Perspektiv på välfärden 2004 (pdf) - Statistiska centralbyrån

Youth
tion that change is a linear function of time. However,
in this particular case the linear assumption
is not realistic. For example, the typical pattern of
income development is not linear continuous
growth. What we observe is rather a curve-linear
development signified by a rapid increase during
the period a person establishes herself on the labour
market, followed by a slower increase as she
gets older, and eventually a decrease by the end of
the life course. The income slope has therefore
been specified with a relation of 0 with the t 0
measure, 2 with th0e t 1 measure, and 3 with the t 2
measure, expressing a curve-linear rather than a
linear function of time. A linear function has been
applied for the measurement of deprivation slope.
The means of the manifest variables are constrained
to zero. This, along with the fixed relations
between the latent variables and the manifest
variables, allows the latent variables to function as
a ‘container’ of parameters of the random coefficients
model. Thus, for the Intercept latent variable,
a mean and a variance are estimated, where
the variance represents individual differences in
the intercept of the growth model. For the Slope
variable as well, a mean and a variance are estimated,
and here the variance parameter represents
individual differences in linear change over time.
Thus, the SEM ap0proach allows straightforward
specification, estimation and interpretation of the
growth models.
Results
In order to arrive at a model that is as parsimonious
as possible, the following approach has been
applied. The starting point is taken in a ‘full
model’ estimating all relationships that follow
from the analytic model in Figure 1. That is, the
variables in the first block affect all the others,
variables in block two are correlated and the variables
in block three are affected by all previous
variables at the same time as they are interrelated,
as shown in the figure. Insignificant estimates (tvalue
less than 1.96) have thereafter been lifted
from the model one by one in order to find a parsimonious
model that fits the data at least as well
as the full model. The strategy resembles what
Jöreskog (1993) called ‘model generating,’ and as
a result only significant estimates are displayed in
the tables 4 . The model has been specified in
4 In addition to those displayed are the correlations
between sets of dummy variables estimated, that is
dummies for class of origin, education and unemployment.
This is necessary to get an acceptable fit of the
model, but reflects nothing more than the obvious fact
that they are mutually exclusive. For the exogenous
class dummies, it is the observed variables that are
allowed to co-vary, while for the endogenous educa-
74
STREAMS 2.5, and estimated with AMOS 4. The
results are displayed in two different tables.
Manifest variables
Table 4 shows parameters for the relationship
between the exogenous variables age, gender and
class of origin, and the set of endogenous variables
that indicate the youth situation. Thus, the
table relates to manifest variables in the two first
blocks of the analytical model (Figure 1).
In table 4 we can see that the older the population
becomes, the more likely it is that they have
attained a longer education, are employed, have
left the nest, and have a child of their own. Hence,
the pattern revealed is more or less what can be
expected when analysing a population that is presumably
in a transitional phase into adulthood.
The next variable, gender, is more interesting, as
it reveals substantial differences between women
and men. Both short- and long-term unemployment
are more common among women. This is
also true for social assistance, although it must be
kept in mind that this variable is somewhat problematic
due to underreporting (see footnote 3). As
expected, women also leave the nest earlier than
do men, and they are also more likely to become a
parent at a young age.
The estimates related to class of origin should
be interpreted as contrasts to the reference group,
which in this case consists of blue-collar workers.
It is apparent that those with middle and higher
white-collar worker class of origin deviate from
the rest. H0ere we find a higher incidence of
higher education, a larger share of students, especially
among the older sections of the population,
which is shown by the impact on the interaction
between being a student and age (older students,
who are usually those involved in tertiary education,
more often come from white-collar homes).
As a natural consequence, being unemployed is
less likely if the class of origin is middle or higher
white-collar worker. It is also less common that
people with a white-collar background have children
of their own at a young age. Thus, table
4confirms a picture that we, on the basis of earlier
research, more or less expected. The reproduction
of a class society shines through the results of the
analysis and there is also a gendered pattern
showing that0 young women seem to become
what is commonly defined as "adults" earlier,
because of their earlier nest leaving and higher
likelihood of becoming a parent at an early age. In
addition to the regression estimates (specified as
causal relations), correlations are also estimated
between the variables relating to block two of
Figure 1. The significant results are shown in
tion and unemployment dummies, it is the error term
that co-varies.