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The aim of this discussion paper is to raise awareness of the conceptual and practical limits of mainstream practices in social measurement and to suggest possible directions for social indicator construction, in view of effectively supporting policies for social sustainability and well-being promotion. We start with a review of the epistemological issues raised by the measurement of social phenomena, investigate the notion of social complexity, and discuss the critical link between it and measurement. We then suggest that social indicators should be primarily designed to build structural syntheses of the data, unfolding the patterns and stylizing the complexity of social phenomena, rather than computed pursuing numerical precision, through hardly interpretable aggregated measures. This calls for tools and algorithms capable of rendering structural information, preserving the essential traits of complexity and overcoming the limitations of classical aggregation procedures. We provide some examples along this line, using real data pertaining to regional well-being in OECD countries.

We study an airport cost function with a four-random components stochastic frontier model that split the inefficiency in two parts: (1) persistent (long-run) and (2) temporary (short-run). We analyze as determinant of both inefficiency types several exogenous factors such as the airport ownership and LCCs share. We apply the model to the Italian airport system during the period 2010–15. We find that (1) average total cost inefficiency is 14%, and that a it is mainly due to temporary inefficiency, (2) that work load units have the largest cost–output elasticity (+0.31%), (3) that price of airport services has the largest cost-input elasticity (+0.50%) while labor has a +0.41% cost elasticity and capital only a +0.09% cost elasticity. There is also evidence of economies of scale. Moreover we find that LCCs reduce both types of inefficiency and that private airports are more cost efficient than public ones. These findings have interesting managerial (e.g., strictly monitoring service costs, expand LCCs flights) and policy (e.g., provide incentive to reduce temporary inefficiency, push airport privatization) implications.

The neural bases of cognitive impairment(s) in alcohol use disorders (AUDs) might reflect either a global brain damage underlying different neuro-cognitive alterations, or the involvement of specific regions mostly affected by alcohol neuro-toxic effects. While voxel-based-morphometry (VBM) studies have shown a distributed atrophic pattern in fronto-limbic and cerebellar structures, the lack of comprehensive neuro-cognitive assessments prevents previous studies from drawing robust inferences on the specificity of the association between neuro-structural and cognitive impairments in AUDs. To fill this gap, we addressed the neuro-structural bases of cognitive impairment in AUDs, by coupling VBM with an in-depth neuropsychological assessment. VBM results highlighted a diffuse pattern of grey matter reduction in patients, involving the key-nodes of the meso-cortico-limbic (striatum, hippocampus, medial prefrontal cortex), salience (insular and dorsal anterior cingulate cortex) and executive (inferior frontal cortex) networks. Grey matter density in the insular and anterior cingulate sectors of the salience network, significantly decreased in patients, explained almost half of variability in their defective attentional and working-memory performance. The multiple cognitive and neurological impairments observed in AUDs might thus reflect a specific executive deficit associated with the selective damage of a salience-based neural mechanism enhancing access to cognitive resources required for controlled cognition and behaviour.

In recent years, increasing attention has been directed toward problems inherent to quality control in healthcare services. In particular, it is necessary to measure effectiveness with respect to improving healthcare outcomes of diagnostic procedures or specific treatment episodes. The performance of hospitals is usually evaluated by multilevel models and other methods for risk adjustment. However, these approaches are not suitable for data with large unobserved heterogeneity. A potentially large source of unobserved heterogeneity comes from the variation of the regression coefficients between groups of individuals sharing similar but unobserved characteristics. To overcome such drawbacks, we propose the multilevel cluster-weighted model, a new mixture model approach for handling hierarchical data.

Over recent years, there has been a noticeable increase in the prescription of psychotropic drugs (PDs), particularly antidepressants (ADs). The aim of this retrospective study was to evaluate the consumption of PDs by all adult employees in Lombardy aged >18 years in the period 2007-2011 and the possible changes induced by the “economic crisis” in this highly productive area of Italy. The subjects were classified by gender, age class, nationality, education and province. During the five-year period, there were 3,554,860 employed adults in Lombardy, of whom 277,865 (7.8%) used PDs. The use of PDs (particularly ADs) was associated with being an Italian woman aged >55 years with a basic education and a blue collar job. The lower prevalence in immigrants may be due to the difficulty in gaining access to health services. The increase in PD use can be attributed more specifically to ADs and anti-epileptic drugs with anxiolytic properties. Although continuously increasing, the use of AD fluctuated and was greater during the fall and winter. The increase involved all of the provinces in Lombardy in a similar manner. In 39% of cases, the use of PDs (and particularly ADs) was limited to one trimester, although the AD guidelines suggest at least 6-12 months of treatment.

This paper considers the estimation of Kumbhakar et al. (J Prod Anal. doi: 10.1007/s11123-012-0303-1 , 2012) (KLH) four random components stochastic frontier (SF) model using MLE techniques. We derive the log-likelihood function of the model using results from the closed-skew normal distribution. Our Monte Carlo analysis shows that MLE is more efficient and less biased than the multi-step KLH estimator. Moreover, we obtain closed-form expressions for the posterior expected values of the random effects, used to estimate short-run and long-run (in)efficiency as well as random-firm effects. The model is general enough to nest most of the currently used panel SF models; hence, its appropriateness can be tested. This is exemplified by analyzing empirical results from three different applications.

In recent years, governments and other stakeholders have increasingly used administrative data for measuring healthcare outcomes and building rankings of health care providers. However, the accuracy of such data sources has often been questioned. Starting in 2002, the Lombardy (Italy) regional administration began monitoring hospital care effectiveness on administrative databases using seven outcome measures related to mortality and readmissions. The present study describes the use of benchmarking results of risk-standardized mortality from Lombardy regional hospitals. The data usage is part of a general program of continuous improvement directed to health care service and organizational learning, rather than at penalizing or rewarding hospitals. In particular, hierarchical regression analyses - taking into account mortality variation across hospitals - were conducted separately for each of the most relevant clinical disciplines. Overall mortality was used as the outcome variable and the mix of the hospitals' output was taken into account by means of Diagnosis Related Group data, while also adjusting for both patient and hospital characteristics. Yearly adjusted mortality rates for each hospital were translated into a reporting tool that indicates to healthcare managers at a glance, in a user-friendly and non-threatening format, underachieving and over-performing hospitals. Even considering that benchmarking on risk-adjusted outcomes tend to elicit contrasting public opinions and diverging policymaking, we show that repeated outcome measurements and the development and dissemination of organizational best practices have promoted in Lombardy region implementation of outcome measures in healthcare management and stimulated interest and involvement of healthcare stakeholders.

Cluster-weighted modeling (CWM) is a mixture approach to modeling the joint probability of data coming from a heterogeneous population. Under Gaussian assumptions, we investigate statistical properties of CWM from both theoretical and numerical point of view; in particular, we show that Gaussian CWM includes mixtures of distributions and mixtures of regressions as special cases. Further, we introduce CWM based on Student-t distributions, which provides a more robust fit for groups of observations with longer than normal tails or noise data. Theoretical results are illustrated using some empirical studies, considering both simulated and real data. Some generalizations of such models are also outlined.

After a short history of the concept of human capital (henceforth HC) in economic thought (Section 1), this study presents the two main methods for estimating the value of the stock of HC - the retrospective and prospective one - with a review of the models proposed (Section 2). These methods are linked both to the theory of HC investment as a rational choice (Section 3), the literature analysing the contribution of HC investment to economic growth and the HC estimating method through educational attainment (Section 4). The more recent literature on HC as a latent variable is also assessed (Section 5) and a new method of estimation where HC is seen both as an unknown function of formative indicators and as a 'latent effect' underlying earned income is proposed (Section 6). Section 7 concludes. © 2010 Blackwell Publishing Ltd.

Dagum and Slottje (2000) estimated household human capital (HC) as a latent variable (LV) and proposed its monetary estimation by means of an actuarial approach. This paper introduces an improved method for the estimation of household HC as an LV by means of formative and reflective indicators in agreement with the accepted economic definition of HC. The monetary value of HC is used in a recursive causal model to obtain short- and long-term multipliers that measure the direct and total effects of the variables that determine household HC. The new method is applied to estimate US household HC for year 2004.

The 2nd Health Econometrics Workshop took place at the Catholic University of Rome in Italy on 15-17 July 2010. The purpose of this meeting was to provide a forum where policy makers, economists and econometricians could discuss the use of statistical and econometric methods to address issues in the field of health economics. There were seven keynote speakers - leading scholars in the subject - invited to give their contributions: Alberto Holly, Stephen Hall, Badi Baltagi, William Greene, Andrew Jones, John Mullahy and Edward Norton. The meeting was attended by 50 participants from around the world, and 17 scientific papers were presented. Some of these works will be published in the forthcoming special issue of Empirical Economics.

STUDY OBJECTIVE—To investigate the utility of capture-recapture methods to estimate prevalence of subjects with alcohol related disorders using multiple incomplete lists. DESIGN—This was a cross sectional study of alcohol related disorders in a large community. SETTING—During 1997 identified cases with known alcohol related disorders were independently flagged by four sources (self help volunteering groups; psychiatric ambulatory; public alcohology service; hospital discharges). PATIENTS—381 records were flagged, corresponding to 349 individual cases from a target population resident in a northern Italy area. MAIN RESULTS—The two sample capture-recapture estimates were clearly biased because of dependencies among sources. Estimates based on log-linear models showed prevalent counts ranged from 2297 (95% confidence intervals: 1524, 3794) to 2523 (95% confidence intervals: 1623, 4627) after adjustment for dependence among sources only or also for heterogeneity in catchability among age categories (