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How does research productivity relate to gender? Analyzing gender differences for multiple publication dimensions

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Abstract

Measures of research productivity have become widely used for obtaining tenure, third-party funding, and additional resources from universities. However, previous studies indicate that men might have a higher research output than women, with mixed conclusions about the factors that drive these differences. This study explores to what extent the research productivity of psychology professors in Germany is related to gender and, furthermore, how any gender gaps can be explained by controlling for individual and organizational factors. In addition, three publication dimensions (publications in top 10% journals, journal articles, and book and collection chapters) are distinguished to determine the effect of gender on research productivity as precisely as possible. A unique data set based on all full professors in psychology in Germany and their publication record in 2013 and 2014 is used (\(N_\mathrm{authors}\) = 294; \(N_\mathrm{articles}\) = 2252, \(N_\mathrm{chapters}\) = 439). Thus, this study provides a current overview of the state of research productivity in an entire discipline after researchers receive tenure and external restrictions are lessened. Our research helps to further locate the point at which gender differences in publication numbers occur. As we are the first to systematically to analyze different publication types, we are able to show that there is no difference in publication numbers for less-prestigious book chapters. However, we find significant gender differences for research productivity in academic journals that are more important for career advancement and peer recognition, even after we control for the most important individual and organizational factors that might explain gender differences. Our results point to the direction that women do research and write manuscripts, but may have different publication patterns: instead of submitting to competitive journals, they may be satisfied with less-prestigious book chapters. As publications in peer-reviewed journals are especially important for career advancement as well as peer recognition, this publication pattern may be disadvantageous for women. Overall, we conclude that additional research to understand these developments is needed that focuses on the motives and beliefs of researchers, both to improve gender equality in academia and to give women better chances to gain recognition and prestige.

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Notes

  1. The dataset is available by the authors upon request.

  2. We used the most popular career focused social networks in Germany “Xing” (www.xing.de) and “LinkedIn” (www.linkedin.com).

  3. To further validate our results, we replicated all analyses by estimating negative-binomial models for the non-standardized dependent variables and established estimates similar to the estimates from the OLS regression models.

  4. We did not calculate the average Journal Impact Factor (JIF) per researchers as others did (e.g. König et al. 2015) because we follow the arguments of Moed (2002) that JIF presents a distorted picture of the actual impact of individual authors. Therefore, we count the number of publications in high-ranking journals as a proxy for higher quality. That does not automatically assume that a high JIF can be used to calculate individual averaged indicators for authors.

  5. Even though several authors (e.g. Cheek et al. 2006; Seglen 1997) argue that the JIF can be used for measuring research quality, we are aware that this is only a proxy for the quality of individual articles. Larivière and Gingras (2010) could show a moderate correlation between JIF and articles’ citation counts. However, high citation counts do not necessary denote high research quality. We relied on a JIF measure computed by the Kompetenzzentrum Bibliometrie that is highly correlated with the JIF provided in the Journal Citation Reports (\(r = 0.96\)).

  6. A list of all cut-off values can be found in the Appendix.

  7. A regular boxplot of the same data can be found in the Appendix. for comparison, see Fig. 3.

  8. In order to check the robustness of our bivariate findings, we conducted a bootstrapped t-test with 1,000 repetitions for each publication dimension. The findings confirm the findings of the regular t-test (see Table 5 in the Appendix).

  9. The full tables include linear and squared terms of the variable career age. Between the models, the coefficient for gender does not change up to the third decimal place.

  10. For WholeCount90 and WholeCount10 the full model is Model 4, for Chapter the full model is Model 3.

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Acknowledgements

The authors are thankful to Jonas Elis, Lukas Fiege, Jakob Kemper, Antonia Velicu and Erik Wenker for research assistance. The authors are grateful to Julia Jerke, David Johann, and the two anonymous reviewers for their comments and helpful hints and to Sybille Hinze and Marion Schmidt for support in the second data collection. The authors are ordered alphabetically. Both authors contributed equally to the conception and design of the study, the data collection process, the analysis and interpretation of the data as well as writing and revising the manuscript. Justus Rathmann is funded by SNSF Starting Grant “CONCISE” (S-64408-01-01).

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Authors and Affiliations

  1. Institute of Political Science, University of Duisburg-Essen, Lotharstr. 65, 47057, Duisburg, Germany

    Sabrina J. Mayer

  2. Department Research System and Science Dynamics, German Centre for Higher Education Research and Science Studies, Schuetzenstr. 6a, 10117, Berlin, Germany

    Sabrina J. Mayer & Justus M. K. Rathmann

  3. Institute of Sociology, University of Zurich, Andreasstr. 15, CH-8050, Zurich, Germany

    Justus M. K. Rathmann

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Appendix

Appendix

Summary statistics

See Table 4.

Table 4 Summary statistics of the data set
Full size table

Box plot of productivity per gender

See Fig. 3.

Fig. 3
figure 3

Distribution of publication counts by gender and publication dimension (including outliers)

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Bootstrapped t-test

See Table 5.

Table 5 Bootstrapped t-test on multiple dimension of research productivity
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Regression tables

Whole count 90

See Table 6.

Table 6 OLS regression on standardized ordinary journal publications
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Whole count 10

See Table 7.

Table 7 OLS regression on standardized top journal publications
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Chapter

See Tables 8, 9.

Table 8 OLS regression on standardized publications in collections
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Table 9 JIF 2014 threshold by WoS subject category (for all used subject categories)
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Mayer, S.J., Rathmann, J.M.K. How does research productivity relate to gender? Analyzing gender differences for multiple publication dimensions. Scientometrics 117, 1663–1693 (2018). https://doi.org/10.1007/s11192-018-2933-1

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