10. Resultados
10.6. Relación gmax-material a 20°C
As discussed above, satisfaction with life as a whole can be examined; it is possible to use the same tools to observe and analyze satisfaction with respect to specific aspects or domains of life, such as health, employment, and housing. This section considers how the satisfaction with different domains may be linked to satisfaction with life as a whole. Although subjective well-being can be understood through either a top-down or bottom-up approach (Diener 1984; Headey, Holmstrom, and Wearing 1985; Lance et al. 1989), this section combines both approaches. In the bottom-up approach, domain satisfactions determine (and are compo- nents of) satisfaction with life as a whole. This yields the so-called two- layer satisfaction model.9 The top-down approach, in contrast, may be visualized by thinking of an individual who is optimistic or pessimistic. That trait not only affects the individual’s outlook on life as a whole (that is, yielding a higher or lower evaluation of life than that of the aver- age individual), but also affects the individual’s evaluation of different domains. The top-down approach aspect may be represented by a variable Z, which is a common determinant of satisfaction with life as a whole and of the domain satisfactions. In essence, that variable captures the psycho- logical traits of the individual.
The two-layer model can be operationalized by asking about respon- dents’ satisfaction with many different domains of life. Examples include satisfaction with job, health, and financial situations; social relationships; marriage; the government; the housing situation; one’s neighborhood; and the supply of urban amenities—the focus of the present volume.
The answers to these questions are domain satisfactions. It is clear that individuals may not be equally satisfied with all domains of life. For instance, a person may be at once highly satisfied with his or her financial situation and highly dissatisfied with his or her health. Satisfaction with life as a whole—say, LS—may be seen as an aggregate measure or as a weighted average of domain satisfactions (DS), where the most important domains are given the most weight. Satisfaction with life thus depends on degree of
an urban quality of life index: theory and methods 81
satisfaction with the various (k) aspects of life, that is, DS1, . . . , DSk. An
example of such a two-layer model is shown in figure 3.2. The underlying idea is that domain satisfactions are formed first, and then their weighted aggregate is satisfaction with life as a whole. In other words, domain sat- isfactions are components of “satisfaction with life as a whole.”
This analysis can be operationalized by the following model equation:
LS = LS(DS1, . . . , DSk). (3.20)
For instance, one might think of a linear aggregate:
LS = a1DS1+ . . . akDSk + eLS, (3.21)
where the DSs are operationalized by the cardinal median or COLS method.
An advantage of this intuitively plausible decomposition is that many variables that have no significant direct impact on LS (called x in figure 3.2) do have a significant impact on one or more domains. For instance, income has a rather limited impact on satisfaction with life as a whole; but it has a rather considerable impact on some of its components— notably, satisfaction with financial situation, health, and job. On the other hand, income may have a positive effect on financial satisfaction while it has a negative effect on health or job satisfaction, because higher income frequently entails a greater workload. The total effect of income on life as a whole is then a weighted addition of the three effects via the three domain satisfactions. It may be that the total effect on life satis- faction (LS) is then rather small or nonsignificant because positive and
Job satisfaction Financial satisfaction House satisfaction Health satisfaction Leisure satisfaction Environment satisfaction
x Satisfaction withlife as a whole
Figure 3.2 Two-Layer Model of Domain and Life
Satisfaction
negative effects on the domain satisfactions cancel out in the aggregate. In other words, the estimated direct effect of income on LS may be too small to matter. Similarly, the presence of electric lights on the streets is not a significant explanatory variable of life satisfaction, but it may be important as an explanatory variable for satisfaction with urban amenities—which, in turn, is a sizable component of satisfaction with life as a whole.
It is obvious that, as a rule, objective variables already used to explain one or more domain satisfactions should not be included a second time as explanatory variables in equation (3.21) for LS as a whole, because doing so would lead to identification problems. It also is evident that more domains and layers may be used than the two that are suggested here. For instance, using a British data set, van Praag and Ferrer-i-Carbonell (2008b) further decompose job satisfaction according to four types of job satisfaction—that is, with pay, security, the work itself, and hours worked. The only requirement for such multilayer decompositions clearly is whether such further differentiations make intuitive and empirical sense. For example, van Praag and Ferrer-i-Carbonell (2008b) use the British Household Panel Survey to decompose life satisfaction into five different domain satisfactions—namely, satisfaction with job, financial situation, health, house, and leisure. They find that, in descending order, the domains health, financial situation, and job situation score the highest. The model was later extended to three layers by distinguishing subdomains of the job situation, such as job security and pay.
The cardinalizations of the domain satisfactions and life satisfaction discussed above significantly simplify computation, permitting the use of OLS and related techniques instead of multiequation probit-type models involving series of highly complex integrations. Van Praag and Ferrer-i- Carbonell (2008b, ch. 4) provide an example in which the same model is estimated by means of ordered probit and by the corresponding COLS- variant for a large panel data set. Whereas ordered probit required a computation time of about 1.5 hours with panel data techniques, the OLS- variant took about 1 minute. The results were virtually the same, except for a proportionality factor. Although the time needed for computation clearly is not very important in itself, the fact that one method was about 90 times faster than the other method cannot be ignored.