WORK CAREER DEFINED BY AUTONOMY IN WORK
Vojko Antončič, Meta Gnidovec
WORK CAREER DEFINED BY AUTONOMY IN
WORK
Abstract
A work career can be defined as series of different working states .
Since autonomy in work is one of the main characteristics that influ-
ence the quality of working life, we have defined the work career of
an individual as a sequence of different states of work autonomy .
Two jobs differ only when they differ in autonomy. In this paper, data
collected retrospectively for the period 1974-1994 are examined .
Event history analysis is used to describe the process of changing
work autonomy. To determine how long autonomy episodes are and
how they end, the Kaplan-Meier method for estimating survivor func-
tion was applied .
Most of the autonomy episodes are of very long duration . During
their work careers individuals generate only one episode, which ends
in inactivity. Since episodes are long, we presume that these episodes
indicate the leaving of the labour market and retirement . The proba-
bility of changing autonomy status is somehow higher in the first five
years, after which changes are rare . Younger and more educated Indi-
viduals are more likely to achieve jobs which afford more autonomy . It
seems that quality of working life defined in terms of work autonomy
doesn't improve during a career.
Keywords: work career, autonomy status, Slovenia, event history
analysis
INTRODUCTION
The quality of working life essentially determines the quality of life . Work,
a basic and ever more important source of national wealth, is thus also a
source of the wealth of each individual . Since systems of social institutions
are mainly based on work, the social status of an individual depends above
all on his working activities and employment status (Svetlik, 1991) .
There are different forms of work and employment, for example temporary
employment, contractual work, work at home, directly paid (unregistered)
jobs, etc . Of these, is probably the paid employment, the working place,
that contemporary society most systematically offers the individual . It pro-
vides him with a regular wage, status and various social and legal rights .
Employment is also a means of satisfying the needs that are not only
instrumental (e .g . the need for autonomy at work, responsibility and initia-
tive, etc .) and are related to occupational values . The hypothesis regarding
relationship between occupational values and work career is well known, it
DR, Vol.XII(1996)22-23
	
169
Vojko Antončič, Meta Gnidovec
states that a work career is a sequence of occupational positions through
which individual convergates towards a state of employment which corre-
sponds to his working values (Antončič, 1985) .
One of the main characteristics of occupational/professional work' from
the individuals' perspective (labour supply) is undoubtedly work autonomy .
The importance of autonomy as a key job characteristics is supported by
numerous writers writing about the quality of working life (Breaugh, 1985) .
People who have no or only little autonomy over their work will usually be
less satisfied with his work career than those who have chances to make
their own decisions and influence what happens in the job . Greater satis-
faction with work undoubtedly contributes to a higher quality of working
life and thus to quality of life .
CONCEPTUAL AND METHODOLOGICAL
ASPECTS OF ANALYSIS
In this article we shall study the state of work autonomy in Slovenia . We
shall focus on the following questions : Do individuals during their work
careers convergate toward more autonomous employment status? Do they
attain greater autonomy, or do they stay more or less on the same working
place with the same autonomy status? How long it takes to achieve higher
level autonomy? Do characteristics such as age, gender and education cre-
ate different opportunities for promotion toward more autonomous work?
To address these questions the work career of an individual is studied .
We define it as a transition from one autonomy state to another . A work
career is thus described as a series of states which are defined by change of
autonomy at work .
Figure 1 :
WORK CAREER DEFINED BY WORK AUTONOMY
Status of work
autonomy
autonomy --
partial
autonomy
no autonomy T- - -
Time
t o
	
t, t, t, t4 t5
interview
170 DR, Vol.XII(1996)22-23
WORK CAREER DEFINED BY AUTONOMY IN WORK
To analyse the process of autonomy change, the usual data, which
describe the state of autonomy only in one single point in time, are not
enough. It is necessary to determine every change of state and the exact
time of change . Therefore retrospective data which define the work career of
an individual are used . 2
As an appropriate (legitimated) indicator of somebody's autonomy at work
the chance to determine what the job consisted of and how it would be
done was used . For each employment that the respondent had had in the
observed period the autonomy status was reported. If a change of working
place was accompanied by a change of autonomy status, this indicates a
new employment status in the working career . The level of autonomy pos-
sessed by an individual was estimated on the basis of the following ques-
tion :
Who decided what you had to do and how you had to do it in that job?
Decisions at work
1 . Respondent himself decided what he had to do and how he would do it
at work .
2. Others decided what he had to do, he decided how to do it .
3. In most cases others decided what he had to do and how he had to do
it .
If the working status was self-employed or farmer we assumed that the
respondent himself decided what he was doing in his work and how he was
doing it .
Autonomy defined in this way determines the following state space : 1 .
autonomy, 2. partial autonomy, 3. no autonomy, which respondent can
possesses during his work career . Number of states is then fine {Y k
k=1,2,3} (there are three possible autonomy states), an event or change of
state may occur in any time point in the observed period (O=T o SI'1ST2<_ . . .) .
Therefore the process of transitions from one autonomy state to another
(work career) can be written as a stochastic process (Blossfeld, 1989) :
(Y,T)={(Yk,Tk) : k=1,2, . . .},
which can be described as
Y={Z(t) : t ? 0},
with Z(t) = Yk-1 for Tk-1 <_ t < Tk , k =1,2 . . . which is a continuous time,
discrete state stochastic process .
To describe this process event history methods are used . The basic statis-
tical model of an event history analysis examines the length of time inter-
vals between consecutive changes of states . In other words, in event history
analysis we examine the time intervals between events . The time interval
from event N-1 to event N is called the waiting time . The unit of our analy-
sis is no longer individual but episode or spell .
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Vojko Antončič, Meta Gnidovec
The term episode denotes the period of time between successive events .
The basic information that is needed to describe an episode is : a) starting
point in time, b) end point in time, c) initial state and d) final (destination)
state .
In studying the duration of an "autonomy" episode, a single spell model
is involved . The observation is of the transition from an initial state to a
final state, there is no discrimination between different possible states . It is
only important that the state of autonomy changes . The duration of an
episode is represented in the statistical model by a nonnegative stochastic
variable T. The distribution of such a variable can be described as a distrib-
ution function
F(t)=P(T<t),
which specifies the probability that the episode's duration is less than t .
The density function is then
f(t)=dF/dt .
Equivalently, we can describe the probability distribution of T by a sur-
vivor function, defined by :
S(t)=P(T?t)
This is the probability that the episode's duration is at least t, that the
event by which the current episode comes to an end occurs later than t . In
other words the survivor function expresses the probability that an individ-
ual remains in the state ("survives") until time t, that is, that an event has
not yet occurred and the episode is still continuing .
The survivor function is just one of the alternative ways to specify the dis-
tribution of random value T. It is related to the distribution function F(t) in
the following way : S(t)= 1 - F(t) . In our analysis we use the survivor func-
tion since it allows for more intuitive description and it facilitates the esti-
mation of the parameters of the model .
To estimate the survivor function two nonparametric estimation methods
can be used (Rohwer, 1994) :
- Life Table Method
- Kaplan - Meier Estimator (Product Limit Estimatior) .
Another possible analysis is when transitions in multiple end states are
considered. When there are two or more destination states that can be
reached from a single given origin state, we speak of competing risks of
reaching each of a number of different destination states from the given ori-
gin state. In our case we have episodes which describe the beginning of an
autonomy state until a change of autonomy status occurs (until the
moment when the origin state changes) and the episode ends . An episode
can end in different ways : autonomy can increase, it can decrease, or the
individual who generates the episode might become inactive . The respon-
dent might become unemployed, retired, a "student" or enter the military
service, etc . And when the working status is "inactivity" no autonomy sta-
172
	
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WORK CAREER DEFINED BY AUTONOMY IN WORK
tus can be defined . To find out the probability that an episode ends in cer-
tain way, specific rates for each possible transition must be calculated . The
sum of these specific rates gives us a risk rate for the change of autonomy
status .
Since the termination of the entire observation period is, as a rule, exoge-
nous (e .g . due to the time point of the retrospective collection of the data),
the endpoint of the last episode of an individual or subject may not be
observed. In such situations, event history data are said to be right cen-
sored . There is also the possibility that event history data are left censored .
This happens when the length of time that an individual or a subject has
already resided in an origin state is unknown . The data set we use in our
analysis is right censored . The observation ended with the retrospective
interview, at which time many episodes were still in progress.3 Therefore
the maximum likelihood method is used for the estimation of the models .
This method also takes into account the right censored episodes under the
assumption that the same stochastic model is valid for all episodes, for cen-
sored and non-censored (regularly ended) episodes .
RESULTS
The results are based on autonomy episodes which were generated by
individuals who were present in the labour market in the period from 1974
to 1994 . The sample totals 1508 such persons who were either employed,
self-employed, unemployed or farming during the observed period. Together
they generate 2255 autonomy episodes . Because of incomplete and/or
incorrect data, 22 episodes had to be excluded from the analysis . Thus
2233 autonomy episodes remained .4
In Table 1 the number of episodes is presented with respect to socio-
demographic groups of respondents . Numerous analyses of the labour mar-
ket showed that the characteristics such as gender, education and age are
important factors which influence individual behaviour in the labour mar-
ket and existing employment patterns . Therefore we assume that these fac-
tors also influence the duration of autonomy episodes .
Table 1 :
NUMBER OF AUTONOMY EPISODES5
number of
	
% average number
episodes of episodes
	 per person
total 2233	100	1 .5	
gender male 1192 53,4 1 .5
	female	1040	46,6	1 .4	
age 18 - 30 years 488 21,9 1 .6
groups 31 - 40 years 691 30,9 1 .8
41 - 50 years 453 20,3 1 .5
	51 and more years	600	26,9	1 .1	
educational primary 676 30,3 1 .4
groups vocational 672 30,1 1 .5
secondary 576 25,8 1 .6
high 308 13,8 1 .6
DR, Vol .XII(1996)22-23 173
Vojko Antončič, Meta Gnidovec	
For each of the 2233 episodes the starting time is known . Respondents
reported the year and month in which the event occurred (change of auton-
omy status). Since we were asking about events that might have happened
20 years ago or even earlier, some respondents were not able to remember
the exact month in which the event occurred . In such case they were only
able to report the season of the year when the event happened . There were
also cases where only the year of the event was known . In such cases we
arbitrarily assigned 7 as the month value (the middle of the year) . 1080
episodes ended within the observed period. For these episodes the ending
time is also known. Since the time was reported in months, the month is
the time unit used for measuring duration .
To find the length of autonomy episodes, regardless of how they termi-
nate, the Kaplan-Meier estimations of survivor function was used . This esti-
mate enables us to also include censored episodes in the analysis. In our
data set there are 1153 censored episodes .
CHANGE OF AUTONOMY STATUS
As we have already established, 1080 episodes ended within the observed
period. On average they last almost 12 years (143 .48 months) . The longest
recorded episode was even 59 years long .
The process of changing autonomy status in the work place can be seen
in Figure 2 . How quickly episodes end is presented with survivor function,
which shows the probability that an individual stays in a given state .
1
UO7
~` 0.5-
0 0
a
	
-
0 60 120 180 240 300 360 420 480 540 600 660 720
Time in Months
Figure 2:
DURATION OF AUTONOMY EPISODE (PRODUCT LIMIT SURVIVOR FUNCTION)
174 DR, Vol.XII(1996)22-23
WORK CAREER DEFINED BY AUTONOMY IN WORK
It can be seen that in the beginning the function decreases more quickly,
especially in first the five years . The median duration of an autonomy
episode is 246 .8 months, which means that half the episodes end after 20
1/2 years. After this period the function again starts to decrease more
quickly (approximately till the 420th month) and thereafter the probability
remains more or less the same .
Autonomy episodes are very long . Half the employed keep the same
autonomy status during their whole work career . On average an individual
generates 1 .5 autonomy episodes in his career (see Table 1) . The function
decrease, which begins after 300 months, indicates both the ending of work
careers and the ending of autonomy episodes . This is the period when most
people retire and leave the labour market. If there is a change in autonomy
at work, it most probably happens in the first five years . In this period 20
of episodes change . Therefore we can assume that these are likely to be
episodes that are generated by persons in their first employment and desig-
nate the period of apprenticeship . The end of the apprenticeship period
usually also means a change of autonomy . The individual becomes more
independent.
CHANGE OF AUTONOMY STATUS AND GENDER
In most countries substantial differences between males and females in
the labour market are apparent. Women are usually referred to as a sec-
ondary labour force. Their employment careers are essentially different
from those of men (very often interrupted, for example, by childbirth) . But
1	
0-5-
Men
	
1
0 60 120 180 240 300 360 420 480 540 600 660 720
Time in Months
Figure 3:
DURATION OF AUTONOMY EPISODE AND GENDER
DR, Vol .XII(1996)22-23 175
Vojko Antončič, Meta Gnidovec
this is not the case in Slovenia . The surveys showed that the differences are
not that great (e .g. Labour Force Survey, 1994) . In Figure 3 survivor func-
tions for men and women are presented .
As can be seen, also in the case of autonomy status, there are no major
differences between men and women . The probability that a person keeps
the origin status, is the same for men and women (minor differences occur
with very long episodes, when the probability of surviving is higher for
women) . The median duration differs by only half a year (men : 243.9
months, women: 250.6 months) .
Statistical tests have also shown that there are no statistically significant
differences between the male and female populations7 .
CHANGE OF AUTONOMY STATUS AND EDUCATION
Does the level of education influence the duration of autonomy episode?
Episodes were classified in four groups according to the attained education-
al level of the person that generated the episode (see Table 1) . All the
groups are about the same size, which allows the calculation of test statis-
tics for differences among groups .
A look at the average duration of episodes shows that longer episodes are
more frequently found at lower educational levels . Thus the median dura-
tion for the group with primary education is 27 years, for the group with
vocational training the figure is 20 years, for secondary education 17 years
and for the group with high education 16 years . People with lower levels of
1	
~ .
C
O 0.5 -
7
	
-
Primary
- t~
Vocational
Secondary
0 ~ " ' I . . II11	''	II'I'
0 60 120 180 240 300 360 420 480 540 600 660 720
Time in Months
Figure 4:
DURATION OF AUTONOMY EPISODE AND EDUCATION
176 DR, Vol.XII(1996)22-23
WORK CAREER DEFINED BY AUTONOMY IN WORK
education change their autonomy status later than members of the other
three educational groups . The differences between these three groups are
not that great .
Survivor functions are shown in Figure 4 .
Test statistics showed that there are significant statistical differences
between educational groups . In the first five years survivor functions are
very similar: for all groups episodes end most quickly in this period (suc-
cessfully completed apprentice period) . After five years greater differences
can be seen : the group with primary education especially differs from oth-
ers, changes of autonomy status are very rare in this group (40 % never
change their autonomy status) . Also among episodes generated by those
with vocational training, after a certain period there are no changes . Sur-
vivor function slowly decreases to the point in which a career ends . The
average duration of episodes in the other two groups are not substantially
different, but still there are some differences . Till the 180th month (15
years) episodes generated by people with secondary education end sooner .
Afterwards it is people with higher education who change their autonomy
status more quickly . People with higher education most often get jobs
which afford a relatively high degree of work autonomy at the very begin-
ning of their career. Chances for "promotion" are thus smaller, since better
jobs are less available . More effort is needed to get such jobs and more
effort means more time needed to achieve change . Therefore people with
higher education change their autonomy status more often then others at
the end of their careers .
CHANGE OF AUTONOMY AND AGE
We also classified episodes into four groups in the case of age . We differ-
entiate between episodes which are generated by people of up to 30 years,
31 - 40 years, 41- 50 years and by people that are 51 or more years old . In
Figure 5 survivor functions are presented .
Differences between survivor functions are most evident in the case of age
groups. This was also proved with test statistics . The older people are the
longer the autonomy episodes, and older people generate fewer episodes on
average than other age groups (Table 1) . Younger people change their
autonomy status much more quickly : in the first three years one third of
autonomy episodes end . The average number of episodes also indicates a
faster rate of change (Table 1), the average duration in this group is 97.2
months and half the episodes end in about 8 years . In the next age group
(31-40 years) the average duration is longer, 156 .47 months . In this group
changes are also faster in the first period, afterwards episodes become
longer and longer. As could be expected, the average durations of the other
two groups are longer, since these people have been present in the labour
market for a longer period . In the third group (41-50 years) the episode
lasts on average 297 .47 months (about 25 years) and in the oldest group
the duration is about 28 years . Averages alone are not especially revealing,
however, we see more, if we look at the survivor functions . In the oldest
group the probability of keeping the same autonomy status is very high, in
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177
Vojko Antončič, Meta Gnidovec	
the first 10 years only one tenth of episodes are ended : In the same period
in the third group one fifth of episodes are ended . After a certain period dif-
ferences become small (survivor functions have the same shape) .
1	
	 ~ ~ N
- •
\
s 0.5-
'Z
	
`mil \
n 18-30 yeas
31-40 years 41 50 years
51 and olds
0 60 120 180 240 300 360 420 480 540 600 660 720
Time in Months
Figure 5 :
DURATION OF AUTONOMY EPISODE AND AGE
The younger the people the shorter the autonomy episodes . Probably their
readiness and desire for changes are stronger then those of older people .
But change itself doesn't mean that there is a transition to a higher auton-
omy status . In order to find out what kind of a change occurs, we need to
know how each episode ends . In the analysis which we have presented thus
far, we haven't differentiated between different possible types of termina-
tion. Therefore, in the next stage of analysis different possible destination
states are considered .
DESTINATION STATE OF AUTONOMY EPISODE
An autonomy episode, defined as the basic unit of an employment career,
can terminate in different ways . It can terminate with a new working place
which allows a higher degree of autonomy, or an individual can retrograde
and get a job which affords him less autonomy. When an episode is ended
with unemployment, autonomy status can not be determined ; neither can
autonomy status be determined when an episode terminates with the con-
clusion of a work career and leaving the labour market .
How episodes terminated in the observed period in the case of the Quality
of Life survey can be seen in Table 2 . Many of the episodes (almost half of
them) ended in inactivity. Those episodes average about 215 .94 months
long (about 18 years), which might be expected considering the fact that
178 DR, Vol.XII(1996)22-23
WORK CAREER DEFINED BY AUTONOMY IN WORK
individuals generate on average of 1 .5 episode during their careers . On
average an individual generates one episode during his work career which
ends in inactivity. Therefore we can assume that these are the last episodes
in someone's career and that transitions to inactivity usually denote leaving
the labour market and retirement .
Table 2 :
DESTINATION STATES OF AUTONOMY EPISODES
Higher
	
Lower Unemploy- Inactivity
	 autonomy	autonomy	ment	
n.o.e . a .n . n .o .e . a .n . n .o .e . a .n . n .o .e . a .n .
P .P .	P.P .	P.p .	P.P .
total	 271	1 .26	78	1 .32	203	1 .83	528	1 .23	
gender male 163 1 .27 51 1 .31 95 1 .98 254 1 .27
	female	108	1 .24	26	1 .37	108	1 .71	274	1 .20	
age 18 - 30 years 35 1 .30 10 1 .11 86 1 .56 58 1 .61
groups 31 - 40 years 121 1 .33 42 1 .50 84 1 .87 82 1 .86
41 - 50 years 79 1 .22 17 1 .21 19 2 .71 62 1 .27
	51 and older	35	1 .13	9	1 .13	14	3.50	326	1 .09	
educa primary 42 0 .76 16 1 .14 63 2 .03 222 1 .20
tional vocational 78 1 .24 23 1 .35 59 1 .74 140 1 .18
groups secondary 81 1 .25 27 1 .35 65 1 .86 118 1 .30
high 69 1 .28 12 1 .50 16 1 .45 48 1 .45
LEGEND : n .o .e . = number of episodes
a .n .p .p . = average number of episodes per person
203 episodes are terminated with unemployment. These episodes are
much shorter than those which end in leaving the labour market, on aver-
age lasting 27 .49 months (2.5 years) . The other third of the episodes (349)
end in states of higher or lower autonomy status at new working places . Of
these episodes, the majority terminate with higher autonomy status (transi-
tion to a better working place) . On average such episodes last 107 .2 months
(approximately 9 years). There are only 78 retrograde episodes, and these
are shorter, with an average duration of 80 .86 months (6.5 years) .
The probability that an episode ends in a certain destination state is
shown in Figure 6 . Kaplan - Meier estimator was again used for estimation,
thus censored episodes were also included in the analysis .
The analysis of duration has already shown that autonomy episodes are
long. The same thing can be seen in Figure 6 : the changes are slow ; In the
average after 120 months (10 years) only one tenth of all episodes change .
Rather faster changes can be seen when an episode ends in transition to
unemployment .8 The probability of someone becoming unemployed is very
high in the first five years (steeper slope), thereafter there are no major
transitions to unemployment . As could be expected, the lowest probability
is of entering a state of lower autonomy . It doesn't often happen that an
DR, Vol .XII(1996) 22-23 179
Vojko Antončič, Meta Gnidovec
1	
. . . . . . . . . .	. . . . . . . . . . . . . . . . . . . . Lower autonomy
Unemployment
~`
	
Higher autonomy
O -
UC
7
0 .5 - 	Inactivness
0
0 l, r t t l, t i i, , l „ r
0 60 120 180 240 300 360
Time in Months
Figure 6:
DURATION OF AUTONOMY EPISODE ACCORDING TO DESTINATION STATES
individual possesses less autonomy at a new working place and the proba-
bility of this type of transition is more or less constant . In the case of tran-
sition to a state of higher autonomy, the situation is about the same .
Changes happen a little faster than in the case of transition to a state of
lower autonomy, but the probability for transition also increases very slowly
here. Thus in the first few years only 10% of episodes change . Transition
happens a little faster in the period between the 70th and the 150th month,
when individuals have some work experience and more chances for promo-
tion. After this period the probability is again less distinct until the moment
when a career ends . If we finally take a look at slope denoting the process of
transition to inactivity, we see that in the first 15 years (180 months) the
rate is about the same as in the case of transition to higher autonomy . But
after 20 years transition to inactivity happens much faster . The probability
of this transition becomes greater and greater, since the longer the episode
the closer the time of retirement .
DESTINATION STATE OF EPISODE AND GENDER
We have already found out that there are no significant statistical differ-
ences between men and women as far as duration of episodes is concerned .
Does the same apply to different possible terminations? The first impres-
sion can be obtained from Table 2. We see that most of the episodes that
are generated by women end in unemployment or in inactivity, while those
generated by men are more likely to take the other two forms . To find out
whether these differences are also statistically significant, survivor func-
tions for all possible transition were calculated separately for women and
men. In Table 3 values of survivor functions for each fifth year are reported .
180 DR, Vol.XII(1996)22-23
WORK CAREER DEFINED BY AUTONOMY IN WORK
Table 3 :
DESTINATION STATE OF EPISODE AND GENDERS
Duration of
	
Higher Lower
episode	autonomy	autonomy
(months) Men	Women	Men	Women	
60 93.91 94.95 96.76 98.45
120 87.50 91 .58 95 .71 97.23
180 82.15 88.00 94 .91 96 .29
240 78.22 83.52 - -
300 76.49 82.16 - -
360 75.59 79.58 - -
Test statistics showed that the differences between men and women are
statistically significantly only in the case of transition to higher or lower
autonomy. Considering first transition to higher autonomy status : survivor
function for women is higher throughout and differences become more
obvious especially after the fifth year . Therefore it seems safe to conclude
that men are more quickly promoted to higher autonomous states than
women. In the first ten years 12 .5 % of men and only 8 .4 % of women are
thus promoted. The same situation can be found in the case of transition to
lower autonomy. The probability of such transition is again higher for men,
though differences grow smaller in time .
DESTINATION STATE OF EPISODE AND EDUCATION
The thesis about education being one of the basic channels of vertical
mobility is well known . Its value especially comes into prominence in the
labour market . In principle, people with higher education possess jobs
which afford them better working conditions, higher wages and better
opportunities to decide what to do in a job and how to do it . Do autonomy
episodes confirm this?
In the group of episodes generated by people with primary education, two
thirds of the episodes (222 out of 343) end in inactivity (see Table 2) . These
are followed by episodes which end in unemployment (18,3%), whereas the
other two states don't appear very often . In the case of the group with voca-
tional education episodes which end in inactivity are also in a majority, but
in this group more episodes end with higher autonomy status ." Roughly
the same applies to the group with secondary school education, although
in this group transitions to unemployment are also quite noticeable . In the
last group, the group with higher education, the likeliest episodes are those
which end in promotion. Half the episodes generated in this group end with
transition to higher autonomy status .
For each possible destination state statistically significant differences
between different educational groups were found . 12 Values for survivor
functions are reported in Table 4 .
DR, Vol .XII(1996)22-23 181
Vojko Antončič, Meta Gnidovec
Table 4 :
DESTINATIONS STATES OF EPISODES AND EDUCATION
End
	
Duration Level of education
(destination) of episode
state	(months)	Primary	Vocational	Secondary	High	
Higher 60 98.52 94.79 93.10 85.62
autonomy 120 96.40 89 .11 85.62 78.55
180 94.44 84.61 80.16 68.07
240 92.13 78.96 74.68 62.04
300 91 .41 77.35 70 .81 -
360 88.91 74.36 65.96 -
Lower 60 98.31 97.39 96.77 96.88
autonomy 120 97.55 96.43 94.75 95.36
180 97.18 95.31 93.82 93.35
240 96.92 94.15 90.24 -
300 - - - -
360 - - - -
Unemploy- 60 91 .56 91 .54 87 .51 93.97
ment 120 90.07 89.67 87.25 -
180 - - 86.99 -
240 - - - -
300 - - - -
360 - - - -
Inactivity 60 95.64 94.43 93.54 96.35
120 90.14 90.60 90.26 91 .10
180 83.62 85.46 81 .29 86.28
240 76.01 76.54 70.38 72.48
300 68 .00 59 .98 60.18 57.84
360 53 .40 46.16 35 .11 43.17
Highly educated individuals are most quickly promoted to states of higher
autonomy. After five years 15 % of them get new jobs which afford higher
autonomy at work. In the group with secondary education 10 years, and in
the group with vocational education 15 years, are needed for this . The sur-
vivor function in group with primary education remains high. In a period of
25 years less then 10 % of the episodes end in higher autonomy . The lowest
survivor functions for "lower autonomy" can be found in the group with sec-
ondary education, where the probability for downward moves is highest .
But differences between groups in the case of transition to lower autonomy
are not great. Individuals with secondary education also tend to have the
quickest transitions to unemployment . In the case of transition to inactivity
there are almost no differences between groups . The survivor function in
the group with high education is highest in the first five years and lowest
five years later. Episodes which last 15 years or more most probably end in
182 DR, Vol.XII(1996)22-23
WORK CAREER DEFINED BY AUTONOMY IN WORK
inactivity in the group with secondary education . The same is true for the
other two groups, especially in the first period (later differences become
greater) .
DESTINATION STATE OF EPISODE AND AGE
In the analysis of duration of autonomy episodes the greatest differences
were found between different age groups . Is this also the case with different
possible destinations? The types of episode generated in different age
groups are shown in Table 2 . In the youngest group the most numerous
episodes are those which end in unemployment . Almost half of them
(45.5%) end in this way . The two middle groups are most strongly repre-
sented by episodes which end in higher or lower autonomy . Thus 36.8%
episodes generated in the group of 31 to 40 years and 44 .6% generated in
the group of 41 to 50 years end in higher autonomy . In the case of lower
autonomy status the inverse applies: more such episodes can be found in
first group (12 .8%; in the other group 9 .6%) . Almost all the episodes which
generated by the oldest group end in inactivity .
In different age groups different possible destinations prevail . Test statis-
tics also proved that there are statistically significant differences .13 The
probabilities of the various transitions are reported in Table 5 .
Table 5 :
DESTINATION STATES OF EPISODES AND AGE GROUPS
End
	
Duration Age groups
(destination) of episode
state	(months)	18-30 years	31-40 years	41-50 ears	51and older
Higher 6U 90 .32 91 .4b
autonomy 120 - 81 .63 88 .92 98 .07
180 - 58.76 83.44 96.20
240 - - 78.02 94 .45
300 - - 73.05 93 .88
360 - - 66 .76 92 .32
Lower 60 96 .87 94 .64 98 .50 99 .52
autonomy 120 - 93 .03 96 .67 99 .39
180 - 89 .63 96 .01 99 .28
240 - - - 98 .79
300 - - -
360 - - -
Unemploy- 60 76 .50 87 .98 87 .51 98 .41
ment 120 - 85 .40 87 .25 98 .11
180 - - 86.99 97 .60
240 - - - -
300 - - -
360 - - - -
Inactivity 60 86 .44 93 .99 97 .97 98 .47
120 74 .10 88 .61 95 .49 94 .45
180 - 79 .91 91 .49 87.42
240 - 69 .71 85 .26 76 .10
300 - - 72.72 65 .53
360 - - 55 .70 48 .70
DR, Vol .XII(1996)22-23 183
Vojko Antončič, Meta Gnidovec
The highest probabilities of transition to new autonomy state can be
found in the youngest group . Only in the case of transition to lower autono-
my is the probability higher in the group of 31 to 40 years . As could be
expected, the lowest probabilities of transition are found in the oldest
group, where the probability of transition to each possible state is the low-
est (survivor function is the highest) . For this group transition to inactivity
is most likely. The younger the group more likely the transition is to occur .
CONCLUSIONS
We can consider there to be two sides in the labour market : on one side
there are people and their characteristics, on the other working places,
positions, companies (social structures, institution, etc .) . In our analysis
we studied changes of work autonomy and its relationship to personal
characteristics such as gender, age and education .
Single episode analysis showed that autonomy episodes are very long .
Half of them last more than 20 years . Thus individuals on average generate
one episode in their work careers, and this ends in inactivity . The probabili-
ty of there being a change of status is higher in the first period . Usually
this happens at the end of a period of apprenticeship .
Since autonomy episodes are very long, almost half of them end in inac-
tivity. These are especially episodes which end in retirement and leaving the
labour market. Very few episodes end in a status of lower autonomy (about
7%). Usually someone decides to change working place when he gets the
opportunity for a new job which affords him more autonomy .
There are no major differences with respect to gender . Usually men and
women stay in the same states equally long . The only difference was found
in transition to higher (or lower) autonomy status, where men more quickly
experience change . More substantial differences are found when different
age groups are considered . Younger change their autonomy status people
much more quickly than older people . Education also plays an important
part in transitions from one state to another : education is the capital which
enables easier promotion to a more autonomous working place .
Since changes of work autonomy rarely occur - the autonomy status of an
individual changes only once during his work career - and since episodes
usually end with transition to inactivity, seems safe to conclude that the
quality of working life (at least with respect to autonomy) does not improve .
NOTES
1 We refer to institutionalised work, to work in which some job is done .
2 The data used are obtained with the questionnaire "Quality of Life in Slovenia -
1994", which covers the period of the last 20 years (1974 -1994) .
3 For example the fifth episode in Figure I . The episode ends in time point t5,
which is unknown since our inquiry ends earlier .
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DR, Vol .XII(1996)22-23
WORK CAREER DEFINED BY AUTONOMY IN WORK
4 The whole sample consisted of 2520 persons from which 1806 were successfully
interviewed . In our analysis 1487 persons were included . 2I persons had to be
excluded. We assume that this exclusion was not systematic and that was not
caused by some characteristic of a respondent which is relevant for our analysis .
Thus we assume that the sample is still unbiased .
5 There is a difference between the number of episodes in the total sample and the
number of episodes according to various socio-demographic groups, since for one of
the autonomy episodes the basic information (gender, age and education) about the
respondent who generated this episode is not known .
6 A nonparametric method, where no presumptions about the distribution of a
process are required . Therefore it is appropriate for the first exploratory analysis .
7 The following test statistics were used : a) Log-Rank (Savage), b) Wilcoxon (Bres-
low), c) Wilcoxon (Tarone-Ware), d) Wilcoxon (Prentice) . All these statistics are includ-
ed in TDA programme package .
8 The average number of episodes per person is also the biggest in the case of
unemployment (l .83 ; see Table 2) .
9 In tables 3, 4 and 5 survivor functions (estimated by the Kaplan-Meier method)
are reported . They are multiplied by 100, therefore they can be interpreted as shares
of persons who stay (survive) in certain state .
10 Hypotheses about differences between men and women can be accepted at sig-
nificant level a = 0 .05 .
11 The average number of persons per episode also shows that individuals with
primary education more rarely enter a state of higher autonomy .
12 Hypotheses about differences among groups can be accepted in the case of
transition to higher autonomy, with significant level a =0 .001 . In the other three
transitions the significant level is a =0 .05 .
13 Hypotheses about differences among groups can be accepted at significant level
a =0.001 .
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