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The skewness of scientific productivity

  • Javier Ruiz-Castillo
  • Rodrigo Costas

This paper exploits a unique 2003-2011 large dataset, indexed by Thomson & Reuters, consisting of 17.2 million disambiguated authors classified into 30 broad scientific fields, as well as the 48.2 million articles resulting from a multiplying strategy in which any article co-authored by two or more persons is wholly assigned as many times as necessary to each of them. The dataset is characterized by a large proportion of authors who have their oeuvre in several fields. We measure individual productivity in two ways that are uncorrelated: as the number of articles per person, and as the mean citation per article per person in the 2003-2011 period. We analyze the shape of the two types of individual productivity distributions in each field using size- and scale-independent indicators. For productivity inequality, we use the coefficient of variation. To assess the skewness of productivity distributions we use a robust index of skeweness, as well as the Characteristic Scores and Scales approach. For productivity inequality, we use the coefficient of variation. In each field, we study two samples: the entire population, and what we call “successful authors”, namely, the subset of scientists whose productivity is above their field average. The main result is that, in spite of wide differences in production and citation practices across fields, the shape of field productivity distributions are very similar across fields. The parallelism of the results for the population as a whole and for the subset of successful authors when productivity is measured as mean citation per article per person, reveals the fractal nature of the skewness of scientific productivity in this case. These results are essentially maintained when any article co-authored by two or more persons is fractionally assigned to each of them.

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Paper provided by Universidad Carlos III, Departamento de Economía in its series Economics Working Papers with number we1402.

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Date of creation: Jan 2014
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Handle: RePEc:cte:werepe:we1402
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  1. Kim, Tae-Hwan & White, Halbert, 2004. "On more robust estimation of skewness and kurtosis," Finance Research Letters, Elsevier, vol. 1(1), pages 56-73, March.
  2. Li, Yunrong & Radicchi, Filippo & Castellano, Claudio & Ruiz-Castillo, Javier, 2013. "Quantitative evaluation of alternative field normalization procedures," Journal of Informetrics, Elsevier, vol. 7(3), pages 746-755.
  3. Albarrán, Pedro & Crespo, Juan A. & Ortuño-Ortín, Ignacio & Ruiz-Castillo, Javier, 2010. "The Skewness of Science in 219 Sub-Fields and a Number of Aggregates," CEPR Discussion Papers 8126, C.E.P.R. Discussion Papers.
  4. Waltman, Ludo & van Eck, Nees Jan, 2013. "A systematic empirical comparison of different approaches for normalizing citation impact indicators," Journal of Informetrics, Elsevier, vol. 7(4), pages 833-849.
  5. Juan A. Crespo & Neus Herranz & Yunrong Li & Javier Ruiz-Castillo, 2014. "The effect on citation inequality of differences in citation practices at the web of science subject category level," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(6), pages 1244-1256, 06.
  6. Herranz, Neus & Ruiz-Castillo, Javier, 2011. "Sub-field normalization in the multiplicative case: High- and low-impact citation indicators," CEPR Discussion Papers 8716, C.E.P.R. Discussion Papers.
  7. Yungron Li & Javier Ruiz-Castillo, 2013. "The impact of extreme observations in citation distributions," Economics Working Papers we1308, Universidad Carlos III, Departamento de Economía.
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