We employ Data Envelopment Analysis to compute the technical efficiency of Italian and English higher education institutions. Our results show that, in relation to the country-specific frontier, institutions in both countries are typically very efficient. However, institutions in England are more efficient than those in Italy when we compare jointly their performances. We also look at the evolution of technical efficiency scores over a four-year period, and find that, in line with an error-correction hypothesis, Italian universities are improving their technical efficiency while English universities are obtaining stable scores. Policy implications are addressed.

Esta es la versión de autor del artículo publicado en:This is an author produced version of a paper published in:Higher Education 59.1 (2010) each "country-specific" frontier; but when compared together, Italian universities seem relatively more efficient. Malmquist indexes show, in both cases, efficiency improvements in the period considered. In the Italian case, this improvement is due to major "technological changes"; that is, the introduction of some structural reforms in the sector (e.g. Bachelor/Master curricula). In the Spanish case, there is an improvement in "pure" efficiency, which is due to new funding models. Further stages of the study underline the role of "regional effects", probably due to different socio-economic conditions in Italy, and to the decentralization process in Spain.JEL classification: C14, H52, I21, I22

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This study investigates efficiency and quality of care in nursing homes. By means of Data Envelopment Analysis (DEA), the efficiency of 40 nursing homes that deliver their services in the north-western area of the Lombardy Region was assessed over a 3-year period (2005-2007). Lombardy is a very peculiar setting, since it is the only Region in Italy where the healthcare industry is organised as a quasi-market, in which the public authority buys health and nursing services from independent providers-establishing a reimbursement system for this purpose. The analysis is conducted by generating bootstrapped DEA efficiency scores for each nursing home (stage one), then regressing those scores on explanatory variables (stage two). Our DEA model employed two input (i.e. costs for health and nursing services and costs for residential services) and three output variables (case mix, extra nursing hours and residential charges). In the second-stage analysis, Tobit regressions and the Kruskall-Wallis tests of hypothesis to the efficiency scores were applied to define what are the factors that affect efficiency: (a) the ownership (private nursing houses outperform their public counterparts); and (b) the capability to implement strategies for labour cost and nursing costs containment, since the efficiency heavily depends upon the alignment of the costs to the public reimbursement system. Lastly, even though the public institutions are less efficient than the private ones, the results suggest that public nursing homes are moving towards their private counterparts, and thus competition is benefiting efficiency.

The increasing availability of statistical data raises opportunities for ‘big’ data and learning analytics. Here, we review the academic literature and research relating to the use of big data analytics in the public sector, and its contribution to public organizations’ performance and efficiency. We outline the advantages as well as the limitations of using big data in public sector organizations and identify research gaps in recent studies and interesting areas for future research.