify parameters that can help in designing policies for improving child well-being. Causal relationship between family well-being and child well-being (Addabbo et al, 2004, 2008a and 2008b, Di Tommaso, 2007, Krishnakumar & Ballon, 2008; Gallego, 2010; Maccagnan, 2011). Effect of parental characteristics differentiated by gender: education, employment time and unpaid care work time, Effect of households characteristics: income levels, no. of household members, no. of siblings

Starting points Theories: Sen’s capabilities approach Robeyns’s approach to capabilities / Proposal of capabilities to check for gender inequalities in Western societies Studies on child well-being and capabilities Capabilities measurement with SEM: Krishnakumar, Ballon (MIMIC – Multiple indicators multiple causes) Kuklys (2005), Di Tommaso (2006, 2007), Addabbo & Di Tommaso (2007), Addabbo et al (2007), Hamid (2009), Gallego (2010) or Maccagnan (2011)

Analysis Population: Spanish children/young people from 10 to 17 years old Database: Spanish Time Use Survey. Information collection period: Full year: 1st October 2002 to 30th September Questionnaires: individual, household and activity diary. Sample: 2,880 young people: 1,419 boys 1,469 girls

Structural equation modelling

Y i = β Yi Y* +ξ i,i = 1,..., m Y ∗ = γ ij X j + ς

Functionings

1. Children’s age (AGE): 8 categories from 10 to 17 years old. Measuring child well being is age dependent, many functionings vary with age and can only be measured at a late stage of the child development. AGEFrequencyPercent , , , , , , , ,2 Total ,0 Independent variables

The variables refer to average monthly net income of the household divided into 8 sections. According to the literature, family income has a positive effect on children’s cognitive and social development as income determines investments in children’s education. However, some studies are showing than when controlling for other variables the impact of income on some children capabilities is not so high as expected (Blau, 1999; Levy & Duncan, 2000; Taylor et al., 2004) 2. Household income level (INCL): INCLCountPercent Under 500€702,4 500€ to 999,99€41814, € to 1.499,99€78327, € to 1.999,99€63121, € to 2.499,99€41714, € to 2.999,99€2017, € to 4.999,99€30110,4 Over 5.000€672,3 Total ,0

NCHFrequencyPercent Cumulative Percent , ,084, ,696,4 4742,699,0 517,699,5 610,399,9 72,1100,0 81,0100,0 Total ,0 Independent variables 4. Number of children at the household (NCH): It is a continuous variable that defines the number of children at the household of the reference person (young). Studies reveal that the number of siblings has a negative effect on children capabilities (Addabbo et al., 2011b, 2008). 3. Number of household members (NHM): It is a continuous variable that defines the number of members of the household reference person (young).

Independent variables Education MothersFathers FrequencyPercentageFrequencyPercentage 1. Without any degree66923,266222,9 2. Primary113739, ,6 3. Secundary30810,735412,3 4. Professional training36012,538613,4 5. University41414,345415,7 Total , ,9 5. Parent’s educational level (MEDU & FEDU): 2 categorical variables that correspond with the educational level of mothers and fathers.

Independent variables Mother’s paid workFrequencyPercentage Unemployed199769, min2157, min2287, min2117,3 > 469 min2378,2 Total ,0 Father’s paid workFrequencyPercentage Unemployed106937, min37813, min51317, min45015,6 > 599 min47816,6 Total ,0 6. Parent’s paid working time (MPLI & FPLI): 2 categorical variables that correspond with the intensity of paid work of mothers and fathers. It still remains unclear which effect is predominant, since the existing research provides conflicting conclusions. Empirical estimates range from parental employment having a negative effect (Baydar & Brooks-Gunn, 1991; Desai et al., 1989), to its having no effect (Blau & Grossberg, 1992), to its being beneficial (Vandell & Ramanan, 1992) because the additional labor income has positive implications for expenditures on goods consumed by the child (Brooks-Gunn et al., 2002; Ermisch & Francesconi, 2005; Bernal, 2008).

Mother’s unpaid workFrequencyPercentage 0 min281, min66623, min74525, min71324,7 > 489 min73625,5 Total ,0 Father’s unpaid workFrequencyPercentage 0 min83228, min44915, min53118, min53518,5 > 179 min54118,7 Total ,0 7. Parent’s unpaid working time (MULI & FULI): 2 categorical variables that correspond with the intensity of unpaid work of mothers and fathers. While mother's care time is considered always as a crucial input in child development, father's time may be equally productive. In Western societies, time spent with children by fathers has increased over time, partly offsetting the decline in mother's time. However, the amount of time a father spends with children seems to be affected by the gender composition of the children (Lundberg, 2005; Lundberg et al., 2007a & 2007b, Mammen, 2005, and Bonke & Esping-Andersen, 2011)). Independent variables

Structural equation model: results Exogenous variables Specification 1Specification 2Specification 3 BoysGirlsBoysGirlsBoysGirls EstP P P P P P Age,371***,691***,379***,693***,400***,737*** Income level,224***,089,109,236***,103,064,169***,025,664 Household members,036,435,055,315 Number of children-,116,012-,017,760 Mother’s education-,075,100,067,229-,082,071,059,290 Father’s education-,083,071-,241***-,088,051-,247*** Mother’s paid working time-,622***-,360***-,614***-,363***-,630***-,351*** Father’s paid working time-,315***-,459***-,311***-,461***-,332***-,481*** Mother’s unpaid working time -,493***-,318***-,490***-,315***-,501***-,313*** Father’s unpaid working time,238***,083,134,234***,076,170,231***,057,322 MEDUcorrMPLT,174***,149*** Functionings Total free time,367***,246***,374***,245***,355***,232*** Variety of activities,215***,113***,216***,112***,216***,107*** Social life,280***,322***,285***,324***,267***,313*** Cultural time,171***,085,002,170***,085,002,175***,099*** Sports, plays, games time,097***-,092***,099***-,091,001,099***-,093*** Domestic & care time,108***,128***,111***,124***,105***,113***

Results & Policy implications High effect of age in the development of children capabilities related to active leisure, entertainment and socialization. Income level has a significant positive effect on boys well-being (not significant for girls). Parents’ paid working time has a significant negative effect on child well- being Mothers’ paid working time is the variable with the largest negative effect on boys Fathers’ paid working time is the variable with the largest negative effect on girls Need for work-family balance policies (parental leave, parental care, flexible working arrangements), both for women and men.

Results & Policy implications Mothers’ unpaid working time has a significant negative effect on child well-being, both boys and girls Fathers’ unpaid working time has a: significant positive effect on boys well-being non-significant effect on girls well-being High inequality on time spent by parents depending on gender affects the resilience of gender stereotypes and prejudices which directly affects boys and girls choices and societal behavior towards men and women. Children well-being & capabilities by gender: Boys: more free total time, more variety of activities, more culture and leisure & playing Girls: more social interaction and care time These results show the existing gender differentials in child development which may affect future capabilities and opportunities of women and men, as well as consequences on total welfare derived from the maintenance of a gender stereotyped society given the educational attainments by gender in OECD countries.