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Job mobility, peer effects, and research productivity in economics

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Abstract

We analyse a comprehensive panel dataset of economists working at Austrian, German, and Swiss universities and investigate how job mobility and characteristics of other researchers working at the same university affect research productivity. On aggregate, we find no influence of these local research characteristics on the productivity of researchers, if we control for their unobserved characteristics. This finding indicates that with today’s information, communication and travelling technologies knowledge spillovers are globally available rather than dependent on physical co-presence. However, we find some evidence that high-productivity researchers could be more likely to benefit from local research characteristics.

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Notes

  1. Furthermore, peers might additionally influence research productivity by creating a competitive environment in which peers at the same university align their efforts (Frank 1985).

  2. Early work by Lotka (1926); Beaver and Rosen 1979) document a positive correlation between the number of co-authorships and research productivity. A more recent study by Lee and Bozeman (2005) finds no impact of co-authorships when adjusting research productivity by the number of authors and when controlling for individual and institutional factors.

  3. Note that in economics the authors typically are named in alphabetical order.

  4. Even though bibliometric methods are imperfect measures of research productivity, they are widely used by university researchers, administrators and their stakeholders (see, e.g., Stephan 2012). Our data performs relatively well according to the quality criteria set out in Harzing (2008) and Adler and Harzing (2009). Concretely, we account for number of authors and the quality of a broad range of general as well as specialized journals that cover the relevant languages. Furthermore, in economics, no weighting of the first author is required and we control for the sub-field of specialization. Finally, we appropriately assign affiliations by using the current and primary affiliation.

  5. Publications in peer-reviewed journals are the most important publication outlets in our sample of economists at German-speaking universities. In the time span under consideration, books and monographs have lost some of their previous high importance and blogs have only started to become important. Assigning a quality weight to a journal is preferred to pure counting of publications or citations and reflects the screening in the review process. Nonetheless, journal rankings based among other criteria on impact factors have a limited explanatory power for the impact of the individual article (early work on this topic include Schubert and Glänzel (1983) and Seglen (1997)). However, for example using instead article citations to account for quality is not possible in the current setting, because in economics with its particular long publication time lag, using citations as a reliable quality measure requires several years of subsequent observations.

  6. For more information regarding the methodology of the Handelsblatt ranking 2011 used in this study refer to Handelsblatt (2011b).

  7. Since we control for the local research environment, \(Mobil_{i,t}\) identifies the pure mobility effect (hypothesis 3a), i.e. the job mobility effect for a given local research environment. Including \(Mobil_{i,t}\) and \(\overline{y}_{-i}\) individually yields qualitatively the same results, suggesting that multicollinearity is not a major concern. These estimates can be obtained from the authors upon request.

  8. By dropping observations for which receiving the first call and mobility coincide, the variable \(Tenured_{i}\) becomes time-invariant.

  9. Due to the large number of journals considered in this study in addition to using the rolling average of publications over two years, only about 10 % of our sample has no research output in a particular year. Hence, it is not surprising that dropping the 154 observations with \(y_{i,j_t,t}\)=0 or using replacement values of 1, 0.1 or 0.001 yield qualitatively the same results. These results can be obtained from the authors upon request.

  10. In principle, it would be possible to analyse the impact delayed by one year by ignoring the data for the year 2008. However, this approach would leave us with a sample size of 442 observations, a time dimension of 2, and only 22 incidences of job mobility. In particular there is not enough data for a further sample split. Due to this data restriction we do not evaluate this additional impact lagged by one year.

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Acknowledgments

We thank Stephan Fretz and Heinrich Ursprung for ongoing support. For helpful comments and suggestions we are grateful to two anonymous referees, Toke Aidt, Matthias Bannert, Tobias Brändle, Martin Gassebner, Anne-Wil Harzing, Bertrand Koebel, Matthias Krapf, Michael Lamla, Martin Paldam, Marc Sangnier and Jan-Egbert Sturm. Furthermore, we are thankful to the participants of the KOF research seminar, of the Lancaster University Economics Department Research Seminar, of the Personnel economics colloquium in Tübingen, of the SMYE in Aarhus, of the EJPE workshop in Castasegna and of the EPCS conference in Cambridge.

Thomas Bolli gratefully acknowledges the financial support of the Swiss National Science Foundation.

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

    Thomas Bolli & Jörg Schläpfer

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Bolli, T., Schläpfer, J. Job mobility, peer effects, and research productivity in economics. Scientometrics 104, 629–650 (2015). https://doi.org/10.1007/s11192-015-1625-3

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