Children’s Subjective Well-Being in Disadvantaged Situations


Descriptive variables

Boys

Girls

Total/percentage

School ownership

State-run

1,758

1,788

3,346 (56.38 %)

Subsidized

1,097

1,066

2,163 (36.45 %)

Private

118

107

225 (7.71 %)

Total

2,973

2,961

5,934

Age

11

410

460

870 (14.95 %)

12

1,857

1,954

3,811 (65.51 %)

13

490

384

874 (15.02 %)

14

156

106

262 (4.50 %)

Total

2,913

2,904

5,817

Country of origin

Born in Spain

2,640

2,617

5,257 (88.81 %)

Born abroad

326

336

662 (11.19 %)

Total

2,966

2,953

5,919

Geographical location

Rural

146

127

273 (4.61 %)

Semi-urban

596

574

1,170 (19.71 %)

Urban

2,231

2,260

4,491 (75.68 %)

Total

2,973

2,961

5,934





7.3.2 Procedure


The schools were randomly selected. Participating pupils, after giving their informed consent, completed a self-administered questionnaire in their own classrooms during school hours, during the 2011–2012 school year. Two trained researchers were present at the time and informed them of the purpose of the study, its anonymous nature and their voluntary cooperation.


7.3.3 Instruments


Data were collected using the questionnaire of the International Survey of Children’s Well-Being (www.​childrensworlds.​org). This questionnaire includes the Domains Satisfaction General Index (DSGI), which was calculated using the arithmetic mean of the 8 indexes for life domains (Casas and Bello 2012; Casas et al. 2013a): home, material belongings, interpersonal relationships, the area where you live, health, use of time, school and personal satisfaction, comprising a total of 26 items using a 11-point scale. In addition to the control variables (age, gender), it also includes a number of items related to situations we have considered possible indicators of social disadvantage.


7.3.4 Data Analysis


Situations of social disadvantage that may be considered dimensions of poverty are analysed using the multidimensional perspective of the Capability Approach (CA) (Chiappero-Martinetti 2000), which addresses people’s relationship with economic failure, but also with health, education, social relations and subjective issues; that is, a more comprehensive approach to social well-being.

On the basis of this multidimensional approach, we used the methodology of fuzzy sets, which allowed us to determine the different degrees of existence of the variable used (Chiappero-Martinetti 2000). We understand that, as constructs, poverty and well-being are considered difficult to bind as a whole due to their complexity, this theory generally being used for comprehensive understanding of these phenomena (Qizilbash and Clark 2005; Lelli 2001); several authors and studies have developed means of measuring poverty from a multidimensional perspective (Chiappero-Martinetti 2000; Comim 2008; Lelli 2001; Qizilbash and Clark 2005; PNUD 2010).

Fuzzy set methodology replaces the well-delineated function (set crisp), in which the indicators are only related to non-member or yes-member values. It is widely used due to its ease of visualization and interpretation, since values are considered equidistant and have a minimum and maximum that refer to each variable’s field of membership (Lelli 2001). In our database, these values range from 0 (non-members) to 10 (members). Note that differences between authors refer primarily to which values are considered maximum and minimum. For example, Comim (2008) and Chiappero-Martinetti (2000) use a parameter of between 0 and 1, whereas Qizilbash and Clark (2005) use values between 0 and 5.

Those variables that are not dichotomous are therefore conceived in a linear function of equidistant values between the parameters 0 and 10 (Table 7.2). This study considers that for each variable and dimension, the closer the values of the children’s responses are to 0, the less poor they will consider themselves. The closer they are to 10, the more they will consider themselves as living in a more socially disadvantaged situation. Thus, using the same strategy as that used by Comim (2008), an average between 0 and 10 was developed for each dimension.


Table 7.2
Dimensions of multidimensional poverty










































































































































Dimensions

Scoresa

0

2.5

5

7.5

10

Education father’s level of education

University upper secondary
 
Primary secondary
 
Did not finish primary

Mother’s level of education

University upper secondary
 
Primary secondary
 
Did not finish primary

Repeated year

No
       

Home and material conditions

Has computer when needed

Yes
     
No

Has internet

Yes
     
No

Has mobile

Yes
     
No

Has clothes in good condition for school

Yes
     
No

No. of bathrooms and toilets in usual residence

2, 3 or more
 
1
 
None

Family has car

1 or 2 or more cars
     
None

No. of books at home in addition to school books

100–500, or over 500
 
50–100
 
Less than 50

Satisfaction with home

Having own space at home

Totally agree

Agree

Neither agree nor disagree

Disagree

Totally disagree

Satisfaction with house/flat itself

Totally satisfied
 
Neither dissatisfied, nor satisfied
 
Totally dissatisfied

Satisfaction with space available to them at home

Totally satisfied
 
Neither dissatisfied, nor satisfied
 
Totally dissatisfied

Subjective poverty

Satisfaction with things they have

Totally satisfied
 
Neither dissatisfied, nor satisfied
   

Concerned about family’s money

Never

Sometimes
 
Often

Always

Family wealth in comparison with other families in the area where they live

Equally wealthy. much more wealthy, or quite wealthy
 
Less wealthy
 
Much less wealthy


a0 is associated with lower rates of social disadvantage; 10 is associated with higher rates of disadvantage

The dimensions explored are as follows:



  • Education, with the following variables: father and mother’s level of education and children repeating a school year;


  • Home and material aspects, with the following variables: family having a computer, internet, a mobile, a car, clothes in good condition, a bathroom and books;


  • Satisfaction with home, with the following variables: satisfaction with own space in the home, with the home itself and with spaces available to them in the home;


  • Subjective poverty perceived by the child, with the following variables: satisfaction with the things they have, concern about family’s money and comparative assessment of family wealth.

Descriptive analyses were conducted of the items on the DSGI with life domains using different variables (father and mother’s education, repeated school year, perception of family wealth, money concerns and satisfaction with the space available at home). These analyses were performed using t-test or variance analysis (ANOVA), with a statistical significance of 0.05.

We then conducted a multifactorial regression analysis using the stepwise procedure (Bisquerra et al. 2004) in order to identify the influence on subjective well-being of the different variables related to poverty, using the 0.05 level of significance. The dependent variable used was the DSGI and the independent variables were the dimensions of poverty: education, home and material conditions, satisfaction with home and subjective perception of poverty. We also performed a multivariate variance analysis (MANOVA), taking as dependent variables the dimensions of education, home and material conditions, satisfaction with home and subjective poverty. The independent variable was perception of wealth and poverty, subdividing the sample into two groups: children who have the perception that their family is less or much less wealthy than others, and those who have the perception that their family is wealthier or much wealthier than others. A polynomial contrast was used.