1. OPPORTUNITIES
AND SOCIAL
CONDITION : THE
ISSP 2009 SURVEY
CASE OF
SWITZERLAND
Author: Carmine Gelormini
[DATA]
[NOME DELLA SOCIETÀ]
[Indirizzo della società]
2. Introduction
Aim of this report is to explain the income
distribution within a sample of observation of 1229
observation, taken from the ISSP 2009 Survey on
Social Inequality referred database, through several
factor related to social stratification and
achievement. Studies on social stratification and
mobilization concern about whether people have
opportunities to get ahead (i.e., receive further
education, get a good job), which should be unrelated
to such ascribed characteristics as race, sex, or
socioeconomic origin. The association between
ascribed characteristics and achievements, is
conceptualized as a measure of inequality of
opportunity, and used to examine whether the
society is open or rigid (Breen & Jonsson,2005). A
positive correlation between the ascribed factor and
achievement is seen as a sign of inequality of
opportunity (Sørensen, 2006; Bourguignon et al.,
2005). Further specification in this area of study focus
in estimating the share of observed inequality in
current earnings which can be attributed to the so
called inequality of opportunity (J. Roemer, 1988).
Roemer offered an influential formalization of the
concept of unequal opportunities, suggesting that
one should separate the determinants of a person’s
3. advantage (i.e. desirable outcomes, such as incomes
or status) into circumstances and efforts. Also
inequality of earning it’s directly connected with
inequality of opportunity, noting that circumstance
variables are economically exogenous by definition,
but that “effort” variables can be affected by
circumstances, as brilliantly noted in the Inequality of
Opportunity in Brazil by François Bourguignon. The
report though will not be focused on the discrepancy
between incomes, bearing in mind these assumptions
more as an interpretation.
Method
The data analyzed are from the ISSP survey on social
inequality and refer to Switzerland. We will examine
the relating to socio-economic stratification. The
scheme will be the following: two unobserved
factors, ascription and self-effort, will be used as
direct predictors of an individual level of income,
while a third latent factor, formely named “other
factors”, will be used as a mediator, to catch if there
is an indirect effect of some factors that are usually
not mentioned as determinant in achieving high level
of income. The following variables will be used:
mother’s and father’s job, cultural family’s heritage
and family’s status of living for the ascription latent
4. factor; respondents’ level of education ( in years of
schooling) and intergenerational mobility (named
difference from father’s to individual’s job) for the
self-effort factor; the type of society, the existence of
a network and the individual’s gender for the so-
called “other factors” factor; level of income
(monthly) for the dependent variable. As far as we
know from previous studies ascription factors and
self-effort of the individuals play a relevant role for
mobility, in terms of position achieved and network
building, and also for revenues. Also some factors are
correlated between them : educational achievement
is a reliable predictor for social position, and
educational achievements of parents are positive
predictors of the social position of their offspring
(Bergman et al, 1998). Furthermore educational
achievement is a function of the educational
achievement of the parents and of the parents social
position, which implies also access to certains
networks. Also interpreting this hyphothesis evidence
suggest that male perform generally better than
women. But the question that will be answer are if
and how much all of these factors play a role in the
differences in income. We will use a structural
equation model for testing the viability of the model,
extrapolating the differents and unique effects.
5. Statistics
INCO
ME
FATH
ER_J
OB
FAMIL
Y
CULT
URE
YOU
R_E
DUC
FAMIL
Y_STA
TUS_L
IVING
INTER
_MOBI
LITY
SE
X
SOC
IET
NET
WK
MOT
HER_
JOB
N
° 1229 1229 1229
122
9 1229 1229
12
29
122
9
122
9 1229
4171
,399
5 - -
14,5
964 -
2,811
2 - 3 - -
M
e
3950
,000
0
6130
,000
0
6,000
0
10,0
000
5,000
0
3,000
0
2,0
00
0 3
3,00
00
5230
,000
0
S
D
2428
,177
96
4006
,111
49
1,835
33
16,4
457
4
1,906
92
1,309
02 - -
,744
24
5192
,541
91
min ,00
1000
,00 1,00 3,00 1,00 1,00 -
1,0
0 1,00 -
MM
ax
9500
,00
7516
4,00 9,00
96,0
0 10,00 7,00 -
5,0
0 3,00 -
A final summary of data will be useful to
understand how they look.
All the independent variables’ values are
obtained through question like the following,
which refers to the first one:
The data have being modeled through SPSS for
further using them in AMOS Graphical,
because missing data are replaced through
FMI procedures. Some statistics have been
deleted because useless and not explanatory,
for example the mean for the variable “family
culture”, “family status of living” , which is
constructed from a scale from 1 to 10, and
indicates the number of books in the house of
6. family and then the different values have been
coded into ten classes, where 10 indicates the
highest number. The coding rules for these
variables can be found in the “ISSP 2009 -
Social Inequality IV GESIS Study No. 5400
Variable Reports”.
Problems arises for the dependent variable,
income, having a 24% of missing data in the
original data, as the table below show.
The variable has been originally constructing
asking the following question
Consider all of your personal income. What is the main source? Please use this card.
Using this card, if you accumulate all sources of your income, which letter best
describes your personally total net income? If you do not know the exact figure, please
give an approximation. Use the part of the card that you know best, weekly income,
monthly income or yearly income.
7. Answers varing from these categories,
0 No own income, not in paid work
500 Less than 1.000 CHF per month; 1350 1.000-1.699 CHF; 2100 1.700-2.499 CHF;
3000 2.500-3.499 CHF
3950 3.500-4.399 CHF; 4750 4.400-5.099 CHF ; 5550 5.100-5.999 CHF ;6650 6.000-
7.299 CHF ; 8400 7.300-9.499 CHF
9500 9.500 CHF and more ; 999990 NAP, other countries ;
999997 Refused ;999998 Don't know
So logically placing mean instead of missing
value make sense as a good approximation also.
Analysis
The analysis has been performed using
AMOS, using a latent variable structural
equation model, inspecting a dependent
8. variable through 2 predictor latent factors
and 1 mediator, as a further latent factor.
The variables are referring to father’s job
and mother’s job .
The arrows goes from the latent factors
directly to the dependent variable, which is
the only observed ( in the rectangle), but
the two main factors point also to the
mediator and through this again to the
dependent.
Results and interpretation:
The final output gives the following results:
9. The Chi-square test showing a p value >
0.05 is significative meaning that for the
theoritical assumption made, the variables
are statistically relevant for affecting the
level of income. Note that it’s generally
argued if chi-square test can produce good
results on large sample; in this case the
sample it’s large, above 1000 obs.
Looking at the regression weights also,
these shows that the two main predictors
have some effects on the income, but it is
not statistically significant, even if the
direction of the coefficients present a clear
situation at least for the effect of ascripted
factors on income, being high and
positively direct. The only coefficients
which shows some relevancy are the level
10. of education on self effort factor, which it’s
clear, although it’s near to zero; and the
respondents’ father jobs which affect
negatively overall the factor ascription,
which is also difficult to explain and
probably unuseless being a conceptual
argument.
On the decomposition of effect side, the
analysis shows overally a strong negative
11. effect of self-effort on the income with a
value of -1,397, and a positive one for
ascription (1,229). This result is also not
very clear, because while ascripted factors
may play a clear important role in earning
money, we still don’t know a lot if education
it’s relevant for benefit more revenues,
being stated almost everywhere, from
newspaper to commercials, that studying is
key factors for gain better jobs which led to
more revenue.
The direct effects confirm what it has been
showed by the total’s summary table, with
a negative coefficient of -1,528 for self
12. effort on income, and a positive one of
1,323 for ascription, leaving the mediator
variables not considered.
The summary of the indirect effects in the
end tells us that the self efforts positively
affect income, through the other factors
variables, for only ,131 stardard deviation
units, while for the ascription factor there
almost no mediation effect.
13. Model fit
CMIN /DF shoud be whithin 5 while we
have here more discrepancy: the value is
1,422.
The estimate for RMSEA is .08, the 90
percent confidence interval is 0.72 – .089
and the probability that the population
RMSEA is less than .05 is 97.3%. Also
PCLOSE should be above ,05 while we
have it here less than 0,05. The RMSE it’s
just on the limit.
14. The CFI (comparative fit index) is as CFI
the cuf-off for a good model for this value is
CFI≥.90
Conclusions:
The data don’t show enough evidence to
produce any causality relationship pattern.
The hyphotesis formulated whithin this
model has to be rejected. The model also
don’t present a clear goodness of fit to the
data, being some index like RMSEA and
CFI relevant, but not for CMIN and chi-
square.
References
Welfare states, family inequality, and
equality of opportunity, Annemette
Sørensen; Elsevier Ltd., 2006
Comparing social stratification schemas:
CAMSIS, CSP-CH, Goldthorpe, ISCO-88,
Treiman, and Wright, Mafred Bergman;
SIDOS, 1998
15. Social Change, Mobility, and Inequality in
Switzerland in the 1990s, Manfred
Bergman, Dominique Joye, Beat Fux;
Swiss Journal of Sociology, 2002
Inequality of Opportunity in Brazil, François
Bourguignon, et al; The World Bank, 2005
Equality of Opportunity,John E. Roemer;
Harvad University Press, 1998
Structural Equation Modelling: Guidelines
for Determining Model Fit.; Daire Hooper,
et al. 2008.
ISSP 2009 - Social Inequality IV GESIS
Study No. 5400 (v4.0.0),
http://dx.doi.org/10.4232/1.12777
Inequality of Opportunity, Income
Inequality and Economic Mobility, Paolo
Brunori, Francisco H. G, F.V. Peragine;
2013