Search

In more recent times, there is an increasing consensus in the field of development study to view poverty as a multidimensional deprivation beyond the more commonly used monetary perspective. Although the multidimensional poverty measurement is gaining more acceptance among policy makers, it is still based on the clear distinction between the poor and non-poor through an arbitrary threshold. One alternative to this shortcoming is offered by a multidimensional poverty measurement with a fuzzy-set approach in which it is possible to recognize deprivations as a matter of degree. The integrated fuzzy approach allows this possibility, although there is an unexplored opportunity of recognising that two or more dimensions can be attributed to a single item or attribute. This paper aims to contribute to this strand of research by introducing a ‘Double-Fuzzy’ approach. The methodology is applied to Tunisia using the Tunisian National Survey on Household Budget and Consumption (HBS) 2015.

The best evaluation for the proportion of defective units in a batch of fruits and vegetables can be achieved by an exhaustive checking of all the boxes in the batch, that is prohibitive to perform in most cases. Usually, only a sample of boxes is checked. In EU countries, EU regulations establish to estimate the proportion of defective units in a batch by the proportion of defective units in the sample, without giving any rule for selecting boxes. Therefore, results are highly dependent on the subjective choice of boxes. In the present study, an objective design-based approach is considered to select boxes from batches, adopting balanced spatial schemes with equal inclusion probabilities. The schemes are able to select samples of boxes evenly spread throughout the batch also ensuring good statistical properties for the proportion of defective units in the sample as estimator of the proportion of defective units in the batch. The performance of these strategies is evaluated by means of a simulation study performed on real and artificial batches of apples, peppers and strawberries. A case study is considered to estimate the proportion of defective units in a batch of courgettes stored in a distribution center of a supermarket chain in Central Italy.

The main scope of this paper consists in presenting an original contribution for the estimation of measures of monetary and multi-dimensional poverty in a longitudinal context; we also provide empirical on how this could be very useful in explaining the different recovery in three Visegrad Countries from the 2008 crisis. The traditional approach of poverty, defined by the dichotomy (poor, non-poor) is replaced by a defining based on the concept “a matter of degree”, which could briefly be defined as the position of an individual in the income distribution, or in any other variable’s distribution taken into account in a multidimensional approach. Another original contribution of the paper consists in proposing rules for the intersection and union of fuzzy sets, which we demonstrate to be appropriate for the study of poverty and deprivation, especially when the approach is applied to a time sequence in a longitudinal perspective; in particular, we propose the new ‘fuzzy “At-persistent-risk-of-poverty rate”, which can be seen as the fuzzy alter ego of the well-known Eurostat Laeken indicator.

This paper proposes a new method for improving the design effect of household surveys based on a two-stage design in which the first stage clusters, or primary selection units, are stratified along administrative boundaries. Improvement of the design effect can result in more precise survey estimates (smaller standard errors and confidence intervals) or reduction of the necessary sample size, that is, a reduction in the budget needed for a survey. The proposed method is based on the availability of a previously conducted poverty mapping, that is, spatial descriptions of the distribution of poverty, which are finely disaggregated in small geographic units, such as cities, municipalities, districts, or other administrative partitions of a country that are linked to primary selection units. Such information is then used to select primary selection units with systematic sampling by introducing further implicit stratification in the survey design, to maximize the improvement of the design effect. The proposed methodology has been implemented for the new 2021 Household Budget Survey in Tunisia, conducted under a cooperation project funded by the World Bank. The underlying poverty mapping is based on the 2015 Household Budget Survey and the 2014 Population and Housing Census.

This chapter focuses on the application of the fuzzy-set approach for the measurement of violence against women (VAW). Gender-based violence is a complex and multi-faceted phenomenon. It is defined by the United Nations (UN) as any act of gender-based violence that results in, or is likely to result in, physical, sexual, or psychological harm or suffering to women, including threats of such acts, coercion or arbitrary deprivation of liberty, whether occurring in public or private life. VAW is a universally recognised concern because it results in harm or suffering. Measuring harm is therefore central to measuring violence. In addition, and equally important, our severity scale gauges relative severity strictly for a given type of violence while remaining intentionally silent about the comparative severity of acts of different types of violence. Economy, simplicity and less arbitrariness are actual advantages in favour of the fuzzy scale, provided the latter yields ‘credible’ results.

This paper aims to show how much and in which way people in Brazil are deprived in terms of indicators directly related to the capacity to prevent and recover from infection with COVID-19. We use the Alkire-Foster (AF) method and a fuzzy-set approach as complements to measure multidimensional poverty within the context of the coronavirus pandemic. We propose two pandemic-specific indexes to account for the vulnerability related to the capacity to prevent infection with and to recover from the disease. The outcomes reveal structural deprivations in the country and considerable inequality among regions and ethnic groups. Rank correlation analyses suggest that the proposed indexes can trace the trends in increasing infection and a higher mortality rate in vulnerable regions. Compared to headcount ratio results, the fuzzy measures have more precise outcomes and are better able to capture the evolution in mortality patterns. Our empirical evidence offers an additional warning that the pandemic responses need to prioritize the most vulnerable groups and reinforces the need for coordinated national action.

This paper analyses multidimensional fuzzy monetary and non-monetary deprivation in households with children by using two different definitions: households with children under 14 years old, and the EU definition of households with dependent children. Eight dimensions of non-monetary deprivation were found using 34 items from the EU-SILC 2016 survey. Dealing with subpopulations, it is essential to compute standard errors for the presented estimators. Thus, a relevant added value of the paper is fuzzy poverty measures and associated standard errors, which were also computed. Moreover, a comparison was made between the measures obtained concerning the two subpopulations across countries. With a focus on Italy, an Italian macro-region is presented.

Over the last few years, there has been increased interest in compiling poverty indicators for children, as well as in providing uncertainty measures that are associated with point estimates. In this paper, we provide point, variance, and interval confidence estimates of the at-risk-of-poverty rate indicator for 33 European countries. Using the 2018 EU-SILC survey, we analysed the spatial distribution of poverty by providing graphical representations at the national level. Our results reveal rates of child poverty that are higher than in the national estimates for most of the countries. By considering the computation of standard errors, we used the bootstrap method thanks to its convenient properties. It is worth noting that, for some countries, such as Finland, Belgium, and Ireland, the confidence intervals do not overlap. These results suggest differences among countries not only in terms of child poverty, but also in terms of social protection and the welfare state.

This article presents the results of the latest poverty and inequality mapping update using the 2012 Living Standards Measurement Study (LSMS) survey and the 2011 census. This mapping builds on the methodology outlined by Chris Elbers and colleagues and further innovates by including new methodological developments. The results presented here allow better understanding of the regional inequalities in welfare across Albania, and thus can help policy makers address them. This is particularly needed because internal migration over the past decade led to large-scale urbanization in some areas and severe depopulation in others. Internal migration is multifaceted, but mostly flows toward Tirana.

Summary In this paper, we present a practical methodology for variance estimation for multi-dimensional measures of poverty and deprivation of households and individuals, derived from sample surveys with complex designs and fairly large sample sizes. The measures considered are based on fuzzy representation of individuals' propensity to deprivation in monetary and diverse non-monetary dimensions. We believe this to be the first original contribution for estimating standard errors for such fuzzy poverty measures. The second objective is to describe and numerically illustrate computational procedures and difficulties in producing reliable and robust estimates of sampling error for such complex statistics. We attempt to identify some of these problems and provide solutions in the context of actual situations. A detailed application based on European Union Statistics on Income and Living Conditions data for 19 NUTS2 regions in Spain is provided.

This paper discusses how objective environmental indicators affect the measure of a country&rsquo, s well-being. The dependent variable in the analysis is subjective well-being (WB), for which the objective environmental variable we use is per capita carbon dioxide (CO2) emissions. The paper refers to the relationship between subjective well-being and a set of objective variables representing the four basic types of capital to satisfy human needs and to ensure the well-being of future generations based on the ecological economic systems. Implementing different mediation models, estimated using structural equation modeling, we discover that the objective environmental variable does not directly affects the country&rsquo, s subjective well-being, while, according to different models, the mediated effects are statistically significant in explaining subjective well-being. The surprising results lead us to think that the environmental risks related to CO2 emissions might not be correctly perceived by the public.

Measuring and comparing levels of violence against women across individuals or countries are controversial exercises, and yet they remain essential to understanding the phenomenon. We develop a scale of severity of violence against women based on fuzzy set theory. The scale can be used to derive fuzzy indexes of violence which account for the prevalence, frequency and severity of violence. Unlike existing alternatives, however, the scale that we propose is based on objective information rather than subjective assessment; it is parsimonious in terms of the amount of information that it requires; and it is less vulnerable to risks of cultural bias. We exploit our scale to unravel a puzzle which arises from the largest survey of violence against women in Europe to date, namely that some of the more gender equal countries record higher prevalence rates for violence from intimate partners. We show that the puzzle is resolved once violence is weighted by severity using our scale instead of being measured by simple prevalence.

We review the Integrated Fuzzy and Relative (IFR) approach with the aim of showing its universal and interdisciplinary feature for defining composite indicators in different settings. This methodology, introduced for measuring poverty by means of a multidimensional perspective and fuzzy logic, shall apply indeed in different research frameworks. The article provides and discusses two practical examples of applications of this methodology in two different settings, namely children wellbeing and agro-food sustainability. Warnings that should be clear to analysts, policymakers, and audience, when computing, examining or reading results from this methodology end the paper.

Equivalence scales have a crucial role in poverty measurement. For Turkey, there are no available up-todate equivalence scales, representing Turkish data. There were no efforts for calculation of equivalence scales at the regional level. The aim of the paper is to calculate and propose an equivalence scale for Turkey and estimate regional differences. Besides the models with Engel method, different equivalence scales were estimated by Almost Ideal Demand System. The results of the first model of AIDS approach composed of 5 age groups, is proposed for Turkey, but for simplicity the results of the second AIDS model could be used as well. In this model, the equivalence scale for Turkey is calculated as 0.65 for each additional adult after the first one and 0.35 for each child. For regional equivalence scales, we use two methods and comare the results. In the first one, regressions were run for each region separately and in the second one, dummy variables introduced. The highest difference in the results of the two methods was observed in Istanbul region. The findings for the regional scales are less reliable as the household size is bigger. This limitation is due to the relatively small size of the data sets. Having surveys with higher sample sizes would enable better results. After getting the results some conclusions could be drawn especially with regard to child cost differences among regions. It was expected to have higher costs for children in poorer regions and the regional results have confirmed this hypothesis. © Betti G., Karadag M. A., Sarica O., Ucar B. Text. 2017.

The linearisation approach to approximating variance of complex non-linear statistics is a well-established procedure. The basis of this approach is to reduce non-linear statistics to a linear form, justified on the basis of asymptotic properties of large populations and samples. For diverse cross-sectional measures of inequality such linearised forms are available, though the derivations involved can be complex. Replication methods based on repeated resampling of the parent sample provide an alternative approach to variance estimation of complex statistics from complex samples. These procedures can be computationally demanding but tend to be straightforward technically. Perhaps the simplest and the best established among these is the Jackknife Repeated Replication (JRR) method. Recently the JRR method has been shown to produce comparable variance for cross-sectional poverty measures (Verma and Betti in J Appl Stat 38(8):1549–1576, 2011); and it has also been extended to estimate the variance of longitudinal poverty measures for which Taylor approximation is not currently available, or at least cannot be easily derived. This paper extends the JRR methodology further to the estimation of variance of differences and averages of inequality measures. It illustrates the application of JRR methodology using data from four waves of the EU-SILC for Spain. For cross-sectional measures design effect can be decomposed into the effect of clustering and stratification, and that of weighting under both methodologies. For differences and averages of these poverty measures JRR method is applied to compute variance and three separate components of the design effect—effect of clustering and stratification, effect of weighting, and an additional effect due to correlation of different cross-sections from panel data—combining these the overall design effect can be estimated.

The Europe 2020 Strategy has formulated key policy objectives or so-called “headline targets†which the European Union as a whole and Member States are individually committed to achieving by 2020. One of the five headline targets is directly related to the key quality aspects of life, namely social inclusion; within these targets, the European Union Statistics on Income and Living Condition (EU-SILC) headline indicators at risk-of-poverty or social exclusion and its components will be included in the budgeting of structural funds, one of the main instruments through which policy targets are attained. For this purpose, Directorate-General Regional Policy of the European Commission is aiming to use sub-national/regional level data (NUTS 2). Starting from this, the focus of the present paper is on the “regional dimension†of well-being. We propose to adopt a methodology based on the Empirical Best Linear Unbiased Predictor (EBLUP) with an extension to the spatial dimension (SEBLUP); moreover, we compare this small area technique with the cumulation method. The application is conducted on the basis of EU-SILC data from Austria and Spain. Results report that, in general, estimates computed with the cumulation method show standard errors which are smaller than those computed with EBLUP or SEBLUP. The gain of pooling SILC data over three years is, therefore, relevant, and may allow researchers to prefer this method.

The main aim of the paper is to contribute to the poverty measurement literature by demonstrating a method to reduce the impact of equivalence scales in poverty measurement. This is accomplished by choosing the most appropriate reference household type. The results showed that one adult household is certainly not suitable for being the reference household type. When one adult household is set as the reference, in the range of no equivalence scale and per capita equivalence scale, poverty head count ratio changes from 1 to 48 %, showing the huge effect of the equivalence scale choice. Also the analyses at household size level showed that one adult household type is not convenient to be the reference household type. On the other hand, no clear distinction could be made between central household types, but the importance of choosing a household type close to the center was demonstrated for Turkish data.

This paper describes a methodology for the construction of a multidimensional index for sustainability assessment in the context of the Sustainable Development Goals (SDGs) of the UN 2030 Agenda. The methodology is designed to properly capture the multidimensional nature of sustainable development and the SDG framework, introducing an innovative Fuzzy Multidimensional Index to measure the performance of Mediterranean countries. The focus is on agro-food sustainability, in-line with the aims of the Partnership for Research and Innovation in the Mediterranean Area (PRIMA). Drawing on fuzzy set theory, a step-by-step procedure was developed: the underlying dimensions of a set of selected indicators for the SDGs are identified by exploratory factor analysis, an innovative weighting method is applied to aggregate the indicators and calculate the country scores for each dimension and the Fuzzy Multidimensional Index. The PRIMA program will be a first step towards the implementation of innovative solutions, by funding international cooperation projects between countries on both sides of the Mediterranean for a decade: the Fuzzy Multidimensional Index will be the primary source of data for evaluating such projects and policies implemented from them, the Index will therefore be able to close a gap in the availability of appropriate data.

The main scope of the paper is to adopt a fuzzy sets approach for the measurement of multidimensional poverty over a period of eight years, from 2007 to 2015, which takes into account the effect of the 2008 economic and financial crisis. In particular, the focus is on the financial dimension of poverty, and its effects on citizens in the EU Mediterranean Area. The empirical analysis, based on the European Union&mdash, Statistics on Income and Living Conditions survey (EU-SILC), covers eight Mediterranean Countries.

Static economic models based on complete demand systems are inadequate for estimating unconditional equivalence scales; in order to capture the effects of demographic changes on consumer behaviour, a life-cycle dynamic model is taken into account. In literature, studies have presented and evaluated longitudinal equivalence scales and intertemporal cost of children but these cannot be applied in practice when equivalence scales are utilised in poverty or income distribution analyses. This paper proposes intratemporal equivalent income scales, which are within period index numbers incorporating intertemporal consumer behaviour.

Quality of life must be measured within a multidimensional framework preferably inclusive of objective and subjective indicators that add to the information gathered through economic or monetary-only indices. This paper reports on the first application of the fuzzy set approach to quality of life measurement. The application uses the 2012 data for Macedonia collected by Eurofound during the third wave of the European Quality of Life Survey. The fuzzy approach, developed in the early 1990s, proves to be highly consistent and efficient in this empirical application when compared to distribution analyses. In addition, it is also statistically robust. Both the theoretical background and the application of the approach are described. The fuzzy set provides relevant added value for both data analysts and data users as it presents results easily and concisely, facilitates comparison and cross-reference between dimensions, and allows for the consolidation of monetary and non-monetary aspects of life quality.

Water use and agricultural practices in the Mediterranean area are unsustainable. The situation is worsened by the increased frequency of droughts and floods, as well as desertification and soil depletion, associated with climate change. The aim of Partnership for Research and Innovation in the Mediterranean Area (PRIMA) is to foster an integrated programme of sustainable food production and water provision in the framework of the water-energy-food nexus. A monitoring tool developed under PRIMA is based on the Sustainable Development Goals, two of which are specifically dedicated to food security (SDG 2) and sustainable management of water (SDG 6). The 12 indicators that have been chosen to be monitored in the Mediterranean area are: Multidimensional Poverty Index (MPI); population overweight (%); land use (%); GHG emissions (total and AFOLU)(tCO2e); cereal yield (kg/ha); agriculture value added (US$/worker); fertilizer consumption (kg/haarable land); crop water productivity (kg/m3); annual freshwater withdrawal for agriculture (%); population served using with safely managed water service (rural, %); population served using with safely managed sanitation (rural, %); amount of agricultural residues used for energy purposes (t). Datasets for these indicators are collected by international bodies such as the World Bank, WHO, FAO and UNFCCC; recent series are available for almost all Mediterranean countries and are constantly updated. The aim of the proposed monitoring tool is to keep track of the impact generated in by PRIMA research and innovation projects Mediterranean countries.

The world has great expectations for how the private sector, both companies and investors, can support the 17 Sustainable Development Goals (SDGs). In fact, it is generally believed that these goals cannot be achieved without strong support from the private sector. But will making the world a better place hurt financial returns? The answer is “No” if companies focus on the SDGs and their associated targets that benefit from strong performance on the material environmental, social, and governance (ESG) issues that matter to investors. In this paper we map the 30 generic ESG issues identified by the Sustainability Accounting Standards Board (SASB) to the SDGs and their targets. We show that some SASB issues are more material for a given SDG than others. We also show that some SASB issues are more important to the SDGs in general than others. We also map the material ESG issues for each of SASB’s 79 industries to the SDGs and to their targets. For each sector, there are particular SDGs where it has high impact and for each SDG there are particular sectors that have a high impact on it, and some sectors are more important to the SDGs in aggregate than others. The same is true at the target level. This mapping can be used as a guide for both companies and investors who want to understand how value-creating ESG performance can contribute to the SDGs. This paper is divided into four parts. Part I explains the motivation for this study. Part II explains our methodology and Part III the results. Part IV concludes with a summary of our results and some reflections on how our mapping methodology can be improved.

This paper proposes new fuzzy measures of monetary poverty and also non-monetary deprivation, providing an economic interpretation of the parameters involved. For non-monetary deprivation, the paper provides a step-by-step procedure: dimensions or groupings of initial items of deprivation are identified using explanatory and confirmatory factor analyses, and a weighting system is applied for the aggregation of individual items into the dimension they represent. The methodology is applied to European Union countries using European Union-Statistics on Income and Living Conditions (EU-SILC) data for the 2011 survey wave.

Quality of life must be measured within a multidimensional framework preferably including objective and subjective indicators of quality of life that add to the information obtained through economic indicators. This paper proposes for the first time, the fuzzy set approach to quality of life measurement. The application uses the 2012 data collected for Germany by Eurofound during the third wave of the European Quality of Life Survey. The fuzzy approach, developed in the early 1990s, proves to be highly consistent and efficient in this empirical application when compared to distribution analyses. In addition, it is also statistically robust. Both the theoretical background and the application of the approach are described. The fuzzy set provides relevant added value for both data analysts and data users as it presents results easily and concisely, facilitates comparison and cross-references between dimensions, and allows for the consolidation of monetary and non-monetary aspects of life quality.

In this paper we present a methodology for the study of longitudinal aspects of monetary and multi-dimensional poverty, and apply this in a multi-country comparative context. The conventional poor/non-poor dichotomy is replaced by defining poverty as a matter of degree, determined by the place of the individual in the income distribution. The same methodology facilitates the inclusion of other dimensions of deprivation into the analysis: by appropriately weighting indicators of deprivation to reflect their dispersion and correlation, we can construct measures of non-monetary deprivation in its various dimensions. An important contribution of the paper is to identify rules for the intersection and union of fuzzy sets appropriate for the study of poverty and deprivation. These rules allow us to meaningfully study the persistence of poverty and deprivation over time. We establish the consistency of the approach when applied to a time sequence of any length. We can thus study longitudinally over time a whole range of indicators of poverty and deprivation, from cross-sectional monetary poverty rates to multi-dimensional indicators of deprivation: in particular, we propose the new “Fuzzy At-persistent-risk-of-poverty rate”, and compare it with the corresponding Laeken indicator adopted by Eurostat.

This paper presents a multidimensional and fuzzy approach to measuring the quality of life in a longitudinal context, with the aim of evaluating the impact of the economic crisis. In the paper the concept of quality of life is shaped by living conditions and resources, behaviour patterns and preferences, as well as perception of satisfaction in key social and economic aspects. The debate is concerned with how to aggregate such various components, variables or items in constructing indicators of quality of life. The methodology is applied to the European quality of life survey cross-sectional data for the rounds 2007 and 2012; the paper aims to evaluating the main changes which occurred in a period ranging from a pre-crisis year to a year where the economic crisis has occurred, but not yet concluded.

The aim of this paper is to study the impact of remittances from labour migrants on welfare, as well as the impact of the use of equivalence scales on poverty, in Tajikistan. The magnitude of remittances sent back is fairly well known from official statistics (e.g. the amount of remittances is regularly monitored by the balance of payment at the Central Bank). In 2009 a change in the flow of money from abroad occurred, and volumes dropped, but it was unknown to what extent; its impact on poverty is still officially unknown. This paper describes a project aimed at conducting a survey to investigate this impact. It would not be possible to do this kind of analysis with a traditional sample survey, since it would generate only a small selection of migrant households. Instead, the new Migration, Remittances and Poverty Survey was used, which was conducted by the Tajik State Statistical Office in August 2010 using an adaptive sampling design from a set of enumeration areas selected mainly from the 2009 Integra...

MicroReg č un classico modello di microsimulazione, tax-benefit, costruito per la Regione Toscana dall'Irpet. L'obiettivo č quello di garantire al policy maker regionale un adeguato strumento di valutazione e programmazione delle politiche fiscali e di welfare, siano esse attuate a livello nazionale e/o regionale. Diversamente da tutti gli altri modelli di microsimulazione sviluppati nel nostro paese, microReg ha un campo di applicazione multiregionale. Esso fornisce una adeguata base di calcolo, a seguito di un qualche intervento di natura pubblica, dei seguenti aspetti: i) distribuzione dei guadagni e delle perdite fra individui e famiglie; ii) impatto sulle misure di povertŕ e disuguaglianza; iii) differenze nei costi e nei benefici tra le regioni; variazioni di gettito nazionali e regionali. Il lavoro presenta la struttura del modello e tre applicazioni empiriche.

This paper identifies methodologies and strategies appropriate for the development of indicators of well-being at the level of subnational regions, thus contributing towards the incorporation of the subnational dimension into public policy making. Considerations in adapting and augmenting country-level indicators for application at the regional level are discussed. Special technical procedures involved in this process are described. These include consolidation of measures defined with reference to different poverty line thresholds in the income distribution, cumulation of data or measures over multiple survey waves and use of small area estimation methods. Empirical illustrations using comparable survey data from European Union countries are provided, to the extent possible with the available microdata. Copyright 2011, Oxford University Press.

In this paper we attempt to measure the educational mismatch, seen as a problem of overeducation, using a multidimensional and fuzzy methodology. Educational mismatch can be difficult to measure because many factors can converge to its definition and the traditional unidimensional indicators presented in literature can offer a restricted view of the problem. We discovered two dimensions that properly define overeducation. The first includes information regarding job satisfaction, the use of expertise and the coherence between study and work, but also the measure on which the traditional indicator is based and the second concern earning aspects. We then calculate a degree of membership to the set of overeducated workers using the defined dimensions. We believe that in this manner we can partially overcome the rigidity of the traditional measures. Our findings suggested that generally women have a degree of mismatch higher than men and the graduates in Pharmacy, Medicine and Engineering are the least overeducated in terms of the first dimension, even if in terms of the earnings dimension they have similar mismatches to the other fields of education. Self-employment and collaboration contracts reduce overeducation when the first dimension is considered; on the other hand, when the second dimension is taken into account graduates with collaboration contracts are the most mismatched out of those having a job. Additionally, university reform introduced in the academic year 2001–2002 in the Italian higher education system is shown as not contributing to a reduction of the overeducation phenomenon.