EXPLAINING SOCIAL EXCLUSION - Institut für Soziologie

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EXPLAINING
SOCIAL EXCLUSION
Edited by Jenny Jarman
International Journal of Sociology and Social Policy Vol. 21 No. 4/5/6 2001
Copyright © 2001 Barmarick Publications.
Authors reserve all rights.
Enhoimes Hall, Patrington, Hüll, East Riding of Yorkshire,
England, HUI2 OPR.
ISSN 0144-333X

Editor:
Review Editor:
Associate Editorst
Barric O. Pettman,
International Institute of Social Economics
Jill Manthorpe,
University of Hüll,
Department of Social Work,
Hüll HU6 7RX
Erik Allardt, University of Helsinki
Simon Bekkcr, The Urban Foundation, Johannesburg
Berch Berberoglu, University of Navada
Robert Bierstedt, University of Virginia
Tom Bottomore, University of Sussex
Sally Bould, University of delaware
Benjamin P. Bowser, California Säte University, Hayward
Otar Brox, University of Tromso
Jose Cazaria, University of Granada
Mohamed Cherkaoui, University of Paris - Sorbonne
John Cross.Vassar College
Y. D. Dam'ie, University of Poona
K. Dobbelaere, Catholic University of Leuven
Tim Dowd, Emory University
John Eldridge, University of Glasgow
Maria-Pilar Garcia, Universidad Simon Bolivar
Anthony Giddens, University of Cambridge
Gergory Gizelis, Academy of Athens
Björn Gustaven, Oslo Work Research Institutes
Leonard M. Henny, State University of Utrech
H. J. Hoffman-Nowotny, University of Zürich
Hsin-Huang Michael Hsiao, Institute of Ethonology, Taiwan
Ronald N. Jacobs, University at Albany
Mette Jensen, Roskilde Universitetcenter
Ken Judge, University of Kent, Canterbury
David Karen, Bryn Mawr College
Lisa A. Keister, The Ohio State University
Hiroshi Komai, University of Tsukaba
Cheng-shu Kao, Tunghal University, Taiwan
Kilma'n Kulsclr, Hungarian Academy of Sciences
Jolanta Kulspinka, University of Lodz
David Macarov, University of Jerusalem
Donald Mac-Rae, London School of Economics
Zdravko Minar, University of Ljubljana
Alfonso Morales, University of Texas at El Paso
Guiseppe Morosini, Universita delgi stud di Torino
Anna Olszewska, Instyut Filozofii i Socjologii, Warsaw
Eise 0yen, University of Bergen
David N. Pellow, Universiry of Colorado at Boulder
Glanfranco Poggi, Iniversity of Edinburgh
A. G. Schutte, University of Wirwatersrand
Duncan Timms, University of Stirling
Renato Treves, University of Milan
Joji Watanuki, Sophia University
Peter Weingart, University of Bielefeid
Wlodzimierz Wesolowski. Polish Academy
Volume 21 Number 4/5/6 2001
Contents
Explaining Social Exclusion
by Jenny Jarman
Page
Social exclusion and public policy: the relationship between
local school admission arrangements and segregation
by poverty"
by Stephen Gorard, Chris Taylor and John Fitz 10
Young People's Explanations and Experiences of Social
Exclusion: Retrieving Bourdieu's Concept of Social Capital
by Virginia Morrow 37
A Story of "Difference", A Different Story: Young Homeless
Lesbian, Gay and Bisexual People
by Shirley Prendergast, Gillian A. Dünne, David Telford 64
Unemployment and Social Mobility in East German
by Reinhold Sackmann, Michael Windzio and
Matthias Wingens 92
Unemployment without Social Exclusion: Evidence from
Young People In Eastern Europe
by Ken Roberts \
New Fatherhood in Practice? Parental Leave in the UK
by Esther Dermott 145
An International Comparative Analysis of Marriage
Patterns and Social Stratification
by Ken Prandy and Frank L. Jones 165
Inclusion or Exclusion? Reflections on the Evidence of
declining racial disadvantage in the British labour market
by Paul Iganski, GeoffPayne and Judy Roberts \4
Risk of Social Exclusion in Later Life: How well do the
pension Systems of Britain and the US accommodate
women's paid and unpaid work
by Jay Ginn 212
3
j

International Journal of Sociology and Social Policy 92
Unemployment and social mobility in East Germany
by Reinhold Sackmann, Professor, EMPAS, University of Bremen,
Michael Windzio, PD, EMPAS, University of Bremen and Matthias
Wingens, PD, University of Bremen
High unemployment rates are still a major problem faced by many
European societies; the Situation is especially grave during the transition
process from a communist to a market economy. We know that unemployment
has effects on the psychological well-being of persons
affected (Kieselbach 2000) and on the functioning of communities hit
by a high concentration of unemployment (Jahoda, Lazarsfeld and
Zeisel 1971). However, our knowledge with regard to the long-term effects
of unemployment on careers is rather limited. Some authors conclude
that unemployment is only a transition phase in the life course
and has a limited effect on long-term careers (Mutz et al. 1995). In contrast,
other authors argue that unemployment is a first step towards
processes of social exclusion, featuring unemployment äs a major
mechanism of disintegrating certain groups from Society (Kronauer,
Vogel and Gerlach 1993). Major social policy interventions fostering
workfare instead of welfare äs well äs programs focussing on employability
build on this kind of social diagnosis.
The effects of youth unemployment on careers may be even
more crucial. Youth unemployment in transition societies is seen äs a
critical life event, because long-term effects of unemployment may not
only leave a "scar" on the careers of the individuals (OECD 1998) but
can also be a cause of major social problems in transition societies, e.g.
criminal behaviour and, or racist behaviour, destabilising the fragile
equilibrium of accelerated social change in transition societies.
Thus, there is a need for empirical research on the long-term effects
of unemployment. Methodologically, longitudinal analysis, especially
event history analysis, was a major improvement for the study of
the connection of life courses and social change. The aim of this article
is to look into the long-term effects of unemployment on social mobility,
using longitudinal data on young East Germans during the transition
period. In order to simultaneously pursue transitions and
Volume 21 Number 4/5/6 2001 93
trajectories, two methods are used: event-history and optimalmatching
analysis.
Data set
For the empirical analysis of the East German Career Study, a longitudinal
data set of young East German labour market entry cohorts is
used, which was set up at the Special Collaborative Centre 186. The
data set (Weymann, Sackmann and Wingens 1999) consists of 3776
young skilled workers who participated in an apprenticeship and
young university graduates, all taking exams in 1985 (before the transformation),
in 1990 (the reunification year) and 1995. The data set is
not representative for all young people in East Germany, but only for
the two most numerous qualification levels: young skilled workers and
Professionals. Thus we restrict generalisations of our results to these
groups. For questions of exclusion, you have to take into account that
the "unqualified" are not part of the sample. However, the "unqualified"
make up a very small group in East Germany, äs they only account
for 3% of the cohort of young people.
Theoretically, we take a dynamic approach to the phenomenon
of exclusion. Cross-sectional data on unemployment can be misleading,
äs they suggest that uniform levels of unemployment refer to a
constant group of the unemployed. Longitudinal studies that use life
event analysis have improved the dynamic analysis of unemployment.
Studies usually focus on processes that cause the selectivity of becoming
unemployed and processes causing exit from unemployment. Research
results from this method suggest that unemployment is a
transient position (Mutz et al. 1995). While results gained with this
method fürthered unemployment research, it also has some blind
spots. If one only looks at single transitions of unemployment, one can
miss its long-term effects. The starting point of our analysis is to look
into the long-term effects of unemployment for careers. To get an answer
to this question, we use two methods: a) we use event-history
analysis to see how unemployment (äs a time-dependent variable) influences
processes of social mobility; b) we use the relatively new
method of optimal matching analysis to find patterns of careers.

International Journal of Sociology and Social Policy 94
Since the 1980s, the new method called optimal matching analysis
enables fmding patterns of sequences of states (Abbott 1995).
Originally this method was used in DNA-analysis in genetics, but recently
there is a growing literature of applications in life-course research
(Abbott and Hrycack 1990; Han and Moen 1999; Halpin and
Chan 1998; Scherer 1998). The optimal matching analysis uses algorithms
that compute the similarity of sequences of states. In a second
step the results of the similarity values are usually grouped by cluster
analysis. The aim of this method is to find types of careers using the information
of whole careers.
Long-term effects of Unemployment spells on social mobility:
Some hypotheses
Research on the long-term effects of Unemployment spells on social
mobility has not been conclusive. There is a set of competing theories
resulting in contradictory hypotheses, which - to a certain extent - can
be tested in empirical research. If one wants to model the effect of unemployment
on mobility in the context of a transformation of a society,
one has to specify what could be meant by the effects of unemployment
on the one band and by the effects of structural transformation on
the other.
For the effects of Unemployment, existing labour market theories
suggest four types of possible effects for the individual. One concerns
the direction of social mobility influenced by a single spell of
Unemployment. Theories of exclusion conclude a spiralling unemployment
effect; unemployment is seen äs a decisive element for
downward mobility (c.f. Kronauer et al. 1993). This view contrasts
with theories of the transitory nature of unemployment, which conclude
that unemployment simply indicates a switch in one's career,
whatever direction this mobility process may take (c.f. Mutz et al.
1995). Hypothesis one is: unemployment only influencesprocesses of
downward social mobility.
The second hypothesis refers to cumulative processes of unemployment.
Exclusion theories argue that unemployment spells are not
evenly distributed among cases. Unemployment may be characterised
Volume 21 Number 4/5/6 2001 95
äs a cumulative process. Some people become unemployed repeatedly.
Signalling theory (Spence 1973) suggests similar effects: the longer
the overall length of unemployment in a person's career, the less likely
that an employer entrusts a rise in position to this person (Inkmann,
Klotz and Pohlmeier 1998). Hypothesis two is: the higher the cumulative
duration of all unemployment spells, the less likely upward social
mobility.
The third hypothesis refers to human capital and coping mechanisms
for unemployment. Human capital theory suggests that the
longer a single unemployment spell, the more likely a loss of human
capital thereafter (Mincer and Ofek 1993). One would conclude that
long spells of unemployment lead to downward social mobility. Contrary
to this is the Suggestion of coping theory: People become more
risk-averse after a spell of long-term unemployment. Therefore, mobility
is reduced after long unemployment spells. In the USA, young
people affected by a deprivation of their parental home caused by unemployment
during the 1930s, were more risk-averse during their
work-history than people who did not have this experience during their
youth (Eider 1974). Similar coping-effects might be the resultof an experience
of unemployment during adulthood. Hypothesis three is: the
longer an unemployment spell, the less likely is it that there will be social
mobility afterwards.
The fourth hypothesis refers to processes of downwardspiralling
mobility. Downward-spiralling suggests that downward mobility
to qualification-inadequate positions is irreversible. Contrary to
this, one might suppose that positions below one's level of qualification
produce a kind of personal tension äs people feel they have more
Potential than their current position suggests. Therefore, they try to rebalance
individual resources and positions by moves of individual
counter-mobility (Tuma 1985; Becker and Zimmermann 1995). Hypothesis
four is that people in a position below their level ofqualification
will be more likely to make transitions of upward mobility.
Sociologically inspired labour market theory underlined the importance
of structural components for the explanation of processes of

International Journal of Sociology and Social Policy 96
mobility in individual careers (Kalleberg and S0rensen 1979). Processes
of individual careers are embedded in a structural process of social
change. An understanding of the macro-social transformation is
important for the explanation of individual mobility. This especially
holds true in the case of accelerated social change e.g. in countries of
transition from a communist economy to a market economy. One advantage
of event-history analysis is that it allows a dynamic analysis of
macro-social processes and individual processes, thus differentiating
micro- and macro-level causation (Biossfeld and Rohwer 1995).
Three possible effects of the structural components of the transition
process on individual mobility are important. A first thesis is on
the general direction of structural change of mobility due to the transition
process. Some theoreticians of the transition suggested a general
trend of downward mobility in East Germany. Like the Mezzogiorno
in Italy, peripheral East Germany loses its own economic potential;
general downward mobility therefore prevails. Contrary to this, modernisation
theory suggests a modernisation vis a vis the backwardness
of the communist economic structure. Especially the modemising
trend towards more employment in the tertiary sector is supposed to be
accompanied by more upward mobility (Zapf 1994; Biossfeld 1989).
Hypothesis five is: a general rise of employment in the tertiary sector
causes more individual upward mobility.
A further thesis refers to the effect of the general level of unemployment
on mobility. One could argue that during periods of recession
the number of involuntary exits rises, forcing people to leave Jobs.
Thus higher unemployment levels would lead to higher mobility levels.
Contrary to this, one Suggestion is that a rise of the general level of
unemployment causes a higher risk in mobility moves (Schettkat
1992; DiPrete and Nonnemaker 1997). Therefore, the overall level of
mobility is lower in times of high unemployment than in times of labour
shortage, when there is more incentive and less risk to move between
Jobs. Hypothesis six is: a general rise of unemployment reduces
individual mobility.
Volume 21 Number 4/5/6 2001 97
A last thesis refers to the connection between structural and organisational
change. As evolutionary Organisation theory suggests
(Hannan and Carroll 1992), social mobility is more likely after new
companies have been formed and old companies have been closed than
after the enlargement and shrinkage of established companies. Haveman
and Cohen (1994) showed that in the banking sector of California
a growing number of Company formations, mergers and closures
caused a higher rate of social mobility of employees. Especially the
quick privatisation policy (Pickel and Wiesenthal 1997) of the East
German transformation (with high rates of firm formations and firm
closures) could have caused more social mobility during this period.
Hypothesis seven is: a general rise offormation of companies raises
the likelihood of upward mobility, whereas a general rise ofclosure of
companies raises the likelihood of downward mobility.
The following event-history analysis focusses on these seven
hypotheses. To reduce the risk of a maladjusted model, we controlled
for a number of variables: labour force experience; occupational prestige
of current Job; gender; children; employment in the public sector;
entry-cohort; number of preceding employment history spells; and rriigration
to West Germany. We will not comment on the results of these
control-variables, which were discussed in other publications (Weymann,
Sackmann and Wingens 1999).
Results of the event history analysis
The aim of our event-history analysis is to find out what the effect of
unemployment on social mobility career transitions is. Young East
Germans, who took their exams in 1985 and 1990, were observed until
the middle of 1997. As unemployment did not exist in GDR the period
"at risk" started in November 1989. As we did know the length of the
employment spells before 1989, this information was used in the
model thus avoiding "left censoring" (Guo 1993). A highly flexible
piecewise constant exponential model with competing risks was used.
The central idea of such a piecewise constant model is to split the process
time axis into intervals and to allow a Variation of the constant between
the intervals. The model allows to control approximately for the

International Journal of Sociology and Social Policy 98
time dependence of the process without a previous decision about the
shape of the function. Time intervals of twelve months were used. An
upward mobility transition was defmed äs a transition between two
Jobs whereby the second Job has an at least 5% higher level of occupational
prestige on the Treiman-Ganzeboom prestige index from 1996.
Downward mobility is defmed likewise with the second Job having an
at least 5% lower level of prestige on the Treiman-Ganzeboom index.
Lateral mobility transitions are defmed äs all other Job changes.
Table l (overleaf) shows the results of the event history analysis.
Numbers in the table show the percentage-effect that can easily be interpreted
(Blossfeld and Rohwer 1995). For example: The risk of a
downward social mobility transition is 1068% higher after an unemployment
spell.
One major result is that spells of unemployment can precede all
kinds of social mobility. Unemployment raises the chance of upward,
downward and lateral social mobility. After an unemployment spell
lateral mobility is more likely than downward mobility, which is more
likely than upward mobility. As all three directions of mobility are
considerably increased, one can conclude that spells of unemployment
mdicate a rather neutral switch in mobility paths. They not only precede
a single direction towards downward mobility and they are not
necessanly the antecedent of a downward Spiral äs suggested by some
versions of exclusion theory.
What are the consequences of cumulative unemployment? Is the
effect of the cumulated length of unemployment spells more singledirectional
than an occasional unemployment spell? The numbers in
Table l give a differentiated answer to this question: Upward mobility
is less likely (-4%) for persons that are characterised by a long record
of unemployment during their career. Employers may use this Information
äs a signal to evaluate that these individuals are less competent
for a higher position. However, it is not necessarily a predictor for a
downward directed career path äs there is no significant effect of cumulative
unemployment on downward mobility. A record of long un-
Volume 21 Number 4/5/6 2001 99
Time: 0-12mths.
Time: 12-24mths.
Time: 24-36 mths.
Time: 36-48 mths.
Time: 48-60 mths.
Table 1:
Predictors of social mobility, pce-model, percentage-effects
Time: more than 60 mths.
Unemployment
Cumulated duration of
unemployment
Duration of preceding unemployment
spell
Current level of prestige lower
than prestige level of certificate
Unemployment level
Development of tertiary sector
Formation of companies
Ciosure of companies
Occupational prestige
Labour-entry cohort 1985
Labour force experience
Migration to West Germany
Employment in public service
Woman
With child younger than 6 yrs.
Woman * with child younger
6 yrs.
Upward
mobility
0.0003
0.0004
0.0004
0.0002
0.0004
0.0005
735%**
-4%*
n. s.
4%**
n. s.
8.5%*
1%**
n.s.
-4%**
286%**
-3%**
n.s.
-46%**
n.s.
n.s.
n.s.
Downward
mobility
0.2193
0.3018
0.3011
0.2564
0.2272
0.3237
1068%**
n.s.
-5%**
-3%**
n.s.
-6%*
n.s.
0.9%**
n.s.
44%*
-1%**
n.s.
-64%**
n.s.
n.s.
-31%**
Lateral
mobility
0.0183
0.0249
0.0216
0.0210
0.0209
0.0228
1307%**
n.s.
-7%**
n.s.
-4%*
n.s.
0.6%**
0.4%*
1%*
-30%*
n.s.
23%**
-24%**
n.s.
n.s.
n.s.

International Journal of Sociology and Social Policy 100
Volume 21 Number 4/5/6 2001 101
Table 1 (continued):
Predictors of social mobility, pce-model, percentage-effects
Number of preceding
employment history spells
Log-likelihood
Spells
Events
Upward
mobility
21%**
Downward
mobility
n.s.
-13315
63185
Lateral
mobility
17%**
2431 (480 upward transitions; 638 downward
transitions; 1313 lateral transitions)
** = significant on 1%-level; * = significant on 5%-level; n.s. = not significant
Special Collaborative Centre 1 86: Hast German Career Study
employment can prevent one from getting a better Job, but it is not the
start of a downward slope.
What happens after a long single unemployment spell in the next
Job? Individuais stay longer in their next Job. The figures indicate that
people behave in a more risk-averse manner after a spell of long-term
unemployment: lateral (-7%) äs well äs downward mobility transitions
(-5%) are reduced after single long unemployment spells.
What is the effect for your further career by taking a position below
the level of qualification of your certificate? Is it the start of a
downward slope or can people rebalance resources and positions by
moves of counter-mobility? Data suggest the latter: People with a
lower level of position in relation to the level of their certificate are
more likely to make an upward mobility transition (+4%). They are
also less likely to make downward mobility transitions (-3%). Both results
indicate a tendency of successful counter-mobility to rebalance
resources and positions.
Let us now look at structural influences on mobility in the transformation
process. What is the effect of a rise in the general unemployment
level on individual mobility? The data suggest only a weak effect
of the unemployment level on mobility. There is no significant effect
on vertical mobility, only lateral mobility is slightly reduced (-4%).
Modemisation theory suggests a rise of upward mobility by the
enlargement of the tertiary sector. Indeed data support this optimistic
picture. The chance of individual upward mobility rises with more positions
in the tertiary sector (+8.5%) and the risk of downward mobility
is reduced (-6%). A growth of the tertiary sector in Hast Germany is
connected by a better opportunity structure for individual mobility.
Evolutionary Organisation theory argues that formation and closure
of companies influence individual mobility more than the enlargement
or shrinkage of established companies. Our data on the East
German transformation, which in its initial stage was characterised by
high numbers of formation and closure of companies, support these
suggestions. The formation of new companies offers opportunities for
upward mobility (+1%) and lateral mobility (+0.6%), whereas the closure
of companies leads to a rise of downward mobility and lateral mobility.
The results of the event-history analysis on the effect of unemployment
for mobility with respect to exclusion theory is rather clear
cut: Unemployment is a transitory element of mobility paths. Rather
than determining the direction of social mobility, it is a rather neutral
switch preceding mobility in all directions. The only result that would
fit the usual exclusion discourse is that people with a long record of cumulative
unemployment are less likely to make upward mobility transitions.
In the event history analysis, we also find some hints that
individuals may not simply be the victims of unfavourable conditions;
they try to actively cope with their position. So, after long-term unemployment
spells, they try to hold on to their new Job longer than people
without this experience.
Results of an optimal matching analysis
Before we refute exclusion theory with the empirical results of the
event history analysis, let us be cautious and question the method used.
Maybe there are limits to the method used that prevent us from discovering
exclusionary phenomena: The focus of event-history analysis is

International Journal of Sociology and Social Policy 102
on the causes of transitions. There are some life-course sociologists
who claim that trajectories are not adequately treated by this kind of
methodology (Abbott and Hrycak 1990). Knowing a lot about transitions
without knowing much about trajectories is like seeing the trees
but not the wood.
Using optimal matching analysis we want to see whether there
are types of careers that show a pattern of exclusion. The focus of this
method is not on single transitions but on whole occupational sequences
and their comparison. For example three sequences of different
persons could look like this: CCUD; CUUD; CCCC, where C
indicates the state "constant Status employment position", U refers to
"unemployed" and D indicates "downward mobility". As each letter is
a measurement of one month the sequence CCCC is an equivalent to
four months in a constant employment position. Optimal matching refers
to a distance measure that is calculated by a comparison of each
sequence with any other. In our example a visual inspection shows that
the sequence CCUD is more similar to the sequence CUUD than to the
sequence CCCC; and CCCC is more similar to CCUD than to CUUD.
With large data sets and long sequences visual inspection is not possible.
Optimal matching computes a matrix of similarity values between
each sequence of the data set. In a second step these distances can be
used in cluster analysis in order to find groups with similar employment
sequences.
In our analysis, states of employment were differentiated into
the following categories: constant Status positions; small downward
mobility positions with a 5-10% lower occupational prestige than the
certificate; large downward mobility positions with a more than 10%
lower occupational prestige, and likewise defined small and large upward
mobility positions. Mobility was measured by comparing the
time-specific prestige score (Ganzeboom and Treiman 1996) with the
prestige score of qualification (according to occupation-specific
graduation or apprenticeship certificate). "Constant Status position"
refers to equivalent values of these two prestige scores. Three states of
non-employment were included: unemployment, studying, and other
kinds of non-employment, which mainly consist of spells of retraining
Volume 21 Number 4/5/6 2001 103
or housewife positions. Similarity distances won by optimal matching
algorithms were clustered afterwards with the Ward algorithm. A four
cluster solution was chosen, which showed distinct patterns of career.
Within these four clusters two favourable and two unfavourable
or ambivalent clusters can be seen. The favourable clusters were called
"in constant position" and "the big winners". The unfavourable and the
ambivalent clusters were labelled "the big loss" and "the risky decisions".
The figures two to five show the aggregate time dependent distributions
of our eight states within each cluster.
100%
80%
60%
40%
20%
0%
Figure 2: Career types, cluster 1: In Constant Position
• unemployment
D non-employment
ü studying
• large dow nw ard mobility
• small dow nw ard mobility
D constant position
M large upw ard mobility
D small upw ard mobility
Time in Months
N=880 47.3%
Special Collaborative Centre 186: East German Career Study
The biggest cluster is "in constant position" with 47% of the
sample. The main characteristic of this group is quite permanent employment
without a change of Status. There are few changes in this
cluster: After 10 months, more than 90% of the persons in this cluster
are employed in positions with a prestige Status that is identical or very

International Journal of Sociology and Social Policy 104
Volume 21Number4/5/62001 105
100%
80%
60%
40%
20%
0%
Figure 3: Career types, cluster 2: The Big Winners
Time in Months
• unemployment
Dnon-employment
Gsludying
large dow nw ard mobility
small dow nw ard mobility
Dconstant Position
N=192 10.3%
Special Collaborative Centre 186: East German Career Study
B large upw ard mobility
Q small upw ard mobility
similar with the qualificational level. On average 1.9% of the persons
of this cluster are unemployed.
The smallest cluster is "the big winners" with 10% of the sample.
Its main characteristic is positions that are considerably higher
than the ones held at the start of the career. At the beginning of the sequence
around 15% of this cluster are studying. These spells of study
are short; after 40 months hardly any students remain in this cluster.
Also "constant position", which was 12% at the beginning, disappears
after 12 months. The percentage of big winners, which could add ten
and more percent to the prestige of their original qualification, rises
from 60% to 97% after 42 months. On average, 3% of the persons in
this cluster are unemployed.
The second biggest cluster is "the big loss" with 24% of the sample.
They had to accept positions that were far below their qualifications
with an occupational prestige level that is ten and more percent
reduced in comparison to the level of their certificate. On average,
people in this group stick to this position for nearly five years within a
100%
r- CO
Figure 4: Career types, cluster 3: The Big Loss
to cn o N-
m

International Journal of Sociology and Social Policy 106 Volume 21 Number 4/5/6 2001 107
100%
Figure 5: Career types, cluster 4: Risky Decisions
Time in Months
• unempfoyment
D non-employment
Dstudying
• lange dow nw ard mobility
• s mall dow nw ard mobility
P constant position
@ large upw ard mobility
(Ismail upw ard mobility
N=351 18.9%
Special Collaborative Centre 186: East German Career Study
encompassing house-wives äs well äs people in retraining. Most people
in this cluster were either dissatisfied with their original occupational
position or were forced to leave it. The growing proportion of
students, which amounts to 20% after 30 months and keeps to this
level until the end of the observed period, indicates the persons' intentions
to improve the Situation by further education. The proportion of
people in upward and downward mobility positions augments during
the period of observation. One can conclude that not everybody is successful
after leaving bis/ her original occupational position. We labelled
this cluster "risky decision" because the high propensity
towards qualification in this cluster indicates that many transitions are
the result of a conscious decision. However a further characteristic of
this cluster is the fact that the unemployment rate is at 12.6%, the highest
of all clusters.
Spells of unemployment are part of all four types of careers. On
average the duration of unemployment within the career patterns differs
from l .4 months in "the constant position" cluster to 9.5 months in
"the risky decisions" cluster, where unemployment is most prominent.
One cannot say that long-term unemployment is the decisive characteristic
of a whole group of young adults in the transformation process
in East Germany.
Looking at patterns of careers reveals two specific risks for
young people in the transition process: one is seen in the cluster "the
big loss". It is the risk of a loss of tradable human capital, which results
in being stuck in a downgrading of one's position. This is not the risk
of exclusion from the labour market, but the risk of a rather permanent
unfavourable inclusion in the labour market. The second risk is more
ambivalent. It is seen in the cluster "risky decisions". This risk is a
product of market individualisation, which äs a phenomenon was quite
new to East Germans. Unfavourable structural processes force a reaction
of the individual to increase chances on the labour market. In reaction
to this Situation the individual can respond differently with results
differentiating persons even further. As a consequence, some for example
may fail and get stuck in a new spell of unemployment after retraining
while others may succeed and get a much better position after
studying. The cluster "the risky decisions" connects all these rather
different paths; its only connecting theme is individualised risk. Exclusion
from the labour market is a part of the story of the careers in this
cluster, but it is not the whole story, äs exclusion even in this cluster is
temporary and individual reactions to counter exclusionary tendencies
are part of the picture.
We tried to find out which variables could be connected with the
distribution of people among different career clusters. To do this, four
binary logit regressions were run, using the membership of a specific
cluster äs a dependent variable with the value l. The value 0 was coded
for persons belonging to one of the other clusters. The profession
"medical doctors" had such an extreme distribution among the clusters
(none of them in the cluster "big winners", hardly any in the cluster
"big loss") that stable estimations could not be reached. Therefore, this
Professional group was excluded from the logit regressions.
Table 6 presents the results of the logit regressions. The level
and content of the Job related qualifications were the strongest predic-

International Journal of Sociology and Social Policy 108
Volume 21 Number 4/5/6 2001 109
Table 6: Predictors of cluster-distribution (binary logit-models, unstandardized
effect coefficients, without medics)
(N=1616)
woman (ref.: meri)
coh.\990(ref.:coh.
1985)
Professions
/ occupations
social scientists
teacher
agronomists
natural scientists
Production/
consumer goods
construction, fitting
administration
other Services
Constant
-2LL
Pseudo R2
(Mc Fadden P3)
X(df)
Constant
position
(N=646)
-1.461 **
l.HOn.s.
1.234 n. s.
4.744 **
-1.636*
reference cat.
-1.215 n. s.
-1.488*
-1.655*
-1.538 n.s.
-1.333*
2006.166
0.077
168.68(9)**
Big
winners
(N=192)
1.188 n.s.
-1.536*
-1.066 n. s.
1.898 n. s.
-2.785 n.s.
reference cat.
5.082 **
7.075 **
13.639**
6.870 **
-27.77 **
988.447
0.152
179.78(9)**
** = significant on 1%-level; * = signiflcant on 5%-leveI
Big
loss
(N=435)
1.152 n.s.
-1.039 n.s.
-1.066 n.s.
-4.504 **
1.918**
reference cat.
-8.196**
-2.127**
-3.003 **
-7.246 **
-1.587**
1691.319
0.101
191.14(9)**
Special Collaborative Centre 186: Hast German Career Study
Risky
decisions
(N=343)
1.354 n.s.
1.170 n.s.
-1.248 n.s.
-2.890 **
-1.256 n.s.
reference cat.
3.671 **
1.905**
1.333 n.s.
3.694 **
-6.172**
1545.895
0.074
124.80(9)**
tor for the four clusters. Part of this association can be explained by the
different vertical level of degrees held by the members of the professional
and skilled occupational groups. Social scientists, teachers,
agronomists and natural scientists are groups that by definition possess
a university degree, while the other occupational groups are skilled
workers after an apprenticeship. A ceiling effect (S0rensen 1979)
causes less upward mobility for the Professionals that already hold
high prestige Jobs. Therefore, there are less Professionals in the cluster
"big winners" than skilled workers after an apprenticeship. A parallel
logic leads to a smaller risk of apprenticeship trained skilled workers
belonging to the cluster "big loss": As skilled workers already have
Jobs of lower prestige, their likelihood to loose a great proportion of
this prestige is lower than in the professions which have a higher prestige.
Another interesting result of the logit regression is that teachers
are much more likely to belong to the cluster "constant position" and
their probability to be in the "risky decisions" cluster is below other
groups. A similar pattern of a protection from risk holds for men
against women and for professions against skilled occupations. There
seems to be a trade-off between the security of "constant position" and
the discontinuity of "risky decisions". While there is some plausibility
in this account, one has to consider that the "explanatory value", the
pseudo R2, of the clusters "risky decisions" and "constant position" is
quite low.
To sum up the results of the optimal matching analysis: the majority
of young qualified adults in Hast Germany are not excluded from
the labour market. A minority face the risk of rather permanent disqualification
or having to counter temporary exclusion from the labour
market. Spells of unemployment are spread over a wide variety of careers,
but they are most numerous in a "risky decisions" cluster.
Discussion
The empirical analysis of our population of young and qualified Hast
Germans strengthened the thesis of unemployment being a switch in
the labour market. Two perspectives structured the empirical analysis:
on the one hand, there was a focus on single transitions between positions
in the labour market, which showed how social mobility is influenced
by different forms of unemployment. The Status "unemployed"
raises the likelihood for mobility eight to fourteen times (according to
the direction of mobility) in relation to all other employment and nonemployment
statuses. A spell of unemployment is usually not the start
of a downward directed career. As such it is more a rather neutral

International Journal of Sociology and Social Policy
switch in career paths. Data analysis showed that the cumulative duration
of all preceding unemployment spells reduces the likelihood of
upward social mobility; however, the likelihood of downward mobility
is not enlarged in this case. Contrary to the supposition of a destabilising
effect of long unemployment spells for the continuity of workrelated
behaviour, analysis shows apreference for risk-avoidance after
unemployment. After experiencing long spells of unemployment, people
tend to hold on to existing Jobs; mobility is avoided. The main result
of the event history analysis is that spells of unemployment in East
Germany are not the first steps of a downward spiralling mobility career.
In the optimal matching analysis the patterning of sequences
was studied. One could argue that there might be a group of persons
with a sequence of permanent unemployment or a combined sequence
of high unemployment and downward mobility. However, the highest
concentration of unemployment within the different career clusters is
not in the "big loss" cluster (with a characteristic of high rates of
downward mobility) but in the "risky decisions" cluster, which is characterised
by many transitions between heterogeneous statuses. Since
unemployment is often part of a "noisy" cluster with many changes, it
is even in the perspective of whole trajectories more a correlation to
Status transitions than a type of trajectory of its own.
These results are important for conceptions of societal exclusion.
From a life-course perspective, the main problem people face in
high unemployment situations is not permanent exclusion, but transitory
exclusion. This empirical perception is an argument against simplistic
conceptions of exclusion which is often wrongly viewed äs a
looming permanent fate. The transitory nature of modern forms of exclusion
is also important for policy issues äs one major aim may be to
structure labour markets in such a way that exclusionary positions can
be left faster.
Volume 21 Number 4/5/6 2001 111
is that older age groups and unqualified persons are not part of the
sample. An analysis including higher age groups may find more longterm
unemployment and more transitions from unemployment to
non-employment. However it is not clear, whether these patterns of
un- or non-employment are seen äs illegitimate forms of "exclusion",
äs the meaning of unemployment in higher age groups may be very
different from its meaning in young adulthood. It would also be necessary
to control results for unqualified young people. However, one has
to take into account that structural constraints in the GDR reduced the
overall number of unqualified youngsters to low margins. This fact is
in itself, although to a lesser degree, a characteristic of the schoolwork-transition
in West Germany.
Further research that is needed consists of systematic international
comparisons in this field. Our empirical analysis on the effects
of the transition process for employment careers in East Germany
leads to a rather optimistic evaluation of this process. It is necessary to
compare these results with other transition countries to find out, what
kind of structural components are necessary components for low degrees
of "scarring" after spells of unemployment in young adulthood.
Our analysis showed that quick structural adaptation to new employment
sectors and a high dynamic of Company formations are two
structural processes that produced new opportunities of upward mobility
for young East Germans, which might be a prerequisite for successful
transition processes. There might be other structural components,
e.g. certain combinations of education and labour market Systems such
äs strong apprenticeship Systems (Shavit and Müller 1998; Sackmann
1998) that are relevant factors for the degree of exclusion following
waves of unemployment. As many of these structural variables are
properties of societies and not attributes of individuals, only international
comparisons have a high potential in specifying some of these
causal effects.
Before drawing such general conclusions, we have to discuss
limitations of our data set. Our sample is only representative for two
qualification groups and certain age groups in East Germany. Relevant

International Journal of Sociology and Social Policy
112
Volume 21 Number 4/5/6 2001 113
Unemployment
Appendix: Variables in the event-history analysis
Cumulated duration of unemployment
Duration of preceding unemployment
spell
Person is in a self-declared state of unemployment
(time-dependent dummy variable).
Accumulation of the length of all unemployment
spells (time dependent variable;
in months).
Time dependent variable; in months.
Ciosure of companies
The number of closures of companies in
the Hast German Bundesland of residence.
For an adjustment to the sizevariance
of the Bundesländer, the average
of absolute formation numbers was
set 100 for each Bundesland. The period
specific variance of closure was expressed
äs percentage deviation of 100. If
the person's residence was outside of
East Germany or if there was a missing
value in the residence calendar, an average
of the deviation values would be attributed.
Time dependent variable.
Current level of prestige lower than prestige
level of certificate
Unemployment level
Difference between the prestige score
(Ganzeboom and Treiman 1996) of
qualification (according to occupationspecificgraduationorapprenticeship
certificate)
minus prestige score of current
Job. Values on interval scale level, positive
values indicating a current position
below qualification, negative values
showing a position above qualification.
Time dependent variable.
East German unemployment rate, quarterly
adjusted time dependent variable.
Occupational prestige
Labour-entry cohort 1985
Labour force experience
Migration to West Germany
SIOPS scores (Ganzeboom and Treiman
1996); time dependent variable.
Reference: entry cohort 1990.
Accumulation of all employment experience;
time dependent variable; in months.
Residence in West Germany, other countries
or not known. Reference: residence
in East Germany. Time dependent
dummy variable.
Development of tertiary sector
Percentage of employment in the East
German tertiary sector, quarterly adjusted
time dependent variable.
Employment in public Service
Reference: employment in private sector.
Time dependent dummy variable.
Formation of companies
_
Absolute number of formation of companies
in East German, divided by 1000;
quarterly adjusted time dependent variible.
Woman
With child younger than 6 yrs.
Reference: man.
Dependent child below age six. Time dependent
dummy variable.
Woman * with child younger 6 yrs.
Interaction term.

International Journal of Sociology and Social Policy
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