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In the last decade, several actions have been undertaken by different institutions to promote women’s careers in academia and increase female representation through gender equality programmes and action plans. The share of female students graduating from universities now exceeds that of male students, and the share of women pursuing an academic career is slowly increasing. Despite the larger influx of female students and junior academics, relevant statistics continue to indicate a so-called scissor effect between the genders that persists through different cohorts of men and women. Even in disciplines where women have been overrepresented among PhDs and at early career stages for many years. There is reason to believe that stereotypes about women in science persist and impede their career progress. Continuing indications of sexism and gender bias in academia suggest that the QB phenomenon may still be present today. This may lead to the provocative conclusion that recent measures intended to prevent biases against women may help perpetuate them. Specifically, involving senior female scientists in supervision and review procedures may harm rather than help the cause it is intended to serve, as there are chances that this eventually results in the provision of less rather than more encouragement and opportunities to young female scientists. This, in turn, may discourage women’s participation in research projects as well. Nonetheless, to better understand the Queen Bee syndrome, it would be useful to distinguish between disciplines and academic ranks. Women’s behaviour may be thus related to the scientific area where they operate and whether they climb the ladder. We aim to perform these further analyses in the near future. Similarly, venues for future research may be tackling the limitations of the dataconsidering the inhomogeneous availability of data on the PRIN calls, the availability of publication gender data averaged over a 5-years time window and for a limited number of Italian universities in the Leiden Rankings.

We propose an innovative use of the information provided by the Leiden Rankings (LR). Although LR only consider research output of major universities reported in Web of Science, statistical analysis of LRs combined with network mapping can reveal the complexity of research performance measurement. Yet, one can identify ''outlying'' institutions that perform significantly below or above expectations; these can be further analyzed using case studies. Outliers can inform and guide science policies about alternative options. Analyzing the case of the Politecnico di Bari, we observe that ''small teams'' led by young and promising scholars can push the performance of a university up to the top of the LR. Supporting ''emerging teams'', as argued by Moed (2017), can thus be an alternative to research support policies, adopted to encourage virtuous behaviors and best practices in research.

In this paper we answer the question of how evaluation should be by proposing a good evaluation of research practices. A good evaluation of research practices, intended as social practices à la MacIntyre, should take into account the stable motivations and the traits of the characters (i.e. the virtues) of researchers. We also show that a good evaluation is also just, beyond the sense of fairness, as working on good research practices implies keep into account a broader sense of justice. After that, we propose the development of a knowledge base for the assessment of "good" evaluations of research practices to implement a questionnaire for the assessment of researchers' virtues. Although the latter is a challenging task, the use of ontologies and taxonomic knowledge, and the reasoning algorithms that can make inferences on the basis of such knowledge represents a way for testing the consistency of the information reported in the questionnaire and to analyze in a correct and coherent way the data gathered through it.

This report presents the current status of the European Tertiary Education Register (ETER), as well as recommendations for its future development. As an outcome of this work, the ETER database is now consolidated in terms of methodology and data content, but also of a technical infrastructure, which allows an efficient management of the data and allows users to access the data through a variety of options (direct download, automated download, API access). As documented in this report, ETER is establishing itself as the reference dataset for European higher education and is being widely used both for policy analysis and for scholarly purposes. The final recommendations of this report deal with further consolidating and extending the system and better embedding it in a broader landscape of higher education databases at the international level. The report advises that, in the long term, ETER should be established as a lasting activity run by the European Commission with the support of EUROSTAT and the participation of member states. To this aim, future ETER activities should focus to a stronger extent on establishing institutional collaborations with statistical data providers, Commission services and users, while maintaining and further developing the data collection, communication and dissemination activities., JRC.B.7-Knowledge for Finance, Innovation and Growth

The emergence of Open Access (OA) publishing has created new economic niches and debates in academic publishing. OA journals offer numerous publication outlets with differing editorial philosophies and business models. Scholars and academic stakeholders must decide which journals offer acceptable value for the direct or indirect costs of academic publishing. Our research uses the Directory of Open Access Journals (N=12,100) to identify various characteristics of OA academic journals that influence journal prices. The Journal Impact Factor (JIF), language, publisher mission, DOAJ Seal, World Bank Economic and Geographic regions of publishers, peer review duration and journal discipline are identified as factors with significant influence over journal price levels. Journals with status endowments (JIF, DOAJ Seal), published in wealthier regions, in medical or science-based disciplines, and with English-based articles are relatively more expensive. Scholarly and political economic inequalities manifest in the prices and benefits offered by different journals and publishers throughout the world.

The issue of quality and its relationship with efficiency and performance is a crucial operational issue in many fields of study including production economics, operations research, engineering and business management. In this paper we provide a methodology for identifying latent quality factors, estimate their statistical significance and analyze their impact on the performance of the production process. This methodology is based on up-to-date computational methods and statistical tests for directional distances. We illustrate the approach using real data to evaluate the performance of European Universities.

Scientific research has been acknowledged as an important knowledge resource for developing technologies. Studying science-technology linkages is crucial to help understand the mechanisms of innovation. Using nanotechnology as a case study, this paper investigates what types of scientific research can help improve the impact of technologies. This study uses backward and forward citation analysis, extracted from the Derwent World Patents Index (DWPI). Non-patent citations (NPCs) from each patent are further connected with records indexed in Web of Science, and the forward citations for the cited articles are collected. On the one hand, our results confirm that there is an important contribution from science to technology. High-impact academic research has significantly contributed to the development of high-impact patents. On the other hand, this study also reveals the heterogeneous pattern of patents citing scientific publications, depending on the organizational type, country, and knowledge origin. Compared to those in the U.S., patents developed by Chinese inventors tend to reply on more recent science but with a narrower scientific scope.

Conditional efficiency measures are needed when the production process does not depend only on the inputs and outputs, but may be influenced by external factors and/or environmental variables (Z). They are estimated by means of a nonparametric estimator of the conditional distribution function of the inputs and outputs, conditionally on values of Z. For doing this, smoothing procedures and smoothing parameters, the bandwidths, are involved. So far, Least Squares Cross Validation (LSCV) methods have been used, which have been proven to provide bandwidths with optimal rates for estimating conditional distributions. In efficiency analysis, the main interest is in the estimation of the conditional efficiency score, which typically depends on the boundary of the support of the distribution and not on the full conditional distribution. In this paper, we show indeed that the rate for the bandwidths which is optimal for estimating conditional distributions, may not be optimal for the estimation of the efficiency scores. We propose hence a new approach based on the bootstrap which overcomes these difficulties. We analyze and compare, through Monte Carlo simulations, the performances of LSCV techniques with our bootstrap approach in finite samples. As expected, our bootstrap approach shows generally better performances and is more robust to the various Monte Carlo scenarios analyzed. We provide in an Appendix the Matlab code performing our experiments.

Directional distances provide useful, flexible measures of technical efficiency of pro- duction units relative to the efficient frontier of the attainable set in input-output space. In addition, the additive nature of directional distances permits negative input or outputs quantities. The choice of the direction allows analysis of different strate- gies for the units attempting to reach the efficient frontier. Simar et al. (2012) and Simar and Vanhems (2012) develop asymptotic properties of full-envelopment, FDH and DEA estimators of directional distances as well as robust order-m and order-α di- rectional distance estimators. Extensions of these estimators to measures conditioned on environmental variables Z are also available (e.g., see Daraio and Simar, 2014). The resulting estimators have been shown to share the properties of their corresponding ra- dial measures. However, to date the algorithms proposed for computing the directional distance estimates suffer from various numerical drawbacks (Daraio and Simar, 2014). In particular, for the order-m versions (conditional and unconditional) only approx- imations, based on Monte-Carlo methods, have been suggested, involving additional computational burden. In this paper we propose a new fast and efficient method to compute exact values of the directional distance estimates for all the cases (full and partial frontier cases, unconditional or conditional to external factors), that overcome all previous difficulties. This new method is illustrated on simulated and real data sets. Matlab code for computation is provided in an appendix.

This paper demonstrates that standard central limit theorem (CLT) results do not hold for means of nonparametric conditional efficiency estimators, and provides new CLTs that do hold, permitting applied researchers to estimate confidence intervals for mean conditional efficiency or to compare mean efficiency across groups of produc- ers along the lines of the test developed by Kneip et al. (JBES, 2015b). The new CLTs are used to develop a test of the “separability" condition that is necessary for second-stage regressions of efficiency estimates on environmental variables. We show that if this condition is violated, not only are second-stage regressions meaningless, but also first-stage, unconditional efficiency estimates are without meaning. As such, the test developed here is of fundamental importance to applied researchers using non- parametric methods for efficiency estimation. Our simulation results indicate that our tests perform well both in terms of size and power. We present a real-world empiri- cal example by updating the analysis performed by Aly et al. (R. E. Stat., 1990) on U.S. commercial banks; our tests easily reject the assumption required for two-stage estimation, calling into question results that appear in hundreds of papers that have been published in recent years.