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Within- and between-department variability in individual productivity: the case of economics

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Abstract

In the social sciences, university departments are the governance units where the demand for and the supply of researchers interact. As a first step towards a formal model of this process, this paper investigates the characteristics of productivity distributions in a unique dataset consisting of 2,530 faculty members with at least one publication who were working in the 81 top world Economics departments in 2007. Individual productivity is measured in two ways: as the number of publications up to 2007, and as a quality index that weights differently the articles published in four journal equivalent classes. The academic age of individuals, measured as the number of years since obtaining a Ph.D. up to 2007, is used to measure productivity per year. Independently of the two productivity measures, and both before and after age normalization, the five main findings of the paper are the following. Firstly, individuals within each department have very different productivities. Secondly, there is not a single pattern of productivity inequality and skewness at the department level. On the contrary, productivity distributions are very different across departments. Thirdly, the effect on overall productivity inequality of differences in productivity distributions between departments is greater than the analogous effect in other contexts. Fourth, to a large extent, this effect on overall productivity inequality is accounted for by scale factors well captured by departments’ mean productivities. Fifth, this high degree of departmental heterogeneity is found to be compatible with greater homogeneity across the members of a partition of the sample into seven countries and a residual category.

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Notes

  1. See Biglan (1973) for an early contribution to department differences in social structure and output within a single university, as well as Agasisti et al. (2012) and the references cited there for the literature on efficiency differences between departments in a single and several universities.

  2. As pointed our in van Raan (2006a, b, 2008), the research group—defined by the internal structure of universities, research institutions, and research and development laboratories of companies—is the more important working floor entity in the natural sciences and the medical research fields. This is not the case in Economics, where the university department is the key organizational unit.

  3. We have compared this list with the first 81 economics departments listed in three other equally acceptable university rankings. The main conclusion is that, apart from differences in the order in which each institution appears in the various rankings, our list has between 70 and 73 departments in common with each of the three other lists (see Albarrán et al. 2014a for further details).

  4. Naturally, extreme observations can also affect any measure of productivity inequality, such as the CV.

  5. For a discussion of robust measures of skewness in the context of the financial literature on stock market returns, see Kim and White (2004), and for the properties of the Groeneveld and Meeden’s measure, see the references in note 5 in PRRC.

  6. In any case, the GM index is not strongly associated to mean productivity: the coefficient of correlation between these two variables is 0.10.

  7. Given the relatively small department sizes, in the double partition mentioned in “The measurement of the importance of productivity differences between research units” section, we distinguish between deciles, that is, Π is made equal to 10.

  8. The first study entails 2.9 million articles published in several years in the 1980–2004 period with a variable citation year from the publication year up to May 2011. Articles were classified into 172 journal subject categories (Li et al. 2013). The second study encompasses 4.4 million articles published in 1998–2003 with a 5-year citation window for each year. Articles were classified into 219 journal subject categories (Crespo et al. 2014).

  9. In this case there is a weak negative correlation between the GM index and mean Q/Age: the coefficient of correlation between these two variables is −0.34. Therefore, the higher is the ranking according to Q/Age, the smaller tends to be the skewness measured by the GM index.

  10. As can be observed in Table 6 in the Appendix, at one extreme, a very small percentage of economists, ranging from 8.3 to 9.5 %, are responsible for 26.6–33.5 % of all quality points (Rice University, the Hebrew University, and the Free University of Amsterdam). At the other extreme, 23.7–35.7 % of all economists account for 49.6–62.1 % of quality points (University of Amsterdam, University College London, University of Wisconsin at Madison, and Johns Hopkins University).

References

  • Agasisti, T., Catalano, G., Landoni, P., & Verganti, R. (2012). Evaluating the performance of academic departments: An analysis of research-related output efficiency. Research Evaluation, 21, 2–14.

    Article  Google Scholar 

  • Albarrán, P., Carrasco, R., & Ruiz-Castillo, J. (2014a). The elite in economics. Working paper 14-14, Universidad Carlos III, July 2014.

  • Albarrán, P., Carrasco, R., & Ruiz-Castillo, J. (2014b). The effect of spatial mobility and other factors on academic productivity. Some evidence from a set of highly productive economists. Working Paper 14-15, Universidad Carlos III, July 2014 (http://hdl.handle.net/10016/19167).

  • Albarrán, P., Crespo, J., Ortuño, I., & Ruiz-Castillo, J. (2011). The skewness of science in 219 sub-fields and a number of aggregates. Scientometrics, 88, 385–397.

    Article  Google Scholar 

  • Albarrán, P., Perianes, A., & Ruiz-Castillo, J. (2013). Differences in citation impact across countries. Working paper 12-29, Universidad Carlos III, November 2013. http://hdl.handle.net/10016/16203 (forthcoming in Journal of the American Society for Information Science and Technology).

  • Albarrán, P., & Ruiz-Castillo, J. (2011). References made and citations received by scientific articles. Journal of the American Society for Information Science and Technology, 62, 40–49.

    Article  Google Scholar 

  • Alvarado, R. (2012). La colaboración de los autores en la literature producida sobre la Ley de Lotka. Ciência da Informação, 40, 266–279.

    Google Scholar 

  • Biglan, A. (1973). Relationships between subject matter characteristics and the structure and output of university departments. Journal of Applied Psychology, 57, 204–213.

    Article  Google Scholar 

  • Carrasco, R., & Ruiz-Castillo, J. (2014). The evolution of the scientific productivity of highly productive economists. Economic Inquiry, 52, 1–16.

    Article  Google Scholar 

  • Crespo, J. A., Herranz, N., Li, Y., & Ruiz-Castillo, J. (2014). The effect on citation inequality of differences in citation practices at the web of science subject category level. Journal of the American Society for Information Science and Technology, 65, 1244–1256.

    Article  Google Scholar 

  • Crespo, J. A., Li, Y., & Ruiz-Castillo, J. (2013). The measurement of the effect on citation inequality of differences in citation practices across scientific fields. PLoS One, 8(3), e58727.

    Article  Google Scholar 

  • Diamond, A. M. (1984). An economic model of the life-cycle research productivity of scientists. Scientometrics, 6, 189–196.

    Article  Google Scholar 

  • Econphd.net Rankings. (2004). http://econphd.econwiki.com/rank/rallec.htm.

  • Groeneveld, R. A., & Meeden, G. (1984). Measuring skewness and kurtosis. The Statistician, 33, 391–399.

    Article  Google Scholar 

  • Kalaitzidakis, P., Mamuneas, T., & Stengos, T. (2003). Rankings of academic journals and institutions in economics. Journal of the European Economic Association, 1, 1346–1366.

    Article  Google Scholar 

  • Kim, T.-H., & White, A. (2004). On more robust estimation of skewness and kurtosis. Finance Research Letters, 1, 56–73.

    Article  Google Scholar 

  • Li, Y., Castellano, C., Radicchi, F., & Ruiz-Castillo, J. (2013). Quantitative evaluation of alternative field normalization procedures. Journal of Informetrics, 7, 746–755.

    Article  Google Scholar 

  • Li, Y., & Ruiz-Castillo, J. (2013). The comparison of normalization procedures based on different classification systems. Journal of Informetrics, 7, 945–958.

    Article  Google Scholar 

  • Lotka, A. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16, 317–323.

    Google Scholar 

  • Oswald, A. (2007). An examination of the reliability of prestigious scholarly journals: Evidence and implications for decision-makers. Economica, 74, 21–31.

    Article  Google Scholar 

  • Perianes-Rodriguez, A., & Ruiz-Castillo, J. (2014). Within and across department variability in individual productivity. The case of economics. Working Paper 14-04, Departamento de Economía, Universidad Carlos III. http://hdl.handle.net/10016/18470.

  • Radicchi, F., Fortunato, S., & Castellano, C. (2008). Universality of citation distributions: Toward an objective measure of scientific impact. Proceedings of the National Academy of Sciences, 105, 17268–17272.

    Article  Google Scholar 

  • Ruiz-Castillo, J. (2014). The comparison of classification-system-based normalization procedures with source normalization alternatives in Waltman and Van Eck (2013). Journal of Informetrics, 8, 25–28.

    Article  Google Scholar 

  • Ruiz-Castillo, J., & Costas, R. (2014). The skewness of scientific productivity. Journal of Informetrics, 8, 917–934.

  • Schubert, A., Glänzel, W., & Braun, T. (1987). A new methodology for ranking scientific institutions. Scientometrics, 12, 267–292.

    Article  Google Scholar 

  • Seglen, P. (1992). The Skewness of Science. Journal of the American Society for Information Science, 43, 628–638.

    Article  Google Scholar 

  • Seglen, P. (1997). Why the impact factor of journals should not be used for evaluating research. British Medical Journal, 314, 498–502.

    Article  Google Scholar 

  • Van Raan, A. F. J. (2005). Fatal attraction: Ranking of universities by bibliometric methods. Scientometrics, 62, 133–143.

    Article  Google Scholar 

  • Van Raan, A. F. J. (2006a). Statistical properties of bibliometric indicators: Research group indicator distributions and correlations. Journal of the American Society for Information Science and Technology, 57, 408–430.

    Article  Google Scholar 

  • Van Raan, A. F. J. (2006b). Performance-related differences of bibliometric statistical properties of research groups: Cumulative advantages and hierarchically layered networks. Journal of the American Society for Information Science and Technology, 57, 1919–1935.

    Article  Google Scholar 

  • Van Raan, A. F. J. (2008). Scaling rules in the science system: Influence of field-specific citation characteristics on the impact of research groups. Journal of the American Society for Information Science and Technology, 57, 408–430.

    Article  Google Scholar 

  • Waltman, L., & Van Eck, N. J. (2013). A systematic empirical comparison of different approaches for normalizing citation impact indicators. Journal of Informetrics, 7, 833–849.

    Article  Google Scholar 

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Acknowledgments

Ruiz-Castillo acknowledges financial support from the Spanish MEC through grant ECO2011-29762. Fernando Gutierrez del Arroyo, Pedro Henrique Sant’Anna, and Ana Moreno’s work in the construction of the dataset, as well as useful conversations with Pedro Albarrán and Raquel Carrasco are deeply appreciated. Comments by two referees led to an improved version of the paper. All remaining shortcomings are the authors’ sole responsibility.

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Authors and Affiliations

  1. SCImago Research Group, Departamento de Biblioteconomía y Documentación, Universidad Carlos III, Getafe, Spain

    Antonio Perianes-Rodriguez

  2. Departamento de Economía, Universidad Carlos III, Getafe, Spain

    Javier Ruiz-Castillo

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  1. Antonio Perianes-Rodriguez
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  2. Javier Ruiz-Castillo
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Correspondence to Javier Ruiz-Castillo.

Appendix

Appendix

See Tables 5 and 6.

Table 5 Characteristics of productivity distributions for the 81 departments, ordered by mean productivity in distribution Q
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Table 6 Results of the CSS approach for productivity distribution Q/Age at the departmental level (Departments are ordered by mean productivity according to Q/Age)
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Perianes-Rodriguez, A., Ruiz-Castillo, J. Within- and between-department variability in individual productivity: the case of economics. Scientometrics 102, 1497–1520 (2015). https://doi.org/10.1007/s11192-014-1449-6

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