Integrating Survey and Administrative Data on Local Social Protection

Welfare systems can be observed according to two different perspectives. The former deals with the supply of social protection, i.e. with the funding and provision of social benefits and the production of social services and goods. The latter focuses on the demand of social protection, and particularly on the characteristics of people benefiting from social protection or asking for it. Typically, data on the supply of social benefits have an administrative nature (registers and budgets data) whereas data on beneficiaries come from sample surveys. In theory, administrative data, being census data, can be detailed by territory. On the contrary, sample surveys are usually planned to provide accurate estimates at the national level or for large sub-national areas. This chapter provides an example on the use of different data sets for the Old age and Family/children functions at the province level (LAU 1 in the EU nomenclature). Data on the supply of benefits derive from the SISSIM (Istat Survey on Interventions and Social Services of Individual and associated Municipalities) and from municipalities' budgets. Data on the demand of social protection come from EU-SILC (European Union - Statistics on Income and Living Conditions), a survey that is annually conducted by Istat in a comparable European framework. Earned benefits are estimated applying small area estimation methods, given that the sample size of the EU-SILC survey at the province level is small, so the traditional design-based estimators usually are unreliable. Results are analysed to understand whether administrative and sample survey data can be used to to compose a coherent picture of social protection delivered at the provincial level

The use of Twitter data to improve small area estimates of households’ share of food consumption expenditure in Italy

The use of big data in many socio-economic studies has received a growing interest in the last few years. In this work we use emotional data coming from Twitter as auxiliary variable in a small area model to estimate Italian households’ share of food consumption expenditure (the proportion of food consumption expenditure on the total consumption expenditure) at provincial level. We show that the use of Twitter data has a potential in predicting our target variable. Moreover, the use of these data as auxiliary variable in the small area working model reduces the estimated mean squared error in comparison with what obtained by the same working model without the Twitter data

Identification of a novel motif in DNA ligases exemplified by DNA ligase IV

DNA ligase IV is an essential protein that functions in DNA non-homologous end-joining, the major mechanism that rejoins DNA double-strand breaks in mammalian cells. LIG4 syndrome represents a human disorder caused by mutations in DNA ligase IV that lead to impaired but not ablated activity. Thus far, five conserved motifs in DNA ligases have been identified. We previously reported G469E as a mutational change in a LIG4 syndrome patient. G469 does not lie in any of the previously reported motifs. A sequence comparison between DNA ligases led us to identify residues 468¿476 of DNA ligase IV as a further conserved motif, designated motif Va, present in eukaryotic DNA ligases. We carried out mutational analysis of residues within motif Va examining the impact on adenylation, double-stranded ligation, and DNA binding. We interpret our results using the DNA ligase I:DNA crystal structure. Substitution of the glycine at position 468 with an alanine or glutamic acid severely compromises protein activity and stability. Substitution of G469 with an alanine or glutamic acid is better tolerated but still impacts upon activity and protein stability. These finding suggest that G468 and G469 are important for protein stability and provide insight into the hypomorphic nature of the G469E mutation identified in a LIG4 syndrome patient. In contrast, residues 470, 473 and 476 within motif Va can be changed to alanine residues without any impact on DNA binding or adenylation activity. Importantly, however, such mutational changes do impact upon double-stranded ligation activity. Considered in light of the DNA ligase I:DNA crystal structure, our findings suggest that residues 470¿476 function as part of a molecular pincer that maintains the DNA in a conformation that is required for ligation

Does uncertainty in single indicators affect the reliability of composite indexes? An application to the measurement of environmental performances of Italian regions

In recent decades, the measurement and evaluation of important social and natural phenomena has significantly evolved, with many traditional measurements based on single variables increasingly being replaced by multi- dimensional approaches. One key aspect of these approaches is the development of composite indexes, usually real-value functions of multiple achievements of a group of units. The achievements in each of the selected dimensions are generally synthesised through one or more variables, often referred to as indicators. When in- dicators are obtained through an estimation process, it is crucial to understand if and how their estimation error – for example, sampling error – affects the resulting composite index. This paper presents a methodology based on a parametric bootstrap technique that evaluates to what extent uncertainty in indicators affects the reliability of the aggregate composite index. The method is applied to four composite indexes measuring the environmental performances of Italian regions based on real population and survey data. To our knowledge, this is the first attempt to measure the impact of indicators’ sampling error on composite indexes. If adequately generalised, our methodology could be used in the presence of measurement errors, non- response issues, or other kinds of non-sampling errors

Small area estimation based on M-quantile models in presence of outliers in auxiliary variables

When using small area estimation models, the presence of outlying observations in the response and/or in the auxiliary variables can severely affect the estimates of the model parameters, which can in turn affect the small area estimates produced using these models. In this paper we propose an M-quantile estimator of the small area mean that is robust to the presence of outliers in the response variable and in the continuous auxiliary variables. To estimate the variability of this estimator we propose a non-parametric bootstrap estimator. The performance of the proposed estimator is evaluated by means of model- and design-based simulations and by an application to real data. In these comparisons we also include the extension of the Robust EBLUP able to down-weight the outliers in the auxiliary variables. The results show that in the presence of outliers in the auxiliary variables the proposed estimator outperforms its traditional version that takes into account the presence of outliers only in the response variable

Poverty Indicators at Local Level: Definitions, Comparisons in Real Terms and Small Area Estimation Methods

The importance of computing poverty measures at sub-national level is nowadays widely attested. Local poverty indicators are relevant both for a detailed planning of the policy actions against poverty and social exclusion, and for the citizens to evaluate their effects. However, there are still open problems to compute adequate sub-national poverty indicators. They refer to: 1) the definition of poverty lines; 2) the methods for accounting the spatial variation of the cost of living to make comparisons in ‘real terms’ between different areas; 3) the use of Small Area Estimation methods when the sample size is not enough to obtain accurate estimates of the indicators at local level. In this paper, we discuss the issues above by presenting some analyses on the impact of using different poverty lines on the value of the poverty rate for the 20 Italian Regions, which represent a planned domain of study in Italy. Then, we estimate the poverty rate for the 110 Italian Provinces, unplanned domains in Italy, by using specific parametric models and SAE methods. The key results highlight strong differences in the territorial distribution of the poverty rate by using national versus sub-national specific poverty lines. The effect of the heterogeneity of the general spatial price indexes on the poverty rates seems instead less important in comparison with the relevant territorial differences in the cost of housing. Moreover, the different methods of estimation of poverty rates at local level provides interesting first results and indicates the route for further research to improve the methods of estimation of poverty at the sub-regional level

Influence of Chemical and Physical Variables on 87Sr/86Sr Isotope Ratios Determination for Geographical Traceability Studies in the Oenological Food Chain

This study summarizes the results obtained from a systematic and long-term project aimed at the development of tools to assess the provenance of food in the oenological sector. 87Sr/86Sr isotope ratios were measured on a representative set of soils, branches, and wines sampled from the Chianti Classico wine production area. In particular, owing to the high spatial resolution of the 87Sr/86Sr ratio in the topsoil, the effect of two mill techniques for soil pretreatment was investigated to verify the influence of the particle dimension on the measured isotopic ratios. Samples with particle sizes ranging from 250 to less than 50 m were investigated, and the extraction was performed by means of the DIN 19730 procedure. For each sample, the Sr isotope ratio was determined as well. The obtained results showed that the 87Sr/86Sr ratio is not influenced by soil particle size and may represent an effective tool as a geographic provenance indicator for the investigated product

Training Attention in Physical Education: Effects on Typically Developing and DCD Children

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Application of data fusion techniques to direct geographical traceability indicators

A hierarchical data fusion approach has been developed proposing multivariate curve resolution (MCR) as a variable reduction tool. The case study presented concerns the characterization of soil samples of the Modena District. It was performed in order to understand, at a pilot study stage, the geographical variability of the zone prior to planning a representative soils sampling to derive geographical traceability models for Lambrusco Wines. Soils samples were collected from four producers of Lambrusco Wines, located in in-plane and hill areas. Depending on the extension of the sampled fields the number of points collected varies from three to five and, for each point, five depth levels were considered. The different data blocks consisted of X-ray powder diffraction (XRDP) spectra, metals concentrations relative to thirty-four elements and the 87Sr/86Sr isotopic abundance ratio, a very promising geographical traceability marker. A multi steps data fusion strategy has been adopted. Firstly, the metals concentrations dataset was weighted and concatenated with the values of strontium isotopic ratio and compressed. The resolved components described common patterns of variation of metals content and strontium isotopic ratio. The X-ray powder spectra profiles were resolved in three main components that can be referred to calcite, quartz and clays contributions. Then, a high-level data fusion approach was applied by combining the components arising from the previous data sets. The results show interesting links among the different components arising from XRDP, the metals pattern and to which of these 87Sr/86Sr Isotopic Ratio variation is closer. The combined information allowed capturing the variability of the analyzed soil samples