Measuring individual transitions means capturing a process with a specific time dimension. The established analysis of school-to-work transitions focuses on single status changes, such as those between education and unemployment. As longitudinal datasets became increasingly available, the periodical character of transitions has deserved attention, mostly in terms of studies that used event history models. But even these kinds of studies continued to focus on single status changes, which are not determined by theory but by the respective research question, or by data availability. Hence, the analysis of micro-level transitions remains selective, because there is no common idea how to explore them. As a result, school-to-work transition research is in danger to overlook important aspects of this life-course trajectory. This paper argues that the reason is the missing theoretical definition of a transition. Given the growing complexity of school-to-work transitions, the status change concept becomes inappropriate for their analysis. The predominance of hypothesis-testing methods together with an underrepresentation of explorative methods lead to a disregard of the process character of school-to-work transitions. This considerably limits the gain of new scientific insights. Recent methodological developments regarding the explorative analysis of longitudinal processes, namely sequence analysis, offer the possibility to cope with the complexity of school-to-work transitions. The paper aims at comparing the advantages and drawbacks of two methods in analysing transitions, and advocates a combined research design between explorative and hypothesis-testing methods.

The transition from school to work remains a question of enduring relevance, despite the fact that there is no shortage of policy activities and research in this field within the past 30 years. It remains obvious that labour market mechanisms are incapable of solving the youth integration problem without government or corporate intervention. A smooth transition into the labour market preserves labour market supply, prevents loss of human capital, and provides life perspectives and independence for young people. Failure at this point may have long-term consequences for people's employment career and for social inequality (e.g., Mroz and Savage 2006; Steijn et al. 2006; Bell and Blanchflower 2010; Julkunen 2010). However, the development of youth unemployment in the OECD within the last two decades shows no improvement. With the economic crisis since 2008, the situation for young people deteriorated in many countries. Additionally, some researchers observed processes of polarisation or segmentation in the youth labour market – that is, increasing risks of social exclusion for specific disadvantaged groups such as immigrants (e.g., Kogan 2004) or low-skilled school leavers (e.g., Solga 2002; Fenton and Dermott 2006). For these reasons, both policy makers and social scientists continue to be interested in school-to-work transition research.

Although a huge body of existing research on school-to-work transitions suggests that there is nothing left to be examined, there are fundamental research questions that remain to be addressed: first, a theory that appropriately defines transitions does not exist. Second, individual transitions are only analysed as single events, not as processes and, third, the predominance of hypothesis-testing methods undermines the conceptualisation of school-to-work transitions.

Shanahan (2000: 683) suggests a couple of methodological innovations, one of which is sequence analysis, a methodological tool that uses the potential of longitudinal data and is capable of dealing with the complexity of the transition process. Sequence analysis is an exploratory rather than a hypothesis-testing method. Given the increasing amount of longitudinal data available, computational tools that aim at reducing complexity in a meaningful manner have increasingly gained importance. Sequence analysis allows for detecting structure in a seemingly chaotic mass of information, while having a holistic view, because sequences are treated as entities. Sequence analysis enables researchers to go beyond simplified definitions of what a transition is, where it starts, or where it ends. However, it has to be applied cautiously and unfolds its full power only within a meaningful research design, at best when combined with other (causal) methods.

The next section deals with theoretical approaches that address school-to-work transitions in order to illustrate a theoretical gap regarding the definition of micro-level trajectories between school and work. The following section reviews school-to-work transition research with a focus on the operationalisation of the transition process. The final section assesses the advantages and the drawbacks of different methods of transition analysis and advocates a strategy of analysing school-to-work transition from different angles.

[…] transition-system research often appears theoretically eclectic and fragmented.

(Raffe 2008: 278)
Despite the central importance of young peoples' labour market entry, an integrated theory of the transition from school to work is not available. One can argue that this not necessarily constitutes a problem, but ‘borrowing’ theories that naturally have a different and more general focus, most probably tend to overlook important aspects of the research object. This section exemplifies a couple of these theories, which researchers apply to school-to-work transitions.

Life course research aims at formulating adequate and sound theoretical rules for pathways, and there have been some attempts, for example the work on individual transition types by Sackmann and Wingens (2003), who examine school-to-work transitions from a life course perspective; or human capital theory, which aims to analyse the relation between education and labour market outcomes. But these approaches have not been able to provide satisfactory explanations of the country differences regarding the micro-level transition from school to work. The standard explanations provided refer to cultural, political, or institutional factors. Related theories suffer from the fact that they are either too general or too specific in nature. In the former case, theories are too abstract that they hardly provide valuable hypotheses for the cases under observation, whereas in the latter case, specific theories are meaningless if applied to all the cases of the basic population.

From the perspective of institutionalism, the Varieties of Capitalism (VoC) approach argues that social protection is strongly related to individual skill investment and, therefore, also related to different skill formation systems. The argument is that firms only invest in specific skills if social protection institutions, such as employment and unemployment protection, safeguard their returns. The absence of such institutions leads to investment in transferable skills by employees and employers (Estevez-Abe et al. 2001). The form of collective skill formation systems can be explained well by the VoC approach, but individual transition patterns cannot be hypothesised.

The theoretical foundations on which research in the field of micro-level school-to-work transitions is based, therefore, remain eclectic (Raffe 2008). Researchers typically borrow theoretical pieces from different disciplines and neighbouring research fields, such as economic labour market theories. For example, human capital theory is used for explaining the effects of educational credentials on labour market outcomes (Becker 1962, 1975). But although this theory is mainly focused on the individual level, it is hardly able to explain how institutions determine the process of the school-to-work transition. Segmentation theory, in its original version, assumes the existence of two segments within the labour market which differ in terms of wages and employment characteristics, while there is no mobility between them (Doeringer and Piore 1971; Edwards et al. 1975; Reich 2008). In later variants of this theory, certain groups, among them youth and school leavers, were identified that constitute labour market segments of their own. Although this theory includes the institutional level by connecting labour market segmentation to the collective skill formation system (Sengenberger 1992), it does not allow for explaining country differences (Ashton 1988). Segmentation theory is often applied to school-to-work transitions, mainly with respect to the incidence of non-standard forms of employment among labour market entrants (e.g., de Vries and Wolbers 2005). But, again, the concrete shape of micro-level school-to-work transitions cannot be explained.

Very close to the original segmentation theory is Marsden's dichotomy of internal vs. occupational labour markets (Marsden 1990, 1999), which is frequently used in school-to-work research as well. It assumes that the situation of young labour market entrants depends to a large extent on the fact that labour markets are structured either internally or occupationally. However, recent studies have questioned the explanatory power of this dichotomy (Gangl 2003; Brzinsky-Fay 2007).

Additionally, some researchers have employed power resources approaches such as insider-outsider theory (Lindbeck and Snower 1989), which refers primarily to the political origins of certain groups being disadvantaged regarding labour market access or employment conditions. The argument here is that job holders – the insiders – are organised in employee organisations and have advantages over job seekers – the outsiders – when it comes to negotiating their wages and working conditions. In the long run, the situation of the outsiders – and school leavers are by definition outsiders – deteriorates. But again, the insider–outsider theory has not been able to explain country differences regarding the school-to-work transition in a satisfactory way.

Apart from human capital theory, these theories aim at explaining micro-level effects (e.g., labour market entry) by macro-level causes (institutions), but they are not able to explain school-to-work transitions in their whole complexity. As a consequence of this theoretical gap, fixed definitions of the key concepts that are usually applied in transition research, such as transition or trajectory (cf. Brzinsky-Fay 2010: 7), do not exist in these theoretical frameworks. The only theoretical framework that directly focuses on individual transitions is the life course perspective.

The term transition is understood as a change between an initial and a destination status. The duration of a transition is not determined explicitly; therefore, it is used to describe either a very short status change or a prolonged process that involves many status changes. Regarding school-to-work transitions, it can be assumed that they become increasingly protracted and involve many status changes; as a result, transitions must be examined in a longitudinal way. Because the definition as single status change is very clear and straightforward, life course analysis to a certain extent became synonymous to transition analysis in a sense of status change analysis (cf. Sackmann and Wingens 2001).

The term trajectory represents a structuralist view, implying that ‘labour market destinations were largely determined by social forces’ that are ‘outside of the control of individual social actors’ (Evans and Furlong 1997: 18). Other authors prefer the use of the term ‘pathway’ instead (e.g., Shanahan 2000). One can understand trajectories as normative transitions arising from certain institutional arrangements, but finally, the concept of trajectory remains quite fuzzy, because of its simultaneous usage to describe longer transition periods. It is not determined, whether trajectories analytically serve as a tool for inter-individual comparison (trajectory as an aggregation of individual processes) or as a tool for intra-individual comparison (trajectory as a certain part within the life course) (George 2009: 164).

The life course perspective conceives life courses as ‘endogenous causal relations’ (Mayer 1990), implying that one status depends on the preceding status(es), no matter what the causal relation looks like. An alternative understanding is that of a holistic logic of the life course, which assumes that life courses are a meaningful composition as a whole. This understanding is seen as a more suitable concept for analysing some of the core propositions of life course research (Aisenbrey and Fasang 2010: 422), namely the standardisation (Kohli 1985), the individualisation (e.g., Buchmann 1989), the de-standardisation (Widmer and Ritschard 2009) and the de-institutionalisation of life courses (Held 1986). Apart from that, researchers have attempted to postulate life-course regimes in connection with those institutional characteristics, that are seen as fundamental within the VoC approach (Mayer 2005). In liberal market economies life courses are based on weaker societal relationships because of the lack of coordination, whereas in coordinated market economies, life courses are based more on long-term commitments. This explains the existence of high investments in vocational skills as well as it allows for hypothesising the form of certain life course trajectories. This approach remarks a promising step towards theorising individual school-to-work transitions, but it remained on a too abstract level. Closely related to these propositions, there was a tension between individual agency and institutional structure, which is also reflected in the methods applied within the life course concept.

The above list of theories employed by researchers illustrates the theoretical gap regarding the definition of transitions and trajectories that constitutes a serious problem for analysis, because central propositions of life course research cannot be examined properly. The same is true for the question how institutional frameworks influence school-to-work transitions. Theoretical answers to these questions can only be obtained by including as many countries as possible (cf. Bynner et al. 1997: 6). There is still need for more exploratory work on micro-level school-to-work transitions, because theoretical statements about how they are influenced by institutions can only be made if the concept of a transition and trajectory is clear, in order to be operationalised appropriately.

It is important to recognize that the transition is a process that occurs over time. The initial education-occupation association is not necessarily equally meaningful in all cases.

(Kerckhoff 2000: 463)
Measuring transitions means capturing a process with a specific time dimension, whose extent needs to be determined by the research question, or by data availability. Analyses of labour market transitions usually examine single status changes, for instance between employment and unemployment, or between education and employment. As I have shown in the previous section, this is also due to lack of theoretical clarity regarding the transition concept. As a consequence, transition analysis is limited to status change or time point analysis, which mostly applies event history models. These models are extremely powerful in looking at the conditions and/or effects of status changes, but they limit the trajectory between education system and labour market to a status change. There have only been few attempts that treat school-to-work transitions as sequences composed of more than one or two statuses (Berger et al. 1993). Usually, cross-sectional data are used for the calculation of aggregate measures (e.g., OECD 2010), whereas longitudinal data are employed predominantly for individual measures.

At the individual level, the basic indicator used in school-to-work transition research is the first transition into employment, but it is not necessarily meaningful. Researchers have tried to detect the crucial status change by constructing concepts such as the ‘first significant job’ (Russell and O'Connell 2001; Korpi et al. 2003) that lasts at least 6 months, or the ‘first job after leaving school for the last time’ (e.g., Arum and Hout 1998), for example. This limitation serves the purpose to exclude short, probably erratic or irrelevant employment periods. However, the determination of the time period that has to be regarded as ‘not significant’ remains to a large extent arbitrary – a problem that cannot even be eliminated by increasing the quality of available data. Kerckhoff explains that ‘[…] the problem of defining the first job becomes the most troubling when studies are based on the very best possible longitudinal data’ (2000: 471).

Unemployment duration of school leavers is also taken as an indicator to describe the quality of the transition (cf. Müller and Gangl 2003), but for the same reasons, its validity seems questionable. The duration of unemployment after leaving school simultaneously measures the duration of search processes, the general availability of jobs on the (youth) labour market as well as mismatch of any kind. Apart from their arbitrariness, both measures have the main disadvantage of seeking to qualify school-to-work transitions by focusing on only one single status change, which in this case is a passage from unemployment to employment. Other labour market statuses – such as inactivity (military service, household and childcare activities) or education beyond compulsory schooling or participation in active labour market programmes – are disregarded, and it is not clear if the school-to-work transition period ends with the first incidence of employment. Transition periods involving more than one employment episode are very different from those showing continuous employment regarding both their nature and their impact on future employment prospects. Because the increase in complexity of labour market entry (Berger et al. 1993) takes place in every industrialised country, the difference in the effects of institutional arrangements on individual school-to-work transitions can only be assessed by taking into account this complexity – that is, by applying longitudinal indicators to longitudinal information.

Researchers also use more elaborated individual-level measures, for example the risk of having a fixed-term job (cf. Gebel 2009; de Vries and Wolbers 2005; Scherer 2004). Fixed-term employment, on the one hand, is seen as a flexible form of employment that allows employers to screen new employees while keeping the flexibility to lay them off when either the economic situation requires retrenchment or the performance of the young employee does not meet productivity expectations. On the other hand, fixed-term contracts imply a shift of economic risk towards labour market entrants in terms of decreasing employment security. The question is, whether fixed-term employment appears as a ‘bridge’ or as a ‘trap’ (e.g., Gash 2008). Apart from these implications, the indicator of fixed-term employment risk, like the aforementioned indicators, only makes sense if observed and analysed in a longitudinal perspective.

Considering school-to-work transitions as periods also helps avoiding certain problems inherent to the comparative analysis of school-to-work transition systems: for example, the definition of what has to be regarded as ‘education’ or ‘work’, which is important for vocational apprenticeship systems. Kerckhoff (2000: 463) states: ‘[…] a decision has to be made as to whether the period spent in the dual system is time in school or at work. That is, does the transition from school to work take place before or after the period in the dual system?’ Another problem for comparative analysis results from country differences regarding the degree of coordination between education and the labour market. When comparing Germany and the USA, Kerckhoff (2000: 465) mentions that multiple entries into the labour force are more common in the USA. Paying attention only to the first status change between education, unemployment, or inactivity on the one hand, and employment on the other, means overestimating the integrative potential of the transition system in the USA, because later exits and re-entries into employment remain disregarded.

Despite all individual-level indicators provide the possibility to analyse school-to-work transitions comprehensively, they all suffer from the ‘time point’ problem. It can be assumed that both the frequency and the nature of the status changes within the school-to-work transition period vary between countries. Therefore, transition analysis that refers only to one single status change most probably leads to biased conclusions. Shanahan (2000: 683) lists a couple of problems arising from researchers’ decision to limit their analysis of the pathway from youth to adulthood to such ‘life course markers’. To address these problems, a couple of methodological innovations are suggested, one of which is sequence analysis.

Apart from these technical questions of how to measure a time process, the question arises whether improving data quality has effects on methodological and theoretical development. One can observe a three-dimensional increase in the size of datasets: first, the number of cases increases because of progress made in survey methodology. Second, the number of variables increases because of growing complexity of research questions. And third, the number of time points for which observations and variables are available increases because of the growing availability of longitudinal datasets. This requires the application of algorithmic explorative methods capable of sorting information in meaningful ways in order to extract crucial commonalities and/or differences in individual pathways.

Since the late 1980s, event history analysis meritedly has become the method naturally connected to (quantitative) life course analysis. However, some of its drawbacks should be noted: first, its reliance on discrete events and its assumption that life courses are stochastically generated lead to an emphasis of individual agency for the formation of life courses. Second, event history models are very sensitive on how the event of interest is operationalised. And third, the predictors of certain events are to a large extent dependent on the occurrence of different kinds of events. These drawbacks have led to critical disputes among scholars since the 1990s (Abbott 1990, 1995), such as the Breiman–Cox-debate in Statistical Science (Breiman 2001), which contributed to the spreading of sequence-analytical methods in the field of transition and life course analysis.

Sequence analysis was originally invented by biologists in order to find out the extent to which two DNA sequences are homologous, or, in other words, to determine the distance between them (Kruskal 1983). The established degree of similarity then allowed for drawing conclusions about a common ancestor of two DNA strands. The first sociologist to used sequence analysis was Andrew Abbott, who analysed musicians’ careers and ritual dances (Abbott 1983; Abbott and Forrest 1986). Sequence analysis was seen as a qualitative tool in the context of historical, narrative sociology. Due to the limited capacity of computers in those years, analysis was restricted to few cases with short sequences. Since the 1990s, researchers have begun to focus on individual sequences, such as class careers (Halpin and Chan 1998), employment biographies (Abbott and Hrycak 1990; Blair-Loy 1999; Pollock et al. 2002), family histories (Elzinga and Liefbroer 2007), school-to-work transitions (Scherer 2001; Schoon et al. 2001; McVicar and Anyadike-Danes 2002; Brzinsky-Fay 2007) and life-course trajectories (Billari and Piccarreta 2005; Wiggins et al. 2007; Martin et al. 2008). As the technical situation improved with the implementation of sequence analysis in the statistical software packages Stata1 and R,2 researchers from different disciplines became able to compare sequences of a large number of individuals, finding out similarities, quantifying certain characteristics or grouping them into ideal types. The increasing number of applications also led to a discussion about the potential and limitations of sequence analysis methods. In recent years, a number of researchers have worked on enhancements of the method itself (Gauthier et al. 2009; Hollister 2009; Studer et al. 2011), some of which are presented in a special issue of Sociological Methods & Research (Brzinsky-Fay and Kohler 2010), as well as an increasing number of sequence analysis applications within the social sciences (Huang et al. 2007; Kogan 2007; Shoval and Isaacson 2007; Quintini and Manfredi 2009; Gauthier et al. 2010; Salmela-Aro et al. 2011; Simonson et al. 2011).

Sequences are ordered listings of elements (MacIndoe and Abbott 2004; Brzinsky-Fay et al. 2006), in which an element can be a certain status (e.g., labour market status) or a physical object (e.g., base pair of DNA) or an event (e.g., dance step). These elements are tied to either fixed points of time (e.g., status in a certain month), or to fixed positions (e.g., protein at position 12). Their specific order is of crucial importance and cannot be changed. Individual school-to-work transition sequences are composed of different labour market statuses, such as employment, unemployment, education, apprenticeship, and inactivity. The resulting complexity of empirical transitions composed from these five statuses is provided in Figure 1, which shows the monthly labour market entry sequences for the first 60 months after leaving school in three countries (France, Spain and Germany). Two things appear quite clear: There are obvious differences between countries; and the variance within countries is remarkably high. In this situation, the application of event history analysis would require the definition of the event of interest, which necessarily must remain arbitrary and disregards meaningful information.

Five-year-long school-to-work transitions in 3 countries.

Figure 1.
Five-year-long school-to-work transitions in 3 countries.

Source: ECHP, own calculations.

Figure 1.
Five-year-long school-to-work transitions in 3 countries.

Source: ECHP, own calculations.

Close modal

The main task of sequence analysis is to reduce complexity by comparing, sorting and grouping these sequences. The result is a typology of transition types that – apart from giving interesting insights – could be used as a well-grounded argument for choosing certain events of interest.3 The comparison of sequences includes visual inspection and calculation of simple descriptive indicators for specific sequence characteristics – such as the length of the sequence, the number of episode changes within a sequence, or the number of different elements in the sequence. Elzinga (2003) proposed measures for ‘turbulence’ and the frequency of certain subsequences to qualify sequence characteristics. The most frequently used technique for comparing sequences is optimal matching (OM). OM defines the distance between two sequences as the number of operations – substitution and deletion/insertion – it takes to transform one sequence into the other. The resulting distance measure is called the ‘Levenshtein distance’ (Levenshtein 1966), and the values are computationally achieved by the Needleman–Wunsch algorithm (Needleman and Wunsch 1970). After having established the similarity between each pair of sequences, the resulting distance matrix can be used as input for a cluster analysis or multidimensional scaling. The result is a classification that consists of a manageable number of sequence types, as shown in Figure 2. These eight ideal types of transitions appear in all countries, but their proportions vary. They can be used to create meaningful subgroups, which can be analysed separately. This kind of longitudinal exploration also provides event history models with good descriptive information, from which this powerful hypothesis-testing method could start, while avoiding unfounded pre-decisions.

Ideal types of school-to-work transitions.

Figure 2.
Ideal types of school-to-work transitions.

Source: ECHP, own calculations.

Figure 2.
Ideal types of school-to-work transitions.

Source: ECHP, own calculations.

Close modal

The application of OM requires the ex-ante definition of costs for the basic operations used to transform one sequence into the other. The supposed ‘subjectivity’ or ‘arbitrariness’ of this definition is one of the main objections against the method (Levine 2000; Wu 2000). In fact, in many cases there is no theoretical basis for choosing the numerical values of the costs of substitution, insertion, or deletion. However, in some cases the computation of these costs via transition frequencies might be a solution, while heuristic testing of automated cost calculation, as recently proposed by Gauthier et al. (2009), might be another. There is no ‘true’ cost structure, but in order to find patterns in the data, different cost structures can be tested iteratively, while the requirements of validity and reliability are considered (Bernard 2000). Another drawback of OM is the one-dimensionality of the elements that the sequences were composed of. The analysis of parallel or multiple sequences (e.g., employment career and family formation simultaneously) has been a serious handicap of the method until lately, a couple of attempts tackled this challenge, mainly in the field of life-course research (Aassve et al. 2007; Piccarreta and Billari 2007; Pollock 2007). The application of sequence analysis provides important potential – as Raffe (2009: 111) points out: ‘The recent quantitative work comparing transition sequences […] represents an important line of development’.

Sequence analysis cannot replace event history models, because it is not a causal method and sequence analytical methods’ contribution to answer the question ‘why?’ is limited by nature. It has a holistic perspective, which reflects the one-sided view on the structural character of life courses ‘as a whole’, while at the same time ignoring individual agency, which is better reflected by event history analysis. For these reasons, sequence analysis should be seen as a complement to event history modelling. The most promising future methodological and theoretical development in transition analysis is the integration of both approaches. For instance, the explorative potential of sequence analysis could serve as a basis for the definition of meaningful events for a confirmatory event history model. Another possibility is that the ideal types that result from the grouping process also allow for group-wise analyses with different events of interest. This allows taking into account long transition processes and increasing the validity of confirmatory results.

This article aims at overcoming a serious limitation in contemporary school-to-work transitions research, which has its origins in an unclear definition of the central concepts ‘transition’ and ‘trajectory’ and in the limitation of school-to-work transition research to hypothesis-testing methods, in the first line event history analysis.

The prolongation of educational processes and the increasing flexibilisation of labour markets in western societies have led to higher complexity of what is used to be referred to as ‘school-to-work transition’. From a life-course research perspective, this period must be conceptualised as a ‘trajectory’ rather than a ‘transition’. The concentration on single status changes has shown that this complexity exists, but it must fail when it comes to a qualitative exploration of how these complexity looks like in detail.

It is shown in this article that all relevant individual-level indicators, which are used in school-to-work transition research, suffer from this ‘time-point problem’. Even if durations are focused on, analyses are limited to single episodes while disregarding other episodes, their sequence, or their composition. In order to avoid arbitrariness and to detect qualitative changes and variations in individual school-to-work transition processes, explorative methods must find their way into the methodological toolbox of researchers. One of these methods, which provide a good and established instrument for the exploration of longitudinal information, is sequence analysis. Within the field of school-to-work transition, some studies already exist, but they must be integrated with each other in a meaningful manner. A coming ‘new wave’ of school-to-work transition research can yield promising new insights into these processes of increasing complexity, if exploration and hypothesis-testing are carried out hand in hand.

1

A platform-independent implementation of various tasks for sequence analysis including optimal matching is the Stata ado-package SQ (Brzinsky-Fay et al. 2006). Additionally, Stata plugins for optimal matching and other algorithms for sequence analysis are available from the website http://teaching.sociology.ul.ie/seqanal/ maintained by Brendan Halpin.

2

For R, the software package ‘TraMineR’ is available (http://mephisto.unige.ch/traminer/), which offers a couple of functions that allow for comprehensive applications of sequence analysis.

3

The result of the sorted and grouped sequences from the sample used in can be found in Brzinsky-Fay (2007).

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Christian Brzinsky-Fay is senior researcher at the WZB Berlin Social Research Center. His main scientific interests are life-course research, education and labour market research and empirical methods.

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