Measurement and Identification of Asset-Poor Households: A Cross-National Comparison of Spain and the United Kingdom

This paper is concerned with the analysis of the wealth dimension of poverty in developed countries, which can hardly be measured by means of the information on household income. We focus in identifying the group of households that lack enough wealth holdings to sustain them during a period of economic crisis in order to quantify asset poverty, and its demographic weight, in two industrialized countries with particularly different household demographics and saving attitudes such as Spain and the United Kingdom. Our results show that the age profile of the asset poor is remarkably similar in the two countries. In both it is individuals in households whose head is under 45 years old who are more likely to be asset poor, even if, when the housing wealth component is excluded, both show that the incidence of asset poverty by head of household age follows a clear U-shape pattern. However, some country-specific differences also arise. For instance, the incidence of wealth poverty in the United Kingdom is twice that of Spain. Using counterfactual analysis we find that, although the different household demographics clearly contribute importantly to this result, there remains a significant part of the asset-poverty gap which is not explained by this relevant factor.

The Household Wealth Distribution in Spain: The Role of Housing and Financial Wealth

We analyse the distribution of household wealth in Spain using the first wave of the Spanish Survey of Household Finances, conducted by the Bank of Spain in 2002. We study the distribution of the different wealth components and, using inequality decomposition techniques, we assess the contribution of each element to overall wealth inequality. We find that wealth is more unequally distributed than income, while housing wealth is much more evenly distributed than financial wealth. Moreover, we identify two groups of wealth components: one disequalizing group, which includes financial wealth, whose value and portfolio share increase with household wealth; and a second more equalizing one, including housing wealth, whose value increases with wealth, but their share in the portfolio does not. Finally, we show that differences between age groups do not explain why wealth is much more unequally distributed than income. Instead, business and home ownership are factors that clearly contribute to explain this feature.Wealth, income, distribution, inequality decomposition.

Measuring poverty using both income and wealth: A cross-country comparison between the U.S. and Spain

In this paper we study the correspondence between a household’s current income and its vulnerability to income shocks in two developed countries: the U.S. and Spain. Vulnerability is measured by the availability of wealth type resources to smooth consumption in a multidimensional approach to measuring poverty, which allows us to identify three groups of households. First, the twice-poor group which includes income-poor households who also lack of an adequate stock of wealth; second, the group of protected-poor households, which are all those income-poor families that have accumulated a buffer stock of wealth resources they can rely on; lastly, the vulnerable-non-poor group, which includes those households above the income-poverty line that do not hold any stock of wealth. The latter are, out of the group of non-poor, those who are more likely to be pushed into economic deprivation in times of economic hardship. Interestingly, the risk of belonging to one of these groups changes over the life-cycle in both countries while the size of the groups differs significantly between Spain and the U.S., although this result is quite sensible to whether one includes the housing wealth component in the wealth measure or not.Multidimensional poverty, income and wealth.

"Measuring Poverty Using Both Income and Wealth: An Empirical Comparison of Multidimensional Approaches Using Data for the U.S. and Spain"

This paper presents a comparative analysis of the approaches to poverty based on income and wealth that have been proposed in the literature. Two types of approaches are considered: those that look at income and wealth separately when defining the poverty frontier, and those in which these two dimensions are integrated into a single index of welfare. We illustrate the implications of these approaches on the structure of poverty using data for two industrialized countries—for example, the United States and Spain. We find that the incidence of poverty in these two countries varies significantly depending on the poverty definition adopted. Despite this variation, our results suggest that the poverty problem is robust to changes in the way poverty is measured. Regarding the identification of the poor, there is a high level of misclassification between the poverty indices: for most of the pairwise comparisons, the proportion of households that are misclassified is above 50 percent. Interestingly, the rate of misclassification in the United States is significantly lower than in Spain. We argue that the higher correlation between income and wealth in the United States contributes to explaining the greater overlap between poverty indices in this country.Wealth; Income; Multidimensional Poverty

The household wealth distribution in Spain: The role of housing and financial wealth.

We analyse the distribution of household wealth in Spain using the first wave of the Spanish Survey of Household Finances, conducted by the Bank of Spain in 2002. We study the distribution of the different wealth components and, using inequality decomposition techniques, we assess the contribution of each element to overall wealth inequality. We find that wealth is more unequally distributed than income, while housing wealth is much more evenly distributed than financial wealth. Moreover, we identify two groups of wealth components: one disequalizing group, which includes financial wealth, whose value and portfolio share increase with household wealth; and a second more equalizing one, including housing wealth, whose value increases with wealth, but their share in the portfolio does not. Finally, we show that differences between age groups do not explain why wealth is much more unequally distributed than income. Instead, business and home ownership are factors that clearly contribute to explain this feature.Wealth, income, distribution, inequality decomposition.

Childcare use and its role in Indigenous child development: evidence from the Longitudinal Study of Indigenous Children in Australia

This paper investigates patterns of childcare use and their influence on the cognitive development of Indigenous children. The influence of childcare on the cognitive outcomes of Indigenous children is less well understood than for non Indigenous children due to a lack of appropriate data. This paper uses data from the Longitudinal Study of Indigenous Children, a unique panel survey that tracks two cohorts of Indigenous children in Australia. This paper focuses on the younger cohort that has been followed from infancy and includes rich information on their childcare use and cognitive outcomes. We find that, compared to Indigenous children who never participated in childcare, Indigenous children who participated in childcare performed better on a range of cognitive outcomes measured across the preschool years. Using regression and propensity score matching techniques we show that this difference is entirely driven by selection into childcare, with children from more advantaged families being more likely to attend formal childcare than children from less advantaged families. However, results from the matching analysis suggest that relatively disadvantaged children might benefit more from attending childcare, as indicated by the positive potential effects found for those who never attended childcare (i.e. the estimated effects had they participated in childcare)

Extended a Priori Probability (EAPP): A Data-Driven Approach for Machine Learning Binary Classification Tasks

[EN] The a priori probability of a dataset is usually used as a baseline for comparing a particular algorithm's accuracy in a given binary classification task. ZeroR is the simplest algorithm for this, predicting the majority class for all examples. However, this is an extremely simple approach that has no predictive power and does not describe other dataset features that could lead to a more demanding baseline. In this paper, we present the Extended A Priori Probability (EAPP), a novel semi-supervised baseline metric for binary classification tasks that considers not only the a priori probability but also some possible bias present in the dataset as well as other features that could provide a relatively trivial separability of the target classes. The approach is based on the area under the ROC curve (AUC ROC), known to be quite insensitive to class imbalance. The procedure involves multiobjective feature extraction and a clustering stage in the input space with autoencoders and a subsequent combinatory weighted assignation from clusters to classes depending on the distance to nearest clusters for each class. Class labels are then assigned to establish the combination that maximizes AUC ROC for each number of clusters considered. To avoid overfit in the combined feature extraction and clustering method, a cross-validation scheme is performed in each case. EAPP is defined for different numbers of clusters, starting from the inverse of the minority class proportion, which is useful for a fair comparison among diversely imbalanced datasets. A high EAPP usually relates to an easy binary classification task, but it also may be due to a significant coarse-grained bias in the dataset, when the task is previously known to be difficult. This metric represents a baseline beyond the a priori probability to assess the actual capabilities of binary classification models.This work was supported in part by the Generalitat Valenciana through the Valencian Institute of Business Competitiveness (IVACE) Distributed Nominatively to Valencian Technological Innovation Centers under Project IMAMCN/2021/1, in part by the Cervera Network of Excellence Project in Data-Based Enabling Technologies (AI4ES) Co-Funded by the Centre for Industrial and Technological Development¿E. P. E. (CDTI), and in part by the European Union through the Next Generation EU Fund within the Cervera Aids Program for Technological Centers under Project CER-20211030.Ortiz, V.; Pérez-Benito, FJ.; Del Tejo Catalá, O.; Salvador Igual, I.; Llobet Azpitarte, R.; Perez-Cortes, J. (2022). Extended a Priori Probability (EAPP): A Data-Driven Approach for Machine Learning Binary Classification Tasks. IEEE Access. 10:120074-120085. https://doi.org/10.1109/ACCESS.2022.32219361200741200851

Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts

[EN] Background The breast dense tissue percentage on digital mammograms is one of the most commonly used markers for breast cancer risk estimation. Geometric features of dense tissue over the breast and the presence of texture structures contained in sliding windows that scan the mammograms may improve the predictive ability when combined with the breast dense tissue percentage. Methods A case/control study nested within a screening program covering 1563 women with craniocaudal and mediolateral-oblique mammograms (755 controls and the contralateral breast mammograms at the closest screening visit before cancer diagnostic for 808 cases) aging 45 to 70 from Comunitat Valenciana (Spain) was used to extract geometric and texture features. The dense tissue segmentation was performed using DMScan and validated by two experienced radiologists. A model based on Random Forests was trained several times varying the set of variables. A training dataset of 1172 patients was evaluated with a 10-stratified-fold cross-validation scheme. The area under the Receiver Operating Characteristic curve (AUC) was the metric for the predictive ability. The results were assessed by only considering the output after applying the model to the test set, which was composed of the remaining 391 patients. Results The AUC score obtained by the dense tissue percentage (0.55) was compared to a machine learning-based classifier results. The classifier, apart from the percentage of dense tissue of both views, firstly included global geometric features such as the distance of dense tissue to the pectoral muscle, dense tissue eccentricity or the dense tissue perimeter, obtaining an accuracy of 0.56. By the inclusion of a global feature based on local histograms of oriented gradients, the accuracy of the classifier was significantly improved (0.61). The number of well-classified patients was improved up to 236 when it was 208. Conclusion Relative geometric features of dense tissue over the breast and histograms of standardized local texture features based on sliding windows scanning the whole breast improve risk prediction beyond the dense tissue percentage adjusted by geometrical variables. Other classifiers could improve the results obtained by the conventional Random Forests used in this study.This work was partially funded by Generalitat Valenciana through I+D IVACE (Valencian Institute of Business Competitiviness) and GVA (European Regional Development Fund) supports under the project IMAMCN/2018/1, and by Carlos III Institute of Health under the project DTS15/00080Pérez-Benito, FJ.; Signol, F.; Perez-Cortes, J.; Pollán, M.; Perez-Gómez, B.; Salas-Trejo, D.; Casals, M.... (2019). Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts. Computer Methods and Programs in Biomedicine. 177:123-132. https://doi.org/10.1016/j.cmpb.2019.05.022S12313217

Water Saving in CSP Plants by a Novel Hydrophilic Anti-Soiling Coating for Solar Reflectors

In this work, results of the outdoor exposure campaign of a newly developed hydrophilic anti-soiling coating for concentrated solar thermal power (CSP) mirrors are presented. The material was exposed for nearly two years under realistic outdoor conditions and the influence of two different cleaning techniques was evaluated. Mirror samples were analyzed during exposure and their reflectance and cleanliness were measured. The performance of the anti-soiling coated mirror samples was compared to conventional uncoated silvered-glass mirrors. The coatings showed appropriate anti-soiling and easy-to-clean behavior, with a mean cleanliness gain of 1 pp and maximum values under strong soiling conditions of up to over 7 pp. Cleanliness of the coated samples stayed higher throughout the whole campaign before and after cleaning, resulting in lower soiling rate compared to the reference material. Taking into account these values and supposing a threshold for cleaning of 96%, the number of cleaning cycles could be decreased by up to 11%. Finally, the coated material showed negligible degradation, not exceeding the degradation detected for the reference material

A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation

[EN] Background and Objective: Breast cancer is the most frequent cancer in women. The Spanish healthcare network established population-based screening programs in all Autonomous Communities, where mammograms of asymptomatic women are taken with early diagnosis purposes. Breast density assessed from digital mammograms is a biomarker known to be related to a higher risk to develop breast cancer. It is thus crucial to provide a reliable method to measure breast density from mammograms. Furthermore the complete automation of this segmentation process is becoming fundamental as the amount of mammograms increases every day. Important challenges are related with the differences in images from different devices and the lack of an objective gold standard. This paper presents a fully automated framework based on deep learning to estimate the breast density. The framework covers breast detection, pectoral muscle exclusion, and fibroglandular tissue segmentation. Methods: A multi-center study, composed of 1785 women whose "for presentation" mammograms were segmented by two experienced radiologists. A total of 4992 of the 6680 mammograms were used as training corpus and the remaining (1688) formed the test corpus. This paper presents a histogram normalization step that smoothed the difference between acquisition, a regression architecture that learned segmentation parameters as intrinsic image features and a loss function based on the DICE score. Results: The results obtained indicate that the level of concordance (DICE score) reached by the two radiologists (0.77) was also achieved by the automated framework when it was compared to the closest breast segmentation from the radiologists. For the acquired with the highest quality device, the DICE score per acquisition device reached 0.84, while the concordance between radiologists was 0.76. Conclusions: An automatic breast density estimator based on deep learning exhibits similar performance when compared with two experienced radiologists. It suggests that this system could be used to support radiologists to ease its work.This work was partially funded by Generalitat Valenciana through I+D IVACE (Valencian Institute of Business Competitiviness) and GVA (European Regional Development Fund) supports under the project IMAMCN/2019/1, and by Carlos III Institute of Health under the project DTS15/00080.Perez-Benito, FJ.; Signol, F.; Perez-Cortes, J.; Fuster Bagetto, A.; Pollan, M.; Pérez-Gómez, B.; Salas-Trejo, D.... (2020). A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation. Computer Methods and Programs in Biomedicine. 195:123-132. https://doi.org/10.1016/j.cmpb.2020.105668S123132195Kuhl, C. K. (2015). The Changing World of Breast Cancer. 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