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The dynamics of productivity in Swiss universities

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Abstract

This paper analyzes the productivity of Swiss university departments between 1995 and 2012. Using a parametric input distance function we estimate and decompose the Malmquist productivity indexes in line with Fuentes et al. (J Product Anal 15:79–94, 2001) and Atkinson (J Bus Econ Stat 21:284–294, 2003). The adopted model is a mixed-effects model with department-specific fixed effects and random time trends. An autoregressive stochastic term is used to model inefficiency shocks with gradual dissipation by adaptation and learning. The results indicate a negative trend in Malmquist index starting from 2002, with an average rate of about 1 % per year. However, our analysis of scale effects indicates that this decline is more or less offset by universities’ constant expansion and the resulting economies of scale. The results point to various patterns of overall productivity change across scientific fields that are closely related to potential productivity gains due to scale economies. In contrast to some previous studies we do not find any significant relation between productivity development and the Bologna reform.

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Notes

  1. Mobility and competition are among the key policy points recommended by many experts such as Aghion et al. (2008).

  2. For more recent applications see Saal et al. (2007) and Das and Kumbhakar (2012).

  3. In addition to the quadratic form, we have considered several alternatives, including cubic time trends, year dummies and another specification with piecewise linear trends in 2- to 4-year intervals. Our preliminary analyses indicated that the results are not sensitive to the specification of the time trends. Similar to Cornwell et al. (1990), Lee and Schmidt (1993), Kneip et al. (2003) and Sickles (2005), we favor a quadratic trend because it allows one to keep the number of trend coefficients within a reasonable limit.

  4. \(\beta _{rs}=\beta _{sr}\), \(\gamma _{mn}=\gamma _{nm}\) and \(\zeta _{rm}=\zeta _{mr}\) \(\forall r,s,m,n\)

  5. In an alternative specification discussed in Sect. 6, we have considered additional variables such as the share of international students and the implementation of the Bologna reform.

  6. See also Eqs. 9, 11 and 12.

  7. We estimate Eq. 8 using the expectation-maximization (EM) algorithm programmed by the xtmixed command in Stata 12.

  8. We also tested a version where we assume that these department-specific trends vary around field-specific mean values with a bivariate normal distribution, that is: (\( \phi _{j}^{1}\),\(\phi _{j}^{2})\sim N\left( \lambda _{f}^{p},\Sigma _{\phi }\right) \). Subscript f denotes the scientific field and the means of this distribution \((\lambda _{f}^{1},\lambda _{f}^{2})\) represent the average time trends for each scientific field.

  9. We assumed no correlation, because our preliminary regressions indicated that the correlation coefficients were mostly insignificant.

  10. “Professors” include full and associate professors, “Lecturers” include assistant professors, lecturers and senior scientific staff. The category “Assistants” contains employed doctoral students and junior scientific staff such as post-doc assistants.

  11. Furthermore, our proxy for the stock of real estate, the floor space, available for about half of the sample period shows relatively little variation over time, suggesting that assuming fixed real estate stock is not unrealistic.

  12. Research funds can be considered as an intermediate output for a university. For further discussion of intermediate and final outputs see Agasisti and Pérez-Esparrells (2009) and Garcia-Aracil and Palomares-Montero (2010).

  13. We tried several alternative replacing values. The estimation results show little sensitivity. The final results reported in this paper are based on replacing all zero values of inputs/outputs by 0.1. The exact number of observations with zero values depends on the variable, varying from 6 for number of assistants to 82 for enrollments.

  14. In fact medical schools throughout Europe have more or less ardently resisted Bologna reforms (Patricio et al. 2012). Switzerland’s medical schools stand out as an exception that integrated Bachelor degrees in their programs (Michaud 2012). The 3-year Bachelor program can be a step toward a Master degree in medicine (physician diploma) but also could lead to other careers.

  15. A positive sign implies that an increase in diversity has a positive impact on productivity.

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Acknowledgments

We would like to thank the Swiss Federal Statistical Office (SFSO) for providing the data and acknowledge the exceptional support we received from their higher education manager, Petra Koller. We are grateful to Lionel Perini, Alain Schatt, Milad Zarin and four anonymous referees for their helpful suggestions. We also thank the participants of the Halle Workshop on Efficiency and Productivity Analysis, 2010, and the European Workshop on Productivity and Efficiency Analysis, 2011, for their comments on earlier versions.

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Authors and Affiliations

  1. ETH Zurich, KOF Swiss Economic Institute, Leonhardstrasse 21, 8092, Zurich, Switzerland

    Thomas Bolli

  2. Faculty of Economics, University of Neuchâtel, A.L. Breguet 2, 2000, Neuchâtel, Switzerland

    Mehdi Farsi

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  1. Thomas Bolli
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  2. Mehdi Farsi
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Correspondence to Thomas Bolli.

Appendix

Appendix

See Table 4 and Fig. 5.

Table 4 Parameter estimates of the input distance function
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Fig. 5
figure 5

Malmquist productivity indices for Models 1–4

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Bolli, T., Farsi, M. The dynamics of productivity in Swiss universities. J Prod Anal 44, 21–38 (2015). https://doi.org/10.1007/s11123-015-0450-2

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