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La pandemia del COVID-19 llevó a la población española a un confinamiento obligatorio entre los meses de marzo y mayo de 2020. Ello supuso cerrar los centros educativos y continuar el resto del curso 2019-2020 sin presencialidad. Este trabajo analiza cómo se desarrolló el proceso educativo durante el confinamiento y estima el impacto que la interacción entre la educación telemática y la situación laboral de los progenitores tuvo sobre la competencia de pensamiento crítico del alumnado. Para ello, utilizamos datos recogidos al principio y al final del curso 2019-2020 sobre el pensamiento crítico del alumnado de 3º ESO de 15 centros educativos de la Comunidad de Madrid. En primer lugar, concluimos que los centros públicos tuvieron mayores dificultades que los centros concertados para continuar con el proceso de enseñanza-aprendizaje. Para seguir las clases telemáticas, el 70% del alumnado en centros concertados disponían de un ordenador de sobremesa o portátil, frente a solo un 40% del alumnado en centros públicos. Además, mientras que el 73% del alumnado de centros concertados destinaron más de 4 horas en total a clases y tareas online, esta cifra solo superó ligeramente el 50% en el alumnado de centros públicos. En segundo lugar, y con carácter general, las diferentes situaciones laborales de padres y madres no tuvieron efectos significativos sobre el pensamiento crítico del alumnado. Sin embargo, encontramos que, para el alumnado con resultados académicos por debajo de la media a principio de curso, el hecho de que la madre teletrabajara durante el cierre escolar tuvo un impacto positivo y significativo sobre sus resultados. Ello sugiere que el teletrabajo permitió a las madres brindar mayor apoyo y supervisión a sus hijos e hijas con bajo rendimiento previo.

During the global economic crisis, unemployment rates increased dramatically across Europe, especially among the least educated population groups. The picture in Spain in 2012, with unemployment rates running at over 20% and youth employment close to 45%, was discouraging. In face of this situation, the Spanish autonomous government of Extremadura launched a programme specifically aimed at motivating unemployed individuals without a school degree to return to education and earn the compulsory secondary education diploma. This paper applies a fuzzy regression discontinuity design to evaluate the impact of this conditional cash transfer programme using administrative data. The results show that the programme did not increase the likelihood of earning the lower secondary education diploma. This finding is a caveat emptor for governments considering similar policies, and remarks again the importance of testing innovations before generalization.

Resumen: Se presenta el caso de un varón de 76 años con endocarditis precoz sobre una bioprótesis aórtica por Propionibacterium acnes. El diagnóstico se estableció con base en el cuadro clínico de fiebre y escalofríos, los hallazgos ecocardiográficos de vegetaciones y absceso periprotésico y el hemocultivo positivo. El paciente fue reintervenido, resecando la bioprótesis y administrando antibióticos durante seis semanas. La evolución postoperatoria fue satisfactoria. Nueve años después de la intervención, el sujeto se encuentra asintomático y sin signos de recidiva. Se comentan algunos aspectos relacionados con la patogenia, el diagnóstico y el tratamiento de esta enfermedad. Abstract: We report the case of a 76-year-old male patient who presented with early prosthetic aortic valve endocarditis due to Propionibacterium acnes. Diagnosis was established based on clinical picture of high temperature and positive blood cultures associated with echocardiographic findings of prosthetic valve vegetations and perivalvular abscess. Patient underwent redo sternotomy and re-replacement of the aortic valve bioprosthesis. He received a full course of antibiotics for six weeks and his postoperative course was uneventful. After follow-up for nine years, the patient has remained asymptomatic without any signs of recurrence. Some aspects related to the pathogenesis, diagnosis and treatment of this condition are discussed.

Despite the importance of analysing how an educational system works across regions, there is a lack of research benchmarking regions’ performance over time. The aim of this research is to measure t...

This paper provides evidence on the effectiveness in the implementation of an educational program which offered public schools in Catalonia (Spain) the opportunity to improve the quality of educati...

The non-compulsory and non-free of charge character of preschool education level before 3 years old in Spain raises some doubts about the exogenous nature of this variable. This article provides new evidence about the impact of preschool years on educational outcomes by exploiting a peculiar Spanish database that supplies information on two classrooms at fourth grade in primary schools where students were randomly allocated into each group. Using an identification strategy that reproduces a natural experiment, we find that belonging to the group with more average years of preschool education significantly and positively impacts on fourthgrade students’ average outcomes. Particularly, an increase of one standard deviation in the difference in years of preschool attendance results in an increase of 0.16 (0.13) standard deviations in the group achievement for mathematics (reading). In view of the importance of preschool education and considering that preschool attendance before 3 years old is related with socio-economic variables, we conclude that extending free preschool education to the 2-year-old age group in Spain should be considered especially for students in families with less favourable backgrounds.

The economic crisis forced politicians to make public finances sustainable. The education sector was one of the most adversely affected by control of public expenditure. This paper analyzes the drivers causing productivity changes of especially vulnerable public schools during the crisis. We use the Hicks-Moorsteen index, which is a seldom applied methodology that leads to feasible results under variable returns to scale. To illustrate the benefits of this index, we use a sample of 298 Catalan public primary schools between 2009 (when budgetary constraints started) and 2014. The results reveal that during the crisis schools improved their total factor productivity by raising academic achievement despite cutbacks in resources. We also found that there was a strong convergence pattern during the financial crisis, driven by the catch-up process of some schools. The findings have important policy implications, suggesting that a monitoring system should be set up for use by policy makers.

This research contributes to the ongoing debate about differences in teachers’ performance. We introduce a new methodology that combines production frontier and impact evaluation insights that allows using DEA as an identification strategy of a treatment with high and low quality teachers within schools to assess their performance. We use a unique database of primary schools in Spain that, for every school, supplies information on two classrooms at 4th grade where students and teachers were randomly assigned into the two classrooms. We find considerable differences in teachers’ efficiency across schools with significant effects on students’ achievement. In line with previous findings, we find that neither teacher experience nor academic training explains teachers’ efficiency. Conversely, being a female teacher, having worked five or more years in the same school or having smaller class sizes positively affects the performance of teachers.

The identification of the causal effects of educational policies is the top priority in recent education economics literature. As a result, a shift can be observed in the strategies of empirical studies. They have moved from the use of standard multivariate statistical methods, which identify correlations or associations between variables only, to more complex econometric strategies, which can help to identify causal relationships. However, exogenous variations in databases have to be identified in order to apply causal inference techniques. This is a far from straightforward task. For this reason, this paper provides an extensive and comprehensive overview of the literature using quasi-experimental techniques applied to three well-known international large-scale comparative assessments, such as PISA, PIRLS or TIMSS, over the period 2004-2016. In particular, we review empirical studies employing instrumental variables, regression discontinuity designs, difference in differences and propensity score matching to the above databases. Additionally, we provide a detailed summary of estimation strategies, issues treated and profitability in terms of the quality of publications to encourage further potential evaluations. The paper concludes with some operational recommendations for prospective researchers in the field.

Average efficiency is popular in the empirical education literature for comparing the aggregate performance of regions or countries using the efficiency results of their disaggregated decision-making units (DMUs) microdata. The most common approach for calculating average efficiency is to use a set of inputs and outputs from a representative sample of DMUs, typically schools or high schools, in order to characterize the performance of the population in the analyzed education system. Regardless of the sampling method, the use of sample weights is standard in statistics and econometrics for approximating population parameters. However, weight information has been disregarded in the literature on production frontier estimation using nonparametric methodologies in education. The aim of this chapter is to propose a preliminary methodological strategy to incorporate sample weight information into the estimation of production frontiers using robust nonparametric models. Our Monte Carlo results suggest that current sample designs are not intended for estimating either population production frontiers or average technical efficiency. Consequently, the use of sample weights does not significantly improve the efficiency estimation of a population with respect to an unweighted sample. In order to enhance future efficiency and productivity estimations of a population using samples, we should define an independent sampling design procedure for the set of DMUs based on the population’s production frontier.

Data envelopment analysis (DEA) has been widely applied to empirically measure the technical efficiency of a set of schools for benchmarking their performance. However, the endogeneity issue in the production of education, which plays a central role in education economics, has received minor attention in the DEA literature. Under a DEA framework, endogeneity arises when at least one input is correlated with the efficiency term. Cordero et al. (European Journal of Operational Research 244:511–518, 2015) highlighted that DEA performs well under negative and moderate positive endogeneity. However, when an input is highly and positively correlated with the efficiency term, DEA estimates are misleading. The aim of this work is to propose a new test, based on defining a grid of input flexible transformations, for detecting the presence of positive endogeneity in inputs. To show the potential ability of this test, we run a Monte Carlo analysis evaluating the performance of the new approach in finite samples. The results show that this test outperforms alternative statistical procedures for detecting positive high correlations between inputs and the efficiency term. Finally, to illustrate our theoretical findings, we perform an empirical application on the education sector.

The aim of this paper is to compare the performance of the conditional nonparametric approach with several traditional nonparametric methods to incorporate the effect of exogenous or environmental variables into the estimation of efficiency measures. To do this, we conduct a Monte Carlo experiment using a translog production function with one output, two discretionary inputs and two exogenous variables to generate simulated data. According to the values of different accuracy measures calculated to evaluate the performance of each method, the conditional data envelopment analysis clearly outperforms all the traditional alternatives.

Analyzing the efficiency of educational systems is one of the main focuses of the policy debate to promote national competitiveness and future economic growth. In this paper, we assess the performance of secondary schools from 36 countries (26 OECD countries and 10 partners) participating in PISA 2012. For this purpose, we apply a robust conditional nonparametric approach that allows us to incorporate the effect of contextual factors at both school and country level in the estimation of efficiency measures. Our results suggest that there is a greater heterogeneity across countries than across schools. Particularly, we find that differences in efficiency estimates are mainly explained by economic indicators and cultural values. In contrast, some factors previously identified as potential determinants of student achievement, like the existence of tracking or central examinations, do not seem to significantly affect the efficiency of secondary schools.

In France and Spain, children born in the same calendar year start school together, regardless of maturity differences due to their birth month. This paper analyses the educational impact of birth month on the probability of grade retention controlling by other covariates. Using the PISA 2009 database for both countries, we do identify a great impact on grade retention since students born in the last months of the year are between 70% and 80% more likely to repeat a grade than children born in the first months of the same year. We conclude that policy interventions are required in those countries to ensure that individuals are not unfairly penalized by their birth month.

The programme for international student assessment (PISA) 2006 Report (OECD, PISA 2006: science competencies for tomorrow’s world, Organisation for Economic Co-operation and Development, Paris in 2007) showed significant differences among Spanish students attending publicly financed schools. Publicly financed schools include entirely public schools and schools that are privately managed but publicly funded. Families with a lower socioeconomic status may self-select into public schools, so a direct efficiency comparison between the two school types could lead to flawed conclusions because of the possible school selection bias. In this paper, we suggest using a propensity score matching approach in order to correctly analyze the impact of school ownership on student performance. After tackling the self-selection problem, we use a stochastic parametric distance function framework to compare student efficiency and productivity in both school types across ten Spanish regions using PISA 2006 data. Furthermore, we propose two original measures to analyze the impact of school ownership on academic performance across regions: the average treatment effect on the treated on the production frontier and the average treatment effect on the treated assuming school inefficiency. We find that, on average, private government-dependent schools are more productive than public schools, although efficiency results across regions are highly divergent.

In this paper, we propose a different way of using the Malmquist index that allows us to further an- alyze the relative performance divergences between two groups of decision-making units (DMUs) over time when only a pseudo-panel database is available. To do this, we extend the Camanho and Dyson (2006) one-period Malmquist-type index (CDMI) for a pseudo-panel database with a new pseudo-panel Malmquist index (PPMI). To illustrate the methodology, we apply it to examine how the performance gap between public and private government-dependent secondary schools in the Basque Country (Spain) performed across three PISA waves (20 06, 20 09 and 2012). The results suggest that performance is per- sistently and significantly higher for private government-dependent schools than for public schools Eva Crespo-Cebada, Francisco Pedraja-Chaparro and Daniel Santín acknowledges the financial support of the Spanish Ministry of Economy and Competitiveness (ECO2014-53072-P). Juan Aparicio is grateful to the same institution for sup- porting this research through grant MTM2013-43903-P.

The aim of this paper is to simulate some education policies which would be consistent with different definitions of equality of opportunity (EO). In concrete, we estimate how educational resources should be distributed in order to achieve equality of educational opportunity in the province of Buenos Aires. For that purpose we use a stochastic production frontier model for 12 years old students. The results change drastically depending on the considered concept of EO. If that means giving everybody the same school inputs, the change is not significant. If we want to equalize the academic outputs, the policy is simply impracticable. But if we want to equalize the academic output considering the efforts made by the pupils, the policy is expensive but achievable.

The aim of the present paper is to examine the observed differences in Students’ test performance across public and private‐voucher schools in Spain. For this purpose, we explicitly consider that education is a multi‐input multi‐output production process subject to inefficient behaviors, which can be identified at student level using a parametric stochastic distance function approach. The empirical application of this model, based on Spanish data from the Programme for International Student Assessment implemented by the Organization for Economic Co‐operation and Development in 2003, allows us to identify different aspects of the underlying educational technology. Among other things, the results provide insights into how student background, peer group, school characteristics and personal circumstances interact with educational outputs. Moreover, our findings suggest that, once educational inputs and potential bias due to school choice endogeneity are taken into account, no further unexplained difference re...

The technology set involved in the estimation of a multi-output production frontier theoretically implies monotonicity on outputs. This is because an efficient firm cannot reduce the vector of outputs holding the vector of inputs fixed while it still belongs to the frontier. In empirical studies dealing with the estimation of parametric distance functions, this hypothesis is often violated by observations with far from average characteristics. To overcome this limitation, we propose a new approach for allowing the easy imposition of monotonicity on outputs in this context. This methodology is tested in the educational sector using Spanish student level data from the Programme for International Student Assessment (PISA) database. The results indicate that a nonnegligible 8.33% of the production units break the monotonicity assumption. Furthermore, although there is no statistically significant difference in efficiency distribution by school ownership, our methodology helps to detect a slight worse mathemat...

One of the main concerns in the European Union (EU) is to improving educational achievements and the efficiency of the educational systems. In this work we analyse the educational efficiency in 18 European countries through a methodology that employs the pupil as decision making unit using PISA database. We calculate a whole frontier using data envelopment analysis (DEA) for the analysed pupils faced up to their own education. Efficiency scores are explained taking into account pupil, school, and country factors affecting the productive process. Results show that simply belonging to a country, repeating grade or even to be educated in a good managed school explains a high proportion of European pupils’ efficiency.

The aim of this article is to show how Artificial Neural Networks (ANN) is a valid semi-parametric alternative for fitting empirical production functions and measuring technical efficiency. To do this a Monte-Carlo experiment is carried out on a simulated smooth production technology for assessing efficiency results of ANN compared with Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). As ANNs provides average production function estimations this article proposes a so-called thick frontier strategy for transform average estimations into a productive frontier. Main advantages of ANN are in contexts where the production function is smooth, completely unknown, contains nonlinear relationships among variables and the quantity of noise and efficiency in data is moderate. Under this scenario, the results display that an ANNs algorithm can detect, better than traditional tools, the underlying shape of the production function from observed data.

Since the late 1990s, Spain has played host to a sizeable flow of immigrants who have been absorbed into the compulsory stage of the education system. In this paper, our aim is to assess the impact of that exogenous increase in the number of immigrant students from 2003 to 2009 on grade retention using Spanish data from PISA 2003 and 2009. For this purpose, we use the difference-in-differences method (DiD), capable of detecting whether the immigrant concentration has had a significant effect on student performance. Within this framework, the control group will be the schools without sampled immigrants from 2003 to 2009 and the treatment group will be schools with immigrant students that experienced a significant increase of immigrants throughout this period. As the percentage of immigrants is different across schools, the DiD methodology is adapted to deal with a dose treatment. What we are looking for then is not simply the average effect of there being or not being foreign students at the school, but the effect of their concentration. In this way, the effect of immigrants joining schools can be isolated and estimated through a DiD dose estimator controlling by other educational variables that also influence school performance. Our results evidenced that their arrival does not on average decrease school promotion rates with respect to 2003 and is even beneficial to native students. Although the concentration of immigrant students at the same school does have a negative impact on immigrant students generating more grade retention, native students are unaffected until concentrations of immigrant students are higher.

From its origin, the techniques for measuring efficiency in schools have been widely applied to evaluate public expenditure in education. The aim of this study is to make a critic revision over the risks of performing efficiency analysis without considering the special characteristics of education, where production function is so complex. Moreover, this paper proposes decision trees in order to analyze the educational sector. This technique is able to generate logical rules to model the educational production function offering an easy framework to take decisions about the allocation of public expenditure in education. To illustrate these ideas this paper conducts an empirical analysis using a Spanish 15 year old students database from PISA (Programme for International Student Assessment) captured in 2000.

The main purpose of this paper is to provide an introduction to artificial neural networks (ANNs) and to review their applications in efficiency analysis. Finally, a comparison of efficiency techniques in a non-linear production function is carried out. The results suggest that ANNs are a promising alternative to traditional approaches, econometric models and non-parametric methods such as data envelopment analysis, to fit production functions and measure efficiency under non-linear contexts.

La incapacidad temporal (IT) en España generó en el año 2001 unos costes directos de cuatro mil millones de euros y unos costes indirectos aproximados de doce mil millones de euros. Los métodos actuales de control de la IT se realizan a posteriori, una vez recibida la baja laboral y principalmente sobre la base de duraciones normativas para cada enfermedad. El objetivo de este trabajo es proponer un modelo para la gestión de la IT con el fin de disminuir de forma racional el coste de la prestación por IT no justificado, por medio del control de su duración de manera individualizada. Para este fin se propone el uso de una herramienta flexible, las redes neuronales artificiales (RNAs), que permita predecir la duración de la baja laboral esperada para cada afiliado activo en función de determinadas variables individuales, además de la enfermedad, que también influyen en el tiempo de baja pero que no tienen en cuenta los métodos actuales. Los resultados empíricos de este trabajo, obtenidos para la provincia de Guadalajara durante el período 1995-2001, muestran cómo el modelo de RNAs permite realizar mejores predicciones individualizadas que un modelo tradicional de regresión lineal múltiple. Además, estas predicciones a priori van a permitir racionalizar el proceso de gestión y control de la prestación económica por IT con el consiguiente ahorro de gasto público. Classification-JEL : H51, I18

The aim of this research is to explore the existence of inefficient behaviors in public high schools in Uruguay and identify its potential drivers. To do so, we perform a two-stage model using PISA 2009 and 2012 databases. In the first stage, we use Data Envelopment Analysis (DEA) to estimate efficiency scores, which are then regressed on school and student contextual variables. This second stage is carried out using four alternative models: a conventional censored regression and three different regression models based on the use of bootstrapping recently proposed in the literature. Our results show that educational efficiency in Uruguayan high schools significantly dropped in nine percentage points between 2009 and 2012. In terms of educational policy recommendations, in order to reduce the inefficiencies in the evaluated public schools in Uruguay, the focus should be put on reducing grade-retention levels and promoting teaching–learning techniques that enhance student’s mathematics study skills and assessing students continuously through test and homework throughout the academic year. In this vein, our findings also show positive effects on public schools’ efficiency of providing the responsibility in the distribution of the school budget to school principals.

In most countries, the public sector considers basic education as a preferential good which is essential for getting the development of a worthy life. Since the Second World War until now, many countries have introduced free, compulsory, and sometimes decentralized educative systems, which have contributed to their economic development.

Background. Dyspepsia diagnoses and treatment decisions are made in situations in which multiple factors must be taken into account. Evolving from neuro-biological insights, artificial neural networks (ANNs) can employ multiple factors in resolving medical prediction, classification, pattern recognition, and pattern completion. The objective of this study was to compare predictive results classifying people with organic dyspepsia with Helicobacter pylori testing (rapid urease test), a scoring system based on patients' symptoms (derived using logistic regression), classification and regression trees (CART) and the most common ANN approach used in medicine: a feed-forward multilayer perceptron (MLP) trained by back-propagation. Methods. A scoring system, CART algorithm, and MLP model were constructed. Predictive accuracy was calculated for them and for Helicobacter pylori testing. Results. MLP model had a sensitivity of 0.91 (0.81 for all data) and a specificity of 0.74 (0.79 for all data) for test data. That compares favorably with Helicobacter pylori testing (sensitivity = 0.80, specificity = 0.43), the scoring system (sensitivity = 0.85, specificity = 0.60), and the CART model (sensitivity = 0.88, specificity = 0.53). Diagnostic accuracy, the area under the curve, was 0.82 using the MLP model, 0.61 using Helicobacter pylori testing, 0.78 using the scoring system, and 0.72 for the test set using CART. Conclusions. The results of the analysis showed that the ANN model derived has better predictive accuracy than Helicobacter pylori testing, than a scoring system based on patients' symptoms and than a decision tree algorithm (CART). ANN model could be used as a predictive tool for organic dyspepsia and would be useful in the process of referral of dyspeptic patients from primary care to endoscopy units.

El objetivo de este trabajo es modelar la evolución de los indicadores de incapacidad temporal. Disponer de modelos capaces de aproximar cuál será la evolución temporal de los indicadores económicos de baja laboral resulta clave por múltiples aspectos. Por un lado, permite tomar medidas de prevención y planificación sanitaria en el caso de que se detecte un aumento en la incidencia de una enfermedad. Por otro lado, la elaboración de un modelo de series temporales nos va a permitir evaluar el impacto de medidas de política de gestión sanitaria. En este trabajo se han utilizado datos suministrados por la Dirección Provincial del INSALUD de la provincia de Guadalajara entre 1989 y 2001. Con estos datos hemos estimando modelos ARIMA para predecir la evolución de los indicadores por de gasto por incapacidad temporal. Además, en Guadalajara se diseñó un plan especial de inspección de las bajas laborales, entre 1994 y 1996. Mediante este modelo se ha contrastado que esta política fue eficaz y supuso una reducción del coste durante ese periodo de un 34%. El análisis efectuado nos permite además afirmar que a pesar de eliminar el componente estacional de la gripe existen otras variables sociolaborales relacionas con el fraude que explican que los procesos de baja no se comporten de forma uniforme en el tiempo.

Este documento se ha creado a raíz de la amplia aceptación de la teoría del capital humano de Becker, de 1964, que considera a la educación no sólo como un bien de consumo sino sobre todo de inversión. El objetivo final de este trabajo es motivar el debate y la reflexión crítica en torno a la universidad que nos gustaría tener en el futuro en Latinoamérica, considerando para ello las distintas opciones que se plantean desde la teoría y desde la evidencia empírica puesta en práctica por otros países.

Non-linear production functions are common in economic theory and in real life, especially in cases with increasing and diminishing returns to scale but there are also contexts where an increase in one input implies a decrease in one output. The aim of this paper is to test how non-linearity affect estimations of technical efficiency obtained by ordinary and corrected least squares (OLS, COLS), data envelopment analysis with constant and variables returns to scale (DEAcrs, DEAvrs), stochastic frontier analysis (SFA) and by multilayer perceptron neural networks with backpropagation (MLP). To do this we will construct a very simple non-linear one input-one output production function and we will obtain different synthetic data with 50, 100, 200 and 300 decision-making units (DMUs). Afterwards we will add up different quantities of noise to the data and finally we will compare real efficiency with estimated values for all techniques named before among the different scenarios. Our results suggest that MLP is a flexible tool to fit production functions and a possible alternative to traditional techniques under non-linear contexts.