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Workforce age and technology adoption in small and medium-sized service firms

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Abstract

This paper provides empirical evidence on the relationship between the age structure of the workforce and the adoption of new or significantly improved technologies. Moreover, it attempts to identify the role of teamwork in this relationship. The econometric analysis is based on data of 356 small and medium-sized German firms from the knowledge-intensive services and ICT services sectors. The results show that, compared to employees younger than 30 years, an older workforce is negatively related to the probability of technology adoption. On the contrary, the dispersion of the employees’ age within the workforce seems not to be connected with the probability of technology adoption. However, in firms with intensive use of teamwork a homogenous workforce in terms of age is positively related to the probability of technology adoption.

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Notes

  1. These sectors contribute to about 8% of the German sales (Statistisches Bundesamt 2006).

  2. For further details on the nine industries, their industrial classification and their distribution within the sample, see the Appendix and Table 4 in the Appendix.

  3. Although the question concerning technology adoption was asked for the fourth time, panel data estimations cannot be provided. The survey among “service providers of the information society” is a very versatile data set where firms participate on an irregular basis. The use of the panel data causes a great loss of observations, and unobserved heterogeneity could not be taken into account because there is only a very tiny fraction of firms for which data are available for more than two subsequent periods.

  4. The 46th wave of the survey includes information on the age structure of the workforce, the qualification level of the employees, the implemented process, product and organisational innovations, the export activity and the existence of foreign competitors.

  5. Firms with fewer than two employees are ignored in the analysis since the variable teamwork is not relevant for them. Thus, including them would bias the sample.

  6. The firms answered the following question: Did you adopt new or significantly improved technologies in the last 12 months (e.g., new electronic data processing systems, Internet)?

  7. For more details on the probit model, see Wooldridge (2002). All calculations and estimations of this paper were done with STATA 10.0.

  8. The share of employees being younger than 25 years and being between 25 and 30 years old has been combined to the group younger than 30 years. They are the reference group.

  9. The share of medium-qualified and unqualified employees is the reference group.

  10. For more details on the linear probability model, see Wooldridge (2002).

  11. See, for example, Karshenas and Stoneman (1995) for a summary.

  12. The sector marketing represents the reference category.

  13. Since the estimated coefficients in a probit model only allow to make a statement on the significance and the sign of an effect but not on its extent, only the marginal effects will be discussed in the following. Table 6 in the Appendix shows the coefficients of the probit estimations.

  14. Due to item non-response for the dummy variable representing service product innovation, the sample size is reduced to 259 observations. Estimations (1) and (2) were also done with this reduced sample. The results did not change qualitatively.

  15. This explanation, however, can be excluded, since the consideration of firm age in the estimation does not change the results.

  16. The coefficients of the probit estimations are shown in Table 8 in the Appendix.

  17. All estimations have also been run with the reduced sample of 259 observations. The results did not change qualitatively.

  18. The method proposed by Ai and Norton (2003) and permuted by Norton et al. (2004) was also used to analyse the effect of the interaction between the weighted Herfindahl index and the dummy variable of teamwork in the probit model. The results did not change and are available on request. The linear probability model predicts seven values outside the 0–1 range of the probability of technology adoption. This is less than 5% and thus another promotion for the use of the linear probability model instead of the probit to analyse the interactions.

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Acknowledgements

I would like to thank Irene Bertschek, Daniel Cerquera, Ulrich Kaiser, Keld Laursen and two anonymous referees for valuable comments and suggestions, as well as the seminar participants at the 2007 European Association of Labour Economists Annual Conference 2007 and the 2008 DRUID-DIME Academy Winter Conference.

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    Jenny Meyer

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Appendix

Appendix

The ZEW quarterly business survey among service providers of the information society includes the following industries (codes of the German Classification of Economic Activities, Edition 2003 in parentheses): software and IT services (71.33.0, 72.10.0–72.60.2), ICT-specialised trade (51.43.1, 51.43.3–3.4, 51.84.0, 52.45.2, 52.49.5–9.6), telecommunication services (64.30.1–0.4), tax consultancy and accounting (74.12.1–2.5), management consultancy (74.11.1–1.5, 74.13.1–3.2, 74.14.1–4.2), architecture (74.20.1–0.5), technical consultancy and planning (74.20.5–0.9), research and development (73.10.1–73.20.2) and advertising (74.40.1–0.2). Table 4 shows how the industries are distributed in the sample.

Table 4 Distribution of industries in the sample
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Table 5 Development of percentage shares of age groups
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Fig. 1
figure 1

Source: ZEW Quarterly business survey among service providers of the information society, 2002, 2003, 2004 and 2005 (in each case 3rd quarter); in each case bandwith = 0.1

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Table 6 Coefficients of probit estimations, age groups
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Table 7 Coefficients of bivariate probit estimations, age groups
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Table 8 Coefficients of probit estimations, Herfindahl index
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Table 9 Coefficients of bivariate probit estimations, Herfindahl index
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Meyer, J. Workforce age and technology adoption in small and medium-sized service firms. Small Bus Econ 37, 305–324 (2011). https://doi.org/10.1007/s11187-009-9246-y

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