The role of non-profit organisations in migration policies: Spain and Italy compared

The aim of the paper is to investigate the relation between Non-Profit Organizations (NP) and immigration. In particular, we examine the services offered to immigrants in Italy and Spain, both noted for their similar patterns of migration. In these countries immigration is characterised by a lack of the so-called “real” (not just legislative) measures that take the needs of immigrants into consideration. Even though emergency policies exist to meet various contingencies, lack of awareness of population movements is probably behind the “absence” of the state.The hypothetic and causal connection between the NP sector and immigration will be studied using selected data. The positive results of the analysis are the first stage in considering the possible substitution of state institutions by the NP sector, with regard to immigrants.     

Flood Risk,Flood Mitigation,and Location Choice: Evaluating the National Flood Insurance Program's Community Rating System

Climate change is expected to worsen the negative effects of natural disasters like floods. The negative impacts, however, can be mitigated by individuals’ adjustments through migration and relocation behaviors. Previous literature has identified flood risk as one significant driver in relocation decisions, but no prior study examines the effect of the National Flood Insurance Program's voluntary program—the Community Rating System (CRS)—on residential location choice. This article fills this gap and tests the hypothesis that flood risk and the CRS‐creditable flood control activities affect residential location choices. We employ a two‐stage sorting model to empirically estimate the effects. In the first stage, individuals’ risk perception and preference heterogeneity for the CRS activities are considered, while mean effects of flood risk and the CRS activities are estimated in the second stage. We then estimate heterogeneous marginal willingness to pay (WTP) for the CRS activities by category. Results show that age, ethnicity and race, educational attainment, and prior exposure to risk explain risk perception. We find significant values for the CRS‐creditable mitigation activities, which provides empirical evidence for the benefits associated with the program. The marginal WTP for an additional credit point earned for public information activities, including hazard disclosure, is found to be the highest. Results also suggest that water amenities dominate flood risk. Thus, high amenity values may increase exposure to flood risk, and flood mitigation projects should be strategized in coastal regions accordingly.     

Inferring gene regulatory networks by an order independent algorithm using incomplete data sets

Analyzing incomplete data for inferring the structure of gene regulatory networks (GRNs) is a challenging task in bioinformatic. Bayesian network can be successfully used in this field. k-nearest neighbor, singular value decomposition (SVD)-based and multiple imputation by chained equations are three fundamental imputation methods to deal with missing values. Path consistency (PC) algorithm based on conditional mutual information (PCA–CMI) is a famous algorithm for inferring GRNs. This algorithm needs the data set to be complete. However, the problem is that PCA–CMI is not a stable algorithm and when applied on permuted gene orders, different networks are obtained. We propose an order independent algorithm, PCA–CMI–OI, for inferring GRNs. After imputation of missing data, the performances of PCA–CMI and PCA–CMI–OI are compared. Results show that networks constructed from data imputed by the SVD-based method and PCA–CMI–OI algorithm outperform other imputation methods and PCA–CMI. An undirected or partially directed network is resulted by PC-based algorithms. Mutual information test (MIT) score, which can deal with discrete data, is one of the famous methods for directing the edges of resulted networks. We also propose a new score, ConMIT, which is appropriate for analyzing continuous data. Results shows that the precision of directing the edges of skeleton is improved by applying the ConMIT score.     

Nonparametric Identification and Estimation of Nonadditive Hedonic Models

This paper studies the identification and estimation of preferences and technologies in equilibrium hedonic models. In it, we identify nonparametric structural relationships with nonadditive heterogeneity. We determine what features of hedonic models can be identified from equilibrium observations in a single market under weak assumptions about the available information. We then consider use of additional information about structural functions and heterogeneity distributions. Separability conditions facilitate identification of consumer marginal utility and firm marginal product functions. We also consider how identification is facilitated using multimarket data.     

Household dynamics and fuelwood consumption in developing countries: a cross-national analysis

Previous research has suggested a link between household dynamics (i.e., average household size and number of households) and environmental impacts at the national level. Building on this work, we empirically test the relationship between household dynamics and fuelwood consumption, which has been implicated in anthropogenic threats to biodiversity. We focus our analysis on developing countries (where fuelwood is an important energy source). Our results show that nations with smaller average households consume more fuelwood per capita. This finding indicates that the household economies of scale are, indeed, associated with the consumption of fuelwood. In addition, we found that number of households is a better predictor of total fuelwood consumption than average household size suggesting a greater relative contribution to consumption levels. Thus, insofar as declining average household sizes result in increased number of households and higher per capita consumption, this trend may be a signal of serious threats to biodiversity and resource conservation. We also found further support for the ??energy ladder?? hypothesis that economic development reduces demand for traditional fuels.     

Economic Transition and Regional Growth: The Case of Albania and Comparator SEECs

The focus of this paper is to investigate the growth performance of Albania and other South Eastern European Countries in the period 1990–2006 by examining traditional growth sources as emerge from standard theoretical models. It focuses on macroeconomic performances and GDP per capita growth rates to examine why countries in the SEE region are lagging behind with respect to other transition economies. The analysis is conducted through stylised growth regressions to estimate convergence rates and coefficients of key factors of growth. Results indicate that real GDP growth in all the eight countries is mainly driven by physical capital accumulation, whereas the contribution of human capital is insignificant or even negative.     

How much reliable are the integrated ‘live’ data? A validation strategy proposal for the non-parametric micro statistical matching

The integration of different data sources is a widely discussed topic among both the researchers and the Official Statistics. Integrating data helps to contain costs and time required by new data collections. The non-parametric micro Statistical Matching (SM) allows to integrate ‘live’ data resorting only to the observed information, potentially avoiding the misspecification bias and speeding the computational effort. Despite these pros, the assessment of the integration goodness when we use this method is not robust. Moreover, several applications comply with some commonly accepted practices which recommend e.g. to use the biggest data set as donor. We propose a validation strategy to assess the integration goodness. We apply it to investigate these practices and to explore how different combinations of the SM techniques and distance functions perform in terms of the reliability of the synthetic (complete) data set generated. The validation strategy takes advantage of the relation existing among the variables pre-and-post the integration. The results show that ‘the biggest, the best’ rule must not be considered mandatory anymore. Indeed, the integration goodness increases in relation to the variability of the matching variables rather than with respect to the dimensionality ratio between the recipient and the donor data set.     

Capability Approach and Multidimensional Well-Being: The Italian Case of BES

This paper lies within the debate on GDP and beyond and provides an empirical assessment of the Italian equitable and sustainable well-being (BES) as a composite measure of multidimensional well-being. From a policy perspective, the goal is to deliver a reading of the Italian BES able to shed light on its usability. The aim is to offer a suitable method for making the information included in the BES more viable for policy design. To this end, the paper is grounded in an interpretation of the Italian BES through the lens of the capability approach and investigates the relations among BES indicators and life domains. The empirical assessment uses Italian data from the national survey “Aspects of daily life” and applies the Structural Equation Model to estimate several life capabilities and their causal path. Outcomes show the interrelated nature of multi-dimensional well-being and prove the role of exogenous causes in the determination of capabilities. Moreover, the paper provides a final ranking analysis where Italian regions are compared based on their position per capability and income performance. Results confirm, on one hand, the disparity of income and well-being measurements and the importance of multidimensional assessment for human development and, on the other, the importance of focusing on the single components of multidimensional well-being and their interconnections.     

Human factors identification and classification related to accidents'causality on hand injuries in the manufacturing industry

The causes of occupational accidents from the perspective of human factors have been a subject which has received little attention into the field of scientific research. The aim of this research was to identify and classify the human factors that influence human errors and failures that cause accidents and injuries specifically on hands. Available studies related to the topic have been developed mainly for aerospace applications and are found insufficient to explain accidents causalities in the manufacturing industry. This research was developed in the assembly industry of automotive harnesses and was conducted following a mixed Cognitive Anthropological approach. This study was developed in two phases. During the first qualitative phase, participants freely listed their knowledge to identify elements of the cultural domain, then and in the second phase they performed the successive pile sort technique for the collection data to classify elements in the cultural domain. Statistical models like Cluster Analysis and Multidimensional Scaling were applied for results' validation purposes. As results, 70 different human factors were identified and in the second phase they were classified into 4 main categories which were: human error, unsafe conditions, individual factors, and organizational factors. Statistical methods validated these results.