ABS Working Paper 2/2018

Societal Change and Educational Trajectories in Germany, 1925–2008

Rolf Becker University of Bern

Department of Sociology of Education Fabrikstrasse 8 CH–3012 Bern

Switzerland E-Mail: rolf.becker@edu.unibe.ch

Karl Ulrich Mayer Max Planck Institute for Human Development

Lentzeallee 94 D–14195 Berlin

Germany E-mail: mayer@mpib-berlin.mpg.de

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Abstract: Societal Change and Educational Trajectories in Germany, 1925–2008

The aim of this study is to unravel the impact of societal change in terms of modernisation in Germany (except the former GDR) on educational attainment and its social disparities for cohorts born between 1919 and 1986. Therefore, we analyse whether modernisation trends have modified access to higher education for consecutive birth cohorts. In order to explain how these processes have had an effect on class differentials of educational attainment, we assume that the interplay of the changing occupational structure on the macro level and intergenerational status maintenance via investment into the offspring’s education is the key mechanism for long-term educational expansion and for decreasing inequalities in the educational system. The empirical bases of our investigation are clusters of time series for macro changes and retrospective individual data of 11 birth cohorts from the German Life History Study and the National Educational Panel Study for educational outcomes. We apply piecewise exponential event-history models to analyse the direct and indirect impacts of societal change on educational trajectories and the social disparities of educational attainment. The results provide an understanding of historical variations in educational attainment associated with modernisation in social, political, economic, and cultural spheres.

Keywords: educational trajectories; educational attainment; societal change; modernisation; social disparities; event history analysis; time series

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1 Introduction In their response to Blossfeld and Shavit’s (1993) seminal study on the development of inequality in educational attainment in the second half of the 20th century, Breen et al. (2009) emphasise that the trend of modernisation was one of the main social processes resulting in educational expansion in terms of increasing participation and in changing class differentials in higher education. Following Erikson and Jonsson (1996), they argue that the general improvement of living conditions in the course of economic development, the growth of welfare state protection, and the provision and reforms of educational institutions have increased overall educational participation and reduced disparities in educational attainment among the offspring of different social classes (Breen et al., 2009: 1479). Economic growth, reduction of family size, extended educational opportunities, the lengthening of compulsory education, and, in particular, declining costs of education were all factors shaping the distinct propensities of children from different social classes to continue onto higher levels of education (Breen et al., 2009: 1479-80). However, in the case of Germany, as for some of the other countries they examined, Breen and his co-authors did not directly test the impact of overall societal change on educational participation and disparities.

Therefore, utilising life course data on 11 birth cohorts, it is the aim of our study to unravel the impact of societal change in Germany on educational trajectories and the social disparities of educational attainment for cohorts born between 1919 and 1986. We want to analyse whether and how long-term societal trends have modified the educational enrolment, transitions, and attainment of consecutive birth cohorts. Overall societal change has often been viewed as the complex process labelled modernisation, i.e. the secular unidirectional transformation towards mass consumption, democratisation, and welfare state protection, as well as technological change and, in terms of industries and occupations, tertiarisation (Lerner, 1968; Zapf, 1994). It is often claimed that modernisation is correlated with the spread of mass education in terms of the introduction of compulsory education and continued education (Meyer et al., 1992). We are well aware of the critical debate surrounding the theoretical concept of modernisation, for example in regard to the work by Eisenstadt (1966, 2000) on multiple modernities, but in this study we still find it useful to postulate – in the spirit of Max Weber (1978) – something like an overall endogenous societal change propelling traditional societies in the direction of “modern” and “postmodern” societies. It cannot be ruled out theoretically that modern states’ investments in the educational system and individuals’ efforts in education are also preconditions of societal change in terms of modernisation, but we draw on empirical research showing that the bulk of evidence indicating that causation runs the other way (e.g. Windolf, 1997).

Inspired by the concept by Hernes (1976) as well as by the programmatic outline by Blossfeld and von Maurice (2011), we seek to reconstruct the consequences of societal change on the individuals’ responses and decisions to that change in regard to their education resulting in changes of educational trajectories and in regard to social disparities of educational attainment for several birth cohorts across 80 years of German history. Reconstructing social history by means of longitudinal and cohort microdata fosters an understanding of the historical variations in educational outcomes associated with societal processes such as modernisation in social, political, economic, and cultural spheres (Mayer, 2015).

2 Theoretical background and hypotheses According to Breen and co-authors (2009), there are direct and indirect effects of modernisation on individuals’ education. Their response to societal change becomes comprehensible by the parents’ motives, which dominate investments in their offspring’s

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education. Aspiration for intergenerational status maintenance – that is, avoidance of downward mobility – is a major aim of the families’ educational strategy (Erikson and Jonsson, 1996; Breen and Goldthorpe, 1997; Stocké, 2007). In the view of these authors, families pursue a strategy ensuring that their offspring acquire a class position and the related standard of living at least as advantageous as that from which they originated. By investing in their children’s education, they seek to avoid, for their children, any class position in life that is worse than the one from which they originated (Breen and Goldthorpe, 1997: 283). The higher their class position and living standards already achieved by the parents, the greater is their motivation for avoiding social demotion (Becker, 2003: 6).

However, in the course of modernisation towards higher work complexity and technical progress, as well as towards increased bureaucratisation and professionalisation since the advent of 20th century (Klein, 2011: 429), the qualificational requirements required to ensure access to positions guaranteeing status maintenance have increased (Becker and Blossfeld, 2017; Mayer and Hillmert, 2003). Since traditional vocations with low qualificational requirements have been replaced by modern vocations requiring higher skills, structural mobility has increased in the process of modernisation (Blau and Duncan, 1967; Müller et al., 1983; Müller, 1975). Due to significant shifts in labour demand favouring higher skilled workers relative to less skilled, in the course of technical progress it becomes necessary for the next generations to attain higher educational certificates in order to avoid downward mobility. On the other hand, the persistent shifting of economic sectors, the changing of occupational structures, and – as a consequence – the upgrading in the class structure might have changed the reference points of status maintenance across generations (Stocké, 2007: 507; Becker, 2009: 106). As an immediate effect of modernisation, across birth cohorts it becomes increasingly necessary for families to invest in their offspring’s continued education.1 Therefore, it is assumed that higher levels of modernisation have resulted in an increased propensity for attaining higher educational qualifications (Hypothesis 1).

This direct effect of modernisation has been different for each of the social classes. The families in the upper service class will be under less pressure since they have always been able to invest in their children’s academic education or alternative means in order to avoid downward mobility. Therefore, they should show the lowest increases in educational attainment and their demand for more education will more likely be “saturated” in the course of modernisation and educational expansion (Breen, 2005; Raftery and Hout, 1993). Therefore, it is expected that social disparities in attainment decrease in the course of modernisation because disadvantaged social classes are able or are forced to catch up on educational attainment (Hypothesis 2).

Effects of macro-level trends of modernisation on individuals’ educational behaviour observed on the micro level might be mediated by societal processes on the meso level, such as the extension of educational opportunities offered (e.g. Becker and Hadjar, 2013). For example, such indirect effects of modernisation could be indicated by the increasing number of academic high schools (Gymnasium) or universities. The more educational opportunities are provided, the more likely are increases in educational participation (Hypothesis 3). Another indirect effect of modernisation could be mediated by the educational behaviour of other collective entities, such as peer groups (Merton, 1949) or older birth cohorts growing up at the same time as succeeding cohorts (Mannheim, 1928). The higher the rates of enrolment in continued schooling or in university training at previous periods, the higher the participation rates in the next periods (Hypothesis 4).

Such reinforcement effects operate as an additional indirect effect of modernisation on sustainable educational expansion. Since the cost for higher education rises at once while the future benefits are rather uncertain, the lower social classes are more likely oriented towards

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the educational decisions of others in order to reduce their uncertainties (Bandura, 1997). Therefore, it is assumed that the general enrolment in continued schooling or in tertiary education representing successful models in the educational system is rather an incentive for members of lower social classes to invest in their children’s continued and higher education (Hypothesis 5).

3 Data, variables, design, and statistical procedure Data sources The empirical analyses are based on two longitudinal data sets. The first is the event-history dataset of the German Life History Study (GLHS) conducted between 1979 and 2005 in the Special Research Unit 3 of the German Science Foundation and at the Max Planck Institute for Human Development in Berlin. The GLHS provides information on educational trajectories and attainment of the birth cohorts 1919–21, 1929–31, 1939–41, 1949–51, 1954–56, 1959–61, 1964 and 1971 (Mayer, 2008, 2015). The educational careers analysed have taken place across different political regimes – the Weimar Republic (1918–33), the Third Reich (1933–45), and the Federal Republic of Germany (since 1949) – in the historical period from 1925 (the school entry of the cohort 1919) to 1999 (the youngest cohort’s year of interview). The cohort born in 1919–21 includes 1,005 West German women and men interviewed between November 1987 and August 1988 by CATI (computer assisted telephone interview), as well as 407 West Germans interviewed personally between August 1985 and August 1986 (Brückner and Mayer, 1987). The cohorts born around 1930, 1940, and 1950 are comprised of 2,171 West German men and women interviewed between October 1981 and March 1983 (Brückner, 1990). 2,008 West Germans born around 1955 and 1960 were interviewed between October 1988 and November 1989 (Brückner and Mayer, 1995). Finally, the data for 2,909 women and men born 1964 and 1971 was collected between June 1998 and February 1999 (Hillmert and Mayer, 2004).

The second data set comes from the project “Working and Learning in a Changing World” (ALWA: Arbeiten und Lernen im Wandel) of the Institute for Employment Research in Nuremberg (Kleinert et al., 2011), which is now part of the National Educational Panel Study. In this project, 10,404 individuals born between 1956 and 1988 were interviewed between August 2007 and April 2008 on, among other things, their schooling, vocational education, and academic training. For our purpose, we have selected 4,346 West German citizens in the birth cohorts 1959–61, 1964–66, 1969–71, 1974–76, 1979–81, and 1984–86, and their educational careers in the 1965–2008 periods. The interview schedules of the ALWA study are based on those of the most recent GHLS projects and are therefore highly comparable. Moreover, fieldwork was conducted by the same survey research institute (www.infas.eu).2

The selection of the birth cohorts was theoretically inspired by the work of Mannheim (1928) on political generations, Ryder (1965) on demographic birth cohorts, and the precursor Norwegian Life History Study by Natalie Rogoff Ramsøy (1973). Thus, the individuals’ imprinting by historical events in important phases of their life course, such as political circumstances and social institutions (Mayer and Müller, 1986), historical breakdowns, and political turmoil (Mayer and Hillmert, 2003; Elder, 1974), and the processes of modernisation (Becker and Blossfeld 2017; Blossfeld 1986) is the main selection criterion. Such events do imprint social characters (Diewald and Mayer, 2009; Elder, 1995) as well as having long-term impacts on the social structures of life courses (Mayer, 1990) and educational careers (Becker and Hadjar, 2013). Besides political caesurae, most of these phases are also related to periods of social, political, and cultural changes in terms of modernisation (Mayer and Huinink, 1990).3

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The respondents’ information on their educational trajectories, as on the other areas of their life course, was collected retrospectively (Brückner, 1990; Mayer, 2008). They were asked to reconstruct both their schooling and vocational or academic training with exact time references for the start and end of each of the episodes in their educational history (Brückner, 1994). Institutionalised states and events such as education and training are usually memorised in quite a reliable manner (Reimer, 2005). In order to minimise memory problems as well as to maximise the validity and reliability of the information, especially in regards to time references and the sequence of educational episodes, for the most recent cohorts, special techniques were used to support the memory process of the interviewees (Brückner and Mayer, 1995; Matthes et al., 2012). As an example, in the modules on life areas such as schooling and training, autobiographical cues were presented and information already available was used for filtering. Additionally, a tool for the checking and amending of data for temporal consistency was used in the CATI, for example. Finally, careful preparation of interviews and data collection (Matthes et al., 2007), systematic inquiries, an intensive editing process, and close inspections of the information on the life courses for chronological consistency supported the quality of the data (Brückner, 1995; Brückner and Mayer, 1998; Matthes et al., 2012).

The quality of retrospective data has been challenged by scholars of survey methodology (e.g. Sudman et al., 1996). Therefore, it seems to be necessary to demonstrate briefly the quality of the event-history data on educational trajectories employed. For the enrolment in upper secondary school (Gymnasium) at the age of 13 years, the data was compared with data from official statistics. In Figure 1, it is shown that the differences between the different data are not systematic for each of the cohorts. The dissimilarity indices are rather low, but it has to be stressed that for cohorts born around 1960 and 1985 the enrolment in the Gymnasium will be somewhat over-estimated – this is usually caused by educational bias in survey response.

Fig. 1: Enrolment of children in Gymnasia in (West) Germany (in % at age 13) – comparison of official statistics and cohort-specific individual data of GLHS and ALWA (1932–1999)

Regarding enrolment in university, the finding is the reverse for individuals at ages between 19 and 23 (Fig. 2). For almost all the birth cohorts, our microdata seemingly tends to

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underestimate the enrolment compared to the time series published in official statistics. However, it has to be taken into account that the latter comparison is limited due to different measures. The official statistics makes use of different birth cohorts at age 19–23 for each of the years: for each point in time along the historical periods, five birth cohorts are taken into account in order to measure the enrolment in university training, while for the GLHS and ALWA data sets just three birth cohorts maximally are considered per date. This might be the main reason for the observed difference. However, it should be emphasised that the patterns of changing enrolments are somewhat similar for the official time series and the cohort data.

Fig. 2: Enrolment in university training in (West) Germany (in % at age 19–23) – comparison between official statistics and cohort-specific individual data of GLHS and ALWA (1918–2008)

Both these comparisons demonstrate the high data quality of the event-history data in regard to educational enrolment and attainment. Our longitudinal data sets provide detailed event-oriented information on the respondents’ episodes of school education, as well as on vocational and academic training in institutionalised contexts of the educational system, which has changed often in the varied history of Germany between 1918 and 2008. On the one hand, this makes it possible to test our hypotheses in a theoretically and methodologically adequate manner. On the other hand, we are able to take into account the multiple time-dependence of educational trajectories in terms of age, period, and cohort on different levels in order to reconstruct the impacts of structural changes on the macro level on social process, such as individual patterns of education observed on the micro level (e.g. Hernes, 1976).

Dependent and independent variables Two kinds of dependent variables are distinguished. On the one hand, we are looking at educational transition at the first branching point of the German educational system and, on the other hand, we mainly focus on the attainment of certificates, such as the eligibility for university access and university degrees.

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Besides the respondents’ cohort membership and gender, one of the explanatory variables is social origin. This is measured by the educational level of the father or step-father since they typically gained at least the same or higher degrees than women becoming mothers in the decades under consideration. The certificates in schooling, vocational training, and academic training have been measured in a rank order indicating their level. The attainments in schooling, vocational training, and academic training are combined in the logic of the well-known CASMIN scheme (Braun and Müller, 1997; Becker and Blossfeld, 2017).

The class position of parental home is measured by the German Employment Class Schema (Mayer and Aisenbrey, 2007: 132). Considering the specific German situation and following the proposal by Goldthorpe (1983) for the definition of social class, it is operationalised by the father’s or step-father’s employment status (self-employed vs. employed), allocation in the hierarchy of the firm (blue-collar worker, employee, civil servant, etc.), and employment in the private or state sector at the respondents’ age of 15 years. The German Employment Class Schema distinguishes hierarchically ordered social classes, such as working class, lower middle class, middle class, and upper service class.

Fig. 3: Birth cohort pattern of social origin in (West) Germany (Source: GLHS and ALWA – authors’ calculation)

For our analysis, it has to be kept in mind that the families’ class position does not only correspond to what in terms of education, vocation, and class position parents aspire to for their children, and to the resources of the parental home that are available for investments in their children’s education, but also to the impact of several dimensions of the modernisation process on educational aspirations, transition, and attainment. The distribution of parental class positions mirrors the social contexts in different historical periods in which their children grew up. Figure 3 shows the historical change of the class structure in Germany and, most notably, the decreasing share of the working and lower middle classes and the increase in the middle and upper service classes across generations. These changes are the basis for the theoretically expected social processes, such as increased structural mobility, increased qualificational requirements, and changing reference points for intergenerational status maintenance.

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The effects of societal changes in terms of modernisation – the so-called period and cohort effects – have been measured following the procedure suggested by Blossfeld (1986). Long-time series of official statistics have been utilised for indicators of modernisation (Zapf and Flora, 1971). Their choice is theoretically driven considering the theoretical background of modernisation theories (e.g. Treiman, 1970) and the recent studies on educational expansion and inequalities (e.g. Blossfeld and Shavit, 1993; Erikson and Jonsson, 1996; Breen and Goldthorpe, 1997; Breen et al., 2009). They measure modernisation in terms of the historical change in economic productivity, government activity, economic structures, and social welfare (Table 1).4 In order to prevent the identification problem in multivariate estimates resulting from highly correlating or invalid time series, confirmatory factor analysis was applied to the 16 time series (Harrington, 2009). The factor termed modernisation has been extracted by means of the main component method and orthogonal factor rotation. It explains 97 per cent of the variance in these different time series.

Table 1: Factor loadings (pattern matrix) and unique variances Variables Factor: Modernisation Uniqueness KMO score Social protection (at current prices) 0.9705 0.0582 0.9382 Public educational spending (in Deutsch mark) 0.9857 0.0284 0.9011 Public consumption (at current prices) 0.9885 0.0229 0.8861 Monthly income of blue-collar workers (2000 = 100%) 0.9922 0.0155 0.9821 Per capita private wealth (at current prices) 0.9873 0.0252 0.9051 Private consumption of education (2010 = 100%) 0.9923 0.0154 0.8888 Private consumption (at current prices) 0.9873 0.0253 0.9245 Absolute number of medical doctors 0.9886 0.0226 0.9357 Absolute number of automobiles 0.9787 0.0422 0.8469 Share of employees in tertiary sector (in %) 0.9934 0.0132 0.9196 National income (at current prices) 0.9885 0.0229 0.8630 Per capita national income (2010 = 100%) 0.9573 0.0835 0.8807 Gross domestic product (at current prices) 0.9893 0.0213 0.8888 Per capital gross domestic product (at current prices) 0.9797 0.0401 0.9529 Investments (at current prices) 0.9867 0.0264 0.9336 Productivity (1950 = 100%) 0.9966 0.0068 0.8819 Overall 15.5301 0.9069 Variance 0.9733

The period-specific factor scores are documented in Figure 4 for the historical period from 1918 to 2015 (the time series of the original indicators may be requested from the authors). We observe an almost linear trend of modernisation accelerating after the Second World War. However, it has to be emphasised that the modernisation trend has been temporarily disturbed by crises such as the hyper-inflation (1923), the Great Depression (1929–32), the Second World War (1943–45), the recession after the German economic miracle (1966–67), the 1973–75 and 1980–82 recessions in the course of the two oil price shocks (1973, 1979), and the latest economic recessions and financial crisis (2001–03 and 2008–09).

The validity of this macro factor has been additionally tested by time series analysis (Table A-1 in the Appendix). On the one hand, it is interesting to see whether there is an effect of modernisation on the educational system – for example, public investments in the institutional setting of individuals’ education – net of other impacts such as demographic changes (measured by the raw birth rates lagged for six years – that is, the time interval between individuals’ birth and first enrolment in primary school – indicating intra-cohort competition on the individual level, challenges to the educational policy on the macro level, and adaptation of educational infrastructure on cohort sizes on the meso level). We find that modernisation, controlled for demographical change, indeed has a direct and positive effect on the number of academic high schools (Gymnasia) (see Fig. 4) and the growth of their numbers across historical periods (Models 1.1 and 1.2).

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Fig. 4: Modernisation, demographic trend, academic high schools, and universities in (West) Germany, 1918–2010

For the nominal number of Gymnasia, the cohort size has a positive effect which is not significant for the growing number of academic high schools. According to the modernisation theory (e.g. Treiman, 1970), modernisation has forced modern states to extend their educational system. This is indicated by the time series on public educational spending being part of the modernisation factor (see Table 1). As predicted by the modernisation theory, it is obvious that the previously increased public educational spending has resulted in a growing number of academic high schools (Model 1.3). Furthermore, there are positive effects of modernisation and enrolment rates in such high schools (see Fig. 1) on the (growing) number of universities (Models 1.4 and 1.5).

Additional to this macro-meso linkage, we confirm a significant meso-macro linkage since the period-specific levels of modernisation and the number of academic schools (see Fig. 4) has a positive effect on the aggregated enrolment of individuals at age 13 in the Gymnasium (Model 2.1): the higher the level of modernisation and the higher the number of academic high schools, the higher the enrolment in the Gymnasium (see Fig. 1). However, there is a negative effect of cohort size on the enrolment, indicating the consequences of intra-cohort competition on limited places in the academic high schools. Additionally, we find that the families’ private expenditure for their offspring’s education (indicated by the private price index for education) has a positive on the enrolment rate in the Gymnasium (Model 2.2). That means in spite of increasing prices for education we witness increasing demand by families for their children’s continued education. The aggregated enrolment in academic university training at age 19–23 (see Fig. 2) depends mainly on the level of modernisation, while cohort sizes and numbers of universities are negligible (Model 2.3). There is also a path dependency of previous developments in the educational system: the larger the increase of academic high schools, the more likely it is on average that individuals are enrolled in university training (Model 2.4).

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Design and statistical procedure

In order to combine the period-specific factor scores and the cohort-specific educational trajectories, we are using the well-established procedure of episode splitting (Blossfeld et al., 2007; Blossfeld, 1986). Therefore, each of the episodes in the individual’s educational trajectory has been divided into yearly sub-episodes and linked to the corresponding yearly factor scores of modernisation (Fig. 4). At the start of each of the sub-episodes, the factor score of the previous legal year has been deployed in order to capture period effects. These measure the impact of the time-continuous change of modernisation on educational trajectories in the historical trend. In this way, it is possible to model structural change on the macro and the meso levels in its impact on the individuals’ educational decision and action on the micro level. Furthermore, the levels of modernisation at the start of educational trajectories (e.g. the first day of school, the end of compulsory schooling, etc.) are being considered as cohort conditions indicating cohort effects on the shape and dynamic of the educational trajectory.

Finally, by means of this procedure, the educational trajectory is being treated as a stochastic process in the highly institutionalised context of the educational system. On the one hand, dropping out from the educational system or changes of educational track are theoretically possible at each point in time. Additionally, the variation of enrolment and attainment across periods and ages in the individuals’ educational trajectories within each of the birth cohorts will also be considered. On the other hand, we are dealing with the problem of unobserved heterogeneity since the educational aspirations are not directly measured, as could be the case in a prospective design. In this way, the impact of societal change on the unmeasured parental aspirations on their children’s education will be taken indirectly into account at least. In contrast, a comparative-static model, such as a binary logit regression in a cross-sectional design, would tend to underestimate the impact of societal change. In our case, the time-continuous progress of modernisation influences transitions and attainments via the individuals’ formation of educational aspiration, namely, the adaptation of their aspirations to time-dependent processes such as the changing educational behaviour of others in the same birth cohort (Hernes, 1976) or overall societal change (Becker and Hadjar, 2013).

It has to be stressed that the episode splitting and the method of data compilation described above do not have influences on the observed durations or on the estimates of the other variables (Blossfeld, 1986). In the logic of dynamic multi-level analysis, it is possible to model the time-dependent characteristics of the macro and meso levels as predictors for events on the individual micro level by utilising parametric procedures of event-history analysis. It is the aim of this kind of modelling to specify the likelihood of events in the educational trajectory, that is, the hazard rate, as a stochastic and time-variant function of individual resources (micro level), of societal change in terms of modernisation (macro level), and of educational system (meso level).

This rate is defined as the marginal value of the conditional probability of having such an event – such as the transition from primary school to lower secondary schools or attaining a credential – in the time interval (t, t+Δt) provided that this event has not occurred before (Blossfeld et al., 2007). In our case, the rate will be estimated on the basis of an exponential distribution: r(t|x(t)) = exp(β’x(t)), whereby x(t) is the time-dependent vector of the exogenous variables whose unknown coefficients β have to be estimated. For each of the short sub-episodes (maximum of 12 months), we are assuming a constant hazard rate. In this way, we are able to model step functions which display the empirically observed hazard function for the entire educational trajectory embedded in different periods of modernisation.

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4 Empirical results Educational expansion in Germany As has often been empirically documented, Germany, like many other countries, has experienced an overall process of educational expansion (Breen et al., 2009; Becker, 2003; Blossfeld, 1993). Across birth cohorts and historical periods, increasing numbers of young people have stayed on in full-time education beyond the minimum compulsory school (Gymnasium), have entered and finished vocational education and training, have increasingly enrolled in upper secondary courses and attained eligibility for university training (Abitur), have entered into some form of tertiary education (academic university and universities of applied sciences) and finished them successfully (university degrees) (Fig. 5).5 In sum, the educational expansion has been carried out by consecutive cohorts.

Fig. 5: Educational career patterns and attainment across birth cohorts (estimates in Table A-2 in the Appendix)

However, it is important to note very significant nonlinearities and even reversals in these educational trends. There is no linear trend of increasing access to continued education and attainment of higher degrees. Compared to the oldest cohort 1919–21, the cohorts born around 1930 and 1940 were disadvantaged due to the Second World War and its aftermath in the immediate post-war periods, while the younger cohorts born after 1945 have benefited from the educational expansion.

Furthermore, previous findings by Müller and Pollak (2004) are confirmed by another type of longitudinal cohort data for Germany. In particular, beside the permanent intergenerational status reproduction, the social classes remaining traditionally disadvantaged in educational attainment have enjoyed considerable progress in the course of educational expansion (Fig. 6).

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Fig. 6: Educational career patterns and attainment across birth cohorts (estimates in Table A-3 in the Appendix)

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Across birth cohorts, children from the working and lower middle classes have disproportionally increased their propensity of being enrolled in the Gymnasium after the primary school, their likelihood of attaining the Abitur and finally of finishing successfully the university training, in comparison with the middle and upper service classes. Compared to the cohort 1919–21, the cohorts born around 1930 and 1940 have been disadvantaged. This is particularly valid for individuals from the lower classes.

Fig. 7: UNIDIFF layer scores for social origin on educational trajectories across birth cohorts

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Finally, indicated by the UNIDIFF layer scores (estimated by log-multiplicative layer-effect model; Erikson and Goldthorpe, 1992), it is evident that the social disparities in regard to the enrolment in Gymnasium, the high school diploma (Abitur) and the university degree (or other training degrees) increased in the periods of Weimar Republic and Third Reich (Fig. 7). In the course of substantial educational expansion since around 1950, the social disparities of the enrolment in Gymnasium (or in other secondary schools after primary school) or the educational attainment such as Abitur stagnated remarkably. However, there are different patterns for schooling and for vocational and university training after schooling. While secondary schools became universal and was decreasingly characterised by social disparities across social classes, the social disparities of the university degree show a cyclical pattern across the cohorts, indicating stagnating disparities in the post-war period.

Direct effects of modernisation on education At the background of the previous findings, our multivariate analysis focuses on access to upper secondary education, on the eligibility for university training and on the attainment in tertiary education. Controlling for gender and social origin, we estimate the direct effects of modernisation on selected events and attainment in the individuals’ educational trajectory, such as the transition to Gymnasium vs. other lower secondary schools at the first branching point, as well as the attainment of high school diploma (Abitur) or university diploma by a multivariate procedure of event-history analysis.6 First, we corroborate the well-known patterns of social disparities (Table 2).

The higher the fathers’ education, the more successful their children are in the educational system. In spite of modernisation and educational expansion across 90 years, social inequalities in the educational system are always correlated with the social stratification. This means that children from the middle and upper service classes are more likely be enrolled in the Gymnasium and attain the higher education diploma (Abitur) than their counterparts in the working and lower middle classes. In sum, in spite of educational expansion, there are still processes of intergenerational status reproduction for the upper social classes.

Second, we find significant cohort effects of modernisation (Models 1, 4, and 7). The higher the level of modernisation at the first day of enrolment, the more likely is the transition to the Gymnasium after finishing the elementary school, and the more likely it is that individuals continue general schooling and attain a high school diploma (Abitur). This cohort effect tells us that the modernisation might have long-lasting impacts on the parents’ and their children’s aspiration, the planning of the educational trajectory, and their educational decisions. And the higher the level of modernisation after finishing schooling, the more likely is the attainment of a university degree. This cohort effect might indicate the long-term impact of modernisation on the individuals’ propensity to start and to finish university training successfully. Additionally, in each of the states and events in the educational trajectory, the period effect of modernisation is more remarkable than the cohort effects (Models 2, 5, and 8). The continuously increasing modernisation might reinforce the individuals’ aspirations in each point in time during their educational trajectory; it seems to be the case that individuals adapt their educational decisions and actions to the current level of modernisation. In sum, this means that the ongoing modernisation stabilises the individuals’ educational trajectories and accelerates the educational expansion across historical periods. Overall, Hypothesis 1 is supported by the estimations..

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Table 2: Cohort and period effects in educational careers in (West) Germany (Source: GLHS and ALWA – authors’ calculations)

Transition after primary school: Gymnasium 1 Attainment of higher education entrance qualification (Abitur) University degree2

Models 1 2 3 4 5 6 7 8 9 Gender

Male –0.037 –0.039 –0.040 –0.024 –0.067 –0.066 0.074 0.066 0.068 (0.042) (0.042) (0.042) (0.029) (0.031)* (0.030)* (0.038)* (0.038) (0.038)

Social origin Education of father 0.175 0.173 0.171 0.124 0.069 0.069 0.109 0.083 0.086 (0.011)*** (0.011)*** (0.011)*** (0.008)*** (0.008)*** (0.008)*** (0.010)*** (0.010)*** (0.010)*** Working class Reference Reference Reference Reference Reference Reference Reference Reference Reference Lower middle class 0.081 0.082 0.096 0.129 0.161 0.203 –0.033 –0.016 0.127 (0.086) (0.086) (0.087) (0.073) (0.072)* (0.105) (0.084) (0.084) (0.144) Middle class 0.816 0.813 0.833 0.545 0.504 0.771 0.442 0.410 0.794 (0.080)*** (0.080)*** (0.080)*** (0.067)*** (0.065)*** (0.096)*** (0.077)*** (0.076)*** (0.131)*** Upper service class 1.056 1.062 1.108 0.635 0.724 1.121 0.549 0.535 1.144 (0.089)*** (0.089)*** (0.089)*** (0.071)*** (0.070)*** (0.097)*** (0.083)*** (0.082)*** (0.129)***

Modernisation Cohort effect 0.123 0.393 0.248 (0.035)*** (0.022)*** (0.029)*** Period effect 0.157 0.479 0.916 1.273 0.604 1.095 (0.031)*** (0.089)*** (0.023)*** (0.071)*** (0.035)*** (0.097)***

Interaction: Modernisation and… Working class Reference Reference Reference Lower middle class –0.098 –0.040 –0.156 (0.110) (0.086) (0.128) Middle class –0.313 –0.376 –0.496 (0.103)** (0.079)*** (0.114)*** Upper service class –0.476 –0.558 –0.751 (0.101)*** (0.076)*** (0.107)***

Intercept –4.687 –4.699 –4.715 –4.224 –4.440 –4.691 –4.178 –4.330 –4.723 (0.076)*** (0.075)*** (0.076)*** (0.063)*** (0.062)*** (0.088)*** (0.071)*** (0.072)*** (0.117)***

log likelihood L0 –12057.05 –12057.05 –12057.05 –5878.792 –5878.792 –5878.792 –5089.477 –5089.477 –5089.477 log likelihood L1 –11248.87 –11241.86 –11225.21 –5430.809 –4957.449 –4926.926 –4812.483 –4812.483 –4812.483 LR chi2 (d.f.) 1430.8 (6) 1430.7 (6) 1398.9 (9) 1952.5 (6) 3235.8 (6) 2856.1 (9) 923.06 (6) 923.06 (6) 923.06 (6) Number of subjects 17,691 17,691 17,691 12,017 12,017 12,017 14,202 14,202 14,202 Number of sub-episodes 86,720 86,720 86,720 73,464 73,464 73,464 50,735 50,735 50,735 Number of events 2,697 2,697 2,697 2,587 2,587 2,587 1,944 1,944 1,944

* p ≤ 0.05; **p ≤ 0.01; *** p ≤ 0.001; hazard rates estimated by exponential model (including episode splitting). 1 Trajectory after elementary school. 2 Academic university degree or degree of university of applied sciences.

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Which of the social classes have profited from the trend of modernisation? Compared to the interaction of period-specific modernisation and origin from the middle and upper service classes (Models 3, 6, and 9), the rate of enrolment in Gymnasium, eligibility for university training (Abitur) and attainment in tertiary education have significantly increased for offspring from the lower middle and working classes in the course of modernisation. On the one hand, in the process of modernisation, it becomes increasingly feasible for the lower classes to catch up in regard to educational attainment with the socially advantaged social classes. On the other, the increases for the upper classes have culminated at a marginal level and perhaps became “saturated”. If the main effects of social origin and modernisation as well as the interaction terms of class origin and modernisation across periods are taken into account, it can be concluded on the aggregate level that the social disparities in the educational trajectories have decreased continuously across the modernisation trend in favour of the lower classes. However, on the macro level, the response to modernisation differs for social classes and has resulted in both the level effect of modernisation (increases in educational enrolment and attainment in tertiary education) and the structural effect of modernisation (decreasing but retaining persistent inequalities in the educational system).

Indirect effects of modernisation According to modernisation theory, internal economic growth and the increasing economic competition in economic markets have forced modern states to extend their educational system in order to exhaust the ‘talent reserves’ in social classes with some social distance from higher education (Treiman, 1970). Therefore, it is interesting to examine if there are any indirect effects of modernisation mediated by the increasing number of schools and universities on educational trajectories (Table 3).

First of all, after control for gender and social origin, in line with Hypothesis 3, we find that the increasing number of academic high schools (see Fig. 4) has a significant positive effect on children’s transition to the highest track on the lower secondary school level (Model 1). However, second, if the level of modernisation and its interaction with the number of academic high schools (Gymnasia) are taken into account, the main effect of modernisation becomes insignificant, while the main effect of the number of academic high schools remains positive and statistically significant. The statistically significant but negative interaction effect tells us that the transition rates have slowed in the course of modernisation and in the course of the expansion of educational opportunities on the lower secondary school level (Model 2). Overall, the result corresponds with the trend documented in Figure 4.

The same main results are found if the impact of previous period-specific aggregated rate of enrolment at age 13 (see Fig. 1) is taken into account in order to detect diffusion processes in the course of modernisation and educational expansion (Model 3). The evidence is in favour of Hypothesis 4. The higher the previous enrolment rates, the higher are the individuals’ transition rates. The estimation of the interaction effect of others’ educational behaviour with individuals’ social origin (Model 4) empirically supports Hypothesis 5; successful models in their social contexts are helpful for lower social classes to make similar educational decisions in favour of continued schooling.

All these findings on the indirect effects of modernisation could be replicated by estimating the enrolment in university training. In accordance with Hypothesis 4, but in contrast to the first transition, we find significant positive direct effects of modernisation and the number of universities as well as an indirect effect indicated by the significant negative interaction effect of modernity level and number of universities. The latter interaction effect reports again that there is a marginal effect of educational expansion in the modernisation trend (Model 5).

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Table 3: Indirect period effects of modernisation on education in (West) Germany – processes of transition and diffusion (Source: GLHS and ALWA – authors’ calculations) Transition after primary school: Gymnasium Enrolment in university training

Models 1 2 3 4 5 6 7 Gender

Male –0.043 –0.035 –0.042 –0.041 0.210 0.242 0.248 (0.042) (0.042) (0.042) (0.042) (0.038)*** (0.039)*** (0.039)***

Social origin Education of father 0.168 0.175 0.169 0.168 0.185 0.187 0.187 (0.011)*** (0.011)*** (0.011)*** (0.011)*** (0.010)*** (0.010)*** (0.010)*** Working class Reference Reference Reference Reference Reference Reference Reference Lower middle class 0.084 0.076 0.083 0.239 0.016 0.002 0.105 (0.086) (0.086) (0.086) (0.294) (0.075) (0.077) (0.194) Middle class 0.804 0.778 0.804 1.561 0.497 0.526 1.083 (0.080)*** (0.080)*** (0.080)*** (0.275)*** (0.069)*** (0.072)*** (0.176)*** Upper service class 1.066 1.024 1.059 2.219 0.671 0.687 1.743 (0.088)*** (0.088)*** (0.088)*** (0.272)*** (0.076)*** (0.079)*** (0.172)***

Modernisation Period effect 0.138 0.338 (0.203) (0.168)*

Number of academic high schools / universities Period effect 0.393 1.778 0.406 (0.050)*** (0.189)*** (0.104)*** Period effect: Modernisation –0.392 –0.090 (0.064)*** (0.027)***

Enrolment in Gymnasium / university training Period effect 0.024 0.059 0.047 0.077 (0.003)*** (0.010)*** (0.002)*** (0.006)*** Period effect: Working class Reference Reference Period effect: Lower middle class –0.007 –0.004 (0.013) (0.008) Period effect: Middle class –0.035 –0.027 (0.012)** (0.007)*** Period effect: Upper service class –0.052 –0.049 (0.011)*** (0.007)***

Intercept –5.566 –8.600 –5.199 –5.955 –4.624 –4.620 –5.261 (0.125)*** (0.433)*** (0.097)*** (0.237)*** (0.187)*** (0.076)*** (0.153)***

log likelihood L0 –12057.058 –12057.058 –12057.058 12057.058 –10944.56 –10944.56 –10944.56 log likelihood L1 –11223.626 –11170.548 –11227.572 –11209.627 –10384.46 –9745.41 –9703.76 LR chi2 (d.f.) 1474.67 (6) 1603.63 (8) 1461.44 (6) 1393.37 (9) 2327.25 (8) 2334.31 (6) 2077.03 (9) Number of subjects 17,691 17,691 17,691 17,691 14,202 14,202 14,202 Number of sub-episodes 86,720 86,720 86,720 86,720 38,279 38,279 38,279 Number of events 2,697 2,697 2,697 2,697 3,121 3,121 3,121

* p ≤ 0.05; **p ≤ 0.01; *** p ≤ 0.001; hazard rates estimated by exponential model (including episode splitting)

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Finally, we find evidence for diffusion processes in favour of socially disadvantaged members of the lower social classes (Models 6 and 7). This means that Hypothesis 5 is supported. On the one hand, the positive effect of previous enrolment rate for university training again indicates reinforcement effects of educational expansion in times of increasing modernity (Model 6). On the other, this development operates particularly in favour of individuals from lower social classes who could catch up with the individuals from upper classes being traditionally enrolled in university training (Model 7). However, once again, the main effects of social origin remain still large and indicate a strong baseline of social disparities in the educational system (the structural effect of persistent inequalities) in spite of the remarkable educational expansion in Germany in the last 90 years.

5 Summary and conclusion The aim of this study was to unravel the direct and indirect impact of long-term societal change on micro level educational outcomes: transitions in educational trajectories, educational attainment, and their social disparities. For a long period of about 80 years (1925–2008), we look at societal change for Germany in three respects: (1) a monotonic trend captured by 16 yearly time series on economic growth, government activities, and living conditions, which we label “modernisation”; (2) succession of birth cohorts as the carriers of social processes; and (3) period-specific developments of the educational system. Besides time series, the investigation is based mainly on event-history data for 11 birth cohorts born between 1919 and 1986 from the GLHS and the ALWA-adult cohorts of the National Educational Panel Study. Our hypotheses are derived from rational action theory suggested by Erikson and Jonsson (1996) and Breen and Goldthorpe (1997), as well as by the conceptual ideas on the sophisticated analysis of social change (Hernes, 1976) and of education as a lifelong process (Blossfeld and von Maurice, 2011). Descriptively, our results document the long-term trend of both the educational expansion (enrolment and attainment) and the decline of social disparities in educational attainment, but also retardations and reversals of these processes (due especially to the Second World War and its aftermath), and finally the continued intergenerational status reproduction of the upper service class via persistent investment in their offspring’s tertiary education. In our dynamic multivariate analysis, we find that strong cohort and period effects of the level of modernisation as well as responses to it differ for the social classes. On the one hand, modernisation is indeed a “motor” of educational expansion in terms of the extension of the educational system and the increasing enrolment in continued schooling and attainment of degrees in the tertiary education. On the other, we have witnessed decreasing social disparities of educational attainment as one of the consequences of modernisation.

Therefore, we do not completely agree with Breen et al. (2009), who claim that “the main sources of change in education over time are variations in the cost, to the student, of secondary and higher education” (Breen and Jonsson, 2005: 228). On the one hand, this hypothesis has not yet been tested over a long period (cf. Becker, 2003). On the other, it is obvious that decreasing costs are necessary but not sufficient in order to explain increasing participation in higher education and declining disparities in education across the educational expansion. For example, Breen and Goldthorpe (1997: 295) argue that counterbalancing the evaluation of costs, benefits and probabilities of success cannot offset social differentials in educational attainment because of class differentials in families’ considerations of status maintenance. Furthermore, it has to be considered that continued education implies additional opportunity and transactions costs, which are – depending on their financial resources – evaluated differently by families according to their position in the class structure. In some

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cases, the apparently free education becomes more expensive for lower classes that it was before. Provided that higher education is free, the question arises: what is the incentive to take advantage of this costless good? To explain the increased investment of lower classes into their children’s continued and higher education, there must be a reason to invest more in the offspring’s education than before. As already argued, the subjectively optimal education suitable for avoiding status demotion is the pivotal educational motivation (Stocké, 2007).

In the case of Germany, however, we are convinced that the effects of modernisation on the class structure and the social classes’ motive of status maintenance comprise the “motor” for educational expansion. Structural mobility as well as increased qualificational requirements for vocations guaranteeing the intergenerational status maintenance at least might be the incentives for changes in educational aspirations and decisions (Becker, 2009). Due to data limitations, we are not able to test this assumption. Therefore, there is an urgent need to test this hypothesis in future research.

Finally, we point to some limitations of our study. The first one is the missing information on the respondents’ and their parents’ educational aspirations and on their decision-making processes at branching points in the educational system. It would be interesting to control for social class to investigate whether and how individuals perceive and evaluate the societal change, as well as how they adapt their aspirations to the modernisation trend and other significant historical circumstances. The second limitation is the exclusion of foreigners and immigrants from the analysis due to the low number of cases in the different birth cohorts (e.g. Becker and Blossfeld, 2017). The third is the exclusion of the educational trajectories in the former GDR (Solga, 1997) because the official statistics were incomplete and less compatible with the data for the western part of Germany. However, the comparison between West and East Germany would be another story than the analysis presented in this study (e.g. Mayer and Solga, 1994). The fourth limitation concerns the incomplete distinction between modernisation and the expansion of the capacities of the educational system as a cause and a consequence. From the perspective of an extended modernisation theory, it seems to be clear that modernisation and its correlates have pushed the dynamics of educational expansion but that education and science were also pivotal factors reinforcing the process of modernisation.

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Endnotes 1 If parents strive for status maintenance, they have a realistic aspiration concerning which class position their offspring could attain by a certain educational certificate. The basis is the parental sense of processes of status attainment and the operation of labour markets (Stocké, 2019). 2 For both data sets, the GLHS and ALWA data, the analysis is restricted to West German citizens due to the specialties in regard to the political and economic system in the former GDR, which would make separate analysis necessary (Solga, 1995). Solga (1997) provides interesting details on inequality of educational opportunities in the former GDR. This is also true for East Germany (Diewald et al., 2006). Foreigners and immigrants have been excluded since they have not been sampled in all of the GLHS cohorts. 3 Pragmatically, the selection of cohorts followed the 10-year distance age-period-cohort design as a sequel to the additional survey of the micro census 1971 (Mayer, 1977; Müller 1978) as well as the findings on specific historical cohorts in this study (Mayer, 1995; Brückner and Mayer, 1987). Specific historical circumstances led to the inclusion of the 1954–56 and the 1964 cohorts. 4 The time series employed are documented in the German System of Social Indicators and Historical time series hosted by GESIS (http://www.gesis.org/histat/en/index). The data were completed with time series published by the Federal Office of Statistics in Wiesbaden (https://www.destatis.de/EN/Homepage.html) on their website, in their yearbooks or in special issues (e.g. Statistisches Bundesamt, 1972). German historical time series published by Rahlf (2015, 2016), Bolt and Van Zanden (2014), Diebolt (1997), Franzmann (2006), Herrlitz et al. (1995), Metz (2005), Sensch (2007, 2008), and Titze et al. (1987) have been used. Economic data has been found in the Picketty-Zucman Wealth-Income data set (Picketty and Zucman, 2014) and in the Angus Maddison Project (http://www.ggdc.net/maddison/maddison-project/home.htm). 5 It has to be emphasised that particularly after 1949 there was a shift of enrolment in the Volks- or Hauptschule [lower secondary school] to the Realschule [intermediate secondary school] to the Gymnasium [upper secondary school] across the cohorts (see Fig. 1). In the course of the educational expansion, the Volks- or Hauptschule lost this dominant position as the main school for the populace (Becker, 2003: 2). 6 Although gender differences are not the subject of our study, it is interesting to report that the gender gaps for the attainment of the higher education entrance qualification (Abitur) as well as university training and the attainment of related certificates cannot be explained completely by reference to different reactions to the modernisation trend. However, for future research, there is a need to investigate the gender differences in detail.

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Appendix

Table A-1: Effect of modernity on educational system and enrolment rate in (West) Germany, 1918–2010 – time series analysis Macro-Meso Meso-Macro

Models Number of academic high schools (1.1)

Growth of number of Gymnasia (1.2)

Number of academic high schools (1.3)

Number of universities (1.4)

Growth of number of universities (1.5)

Rate of enrolment in Gymnasium (2.1)

Rate of enrolment in Gymnasium (2.2)

Freshmen rate at universities (2.3)

Freshmen rate at universities (2.4)

Modernity level 0.602 118.2 3.760 13.32 12.13 (0.025)*** (8.887)*** (1.075)*** (1.276)*** (0.191)**** Birth rates 0.011 –0.295 –0.004 (0.005)* (0.086)*** (0.076) # Academic high schools 12.78 5.994 (15.50) (1.665)*** # Universities –0.009 (0.010) Public educational spending 8.545 (0.272)** Private educational spending 0.255 (0.007)*** Δ Modernity level 0.505 136.3 (0.155)** (24.49)*** Δ Birth rates 0.082 (0.260) Δ Academic high schools –11.05 5.041 (31.06) (2.354)** Constant 2.039 –0.080 1.757 131.3 –0.286 11.03 7.978 15.08 13.43 (0.082)*** (0.260) (0.022)**** (33.90)*** (0.835) (3.628)** (0.384)*** (2.179)*** (0.194)**** ARMA

L.ar 0.784 0.763 (0.142)*** (0.157)**** L.ma –0.809 –0.724 (0.000) (203.486) L2.ma –0.191 –0.276

(0.350) (56.154) Sigma

Constant 0.075 10.16 (0.011)*** (1,033) Adjusted R2 0.95 0.92 0.98 0.94 0.94 0.98 0.98 Number of sub-episodes 92 91 92 92 91 92 92 92 92

† p ≤ 0.1; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001

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Table A-2: Educational career patterns and attainment across birth cohorts (Source: GLHS and ALWA – authors’ calculations)

Gymnasium Abitur Abitur1 No school degree2 General VET3 University degree University for applied science No training degree

Cohort 1919-21 Reference Reference Reference Reference Reference Reference Reference Cohort 1929-31 –0.449 0.293 –0.073 0.485 0.012 0.117 Reference 0.007 (0.128)*** (0.174) (0.147) (0.197)* (0.039) (0.182) (0.057) Cohort 1939-41 –0.512 0.146 –0.218 0.031 0.098 0.222 0.436 –0.419 (0.128)*** (0.180) (0.155) (0.212) (0.034)** (0.166) (0.341) (0.063)*** Cohort 1949-51 –0.152 0.872 0.172 –0.234 0.097 0.826 0.518 –0.662 (0.114) (0.133)*** (0.112) (0.221) (0.034)** (0.136)*** (0.327) (0.064)*** Cohort 1954-56 0.271 1.667 0.453 –0.369 0.079 0.749 0.765 –0.824 (0.094)** (0.108)*** (0.094)*** (0.223) (0.032)* (0.126)*** (0.300)* (0.057)*** Cohort 1959-61 0.462 1.674 0.513 –0.547 0.210 0.732 0.978 –0.723 (0.078)*** (0.098)*** (0.085)*** (0.188)** (0.028)*** (0.118)*** (0.286)*** (0.045)*** Cohort 1964-66 0.343 1.516 0.390 0.254 0.282 0.564 1.034 –0.681 (0.078)*** (0.099)*** (0.086)*** (0.172) (0.027)*** (0.119)*** (0.284)*** (0.043)*** Cohort 1969-71 0.496 1.664 0.496 0.101 0.225 0.526 0.739 –0.384 (0.079)*** (0.098)*** (0.085)*** (0.176) (0.027)*** (0.122)*** (0.289)* (0.042)*** Cohort 1974-76 0.474 1.969 0.679 –0.271 0.174 1.109 0.877 –0.741 (0.120)*** (0.111)*** (0.099)*** (0.256) (0.049)*** (0.139)*** (0.338)** (0.086)*** Cohort 1979-81 0.660 2.009 0.678 0.099 0.140 0.790 0.521 –0.311 (0.108)*** (0.106)*** (0.093)*** (0.216) (0.050)** (0.149)*** (0.351) (0.062)*** Cohort 1984-86 0.768 1.921 0.555 0.218 (0.093)*** (0.102)*** (0.091)*** (0.194) Constant –3.912 –4.907 –3.386 –4.471 –1.868 –4.294 –5.373 –1.957 (0.066)*** (0.093)*** (0.080)*** (0.162)*** (0.023)*** (0.109)*** (0.275)*** (0.031)*** N of sub episodes 119,058 97,305 52,040 86,211 43,550 66,527 60,591 66,527 N of events 3,239 3,057 2,722 1,010 7,902 1,646 639 5,719 N of subjects 23,613 16,008 7,500 16,008 13,499 19,004 16,885 19,004

* p ≤ 0.05; **p ≤ 0.01; *** p ≤ 0.001 1 Without individuals in lower secondary school (Volks/Hauptschule or special schools or miscellaneous schools). 2 Only individuals enrolled in secondary school. 3 Without individuals eligible for university training.

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Table A-3: Educational career patterns and attainment across social classes (Source: GLHS and ALWA – authors’ calculations) Gymnasiuma Abitura Abiturab University degree

Working & lower middle class

Middle & upper service class

Working & lower middle class

Middle & upper service class Middle & lower classes Upper service class Working & lower

middle class Middle & upper service class

Cohort 1919-21 Reference Reference Reference Reference Reference Reference Reference Reference Cohort 1929-31 –0.404 –0.009 –0.130 0.715 –0.346 0.096 0.296 0.376 (0.287) (0.147) (0.514) (0.172)*** (0.264) (0.165) (0.481) (0.192) Cohort 1939-41 –0.314 –0.259 0.227 0.363 –0.088 –0.384 0.619 0.326 (0.282) (0.147) (0.455) (0.188) (0.223) (0.220) (0.435) (0.178) Cohort 1949-51 0.294 0.023 1.370 0.955 0.316 –0.065 1.457 0.859 (0.238) (0.135) (0.326)*** (0.142)*** (0.163) (0.165) (0.370)*** (0.145)*** Cohort 1954-56 0.804 0.158 2.374 1.448 0.472 0.414 1.424 0.617 (0.215)*** (0.107) (0.295)*** (0.113)*** (0.149)** (0.123)*** (0.361)*** (0.134)*** Cohort 1959-61 0.984 0.357 2.381 1.487 0.568 0.423 1.569 0.526 (0.189)*** (0.088)*** (0.274)*** (0.103)*** (0.133)*** (0.115)*** (0.345)*** (0.125)*** Cohort 1964-66 0.891 0.225 2.242 1.331 0.483 0.271 1.161 0.429 (0.188)*** (0.087)* (0.274)*** (0.104)*** (0.133)*** (0.117)* (0.351)*** (0.126)*** Cohort 1969-71 0.980 0.352 2.337 1.465 0.555 0.365 1.085 0.359 (0.193)*** (0.088)*** (0.275)*** (0.103)*** (0.133)*** (0.115)** (0.359)** (0.128)** Cohort 1974-76 0.970 0.327 2.723 1.730 0.743 0.407 1.500 0.958 (0.271)*** (0.137)* (0.303)*** (0.117)*** (0.152)*** (0.136)** (0.427)*** (0.143)*** Cohort 1979-81 1.083 0.497 2.941 1.715 0.763 0.493 1.813 0.487 (0.257)*** (0.121)*** (0.285)*** (0.112)*** (0.144)*** (0.126)*** (0.391)*** (0.162)** Cohort 1984-86 1.285 0.575 2.765 1.648 0.640 0.394 (0.222)*** (0.105)*** (0.282)*** (0.108)*** (0.140)*** (0.124)** Constant –5.099 –3.448 –6.081 –4.503 –3.580 –3.026 –5.630 –3.863 (0.170)*** (0.073)*** (0.265)*** (0.098)*** (0.127)*** (0.110)*** (0.330)*** (0.114)*** N of sub episodes 57,692 61,366 40,079 57,226 31,316 16,522 27,532 38,995 N of events 723 2,516 727 2,330 1,445 1,099 332 1,314 N of subjects 10,686 12,927 6,949 9,059 4,685 2,162 8,117 10,887

* p ≤ 0.05; **p ≤ 0.01; *** p ≤ 0.001 a Exponential model (episode splitting included). b Without individuals in lower secondary school (Volks/Hauptschule or special schools or miscellaneous schools).