Various mechanisms, including lower productivity, discrimination and composition effects, have been cited to explain the disadvantaged position on the labor market of young people, women, and persons nearing the end of their working life. This article relies on two hypotheses. First we adopt the perspective of life cycle theory, therefore this phenomenon is also understood as a consequence of giving priority on the job market to fathers between 35 and 40 years old. Second we consider that having a job and the level of earnings are two different dimensions of the labor market and of the way people can be advantaged or disadvantaged. In the case of the Czech Republic, Poland and Hungary in the period running from the 1980s to the early 2000s, the arrangements made vary in time and by country, as is shown by employment chances and earnings for a variety of groups, corresponding here to the life stages of youth, motherhood or fatherhood, and old age. This information brings to light which groups are disadvantaged and in what way. The configurations observed change with the countries’ economic and socio-political transformations, becoming more diverse by country at the end of the period. Older workers appear excluded from the labor market in Hungary; young people in Poland are integrated but paid relatively low wages; in the Czech Republic, where employment rates are relatively high, mothers are less likely to work.

Although this paper focuses on Central European countries it deals with an issue that has become crucial all over Europe and maybe in every industrialized society in the last decades, namely: how to share work and the incomes from work when the workers outnumber the jobs? I will illustrate some answers to this question by focusing on a narrower problem: how was the access to jobs and earnings related to one's life cycle position in Czech, Polish and Hungarian societies during the end of the communist era and the first decade of post-communism?

Indeed in the Western European and North American societies, the emergence and persistence of often massive unemployment have led to some major changes: rising earnings inequality and enduring exclusion from the labor market for some segments of the population (mostly females, migrants, young people and sometimes older workers). Males between 35 and 40 years old, especially fathers, have been relatively protected from these changes. However, the different societies have dealt with this issue in specific ways, and the welfare regime literature (Esping-Andersen 1990) has very thoroughly shown both how this crisis in the job market has affected the welfare systems, and how each type of welfare regime has reacted. For instance, the United States has managed to contain unemployment by developing low-skilled, poorly paid jobs in the service sector; while countries such as France and Germany have defended insiders’ wages and job stability at the cost of much higher unemployment rates. Thus, I will address the two following questions: which groups bear the costs of this job shortage? And what form do these costs take – is it non-employment or employment with low earnings?

The Central European societies I focus on here have met specific conditions which have made the transformation of their labor markets even more striking. The collapse of the communist regimes ruling since the late 1940s gave birth to a tremendous economic crisis (it was even worse in the post-soviet countries). This economic crisis was already noticeable in the 1980s, but after 1989 it produced unemployment which had been almost forgotten during the previous 40 years. Although after 1995 economic growth resumed, labor force participation has remained low and the destruction of the communist institutional settings on the labor market have not yet been replaced by effective collective bargaining. Thus, these three countries have come to face the same problem of sharing resources now scarce: jobs and their subsequent earnings. What is more, their model of welfare regime under communism could be sketched out as a caricature of the corporatist model (Esping-Andersen 1990) since it put a very strong emphasis on the link between jobs and social rights, and on the family as the relevant unit for social policies. This study can therefore shed new light on other more classical cases such as France or Germany where the welfare system reforms are subject to intense social and political debate.

Here I will focus on how job earnings are shared among people, from the specific perspective of life stages. Namely: is it also the case in Central Europe that males aged around 35, especially those with children, are privileged in the labor market? Among the other life cycle positions which are typically vulnerable (young people, mothers, older workers), who bears the costs of job scarcity? What form do these costs take: exclusion from full-time employment or low earnings? Finally, have the three countries dealt with this problem in the same way? What has changed over time?

In order to answer these questions, I build on the following hypotheses, which will be elaborated in more detail in the next section. First, I assume that the rewards associated with a life stage cannot be reduced to its economic productivity. Following Polanyi (1944) I will on the contrary insist on the fact that their value is not completely a result of market mechanisms. More precisely, the preference for fathers aged around 35 rests both on the fact that they are in their prime from the economic point of view, and from the fact that their income ensures the physical reproduction of the society, a collective stake that should not be overlooked. Second, in order to examine this issue I shall move from an analysis in terms of age groups to a life stage perspective. Riley's notion of gendered life course (Riley 1988) will prove very useful to take gender into consideration. However, the value of a life stage is not fixed and universal. On the contrary I assume that it will change according to the institutional context and probably across countries. Central European societies which have experienced very fast social change during the last two decades are a very good test for this third hypothesis. Finally, earnings analyses very often focus only on those having earnings. On the contrary I do not limit my analysis to those having a job or even to the active and unemployed population. Indeed I consider that early-retirement schemes, paid parental leave and other specific benefits allowing someone to withdraw from the labor force take part in the definition of those for whom employment is the only conceivable source of income and those for whom alternatives have been designed.

In order to answer to these questions, I will examine the relative earnings chances and the relative employment chances for specific life stages (youth, motherhood, and end of working life) with the case of the 35-year-old fathers as a reference point, for each country, in the 1980s, in 1993 and at the turn of the century. These chances are computed (through analysis of variance and logistic regression) from different but highly comparable datasets. The Social Stratification in Eastern Europe survey carried out in 1993 in each country, the Polish panel Polpan, and the surveys collected in Hungary by the Tárki have been designed for the same kind of research questions. The Czech Microcensus (2002) is more specific.

The first section presents a review of the literature so as to specify the problem and clarify hypotheses; the second, methods and data; the third analyzes the results.

To specify what is at issue, I first analyze the economic and social dimensions of the labor market with reference to Karl Polanyi's theses. I then present the economic conditions of the post-communist transformation, and its consequences for the labor market. Finally, I explain how I conceive of age, its meaning and its possible role on the job market, before returning to the distinction between employment access and wages.

2.1 Economic and social stakes on the labor market

Regardless of whether the economic theory of the labor market is used to model micro-economic reasoning or corresponds to a more institutionalist perspective, that theory always measures reality in terms of a purely, perfectly competitive market (Kalleberg and Sorensen 1979) and seeks to explain any ‘imperfections’ without surrendering its main hypothesis: market actors are rational, maximizing agents of their own ‘utility’.

In response to the observation that wages tend to increase over the course of a career, economics proposed mainly two explanatory models. The idea operative in Lazear's model (Lazear 1990) is that if a firm wishes to keep and motivate its employees, it may be worthwhile to pay them wages that increase faster than their productivity, offsetting this in advance with below-productivity wages at the outset. It is then in the employees’ interest to remain in the company so as to benefit from this ‘deferred payment’ at the end of their careers, at a time when their productivity will be falling. According to this reasoning, the young are ‘underpaid’ for their skills and abilities while older workers are overpaid. Institutionalist theories link seniority wages to the bargaining power of insiders (often unionized) who manage to keep their career prospects good regardless of their productivity level, to the detriment of young newcomers who may be better qualified yet remain in peripheral positions. The newcomers are understood not only to receive lower wages, but also lower-quality jobs in terms of stability and social protection. Here work is no longer merely an input in the firm's production function; the worker is taken into account, but always as a strategizing actor trying to get the best price for his commodity – his labor.

On the opposite, sociologists often follow Polanyi (1944) according to whom the labor market is embedded in social norms and institutions. For this reason labor cannot be considered as an ordinary commodity whose price depends only on supply and demand. Indeed most of the labor supply does not depend on the level of wages, but on the necessity to earn one's life. Thus, collective norms necessarily make themselves felt2 so that the market system remains compatible with the society's survival (its sustainability, we would say today) which is basically the survival of the highest possible number of its members. It is this aspect of the labor market that I wish to illustrate, showing how differences in wages and access to employment reveal priorities that the economic efficiency calculation cannot fully explain, in precisely those countries where collective actors do not have a strong presence in industrial relations. These priorities are revealed by both employers’ practices and potential workers’ behavior – i.e., being occupied or unoccupied – whether voluntary or not, while also depending on welfare arrangements.

2.2 The economic transformation

With the fall of the communist regimes in 1989, Central European countries fell into sharp economic recession.3 Janos Kornai (2001) used the term ‘transformational recession’ to designate those years, characterized by falling buying power and rising unemployment and inequality, which came with factory bankruptcies and restructurations, capital privatization, redirecting of foreign trade, etc. Figures 1–3, extracted from the TransMONEE database (Unicef 2007), illustrate how the economic situation in the three countries has evolved since 1989. These societies went abruptly from a situation where having a job was a right – in Czechoslovakia it was actually a duty inscribed in the constitution – to a shortage of stable jobs, a situation that generated unemployment and massive early retirement. The communist wage system – wages determined centrally and linked to a wide assortment of bonuses and assistance from state-owned companies – collapsed. Collective bargaining could not make up for the loss as it got reduced to a minimum and had very little regulatory power (Lado 2002); workers’ unions had lost their legitimacy as well as their ideological touchstones, and employers had to create their organizations from scratch (Koltay 2002). In the three countries studied, tripartite commissions have now succeeded in fixing a minimum legal wage, but decisions on wage levels and increases are usually left up to company bosses, who bargain on a case-by-case basis. Growth has returned, but employment rates remain low – 66.7% in Hungary and 60.4% in Poland for the 20–59 age range in 2005 – except in the Czech Republic, where the rate is 75.4%.4 The countries thus moved from nearly full employment plus a welfare regime comparable to the corporatist model to a shortage of jobs in an institutional context of declining corporatism.

Figure 1. 

Development of real GDP in Central Europe (100 in 1989).Source: Transmonee Database, Unicef (2007).

Figure 1. 

Development of real GDP in Central Europe (100 in 1989).Source: Transmonee Database, Unicef (2007).

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Figure 2. 

Development of real average wages in Central Europe (100 in 1989).Source: Transmonee Database, Unicef (2007)Note: For Poland there is a break in the definition of wages between 1998 and 1999: employees’ social contribution are included from 1999 on.

Figure 2. 

Development of real average wages in Central Europe (100 in 1989).Source: Transmonee Database, Unicef (2007)Note: For Poland there is a break in the definition of wages between 1998 and 1999: employees’ social contribution are included from 1999 on.

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Figure 3. 

Employment rates in Central Europe since 1989 (% of population aged 15–59).Source: Transmonee Database, Unicef (2007).

Figure 3. 

Employment rates in Central Europe since 1989 (% of population aged 15–59).Source: Transmonee Database, Unicef (2007).

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2.3 The labor market and the post-communist transformation

Can we really speak of a labor market for the Central European socialist societies? Večerník answers in the affirmative and offers the following description:

In fact, one might rather consider the entire administration of the labour-force under socialism as one big internal market with many preferences, special rules, and both vertical and horizontal social structures. Within this huge primary sector there is another which is the closed internal market of the top party, the state and the economic bureaucracy [the Nomenklatura]. If we are looking for labour-market segmentation in socialist countries, we have to use another perspective. There are large groups of workers who are permanently seeking a new job. They might be considered as the secondary, peripheral sector of the labour market. (Večerník 1991)

However, these workers were not necessarily paid at a lower rate than others; in general they tried to find positions with more fringe benefits (bonuses, housing, working conditions and in-kind advantages); this was what firms used to attract and keep good workers, since they were not free to set wages. In fact, until nearly 1989, high turnover was associated with a scarcity of workers, particularly skilled ones (cf. Kornai 1992).

On internal markets in general, seniority often plays a major role, and men often have an advantage over women because their careers are more continuous (Kalleberg and Sorensen 1979). In this connection the socialist regimes are no exception. Večerník (1991) shows that gender was the first determinant of wage inequalities in Czechoslovakia, and while educational level counted, age took on more weight with time; the peak of the wage curve shifted regularly to the right, reflecting the advantage of older workers. The post-1989 shutdowns, restructurings, and privatizations of major state-owned companies left internal markets severely shaken. Sociologists have observed that in general education had an increasingly central role on the labor market, in determining not only wages but also employment chances (Fazekas and Koltay 2002; Heyns 2005).

Moreover, institutional conditions for unoccupied persons have changed greatly since the communist period of nearly full employment. During the first half of the 1990s, the many early retirements and a fairly generous approach to disability pensions allowed a great number of workers nearing the end of their working life to avoid unemployment (Večerník and Matěju 1999). The eligibility conditions for unemployed status and unemployment compensation became increasingly restrictive (Boeri 2000), which makes it difficult to make intertemporal comparisons between declared unemployed and simply unoccupied persons.5 If we are to analyze the probability of getting a job, the reference population has to be defined with care. I consider all persons aged 20–59 who do not state they are a student6 as potentially occupied. It may seem surprising to rank retirees, early retirees, and disability pensioners among the potentially occupied, but the choice seems to me justified by the means that were used to handle lay-offs and decrease unemployment, such as the massive use of early retirement (though this varied over time) and disability pensions (Večerník and Matěju 1999).

On Central European labor markets, the sharpest changes seem to have been a weakening of internal markets and collective bargaining power, the increasingly important role of educational degree, and increasingly blurred boundaries between being out of the labor force, unemployed, and gainfully occupied. All these institutional changes and their consequences at the individual level create selection effects that vary across time and across country. When it comes to earnings determinants, they are part of the story and both aspects (access to jobs, and level of earnings) need to be confronted at each time-point in each country.

2.4 From age to the life cycle

Gary Becker's human capital theories (Becker 1964) and Mincer's empirical application of them (Mincer 1974) are an obligatory reference for analyzing the effects of age on wage. They illustrate the most widespread understanding of age: age as a proxy for experience. Still, in a labor market characterized by unemployment and discontinuous careers, this definition is less and less relevant (Meurs and Ponthieux 2000). Moreover, employers may perceive age in different ways. Do they see older workers as figures of experience and authority, or as employees whose skills may be obsolete? Are young people seen as inexperienced, immature, mobile, or as better trained than older workers, more reactive and flexible? The perceived economic value of workers in different life stages – what could be called imputed productivity – is ambiguous. As mentioned above, internal markets favor seniority wages. Institutional arrangements thus also affect how the different ages are valued in terms of wages. In Central Europe the internal markets came apart along with the big state-owned companies. This probably explains in part the decreasing role of age in determining wages that has been observed by a number of researchers (cf. Heyns 2005). However, other researchers have developed more refined hypotheses. Kertesi and Köllo (1999) and more recently Diewald (2006) claim that experience acquired under communist rule did indeed lose value, whereas experience acquired after 1989 has remained relevant. This penalized workers who were at the end of their careers in the 1990s; on the contrary, in the early 2000s those who were young in the 1980s are in a position to capitalize on their ‘post-communist’ experience7 (Smith 2001).

A great number of studies on wage profiles take men only into account. Women's careers seem more complex, in my opinion because the gendered life cycle (Riley 1988) sometimes seems a complication specific to women. For employment chances the life cycle seems to me the relevant level of analysis for both women and men. Diewald (2006) shows that in the former GDR, economic layoffs in the early 1990s clearly spared men in mid-career. Post-reunification East German society was increasingly marked by the ‘male bread-winner’ model, and male heads-of-household were given employment priority. It is therefore aging workers whose children are already adult who were being ‘sacrificed’, while young people had to wait for better days to get hired. Similarly, Louis Chauvel (2006) has shown that youth unemployment in France was tolerable as long as male heads-of-household remained relatively protected. In my analyses having a young child has a positive effect on men's employment chances (and a negative effect on women's). All this suggests a largely implicit collective choice in favor of a group defined not so much by age as by stage in the life cycle: fathers of approximately 35 years. In a context of economic difficulties, other groups are in turn disadvantaged. It could be objected that employers have only chosen to keep the most productive workers, or lay off those for whom there are institutional arrangements such as early retirement. But what the use of early retirement (or paid parental leave) shows is precisely how public policy inclines us to think that older workers (or mothers) do not need a job while male heads-of-household have no alternative to working. There is no ‘social wage’ for fathers of approximately 35 years.

I make the following two hypotheses. As it may be misleading to speak of age in explaining wage differentials since it suggests a quantitative variable with an objective value for number of years, I prefer to speak of life stages, in connection with the importance I attribute to the (gendered) stages of the life cycle. I furthermore hypothesize that the ‘male heads-of-household’ population has priority on the labor market not only because these men have mouths to feed, but also because they represent a good compromise between experience and energy and because their social role and economic situation require them to work and earn a decent wage. In fact, the Central European welfare regime may reasonably be described as highly corporatist (in part because of the German influence) with components of the liberal model added after the post-communist reforms.

This group may now be compared with three other types of workers who are particularly vulnerable on the labor market: mothers, young people and workers near the end of their working life. In a context of economic rigor and major institutional change, the initial question – effect of age on the labor market – thus becomes, what socio-demographic group has been most disadvantaged on the job market? Which has been perceived as the least productive or given the lowest priority? Are the same trends observed for all the countries in question?

The problem can be summarized thus. Central European countries, like other developed countries, are undergoing employment scarcity and budget tightening. These difficulties are harsher for certain groups than others, and men in mid-career continue to have priority, for both economic reasons and reasons of ‘welfare regime sustainability’, except if generation effects restrict their access to good jobs (Chauvel 2006). The fact that certain groups are bearing the brunt of these difficult situations is due to employers’ representations and choices, but also the way public policy is oriented (passive and active policies for handling unemployment) and the representations of occupied and potentially occupied persons themselves. Moreover, the disadvantages they are subjected to may be of various kinds. The worst situation is of course a combination of limited access to employment and low wages when one does find work. For these persons we may speak of exclusion or abandon; we may even say in some case that they are being sacrificed. But other configurations are possible. The American model of available employment at the cost of low wages may be seen as one of integration, whereas the French situation, where it is difficult to get a permanent job but the wage is a proper one, may be described as intensely selective – by educational level, for example.

I have chosen three potentially ‘vulnerable’ stages of the life cycle – motherhood, youth, and end of the working life – in order to assess

  • -

    what type of disadvantage is each stage subjected to: low wages or low employment chances;

  • -

    whether one of these life stages is systematically associated with the strongest disadvantage;

  • -

    whether one of them has a kind of priority somewhat comparable to fathers of approximately 35 years;

  • -

    developing trends; and

  • -

    whether the situations in the three countries are comparable and involve the same dynamic or if different societal trends may be seen.

I make the following hypotheses:
  • -

    age generates smaller and smaller wage differentials, and greater differences in access to employment. In the 1980s young people were paid less than their elders, whereas in the 2000s their main problem is finding a job, though when they do find one the wage is decent;

  • -

    education is playing an increasing role in relation to both wages and employment chances, though it was not insignificant under communism (Diewald et al. 2006); and

  • -

    the countries were more similar to each other under communism than afterwards. Communist institutions were extraordinarily similar: the same educational system, the same industrial structures, the same type of state-owned company policies. In post-communism, different transformation strategies were followed and the countries’ economies did not react in identical ways, as shown by the unemployment rates in Table 1.

TABLE 1. 
Central European unemployment rates (as defined by the ILO) in 1993 and 2003
19932003
Czech Republic 4.3 7.8 
Hungary 11.9 5.9 
Poland 14.9 19.3 
19932003
Czech Republic 4.3 7.8 
Hungary 11.9 5.9 
Poland 14.9 19.3 

Source: Labour Force Surveys, Eurostat.

What exactly is meant here by ‘disadvantaged’? Table 1 clearly shows that we cannot be satisfied with analyzing earnings level. There are different kinds of disadvantages, and low employment chances may combine with low income. I have not used the Heckman procedure (Heckman 1979), because it has come under increasing criticism for both its theoretical justifications (Diewald et al. 2006) and its lack of statistical robustness (Blau and Kahn 1996). Moreover, it is not immediately relevant to our purposes, which are to analyze selection equations in themselves and compare them to wage equations.

I have therefore done logistic regressions that predict full-time employment for potential workers as defined above, and variance analysis that predicts employment income for full-time gainfully occupied persons. The results of these two regressions have been mapped to show how priorities and exclusion are organized on the labor market and to compare the countries over time. The difficulty was to choose equivalent variables, that make sense and are common to all the surveys (i.e., same recoding system). The detailed results of the regressions are shown in the appendix.

4.1 Employment chances

The chances of having a job are measured by logistic regression. The population is all persons from 20 to 60 except students. The dependent variable is having a paid full-time job; i.e., over 30 hours a week, including for the self-employed, though self-employment of course does not fit the strict sense of having a job but is rather an earning alternative to employee status.

The explaining variables that indicate the life cycle position are age (5-year age groups), sex, and having a child under six. We have also included educational level (primary, apprenticeship, vocational high school, general high school, higher education degree). Interactions between sex and parent status, sex and educational level, sex and age have all been taken into account. The reference educational level is apprenticeship, because this is the most common level in all three countries due to the combined German and Soviet influence on Central European educational systems (number of years in school was not always available).

4.2 Wages

I did variance analysis to predict wages. The covered population is persons with a full-time job in the sense of the logistic regressions above. The dependent variable is the logarithm of earnings. Perquisites, bonuses, etc., were omitted because this information was not available for all countries, even though this affects the results for the communist period because perquisites at that time were a major component of household income and state-owned company policy. It is not possible on the basis of current studies to determine whether in-kind income during that period weakened or strengthened wage inequalities. My impression is that it benefited the Nomenklatura (which had access to reserved goods) and accentuated differences across industries.

Explaining variables are

  • -

    Individual characteristics: age, sex, educational level. Having a child was not taken into account because it has little theoretical relevance (at least for the problem at hand, since I am not particularly interested in gender discrimination) or statistical significance. However, I did take into account interaction between sex and age. Therefore age and sex are the only variables standing for life-cycle position.

  • -

    Job characteristics: type of job (self-employed, private sector employee, public sector employee);8 industry (farming, industry, non-market services,9 market services); supervisorial position or not (does respondent have subordinates?). The last Czech survey gives no data on this last variable so it was replaced with membership (or not) in ISCO groups 1 (legislators, senior officials and managers) and 2 (professionals).

4.3 Comparison

I then mapped the results of the two regressions, on a plane comparable to Figure 4. The vertical axis is earnings, the horizontal one employment. Only those points corresponding to the designated groups are shown. The situation of persons with a higher education degree (university or technical) illustrates the ‘bonus’ thereby obtained.

Figure 4. 

Confronting employment and earning chances.

Figure 4. 

Confronting employment and earning chances.

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TABLE 2. 
Analysis of life stages (key for Figures 5–7)
GroupGenderAgeChild (for employment only)Educational level
M20uni Male 20-24 years No Higher education 
M20ap Male 20-24 years No Apprenticeship 
F30uni Female 30-34 years Yes Higher education 
F30ap Female 30-34 years Yes Apprenticeship 
M35uni Male 35-39 years Yes Higher education 
M35ap Male 35-39 years Yes Apprenticeship 
M55uni Male 55-59 years No Higher education 
M55ap Male 55-59 years No Apprenticeship 
GroupGenderAgeChild (for employment only)Educational level
M20uni Male 20-24 years No Higher education 
M20ap Male 20-24 years No Apprenticeship 
F30uni Female 30-34 years Yes Higher education 
F30ap Female 30-34 years Yes Apprenticeship 
M35uni Male 35-39 years Yes Higher education 
M35ap Male 35-39 years Yes Apprenticeship 
M55uni Male 55-59 years No Higher education 
M55ap Male 55-59 years No Apprenticeship 

Coordinates:

  • -

    Abscissa: employment. The beta coefficient corresponds to characteristics studied. Fathers of approximately 35 years who have done an apprenticeship are the reference; coefficient = 0.

  • -

    Ordinate: earnings; difference between the prediction for the given characteristics and the prediction for males aged 35 to 40 who have done an apprenticeship.10

The graph scale is of course relative, because employment rates have varied greatly over time and by country. The aim then is to compare point configurations to each other rather than to compare coordinates for a given group from country to country and survey to survey.

4.4 The data

Since dynamism is central here, I have chosen three dates. The first surveys were done in the 1980s, the end of the communist period. The second in 1993, at the heart of the ‘transformational recession’ (Kornai et al. 2001): how do labor markets just freed from the socialist strait jacket handle an economic crisis involving a fall in GDP of over 20%, a near 20% loss in jobs, and a fall in real income of 15–30%? Lastly, surveys from the turning point in the early 2000s, when the economies of the three countries began to grow again. Though employment rates were still very low, unemployment was no longer increasing and the countries were getting ready to join the European Union. What were labor market conditions like at that recent date?

I could not use Structure of Earnings Survey data from surveyed companies because it does not include farmers (17.5% of gainfully occupied Poles were farmers in 2000) or companies with fewer than ten employees (20% of gainfully occupied Poles work in small companies). I have therefore used surveys bearing on smaller samples but covering the entire population.

The 1993 Social Stratification in Eastern Europe (Szelenyi and Treiman 1993) survey permits comparison because it used the same questionnaire and homogeneous samples and coding for all three countries.

For Poland in 1988 and 2003 I used the first and last wave of the POLPAN panel survey (Slomczynski 2003). Younger cohorts were regularly added to the panel to allow for independent analysis of each wave. The questionnaires are highly standardized, and attrition was remarkably low. However, due to budget cuts, only 2000 of the 6000 individuals questioned in 1988 could be questioned in 1993; this explains sample size difference. Otherwise, attrition has been remarkably weak and weights facilitate a cross-sectional use of the dataset.

For Hungary the 1986 TARKI Basic Survey (Kolosi 1986) provided all the necessary variables (in English). For the Czech Republic, the 1984 Czechoslovak Social Structure and Mobility Survey (Linhart et al. 1984) was perfectly adapted to my purposes. Both of these surveys are very similar to the Social stratification in Eastern Europe survey which is partly thought as a follow-up of these earlier studies. The sampling quality is considered very good (low non-response rate).

In order to survey the last time-point in Hungary I have used the 1998 Tarki Household Monitor (Szivós and Tóth 1998). Unfortunately the Czechs have no survey of comparable quality after 1993. I therefore used the 2002 Microcensus11 (CZSO 2002) despite the fact that this survey, focused on income, offers only rudimentary information on the variables of interest to sociologists. There is no distinction between types of secondary education (the reference here is ‘secondary without degree’, which corresponds to category 3C of the ISCED classification); no question on supervisory nature of position or distinction between public and private companies. Lastly, the Czech Bureau of Statistics systematically imputes non-responses and answers that it considers unrealistic without explaining how it proceeded. This is probably the weakest of our dataset, mainly because of the poor information on the data collection process.

Tables 3 and 4 12 present variable distributions for potentially occupied persons (i.e., included in logistic regressions predicting chances of having a full-time job) in 1993.

TABLE 3. 
Education level by sex (among potentially occupied persons in 1993)
CountryGenderPrimaryApprenticeshipSecondary vocationalSecondary academicHigher education degreeTotal
Czech Republic Male 12.0 50.6 19.9 5.8 11.7 100 
 Female 31.0 32.8 21.1 8.3 6.8 100 
 Total 21.5 41.7 20.5 7.1 9.2 100 
        
Hungary Male 25.3 43.6 14.3 6.7 10.1 100 
 Female 39.7 23.0 13.3 14.2 9.8 100 
 Total 32.2 33.7 13.8 10.3 10.0 100 
        
Poland Male 23.3 41.1 17.8 8.9 8.8 100 
 Female 24.7 24.7 21.6 20.9 8.2 100 
 Total 24.0 33.0 19.7 14.8 8.5 100 
CountryGenderPrimaryApprenticeshipSecondary vocationalSecondary academicHigher education degreeTotal
Czech Republic Male 12.0 50.6 19.9 5.8 11.7 100 
 Female 31.0 32.8 21.1 8.3 6.8 100 
 Total 21.5 41.7 20.5 7.1 9.2 100 
        
Hungary Male 25.3 43.6 14.3 6.7 10.1 100 
 Female 39.7 23.0 13.3 14.2 9.8 100 
 Total 32.2 33.7 13.8 10.3 10.0 100 
        
Poland Male 23.3 41.1 17.8 8.9 8.8 100 
 Female 24.7 24.7 21.6 20.9 8.2 100 
 Total 24.0 33.0 19.7 14.8 8.5 100 

Source: Social Stratification in Eastern Europe dataset (1993).

TABLE 4. 
Share (%) of persons with a child under 6 in the household (among potentially occupied persons in 1993)
AgeCzech RepublicHungaryPoland
20-24 16.0 13.4 36.1 
25-29 43.1 39.7 52.1 
30-34 35.9 35.2 45.0 
35-39 13.5 21.1 24.5 
40-44 5.8 8.5 12.9 
45-49 3.5 5.8 9.3 
50-54 5.1 6.4 12.1 
55-59 1.9 5.9 8.8 
AgeCzech RepublicHungaryPoland
20-24 16.0 13.4 36.1 
25-29 43.1 39.7 52.1 
30-34 35.9 35.2 45.0 
35-39 13.5 21.1 24.5 
40-44 5.8 8.5 12.9 
45-49 3.5 5.8 9.3 
50-54 5.1 6.4 12.1 
55-59 1.9 5.9 8.8 

Source: Social Stratification in Eastern Europe dataset (1993).

Are there more common points between the Hungarian labor market in 1986, 1993 and 1998 or between the Czech, Hungarian and Polish labor market in 1993? Is it the country or the period effect that come first in shaping the labor market institutions?

In presenting the results in chronological order, I keep in line with my dynamic approach: the focus is on the specificities of each period, which are expected to prevail over the peculiarities of each country. However the smaller or greater variation between countries might as well be a characteristic of the transformation's stages.

5.1 Under communism

In analyzing the maps based on surveys from the communist period (Figure 5), it is important to have in mind the specificity of pre-1989 employment norms: retirement at 55 for women and 60 for men, ready access to disability pensions, early retirement for certain occupations (miners, fire-fighters). In the Czech Republic unemployment was officially non-existent during this period, whereas in Poland and Hungary it began to be acknowledged in the late 1980s.

Figure 5. 

Employment and earning chances under communism.

Figure 5. 

Employment and earning chances under communism.

Close modal

Common points across countries stand out under communism: mothers with little education and older men were much less likely to be working than the other groups, while working men over 55 greatly benefited from higher education in terms of high earnings. This echoes Večerník's remark on the difference between men and women and the age-wage link (Večerník 1991).

More highly educated mothers were often working (except in Poland). But the education-based earnings differential increased with age, suggesting that both seniority and education were required for climbing supervisorial and wage ladders – or that the older cohorts were monopolizing the good jobs.

5.2 During the ‘transformational recession’

The maps built from the 1993 dataset show strong similaritiesFigure 6, with education becoming the core variable on the labor market of the three countries.

Figure 6. 

Employment and earning chances in 1993.

Figure 6. 

Employment and earning chances in 1993.

Close modal

First, education has become the key variable on the labor market, determining not only wage level but also access to employment altogether (except in Poland). Second, ‘mothers’ were strongly penalized on the job market: at the same educational level they were the poorest paid in all three countries, and only men over 55 were less likely than mothers to have a job. The Czech and Hungarian point clusters clearly form a diagonal, suggesting combined inequalities. Poland here is a surprising exception: education affected only mothers’ access to employment. In that period of economic crisis and institutional overhaul, age became extremely secondary on the labor market. Still, workers over 55 were much less likely to be working than the younger generation. This may be understood as the effect of early retirement, used massively to limit rises in unemployment while businesses worked to restore their productivity and adapted to the fall in Soviet orders by drastically reducing their personnel (Večerník and Matěju 1999; Diewald et al. 2006). Education became central on the labor market during this period of intense economic crisis, in which the oldest careers came to a sudden end.

5.3 After the storm

At the turn of the millennium and the return of growth, labor market configurations began to diversify by countryFigure 7 – this is surely the main new phenomenon. After describing the changes in each country, we return to the situation of young graduates since 1993.

Figure 7. 

Employment and earning chances around year 2000.

Figure 7. 

Employment and earning chances around year 2000.

Close modal

In Hungary, older workers are less likely to have jobs and are paid lower wages than any other man with the same educational level. They seem to have been pushed out of the labor market, as if employers no longer valued their skills and experience. Nonetheless, retirement age went up and early retirement has almost disappeared. Only having a high-level degree – rare for men born around 1940 and educated under communism – gives older men reasonable employment chances, and their wages remain relatively low. Young people are competing directly with fathers of approximately 35 years in terms of both access to employment and wages. When the economy started to grow in 1996 and employment rates began rising in 1998, young people moved to the forefront, and their educational degrees affected wages but not employment chances. Post-communist Hungary seems to have taken up the early communist slogan, ‘Make room for the young’. Young people's success is concomitant with the exclusion or abandoning of older workers; they seem to have the same priority as fathers of approximately 35 years when it comes to getting a good job.

In Poland, older workers find themselves up against a highly selective labor market. Those who have a job are very well paid, but employment depends nearly entirely on educational level. If we were analyzing only wages, we might think that gainfully occupied persons nearing the end of their career have managed to hold onto their well-paid positions of authority, even at low skill levels. In reality, a wide fringe of these potentially occupied persons are very simply excluded from the job market.

By contrast, young people seem as fully integrated as possible into the job market – at the cost of very low earnings. For them, as for persons in mid-career, educational level increases wages but does not significantly affect employment chances.

The Polish labor market thus seems a combination of American-style compromises (access to employment but low pay) for young people and continental ones for older workers (highly selective market but good pay). This makes sense if we remember that Poland has the highest proportion of atypical jobs (according to Eurostat figures, 15.4% of Polish jobs in 2003 were fixed-term contracts, as opposed to 7.3% in Hungary and 8.1% in the Czech Republic), jobs relevant first and foremost for labor market newcomers – young people. In contrast, older workers who have been able to keep their jobs have also preserved some of the institutional characteristics operative when they arrived on the job market.

For the Czech Republic, the first thing to note is that getting a job is not as serious a matter as in Poland: the Czech unemployment rate is much lower (see Table 2). The points are clustered quite closely around ‘father apprentices’; mothers are less likely to find a job, and likely to be paid lower wages than men at the same educational level, even though the men considered here are 5 years older. Having a degree slightly improves employment chances, but above all it improves wages, and this ‘bonus’ itself improves with age. In the end, the specificity of the Czech Republic in the early 2000s is clearly the female life-cycle effect. This is perhaps explained by the generous Czech welfare system, not so much in terms of benefit level as duration of payouts: whereas in 1989 Hungarians and Poles had to face the political transition and the transformation crisis with record public debt and austerity measures, Czechoslovakian budgeting had been extremely conservative since 1969; there had been little or no borrowing from Western countries, and the country was therefore able to fund social measures for absorbing the shock of transformation. Moreover, mothers’ limited access to employment is highly relative. The average employment chances are higher than in the neighboring countries, and we can therefore see mothers’ relative absence from the labor market as reflecting a choice made possible by the relative ease with which their spouses find jobs.

Lastly, young male university graduates are experiencing a mixed situation compared to ‘father’ graduates. In comparison with older workers and women, they hardly seem to be losing out. But while growth has returned in the three countries, and given that the situation of young people on the labor market is extremely sensitive to economic ups and downs, it is harder than reasonably expected for young male university graduates to get a job. For wages, young university graduates have a real advantage in Hungary, whereas in the other countries they are clearly considered newcomers who have to prove themselves. Their situation thus seems less drastic than that of their elders close to retirement, but it is still not very reassuring. All in all, young graduates may well have found a more advantageous compromise in 1993 – they were well paid everywhere, and in the Czech Republic readily found employment. What's more, workers at the end of their careers have greater access to social assistance than young people, who can only collect unemployment compensation if they have paid into the system. The history of transformation effects is therefore not over. We have to see whether the careers of these year-2000 young people are not blocked by the previous cohort, graduates of 1989, who may be in a position to grab up the opportunities generated by transformation.

If we retrace recent developments in Central Europe, we see that the situation of the three countries under communism corresponds to Večerník's results (1991) and his understanding of socialist labor markets. The life cycle was the main determinant of employment chances (which were high), while education most strongly affected women's employment chances and wages, and end-of-career wages.

During the transformational recession, though the countries were following distinct reform paths – shock therapy in Poland, delayed reforms in the Czech Republic due to a social-democratic compromise, the continuation of changes initiated before 1989 in Hungary – what comes to the fore is the similarity of the three, as if the severity of the crisis affected all labor markets in the same way. Paradoxically, it was in this context that education had the greatest influence on individuals’ chances on the job market, but this can also be understood to confirm the falling value of experience acquired under communism.

At the turn of the twenty-first century we see national specificities taking shape, as if the recovered economic dynamism made society-specific orientations visible once again. The relative position of older workers suggests that the experience acquired by the younger cohorts since 1989 is indeed more highly valued. The Polish situation seems a particular case of high selectiveness for the over-55s at precisely the moment the three countries raised retirement age.

The second aspect to be stressed is precisely this situational diversity, not only among countries but also across population groups. It is not merely a question of national models – integration through low wages for one country, exclusion and high wages in the others – but also different types of compromises, from integration (young Poles) to priority (young Hungarians) by way of harsh selectiveness (older Poles) and even outright, more or less voluntary abandon (older Hungarians). At the macro level, these compromises represent possible outcomes of the confrontation between the imperatives of economic efficiency and social cohesion. At a smaller scale, they result from choices and bargaining by a variety of individual and collective actors (employers, public policymakers, potential workers). Older workers are clearly the hardest hit, especially in terms of employment – this corroborates the observation by Diewald et al. (2006) at the end of their analysis of the former GDR – while young people are becoming increasingly better integrated through employment, though their earnings vary by country. In this perspective, the Czech Republic seems a case apart because older workers have not withdrawn from the labor market as much as mothers. However flexibility, even in the frame of a full-time job, is not taken into account here. Wallace (2003) has shown through a survey carried out in different Western and Eastern European countries (including the Czech Republic and Hungary) that it affects younger workers more than the other age groups.

Clearly what is now needed is a comparative examination of Central European welfare institutions of the sort that has already been done so effectively for ‘Western’ ones, including a grid for interpreting national differences. The results presented here suggest that Central Europe is somewhere between the conservative model (particularly relevant for the Czech Republic, where the difference in men's and women's situations somewhat recalls the German model) and the liberal model (especially in Hungary, where education plays a crucial role). This is not at all surprising. The communist welfare institutions may be described as ultraconservative (secure income and the crucial tie between employment and benefits were two powerful ways to buy social peace and discipline), whereas the liberal model was the first – and long the only – reference for the Western advisors who came to guide Central European government reform processes. It is also essential to analyze the income of persons ‘outside’ employment, particularly their earnings. Diewald (2006) has shown that in the former GDR, persons who took early retirement experienced this halt to their activity as a kind of relegation and loss of control over their lives, even as their income level remained high and their consumption level was higher than ever before for retirees. This suggests an issue also relevant for countries such as Germany and France which are reforming their ‘conservative’ welfare regime: changes in welfare regimes alter everyone's life chances in ways linked especially to his/her education, gender, and life stage, all characteristics which at this point may be considered ascriptive. Will this necessarily lead to unfulfilled promises for some social groups? Or are these promises not kept only because of the economic conditions? Indeed in Poland and Hungary, people with some education who were in the middle of their life course at the end of the communist regime were still expecting their highest earnings chances (associated with the end of working life). After 1989, as they come closer to this stage of their life course, it yields less and less the rewards it once granted.

It is worthwhile noting once again the paradox that the situation closest to a ‘knowledge-based meritocracy’ corresponded to one of acute economic crisis. When education reigns supreme on the labor market – to the exclusion of age and life-cycle – whole population groups can be excluded from employment or paid very low wages throughout their lives since life-long learning remains rare. How can people start a family under these conditions? Leaving it up to the ‘knowledge-based meritocracy’, which takes into account only individual characteristics as valued at the micro-economic level, to determine access to employment and wage levels imperils the society's future and its cohesion. This calls into question the strategy of Western European countries, which for the last 30 years have been struggling unsuccessfully to pull themselves out of economic stagnation and yet are working to promote meritocracy as a norm, in keeping with the Lisbon strategy.

Lastly, we have seen that the social and economic value attributed to a given age group can vary over time. This is clear for older workers: Hungarians in this life-cycle stage lost their privileges (high earnings) without becoming any better integrated in the labor market; Poles face much tighter selectiveness than before. Only the Czechs seem to have compensated for the relative drop in wages by a higher employment rate. This brings us back to the problem mentioned in the introduction, i.e., the generational dimension of the developments presented, in terms of explanations and consequences but also generational solidarity by way of direct transfers within families (Attias-Donfut 2000). When persons at the end of their working life experience a loss of social status, does this reflect anything more than alternation between more and less fortunate birth cohorts? Are the difficulties of young Poles compensated for by the aid they receive from their families? Were these family assistance networks as effective during the period of economic recession, when the least educated were hardest hit, so their kin group was more likely to be as destitute as they? Clearly the interest of studying Central Europe goes beyond getting to know new European Union members. The dynamics at work in Central European countries during the last 20 years have much to teach social scientists, including about Western European countries.

1

Trans. Amy Jacobs. An earlier version of this paper has been presented at the RC28 meeting in Brno, 24–27 May 2007.

2.

Inevitably, society took measures to protect itself’ (Polanyi 1944, my italics).

3.

In Poland the 1980s were already years of economic recession.

4.

Labour Force Surveys, Eurostat (2005).

5.

In Hungary in 2004, the 20–24 age group represented 17.4% of unemployment (according to the ILO definition) but 13.3% of the national employment office's unemployed lists.

6.

I have also excluded prisoners, women on maternity leave (but not parental leave) and persons doing their military service when the information was available. These categories represent a very minor share of the samples.

7.

Kaufman and Spilerman (1982) show how the same dynamics affect the access to jobs and therefore, the age structure of detailed occupations: it depends both on the effect of age (is the occupation subject to promotions or not?) and of history (if an occupation did not exist until recently, its incumbents tend to be younger).

8.

The distinction was not available in this form in the Czech data in 1984 and 2002.

9.

Non-market services: education and research, culture, health, social services, administration. All others are considered market services.

10.

The scale on the ordinate axis is multiplicative because the predictions are derived from earnings logarithms. A one-point difference signifies a wage approximately 2.7 times higher (the e value). On the abscissa, a one-point difference signifies a 2.7 odds-ratio.

11.

I consulted these Microcensuses under the supervision of Jiří Večerník during a research stay in 2005.

12.

Clearly persons most likely to have a young child are somewhat younger than the age groups chosen here, but I wanted to capture the situation of persons likely to have fully arrived on the job market. According to Louis Chauvel (2006) the age of 30 in France is a significant cut-off point beyond which situations may be considered stable – which of course does not mean that difficulties disappear.

Appendix

1. Selection equations predicting full-time gainful occupation

The tables display the odds-ratios (Exp(B)).

(a) Hungary

App-1a198619931998
Age 
 20–24 1,632 0,644* 0,744 
 25–29 1,827 0,741 0,796 
 30–34 1,205 0,787 0,606* 
 35–39 1,000 1,000 1,000 
 40–44 0,717 1,032 0,808 
 45–49 0,470 0,865 0,534* 
 50–54 0,223* 0,596* 0,512* 
 55–59 0,089* 0,209* 0,233* 
    
Education 
 Primary 0,460* 0,469* 0,391* 
 Vocational 1,000 1,000 1,000 
 Sec. Technical 1,331 1,264 1,193 
 Sec. General 0,627 0,833 1,291 
 Higher ed. 0,719 2,728* 1,653* 
    
Female 0,219* 0,776 0,452* 
Has a child 3,950* 1,167 1,003 
Female * has a child 0,126* 0,414* 0,438* 
    
Intercept 41,498* 3,586* 2,905* 
R2 (Nagelkerke) 0,423 0,209 0,236 
4025 3222 2244 
Frequency of full-time occupation 0,856 0,660 0,358 
App-1a198619931998
Age 
 20–24 1,632 0,644* 0,744 
 25–29 1,827 0,741 0,796 
 30–34 1,205 0,787 0,606* 
 35–39 1,000 1,000 1,000 
 40–44 0,717 1,032 0,808 
 45–49 0,470 0,865 0,534* 
 50–54 0,223* 0,596* 0,512* 
 55–59 0,089* 0,209* 0,233* 
    
Education 
 Primary 0,460* 0,469* 0,391* 
 Vocational 1,000 1,000 1,000 
 Sec. Technical 1,331 1,264 1,193 
 Sec. General 0,627 0,833 1,291 
 Higher ed. 0,719 2,728* 1,653* 
    
Female 0,219* 0,776 0,452* 
Has a child 3,950* 1,167 1,003 
Female * has a child 0,126* 0,414* 0,438* 
    
Intercept 41,498* 3,586* 2,905* 
R2 (Nagelkerke) 0,423 0,209 0,236 
4025 3222 2244 
Frequency of full-time occupation 0,856 0,660 0,358 

(b) Poland

App-1b198819932003
Age 
 20–24 2,995 0,826 1,044 
 25–29 8,384* 0,912 1,954 
 30–34 1,988 1,098 1,162 
 35–39 1,000 1,000 1,000 
 40–44 1,865 1,392 1,621 
 45–49 1,126 0,653 1,582 
 50–54 0,707 0,589* 1,180 
 55–59 0,887 0,289* 0,439* 
    
Education 
 Primary 0,590 0,709 0,623* 
 Vocational 1,000 1,000 1,000 
 Sec. Technical 0,612 1,317 1,227 
 Sec. General  0,739 1,359 
 Higher ed. 0,734 1,085 2,273* 
    
Female 2,379 0,380* 1,188 
Has a child 2,871* 1,324 2,214* 
Female * has a child 0,189* 0,466* 0,338* 
    
Intercept 20,107* 3,106* 1,170 
R2 (Nagelkerke) 0,118 0,130 0,187 
3209 2756 1186 
Frequency of full-time occupation 0,965 0,657 0,567 
App-1b198819932003
Age 
 20–24 2,995 0,826 1,044 
 25–29 8,384* 0,912 1,954 
 30–34 1,988 1,098 1,162 
 35–39 1,000 1,000 1,000 
 40–44 1,865 1,392 1,621 
 45–49 1,126 0,653 1,582 
 50–54 0,707 0,589* 1,180 
 55–59 0,887 0,289* 0,439* 
    
Education 
 Primary 0,590 0,709 0,623* 
 Vocational 1,000 1,000 1,000 
 Sec. Technical 0,612 1,317 1,227 
 Sec. General  0,739 1,359 
 Higher ed. 0,734 1,085 2,273* 
    
Female 2,379 0,380* 1,188 
Has a child 2,871* 1,324 2,214* 
Female * has a child 0,189* 0,466* 0,338* 
    
Intercept 20,107* 3,106* 1,170 
R2 (Nagelkerke) 0,118 0,130 0,187 
3209 2756 1186 
Frequency of full-time occupation 0,965 0,657 0,567 

(c) Czech republic

App-1c198419932002
Age 
 20–24 0,571 1,461  0,518* 
 25–29 1,292 1,105  0,773 
 30–34 0,779 1,204  1,037 
 35–39 1,000 1,000  1,000 
 40–44 0,524 1,537  0,842 
 45–49 0,596 1,204  1,055 
 50–54 0,211* 0,779  0,526* 
 55–59 0,100* 0,343*  0,346* 
     
Education 
 Primary 0,404* 0,701 Primary 0,224* 
 Vocational 1,000 1,000 Sec. no exam 1,000 
 Sec. Technical 1,430 1,525* Sec. exam 2,009* 
 Sec. General 0,441* 1,756   
 Higher ed. 1,459 1,461 Higher ed. 2,957* 
     
Female 0,268* 0,655  0,430* 
Has a child 2,060* 1,568  1,142 
Female * has a child 0,203* 0,436*  0,130* 
     
Intercept 69,424* 7,435*  7,119* 
R2 (Nagelkerke) 0,309 0,268  0,304 
12926 3950  10080 
Frequency of full-time occupation 0,910 0,817  0,735 
App-1c198419932002
Age 
 20–24 0,571 1,461  0,518* 
 25–29 1,292 1,105  0,773 
 30–34 0,779 1,204  1,037 
 35–39 1,000 1,000  1,000 
 40–44 0,524 1,537  0,842 
 45–49 0,596 1,204  1,055 
 50–54 0,211* 0,779  0,526* 
 55–59 0,100* 0,343*  0,346* 
     
Education 
 Primary 0,404* 0,701 Primary 0,224* 
 Vocational 1,000 1,000 Sec. no exam 1,000 
 Sec. Technical 1,430 1,525* Sec. exam 2,009* 
 Sec. General 0,441* 1,756   
 Higher ed. 1,459 1,461 Higher ed. 2,957* 
     
Female 0,268* 0,655  0,430* 
Has a child 2,060* 1,568  1,142 
Female * has a child 0,203* 0,436*  0,130* 
     
Intercept 69,424* 7,435*  7,119* 
R2 (Nagelkerke) 0,309 0,268  0,304 
12926 3950  10080 
Frequency of full-time occupation 0,910 0,817  0,735 

2. Earnings equations predicting the log of declared earnings

(a) Hungary

App-2a198619931998
Age 
 20–24 −0,231* −0,136* −0,099 
 25–29 −0,124* −0,114* 0,009 
 30–34 −0,054* −0,035 0,030 
 35–39 reference reference reference 
 40–44 −0,005 0,012 0,002 
 45–49 0,044 0,087* 0,028 
 50–54 0,095* 0,110* 0,033 
 55–59 0,010 0,081 0,043 
    
Gender 
 male reference reference reference 
 female −0,263* −0,219* −0,168* 
    
Education 
 primary −0,033 −0,117* 0,114 
 vocational reference reference reference 
 sec. Technical 0,132* 0,156* 0,285* 
 sec. General 0,099* 0,204* 0,171 
 higher ed. 0,296* 0,487* 0,612* 
    
Ownership 
 private  0,067* 0,014 
 public reference reference reference 
 self-employed 0,093* 0,172* 0,001 
    
Supervision 
 yes 0,152* 0,164* 0,364* 
 no    
    
Industry 
 agriculture −0,149* −0,114* −0,027 
 manufacturing reference reference reference 
 “public services” −0,063* −0,079* −0,075 
 other services −0,070* −0,001 −0,001 
    
Intercept 11,721* 11,705* 11,646* 
Adjusted R2 0,388 0,394 0,147 
3 379 2 125 1 198 
App-2a198619931998
Age 
 20–24 −0,231* −0,136* −0,099 
 25–29 −0,124* −0,114* 0,009 
 30–34 −0,054* −0,035 0,030 
 35–39 reference reference reference 
 40–44 −0,005 0,012 0,002 
 45–49 0,044 0,087* 0,028 
 50–54 0,095* 0,110* 0,033 
 55–59 0,010 0,081 0,043 
    
Gender 
 male reference reference reference 
 female −0,263* −0,219* −0,168* 
    
Education 
 primary −0,033 −0,117* 0,114 
 vocational reference reference reference 
 sec. Technical 0,132* 0,156* 0,285* 
 sec. General 0,099* 0,204* 0,171 
 higher ed. 0,296* 0,487* 0,612* 
    
Ownership 
 private  0,067* 0,014 
 public reference reference reference 
 self-employed 0,093* 0,172* 0,001 
    
Supervision 
 yes 0,152* 0,164* 0,364* 
 no    
    
Industry 
 agriculture −0,149* −0,114* −0,027 
 manufacturing reference reference reference 
 “public services” −0,063* −0,079* −0,075 
 other services −0,070* −0,001 −0,001 
    
Intercept 11,721* 11,705* 11,646* 
Adjusted R2 0,388 0,394 0,147 
3 379 2 125 1 198 

(b) Poland

App-2b198819932003
Age 
 20–24 −0,074 −0,135* −0,410* 
 25–29 −0,072 −0,187* 0,100 
 30–34 −0,066 −0,097* 0,043 
 35–39 reference reference reference 
 40–44 −0,131* 0,009 0,131 
 45–49 0,095 −0,008 0,074 
 50–54 0,179 0,007 0,059 
 55–59 0,083 −0,040 0,230 
    
Gender 
 male reference re ference reference 
 female −0,308* −0,290* −0,222* 
    
Education 
 primary −0,052* −0,061 0,009 
 vocational reference reference reference 
 sec. Technical 0,100* 0,231* 0,252* 
 sec. General 0,115* 0,228* 0,504* 
 higher ed. 0,271* 0,530* 0,593* 
    
Ownership 
 private 0,171* −0,053 0,076 
 public reference reference reference 
 sel-employed 0,560* 0,128* 0,276* 
    
Supervision 
 yes 0,120* 0,230* 0,049 
 no    
    
Industry 
 agriculture −0,611* −0,168* −0,693* 
 manufacturing reference reference reference 
 “public services” −0,297* −0,136* −0,030 
 other services −0,095* −0,034 0,022 
    
Intercept 6,344* 5,369* 8,260* 
Adjusted R2 0,365 0,337 0,331 
3 126 1 781 642 
App-2b198819932003
Age 
 20–24 −0,074 −0,135* −0,410* 
 25–29 −0,072 −0,187* 0,100 
 30–34 −0,066 −0,097* 0,043 
 35–39 reference reference reference 
 40–44 −0,131* 0,009 0,131 
 45–49 0,095 −0,008 0,074 
 50–54 0,179 0,007 0,059 
 55–59 0,083 −0,040 0,230 
    
Gender 
 male reference re ference reference 
 female −0,308* −0,290* −0,222* 
    
Education 
 primary −0,052* −0,061 0,009 
 vocational reference reference reference 
 sec. Technical 0,100* 0,231* 0,252* 
 sec. General 0,115* 0,228* 0,504* 
 higher ed. 0,271* 0,530* 0,593* 
    
Ownership 
 private 0,171* −0,053 0,076 
 public reference reference reference 
 sel-employed 0,560* 0,128* 0,276* 
    
Supervision 
 yes 0,120* 0,230* 0,049 
 no    
    
Industry 
 agriculture −0,611* −0,168* −0,693* 
 manufacturing reference reference reference 
 “public services” −0,297* −0,136* −0,030 
 other services −0,095* −0,034 0,022 
    
Intercept 6,344* 5,369* 8,260* 
Adjusted R2 0,365 0,337 0,331 
3 126 1 781 642 

(c) Czech republic

App-2c198419932002
Age 
 20–24 −0,229* 0,008  −0,396* 
 25–29 −0,110* −0,022  −0,231* 
 30–34 −0,031* 0,041  −0,057 
 35–39 reference reference  reference 
 40–44 0,028* 0,080*  −0,027 
 45–49 0,049* 0,058*  0,015 
 50–54 0,054* 0,047  −0,003 
 55–59 0,006 0,106*  −0,051 
     
Gender 
 male reference reference  reference 
 female −0,316* −0,254*  −0,322* 
     
Education 
 primary −0,013 0,017 primary −0,189* 
 vocational reference reference sec no exam reference 
 sec. Technical 0,120* 0,166* sec exam 0,222* 
 sec. General 0,121* 0,202*   
 higher ed. 0,300* 0,424* higher ed. 0,494* 
     
Ownership 
 private  −0,036 employed reference 
 public not relevant reference   
 sel-employed  0,278* self-employed −0,283* 
     
Supervision 
 yes 0,147* 0,178*  0,202 
 no     
     
Industry 
 agriculture −0,068* −0,090*  −0,217* 
 manufacturing reference reference  reference 
 “public services” −0,077* −0,023  −0,104* 
 other services −0,059* −0,013  0,102* 
     
Intercept 8,246* 5,693*  9,550* 
Adjusted R2 0,427 0,339  0,309 
11 515 2 789  8 202 
App-2c198419932002
Age 
 20–24 −0,229* 0,008  −0,396* 
 25–29 −0,110* −0,022  −0,231* 
 30–34 −0,031* 0,041  −0,057 
 35–39 reference reference  reference 
 40–44 0,028* 0,080*  −0,027 
 45–49 0,049* 0,058*  0,015 
 50–54 0,054* 0,047  −0,003 
 55–59 0,006 0,106*  −0,051 
     
Gender 
 male reference reference  reference 
 female −0,316* −0,254*  −0,322* 
     
Education 
 primary −0,013 0,017 primary −0,189* 
 vocational reference reference sec no exam reference 
 sec. Technical 0,120* 0,166* sec exam 0,222* 
 sec. General 0,121* 0,202*   
 higher ed. 0,300* 0,424* higher ed. 0,494* 
     
Ownership 
 private  −0,036 employed reference 
 public not relevant reference   
 sel-employed  0,278* self-employed −0,283* 
     
Supervision 
 yes 0,147* 0,178*  0,202 
 no     
     
Industry 
 agriculture −0,068* −0,090*  −0,217* 
 manufacturing reference reference  reference 
 “public services” −0,077* −0,023  −0,104* 
 other services −0,059* −0,013  0,102* 
     
Intercept 8,246* 5,693*  9,550* 
Adjusted R2 0,427 0,339  0,309 
11 515 2 789  8 202 

*Denotes that P<0.05.

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Marie Plessz is a PhD student in sociology at the Observatoire Sociologique du Changement (Observatory for Social Change), Sciences Po/CNRS. Her research focuses on social change, labour markets and the life cycle.

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