Spatial Disaggregation of Social Indicators: An Info-Metrics Approach

In this paper we propose a methodology to obtain social indicators at a detailed spatial scale by combining the information contained in census and sample surveys. Similarly to previous proposals, the method proposed here estimates a model at the sample level to later project it to the census scale. The main novelties of the technique presented are that (i) the small-scale mapping produced is perfectly consistent with the aggregates -regional or national- observed in the sample, and (ii) it does not require imposing strong distributional assumptions. The methodology suggested here follows the basics presented on Golan (2018) by adapting a cross-moment constrained Generalized Maximum Entropy (GME) estimator to the spatial disaggregation problem. This procedure is compared with the equivalent methodology of Tarozzi and Deaton (2009) by means of numerical experiments, providing a comparatively better performance. Additionally, the practical implementation of the methodology proposed is illustrated by estimating poverty rates for small areas for the region of Andalusia (Spain).

     

An Empirical Approach to the Poverty Indicators Based on Revealed Parameters: The Case of Spain

This article investigates the issue of measuring the proportion of the population affected by the phenomenon of poverty in Spain on the basis of the relationship between the severe material deprivation rate and the at-risk-of-poverty rate by regions. The current definitions of equivalence units and equivalent income were analysed and an innovation to allow the introduction of a parametric model, based on the revealed annual consumption of the main different type of households and on the regional purchase power parities, was subsequently proposed. In the case of Spain it was discovered empirically that the equivalence units are not constant, as maintained until now, but that they vary over time. An improvement between the relations of the two rates was obtained by means of the application of these revealed scales. The regional composition was also improved. The results could serve as a base for future studies to provide more detailed information about the breakdown of the relative poverty within each country and, thereby, to meet the relevant information requests, at national and international level, to support the implementation of public policies in this area.     

An Outcomes-Based Assessment of Quality of Life in Social Services

The goal of this article consists of describing the calibration of an instrument to assess quality of life-related personal outcomes using Rasch analysis. The sample was composed of 3.029 recipients of social services from Catalonia (Spain) and was selected using a probabilistic polietapic sample design. Results related to unidimensionality, item and person separation reliability, calibration, items’ level of difficulty, response categories and differential item functioning by gender and type of collective are provided and point out the general fit of the data to the model. However, it is recommended to include more difficult items and eliminate one that seems not to be adequate.     

Core Indicators to Assess Quality of Life in Population with Brain Injury

Assessing quality of life of people with brain injury has a relevant role for developing strategies focused on personal outcomes that allow us to guide good practices and rehabilitation. So far, assessment of quality of life for this population has been restricted to an evaluation of personal outcomes from a health-related quality of life perspective. This approach it is mostly centered on physical health, however, quality of life needs to be addressed from a holistic and multidimensional perspective. The goal of this study is to identify core indicators of quality of life in brain injury based on a comprehensive theoretical model focused on the most relevant aspects of this population functioning. A Delphi study was carried out to obtain the specific core indicators of quality of life for this population. The methodology used to reach a consensus about the best indicators and items to measure quality of life involved four rounds and 14 experts on rehabilitation of people with brain injury. The Delphi study provided evidence of content validity for the field-test version of a new scale that will be applied to a wide sample in order to empirically check its suitability for this population.     

Quality of Life in the City of Delhi: An Assessment Based on Access to Basic Services

The quality of life is a fundamental aspect of development and advancement of human societies. However, measuring and expressing the quality of life in any given setting has proved difficult because it includes multiple dimensions. Further, methods based on questionnaire surveys have to contend with responses that are inexact and difficult to quantify. Here I estimate the quality of life of people living in the Indian city of Delhi using fuzzy sets theory, an approach that is designed to handle inexact or ‘fuzzy’ outcomes. Using a stratified random sample set of 330 households, I compare different locations in Delhi based on their access to seven basic services that is assumed to depict the quality of life. I found that the majority of services (in particular, the overall maintenance and transport services) are poor in resettlement colonies, unauthorised colonies, and urbanised villages. The quality of services improves in colonies under the jurisdiction of the Delhi Cantonment Board, the New Delhi Municipal Council, and approved colonies of the Delhi Development Authority. The overall patterns suggest that the differences in satisfaction and access are primarily influenced by location, and within each location they are influenced by economic conditions. Over 36 % of Delhi’s households, which are classified as ‘definitely poor’ and ‘extremely vulnerable’, may be deprived of transport services, around 44 % are deprived of overall maintenance services and over 29 % lack well-maintained green spaces in their neighbourhood. The analysis should draw the attention of policymakers on spatial aspects of development planning.     

A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms

Despite growing understanding of resilience as a process associated with both individual capacities and physical and relational resources located in social ecologies, most instruments designed to measure resilience overemphasize individual characteristics without adequately addressing the contextual resources that support resilience processes. Additionally, most resilience studies have focused on children and youth, without significant attention to social ecological factors that promote post-risk adaptation for adults and how this is measured. Consequently, a key issue in the continued study of adult resilience is measurement instrument development. This article details adaptation of the Child and Youth Resilience Measure for use with an adult population. The article draws on data from a mixed methods study exploring the resilience processes of Irish survivors of clerical institutional abuse. The sample included 105 adult survivors (aged 50–99) who completed the RRC-ARM and the Warwick–Edinburgh Mental Well-being Scale (WEMWBS) during the first phase of the study. Exploratory factor analysis, Cronbach Alpha and MANOVA were conducted on the data. EFA identified five factors; social/community inclusion, family attachment and supports, spirituality, national and cultural identity, and personal competencies. The RRC-ARM shows good internal reliability and convergent validity with the WEMWBS, with significant differences on scale scores for men and women, as well as place of residence. This exploratory adaptation supports the potential of the RRC-ARM as a measure of social ecological resilience resources for adult populations and may have particular applications with vulnerable communities. Further validation is required in other contexts and specifically with larger samples.     

Synthetic Indicators of Quality of Life in Europe

For more than three decades now, sociologists, politicians and economists have used a wide range of statistical and econometric techniques to analyse and measure the quality of life of individuals with the aim of obtaining useful instruments for social, political and economic decision making. The aim of this paper is to analyse the advantages and disadvantages of three possible methodologies for obtaining synthetic indicators for the area of welfare and quality of life. These methodologies are Principal Components Analysis, Data Envelopment Analysis and Measure of Distance P₂. Furthermore this paper analyses quality of life in the European Union (EU), as a methodological exercise to demonstrate the principles of calculation, implications and differences between the three indicator-construction approaches. This analysis is particularly useful in a scene like the EU, immersed in a deep transformation process and with profound cultural, economic and social inequalities. Therefore, an analysis of the quality of life and well-being of its inhabitants can play a major role in ironing out such differences.     

Quality of Community Life Among Rural Residents: An Integrated Model

The purpose of this research was to explore the relationships among housing satisfaction, community attachment and community satisfaction and the complex mechanisms involved in predicting community satisfaction among residents in rural communities. The role of housing satisfaction and community attachment in predicting community satisfaction was of particular interest. A structural equation model of community satisfaction was tested with mail survey data drawn from a randomly selected rural sample of 974 households. The results of this study confirmed the influences and mediating role of community attachment and housing satisfaction in predicting community satisfaction. “Spillover” effects from “lower levels of life concerns” (e.g., satisfaction with local services, assessment of current housing conditions) in perceived community satisfaction were found that support previous research (Sirgy and Cornwell in Social Indicators Research 59:79–114, 2002).     

Poverty in People with Disabilities: Indicators from the Capability Approach

People with disability are particularly exposed to poor living conditions: on one hand they have more difficulties in getting an income cause to their inabilities, on the other hand conditions of poverty increase the risk of disability. However, little rigorous quantitative research has been undertaken to measure the real impact of disability on poverty. Methods based on monetary set (income and consumption) may not reflect the real possibility to convert resources into the fulfilment of personal goals. The capability approach, developed by A. K. Sen, offers a useful framework for the evaluation and assessment of individual well-being considering both monetary and non-monetary resources. The study describes a methodology, derived from the capability approach, to estimate the additional income needed for various types of household with and without disabled members to ensure the same satisfaction with economic resources. The method has been applied to the Italian contest, where 2.6 million people with disability live, and then the results are compared with the findings from an analogous study conducted in UK. The study reveals that disabled people in Italy need an income level two times higher to enjoy the same level of economic satisfaction, while in UK 50% of extra resource are sufficient. The findings suggests that traditional evaluations based on consumption lead to an underestimation of the needs of household containing disabled members. It follows that the financial benefits envisaged by actions planned by government through current instruments may not be completely effective.     

A Measure of Well-Being Across the Italian Urban Areas: An Integrated DEA-Entropy Approach

In recent years, there has been an increasing proliferation of initiatives focusing on the concept of quality of life and well-being. At the centre of these studies there is the recognizing that the GDP offers only a partial perspective of factors affecting people’s lives. Following this line of the research, this paper is aimed at computing the well-being efficiencies of a sample of Italian Province capital cities, using a methodological approach that combines data envelopment analysis (DEA) with Shannon’s entropy formula. To avoid subjectivity in choosing a representative set of variables that proxy the phenomenon under study, we rely on the theoretical framework adopted by the Italian National Institute of Statistics (ISTAT) within the equitable and sustainable well-being (BES) project. The dashboard of indicators included in the analysis are related to the Ur-BES initiative, promoted by ISTAT to implement the BES framework at cities level. In a first step of the analysis, an immediate focus on separate dimensions of urban well-being is obtained by summarizing the plurality of available indicators through the building of composite indices. Next, the adopted integrated DEA–Shannon entropy approach has permitted to increase the discriminatory power of DEA procedure and attain a more reliable profiling of Italian Province capital cities well-being efficiencies. The results show a marked duality between the Northern and Southern cities, highlighting important differences in many aspects of human and ecosystem well-being.     

An Introduction to ‘Benefit of the Doubt’ Composite Indicators

Despite their increasing use, composite indicators remain controversial. The undesirable dependence of countries’ rankings on the preliminary normalization stage, and the disagreement among experts/stakeholders on the specific weighting scheme used to aggregate sub-indicators, are often invoked to undermine the credibility of composite indicators. Data envelopment analysis may be instrumental in overcoming these limitations. One part of its appeal in the composite indicator context stems from its invariance to measurement units, which entails that a normalization stage can be skipped. Secondly, it fills the informational gap in the ‘right’ set of weights by generating flexible ‘benefit of the doubt’-weights for each evaluated country. The ease of interpretation is a third advantage of the specific model that is the main focus of this paper. In sum, the method may help to neutralize some recurring sources of criticism on composite indicators, allowing one to shift the focus to other, and perhaps more essential stages of their construction. An abridged version of this paper was presented at the Workshop on European Indicators and Scoreboards, organised by DG Education and the Joint Research Centre within the auspices of CRELL, in Brussels, October 24–25, 2005.     

Using Quality of Care Indicators to AssessFamily Planning Program: Xuanwu,Nanjing

Tilequalityofcareprogramwaslaunchedill1997inXuanwuDistrict,Nalljing,JiangsuProvincecenteringoilone-stopreproductivehealtllservices.Witlltheunfoldingoftheprogram,theDistrictFamilyPlanningCommissionadopted11ewappraisalindicatorsin1998tofacilitateprogress.DemographicsAreaf81squarekmPopulatiollf389,000Womenofreproductiveagef105,000Marriedwomellofreproductiveagef76,000BackgroulldTwoKAP(knowledge,attitudeandpractice)surveyswerecollductedbytheNalljillgFamilyPlanllingCommissiOllill1996and1997…     

A Common Weight Approach to Construct Composite Indicators: The Evaluation of Fourteen Emerging Markets

This study presents an ongoing project, emerging market (EM) evaluation project, of the Taiwan Institute of Economic Research (TIER). The purpose of this project is to construct a composite indicator (CI) named as growth potential index (GPI) for selecting the promising EMs, in which to begin new or expand existing business is attractive to governments, firms, and investors. However, weight determination is one of the most difficult tasks in the construction process of a CI. A new approach inspired by the Z score and rooted in data envelopment analysis (DEA) is proposed to objectively determine the common weights for constructing the GPI without requiring data normalisation beforehand. The same dataset is used to compare the proposed common weight approach with the equal weighting method (currently used by the TIER), the widely used DEA-CI model, and the first common weight DEA-CI model. Spearman’s rank correlation test revealed a high positive correlation between the GPIs obtained by the proposed approach and each considered method. The major findings include: (1) China is the most promising EM; (2) Argentina, China, Malaysia, Poland, and Russia are above-average EMs; (3) India, Indonesia, Saudi Arabia, South Africa, and Thailand are below-average EMs; and (4) of the so-called BRIC countries (Brazil, Russia, India, and China), China is the best EM, and India is the worst EM.     

A Mixed Method Approach to Quality of Life Research: A Case Study Approach

Increased use of qualitative and quantitative methods in quality of life projects necessitates an examination of how to effectively work within a mixed method framework. The research objectives of this paper are to (1) operationalize the two goals of mixed method research (confirmation and comprehension) and (2) develop a strategy for using mixed methods in quality of life research. Face-to-face interview (qualitative) and telephone survey (quantitative) data from the Community–University Institute for Social Research Quality of Life (CUISR QoL) project in Saskatoon, Saskatchewan were used for operationalization. Overall, confirmation and comprehension were challenging concepts to operationalize. Seven benefits and four guidelines were developed and are presented as dynamic, rather than definitive, structures.     

Nowcasting Indicators of Poverty Risk in the European Union: A Microsimulation Approach

The at-risk-of-poverty rate is one of the three indicators used for monitoring progress towards the Europe 2020 poverty and social exclusion reduction target. Timeliness of this indicator is critical for monitoring the effectiveness of policies. However, due to complicated nature of the European Union Statistics on Income and Living Conditions (EU-SILC) poverty risk estimates are published with a 2–3 years delay. This paper presents a method that can be used to estimate (“nowcast”) the current at-risk-of-poverty rate for the European Union (EU) countries based on EU-SILC microdata from a previous period. The EU tax-benefit microsimulation model EUROMOD is used for this purpose in combination with up to date macro-level statistics. The method is validated by using EU-SILC data for 2007 incomes to estimate at-risk-of-poverty rates for 2008–2012 and to compare the predictions with actual EU-SILC and other external statistics. The method is tested on eight EU countries which are among those experiencing the most volatile economic conditions within the period: Estonia, Greece, Spain, Italy, Latvia, Lithuania, Portugal and Romania.     

An Economic Approach to Achievement and Improvement Indexes

This study proposes a usefulalternative to the ``aggregate deprivationindex' which is used to measure the well-beingsof individuals in different countries orgeographic locations. Furthermore, we alsopropose an improvement index which alleviateswell known difficulties associated withovertime comparisons of ``aggregate deprivationindex'. While deriving our indexes, we pursuedan economic approach to index numbers theoryand relied on the assumptions of optimizingbehavior. The proposed achievement index hasits roots in the theory of quantity indexeswhose axiomatic properties are wellestablished. The roots of our improvement indexon the other hand, is well grounded in theproductivity growth literature. The study alsoprovides a numerical example.     

Subjective Indicators Construction by Distance Indices: An Application to Life Satisfaction Data

The construction of subjective indicators for measuring phenomena expressed in an ordinal scale is a central issue in social sciences, particularly in sociology and psychology. In this paper, we propose the use of a subjective indicator by groups of units (for example, by geographical area) based on the ‘distance’ between the empirical cumulative distribution and a hypothetical cumulative distribution of reference. This approach allows to avoid the awkward question of the ‘quantification’ of an ordinal variable, i.e., the conversion of an ordinal variable into an interval variable. As an example of application, we consider life satisfaction data coming from the annual multipurpose survey on “Aspects of Daily Life”, carried out by the Italian National Institute of Statistics, and we present a comparison with some classical methods.     

Measuring Substance Abuse Treatment Need Among Adults in Florida: A Social Indicators Approach

Determining the size and demographiccharacteristics of substance abuse populationsis extremely important for implementing publicpolicies aimed at the control of substanceabuse. Such information not only assists in theallocation of limited treatment resources bythe state, but also in the monitoring ofsubstance abuse trends over time and in theevaluation of innovative policy initiatives. Inthis study, we develop three composite measuresof treatment need. We then use these measuresto estimate treatment need for alcohol abuseand for controlled substance abuse within eachof Florida's 67 counties. This study providesan important empirical component of communityplanning, quantifying and, to a limited degree,specifying the level of need for the substanceabuse treatment of community residents. Anadditional benefit is the development of a costeffective and unobtrusive methodology fordetermining empirically when levels of need arechanging so that treatment levels can beadjusted accordingly. With proper use,policymakers can readily employ the methodologydeveloped in this study in Florida andelsewhere to make better-informed decisions inthe allocation of finite substance abusetreatment resources.