Ageing, productivity and wages in Austria: sector level evidence

Abstract

In this paper we analyse the link between the age structure of the labour force and average labour productivity as well as average wage across industrial sectors. While this intermediate economic level has been under-explored up to now, we will argue that it provides valuable insights in several respects. Our analysis is based on a panel data set ranging over 6 years (2002–2007). It covers the sectors of mining, manufacturing and market oriented services in the Austrian economy. Our results exhibit a positive correlation of the share of older employees and productivity, whereas we cannot find any evidence for a significant relationship of the share of younger employees and productivity. Moreover, the estimated age-wage pattern does not hint at an over-payment of older employees.

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Fig. 1

Notes

  1. 1.

    In this paper we consider the terms industry and sector as synonyms.

  2. 2.

    For simplifying reasons we abstain from time subscripts in the following.

  3. 3.

    An alternative way in order to abstain from the assumption of perfect substitutability would be to implement a Cobb-Douglas type aggregate of labour, see e.g. Prskawetz et al. (2008).

  4. 4.

    This term is similar to the “relative (marginal) productivity differential” of a trained worker compared to an untrained worker \( \frac{{{\text{MP}}_{\text{T}} - {\text{MP}}_{\text{U}} }}{{{\text{MP}}_{\text{U}} }} \) in Konings and Varnomelingen (2011), p. 5.

  5. 5.

    Based on the estimated coefficients for γ k we were able to verify this approximation for our data set.

  6. 6.

    Note that we are consistent with our empirical approach (see Sect. 5) in dividing output and capital through L i instead of L * i .

  7. 7.

    In fact, X i may encompass several sector specific characteristics \( j:\sum\nolimits_{j = 1}^{n} {\delta_{j} X_{ij} } \).

  8. 8.

    Hence we include variables in X i that may explain the total factor productivity (Solow residual) A.

  9. 9.

    Although average wages rather present an approximate measure, especially at the present level of analysis we prefer it as opposed to individual wages in order to achieve best possible comparability with the productivity outcome.

  10. 10.

    NACE (Nomenclature of economic activities) is a code that represents the classification of economic activities within the European Union, while OeNACE accords to the Austrian version. While all other levels of OeNACE are identical with the corresponding levels of NACE an additional hierarchical level—the national sub-classes—was added to represent the Austrian economy in a more detailed and specific way. For details see European Commission (2002) and Statistics Austria (2003). Based on the classification of our data we use the OeNACE version of 2003.

  11. 11.

    The Main Association of Austrian Social Security Institutions provided us with these data for our particular research purpose. Thus, except for the manufacturing sector (NACE D), where data are available for subsections DA-DN, the information is provided for NACE sections. Data on NACE DF (“Manufacture of coke, refined petroleum products and nuclear fuel”) are not available from Statistics Austria due to secrecy reasons.

  12. 12.

    Basically social security data contain all employees (white-collar and blue-collar workers, home workers, apprentices, full-time and part-time workers) and self-employed persons. The data set we received for our particular research purpose is, however, restricted.

  13. 13.

    Since labour productivity is calculated based on the structural business statistics, while age shares emanate from social security data, this imbalance might theoretically lead to a bias of the results. For instance, self-employed persons contribute to value added, whereas they do not count for the age distribution. We expect that this does not lead to a systematic bias, as their age distribution is assumed to accord to the overall age distribution of the employees.

  14. 14.

    Sector specific results are not shown in Table 1 but can be obtained from the authors on request.

  15. 15.

    This definition is similar to that of the European Commission (2003). The Commission’s definition not only contains limits of staff headcounts but also financial ceilings (for annual turnover and annual balance sheet total). Since we do not have access to these financial indicators, we solely adopt the staff headcount limit.

  16. 16.

    Another useful variable to explain differences between labour productivity across sectors would be tenure as it measures experience of workers. Due to the lack of data we are not able to include such a variable. Therefore our estimated age-wage pattern might capture not only the pure ability effect based on age but might be influenced by experience based on job tenure.

  17. 17.

    The equality between the age effects on labour productivity and wages can be tested by comparing the estimated coefficients. This is done by regressing the difference between (the natural logarithm of) labour productivity and (the natural logarithm of) average wages per capita (=productivity-pay gap) on the same set of regressors as the production function and the wage equation. The estimated coefficients for the age shares correspond to the difference between the coefficients of the production function and the wage equation. Based on this proceeding we cannot reject the null-hypotheses that the coefficient for the share of old employees within the productivity-pay-gap regression is equal to zero.

  18. 18.

    We are aware of the fact, that pure age should actually be disentangled from tenure effects, which unfortunately is not properly possible up to now.

  19. 19.

    This methodology analyzes data using exploratory graphs, which are frequently used to diagnose characteristics of the data. For details see e.g. Raveh (2000) and Mahlberg and Raveh (2012).

  20. 20.

    We are grateful to an anonymous referee for the hint that age-productivity and age-wage profiles might differ across economic sectors.

  21. 21.

    We are very grateful to René Böheim for providing us these results.

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Acknowledgments

This research was financed by the Austrian Science Fund (FWF) under grant No. P 21475-G16. The authors gratefully acknowledge valuable discussions and comments (on earlier versions of the paper) from two anonymous referees, René Böheim, Jesús Crespo-Cuaresma, Bernd Fitzenberger, Michael Hauser and Thomas Zwick as well as the participants at the Fifth European Workshop on Labour Markets and Demographic Change in Berlin in 2010, the Annual Meeting of the Austrian Economic Association 2010 in Vienna and the Sixth North American Productivity Workshop in Houston in 2010. The usual disclaimer applies.

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Correspondence to Bernhard Mahlberg.

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Mahlberg, B., Freund, I. & Prskawetz, A. Ageing, productivity and wages in Austria: sector level evidence. Empirica 40, 561–584 (2013). https://doi.org/10.1007/s10663-012-9192-9

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Keywords

  • Ageing workforce
  • Production function
  • Age-productivity profile
  • Age-wage pattern

JEL Classification

  • J14
  • J24
  • J82