In this paper the efficiency of the Spanish Police Service is analysed using Data Envelopment Analysis (DEA). The analysis concentrates on the police activities related to the solving of crimes. The theoretical and empirical study of the police production function under this dimension leads to a consideration of the units analysed as centres which carry out their activities with nonidentical technologies and using common inputs. Accordingly, the multiactivity DEA model developed by Mar Molinero (Journal of the Operational Research Society, 47, 1996, 1273-9) is applied, one that is designed to estimate the efficiency of institutions which face several production functions using shared inputs. The results demonstrate the important differences that exist between the centres with respect to the distinct activities analysed, both as regards the efficiency rates and the factors which determine them.

This study examines the development of mathematical and financial literacy skills amongst 471 students in Spain. Most studies on this topic have looked at either one or the other skill but they have not examined the relationship between the two.  The use of simultaneous equations has enabled us to do so. The aim of the paper is to disentangle the factors determining the financial skills of young consumers in Spain. To do this, the PISA Financial Literacy Assessment conducted by the OECD in 2012 is used. Our paper’s main contribution lies in the methodological way to deal with the empirical challenges overshadowing our study. Particularly, our methodological strategy is defined by the application of a multi‐level model of simultaneous equations (MSiEM). This method allows us to take into account the simultaneous determination of math and financial skills at school and the nested structure of the database. This MSiEM permits the identification of the determinants of financial skills, differentiating between the influences operating at school level from those at student level. A first conclusion is that the development of financial abilities of young consumers is mediated by their mathematical skills. A second conclusion, in accordance with those of other international studies, is the importance of the family on the financial literacy of 15‐year‐old Spanish students. The family, a first‐order determinant in reading, science and math scores, is also a key variable in the development of financial skills. Finally, school type (public vs. private) does not display any effect on either the financial or math performance of Spanish young people.

The aim of this paper is to test whether the distribution of students by social, cultural and racial characteristics is homogeneous between Spanish public (PS) and publiclysubsidised private schools (PSPS) or whether segregation exists between the profile of pupils attending each type of school. The theoretical framework is based on the contributions of researchers into school choice policies, while the empirical application uses a 2005 questionnaire answered by the final-year secondary school students of the Spanish region of Aragón. We quantify the degree of internal segregation within each sector (PS and PSPS) and estimate a probit model in order to discover which factors determine the choice of a publicly-subsidised private school. We conclude that the distribution of pupils between PS and PSPS follows a clear socioeconomic pattern which favours privately-owned schools. Our study offers an additional result, namely, that cream-skimming processes are more recurrent within the publicly-subsidised sector, which is shown to be far more selective than the public sector in its distribution of pupils. Finally, it is found that the higher the socioeconomic status, the higher the probability of choosing PSPS, suggesting that the segregation found in this paper may be caused partly by the choice patterns of Spanish families.

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Measurement of research activity still remains a controversial question. The use of the impact factor from the Institute for Scientific Information (ISI) is quite widespread nowadays to carry out evaluations of all kinds; however, the calculation formula employed by ISI in order to construct its impact factors biases the results in favour of knowledge fields which are better represented in the sample, cite more in average and whose citations are concentrated in the early years of the articles.In the present work, we put forward a theoretical proposal regarding how aggregated normalization should be carried out with these biases, which allows comparing scientific production between fields, institutions and/or authors in a neutral manner. The technical complexity of such work, together with data limitations, lead us to propose some adjustments on the impact factor proposed by ISI which -although they do not completely solve the problemreduce it and allow glimpsing the path towards more neutral evaluations. The proposal is empirically applied to three analysis levels: single journals, knowledge fields and the set of journals from the Journal Citation Report.