The introduction of mandatory inter-municipal cooperation in small municipalities: preliminary lessons from Italy

PurposeThis article studies effects of mandatory inter-municipal cooperation (IMC) in small Italian municipalities. Data from 280 small Italian municipalities on effects of IMC in terms of higher efficiency, better effectiveness of local public services, and greater institutional legitimacy of the small municipalities participating in IMC have been investigated against four variables: size; geographical area; type of inter-municipal integration and IMC membership (the presence in the IMC of a bigger municipality, the so-called big brother).Design/methodology/approachData were gathered from a mail survey that was sent to a random sample of 1,360 chief financial officers acting in municipalities of under 5,000 inhabitants, stratified by size (0–1,000 and 1,001–5,000) and geographic area (North, Center, and South) criteria. To analyze dependency relationships between the three potential effects of participating in IMC and possible explanatory variables, we used a logistic regression model as the benefits were binarily categorized (presence or absence of benefits).FindingsFindings show that in more than two-thirds of the municipalities participating in IMC there were benefits in terms of costs reduction and better public services, whereas greater institutional legitimacy was detected in about half of the cases. Our statistical analysis with logistic regression highlighted that IMC type is particularly critical for explaining successful IMC. In particular, positive effects of IMC were mainly detected in those small municipalities that promoted a service delivery organization rather than participating in service delivery agreements or opting for mixed arrangements of joint public services delivery.Originality/valueThe paper focuses on small municipalities where studies are usually scant. Our analysis highlighted that the organizational setting is particularly critical for explaining successful IMC

Estimation Procedures for latent Variable Models with psychological Traits

The starting point for this thesis is a concrete problem: to measure, using statistical models, aspects of subjective perceptions and assessments and to understand their dependencies. The objective is to study the statistical properties of some estimators of the parameters of regression models with variables affected by measurement errors. These models are widely used in surveys based on questionnaires developed to detect subjective assessments and perceptions with Likert-type scales. It is a highly debated topic, as many of the relevant aspects in this field are not directly observable and therefore the variables used to estimate them are affected by measurement errors. The models with measurement errors were very thorough in literature. In this work we will developed two of the most used approaches that the authors have with this topic. Obviously, according to the approach chosen, different models were proposed to estimate the relationships between variables affected by measurement error. After exposing the main features of these models, the thesis focuses on providing an original contribution to comparative analysis of the two presented approaches

Formative and reflective models: state of the art

Although the dispute between formative models and reflective models is not exactly recent, it is still alive in current literature, largely in the context of structural equation model. There are many aspects of SEM that should be considered in deciding the right approach. This work is intended to be a brief presentation of the state of the art for SEM based on covariance matrices. I outline the different positions on five particular issues: causality, selection of observed measures, internal consistency, identifiability and measurement error

Using Structural Equation and Item Response Models to Assess Relationship between Latent Traits

We deepen the two main approaches to the problem of measurement error in social sciences, the Structural Equation Models (SEM) and the Item Response Theory Models (IRM), comparing two different estimation procedures. The One-step procedure (related to SEM) requires that researcher specifies a complete model of both measurement aspects (single link between the latent variable and its indicators) and structural aspects (links between different latent variables), with the model parameters estimated simultaneously. In the Two-step procedure (related to IRM), we first estimate the measures (one for each construct), then we will assess, through a regression model, the relationships between these measures and the latent variables that they represent. Our aim is to define a Two-step method that, using information obtained in the first step about the measurement error, presents low levels of bias and loss of efficiency, as close as possible to that of One-step method

Evaluation of human capital in education-based perspective

This paper considers one of the intangible aspect of human capital: the university knowledge accumulation. It is relevant both for academic management and for recruitment world. In the former case it can be an useful guide to identify the characteristics of clever students, while in the latter case it can be applied to worker selection. Because of the velocity of credit acquisition is not sign of cleverness, it becomes important to analyze different aspects of university human capital accumulation. We will investigate it through latent growth modeling on administrative data come from an Italian university

Utilizzare la Statistica per il Monitoraggio dei grandi eventi sociali a Brescia Smart City

Da un anno Comune e Università di Brescia collaborano alla sperimentazione delle possibilità offerte dai dati di telefonia mobile per ottenere indicazioni utili a migliorare la qualità dei grandi eventi sociali che si svolgono in città. Le manifestazioni del 2013 individuate per avviare tale attività sono molto conosciute al grande pubblico: la gara automobilistica storica Mille Miglia e la competizione ciclistica Giro d’Italia, che per la prima volta si concludeva a Brescia. Ma la sperimentazione continua anche nel 2014, con il monitoraggio della manifestazione cittadina chiamata Notte Bianca. Dopo aver superato alcuni problemi tecnici legati al trattamento e alla sintesi dei dati ottenuti nelle tre occasioni, l’analisi statistica ha permesso di valutare le grandi potenzialità di questa fonte informativa, a supporto della programmazione di tali eventi e più in generale del marketing territoriale

Distance Measures for Unweighted Undirected Networks: A Comparison Study

Networks are mathematical structures that allow the representation of complex systems by jointly modelling the elements of the system and the relationships that exist among them. To analyse different contexts or systems, methodological tools are necessary to allow for the quantitative estimation of the differences existing between two or more networks. For this purpose, various tools have been proposed in the literature. This study is an exploratory analysis of the impacts that different methods (distances and spectral methods) have on the comparative evaluation of two networks. The analyses were conducted through a simulation study that considered three different perturbation schemes to investigate the behaviour of each method with increasing randomness in the perturbation scheme (i.e., edge removal). Results show that the distances between adjacency matrices are sensitive only to changes in the network density, while spectral methods are sensitive to changes in both the network density and the degree of the nodes

Using Surrogate Models and Variable Importance to better Understand Random Forests Regression Fitting

Interpretability mechanisms helping users in better understanding machine learning models are crucial for Artificial Intelligence acceptance. In this manuscript, our experience in interpretation of random forest regression via surrogate models, i.e. models trying to replicate in an interpretable framework an original fitting difficult to understand, is reported. It is shown how, beyond classical R2 analysis, adequacy of surrogate models can be assessed via variable importance analysis

Assessing the risk of establishment and transient populations of Spodoptera frugiperda in Europe

The fall armyworm, Spodoptera frugiperda (J.E. Smith), is an invasive pest threatening crop production and food security worldwide. High concerns are linked to the potential establishment of the species in Europe. The high migratory capacity of S. frugiperda causes concerns about the potential impacts of transient populations invading new areas from suitable hotspots. In the present work, we developed and used a physiologically-based demographic model to quantitatively assess the risks of S. frugiperda in Europe. The risks were assessed considering a best-, a median-, and a worst-case scenario. The Mediterranean coastal areas of Southern Europe resulted particularly suitable for the establishment of the species, with suitable areas reaching even higher latitudes, in the worst-case scenario. In Europe, up to four generations per year were predicted. The predicted yearly average number of moths per trap per week (± standard deviation) was 5 (± 4), 17 (± 5), and 139 (± 22) in the best, median-, and worst-case assessment scenarios, respectively. Model results showed that Southern and Central Europe up to the 48th parallel north might be exposed to the risk of transient populations. Depending on the latitude and on the period of arrival of the propagule, 1–2 transient generations per year might be expected. The model can be used to define strategies for reducing the risks of establishment of the pest at the country level. Predictions on the dynamics and phenology of the pest can also be used to support its management at the local level

Clustering of feeding strategies to improve the evaluation of enteric and slurry methane emissions in dairy cows: an observational study based on Italian dairy farms

The dairy sector is facing increasing challenges in terms of its environmental impact. Methane(CH4) is a focal point of research due to its role in enteric emissions from livestock. This studyinvestigates the effects of various feeding strategies on CH4 emissions from lactating Holsteincows fed total mixed ration (TMR) silage-based diets. Four different equations for estimatingCH4 emissions were chosen according with accuracy and equation variables, and then comparedchecking whether diet composition has an effect on average emission levels. Only Mills equationdetected differences between nutritional clusters. Considering this equation on average, the CH4emissions were equal to 460.36 ± 46.95 g/d, 18.90 ± 1.57 g/kg DMI, 12.89 ± 2.83 g/kg FPCM,equal to a loss of 5.93% of gross energy intake. Clustering based on feed composition identifiedfour distinct groups of diets, with no statistically significant difference in CH4 emissions. Thehighest emissions were found in the nutritional cluster with higher fibre and starch content,with methane production (MeP) reaching 485.85 g/d, 19.47 kg/kg DMI and 14.82 kg/kg FPCM.This indicates that diet nutrients profile significantly impacts CH4 emissions, underscoring theimportance of adopting sustainable feeding strategies in dairy production. Notably, a positivecorrelation exists between MeP and milk productivity, while methane intensity negatively corre-lates with feed efficiency. The findings emphasise the necessity for context-specific emission fac-tors and underscore the importance of implementing sustainable feeding practices to mitigateCH4 emissions enhancing the efficiency of dairy production systems