Transcript TIME USE AND CHILDREN’S WELLBEING: IMPLICATIONS FOR PUBLIC POLICIES New Directions in Welfare Congress OECD HQ (06-07/07/2011, Paris) Gálvez Muñoz, Lina; Rodríguez Modroño, Paula; Domínguez-Serrano,

TIME USE AND CHILDREN’S WELLBEING: IMPLICATIONS FOR PUBLIC
POLICIES
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)
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 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
Capabilities:
Social Relations
Education and knowledge
Domestic work and unpaid care
Leisure & playing activities
y*
X
Y
Functionings
y1
Total active leisure time
y2
Variety of activities
y3
Social time
y4
Cultural time
y5
Unpaid domestic work time
y6
Sports, hobbies and games time
Observable exogenous factors of the structural equation
x 1 Age
x6
Father's educational level
x 2 Household income level
x7
Paid working time of the mother
x 3 No. of household members
x8
Paid working time of the father
x 4 Number of children
x9
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
400
400
800
Social life
1
2
3
5
1
1,00
300
4
Variety of activities
Total Free Time
300
2,00
600
2
3,00
3
4,00
4
5,00
5
200
7
8
200
9
0
0
0
Boys
Boys
Girls
Boys
Girls
500
1.200
1.000
Culture
600
2
1
3
2
4
3
5
4
Unpaid domestic &
care work
1
2
3
400
4
5
5
Number
Number
300
600
500
Sports, hobbies &
games
400
1
800
Girls
Sex
Sex
Sex
Number
400
200
100
100
Number
Frequency
Frequency
6
300
200
400
200
100
200
100
0
0
Boys
Girls
Sex
Boys
Girls
Sex
0
Boys
Girls
Sex
Independent variables
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
2. Household income level (INCL):
 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)
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
2.888
100,0
Total
Independent variables
3. Number of household members (NHM):

It is a continuous variable that defines the number of members of the household
reference person (young).
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).
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
5. Parent’s educational level (MEDU & FEDU):
 2 categorical variables that correspond with the
educational level of mothers and fathers.
Mothers
Fathers
Education
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
Independent variables
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 paid work
Frequency
Percentage
Unemployed
1997
69,1
1-279 min
215
280-409 min
Father’s paid work
Frequency
Percentage
Unemployed
1069
37,0
7,4
1-419 min
378
13,1
228
7,9
420-509 min
513
17,8
410-469 min
211
7,3
510-599 min
450
15,6
> 469 min
237
8,2
> 599 min
478
16,6
2.888
100,0
2.888
100,0
Total
Total
Independent variables
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)).
Mother’s unpaid work
Frequency
Percentage
0 min
28
1,0
1-229 min
666
230-359 min
Father’s unpaid work
Frequency
Percentage
0 min
832
28,8
23,1
1-39 min
449
15,5
745
25,8
40-89 min
531
18,4
360-489 min
713
24,7
90-179 min
535
18,5
> 489 min
736
25,5
> 179 min
541
18,7
2.888
100,0
Total
2.888
100,0
Total
Structural equation model: results
Specification 1
Exogenous variables
Boys
Specification 2
Girls
Boys
Specification 3
Girls
Boys
Girls
Est
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
Mother’s unpaid working
time
Father’s unpaid working time
-,315
***
-,459
***
-,311
***
-,461
***
-,332
***
-,481
***
-,493
***
-,318
***
-,490
***
-,315
***
-,501
***
-,313
***
,238
***
,083
,134
,234
***
,076
,170
,231
***
,057
,322
,174
***
,149
***
MEDU
corr
MPLT
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.