Abstract
This study investigates the relationship between occupational choice and unemployment over the professional career with German administrative linked employer–employee data that track more than 800,000 graduates from vocational education over 25 years. Using short-run fluctuations in local and sectoral occupation-specific labor demand as instruments, it finds that choosing an occupation that later turns out to suffer from low or negative employment growth has a statistically and economically significant impact on unemployment over the professional career. On average, an unanticipated one-standard deviation decrease in occupation-specific employment growth raises unemployment by about 116 days over the life cycle.
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Notes
Because the linked employer–employee data set only includes East German individuals from around 1991 onward, these are excluded from the analysis. All individuals in the sample experienced a recession at about the time they entered the labor market, when the second energy crisis hit West Germany (this happened in 1981/1982, i.e., somewhat later than for instance in the USA).
Equation 2.1 assumes that there is a linear relationship between \(\Delta L^{\eta }_{i}\) and lifetime unemployment. An important sensitivity check below shows that this seems to be a good approximation for the relationship between the two variables but that in fact the impact of \(\Delta L^{\eta }_{i}\) on \(u_{i}\) becomes somewhat stronger with increases in \(\Delta L^{\eta }_{i}\).
Lange and Neuser (1985) provide an overview of the career counseling services provided by Germany’s Federal Employment Agency in the early 1980s. According to the overview, in the years 1981, 1982 and 1983 about 1,200,000, 1,300,000 and 1,400,000 clients were provided with career counseling in the Agency’s regional offices, respectively. In addition to that, in each of the 3 years the regional offices organized around 90,000 information sessions in secondary schools and more than 20,000 other events.
Cf. von Henninges et al. (1976) for details on the impact of the Institute for Employment Research’s analytical and advisory work regarding trends and forecasts of occupation-specific labor demand on the Federal Employment Agency’s career guidance services in the 1970s.
For instance, a worker’s Occupational Sector might be manufacturing occupations, his or her Occupational Sub-sector nutritional occupations and his or her Occupational Group baker or confectioner. Cf. Hoffmeyer-Zlotnik and Warner (2014) for details on the German Classification of Occupations.
In spite of the Federal Employment Agency’s efforts to provide occupational guidance, prospective trainees in Germany’s vocational education system might potentially form their expectations through different processes, for instance based on past occupation-specific employment growth. In fact, a growing body of literature including Betts (1996), Jensen (2010), Nguyen (2010) and Wiswall and Zafar (2015) documents that students across the globe tend not to be well aware of labor market prospects by occupation or program of study. At the same time, an alternative specification of estimation Eq. 2.5 where unanticipated occupation-specific labor demand shocks are constructed by subtracting from actual occupation-specific employment growth past observed occupation-specific employment growth (over the 5 years prior to trainees’ entry into the vocational education system) shows no statistically significant relationship between this variable and lifetime unemployment. Results from this alternative specification are not depicted here but available upon request.
There are also comprehensive schools (Gesamtschulen) and some other less prevalent school types that offer some kind of combination of the three main types of secondary schools.
Cf. Hunt (1995) for a detailed analysis of the relationship between being registered as unemployed and receiving unemployment benefits in Germany for the first half of this study’s observation period. According to Hunt (1995), between 1983 and 1988 87% of individuals registered as unemployed drew unemployment benefits.
Comparable tables for employment growth between 1981 and 2006 as well as between 1982 and 2007 are not depicted here but available upon request. They show very similar patterns.
Cf. “Appendix A” for summary statistics for the control variables as well as for details on sample selection and data cleansing.
Even two-way clustering might be considered. Unfortunately, to the author’s best knowledge no procedure exists to perform two-way clustering in a Tobit IV regression. Outputs of regressions not reported here but available upon request demonstrate that regular IV regressions that cluster standard errors both on the county and the sector level confirm a statistically significant impact of occupational choice on lifetime unemployment.
This study’s sample covers 327 counties but only 143 labor market districts.
Estimates for a third group, trainees pursuing a career in agricultural or mining occupations, are omitted given the comparatively small size of this group.
Cf. Aiyar and Ramcharan (2010) for a discussion of the role of luck in labor markets. An alternative interpretation of this study’s main finding might put less emphasis on the effects of occupational choice at the beginning of the professional career (i.e., a decision) and more on the occurrence of an unanticipated drop in occupation-specific employment growth (i.e., an event). The alternative interpretation emphasizes this study’s connection to research by Oreopoulos et al. (2012) that demonstrates substantial and long-lasting wage losses for some groups of young displaced workers and by Schmillen and Umkehrer (2017) and others according to which early-career unemployment can have long-term scarring effects.
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Acknowledgements
I thank Joachim Möller, Philipp vom Berge, David Card, Richard Frensch, Jolien Helsel, Patrick Kline, Nels Lind, Juliane Parys, Jesse Rothstein, Matthias Umkehrer as well as conference and seminar participants in Berkeley, Carlisle, Frankfurt, Nuremberg, Regensburg and Vancouver and two anonymous reviewers for helpful comments and suggestions. Special thanks are due to Heiko Stüber for providing insight and expertise that greatly assisted this research. An early part of this research was conducted while I was visiting the University of California, Berkeley, whose hospitality is gratefully acknowledged. The findings, interpretations and conclusions expressed in this paper are entirely my own. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
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Appendix A: sample selection, data cleansing and summary statistics
Appendix A: sample selection, data cleansing and summary statistics
As mentioned in Sect. 2, this study focuses on 852,872 individuals that entered the West German labor market in 1981, 1982 or 1983. All of these men and women graduated from Germany’s vocational education system. East Germans are excluded, because their employment history has only been recorded in the IEB data since the early 1990s. Thus, all analyses are based on a group that is fairly homogeneous with regard to experience, training and background.
While the information contained in the administrative linked employer–employee data set can generally be considered highly reliable, it is not completely free of questionable information. For example, the IEB contain a small number of occupational codes that have been documented as erroneous. In order to ensure valid and undistorted results, all main and control variables including lifetime unemployment and the unanticipated occupation-specific employment growth were carefully checked and implausible data points were replaced with missing values.
Table 10 presents summary statistics for all variables. It shows for instance that women comprise 42% of the sample and that only 3% of sampled individuals held an Abitur upon graduation from vocational education. Concerning the age at graduation from vocational education, 8% of individuals were 17 years old or younger, and 25% were 18 years old, 29% 19 years old, 20% 20 years old and 18% 21 years old or older. Almost half of trainees were in training firms with less than 25 workers (a reflection of the strong role of small- and medium-sized firms in Germany’s vocational education system), 10% in firms with 25–50 workers, 9% in firms with 51–100 workers, another nine percent in firms with 101–250 workers, 7% in firms with 251 to 500 workers, 6% in firms with 501–1000 workers and 10% in firms with 1000 workers or more. The median daily wage of the training firm was euro 41.80.
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Schmillen, A. Vocational education, occupational choice and unemployment over the professional career. Empir Econ 57, 805–838 (2019). https://doi.org/10.1007/s00181-018-1484-x
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DOI: https://doi.org/10.1007/s00181-018-1484-x