Abstract Life course research accentuates that employmenttrajectories are governed by individual determinants and en-dogenous causalities; thus, the start to the employment ca-reer enduringly affects workers’ future mobility patterns.However, their actions are always embedded within a par-ticular framework: Their employment trajectories are influ-enced by firm-specific opportunity structures, regional het-erogeneities, and the business cycle. This article focuseson the structural factors framing worker’s mobility pro-cesses.
Structural and cyclical determinants were assessed bycombining a German linked employer-employee datasetwith data on regional economic characteristics from the sta-tistical “spatial planning regions”. The hierarchically clus-tered data were explored with multilevel analysis models.These identified the key factors influencing employmentstability; the determinants of upward, lateral, and down-ward interfirm mobility; and transitions leading to unem-ployment.
Our results show that employees can minimize the en-dogenous causality by taking advantage of particular frame-work conditions: A firm’s investments in further trainingand internal infrastructure impact positively on employmenttrajectories, and work councils increase employment stabil-ity, especially during periods of economic growth. In con-trast, employment trajectories are destabilized by disadvan-tageous firm demographics and intensive use of fixed-termemployment. During an economic downswing, employmentopportunities are better in densely populated areas, whereas
M. Dütsch (B) · Prof. Dr. O. StruckChair of Labour Studies, Otto-Friedrich University of Bamberg,Feldkirchenstraße 21, 96052 Bamberg, Germanye-mail: [email protected]: http://www.uni-bamberg.de/arbeitswiss
unemployment risks dominate in rural areas. In the periodof economic growth, all employees within a region bene-fit from a higher local level of human capital regardless ofqualification level, whereas during an economic downturn,skill segregation prevails and only the highly qualified ben-efit.
Keywords Job duration · Employment career · Structuraleffects · Linked employer-employee data
JEL Classification J62 · J64 · M51 · O15
Erwerbsverläufe in Deutschland: Zur Bedeutungbetrieblicher Charakteristika und regionalerDisparitäten
Zusammenfassung Die Lebensverlaufsforschung betontdie Bedeutung individueller Faktoren sowie des endogenenKausalzusammenhangs für den Erwerbsverlauf. Demnachbestimmt insbesondere der Einstieg in die Erwerbsphasezukünftige Chancen und Risiken im Erwerbsleben. Aller-dings ist zu berücksichtigen, dass Arbeitskräfte innerhalbspezifischer Rahmenbedingungen agieren. So werden derenErwerbsverläufe durch betriebliche Gelegenheitsstrukturengeprägt. Zudem handeln Arbeitnehmer und Arbeitgeber inunterschiedlich strukturierten Regionen. Schließlich sindauch konjunkturelle Einflüsse zu beachten. Deshalb rich-tet der vorliegende Artikel den Fokus auf die Untersuchungstruktureller Einflussfaktoren.
Um strukturelle und konjunkturelle Determinanten abbil-den zu können, wurde ein Linked Employer-Employee Da-tensatz des IAB und Daten zu regionalen Charakteristikaauf Ebene der Raumordnungsregionen verknüpft. Die Aus-wertung der hierarchisch geclusterten Daten wurde anhandvon Mehrebenenmodellen durchgeführt. Zunächst wurden
die Einflussfaktoren auf die Beschäftigungsstabilität und da-nach die Determinanten von Aufstiegen, lateraler Mobili-tät und Abstiegen bei direkten Betriebswechseln sowie vonÜbergängen in Arbeitslosigkeit erforscht.
Unsere Ergebnisse zeigen, dass die zweifellos vorhande-nen endogenen Kausalzusammenhänge im Erwerbsverlaufdann an Bedeutung verlieren, wenn Beschäftigte sich struk-turelle Einflussfaktoren zunutze machen können: Demnachwirken betriebliche Investitionstätigkeiten in Weiterbildungsowie in die Infrastruktur positiv auf Erwerbsverläufe. Be-triebsräte und Personalvertretungen erhöhen vor allem ineiner guten konjunkturellen Situation die Beschäftigungs-stabilität. Hingegen können eine ungünstige Organisations-demografie sowie die intensive Nutzung von Befristungenzur Destabilisierung des Erwerbsverlaufs führen. In einerkonjunkturellen Abschwungphase bieten dichter besiedelteRäume bessere Beschäftigungsoptionen, während in länd-lichen Gegenden Beschäftigungs- und Arbeitslosigkeitsrisi-ken herrschen. Von einer hohen regionalen Humankapital-ausstattung profitieren im Aufschwung alle Qualifikations-gruppen, während im Abschwung eine Segregation bezüg-lich der Qualifikationsgruppen zu beobachten ist.
1 Introduction
The employment period is a decisive phase in the lifecourse. It strongly influences opportunities in life, espe-cially with regard to wage levels and welfare state entitle-ments (Heinz 2006; Vobruba 2000). As a result, job stabil-ity has a high value from the worker’s perspective. It shel-ters from the risk of unemployment and allows the develop-ment of human capital (Blossfeld et al. 2006; Boockmannand Steffes 2010; Winkelmann and Zimmermann 1998).However, recent studies have concluded that the Germanlabor market is characterized by a significant and grow-ing proportion of mobile workers (Blossfeld et al. 2006;Giesecke and Heisig 2011). Despite this change, modern ap-proaches to understanding employment systems show thatjob stability can also be assured in open employment sys-tems when there are adequate opportunities for interfirmmobility (Alewell and Hansen 2012; Lepak et al. 2006;Struck and Dütsch 2012). These allow workers to both pre-serve and further develop their occupational skills through-out their employment careers. However, downward mobilityor transitions to unemployment cause a loss of qualificationsand lead to unfavorable labor market chances in the future(ebd.).
To date, most research has focused on the individualdeterminants influencing employment trajectories. The im-portant role of factors such as gender, nationality, educa-tional level, and the particular age cohort in explaining em-ployment (dis-)continuities has been widely documented
(Bergemann and Mertens 2004; Giesecke and Heisig 2011;Hillmert et al. 2004). Furthermore, it is accentuated that em-ployment trajectories are governed by endogenous causal-ity; cohort analyses indicate that a poor start to the employ-ment career may impact negatively on future mobility pat-terns (Blossfeld 1986; Hillmert et al. 2004).
Although Coleman (1990) has pointed to the importanceof the broader social context for individual behavior, re-search on labor mobility has paid less attention to struc-tural effects. Thus, the impact of firm-specific factors and re-gional disparities on employment trajectories has remainedlargely unexplored. Because “new structuralism” stressesthe significance of accounting for firm characteristics (Baronand Bielby 1980), it seems pertinent to consider these factorsfurther. Moreover, spatial economics, especially the semi-nal theory on “new economic geography” (Krugman 1991),has triggered a wave of empirical work on spatial analy-sis. Using this theoretical approach, several economists haveshown the relevance of regional factors for the developmentof both employment and wages in Germany (Blien 2001;Blien et al. 2001; Möller and Tassinopoulos 2000).
This article seeks to contribute more fully to researchon employment trajectories by focusing on three struc-tural framework conditions in greater detail. These are(1) whether and to what extent job exits as well as up-ward, lateral, or downward interfirm mobility and transi-tions into unemployment are influenced by firm-specificcharacteristics; and (2) whether and to what extent thesemobility processes correlate with various regional determi-nants. Additionally (3) different economic conditions willbe taken into account to analyze their impact on labor mo-bility. Thereby, it should be studied if employees can min-imize the significant effect of individual determinants andendogenous causalities, which are stated by life course re-searchers, by taking advantage of particular framework con-ditions. The article studies these conditions by combininga German linked employer-employee dataset with data onregional characteristics from the Federal Institute for Re-search on Building, Urban Affairs and Spatial Development(BBSR). Based on this hierarchically structured dataset, itdeploys a multilevel framework to evaluate the different mo-bility processes. The article starts by reviewing the currentstate of research in Sect. 2. Theoretical considerations arepresented in Sect. 3. Data and the estimation strategy aredescribed in Sect. 4. Section 5 contains the empirical resultson employment careers in Germany. Finally, Sect. 6 sum-marizes the findings.
2 Current state of research
Several recent empirical studies have demonstrated a rangeof effects of firm characteristics on employment careers.
Employment trajectories in Germany: do firm characteristics and regional disparities matter? 109
Grotheer et al. (2004) looked at the job stability of employ-ees who had just joined a firm. They found a stabilizing ef-fect of work councils on employment as well as a strongcorrelation between the prevalence of part-time or fixed-term employment and employees leaving the firm. A lackof opportunities for promotion owing to a firm’s age demo-graphics was shown to have only a slightly positive effecton the probability of leaving a firm. According to Boock-mann and Steffes (2005, 2010), a positive effect was evidentfrom those firms that provided opportunities for further edu-cation. Work councils decreased job exit rates especially forblue-collar workers. Bergemann and Mertens (2004) foundhigher risks of dismissals in smaller firms along with lowerrates of layoffs and voluntary departures in larger firms.Moreover, Giesecke and Heisig (2011) showed that menworking in larger firms are much more likely to change em-ployer.
Only a few studies have assessed the effect of regional in-dicators on job stability or employment trajectories. Accord-ing to Grotheer et al. (2004), fluctuations in production anddemand as well as high regional unemployment rates pro-voke a change of employer in western Germany, whereasmobility between firms and exits into unemployment arelower in eastern Germany. Boockmann and Steffes (2005)have reported similar results on unemployment rates withineach German state. Whereas in western Germany, they couldobserve no definite link between higher rates of unemploy-ment and job stability, the former proved to have a stabi-lizing effect in eastern Germany. Furthermore, a higher un-employment rate increases the risk of being made redundantafter completing tenure of employment in western Germany.In eastern Germany, interfirm changes are less probable forwomen. However, in a further study of male employees,Boockmann and Steffes (2010) failed to find a significantimpact of higher unemployment rates on job stability, butthat it did lead to a decrease in mobility between westernGerman firms.
Some current research shows the effect of the economiccycle on employment careers. Erlinghagen (2005) found anincreased probability of involuntary dismissal during pe-riods of economic decline in western Germany between1985 and 2001. According to Giesecke and Heisig (2011),the probability of changing job increases during periods ofeconomic growth. Struck et al. (2007) found more volun-tary fluctuation in periods of growth and higher levels ofstability among long-term employees. Hübler and Walter(2009) identified a contracyclical risk of dismissal as wellas a procyclical risk of terminating employment. Althoughmacrolevel studies cannot fully explain the reasons for theseemployment dynamics, they do support the assertion that jobchanges occur in a procyclical environment (Fitzenbergerand Garloff 2007; Schaffner 2011). International studies in-dicate that wages are also subject to procyclical fluctua-tions: They fall in periods of economic decline for both
existing staff and new entrants (Devereux and Hart 2006;Hart 2006).
To summarize, the literature reveals that several empir-ical studies have focused on firm characteristics and eco-nomic conditions and their impact on employment stabilityas well as the probability of becoming unemployed. How-ever, there has been insufficient research on interfirm mo-bility processes. Regional heterogeneities—with the excep-tion of East-West comparisons—are mostly neglected in re-search on employment trajectories, even though macrolevelstudies point to considerable effects of regional disparitieson employment and wages. Bearing this in mind, the fol-lowing analysis of mobility processes aims to close theseidentified research gaps. It examines the impact of firm-specific and regional determinants as well as economic con-ditions on job exits; on upward, lateral, or downward inter-firm mobility rates; and on transitions into unemployment.The next section presents the theoretical background andformulates the hypotheses to be tested in the empirical anal-yses.
3 Theoretical background
3.1 Firm-specific factors and the career path
Recent life-course research has studied the effect of inter-nal firm processes and structures on individual career op-portunities, wages, and socioeconomic status (Ahrne 1994;Baron and Bielby 1980; Struck 2006). Employment careersand mobility processes are perceived as the outcome of aninteraction between employers and employees. The dynam-ics of this interaction stem from the labor market segmen-tation practiced by firms within the framework of the insti-tutional setting (Doeringer and Piore 1971; Sengenberger1987). With this in mind, recent approaches in human re-source management systems (HRMS) as well as employ-ment systems derive segmentation processes from a firm’sinternal labor and employment organization (Hendry 2003;Lepak et al. 2006; Struck and Dütsch 2012). They ar-gue that firms are made up of and apply different em-ployment systems that vary in the average duration of theemployment relationship and the opportunities for mo-bility. Therefore, openness to external markets dependson the internal labor organization, the technical equip-ment, and the demand for professionally accredited qual-ifications. However, it also depends on the availability ofworkers within both internal and external labor markets(ibid.).
In this context, vacancy chain models explain internalemployment trajectories by supposing that employment sys-
110 M. Dütsch, Prof. Dr. O. Struck
tems are characterized by either more open or more closedpositioning systems (Sørensen 1977). When an employ-ment system is relatively closed due to institutional arrange-ments such as collective agreements, vacancies normallyarise through voluntary departures from the firm. In this sit-uation, the worker occupying the next lower position in thehierarchy will be promoted to the vacant position (ibid.). Ap-proaches in organizational demography extend this vacancychain model to account for the impact of a firm’s demo-graphic structures on internal career options (Mittman 1992;Pfeffer 1985). Thus, the age distribution of the workforcecan block promotion opportunities for employees positionedahead of a large age cohort. This creates an employment en-vironment that may encourage voluntary interfirm mobility.This leads to the following hypotheses:
H1: The probability of workers leaving their employers willincrease when internal promotion opportunities areblocked.
H2: Because the mobility processes triggered by blockedpromotion opportunities are voluntary, they will resultin lateral or upward interfirm job changes.
Regarding firm size, it is often thought that smaller firmsshow a higher rate of staff turnover than larger ones due totheir more limited capacity to adapt to changing market con-ditions. Larger firms are in a better position to cope with, forexample, sudden fluctuations in sales revenue, because theycan balance out lost sales in one product area through gainsin another (Struck 2006). Furthermore, they can offer moreemployment opportunities and promotion prospects. Hence,larger firms are characterized as being able not only to offermore possibilities for changing jobs, both laterally and verti-cally, but also to retain more staff than smaller firms (Baronand Bielby 1980; Carroll and Mayer 1986). This leads totwo further hypotheses:
H3: The larger a firm, the higher its job stability.H4: Interfirm job changes following employment in a larger
firm will be mostly voluntary and therefore lead morefrequently to lateral or even upward mobility.
Owing to processes of economic transnationalism (Bloss-feld et al. 2006; Giesecke and Heisig 2011) and the shiftingof sociostructural frameworks (Struck 2006), firms are in-creasingly taking advantage of instable and fixed-term em-ployment relationships in order to remain competitive by ex-ploiting the potential of flexibility. In line with segmenta-tion theory, these atypical types of employment are assignedto a firm’s noncore workforce (Doeringer and Piore 1971;Sengenberger 1987). It is assumed that both job mobilityrates are high and human capital cannot be embedded ormaintained within the noncore workforce (Blossfeld et al.2005; Struck and Dütsch 2012). Moreover, fixed-term em-
ployment is often attributed to the noncore area, because em-ployers can circumvent the relatively strict employment pro-tection regulations and minimize transaction costs when lay-ing workers off (Hohendanner 2010). Fixed-term employeestherefore face a risk of receiving less stable jobs. As a result,when there is a large share of fixed-term employees, a firm’semployment system is considered to be more open (ibid.;Struck and Dütsch 2012). This leads to the next hypothe-sis:
H5: The higher the fixed-term share of a firm’s workforce,the lower the job stability.
The heightened structural and demographic changes haveengendered an extensive discussion on the past and futurerole of education as well as on the acquisition of competen-cies (Büchel and Pannenberg 2004; Dieckhoff 2007). Life-long learning and the completion of further training are con-sidered to be highly important if individuals are to possessup-to-date knowledge and maintain or improve their socialstatus. Moreover, further training programs are consideredto be highly relevant if firms are to stay competitive, be-cause they ensure a constant adaption of their employeesto modern technologies and work processes (ibid.). This isin keeping with human capital theory that emphasizes theimportance of education and training in raising the produc-tivity of workers by increasing their cognitive abilities andtheir individual capability (Becker 1962). However, there isno guarantee that workers receiving further training will re-main in a firm. Therefore, employers try to reduce voluntaryquits and avoid “sunk costs” (Neubäumer 2006) by payinghigher wages and offering promotion opportunities (Bücheland Pannenberg 2004; Dieckhoff 2007). Furthermore, it isassumed that firms providing further training are regarded asgood opportunity structures by both their own staff and ex-ternal third parties. This indicates a higher reputation com-pared to firms that do not offer further training. Employ-ees may profit from a firm’s high reputation, because theirown individual reputation is also enhanced by the reputa-tion of the group or organization they belong to (Backes-Gellner and Tuor 2010; Tirole 1996). Hence, a firm’s goodreputation is considered to be a positive signal that is alsoascribed to the current staff. Workers leaving such a firmmay therefore achieve lateral or upward interfirm mobil-ity either because of their gain in productivity due to thefurther training they have received or because of the as-cribed positive signal. This results in the next two hypothe-ses:
H6: Those firms that offer further training opportunitieswill provide more stable jobs.
H7: Workers leaving firms that offer further training pro-grams will be able to achieve interfirm job change andavoid downward mobility.
Employment trajectories in Germany: do firm characteristics and regional disparities matter? 111
Investment in modern technologies often requires spe-cific human capital and specific training (Bresnahan et al.2002). Workers employed in firms working with the lat-est technology should be able to accumulate human capi-tal that is more specific and up to date than those workingin firms using outdated technology. However, modern ma-chinery and equipment may increase not only workers’ pro-ductivity but also the employer’s efforts to prevent voluntaryquits (Neubäumer 2006). Job stability is therefore assumedto be high in such firms (Boockmann and Steffes 2010). Fur-thermore, modern firms can be viewed as good opportunitystructures that, in turn, suggest a comparatively favorablereputation. Employees may therefore benefit from a firm’smodern technologies by being ascribed positive signals andgaining the opportunity to quickly find another employer.The hypotheses are as follows:
H8: A high level of technological development will increasejob stability in a firm.
H9: Employees who have worked in modern firms will beable to manage immediate transitions between firms.
Work councils are regarded as an instrument for disciplin-ing employers who take an opportunistic approach to em-ployment, information, or payment (Jirjahn 2009; Mohren-weiser et al. 2012). Especially when employers break nonen-forceable and, therefore, implicit contracts about particularworking conditions by announcing, for example, lay-offs,employee representation is deemed to promote industrialdemocracy through its legal co-determination rights (ibid.).Based on their veto rights as well as participation and con-sultation rights, work councils are able to increase employ-ees’ bargaining power. Because they can delay decisions,work councils may increase an employer’s transaction costs.Moreover, worker co-determination increases employees’work satisfaction resulting in fewer voluntary quits (Pfeifer2011). For this reasons, it is hypothesized:
H10: The presence of work councils and employee repre-sentatives will ensure the closure of internal employ-ment systems and thereby increase employment stabil-ity.
3.2 Region-specific factors and the career path
What several established labor market theories have in com-mon is a tendency to avoid addressing and explaining therole of macrostructural factors on the labor market (Fujitaet al. 2001). In contrast, research on regional economics,which has become increasingly significant within economicscience in recent years (ibid.; Krugman 1991, 1998), focuseson explaining regional heterogeneities and their effects onregional growth. Krugman (1991) has developed a core-periphery model based on the work of Hirschman (1958)
that considers a range of divergent centripetal and centrifu-gal forces. It looks at the effect of positive external factorsand points to the mutual relationship between economies ofscale, transportation costs, and migration. Thus, centripetalforces lead to urbanization effects because they encouragethe concentration of economic activities within a certain ge-ographical area. Industrial centers are strengthened becausefirms and employees capitalize agglomeration advantages.In the case of high economies of scale, a company tries tolimit production to one single facility and to serve the marketfrom there. In order to reduce transportation costs, the com-pany will set up in a location with a high population densityand, therefore, higher demand. Both the workforce and firmsare attracted to a regional economy in order to realize theagglomeration advantages given by a larger potential salesmarket and employee pool (Krugman 1991). Hence, lowertransportation costs and higher economies of scale increasethe likelihood of development for economic centers and pe-ripheries alike.
According to Fassmann and Meusburger (1997), a pri-mary segment of the labor market, characterized by stablejobs, good wages, promotion prospects, and a predominanceof more highly qualified employees, will be found in cen-tral or core economic locations. This is dependent on stablelevels of demand and higher economies of scale. Further-more, lateral or even upward interfirm mobility is higher indenser regions, because the same location is shared by sev-eral other competitors and potential employers. In contrast,a secondary segment of the labor market characterized byinstable and badly paid jobs, lower qualifications, marginalpromotion prospects, and high unemployment will take rootin rural areas. This is due to poor market efficiency, insta-ble levels of demand, and the fact that other potential em-ployers are located far away. The following hypotheses areproposed:
H11: Employees who work in core areas will benefit fromagglomeration advantages through high job stability.
H12: In denser regions, lateral and upward interfirm mo-bility will take place to a greater extent.
H13: Job stability will be lower and unemployment riskswill be higher in rural areas.
A further approach to regional research—endogenousgrowth theory—has established a link between the quali-fication structures of a regional workforce and economicdevelopment. It contests the assumption of neoclassical la-bor market theory that long-term economic growth is de-termined exogenously (Lucas 1988). Instead, it emphasizesthe dependence of regional economic growth potential onthe level of skills and knowledge available in the region. Be-cause employees’ productivity increases alongside the ac-quisition of human capital, the strength of locally embeddedhuman capital is considered to be the “engine of growth”
112 M. Dütsch, Prof. Dr. O. Struck
(Lucas 1988) over and above any technological progress. Inthis theory, all groups of workers and firms in a region mightbenefit from productivity gains. According to Lucas (1988),this is a result of positive external effects caused particu-larly by productivity gains within certain groups of work-ers (e.g., the highly skilled). These spillover effects maybe due to, for example, signaling effects and/or the sup-ply chain. Blien and Wolf (2002) as well as Farhauer andGranato (2006) have confirmed this assumption. In contrast,however, other studies have shown a divergent developmentin terms of employment and wages: In line with the the-sis of skill segregation, especially highly qualified employ-ees are seen to profit from an improvement in the regionalskill-level structure (Gerlach et al. 2002; Schlitte et al. 2010;Stephan 2001). The following hypotheses are derived:
H14: The higher the local level of human capital, the morestable employment will be for all workers. This sta-bility may be realized internally or through lateral orupward interfirm mobility.
H15: A high local level of human capital results in skill seg-regation. As a result, the jobs of lower skilled employ-ees will be less stable, whereas the highly skilled willprofit through stable jobs or even upward interfirmmobility.
3.3 Career paths and the business cycle
The effects of cyclical fluctuations on employee mobility inthe labor market can be illustrated with the sorting model.This explains labor market fluctuations and the efficient re-allocation of employees to workplaces (Hinz and Abraham2008; Struck 2006). It posits that seeking new employmentwhile in work will result in a change of job only if it holdsthe promise of higher wages or other nonmonetary bene-fits as well as compensation for the extra expenses incurredthrough job seeking. Therefore, this model provides an eco-nomic indicator of employee behavior in terms of the deci-sion to terminate an employment contract. Assuming that aperiod of economic growth leads to the generation of bet-ter paid jobs, then not only will interfirm mobility rise dueto voluntary job transitions but also, parallel to this, averagejob stability will decline. On the other hand, during a reces-sion, only a few attractive jobs will be generated and therewill be hardly any margin for wage increases. Thus, the in-centive for voluntary mobility declines, whereas involuntarylayoffs and unemployment risks rise (ebd.). This producesthe following hypothesis:
H16: An economic upswing will lead to upward interfirmmobility; in contrast, a cyclical downturn will triggertransitions into nonemployment.
The next section describes the data and the estimationstrategy used to test the hypotheses.
4 Data and method
4.1 Data and sample definition
The database for the following empirical analyses is theGerman LIAB, a linked employer-employee dataset fromthe “Institute for Employment Research” (Jacobebbinghaus2008). It combines data on employees with the “IAB Es-tablishment Panel”, which is a representative annual sur-vey of 16,000 business establishments (Fischer et al. 2008).This study uses the “LIAB longitudinal version 2”, whichincludes approximately 9,700 firms that took part in the sur-vey continuously between either 1999 and 2001 or 2000 and2002. The employment and welfare recipient histories forthe period from 1993 to 2006 were drawn from those per-sons who were employed in any of the LIAB firms for atleast one day between 1997 and 2003.
Data on employees is taken from two different sources.First, the “employee history” contains data on individualemployment history records submitted by employers to theGerman public pension insurance system. The reliabilityof this data is high, because failure to supply accurate in-formation is a legal misdemeanor that may even result ina summary offense. One exception is individual informa-tion on the education variable that is adjusted by imputa-tion (Fitzenberger et al. 2005). The employment statisticsregister covers about 80 % of total employment. Moreover,the “benefit recipient history” contains data on the receipt ofunemployment benefits, unemployment assistance, or main-tenance allowance. Basic personal data on individual em-ployment histories is left-censored and can thus be trackedfrom January 1, 1993 onward. The generated data enables usto identify the three labor market states “unemployment”,“new employment”, and “employment gap” However, it isnot easy to identify all periods of unemployment becauseinformation is recorded only for the periods in which a per-son receives unemployment benefits from the German Fed-eral Employment Agency. As a result, the data do not ac-count for those unemployed people who were not officiallyregistered as such. Thus, a cleansing procedure is used togenerate the three labor market states detailed above. Ajob change between firms is defined as a period of unem-ployment not exceeding 90 days. It is likely that in mostof these cases, workers already knew their new employerswhen the previous job ended. An unemployment period isdefined, moreover, as a period in which the job seeker re-ceives unemployment benefits for at least one day within a90-day period. Finally, the state “employment gap” is acti-vated when no change of employment has occurred nor haveunemployment benefits been received within the 90-day pe-riod. Thus, this data allows us to construct complete em-ployment biographies for those employees covered by theLIAB.
Employment trajectories in Germany: do firm characteristics and regional disparities matter? 113
Fig. 1 Observation periods and the economic cycle
In addition, the LIAB dataset is merged with data on re-gional characteristics derived from the Federal Institute forResearch on Building, Urban Affairs and Spatial Develop-ment (BBSR). This contains information on rates of un-employment, GDP per capita, regional typologies with re-gard to population density, and their place as core or periph-eral regions, as well as the share of students. This data isbased on annual averages. The identified indicators exist forthe 97 German spatial planning regions, and this is consid-ered adequate for analyzing regional labor markets (Rend-tel and Schwarze 1996; Schwarze 1995). Thus, this gen-erated dataset permits simultaneous analyses of employers,employees, and the regional context. As noted in Sect. 3.3,it is necessary to account for cyclical effects on employmenttrajectories. Therefore, terminations of employment in 1999and 2002 were examined. As Fig. 1 shows, using the out-put gap1 as well as the unemployment rate, 1999 was char-acterized by economic growth; 2002, by decline (cf. alsoSachverständigenrat 2008, 2009).
The period covered in T1 examines persons who werealready employed on January 1, 1999 or were employed be-tween January 1, 1999 and December 31, 1999. T2, on theother hand, covers employees who were part of the work-force as of January 1, 2002 or were employed between Jan-uary 1, 2002 and December 31, 2002.
The data used here is restricted to full-time employeesaged 25–52 years. Thus, individuals in vocational training orstudents working during the university break are excluded.This also helps to avoid any confusion between those exit-ing employment and those taking early retirement. Further-more, employees whose income is above the income assess-ment ceiling are excluded because this information is cen-sored. These conditions provide a sample of 370,779 per-sons, 1,836 firms, and 97 regions during 1999 along with363,339 workers, 2,140 firms, and 97 regions during 2002.
1The definition proposed by the expert advisory board is used to datedistinct cyclical up- and downswing phases. The concept of the expertadvisory board (Sachverständigenrat 2008: 78ff.) reflects the so-calledoutput gap, that is, the relative deviance of the GDP from the produc-tion potential given as a percentage.
4.2 Econometric method
In the following, multivariate analyses are performed us-ing data covering workers, firms, and regions. This struc-turing of the data from the level of workers to regions is animportant aspect when choosing an estimation procedure.Moulton (1986, 1990) has noted that the inclusion of meso-and macrolevel variables in a standard regression analysisin which observations are assumed to be independent leadsto an inefficient estimation of the coefficients and to biasedstandard errors. Therefore, multilevel models are preferable,because they allow a grouping of individuals i within firmsj nested in regions k by considering residuals at the firmand the regional level. These residuals represent unobservedcharacteristics that cause correlations between outcomes foremployees from the same firm and region.
The empirical analyses are performed with the followingthree-level logistic random intercept model (Rabe-Heskethand Skrondal 2008; Skrondal and Rabe-Hesketh 2003):
logit{Pr
(yijk = 1 | xijk,C
(2)jk ,C
(3)k
)}
= β0 + β1xijk + C(2)jk ,C
(3)k
in which β0 represents the regression constant, β1 refersto the regression coefficients, and xijk is a vector with ex-planatory variables at the individual, firm, and regional lev-els. Finally, C
(2)jk and C
(3)k denote the random effects that
are assumed to be independent not only of each other butalso across clusters. C
(2)jk is also assumed to be indepen-
dent across units. Based on this three-level approach, em-ployment trajectories are assessed using a two-stage pro-cedure: First, we estimate the risk of job exit; second, weuse four further three-level logistic random intercept modelsto explore the following destination states: “upward job-to-job mobility” (defined as an increase in wages of at least10 %), “lateral job-to-job mobility”, “downward job-to-jobmobility” (defined as a decrease in wages of more than 5 %),and “unemployment”.2 All dependent variables are coded asdummy variables. In the first step, the value 1 represents ajob exit; it takes the value 0, when the employee remains inthe firm. In the second step, the value 1 respectively denotesone of the destination states; the value 0 subsumes all otheremployment states in each case.3
2We use asymmetric boundaries for upward and downward mobility,because we assume that employees perceive even slight reductions inwages as a worsening of their situation. In contrast, workers may per-ceive slight increases in wages as being normal and matching inflation.Therefore, the boundary for upward mobility is higher than that fordownward mobility. The exit state “employment gap” is not reportedfor purposes of clarity. These results are available from the authors onrequest.3Some descriptive statistics on the dependent variables are reported inTable 4 in the Appendix.
at-tests and chi-square tests were performed to explore the differencesbetween the two yearsbPercentages do not add up to exactly 100 due to imprecise roundingcThe total number of observations was 370,779 in 1999 and 363,339 in2002Source: Linked Employer-Employee Data (LIAB); own calculations
This analysis is carried out using a large set of 50 ex-planatory variables that can be divided into those concerningindividual, firm-specific, and region-specific factors. Look-ing at individual factors, information includes details ongender, age, level of education, nationality, job position, aswell as on the corresponding firm entrance cohorts and onprevious periods of employment. Firm-specific characteris-tics include firm size, age distribution, details on contractsand investments, the presence or absence of work councils,and the employment sector. Region-specific factors concernthe differentiated types of regions, the level of human capi-tal, productivity, and the unemployment rate.4
5 Results
5.1 Transition patterns after leaving employment
First, descriptive transition rates of the full-time employedare examined for the years 1999 and 2002 in order to obtainan indication of mobility patterns during different economicand business cycles. These are illustrated in Table 1.
Results show that in the majority of cases, irrespec-tive of the economic environment and in both years, al-most 10 % of employees left their firm. Approximately20 % of workers attained a higher position through in-terfirm mobility in 1999, a period of economic growth,whereas 55 % were not in employment. During the pe-riods of economic decline, only 14 % of employees im-
4Descriptive statistics of individual, firm-specific, and region-specificcharacteristics are reported in Tables 5 to 7 in the Appendix.
proved their employment status through interfirm mobility.In contrast, 63 % moved into nonemployment. These vary-ing patterns of mobility corroborate Hypothesis 16. On thewhole, results are in line with the procyclical fluctuationsin employment mobility (Fitzenberger and Garloff 2007;Hübler and Walter 2009; Schaffner 2011) and procyclicalwage increases (Devereux and Hart 2006; Hart 2006) foundin other research.
5.2 Examination of the variance components
Three-level logistic random intercept models are estimatedto analyze the several destination states.5 In models withoutexplanatory variables (intercept-only models), the variancein the outcome variable can be decomposed into proportionsassociated with the individual level, the firm level, and theregion level. For this purpose, the random part of the three-level models is explored by considering the estimated resid-ual intraclass correlation ρ for the latent responses. It is as-sumed that the level-1 error variance is equal to π2/3 for thelogistic link function (see, e.g., Rabe-Hesketh and Skrondal2008). Then we can obtain for the similarity of employees i
within the same region k:
ρ(region) = ψ(3)
ψ(2) + ψ(3) + π2/3.
Within the same firm j (and the same region k), we get:
ρ(firm, region) = ψ(2) + ψ(3)
ψ(2) + ψ(3) + π2/3.
The intercept-only analyses show that the values of ρ(firm,
region) vary between 0.202 and 0.416 in 1999 and between0.325 and 0.482 in 2002 for the observed exit states; thoseof ρ(region) range between 0.010 and 0.011 in 1999 as wellas in 2002.6 These findings indicate, on the one hand, thatmost of the variation can be ascribed to employees; on theother hand, that significantly more variation is attributableto firms rather than to regions.
The next section addresses which firm and regional fac-tors influence the opportunities and risks in employmentcareers in the two different cyclical phases by estimatingthree-level logistic random intercept models with explana-tory variables. These results are illustrated in Table 2. Al-though all of the explanatory variables are reported, we in-terpret only those that are relevant for testing the hypothe-ses.
5All estimations were performed using the lmer function from the lme4package of the statistical software R. This was very time consuming,because it took 30 days to get the results.6The complete estimates are given in Table 8 in the Appendix.
Employment trajectories in Germany: do firm characteristics and regional disparities matter? 115
Tabl
e2
Indi
vidu
al,fi
rm-s
peci
fic,a
ndre
gion
-spe
cific
fact
ors
influ
enci
ngem
ploy
men
ttra
ject
orie
s(t
hree
-lev
ello
gist
icra
ndom
inte
rcep
tmod
els)
Inde
pend
entv
aria
bles
1999
2002
Exi
tfro
mjo
bIn
terfi
rmca
reer
path
Une
mpl
oym
ent
Exi
tfro
mjo
bIn
terfi
rmca
reer
path
Une
mpl
oym
ent
Upw
ard
mob
ility
Lat
eral
mob
ility
Dow
nwar
dm
obili
tyU
pwar
dm
obili
tyL
ater
alm
obili
tyD
ownw
ard
mob
ility
Indi
vidu
alfa
ctor
s
Sex
(1=
fem
ale)
0.43
2∗∗∗
−0.4
08∗∗
∗−0
.264
∗∗∗
−0.1
39∗∗
0.24
5∗∗∗
0.56
8∗∗∗
−0.2
53∗∗
∗−0
.158
∗∗∗
0.22
80.
184∗
∗∗
Nat
iona
lity
(1=
fore
ign)
0.02
3−0
.105
∗−0
.502
∗∗∗
−0.1
25∗
0.35
8∗∗∗
0.00
1−0
.221
∗∗∗
−0.6
39∗∗
∗−0
.181
0.29
1∗∗∗
Age
:Ref
eren
ce:2
5–34
year
s
35–4
4ye
ars
(1=
yes)
−0.3
54∗∗
∗−0
.601
∗∗∗
−0.2
53∗∗
∗−0
.274
∗∗∗
0.10
6∗∗∗
−0.3
46∗∗
∗−0
.572
∗∗∗
0.00
8−1
.154
∗∗∗
0.02
1
45–5
2ye
ars
(1=
yes)
−0.3
26∗∗
∗−0
.858
∗∗∗
−0.3
41∗∗
∗−0
.484
∗∗∗
0.29
7∗∗∗
−0.2
63∗∗
∗−0
.898
∗∗∗
0.34
6∗∗∗
−1.3
200.
121∗
∗∗
Hig
hest
educ
atio
nle
vel:
Ref
.:Se
cond
ary
scho
olan
dvo
catio
nalt
rain
ing
No
voca
tiona
ltra
inin
g(1
=ye
s)0.
111∗
∗∗0.
326∗
∗∗−0
.141
∗∗−0
.388
∗∗∗
0.08
5∗∗
0.16
9∗∗∗
0.04
3−0
.116
∗−0
.277
0.06
2∗
Adv
ance
dse
cond
ary
scho
olan
dvo
catio
nal
trai
ning
(1=
yes)
0.02
10.
379∗
∗∗−0
.012
0.05
5−0
.207
∗∗∗
0.04
20.
289∗
∗∗0.
013
−0.4
23−0
.223
∗∗∗
Uni
vers
ityde
gree
(1=
yes)
−0.3
03∗∗
∗0.
136∗
∗0.
007
−0.3
43∗∗
∗−0
.367
∗∗∗
−0.5
20∗∗
∗−0
.362
∗∗∗
−0.4
61∗∗
∗−1
.895
∗∗∗
−0.3
20∗∗
∗
Job
Posi
tion:
Ref
.:Sk
illed
blue
-col
lar
Uns
kille
dbl
ue-c
olla
r(1
=ye
s)0.
318∗
∗∗−0
.133
∗∗0.
088∗
0.27
9∗∗∗
0.66
5∗∗∗
0.30
9∗∗∗
0.08
20.
092∗
0.47
3∗∗
0.57
4∗∗∗
Mas
ter
craf
tsm
an(1
=ye
s)−0
.274
∗∗∗
−0.4
99∗∗
∗0.
331∗
∗∗−0
.503
∗∗∗
−0.3
21∗∗
−0.3
87∗∗
∗0.
442∗
∗∗−0
.142
−1.4
97∗
−0.5
95∗∗
∗
Whi
te-c
olla
r(1
=ye
s)−0
.225
∗∗∗
−0.1
49∗∗
∗−0
.001
−0.4
83∗∗
∗−0
.314
∗∗∗
−0.1
39∗∗
∗0.
271∗
∗∗0.
341∗
∗∗−0
.880
∗∗∗
−0.3
65∗∗
∗
Coh
orts
and
prev
ious
empl
oym
ents
tate
:Ref
.:Pe
rman
ently
empl
oyed
Firs
tem
ploy
men
t(1
=ye
s)0.
968∗
∗∗1.
794∗
∗∗0.
173
0.45
61.
318∗
∗∗0.
803∗
∗∗0.
813∗
∗∗−0
.152
3.06
7∗∗∗
1.18
1∗∗∗
Ent
ranc
eat
mos
t1ye
arag
o(1
=ye
s)×
Shar
eof
empl
oym
ent
2.21
1∗∗∗
3.96
6∗∗∗
1.73
3∗∗∗
3.32
4∗∗∗
2.18
1∗∗∗
2.16
8∗∗∗
3.72
7∗∗∗
1.37
9∗∗∗
3.18
4∗∗∗
2.11
0∗∗∗
Ent
ranc
eat
mos
t1ye
arag
o(1
=ye
s)×
Shar
eof
unem
ploy
men
t3.
516∗
∗∗4.
151∗
∗∗1.
378∗
∗∗3.
740∗
∗∗4.
779∗
∗∗3.
244∗
∗∗3.
607∗
∗∗1.
055∗
∗∗3.
058∗
∗∗4.
330∗
∗∗
Ent
ranc
eat
mos
t1ye
arag
o(1
=ye
s)×
Shar
eof
none
mpl
oym
ent
2.74
2∗∗∗
3.63
8∗∗∗
1.03
0∗∗∗
3.19
7∗∗∗
2.75
2∗∗∗
3.06
8∗∗∗
3.78
4∗∗∗
0.52
0∗∗∗
3.27
9∗∗∗
2.48
3∗∗∗
Ent
ranc
e1–
5ye
ars
ago
(1=
yes)
×Sh
are
ofem
ploy
men
t1.
646∗
∗∗3.
925∗
∗∗1.
710∗
∗∗2.
800∗
∗∗1.
107∗
∗∗1.
533∗
∗∗3.
564∗
∗∗1.
488∗
∗∗2.
748∗
∗∗1.
016∗
∗∗
Ent
ranc
e1–
5ye
ars
ago
(1=
yes)
×Sh
are
ofun
empl
oym
ent
1.46
1∗∗∗
2.27
5∗∗∗
0.73
7∗∗∗
1.55
4∗∗∗
2.69
1∗∗∗
1.47
7∗∗∗
1.62
6∗∗∗
−0.5
42∗∗
1.36
5∗∗∗
2.47
6∗∗∗
Ent
ranc
e1–
5ye
ars
ago
(1=
yes)
×Sh
are
ofno
nem
ploy
men
t1.
097∗
∗∗2.
420∗
∗∗0.
504∗
∗∗1.
165∗
∗∗1.
585∗
∗∗1.
103∗
∗∗1.
944∗
∗∗−0
.407
∗∗1.
388∗
∗∗1.
449∗
∗∗
Ent
ranc
em
ore
than
5ye
ars
ago
(1=
yes)
×Sh
are
ofem
ploy
men
t3.
851∗
∗∗6.
100∗
∗∗2.
832∗
∗∗4.
274∗
∗∗1.
094∗
∗∗3.
837∗
∗∗5.
523∗
∗∗2.
908∗
∗∗4.
879∗
∗∗0.
968∗
∗∗
Ent
ranc
em
ore
than
5ye
ars
ago
(1=
yes)
×Sh
are
ofun
empl
oym
ent
0.13
9−0
.548
−0.2
56−0
.538
1.09
5∗∗∗
−0.0
78−6
.138
∗−2
.968
∗∗∗
−2.0
001.
013∗
∗∗
Ent
ranc
em
ore
than
5ye
ars
ago
(1=
yes)
×Sh
are
ofno
nem
ploy
men
t0.
509∗
∗∗−4
.672
∗∗∗
−1.1
59∗
−5.9
93∗∗
0.00
40.
242∗
∗−5
.702
∗∗∗
−1.4
21∗∗
−6.9
97∗∗
∗0.
400∗
∗
116 M. Dütsch, Prof. Dr. O. Struck
Tabl
e2
(Con
tinu
ed)
Inde
pend
entv
aria
bles
1999
2002
Exi
tfro
mjo
bIn
terfi
rmca
reer
path
Une
mpl
oym
ent
Exi
tfro
mjo
bIn
terfi
rmca
reer
path
Une
mpl
oym
ent
Upw
ard
mob
ility
Lat
eral
mob
ility
Dow
nwar
dm
obili
tyU
pwar
dm
obili
tyL
ater
alm
obili
tyD
ownw
ard
mob
ility
Fir
m-s
peci
ficfa
ctor
s
Age
dist
ribu
tion
(Blo
cked
prom
otio
n-op
port
uniti
es:1
=ye
s)1
0.15
9∗∗∗
0.02
5−0
.199
∗0.
095∗
0.13
5∗∗∗
0.10
5∗∗∗
−0.0
350.
409∗
∗∗0.
481
0.08
0∗∗
Firm
size
:Ref
.:Sm
allfi
rm
Smal
l–m
ediu
m-s
ized
firm
(1=
yes)
−0.1
65∗∗
−0.1
880.
377∗
0.17
3−0
.408
∗∗∗
−0.3
81∗∗
∗−0
.104
−0.0
080.
759
−0.5
11∗∗
∗
Med
ium
-siz
edfir
m(1
=ye
s)−0
.274
∗∗∗
−0.2
96∗∗
0.43
0∗0.
184
−0.6
33∗∗
∗−0
.425
∗∗∗
−0.1
660.
138
1.22
9−0
.828
∗∗∗
Lar
ger
firm
(1=
yes)
−0.2
68∗∗
∗−0
.137
0.32
6∗0.
195
−0.7
58∗∗
∗−0
.557
∗∗∗
−0.5
30∗∗
∗−0
.191
−0.1
68−1
.110
∗∗∗
Aty
pica
lem
ploy
men
t(Sh
are
offix
ed-t
erm
empl
oyee
s)0.
989∗
∗∗0.
307
1.03
5∗∗∗
0.38
31.
362∗
∗∗0.
962∗
∗∗0.
049
−0.0
331.
303
1.56
1∗∗∗
Inve
stm
ents
infu
rthe
rtr
aini
ng(1
=ye
s)−0
.212
∗∗∗
−0.0
07−0
.325
∗∗∗
−0.2
41∗
−0.1
78∗∗
−0.3
16∗∗
∗0.
132
−0.4
86∗∗
∗−0
.946
∗−0
.314
∗∗∗
Tech
nolo
gica
lsta
teof
mac
hine
ryan
deq
uipm
ent
(1=
stat
e-of
-the
-art
equi
pmen
t)−0
.145
∗∗∗
0.03
7−0
.225
∗∗−0
.327
∗∗∗
−0.3
68∗∗
∗−0
.006
0.26
0∗∗∗
0.08
6∗−.
0683
−0.2
01∗∗
∗
Co-
dete
rmin
atio
n(W
orks
coun
cil:
1=
yes)
−0.2
12∗∗
∗−0
.526
∗∗∗
−0.6
27∗∗
∗−0
.126
0.03
0−0
.070
−0.2
53∗∗
∗−0
.227
∗∗∗
0.27
4−0
.030
Sect
or:R
ef.:
Man
ufac
turi
ngin
dust
ry
Agr
icul
ture
,for
estr
y,an
dm
inin
g(1
=ye
s)−0
.347
∗∗∗
−1.3
61∗∗
∗−0
.536
∗∗−0
.201
0.67
4∗∗∗
0.62
1∗∗∗
1.40
1∗∗∗
0.67
5∗∗∗
1.63
1∗0.
039
Con
stru
ctio
n(1
=ye
s)0.
548∗
∗∗−0
.200
∗∗−0
.029
0.26
0∗∗
0.93
8∗∗∗
0.73
9∗∗∗
0.17
10.
095
1.04
50.
792∗
∗∗
Tra
de(1
=ye
s)0.
213∗
∗∗0.
149∗
0.11
50.
514∗
∗∗0.
113
0.12
30.
102
0.03
90.
956
0.13
5
Serv
ices
for
firm
s(1
=ye
s)0.
117
0.33
0∗∗∗
−0.2
790.
011
0.13
30.
186
0.12
00.
649∗
∗∗0.
894
−0.0
27
Oth
erse
rvic
es(1
=ye
s)0.
134∗
∗∗−0
.037
0.23
2∗∗
−0.0
37−0
.106
−0.0
25−0
.105
−0.1
69∗∗
∗0.
388
−0.2
19∗∗
∗
Reg
ion-
spec
ific
fact
ors
Type
sof
regi
on:R
ef.:
Den
sely
popu
late
dag
glom
erat
ions
Agg
lom
erat
ions
with
outs
tand
ing
cent
ers
(1=
yes)
0.29
3∗∗∗
0.70
8∗∗∗
0.63
7∗∗∗
−0.0
170.
126
0.20
9∗∗
0.75
8∗∗∗
0.45
7∗∗∗
1.10
2∗∗
0.15
6
Urb
aniz
edar
eas
ofhi
gher
dens
ity(1
=ye
s)0.
397∗
∗∗0.
773∗
∗∗0.
748∗
∗∗0.
130
−0.0
55−0
.001
0.11
40.
454∗
∗∗−0
.397
−0.1
62
Urb
aniz
edar
eas
ofm
ediu
mde
nsity
and
larg
ere
gion
alce
nter
s(1
=ye
s)0.
176∗
∗∗0.
062
0.27
7∗0.
009
0.14
10.
113
0.55
8∗∗∗
0.36
2∗∗∗
−0.4
46−0
.085
Urb
aniz
edar
eas
ofm
ediu
mde
nsity
with
outl
arge
regi
onal
cent
ers
(1=
yes)
0.08
6−0
.057
0.51
3∗∗
0.18
6−0
.040
0.08
40.
873∗
∗∗0.
054
−1.2
35−0
.313
∗
Rur
alar
eas
ofhi
gher
-den
sity
(1=
yes)
0.13
8∗−0
.330
∗∗0.
321∗
0.24
20.
218
0.03
20.
219
0.12
80.
616
−0.0
00
Rur
alar
eas
oflo
wer
-den
sity
(1=
yes)
0.11
00.
043
0.01
030.
068
0.16
70.
208∗
0.20
30.
213∗
−0.9
510.
224∗
Prod
uctiv
ity(G
DP
per
capi
ta)
0.04
0∗∗∗
0.02
1∗∗
0.02
7∗∗∗
0.01
7∗−0
.035
∗∗∗
−0.0
13∗
−0.0
12−0
.015
∗∗∗
−0.2
94∗∗
−0.0
07
Une
mpl
oym
entr
ate
−0.0
17∗∗
∗−0
.014
−0.0
21∗
−0.0
190.
014
−0.0
08∗
−0.0
36∗∗
∗−0
.020
∗∗∗
−0.7
48∗∗
∗0.
030∗
∗∗
Employment trajectories in Germany: do firm characteristics and regional disparities matter? 117
Tabl
e2
(Con
tinu
ed)
Inde
pend
entv
aria
bles
1999
2002
Exi
tfro
mjo
bIn
terfi
rmca
reer
path
Une
mpl
oym
ent
Exi
tfro
mjo
bIn
terfi
rmca
reer
path
Une
mpl
oym
ent
Upw
ard
mob
ility
Lat
eral
mob
ility
Dow
nwar
dm
obili
tyU
pwar
dm
obili
tyL
ater
alm
obili
tyD
ownw
ard
mob
ility
Acc
umul
atio
nof
hum
anca
pita
l(Sh
are
ofst
uden
ts)
−0.0
02−0
.003
0.00
8∗∗
0.00
50.
004
0.00
10.
009∗
∗∗0.
010∗
∗∗0.
154∗
∗∗−0
.003
Con
stan
t−3
.357
∗∗∗
−6.7
06∗∗
∗−6
.002
∗∗∗
−6.9
87∗∗
∗−4
.643
∗∗∗
−2.9
98∗∗
∗−7
.077
∗∗∗
−4.7
83∗∗
∗−6
.297
∗∗∗
−4.1
89∗∗
∗
Epi
sode
s(p
erso
ns)
370,
779
370,
779
370,
779
370,
779
370,
779
363,
339
363,
339
363,
339
363,
339
363,
339
Epi
sode
s(fi
rms)
1,83
61,
836
1,83
61,
836
1,83
62,
140
2,14
02,
140
2,14
02,
140
Epi
sode
s(r
egio
ns)
9797
9797
9797
9797
9797
Res
idua
lvar
ianc
e(fi
rms)
0.20
80.
109
0.77
50.
591
0.53
10.
183
0.27
40.
024
1.24
80.
738
Res
idua
lvar
ianc
e(r
egio
ns)
0.07
50.
067
0.05
40.
048
0.00
90.
044
0.05
40.
010
0.09
00.
052
Log
likel
ihoo
d(fi
nalv
alue
s)−8
0,26
9−2
0,59
7−1
9,15
1−1
5,14
4−3
0,91
5−7
4,99
1−1
4,46
6−1
8,37
7−1
2,83
3-3
1,67
0
Not
es:T
hede
pend
entv
aria
bles
are
code
das
dum
my
vari
able
s.In
the
colu
mns
1an
d6
the
valu
e1
repr
esen
tsa
job
exit;
the
valu
e0
deno
tes
that
the
empl
oyee
rem
ains
inth
efir
m.I
nth
eco
lum
ns2–
5an
d7–
10th
eva
lue
1in
dica
tes
one
ofth
ede
stin
atio
nst
ates
,res
pect
ivel
y,w
here
asth
eva
lue
0su
bsum
esal
lof
the
othe
rem
ploy
men
tsta
tes.
Inea
chre
gres
sion
the
who
lesa
mpl
eis
used
.Reg
ress
ion
coef
ficie
nts
are
repo
rted
afte
rth
ree-
leve
llog
istic
rand
omin
terc
epte
stim
atio
ns.
∗ p<
0.05
;∗∗
p<
0.01
;∗∗∗
p<
0.00
11
“1”
indi
cate
sth
atan
empl
oyee
ispo
sitio
ned
ahea
dof
the
med
ian
age
inth
ein
tern
alag
edi
stri
butio
n.So
urce
:Lin
ked
Em
ploy
er–E
mpl
oyee
Dat
a(L
IAB
);ow
nca
lcul
atio
ns
118 M. Dütsch, Prof. Dr. O. Struck
5.3 Firm-specific determinants
It has been stated that internal career progression is influ-enced by a firm’s demographics. Results show that it is ac-tually those workers positioned ahead of a large age cohortwho are most likely to leave their firm.7 This is in line withHypothesis 1. Furthermore, employees are at greater riskof finding themselves unemployed or having their promo-tional prospects restricted when they change firm. This find-ing contradicts the lateral or upward interfirm job changespredicted in Hypothesis 2. In case of job exits, transitionsinto another employment seem to be difficult. Due to the de-mographic structure, we conclude that blocked promotionopportunities raise the probability of leaving a firm and, fur-thermore, destabilize employment trajectories.
Moreover, it has been argued that career progressionprospects and employment options vary according to firmsize. Results indicate that the larger the firm, the lower therate of exits. This was especially apparent in the compar-atively low unemployment risks during the cyclical declinein 2002.8 Thus, Hypothesis 3 cannot be rejected. These find-ings confirm that larger firms are able to strengthen the clo-sure of their employment systems from the external job mar-ket (Struck 2006). Regarding changes from large firms dur-ing the period of economic upswing in 1999, lateral transi-tions between firms are particularly prominent. Those em-ployees who change employment in the economic downturnseem restricted in their ability to increase income. There-fore, Hypothesis 4 that job exits occur mostly voluntarilyand lead more frequently to lateral or even upward mo-bility can be confirmed only in the case of a cyclical up-swing.
Turning to atypical employment, we examined the sig-nificance of fixed-term contracts9 and found that job sta-bility declines according to the level of fixed-term employ-ment within the firm—independent of the economic envi-ronment. In addition, unemployment risks increase after ex-iting a firm. This is in line with Hypothesis 5. Risks are re-duced only during the cyclical periods of growth when em-ployees can take advantage of lateral interfirm changes. To
7The dummy variable “blocked promotion opportunities” is coded with1 when a person’s position was ahead of the median of the age distri-bution within a firm. This is especially the case when an older agecohort is strongly represented within a firm and the own age cohort isahead. Then, promotion opportunities for the succeeding younger co-horts should be blocked by the older cohorts. It should be noted thatthis modeling is aimed toward left-skewed or normally distributed agepatterns, because it insufficiently captures bimodal or multimodal agedistributions.8For this purpose, the indicated coefficients are transformed into oddsratios exp(β) representing the delogarithmized logit coefficients. Theycan be expressed as probabilities(exp(β) − 1) · 100.9The intensity of use of fixed-term employees by the firms is based ontheir share in the entire workforce.
summarize, firms using a high share of fixed-term employ-ment arrangements offer disadvantageous opportunity struc-tures, because they increase risks in the employment trajec-tory (see also Grotheer et al. 2004; Struck 2006; Struck andKöhler 2004).
Hypothesis 6 assumed that firms providing further train-ing would offer more stable jobs. This is indeed supportedby our results, because employment stability is higher dur-ing cyclical up- and downswings in firms that invest in fur-ther training. Furthermore and in line with Hypothesis 7,employees who leave a firm providing further training face acomparatively low risk of unemployment and are exposed toless lateral mobility or decline. Evidently, employees work-ing in such a firm profit from the increase in their produc-tivity gained from the further training they have received(Büchel and Pannenberg 2004; Dieckhoff 2007) and fromthe positive signals ascribed to them (Backes-Gellner andTuor 2010; Tirole 1996).
Investment in the firm’s infrastructure is also taken intoaccount. Firms with state-of-the-art technology and equip-ment offer high job stability during periods of growth; how-ever, they cannot retain their employees during an eco-nomic slowdown. Therefore, Hypothesis 8 holds only dur-ing an economic upswing. Employees exiting a modernfirm are comparatively well protected from downward in-terfirm mobility as well as from unemployment during acyclical upswing; during an economic slowdown, they profitfrom lower unemployment risks. In the case of transitionsbetween firms, they manage to maintain or even improvetheir income. Thus, Hypothesis 9 is especially supportedin times of an economic downturn. These findings demon-strate that firms with state-of-the-art technology and equip-ment raise workers’ productivity (Bresnahan et al. 2002) andpossess a comparatively good reputation that is transferredto their employees through positive signals (Backes-Gellnerand Tuor 2010; Tirole 1996).
Hypothesis 10 stated that work councils and employeerepresentatives ensure the closure of internal employmentsystems and thereby increase employment stability. Accord-ing to our analysis, a stabilizing effect of work councils canbe observed only in a period of economic growth. Duringdecline, personnel layoffs are still implemented even whenthese worker representation institutions are present. Theseresults replicate recent studies on mobility identifying a sta-bilizing effect of work councils and employee representation(Boockmann and Steffes 2005, 2010; Grotheer et al. 2004).However, if the cyclical economic phases are modeled ex-plicitly, it seems that internal closure is impossible during acyclical downturn.
5.4 Region-specific determinants
This section examines how far region-specific factors cor-relate with employment trajectories. Hypothesis 11 predicts
Employment trajectories in Germany: do firm characteristics and regional disparities matter? 119
that employees who work in core areas will benefit from ag-glomeration advantages in terms of high job stability. Thisis particularly true for densely populated agglomerations intimes of a cyclical upswing. In agglomerations with out-standing centers, in urbanized areas of higher density, andin urbanized areas of medium density and large regionalcenters, however, interfirm promotions as well as lateralchanges can be realized more regularly. This confirms Hy-pothesis 12. During an economic downturn, support is foundfor Hypothesis 13, because in rural areas of lower popu-lation density, higher job instability is often accompaniedby a greater risk of unemployment. Our findings indicate—as stated by Fassmann and Meusburger (1997)—that urban-ized areas offer more and better options for employment; incontrast, rural areas are exposed to increased unemploymentrisks, especially during the cyclical periods of economic de-cline.
To determine the significance of the local accumulationof human capital for employment trajectories, we use re-gional demographic data on the share of students. Hypoth-esis 14 states: “The higher the local level of human cap-ital, the more stable employment will be for all workers.This stability may be realized internally or through lateralor upward interfirm mobility.” Results show that regionaldisparities during periods of growth have only a marginalimpact on employment trajectories, whereas in periods ofdecline, the probability of changes between firms rises inaccordance with the local accumulation of human capital.Because these findings do not indicate whether or not toreject Hypothesis 14, we carried out a cross-level com-parison between each of the identified qualification groupsand the regional accumulation of human capital.10 Table 3shows that the less qualified benefit from a higher locallevel of human capital by realizing upward or lateral in-terfirm mobility during an economic upturn. The same ap-plies to those employees who have successfully completedadvanced secondary education and vocational training. Incontrast, those employees who have completed secondaryschool and vocational training have more stable employ-ment when the local level of human capital is higher. Thisfinding also transfers to more highly qualified employees:They are more likely to avail of the external job mar-ket for upward mobility and are in little danger of down-ward mobility or unemployment. This confirms Hypoth-esis 14, because all qualification groups benefit from ahigher regional level of human capital—as reported by Blienand Wolf (2002) and by Farhauer and Granato (2006). Incomparison, during a downturn, employees with no voca-tional training are employed comparatively insecurely de-
10Results are taken from separate estimations that are otherwise iden-tical to those displayed in Table 2. Likelihood ratio tests reveal that allinteraction effects are highly significant.
spite the higher local level of human capital and are alsoat a higher risk of downward mobility. In an economic de-cline, only those employees with a secondary school andvocational training certificate profit from the higher stockof human capital to achieve interfirm promotions. This alsoapplies to highly qualified workers who are in stable em-ployment. The findings are in line with Hypothesis 15 andother studies (Gerlach et al. 2002; Schlitte et al. 2010;Stephan 2001) suggesting an increase in skill segregationdue to a high local level of human capital. Thus, during adownturn, skill segregation exerts an unfavorable effect onlow skilled employees.
6 Conclusions
Life course research emphasizes the significance of individ-ual factors and endogenous causalities for employment ca-reers; thus, the start to the employment career enduringlyaffects workers’ future mobility patterns (Blossfeld 1986;Hillmert et al. 2004). By focusing in greater detail on struc-tural effects this article extends current research on individ-ual determinants of employment trajectories. Because em-ployees act within specific contexts (Coleman 1990), thisstudy has paid particular attention to exploring how firmcharacteristics and regional determinants impact on employ-ment trajectories. It has also related these trajectories to pe-riods of both economic growth and decline. To date, sucha comprehensive survey could not be undertaken due tothe lack of an adequate dataset and very long computationtimes.
To gain a fuller picture of structural and cyclical deter-minants, a German linked employer-employee dataset pro-vided by the IAB was merged with data on regional char-acteristics taken from the Federal Institute for Research onBuilding, Urban Affairs and Spatial Development (BBSR).Regional indicators were investigated in each of the 97 spa-tial planning regions. The data analysis was carried out inthree steps. First, the frequency of exits from a firm and theconsequences of these exits were explored descriptively dur-ing both a period of economic growth in 1999 and a periodof economic decline in 2002. Second, the decisive factorsinfluencing employment stability were identified and ana-lyzed with multilevel models that permit a hierarchical clus-tering of the data. Third, the determinants of interfirm up-ward, downward, and lateral mobility as well as transitionsinto unemployment were considered.
At first, it could be shown that regardless of whetherin a period of growth or decline, almost 10 % of employ-ees left their firm. Moreover, the identified exit states sug-gested a procyclical mobility of employment as well as
120 M. Dütsch, Prof. Dr. O. Struck
Tabl
e3
Cro
ss-l
evel
effe
cts
onth
ehi
ghes
tedu
catio
nle
vela
ndth
elo
cala
ccum
ulat
ion
ofhu
man
capi
tal(
thre
e-le
vell
ogis
ticra
ndom
inte
rcep
tmod
els)
Inde
pend
entv
aria
bles
1999
2002
Exi
tfro
mjo
bIn
terfi
rmca
reer
path
Une
mpl
oym
ent
Exi
tfro
mjo
bIn
terfi
rmca
reer
path
Une
mpl
oym
ent
Upw
ard
mob
ility
Lat
eral
mob
ility
Dow
nwar
dm
obili
tyU
pwar
dm
obili
tyL
ater
alm
obili
tyD
ownw
ard
mob
ility
Cro
ss-l
evel
-Effe
cts:
Hig
hest
educ
atio
nle
vel×
Loc
alac
cum
ulat
ion
ofhu
man
capi
tal
No
voca
tiona
ltra
inin
g(1
=ye
s)×
Shar
eof
stud
ents
0.00
6∗∗∗
0.01
3∗∗∗
0.00
9∗∗
0.00
60.
001
0.00
5∗∗∗
0.00
70.
013∗
∗∗0.
014∗
∗∗−0
.000
Seco
ndar
ysc
hool
and
voca
tiona
ltra
inin
g(1
=ye
s)×
Shar
eof
stud
ents
−0.0
04∗∗
−0.0
030.
005
0.00
1−0
.002
0.00
10.
010∗
∗∗0.
005
0.00
2−0
.002
Adv
ance
dse
cond
ary
scho
olan
dvo
catio
nal
trai
ning
(1=
yes)
×Sh
are
ofst
uden
ts0.
007∗
∗∗0.
024∗
∗∗0.
013∗
∗0.
004
−0.0
07−0
.001
0.00
8−0
.003
0.00
9−0
.004
Uni
vers
ityde
gree
(1=
yes)
×Sh
are
ofst
uden
ts−0
.004
∗∗0.
008∗
∗0.
009∗
∗−0
.015
∗∗∗
−0.0
11∗∗
−0.0
06∗∗
∗−0
.006
−0.0
13∗∗
∗−0
.006
−0.0
00
Epi
sode
s(p
erso
ns)
3707
7937
0,77
937
0,77
937
0,77
937
0,77
936
3,33
936
3,33
936
3,33
936
3,33
936
3,33
9
Epi
sode
s(fi
rms)
1836
1836
1836
1836
1836
2140
2140
2140
2140
2140
Epi
sode
s(r
egio
ns)
9797
9797
9797
9797
9797
Not
es:T
hede
pend
entv
aria
bles
are
code
das
dum
my
vari
able
s.In
the
colu
mns
1an
d6
the
valu
e1
repr
esen
tsa
job
exit;
the
valu
e0
deno
tes
that
the
empl
oyee
rem
ains
inth
efir
m.I
nth
eco
lum
ns2–
5an
d7–
10th
eva
lue
1in
dica
tes
one
ofth
ede
stin
atio
nst
ates
,res
pect
ivel
y,w
here
asth
eva
lue
0su
bsum
esal
lof
the
othe
rem
ploy
men
tsta
tes.
Inea
chre
gres
sion
the
who
lesa
mpl
eis
used
.Reg
ress
ion
coef
ficie
nts
are
repo
rted
afte
rth
ree-
leve
llog
istic
rand
omin
terc
epte
stim
atio
ns.M
odel
sad
ditio
nally
cont
ain
alle
xpla
nato
ryva
riab
les
whi
char
ere
port
edin
Tabl
e2.
∗ p<
0.05
;∗∗
p<
0.01
;∗∗∗
p<
0.00
1So
urce
:Lin
ked
Em
ploy
er–E
mpl
oyee
Dat
a(L
IAB
);ow
nca
lcul
atio
ns
Employment trajectories in Germany: do firm characteristics and regional disparities matter? 121
a procyclical development of wages. Thus, more interfirmpromotions could be achieved during the period of eco-nomic growth than during the economic downturn. In con-trast, fewer workers moved into a period of nonemploy-ment during an economic upswing than during a decline.This information served as a backdrop for examining thesignificance of firm characteristics and region-specific fac-tors.
Looking at firm-specific determinants, firm demograph-ics correlated with processes of mobility. Thus, closed pro-motion opportunities destabilize employment trajectoriesfor a part of the labor force. The multiple and diverse em-ployment opportunities for career progression in larger firmsrevealed that they have more of a closed employment sys-tem. Furthermore, firms making a stronger use of fixed-termemployment had very unfavorable opportunity structures,because this practice increased employment instability andthe risk of unemployment. Accordingly, firms that invest infurther training or their infrastructure not only improve em-ployment opportunities but also create the conditions for in-terfirm mobility processes due to the positive signaling ef-fect ascribed to their employees. Especially in a positiveeconomic environment, work councils and employee repre-sentation increase employment stability. However, during aturn for the worse in the economy, work councils are unableto prevent dismissals.
Concerning region-specific characteristics, we analyzedthe association between different settlement structures anddiverse mobility patterns. Especially in periods of economicgrowth, more densely populated areas offer more and betteremployment opportunities. During periods of economic de-cline, in contrast, employees in rural areas face a greater riskof unemployment. Hence, the unequal employment oppor-tunities in differently structured regions suggest a regionalsegmentation of the job market. Concerning the local accu-mulation of human capital, we found only a marginal in-fluence on employment trajectories. It was only during aneconomic downturn that employees profited from a higherregional level of human capital, because transitions betweenfirms became more frequent. A differentiation of the effectof the local level of human capital according to qualifica-tion groups revealed a two-sided story depending on thestate of the economic cycle. All skill groups profit from ahigher level of human capital during an economic upturn,whereas skill segregation is more apparent during a down-turn.
In summary, it could be shown that employees can mini-mize the significant impact of individual determinants andendogenous causalities on employment careers (Blossfeld1986; Hillmert et al. 2004) by taking advantage of goodopportunity structures and framework conditions. Thereforefirm characteristics and region-specific factors as well as
economic conditions play an important role in career mo-bility patterns. This finding gains further significance par-ticularly in light of the following three developments: First,in recent years, market volatility due to processes of eco-nomic globalization and transnationalization has been lead-ing to ever shortening economic cycles. Second and re-lated to this, human resource policy has changed particu-larly through an increased use of atypical employment con-tracts. Third, several political initiatives, such as the Euro-pean initiative for regional development and the promotionof metropolitan regions as well as the German initiative toshift decision-making powers from the central governmentto local and regional units, have increased the significanceof regional structures for growth and employment (Blienet al. 2001). Future research on employment careers willneed to examine both structural and cyclical effects in moredetail.
Executive summary
Life course research accentuates that employment trajecto-ries are governed by individual determinants and endoge-nous causalities; thus, the start to the employment career en-duringly affects workers’ future mobility patterns. However,their actions are always embedded within a particular frame-work: Their employment trajectories are influenced by firm-specific opportunity structures, regional heterogeneities, andthe business cycle. This article therefore extends current re-search on individual determinants of employment trajecto-ries by focusing in greater detail on structural effects. Todate, such a comprehensive survey could not be undertakendue to the lack of an adequate dataset and very long compu-tation times.
To gain a fuller picture of structural and cyclical deter-minants, a German linked employer-employee dataset pro-vided by the IAB is merged with data on regional charac-teristics taken from the Federal Institute for Research onBuilding, Urban Affairs and Spatial Development (BBSR).Regional indicators are investigated in each of the 97 spatialplanning regions. The data analysis is carried out in threesteps. First, the frequency of exits from a firm and the con-sequences of these exits are explored descriptively duringboth a period of economic growth in 1999 and a period ofeconomic decline in 2002. Second, the decisive factors in-fluencing employment stability are identified and analyzedwith multilevel models that permit a hierarchical cluster-ing of the data. Third, the determinants of interfirm upward,downward, and lateral mobility as well as transitions intounemployment are considered.
At first, it can be shown that regardless of whether ina period of growth or decline, almost 10 % of employees
122 M. Dütsch, Prof. Dr. O. Struck
left their firm. Moreover, the identified exit states suggest aprocyclical mobility of employment as well as a procycli-cal development of wages. Thus, more interfirm promotionscan be achieved during the period of economic growth thanduring the economic downturn. In contrast, fewer workersmove into a period of nonemployment during an economicupswing than during a decline. This information serves as abackdrop for examining the significance of firm characteris-tics and region-specific factors.
Looking at firm-specific determinants, firm demograph-ics correlate with processes of mobility. Thus, closed promo-tion opportunities destabilize employment trajectories for apart of the labor force. The multiple and diverse employmentopportunities for career progression in larger firms revealthat they have more of a closed employment system. Fur-thermore, firms making a stronger use of fixed-term employ-ment have very unfavorable opportunity structures, becausethis practice increases employment instability and the riskof unemployment. Accordingly, firms that invest in furthertraining or their infrastructure not only improve employmentopportunities but also create the conditions for interfirm mo-bility processes due to the positive signaling effect ascribedto their employees. Especially in a positive economic en-vironment, work councils and employee representation in-crease employment stability. However, during a turn for theworse in the economy, work councils are unable to preventdismissals.
Concerning region-specific characteristics, we analyzethe association between different settlement structures anddiverse mobility patterns. Especially in periods of economicgrowth, more densely populated areas offer more and betteremployment opportunities. During periods of economic de-cline, in contrast, employees in rural areas face a greater riskof unemployment. Hence, the unequal employment oppor-tunities in differently structured regions suggest a regionalsegmentation of the job market. Concerning the local accu-mulation of human capital, we find only a marginal influenceon employment trajectories. It is only during an economicdownturn that employees profit from higher regional levelsof human capital, because transitions between firms becomemore frequent. A differentiation of the effect of the locallevel of human capital according to qualification groups re-veal a two-sided story depending on the state of the eco-nomic cycle. All skill groups profit from a higher level ofhuman capital during an economic upturn, whereas skill seg-regation is more apparent during a downturn.
In summary, it can be shown that employees are able min-imize the significant impact of individual determinants andendogenous causalities on employment careers by taking ad-vantage of good opportunity structures and framework con-ditions. Therefore firm characteristics and region-specificfactors as well as economic conditions play an importantrole in career mobility patterns.
Kurzfassung
In der Lebensverlaufsforschung werden die Bedeutung in-dividueller Faktoren sowie des endogenen Kausalzusam-menhangs für den Erwerbsverlauf betont. Demnach bes-timmt der Einstieg in die Erwerbskarriere nachhaltig daszukünftige Mobilitätsverhalten der Beschäftigten. Aller-dings agieren diese immer auch innerhalb spezifischerRahmenbedingungen. Erwerbsverläufe werden durch fir-menspezifische Gelegenheitsstrukturen, regionale Heteroge-nitäten und die jeweilige konjunkturelle Situation beein-flusst. Vor diesem Hintergrund erweitert dieser Beitrag dieaktuelle Forschung zu den individuellen Determinanten vonErwerbsverläufen, indem der Fokus auf strukturelle Fak-toren gerichtet wird. Solche Analysen konnten bislang auf-grund des Fehlens adäquater Daten und sehr langer Rechen-zeiten nicht durchgeführt werden.
Um ein umfassendes Bild der strukturellen und konjunk-turellen Faktoren zu erhalten, wird ein deutscher Linked-Employer-Employee Datensatz des Instituts für Arbeits-markt- und Berufsforschung (IAB) mit Informationen zuregionalen Charakteristika aus dem Bundesinstitut für Bau-,Stadt- und Raumforschung (BBSR) verknüpft. Die re-gionalen Indikatoren werden auf der Ebene der 97 Raum-ordnungsregionen untersucht. Die Datenanalyse erfolgt indrei Schritten. Zunächst werden deskriptiv die Anzahl derAustritte von Beschäftigten aus Betrieben sowie die an-schließenden Arbeitsmarktstatus sowohl in der konjunk-turellen Aufschwungphase im Jahr 1999 als auch in derAbschwungphase im Jahr 2002 betrachtet. Zweitens wer-den die Einflussfaktoren auf die Beschäftigungsstabilität an-hand von Mehrebenenmodellen, welche die hierarchischeClusterung der Daten berücksichtigen, analysiert. Drittenswerden ebenfalls multivariat die Determinanten von Auf-stiegen, lateraler Mobilität und Abstiegen bei direkten Be-triebswechseln sowie von Übergängen in Arbeitslosigkeiterforscht.
Zunächst kann gezeigt werden, dass unabhängig vonder konjunkturellen Situation ca. 10 % der Beschäftigtenihren Betrieb verlassen haben. Zudem verdeutlichen die an-schließenden Arbeitsmarktstatus eine prozyklische Beschäf-tigungsmobilität sowie eine prozyklische Entwicklung derLöhne. So liegt eine höhere zwischenbetriebliche Mobilitätwährend des wirtschaftlichen Aufschwungs im Vergleichzur Situation während des Abschwungs vor. Hingegen gibtes weniger Übergänge in eine Phase der Nichtbeschäftigungim wirtschaftlichen Aufschwung. Diese Informationen die-nen als Ausgangsbefunde für die Untersuchung des Ein-flusses betrieblicher Charakteristika und regionaler Dispari-täten auf individuelle Erwerbsverläufe.
Hinsichtlich der firmenspezifischen Determinanten be-sitzt die Betriebsdemografie eine Bedeutung für Mobilitäts-
Employment trajectories in Germany: do firm characteristics and regional disparities matter? 123
prozesse. Demnach destabilisieren versperrte Aufstiegs-wege die Erwerbsverläufe eines Teils der Belegschaft.Größere Betriebe weisen aufgrund der vielfältigeren Be-schäftigungsmöglichkeiten stärker geschlossene Beschäf-tigungssysteme auf. Darüber hinaus bieten Betriebe, diein größerem Umfang befristete Arbeitsverträge einsetzen,ungünstige Gelegenheitsstrukturen, da durch diese Praxisdie Beschäftigungsinstabilität sowie das Arbeitslosigkeits-risiko steigen. Hingegen erhöhen Betriebe, die in die Wei-terbildung ihrer Arbeitskräfte oder in ihre Infrastruktur in-vestieren, nicht nur die Beschäftigungsstabilität, sondernermöglichen auch adäquate zwischenbetriebliche Mobi-litätsprozesse aufgrund von positiven Signalen, die ihrenBeschäftigten zugeschrieben werden. Vor allem währendeiner guten konjunkturellen Situation erhöhen Betriebsräteund Arbeitnehmervertretungen die Beschäftigungsstabilität.Allerdings sind Betriebsräte während eines konjunkturellenAbschwungs nicht mehr in der Lage, Entlassungen zu ver-hindern.
Mit Blick auf regionsspezifische Charakteristika werdenzunächst der Zusammenhang zwischen verschiedenen Sied-lungsstrukturen und der Beschäftigungsmobilität analysiert.Insbesondere während eines konjunkturellen Aufschwungsbieten dichter besiedelte Gebiete mehr und bessere Beschäf-tigungsmöglichkeiten. In Zeiten des konjunkturellen Ab-schwungs sind die Beschäftigten in ländlichen Gebietenhingegen einem größeren Arbeitslosigkeitsrisiko ausgesetzt.Folglich deuten die ungleichen Beschäftigungsmöglich-keiten in den unterschiedlich strukturierten Regionen aufeine regionale Segmentierung des Arbeitsmarktes hin. Be-züglich der regionalen Humankapitalausstattung finden wirnur einen geringen Einfluss auf Erwerbsverläufe vor. Le-
diglich während des wirtschaftlichen Abschwungs profi-tieren die Beschäftigten von einer höheren regionalen Hu-mankapitalausstattung, da zwischenbetriebliche Übergängehäufiger auftreten. Eine weitere Differenzierung der Wir-kung der regionalen Humankapitalausstattung nach Qual-ifikationsgruppen ergibt in Abhängigkeit von der kon-junkturellen Situation ein zweigeteiltes Bild. Von einerhohen regionalen Humankapitalausstattung profitieren imAufschwung alle Qualifikationsgruppen, während im Ab-schwung eine Segregation bezüglich der Qualifikations-gruppen zu beobachten ist.
Zusammenfassend zeigen die Befunde, dass die zweifel-los vorhandenen endogenen Kausalzusammenhänge im Er-werbsverlauf dann an Bedeutung verlieren, wenn Beschäf-tigte sich strukturelle Einflussfaktoren zunutze machen kön-nen. Aus diesem Grund besitzen betriebliche Charakteris-tika und regionale Disparitäten sowie die konjunkturellenRahmenbedingungen mit Blick auf Erwerbsverläufe einehohe Relevanz.
Acknowledgements We would like to thank Ute Leber and twoanonymous referees for very helpful comments and suggestions. Wealso appreciate comments received from participants of the User Con-ference of the IAB Establishment Panel Survey in October 2012.
Appendix
See Tables 4–8.
Table 4 Description of thedependent variables
Source: LinkedEmployer–Employee Data(LIAB); own calculations
Variable Mean Description
1999 2002
Exit from job 0.092 0.083 Dummy = 1 if employee leaves the firm; Dummy = 0 ifemployee remains in firm
Interfirm upwardmobility
0.018 0.011 Dummy = 1 if interfirm upward mobility takes place;Dummy = 0 if another employment status is observed
Interfirm lateralmobility
0.014 0.012 Dummy = 1 if interfirm lateral mobility takes place;Dummy = 0 if another employment status is observed
Interfirm downwardmobility
0.009 0.008 Dummy = 1 if interfirm downward mobility takes place;Dummy = 0 if another employment status is observed
Unemployment 0.024 0.025 Dummy = 1 if employee becomes unemployed; Dummy= 0 if another employment status is observed
Number ofobservations
370,779 363,339
124 M. Dütsch, Prof. Dr. O. Struck
Table 5 Description ofindividual characteristics
1 Percentages do not add up toexactly 100 due to impreciserounding off.Source: LinkedEmployer–Employee Data(LIAB); own calculations
Characteristics 1999 2002Mean Standard Deviation Mean Standard Deviation
Sex (1 = female) 28.87 0.45 27.00 0.44Nationality (1 = foreign) 7.39 0.26 7.92 0.27Age in years1
Share of employment 0.32 –/– 0.33 –/–Share of unemployment 0.05 –/– 0.04 –/–Share of nonemployment 0.07 –/– 0.08 –/–First employment 0.03 –/– 0.02 –/–Permanently employed 0.53 –/– 0.53 –/–Cohorts1
Entrance at most 1 year ago 16.99 –/– 16.07 –/–Entrance 1–5 years ago 25.00 –/– 27.10 –/–Entrance more than 5 years ago 58.01 –/– 56.82 –/–Number of observations 370,779 363,339
1 Percentages do not add up toexactly 100 due to impreciserounding off.2 “1” indicates that theestablishment hasstate-of-the-art equipment; “5”indicates that the equipment isobsolete.Source: LinkedEmployer–Employee Data(LIAB); own calculations
Characteristics 1999 2002Mean Standard Deviation Mean Standard Deviation
Simple tasks 0.18 –/– 0.19 –/–Qualified tasks 0.83 –/– 0.81 –/–Share of fixed-term employees 0.05 –/– 0.04 –/–Investments in further training 76.85 –/– 76.64 –/–Technological state of machineryand equipment2
2.92 –/– 2.84 –/–
Works council (1 = yes) 50.11 0.50 49.91 0.50Sector1
Agriculture, forestry, and mining 4.74 –/– 4.11 –/–Construction 15.41 –/– 12.06 –/–Manufacturing industry 33.71 –/– 39.44 –/–Trade 12.53 –/– 12.29 –/–Services for firms 6.48 –/– 7.24 –/–Other services 21.79 –/– 19.95 –/–Number of observations 1,836 2,140
Employment trajectories in Germany: do firm characteristics and regional disparities matter? 125
Table 7 Description of theregional distribution ofemployment-relevant factors
1 Percentages do not add up toexactly 100 due to impreciserounding off.Source: LinkedEmployer–Employee Data(LIAB); own calculations
Characteristics 1999 2002
Mean Standard Deviation Mean Standard Deviation
Types of region1
Densely populatedagglomerations
24.36 –/– 24.36 –/–
Agglomerations with outstandingcenters
23.55 –/– 23.55 –/–
Urbanized areas of higherdensity
14.58 –/– 14.58 –/–
Urbanized areas of mediumdensity and large regional centers
17.96 –/– 17.96 –/–
Urbanized areas of mediumdensity without large regionalcenters
2.84 –/– 2.84 –/–
Rural areas of higher density 12.18 –/– 12.18 –/–
Rural areas of lower density 4.53 –/– 4.53 –/–
Share of students 47.57 13.76 19.69 14.46
Unemployment rate 11.77 4.67 11.08 5.21
GDP (per capita) 22.63 5.33 24.00 5.66
Number of observations 97 97
Table 8 Estimation results for intercept-only models (three-level logistic random intercept models without explanatory variables)
Notes: The dependent variables are coded as dummy variables. In the columns 1 and 6 the value 1 represents a job exit; the value 0 denotes, whenthe employee remains in the firm. In the columns 2–5 and 7–10 the value 1 indicates one of the destination states, respectively, whereas the value0 subsumes all of the other employment states. In each regression the whole sample is used.Source: Linked Employer–Employee Data (LIAB); own calculations
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Matthias Dütsch studied Social Sciences at the University of Erlangen-Nuremberg. Since 2009 he is a research assistant at the Chair of La-bor Studies at the Otto-Friedrich-University of Bamberg. His researchinterests focuses on atypical employment, employment systems andlabor mobility.
Prof. Dr. Olaf Struck is Professor of Labor Studies at the Otto-Friedrich-University of Bamberg. He is head of the Pillar 2 “Learn-ing Environments” in the National Education Panel Study (NEPS) andhead of Pillar 3 “Changes in Human Capital, Labour Markets and De-mographic Structures and their Impact on Social Inequality in Mod-ern Societies” in the Bamberg Graduate School of Social Sciences(BAGSS). Furthermore, he is on the board of the “German Associa-tion for Labour Market Research”. Research interests: empirical labormarket research, human resource management, further education andlifelong learning.
