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The increasing availability of longitudinal data (repeated numerical observations of the same units at different times) requires the development of flexible techniques to automatically detect errors in such data. Besides standard types of errors, which can be treated with generic error correction techniques, large longitudinal datasets may present specific problems not easily traceable by the generic techniques. In particular, after applying those generic techniques, time series in the data may contain trends, natural fluctuations and possible surviving errors. To study the data evolution, one main issue is distinguishing those elusive errors from the rest, which should be kept as they are and not flattened or altered. This work responds to this need by identifying some types of elusive errors and by proposing a statistical-mathematical approach to capture their complexity that can be applied after the above generic techniques. The proposed approach is based on a system of indicators and works at the formal level by studying the differences between consecutive values of data series and the symmetries and asymmetries of these differences. It operates regardless of the specific meaning of the data and is thus applicable in a variety of contexts. We implement this approach in a relevant database of European Higher Education institutions (ETER) by analyzing two key variables: "Total academic staff" and "Total number of enrolled students", which are two of the most important variables, often used in empirical analysis as a proxy for size, and are considered by policymakers at the European level. The results are very promising. [ABSTRACT FROM AUTHOR]

In this article, we contribute to the scant literature covering quantitative studies on the determinants of the non-academic staff incidence in higher education institutions by analysing how the proportion of non-academic staff is related to key features such as size, prestige, year of foundation and financial structure of universities. We apply nonlinear regression analysis to compare HEIs across Europe and the USA, taking into account time and cross-country heterogeneity of the two balanced panel datasets concerning European and American universities over a period of 6 years (2011–2016 for Europe and 2012–2017 for the USA). Evidence suggests that in both Europe and the USA, public and larger (if sufficiently large) as well as more research-oriented units are characterised by a higher proportion of non-academic staff. In Europe, we observe an inverted U-shaped effect of the share of non-personnel expenditure and the foundation year on the proportion of non-academic staff, while the proportion of non-academic staff decreases with the share of core and third-party funding. For the USA, we obtain similar findings except that the share of core funding and third-party funding is characterised by a U-shaped effect, and the impact of the share of non-personnel expenditure has no empirical effect on the proportion of non-academic staff. Additionally, we discover that some factors that contribute to the proportion of non-academic staff may constitute indicators of performance, suggesting the need for further research to extend our knowledge on the complex issue of the role played by non-academic staff in university performance. [ABSTRACT FROM AUTHOR]

In the evaluation of research, the same unequal structure present in the production of research is reproduced. Despite a few very productive researchers (in terms of papers and citations received), there are also few researchers who are involved in the research evaluation process (in terms of being editorial board members of journals or reviewers). To produce a high number of papers and receive many citations and to be involved in the evaluation of research papers, you need to be in the minority of giants who have high productivity and more scientific success. As editorial board members and reviewers, we often find the same minority of giants. In this paper, we apply an economic approach to interpret recent trends in research evaluation and derive a new interpretation of Altmetrics as a response to the need for democratization of research and its evaluation. In this context, the majority of pygmies can participate in evaluation with Altmetrics, whose use is more democratic, that is, much wider and open to all. [ABSTRACT FROM AUTHOR]

[10] introduce an open source natural language processing and text mining method for topic evolution, emerging topic detection and research trend forecasting for all kinds of data-tagged text. [2] propose a micro investigation of research dynamics modelling topic disappearance by using a cross-collection topic model. [Extracted from the article]

Purpose: This paper relates the definition of data quality procedures for knowledge organizations such as Higher Education Institutions. The main purpose is to present the flexible approach developed for monitoring the data quality of the European Tertiary Education Register (ETER) database, illustrating its functioning and highlighting the main challenges that still have to be faced in this domain. Design/methodology/approach: The proposed data quality methodology is based on two kinds of checks, one to assess the consistency of cross-sectional data and the other to evaluate the stability of multiannual data. This methodology has an operational and empirical orientation. This means that the proposed checks do not assume any theoretical distribution for the determination of the threshold parameters that identify potential outliers, inconsistencies, and errors in the data. Findings: We show that the proposed cross-sectional checks and multiannual checks are helpful to identify outliers, extreme observations and to detect ontological inconsistencies not described in the available meta-data. For this reason, they may be a useful complement to integrate the processing of the available information. Research limitations: The coverage of the study is limited to European Higher Education Institutions. The cross-sectional and multiannual checks are not yet completely integrated. Practical implications: The consideration of the quality of the available data and information is important to enhance data quality-aware empirical investigations, highlighting problems, and areas where to invest for improving the coverage and interoperability of data in future data collection initiatives. Originality/value: The data-driven quality checks proposed in this paper may be useful as a reference for building and monitoring the data quality of new databases or of existing databases available for other countries or systems characterized by high heterogeneity and complexity of the units of analysis without relying on pre-specified theoretical distributions. [ABSTRACT FROM AUTHOR]

Directional distances provide useful, flexible measures of technical efficiency of production 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 strategies for the units attempting to reach the efficient frontier. Simar et al. (Eur J Oper Res 220:853–864, 2012) and Simar and Vanhems (J Econom 166:342–354, 2012) develop asymptotic properties of full-envelopment, FDH and DEA estimators of directional distances as well as robust order-m and order- α directional distance estimators. Extensions of these estimators to measures conditioned on environmental variables Z are also available (e.g., see Daraio and Simar in Eur J Oper Res 237:358–369, 2014). The resulting estimators have been shown to share the properties of their corresponding radial measures. However, to date the algorithms proposed for computing the directional distance estimates suffer from various numerical drawbacks (Daraio and Simar in Eur J Oper Res 237:358–369, 2014). In particular, for the order-m versions (conditional and unconditional) only approximations, 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". [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including higher education policy, public spending and technological changes on the research environment.

This paper proposes a holistic framework for the development of models for the assessment of research activities and their impacts. It distinguishes three dimensions, including in an original way, data as a main dimension, together with theory and methodology. Each dimension of the framework is further characterized by three main building blocks: education, research, and innovation (theory); efficiency, effectiveness, and impact (methodology); and availability, interoperability, and "unit-free" property (data). The different dimensions and their nine constituent building blocks are attributes of an overarching concept, denoted as "quality." Three additional quality attributes are identified as implementation factors (tailorability, transparency, and openness) and three "enabling" conditions (convergence, mixed methods, and knowledge infrastructures) complete the framework. A framework is required to develop models of metrics. Models of metrics are necessary to assess the meaning, validity, and robustness of metrics. The proposed framework can be a useful reference for the development of the ethics of research evaluation. It can act as a common denominator for different analytical levels and relevant aspects and is able to embrace many different and heterogeneous streams of literature. Directions for future research are provided. [ABSTRACT FROM AUTHOR]

The need for new indicators on universities is growing enormously. Governments and decision makers at all levels are faced with the huge opportunities generated by the availability of new knowledge and information and, simultaneously, are pressed by tight budget constraints. University rankings, in particular, are attracting policy and media attention, but at the same time receive harsh methodological criticism. After summarizing the main criticisms of rankings, we describe 2 trends in the user requirements for indicators; namely, granularity and cross-referencing. We then suggest that a change in the paradigm of the design and production of indicators is needed. The traditional approach is one that not only leverages on the existing data but also suggests heavy investment to integrate existing databases and to build up tailored indicators. We show, based on the European universities case, how the intelligent integration of existing data may lead to an open-linked data platform which permits the construction of new indicators. The power of the approach derives from the ability to combine heterogeneous sources of data to generate indicators that address a variety of user requirements without the need to design indicators on a custom basis. [ABSTRACT FROM AUTHOR]

We analyse the effect of competition on technical efficiency of Italian airports by applying a novel conditional nonparametric frontier analysis for the first time to the airport industry. We find that competition affects mostly the frontier of best performers, whilst airports that are lagging behind are less influenced. A novel two stage approach shows that, on average, competition has a negative impact on technical efficiency. We estimate a measure of pure efficiency , whitened from the main effect of the competition, whose distribution has a bi-modal shape, indicating the existence of two differently managed groups of airports. [ABSTRACT FROM AUTHOR]

The under-representation of women in science and technology is a matter of current great concern. Obtaining patent-based indicators by gender is crucial to analyse the situation of women in innovation, identify potential cases of gender inequalities, and support policy measures to promote gender balance. In this article we analyse men and women involvement in Spanish patents applied to the European Patent Office during 1990–2004. At least one female inventor is present in 18% of the patents; female contribution amounts to 9% of the total and 14% of inventors are women. Female involvement is above average in the academic public sector and in specific thematic areas, while the share of women in industry is very low. Female inventors show a stronger trend to collaborate than their male counterparts, as measured through their lower percentage of single-inventor patents and their higher co-inventorship index. Over the 15-year period under analysis, women’s involvement in patenting activity shows an upward trend, especially at universities and in cross-gender teams and the specialization profile of women tends to expand. Main difficulties in the development of patent-based indicators by gender are pointed out and the relevance of indicators to monitor the situation of women in innovation activity is highlighted. [ABSTRACT FROM PUBLISHER]

The performance of economic producers is often affected by external or environmental factors that, unlike the inputs and the outputs, are not under the control of the Decision Making Units (DMUs). These factors can be included in the model as exogenous variables and can help to explain the efficiency differentials, as well as improve the managerial policy of the evaluated units. A fully nonparametric methodology, which includes external variables in the frontier model and defines conditional DEA and FDH efficiency scores, is now available for investigating the impact of external-environmental factors on the performance. In this paper, we offer a state-of-the-art review of the literature, which has been proposed to include environmental variables in nonparametric and robust (to outliers) frontier models and to analyse and interpret the conditional efficiency scores, capturing their impact on the attainable set and/or on the distribution of the inefficiency scores. This paper develops and complements the approach of Bădin et al. () by suggesting a procedure that allows us to make local inference and provide confidence intervals for the impact of the external factors on the process. We advocate for the nonparametric conditional methodology, which avoids the restrictive 'separability' assumption required by the two-stage approaches in order to provide meaningful results. An illustration with real data on mutual funds shows the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

In the last decades of the twentieth century universities in Europe and other OECD countries have undergone a profound transformation. They have evolved from mainly élite institutions for teaching and research to large (public and private) organisations responsible for mass higher education and the production and distribution of new knowledge. Increasingly, new knowledge is produced by universities not only for its own sake but also for potential economic gains. [ABSTRACT FROM AUTHOR]

The paper addresses the issue of characterization and classification of universities in the European system, by using the recently developed Aquameth dataset. Preliminary cluster analysis based on structural variables identifies systematic differences in size. However, this structural distinction is associated with differences in strategic orientation of universities (towards research or towards teaching, respectively) only in a few countries. In most European countries there are no discernible differences across universities along these dimensions. The paper argues that countries in which universities are more differentiated according to research or teaching dimensions have implemented differentiation policies through a variety of policy instruments. In turn, these countries also are ranked high in international rankings of universities. This suggests a structural linkage between the poor performance of European universities in research-based rankings and the lack of differentiation. [ABSTRACT FROM AUTHOR]

Higher education institutions (HEIs) are crucial to the development of the European Research Area. However, unlike in the USA, the availability of quantitative indicators for individual HEI at the European level is severely limited by several methodological issues, data availability problems and national institutional constraints. The paper discusses strategies for collecting and validating data from different national sources; the limitations of the available data for different categories of indicators and, finally, the influence of the heterogeneity of the national HEIs on comparability at the European level. Based on the experience of two recently completed projects, the paper shows that, despite these problems, it is possible to collect relatively coherent data on European HEIs and to develop a set of consequential indicators. Further, it provides advice on how to exploit them for comparative purposes in a sensible way. It concludes by indicating areas where major improvements are urgently needed, and advocates a European S&T Indicators Platform to maintain and develop these data sets long term. [ABSTRACT FROM AUTHOR]

Data on scientific productivity at institutes of the French INSERM and at biomedical research institutes of the Italian CNR for 1997 were analysed. Available data on human capital input and geographical agglomeration allowed the estimation and comparison of efficiency measures. Nonparametric envelopment techniques were used, and robust nonparametric techniques were applied in this work for the first time for evaluating scientific productivity. They are shown to be useful tools to compute scientific productivity indicators and make institutional comparative analyses. Taking into account a large number of methodological problems, a meaningful and rigorous indirect comparison is made possible. Several possible explanations of the observed differences in productivity are commented on. [ABSTRACT FROM AUTHOR]

This work discusses the issue on how to include data about property and violent crimes in the production technology for the assessment of police technical efficiency. It applies recent advances in Directional Distances and Nonparametric Estimators. We claim that crime is an external variable not under the control of the decision units in view of the fact that it is exogenously determined. The results from the Conditional Directional Distance Analysis can be relevant to cities with high property misdemeanors and homicide rates. Our analysis may be helpful to obtain a more robust and fair classification of police and justice units under similar circumstances, determine the empirical effect of crime on police productivity, their optimal input–output relationship, explore potential associations and compensation effects, and rewarding efficient policy makers in the prevention of crime based on measures of police efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

Educational Institutions data constitute the basis for several important analyses on the educational systems; however they often contain not negligible shares of missing values, for several reasons. We consider in this work the relevant case of the European Tertiary Education Register (ETER), describing the Educational Institutions of Europe. The presence of missing values prevents the full exploitation of this database, since several types of analyses that could be performed are currently impracticable. The imputation of artificial data, reconstructed with the aim of being statistically equivalent to the (unknown) missing data, would allow to overcome these problems. A main complication in the imputation of this type of data is given by the correlations that exist among all the variables. We propose several imputation techniques designed to deal with the different types of missing values appearing in these interconnected data. We use these techniques to impute the database. Moreover, we evaluate the accuracy of the proposed approach by artificially introducing missing data, by imputing them, and by comparing imputed and original values. Results show that the information reconstruction does not introduce statistically significant changes in the data and that the imputed values are close enough to the original values. • Imputation methodology for educational data using information reconstruction and donors. • Designed to satisfactory impute the difficult case of interconnected time series. • Application to an important real case: European Tertiary Education Register (ETER). • Uses a formal and data-driven approach, hence applicable in other contexts. • Analysis of the imputation accuracy by using artificial missing data. [ABSTRACT FROM AUTHOR]

Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to measure system performance, they lack a statistical framework for inference, due in part to the complex structure of network processes. We fill this gap by developing a general framework to infer the network structure in a Bayesian sense, in order to better understand the underlying relationships driving system performance. Our approach draws on recent advances in information science, machine learning and statistical inference from the physics of complex systems to estimate unobserved network linkages. To illustrate, we apply our framework to analyze the production of knowledge, via own and cross-disciplinary research, for a world-country panel of bibliometric data. We find significant interactions between related disciplinary research output, both in terms of quantity and quality. In the context of research productivity, our results on cross-disciplinary linkages could be used to better target research funding across disciplines and institutions. More generally, our framework for inferring the underlying network production technology could be applied to both public and private settings which entail spillovers, including intra- and inter-firm managerial decisions and public agency coordination. This framework also provides a systematic approach to model selection when the underlying network structure is unknown. [ABSTRACT FROM AUTHOR]

In the 2000s, Italy liberalized its electricity and natural gas markets, and in 2016, it separated retail activities from other activities within these markets. Such differentiation is unique within Europe. Italy is an ideal case for investigating which operational factors may increase profit margins among electricity and gas retailers. The present analysis analyzes 120 retail operators in the Italian electricity and gas markets in 2020, using two models to assess their cost and commercial efficiencies in achieving high direct margin (defined as the difference between revenues and raw materials cost). The findings show a positive effect of size on efficiency. • We analyze the Italian electricity and natural gas retail market in 2020. • We develop two benchmarking models to investigate cost and commercial efficiencies. • We analyze which features bring advantages in achieving high margins to Italian retailers. • We find evidence of economies of scale. • A demand aggregator could provide high margins for small operators. [ABSTRACT FROM AUTHOR]

Higher education institutions (HEIs), based on learning, innovation, and research, can support the progress of civil society. Many HEIs are implementing sustainability practices and projects to counteract climate change, often involving youth participation. The present study aimed at identifying how sustainable communities may be fostered in a university setting. To that end, a questionnaire was administered to engineering students at the start and end of a course on energy issues, assessing their perceptions of sustainability using multi-criteria decision analysis. The results showed that students placed greater value on sustainability at the end of the course. Additionally, the findings highlight that the implementation of projects aimed at tackling real problems may be useful for disseminating knowledge and sustainable practices. The main implications of this study indicate that sustainable communities in academia lay on six foundational pillars: sustainable education, energy (and resource) independence, subsidies in support of the green economy, initiatives aimed at reducing the carbon footprint, energy community development, and new green professional opportunities. [ABSTRACT FROM AUTHOR]

Purpose: The main objective of this work is to show the potentialities of recently developed approaches for automatic knowledge extraction directly from the universities' websites. The information automatically extracted can be potentially updated with a frequency higher than once per year, and be safe from manipulations or misinterpretations. Moreover, this approach allows us flexibility in collecting indicators about the efficiency of universities' websites and their effectiveness in disseminating key contents. These new indicators can complement traditional indicators of scientific research (e.g. number of articles and number of citations) and teaching (e.g. number of students and graduates) by introducing further dimensions to allow new insights for "profiling" the analyzed universities. Design/methodology/approach: Webometrics relies on web mining methods and techniques to perform quantitative analyses of the web. This study implements an advanced application of the webometric approach, exploiting all the three categories of web mining: web content mining; web structure mining; web usage mining. The information to compute our indicators has been extracted from the universities' websites by using web scraping and text mining techniques. The scraped information has been stored in a NoSQL DB according to a semi-structured form to allow for retrieving information efficiently by text mining techniques. This provides increased flexibility in the design of new indicators, opening the door to new types of analyses. Some data have also been collected by means of batch interrogations of search engines (Bing, www.bing.com) or from a leading provider of Web analytics (SimilarWeb, http://www.similarweb.com). The information extracted from the Web has been combined with the University structural information taken from the European Tertiary Education Register (https://eter.joanneum.at/#/home), a database collecting information on Higher Education Institutions (HEIs) at European level. All the above was used to perform a clusterization of 79 Italian universities based on structural and digital indicators. Findings: The main findings of this study concern the evaluation of the potential in digitalization of universities, in particular by presenting techniques for the automatic extraction of information from the web to build indicators of quality and impact of universities' websites. These indicators can complement traditional indicators and can be used to identify groups of universities with common features using clustering techniques working with the above indicators. Research limitations: The results reported in this study refers to Italian universities only, but the approach could be extended to other university systems abroad. Practical implications: The approach proposed in this study and its illustration on Italian universities show the usefulness of recently introduced automatic data extraction and web scraping approaches and its practical relevance for characterizing and profiling the activities of universities on the basis of their websites. The approach could be applied to other university systems. Originality/value: This work applies for the first time to university websites some recently introduced techniques for automatic knowledge extraction based on web scraping, optical character recognition and nontrivial text mining operations (Bruni & Bianchi, 2020). [ABSTRACT FROM AUTHOR]

The main objective of this work is to show the potentialities of recently developed approaches for automatic knowledge extraction directly from the universities' websites. The information automatically extracted can be potentially updated with a frequency higher than once per year, and be safe from manipulations or misinterpretations. Moreover, this approach allows us flexibility in collecting indicators about the efficiency of universities' websites and their effectiveness in disseminating key contents. These new indicators can complement traditional indicators of scientific research (e.g. number of articles and number of citations) and teaching (e.g. number of students and graduates) by introducing further dimensions to allow new insights for "profiling" the analyzed universities. Webometrics relies on web mining methods and techniques to perform quantitative analyses of the web. This study implements an advanced application of the webometric approach, exploiting all the three categories of web mining: web content mining; web structure mining; web usage mining. The information to compute our indicators has been extracted from the universities' websites by using web scraping and text mining techniques. The scraped information has been stored in a NoSQL DB according to a semi-structured form to allow for retrieving information efficiently by text mining techniques. This provides increased flexibility in the design of new indicators, opening the door to new types of analyses. Some data have also been collected by means of batch interrogations of search engines (Bing, www.bing.com) or from a leading provider of Web analytics (SimilarWeb, http://www.similarweb.com). The information extracted from the Web has been combined with the University structural information taken from the European Tertiary Education Register (https://eter.joanneum.at/#/home), a database collecting information on Higher Education Institutions (HEIs) at European level. All the above was used to perform a clusterization of 79 Italian universities based on structural and digital indicators. The main findings of this study concern the evaluation of the potential in digitalization of universities, in particular by presenting techniques for the automatic extraction of information from the web to build indicators of quality and impact of universities' websites. These indicators can complement traditional indicators and can be used to identify groups of universities with common features using clustering techniques working with the above indicators. The results reported in this study refers to Italian universities only, but the approach could be extended to other university systems abroad. The approach proposed in this study and its illustration on Italian universities show the usefulness of recently introduced automatic data extraction and web scraping approaches and its practical relevance for characterizing and profiling the activities of universities on the basis of their websites. The approach could be applied to other university systems. This work applies for the first time to university websites some recently introduced techniques for automatic knowledge extraction based on web scraping, optical character recognition and nontrivial text mining operations (Bruni & Bianchi, 2020). [ABSTRACT FROM AUTHOR]

European governments increasingly employ competitive university funding to improve performance in higher education. The framework that is developed in this paper suggests a donor-specific trade-off between fostering best performing universities and increasing university efficiency when introducing competitive funding in the university sector. We test this assertion based on a university-level panel dataset across eight European countries from 1994–2006. Estimating a simultaneous two-stage Stochastic Frontier Approach, we find that international public funds decrease the productivity of the best performing universities, which suggests a non-negligible effect because of the administrative burden induced by competitive funding. However, the competition for international public funds also disciplines universities as evidenced by a positive impact on efficiency. Conversely, tuition fees enhance the productivity of the best performing universities but increase the spread of universities with lower productivity, which suggests a strong sorting effect. [ABSTRACT FROM AUTHOR]