Competencies for young European higher education graduates: labor market mismatches and their payoffs

Articolo su competenze acquisite vs richieste e loro relazione con remunerazione e soddisfazione nel mercato del lavoro: analisi comparativa a livello europe

Conditional Nonparametric Frontier Models for Convex and Non Convex Technologies: a Unifying Approach

The explanation of productivity differentials is very important to identify the economic conditions that create inefficiency and to improve managerial performance. In literature two main approaches have been developed: one-stage approaches and two-stage approaches. Daraio and Simar (2003) propose a full nonparametric methodology based on conditional FDH and conditional order-m frontiers without any convexity assumption on the technology. On the one hand, convexity has always been assumed in mainstream production theory and general equilibrium. On the other hand, in many empirical applications, the convexity assumption can be reasonable and sometimes natural. Leading by these considerations, in this paper we propose a unifying approach to introduce external-environmental variables in nonparametric frontier models for convex and non convex technologies. Developing further the work done in Daraio and Simar (2003) we introduce a conditional DEA estimator, i.e., an estimator of production frontier of DEA type conditioned to some external-environmental variables which are neither inputs nor outputs under the control of the producer. A robust version of this conditional estimator is also proposed. These various measures of efficiency provide also indicators of convexity. Illustrations through simulated and real data (mutual funds) examples are reported.Convexity, External-Environmental Factors, Production Frontier, Nonparametric Estimation, Robust Estimation.

Introducing Environmental Variables in Nonparametric Frontier Models: a Probabilistic Approach

This paper proposes a general formulation of a nonparametric frontier model introducingexternal environmental factors that might influence the production process butare neither inputs nor outputs under the control of the producer. A representation isproposed in terms of a probabilistic model which defines the data generating process.Our approach extends the basic ideas from Cazals, Florens and Simar (2002) to thefull multivariate case. We introduce the concepts of conditional efficiency measure andof conditional efficiency measure of order-m. Afterwards we suggest a practical wayfor computing the nonparametric estimators. Finally, a simple methodology to investigatethe influence of these external factors on the production process is proposed.Numerical illustrations through some simulated examples and through a real data seton Mutual Funds show the usefulness of the approach.production function, frontier, nonparametric estimation, environmental factors,robust estimation.

International parity relationships between Germany and US: a multivariate time series analysis for the post Bretton-Woods period

This paper investigates the effects of replacing the consumer price index (CPI) with the wholesale price index (WPI) in the cointegrating in-ternational parity relationships found by Juselius and MacDonald (2000).AR model, cointegration, purchasing power parity, un-covered interest rate parity.

Altmetrics as an Answer to the Need for Democratization of Research and Its Evaluation

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

In Defense of Merit to Overcome Merit

: Bibliometric indicators such as the number of published articles and citations received are subject to a strong ambiguity. A high numerical value of bibliometric indicators may not measure the quality of scientific production, but only a high level of activity of a researcher. There may be cases of good researchers who do not produce a high number of articles, but have few research products of high quality. The sociology of science relies on the so-called "Matthew effect," which is inspired by Matthew's Gospel on Talents. "Those that have more will have more" seems to support the idea that those that publish more, merit to have higher bibliometric indicators, and to be recognized for their major results. But is this really the case? Can bibliometric indicators be considered a measure of the merit of scholars or they come from luck and chance? The answer is of fundamental importance to identify best practices in research assessment. In this work, using philosophical argumentation, we show how Christian theology, in particular St. Thomas Aquinas, can help us to clarify the concept of merit, overcoming the conceptual ambiguities and problems highlighted by the existing literature. By doing this, Christian theology, will allow us to introduce the evaluation framework in a broader perspective better suited to the interpretation of the complexity of research evaluation

A robust nonparametric approach to the analysis of scientific productivity

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 for. Nonparametric envelopment techniques were used, and robust nonparametric techniques was applied in this work for the first time for evaluating scientific productivity. It is shown as a useful tool 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

Optimal Bandwidth Selection for Conditional Efficiency Measures: a Data-driven Approach

In productivity analysis an important issue is to detect how external (environmental) factors, exogenous to the production process and not under the control of the producer, might influence the production process and the resulting efficiency of the firms. Most of the traditional approaches proposed in the literature have serious drawbacks. An alternative approach is to describe the production process as being conditioned by a given value of the environmental variables (Cazals, Florens and Simar, 2002, Daraio and Simar, 2005). This defines conditional efficiency measures where the production set in the input × output space may depend on the value of the external variables. The statistical properties of nonparametric estimators of these conditional measures are now established (Jeong, Park and Simar, 2008). These involve the estimation of a nonstandard conditional distribution function which requires the specification of a smoothing parameter (a bandwidth). So far, only the asymptotic optimal order of this bandwidth has been established. This is of little interest for the practitioner. In this paper we fill this gap and we propose a data-driven technique for selecting this parameter in practice. The approach, based on a Least Squares Cross Validation procedure (LSCV), provides an optimal bandwidth that minimizes an appropriate integrated Mean Squared Error (MSE). The method is carefully described and exemplified with some simulated data with univariate and multivariate environmental factors. An application on real data (performances of Mutual Funds) illustrates how this new optimal method of bandwidth selection outperforms former methods.Nonparametric efficiency estimation, conditional efficiency measures, environmental factors, conditional distribution function, bandwidth.

Bibliometric indicators: the origin of their log-normal distribution and why they are not a reliable proxy for an individual scholar’s talent

There is now compelling evidence that the statistical distributions of extensive individual bibliometric indicators collected by a scholar, such as the number of publications or the total number of citations, are well represented by a Log-Normal function when homogeneous samples are considered. A Log-Normal distribution function is the normal distribution for the logarithm of the variable. In linear scale it is a highly skewed distribution with a long tail in the high productivity side. We are still lacking a detailed and convincing ab-initio model able to explain observed Log-Normal distributions-this is the gap this paper sets out to fill. Here, we propose a general explanation of the observed evidence by developing a straightforward model based on the following simple assumptions: (1) the materialist principle of the natural equality of human intelligence, (2) the success breeds success effect, also known as Merton effect, which can be traced back to the Gospel parables about the Talents (Matthew) and Minas (Luke), and, (3) the recognition and reputation mechanism. Building on these assumptions we propose a distribution function that, although mathematically not identical to a Log-Normal distribution, shares with it all its main features. Our model well reproduces the empirical distributions, so the hypotheses at the basis of the model are not falsified. Therefore the distributions of the bibliometric parameters observed might be the result of chance and noise (chaos) related to multiplicative phenomena connected to a publish or perish inflationary mechanism, led by scholars' recognition and reputations. In short, being a scholar in the right tail or in the left tail of the distribution could have very little connection to her/his merit and achievements. This interpretation might cast some doubts on the use of the number of papers and/or citations as a measure of scientific achievements. A tricky issue seems to emerge, that is: what then do bibliometric indicators really measure? This issue calls for deeper investigations into the meaning of bibliometric indicators. This is an interesting and intriguing topic for further research to be carried out within a wider interdisciplinary investigation of the science of science, which may include elements and investigation tools from philosophy, psychology and sociology

Efficiency and University Size: Discipline-wise Evidence from European Universities

Strategic management of universities must build the best possible relation between inputs and outputs. One relevant question, in this perspective, is whether the unit is making the best use of existing resources, or whether technical efficiency is in place. Here we address the question of technical efficiency with respect to university’s size. The crucial concept in this analysis is conditional efficiency and the ratio of size-conditional to unconditional efficiency measures. In particular we take use of robust order-m efficiency scores presented in Cazals, Florens and Simar (2002) and generalized in Daraio and Simar (2005a,b) to analyze data from four European countries and four different research fields. Our results are still explorative and mainly show how heterogeneous international datasets could be used to analyze productivity differences.Universities;efficiency;International comparisons