The Regulation of Interdependent Markets

We examine the issue of whether two monopolists which produce substitutable goods should be regulated by one (centralization) or two (decentralization) regulatory authorities, when the regulator(s) can be partially captured by industry. Under full information, two decentral- ized agencies - each regulating a single market - charge lower prices than a unique regulator, making consumers better off. However, this leads to excessive costs for the taxpayers who subsidize the …rms, so that centralized regulation is preferable. Under asymmetric informa- tion about the firms' costs, lobbying induces a unique regulator to be more concerned with the industry's interests, and this decreases social welfare. When the substitutability between the goods is high enough, the firms'lobbying activity may be so strong that decentralizing the regulatory structure may be social welfare enhancing.regulation, lobbying, asymmetric information, energy markets

Distributional effects of price reforms in the Italian utility markets

In this paper we analyse some distributional effects of the reforms of water and energy services in Italy. We first document the new regulation setting in these services, illustrating the dynamics of utility prices and of household expenditure in the period 1998-2005. We then propose a way to measure the affordability of public utilities, in order to investigate how many households would incur a potentially excessive burden, if they consumed a minimum quantity of utility services. Finally, we calculate this index on data from the ‘Survey on Family Budgets’. Our results show how the affordability of utility bills varies from region to region depending on climate, income, family endowment and size. The analysis – also based on a counterfactual exercise – finds that so far, utility reforms do not seem to have produced any negative effects on weaker households.Affordability, Public Utilities, Regulation, Gas, Electricity, Water

Restructuring Italian Utility Markets: Household Distributional Effects

Competition in public utility sectors has been encouraged in recent years throughout Europe. In this paper we try and analyse the welfare effects of these reforms in Italy, with particular attention to water and energy goods. The first step is to introduce a sensible measure of affordability of public utilities and to see how many households fall below a critical threshold. This issue is analysed stressing how climatic conditions dramatically affect households’ expenditure and how the affordability of utility bills varies a lot from region to region. So far, utilities’ reforms do not seem to have produced negative effects on the weaker group of households.Consumer behaviour, Public utilities, Regulation, Gas, Electricity, Water

Toward defining and measuring the affordability of public utility services

This paper reviews the progress made in the literature toward defining and measuring the affordability of utilities. It highlights the relative merits of alternate affordability metrics; the practical challenges to their operationalization, including the underlying data requirements; and their implications for the design, evaluation, and implementation of appropriate affordability programs.Access to Finance,Economic Theory&Research,Town Water Supply and Sanitation,Public Sector Economics&Finance,Rural Poverty Reduction

Graph-based analysis of textured images for hierarchical segmentation

International audienceThe Texture Fragmentation and Reconstruction (TFR) algorithm has been recently introduced to address the problem of image segmentation by textural properties, based on a suitable image description tool known as the Hierarchical Multiple Markov Chain (H-MMC) model. TFR provides a hierarchical set of nested segmentation maps by first identifying the elementary image patterns, and then merging them sequentially to identify complete textures at different scales of observation. In this work, we propose a major modification to the TFR by resorting to a graph based description of the image content and a graph clustering technique for the enhancement and extraction of image patterns. A procedure based on mathematical morphology will be introduced that allows for the construction of a color-wise image representation by means of multiple graph structures, along with a simple clustering technique aimed at cutting the graphs and correspondingly segment groups of connected components with a similar spatial context. The performance assessment, realized both on synthetic compositions of real-world textures and images from the remote sensing domain, confirm the effectiveness and potential of the proposed method

A CNN-based fusion method for feature extraction from sentinel data

Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use of optical remote sensing for Earth monitoring applications. A possible alternative is to benefit from weather-insensitive synthetic aperture radar (SAR) images. In many real-world applications, critical decisions are made based on some informative optical or radar features related to items such as water, vegetation or soil. Under cloudy conditions, however, optical-based features are not available, and they are commonly reconstructed through linear interpolation between data available at temporally-close time instants. In this work, we propose to estimate missing optical features through data fusion and deep-learning. Several sources of information are taken into account—optical sequences, SAR sequences, digital elevation model—so as to exploit both temporal and cross-sensor dependencies. Based on these data and a tiny cloud-free fraction of the target image, a compact convolutional neural network (CNN) is trained to perform the desired estimation. To validate the proposed approach, we focus on the estimation of the normalized difference vegetation index (NDVI), using coupled Sentinel-1 and Sentinel-2 time-series acquired over an agricultural region of Burkina Faso from May–November 2016. Several fusion schemes are considered, causal and non-causal, single-sensor or joint-sensor, corresponding to different operating conditions. Experimental results are very promising, showing a significant gain over baseline methods according to all performance indicators

Minimal disease activity in patients with psoriatic arthritis treated with ustekinumab: results from a 24-week real-world study

Psoriatic arthritis (PsA) is a chronic inflammatory joint disease affecting around 40% of psoriasis patients. Minimal disease activity (MDA) criteria have been proposed to identify a state of low disease activity, one of the principal goals of treatment for psoriatic disease. This study investigated treatment with ustekinumab (UST) in the context of a real-world setting. Thirty-four PsA patients who had failure or inadequate response to conventional synthetic disease-modifying antirheumatic drugs or to anti-tumour necrosis factor alpha were enrolled. Demographic and clinical features, MDA criteria, and the impact of psoriatic skin manifestations on patients' quality of life (QoL) using the dermatology life quality index (DLQI) questionnaire were evaluated at baseline and after 24-week treatment. Adverse events were recorded. At week 24, 70.5% of patients (n = 24) achieved MDA. A sub-analysis of dermatological indices of the MDA criteria showed that the psoriasis area severity index score was significantly improved and body surface area was significantly decreased at 24 weeks compared with that at baseline (both p < 0.001). For the rheumatologic indexes, tender joint count, swollen joint count, and tender entheseal points were all significantly improved at 24 weeks of therapy (all p < 0.01 vs. baseline). Mean DLQI value decreased approximately fourfold, and there were no safety concerns. The achievement of MDA as well as the significant improvement in DLQI and lack of adverse events in the context of a real-life setting shown here confirms the efficacy and safety of UST in PsA

Psoriatic arthritis: epidemiological and clinical aspects in a cohort of 1.306 italian patients

Because there is the impression that psoriatic arthritis is a composite disorder with mild forms close to more severe and aggressive ones, we conducted a multicenter study with the aim of characterizing disease expression in a large cohort of Italian patients. One-thousand-three-hundred-six patients fulfilled inclusion criteria and were analyzed in this study. Psoriasis antedated the onset of arthritis in the majority of the cases (67.7%). More rare was inverse or simultaneous onset which occurred in 17.3% and 15.0% of the cases, respectively. Peripheral articular involvement (mono-oligo or polyarthritis) was recorded in 88.7% of the cases while spondylitis occurred in 11.3%. Peripheral enthesopathies were found in 28.1% of the cases with a marked occurrence in patients with axial involvement (64.5% vs 35.5% in oligo or polyarthritis). Abnormal levels of ESR and CRP respectively occurred in 52.2% and in 52.6% of the cases, while rheumatoid factor was detected in 5.0% of the cases. On the basis of distribution of joint involvement, symmetry and presence of peripheral enthesopathies we recognized three clusters of arthritis. Patients included in Cluster 1 and Cluster 2 showed a severe form of polyarthritis in most of the cases (82.9%), with increased serum levels of inflammatory indices in more than 85% of the cases. Almost all the hospitalized patients (97.1%) were included in this two clusters. They markedly assumed steroids and methotrexate or another DMARD. About half of the patients (51.1%) included in Cluster 3 showed mono-oligo articular involvement. Serum inflammatory indices were increased in 20.8% of the cases while hospitalization occurred only in 2.9% of the cases and NSAIDs were the treatment of choice. The evidence in our country of a large prevalence of severe forms of arthritis needing specific and aggressive approach outlines the requirement of an intense educational action aimed at increasing the awareness of this condition