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Regional Population Structure and Young Workers’ Wages

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Modelling Aging and Migration Effects on Spatial Labor Markets

Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

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Abstract

This paper estimates the effect that changes in the size of the youth population have on the wages of young workers. Assuming that differently aged workers are only imperfectly substitutable, economic theory predicts that individuals in larger age groups earn lower wages. We test this hypothesis for a sample of young, male, full-time employees in Western Germany during the period 1999–2010. Based on instrumental variables estimation, we show that an increase in the youth share by one percentage point is predicted to decrease a young worker’s wages by about 3%. Moreover, our results suggest that a substantial part of this effect can be ascribed to members of larger age groups being more likely to be employed in lower-paying occupations.

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Notes

  1. 1.

    To ensure comparability with the empirical analysis of this paper, the reported numbers refer to Western Germany (excluding West Berlin). With the availability of the 2011 census, the basis for estimating population variables has changed. As the population measures in this paper are based on pre-census data, we also use the population projections that are derived from this data rather than the recently released projections that make use of the 2011 census.

  2. 2.

    Notable exceptions can be found in the migration literature where many studies conclude that natives’ wages are not negatively affected by age-specific immigration (Ottaviano and Peri 2012). A possible explanation for this finding is that migrants are complements rather than substitutes for native labour.

  3. 3.

    Data comes from the Federal Statistical Office and has been obtained through the following link: https://www-genesis.destatis.de/genesis/online/link/tabellen/12411*.

  4. 4.

    The upper bound of this corridor (Obergrenze der mittleren Bevölkerung) differs by assuming that annual net immigration will increase steadily to 200,000 in the year 2020 before plateauing at that level. Despite this difference the projection for the youth share is very similar (the largest difference between both projections amounts to 0.25 percentage points in the year 2040).

  5. 5.

    These age groups are the children of the large post-World War II birth cohorts. This increase therefore reflects the large size of the parental generation.

  6. 6.

    The results of this and all other robustness checks can be found in the Appendix to this paper.

  7. 7.

    For individuals holding more than one job at the same time it would in principle be possible to use total earnings from all jobs rather than just the wage earned in one job as the relevant dependent variable. We abstain from doing so as our focus is on how the supply of young workers affects the wages earned in a particular job. Similar results to those shown in Table 13.1 are obtained when observations with more than a full-time job are removed from the sample.

  8. 8.

    Inflation-adjustment is done using the consumer price index (base year: 2010). The data comes from the Federal Statistical Office and has been obtained through the following link: https://www.destatis.de/DE/ZahlenFakten/GesamtwirtschaftUmwelt/Preise/Verbraucherpreisindizes/Tabellen_/VerbraucherpreiseKategorien.html?cms_gtp=145110_slot%253D2&https=1.

  9. 9.

    Comparable results are obtained when an individual’s place of residence is used instead.

  10. 10.

    The specification of Eq. 13.1 can also be interpreted as a special case of the model provided by Card and Lemieux (2001) in as far as our analysis also assumes imperfect substitutability across age groups but considers only the age group 15–24 in the empirical analysis.

  11. 11.

    We abstain from estimating a model that includes fixed effects at the individual level. Since 44% of observations come from individuals that are included in the sample only once, estimation of such a model suffers from an insufficient degree of within-variation. Notice that for consistent estimation of the youth share’s marginal effect, a fixed effects approach would only be required in the presence of unobserved, time-invariant heterogeneity at the individual level that is correlated with the youth share.

  12. 12.

    Comparable results are obtained when all variables are averaged across the individuals in a region-year cell and the regression is weighted by the number of observations per cell (see Angrist and Pischke 2009).

  13. 13.

    We have also estimated Eq. 13.1 using mutually exclusive sets of age and experience dummies. Changing the specification in this way has no effect on the estimated youth-share coefficients.

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Acknowledgements

The authors would like to thank Stefan Fuchs, Bernd Hayo, John Moffat, Norbert Schanne and two anonymous referees for their advice and are grateful for comments from the participants of IAB’s regional research network meeting in Aalen, the 54th Conference of the European Regional Science Association (ERSA), the 5th ifo Workshop Arbeitsmarkt und Sozialpolitik, the joint IAB Regional Science Academy workshop in Amsterdam and the 28th Conference of the European Association of Labour Economists (EALE). The Federal Institute for Research on Building, Urban Affairs and Spatial Development kindly provided the shapefile of the German labour-market regions. Our thanks also go to Annie Roth for proof-reading an earlier version of this paper.

Author information

Authors and Affiliations

  1. Institute for Employment Research (IAB), Frankfurt am Main, Germany

    Alfred Garloff

  2. Institute for Employment Research (IAB), Düsseldorf, Germany

    Duncan Roth

Authors
  1. Alfred Garloff
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  2. Duncan Roth
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Correspondence to Duncan Roth .

Editor information

Editors and Affiliations

  1. Schar School of Policy and Government, Arlington, VA, USA

    Roger R. Stough

  2. KTH Royal Institute of Technology, Stockholm, Sweden

    Karima Kourtit

  3. JADS (Jheronimus Academy of Data Science), ’s-Hertogenbosch, The Netherlands

    Peter Nijkamp

  4. Institute for Employment Research (IAB), Nuremberg, Germany

    Uwe Blien

Appendix

Appendix

Table 13.2 Descriptive statistics
Full size table
Table 13.3 Use of 31 December as the reference date
Full size table
Table 13.4 Exclusion of observations with more than a full-time job
Full size table
Table 13.5 Use of an individual’s place of residence
Full size table

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Garloff, A., Roth, D. (2018). Regional Population Structure and Young Workers’ Wages. In: R. Stough, R., Kourtit, K., Nijkamp, P., Blien, U. (eds) Modelling Aging and Migration Effects on Spatial Labor Markets. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-68563-2_13

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