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 pointsTheories:

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)

AnalysisPopulation: 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 2003.Questionnaires: individual, household and activity

diary.

Sample:2,880 young people:

1,419 boys 1,469 girls

Structural equation modelling

Y* Children well-being Y Functionings

y *

Capabilities:Social Relations Education and knowledgeDomestic work and unpaid care Leisure & playing activities

y 1 Total active leisure time

y 2 Variety of activities

y 3 Social time

y 4 Cultural time

y 5 Unpaid domestic work time

y 6 Sports, hobbies and games time

X Observable exogenous factors of the structural equation

x 1 Age x 6 Father's educational level

x 2 Household income level x 7 Paid working time of the mother

x 3 No. of household members x 8 Paid working time of the father

x 4 Number of children x 9 Unpaid domestic work of the mother

x 5 Mother's educational level x 10 Unpaid domestic work of the father

Structural equation modelling

Y i = β Yi Y* +ξ i, i = 1, . . . , m

Y∗ = γ ij Xj + ς

Functionings

SexGirlsBoys

Num

ber

500

400

300

200

100

0

54321

Sports, hobbies & games

SexGirlsBoys

Num

ber

1.200

1.000

800

600

400

200

0

54321

Culture

SexGirlsBoys

Num

ber

800

600

400

200

0

54321

Social life

SexGirlsBoys

Num

ber

600

500

400

300

200

100

0

54321

Unpaid domestic & care work

SexGirlsBoys

Freq

uenc

y

400

300

200

100

0

5,004,003,002,001,00

Total Free Time

SexGirlsBoys

Freq

uenc

y

400

300

200

100

0

987654321

Variety of activities

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.

AGE Frequency Percent

10 349 12,1

11 334 11,6

12 349 12,1

13 378 13,1

14 389 13,5

15 381 13,2

16 357 12,4

17 351 12,2

Total 2.888 100,0

Independent variables

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):

INCL Count Percent

Under 500€ 70 2,4

500€ to 999,99€ 418 14,5

1.000€ to 1.499,99€ 783 27,1

1.500€ to 1.999,99€ 631 21,8

2.000€ to 2.499,99€ 417 14,4

2.500€ to 2.999,99€ 201 7,0

3-000€ to 4.999,99€ 301 10,4

Over 5.000€ 67 2,3

Total 2.888 100,0

NCH Frequency Percent Cumulative Percent

1 1.034 35,8 35,8

2 1.415 49,0 84,8

3 335 11,6 96,4

4 74 2,6 99,0

5 17 ,6 99,5

6 10 ,3 99,9

7 2 ,1 100,0

8 1 ,0 100,0

Total 2.888 100,0

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

EducationMothers Fathers

Frequency Percentage Frequency Percentage

1. Without any degree 669 23,2 662 22,9

2. Primary 1137 39,4 1028 35,6

3. Secundary 308 10,7 354 12,3

4. Professional training 360 12,5 386 13,4

5. University 414 14,3 454 15,7

Total 2888 100,0 2884 99,9

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 work Frequency Percentage

Unemployed 1997 69,1

1-279 min 215 7,4

280-409 min 228 7,9

410-469 min 211 7,3

> 469 min 237 8,2

Total 2.888 100,0

Father’s paid work Frequency Percentage

Unemployed 1069 37,0

1-419 min 378 13,1

420-509 min 513 17,8

510-599 min 450 15,6

> 599 min 478 16,6

Total 2.888 100,0

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 work Frequency Percentage

0 min 28 1,0

1-229 min 666 23,1

230-359 min 745 25,8

360-489 min 713 24,7

> 489 min 736 25,5

Total 2.888 100,0

Father’s unpaid work Frequency Percentage

0 min 832 28,8

1-39 min 449 15,5

40-89 min 531 18,4

90-179 min 535 18,5

> 179 min 541 18,7

Total 2.888 100,0

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 variablesSpecification 1 Specification 2 Specification 3

Boys Girls Boys Girls Boys GirlsEst P Est P Est P Est P Est P Est 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

MEDU corr MPLT ,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 implicationsHigh 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-beingMothers’ paid working time is the variable with the largest

negative effect on boysFathers’ 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 implicationsMothers’ unpaid working time has a significant negative

effect on child well-being, both boys and girlsFathers’ unpaid working time has a:

significant positive effect on boys well-beingnon-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 & playingGirls: 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.