Innovation, Information Technologies and Human Capital in the German Service Sector

  • Georg Licht
Part of the Economics of Science, Technology and Innovation book series (ESTI, volume 17)


In the last decade most industrialized countries experienced a steady growth of value added and employment in the service sector. The employment growth of the services was a major engine for additional jobs, which helped to overcome the decrease in the number of jobs in manufacturing. Given the historically high level of unemployment in Germany today, stimulating the development of the service sector is of great concern to policy makers. In addition, international comparisons reveal that the employment share of the service sector in Germany still accounts for nothing like the proportion of jobs it currently provides in the USA or Canada, for example.2 In 1994, almost two-thirds of Germany’s gainfully employed population were working in the service sector (including publicsector employees). This means that its ratio to the other sectors has reversed within just 35 years. This ongoing transformation into a service society becomes all the clearer when we also examine the changes in activity structures throughout the manufacturing sector, where for years now we have been seeing an accelerating shift away from traditional production operations and towards more service-oriented activities.


Human Capital Service Sector Employment Growth Business Service Service Innovation 
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  1. 1.
    I would like to thank Thorsten Doner, Günther Ebling, Norbert Janz and Hildrun Niggeman for their ongoing efforts in preparing the innovation data set used in this study. I also thank Engelbert Beyer at the German Ministry of Education and Research for stimulating me to work on the relation of innovation in services and their labour market impacts. Financial support by the DFG is gratefully acknowledged.Google Scholar
  2. 2.
    However, international comparisons of employment shares are sometime misleading when hours worked are not taken into account and some employee groups are systematically not included in this comparison. This is relevant with regard to the so called 630-DM-jobs in Germany (see DIW 1998 for more details and some alternative calculations of German US comparisons).Google Scholar
  3. 3.
    See for details on the measurement problem a recent special issue of the Canadian Journal of Economics (1999, Vol. 32 April).Google Scholar
  4. 4.
    The MIP-S was commissioned by the federal ministry of science and technology (BMBF). The Fraunhofer Institut für System-und Innovationsforschung (ISI) and infas a private company specialized in conducting surveys are also involved in this project. The concept of the MIP-S and further results are presented in Licht et al. (1996)Google Scholar
  5. 5.
    This seems to be the best available data source. No official register is available for service sector enterprises. The most recent data from the statistical on e.g. firm size or industry distribution of service sector firms is for 1987. The Federal employment office publishes data based on the establishment level. However, this data only covers establishments with at least one employee, which is subjected to mandatory membership in the employment security system. This data only comprise figure on the number of establishment and the number of employees with mandatory contributions to the social security system. No sales or other data are available. Also, no data on firm’s addresses etc. is available to third parties.Google Scholar
  6. 6.
    As telecom deregulation in Germany leads to a strongly rising number of telecom service providers in the second half of the nineties additional telecom companies were integrated in the second wave making a separate treatment of the sector possible.Google Scholar
  7. 7.
    Although the coverage of sectors is somewhat different Baldwin et al. (1998) report lower figures for the share of innovative firms in Canada. Moreover, in most European countries (except for Sweden and UK) the share of innovative firms is also lower than in Germany.Google Scholar
  8. 8.
    We will discuss these issues only shortly and refer the reader e.g. to our recent book (Licht et al. 1997) or the paper by Ian Miles in this volume.Google Scholar
  9. 9.
    Innovating companies are defined — according to the OSLO manual — as companies which introduce new or significantly improved products and implement significantly new production process within a three years period (1994-1996).Google Scholar
  10. 10.
    Data for these aggregates refer to the year 1997. For details see Janz et al. 1999a and 1999b. Detailed data for 1997 are not yet available.Google Scholar
  11. 11.
    The list is more detailed than the technologies depicted in Table 2. In the case of information technologies it considers of the usage of mainframes, workstations, PCs, packaged standard software, data bases, high quality net works (e.g. ISDN) or multimedia technology. Environmental technologies comprise e.g. recycling, energy saving techniques, pollution control measurement.Google Scholar
  12. 12.
    However, the observation that IT and other technologies often occurs in the same firm at the same time not necessarily is an indicator of technology fusion going on. It can well be the case that two separate innovations are made one using IT and the other using e.g. environmental technologies.Google Scholar
  13. 13.
    The reverse causality — innovation influences the skill structure — is inferred in the next section in more detail.Google Scholar
  14. 14.
    This figure is constructed in the following way. We first calculated the share of employees in each skill group who is working for companies, which expect to expand their workforce in this skill group. Then we calculated the skill group specific share of employees working for companies, which plan to contract the workforce in this skill group. Finally we net out the employment expectation in each skill group by subtracting the number of employees in contracting firms from the number of employees in expanding firms. The resulting number is a good proxy for the expected growth rates as evident from the business cycle research has proven (Carlson and Parkin 1975 and Batchelor and. Orr 1988). Therefore, the larger this number is the higher is the expected employment growth.Google Scholar
  15. 15.
    This is done using separate order probit regression models for each skill group. Kaiser (1998) uses a more refined econometric method considering the correlation between the error terms of these equations leading to more efficient estimates. His results confirm the interpretation made here.Google Scholar

Copyright information

© Springer Science+Business Media New York 2000

Authors and Affiliations

  • Georg Licht
    • 1
  1. 1.Zentrum für Europäische Wirtschaftsforschung (ZEW)MannheimGermany

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