Gender bias in academic recruitment

Abstract

It is well known that women are underrepresented in the academic systems of many countries. Gender discrimination is one of the factors that could contribute to this phenomenon. This study considers a recent national academic recruitment campaign in Italy, examining whether women are subject to more or less bias than men. The findings show that no gender-related differences occur among the candidates who benefit from positive bias, while among those candidates affected by negative bias, the incidence of women is lower than that of men. Among the factors that determine success in a competition for an academic position, the number of the applicant’s career years in the same university as the committee members assumes greater weight for male candidates than for females. Being of the same gender as the committee president is also a factor that assumes greater weight for male applicants. On the other hand, for female applicants, the presence of a full professor in the same university with the same family name as the candidate assumes greater weight than for male candidates.

Appendix: Measuring research productivity

Research activity is a production process in which the inputs consist of human resources and other tangible (scientific instruments, materials, etc.) and intangible (accumulated knowledge, social networks, etc.) resources, and where outputs have a complex character of both tangible nature (publications, patents, conference presentations, databases, protocols, etc.) and intangible nature (tacit knowledge, consulting activity, etc.). The new-knowledge production function thus has a multi-input and multi-output character. The principal efficiency indicator of any production system is labor productivity.

The calculation of labor productivity requires a few simplifications and assumptions. In the hard sciences, the prevalent form of codification of research output is publication in scientific journals. As a proxy of total output, in this work we consider only the specific publications (articles, article reviews, and proceeding papers) indexed in the Thomson Reuters WoS.

When measuring labor productivity, if there are differences in the production factors available to each scientist then one should normalize by them. Unfortunately relevant data are not available at the individual level in Italy. The first assumption then is that resources available to professors within the same field are the same. The second assumption is that the hours devoted to research are more or less the same for all professors. Given the characteristics of the Italian academic system as depicted in “Recruitment in Italian universities” section, the above assumptions appear acceptable.

Because of the differences in the publication intensity across fields, a prerequisite of any distortion-free performance assessment is the classification of each researcher in one and only one field (Abramo et al. 2013a).

Most bibliometricians define productivity as the number of publications in the period of observation. Because publications have different values (impact), we prefer to adopt a more meaningful definition of productivity, i.e. the value of output per unit value of labor, all other production factors being equal. The latter recognizes that the publications embedding new knowledge have different value or impact on scientific advancement, which bibliometricians approximate with citations or journal impact factors. Provided that there is an adequate citation window (at least 2 years) the use of citations is always preferable (Abramo et al. 2011). Because citation behavior varies by field, we standardize the citations for each publication with respect to the average of the distribution of citations for all the Italian cited publications of the same year and the same WoS subject category.¹⁰ Furthermore, research projects frequently involve a team of professors, which is registered in the co-authorship of publications. In this case we account for the fractional contributions of scientists to outputs, which is at times further signaled by the position of the authors in the byline.

At the individual level, professors of the same academic rank in this specific case, we can measure the average yearly productivity, named Fractional Scientific Strength (FSS), in the following way¹¹:

$${\text{FSS}} = \frac{1}{t}\mathop \sum \limits_{i = 1}^{N} \frac{{c_{i} }}{{\bar{c}}}f_{i}$$

where t = number of years of work of the researcher in the period of observation; N = number of publications of the researcher in the period of observation; c _i  = citations received by publication i; $\bar{c}$ = average of the distribution of citations received for all cited publications of the same year and subject category of publication i; f _i  = fractional contribution of the researcher to publication i.

Fractional contribution equals the inverse of the number of authors in those fields where the practice is to place the authors in simple alphabetical order but assumes different weights in other cases. For the life sciences, widespread practice in Italy is for the authors to indicate the various contributions to the published research by the order of the names in the byline. For the life science SDSs, we give different weights to each co-author according to their order in the byline and the character of the co-authorship (intra-mural or extra-mural) [see Abramo et al. 2013b, c]. If first and last authors belong to the same university, 40 % of citations are attributed to each of them; the remaining 20 % are divided among all other authors. If the first two and last two authors belong to different universities, 30 % of citations are attributed to first and last authors; 15 % of citations are attributed to second and last author but one; the remaining 10 % are divided among all others.¹²

Data on research staff of each university, such as years of employment in the observed period, academic rank and their SDS classification are extracted from the database on Italian university personnel, maintained by the Ministry for Universities and Research.¹³ Unfortunately, information on leaves of absence is not available and cannot be accounted for in the calculation of yearly productivity, to the disadvantage of women on maternity leave in the period of observation. The bibliometric dataset used to measure FSS is exctracted from the Italian Observatory of Public Research (ORP), a database developed and maintained by the authors and derived under license from the Thomson Reuters WoS. Beginning from the raw data of the WoS, and applying a complex algorithm for reconciliation of the author’s affiliation and disambiguation of the true identity of the authors, each publication (article, article review and conference proceeding) is attributed to the university scientist or scientists that produced it (D’Angelo et al. 2011). Thanks to this algorithm we can produce rankings of research productivity at the individual level, on a national scale. Based on the value of FSS we obtain, for each SDS, a ranking list expressed on a percentile scale of 0–100 (worst to best) for comparison with the performance of all Italian colleagues of the same academic rank and SDS.