Multidimensional Poverty Measurement

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Multidimensional Poverty Measurement in Tunisia: Distribution of Deprivations across Regions

Multidimensional Poverty Measurement in Tunisia: Distribution of Deprivations across Regions

The data we use are taken from the 2010 Tunisian national consumption survey conducted by the INS (National Statistics Institute of Tunisia).This survey covers the entire country (large cities, small and medium towns and rural areas) and encompasses three areas of study: i) the expenses and acquisitions of households, ii) the food consumption and nutritional status of households, and iii) household access to community services (i.e., health and education).This survey is a primary source of information on the nutritional situation of the Tunisian population and on their access to public services such as health care, education and transport at the regional level. The sampling frame is stratified in two geographic criteria: the governorate and the living environment. The strata used are the following: large corresponding municipalities with over 100,000 citizens, neighboring towns of small and medium municipalities, and a layer of outlying communities. The 2010 Tunisian national consumption survey covered a sample of 13392 households theoretically drawn in a random survey. The survey covers the entire year, allowing INS to assess the level of consumption and welfare of Tunisian people based on their geographic, demographic and socioeconomic characteristics. Difficulties in procuring basic resources, such as education, health, etc., in different areas of Tunisia are converted intohousehold expensesand the downstream generation of difficult living conditions. For example, the absence of health services or schools in rural areas implies additional fees for transport, which aggravates the situation of a poor household. The links between dimensions motivate our empirical analysis space. We use household spending in order to analyze regional multidimensional poverty. This approach is generally preferred because expenses seem much more closely related
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Recognition Method of Multidimensional Poverty Based on Mahalanobis Taguchi System

Recognition Method of Multidimensional Poverty Based on Mahalanobis Taguchi System

Poverty has traditionally been seen as lacking income (or consumption). However, over the past 40 years, this multidimensional concept of poverty has been queried, and Scholars tend to analyze the poverty from a multi-dimensional perspective, according to the seminal works of Sen (1976) [1]. From this pers- pective, income is not the only indicator of poverty. The reasons are mainly re- flected in two aspects. On the one hand, existing studies have shown that there are often high tolerance and exclusion errors between people with low incomes and those deprived of other aspects of human well-being (Baulch and Maasset 2003; Ruggeri Laderchi et al . 2003) [3] [4]. On the other hand, the disadvantage of the monetary-metric income approach is that not all non-monetary characte- ristics can be directly measured. The reason is that the markets do not work well in many developing countries (Bourguignon and Chakravarty 2003) [5]. There- fore, although income is a significant dimension to evaluate human develop- ment, other dimensions should be considered to measure human deprivation such as education, living standards, health, and assets. Multidimensional poverty measurement and recognition have been the main research direction since its conceptual foundation was put forward by Sen. A variety of multidimensional poverty measurement methods were proposed. For example, Hagenaars et al . (1987) constructed the multi-dimensional poverty index system from the two dimensions of leisure and income [6]. Lugo et al . (2009) built a multidimension- al poverty measurement model based on information theory [7]. Tsui (2002) and Bourguignon (2003) used axiomatic methods to measure the multi-dimensional poverty index [8] [9]. Alkire (2011) put forward the AF method based on axi- omatization [10]. The core of the AF method is the “double critical value” me- thod. First, the critical value of each index is used to judge whether the object is deficient in this dimension; Then, the critical value of all dimensions is estab- lished to judge whether the individual belongs to multidimensional poverty. However, this method belongs to the unitary statistical method, which is essen- tially an extension of the analysis method of one-dimensional poverty. It can only examine the contribution of a single dimension to the multi-dimensional poverty, and cannot recognize the poor households from a multidimensional perspective.
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Multidimensional Rank Based Poverty Measures A Case Study: Tunisia

Multidimensional Rank Based Poverty Measures A Case Study: Tunisia

The important contributions to multidimensional poverty measurement have been the extension to the multidimensional context of some members of the general class of additively separable poverty measures. In fact, using a two-stage aggregation procedure, Chakravarty, Mukherjee and Ramade (1998), Bourguignon and Chakravarty (1998, 2003) extended the FGT (1984) class of poverty measures and Tsui (2002) extended the the more general class of sub- group consistent poverty measures. Following Gajdos and Weymark (2005), we have used a reversed-order two-stage aggregation procedure to construct classes of multidimensional non- additively separable poverty measures. For that, we have developed an extension to the multidimensional context of classes of both relative and absolute generalized Sen-Shorrocks- Thon (SST) measures developed by Chtioui and Ayadi (2013). The purpose of constructing a poverty measure is to be used by the decision-maker to help them know the extension of the poverty. It is more interesting to have a measure with a parameter where each value corresponds to a particular social judgement. This offers to the decision-maker a large choice of values, and he can choose the value that represents more his value judgements. The multidimensional poverty measures we have developed offer this property where there are function of parameters (as the parameter of aversion to relative deprivation, the parameter representing the weight to assign to the attribute in the multidimensional poverty measure, the system of weighting that is concerned with the well-being of the poor in each attribute distribution where greater weight is given to those at the bottom of the distribution).
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Impact and Intensity of Multidimensional Poverty in J&K

Impact and Intensity of Multidimensional Poverty in J&K

argument which is put forward is that income based measures, measure the capacity of the households to purchase goods though the market but it doesn’t capture access to the public goods and service which are not captures with income. Thus some of the indices such as HDI and HPI evolved. These measures capture non market deprivations but man problem with these measures is that these fail to measure individual deprivations but measures average achievements for the county as whole. Multidimensional poverty measurement has replaced Human Poverty Index. This measurement has been developed by Alkire and Santos 2010 and corrects these shortcomings of HPI and HDI. This measure is very flexible as the dimensions and indicators to be chosen can be varied from country to country and region to region with varying weights. Though economic growth with social justice has been one of the main objectives of economic planning in India but much of literature has focused on the inequalities among different economic groups. But in plural society as that of India, it becomes very important to look at the socioeconomic condition of people belonging to different caste and religions. There has been a lot of hue and cry that reservation system has put those groups who don’t get it in a relative disadvantageous position’s. Some put forward the view the protective discrimination has not been much of the use to vast majority of poor in these groups but to handful of influential people. Thus while measuring poverty required attention is to be paid to see health, education and nutritional status of these groups visa rest of the populations.
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Multidimensional Poverty Index (MPI) in Agricultural Household in Indonesia

Multidimensional Poverty Index (MPI) in Agricultural Household in Indonesia

Since 2010, the United Nations Development Program (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) agreed on a new poverty reduction initiative through the Multidimensional Poverty Index (MPI) published in Human Development Report (HDR) 2010 (Budiantoro et al, 2013). The study of multidimensional poverty for the Indonesian case is also not new. The research that analyzes multidimensional poverty in Indonesia through MPI calculations has been done by OPHI since 2010 and Budiantoro, et al from Prakarsa associations in 2013. The studies also measure multidimensional poverty in rural areas.
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Assessment of Aspect Based Multidimensional Poverty in Urban Haryana

Assessment of Aspect Based Multidimensional Poverty in Urban Haryana

The multidimensional poverty index (MPI) is the product of two measures, multidimensional headcount ratio (H) and intensity of poverty (A) (Alkire and Foster, 2011). Here MPI was computed with comprising the indicators from the aspects of drinking water facilities, sanitation facilities and housing facilities for urban areas in Haryana. The multidimensional headcount ratio is the proportion of multidimensional poor to the total population. The intensity of poverty is the average weight of deprivations experienced by the multidimensionally poor at a time. Mohanty (2011, 2012) used the unit level data from NFHS-3 and linked multidimensional poverty with health and health care utilisation. Children belonging to multidimensional poor households are more likely to be deprived of health care and lower survival. De and Dutta (2014) made an attempt to measure the Multidimensional Poverty Index of Toto community in Jalpaiguri district of West Bengal. Poverty was measured in Human Development Reports through the Human Poverty Index (HPI) from 1997-2009. In 2010, the MPI replaced the HPI. The MPI addresses the imperfections of HPI by allowing comparisons across countries or regions of the world, as well as within-country comparisons between regions, ethnic groups, rural and urban areas. The MPI reveals the combination of deprivation that strikes a household at the same time. A household is considered as multidimentionally poor if it is deprived in some combination of indicators whose weight sum is 30% or more of the dimension.
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Multidimensional Measurement of Poverty in Pakistan

Multidimensional Measurement of Poverty in Pakistan

The literature on Multidimensional poverty recognizes three broad classes of measures (Deutsch and Silber, 2005). The axiomatic approach to poverty measure, The Fuzzy set approach, and the information theory approach. Tsui (2002) develops an axiomatic framework for multidimensional poverty and derives two relative multi dimensional poverty measures, one was the generalization of Chakravarty’s (1983) unidimensional class of poverty indices, and other was the generalization of Watt’s (1968) poverty index. Bourguignon and Chakravarty (2003) present a distinct family of multidimensional poverty indices; they distinguish two groups of multidimensional poverty, depending on whether they are treating dimensions to be independent or to have some substitutability or complimentarity. They replace the subgroup consistency of Tsui (2002) with separability axiom. They accept both poverty non- decreasing rearrangement and poverty non-increasing rearrangement. For both group of indices they recommend extension of FGT class of indices. Fuzzy set approach in a multidimensional framework was first used by Cerioli and Zani (1990) who drew inspiration from the theory of Fuzzy sets initiated by Zadeh (1965). Cerioli and Zani’s original approach was later developed by sheli and lemmi (1995) giving origin to the so called Totally Fuzzy and Relative (TFR) approach Betti (2007). The distance function approach is widely used in Efficiency analysis. Lovell et al. (1994) were the first who used this for the analysis of household behavior. Information theory was originally developed by engineers in the field of communications. Theil (1967) was probably the first one to apply this theory to economics. One of the basic concepts used in such an approach is the logarithm of a probability [Deutsch & Silber 2005]. Information theory has been applied
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Languages, Symbolic Power and Multidimensional Poverty in the Context of Pakistan

Languages, Symbolic Power and Multidimensional Poverty in the Context of Pakistan

This paper engages with the question of languages in education and language policy in the multilingual context of Pakistan, from the perspective of its impact on multidimensional poverty. Poverty is interpreted as ‘capability deprivation’ following Amertya Sen’s capability approach, while Bourdieu’s social critical theory inform the analysis. The paper is based on findings from a 3-year qualitative study, funded by Research Consortium on Educational Outcomes and Poverty (RECOUP). The methods of data collection included participant observation, documentary analysis and interview data from 16 cases of private and government school graduates, in two provinces. Each case comprised of a final-year secondary school student and his/ her same sex 5-6 year older sibling. The findings reveal that the symbolic power of English in the country, in contrast to its restricted access, and the concurrent devaluation of the local linguistic capital reinforced the structures that nest inequality and poverty. This restrained the agency and of the already socioeconomically disadvantaged government school participants to achieve valued goals as inequality was unleashed in the multiple dimensions of their lives: cognitive, social, affective, economic, and physical. The paper argues for more inclusive language policies and languages in education.
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Distribution-sensitive multidimensional poverty measures with an application to India

Distribution-sensitive multidimensional poverty measures with an application to India

also noting that Bourguignon and Chakravarty (2003) present similar measures. But like Bourguignon and Chakravarty, they treat 𝛾 < 1 and 𝛾 > 1 as alternative possibilities, which they note requires dimensions to be all substitutes or all complements, and with an equal strength across dimensions. They consider this to be “unduly restrictive” and hence present 𝛾 = 1 as their “basic neutral case”. However, since like Bourguignon and Charkarvarty (2003), Alkire and Foster (2011a) stop short of presenting anything like the cross- dimensional convexity axiom (which accords normative primacy to the 𝛾 > 1 case), the distribution-sensitive measure 𝑀(𝛼, 𝛽; 𝑦), though anticipated by Alkire and Foster, has hitherto lacked axiomatic justification. In addition, as already noted above, the Alkire-Foster measures on account of their use of the dual cut-off violate the strong transfer axiom. Fourth, it is also worthwhile commenting briefly on a related set of “inequality-sensitive” multidimensional poverty measures in Rippin (2012a, 2012b) which take the form
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Chronic health conditions and poverty: a cross-sectional study using a multidimensional poverty measure

Chronic health conditions and poverty: a cross-sectional study using a multidimensional poverty measure

territories, including rural and urban populations — however, those in very remote areas were excluded. As these areas make up only 1% of the population, the ABS deemed that this would not affect the robustness of the data. 29 Both private dwellings and care-accommodation establishments were included in the sample, with a response rate of 89% for private dwellings and 92% for care-accommodation establishments. 30 Despite the high response rate for the survey, the potential for non-response bias cannot be excluded. It has been noted previously in Australia that people with lower education attainment have been less likely to participate in surveys 31 ; hence, this paper may underestimate the number of people in multi- dimensional poverty. The ABS sought to reduce non- response bias through survey design and estimation proce- dures, 30 and the use of weighted data in this analysis would also reduce non-response bias, although it cannot be excluded entirely. The original 2003 SDAC data were weighted by the ABS against the 2001 Census of Population and Housing to represent the Australian popu- lation in 2003 by broad population variables such as age, sex, state/territory and section of state. 27
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Gender and Multidimensional Poverty in Nicaragua: An Individual based Approach

Gender and Multidimensional Poverty in Nicaragua: An Individual based Approach

As the Goal 1 of the SDGs indicates, the conceptual understanding of poverty has been enhanced and deepened considerably in the past decades, especially following Amartya Sen’s influential work on his capability approach (Thorbecke, 2008) 1 , and currently there is a widespread consensus that poverty is a multidimensional phenomenon (Atkinson, 2003; Ferreira & Lugo, 2013; Silber & Yalonetzky, 2013). Consequently, poverty analysis and its measurement should not be based solely on income, or any monetary indicator, as it is unable to capture key well-being dimensions such as life expectancy, the provision of public goods, literacy, security, freedom and so on (Bourguignon & Chakravarty, 2003; Chakravarty & Lugo, 2016; Kakwani & Silber, 2008a; Stiglitz, Sen, & Fitoussi, 2009a, 2009b; Whelan, Nolan, & Maître, 2014); as Sen (2000b) noted: “Human lives are battered and diminished in all kinds of different ways” (p. 18). As a result of this awareness, poverty research has shifted the emphasis from a unidimensional to a multidimensional approach (Chakravarty & Lugo, 2016; Duclos & Tiberti, 2016; Pogge & Wisor, 2016), which has been considered by Kakwani & Silber (2008a) as “the most important development of poverty research in recent years” (p. xv), and diverse approaches have been proposed in the literature to measure poverty in a multidimensional setting 2 .
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Poverty Measurement and Analysis

Poverty Measurement and Analysis

∑ Depth of poverty (poverty gap). This provides information regarding how far off households are from the poverty line. This measure captures the mean aggregate income or consumption short- fall relative to the poverty line across the whole population. It is obtained by adding up all the shortfalls of the poor (assuming that the nonpoor have a shortfall of zero) and dividing the total by the population. In other words, it estimates the total resources needed to bring all the poor to the level of the poverty line (divided by the number of individuals in the population). This meas- ure can also be used for nonmonetary indicators, provided that the measure of the distance is meaningful. The poverty gap in education could be the number of years of education needed or required to reach a defined threshold (see technical note A.6 for a discussion of this and other ex- amples of the application of poverty measurement tools to nonmonetary indicators). In some cases, though, the measure does not make sense or is not quantifiable (for example, when indi- cators are binary, such as literacy, in which case only the concept of the headcount can be used). Note also that, as discussed in technical note A.1, the poverty gap can be used as a measure of the minimum amount of resources necessary to eradicate poverty, that is, the amount that one would have to transfer to the poor under perfect targeting (that is, each poor person getting exactly the amount he/she needs to be lifted out of poverty) to bring them all out of poverty.
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Computation of multidimensional poverty index: a case study

Computation of multidimensional poverty index: a case study

2.1 Poverty Incidence and Intensity Measurement Monetary indicators like income or consumption have been used to measure poverty quite often. This measurement considers only one dimension and it is monetary perspective of observing poverty conventionally. The monetary figure is calculated on the basis of goods and services valued at existing market price required to maintain a minimum life standard. If any person cannot afford funds to maintain this minimum life standard, then he deems as a poor or marginal person living below poverty line. There is no doubt that monetary measurement for poverty is an extremely worthwhile information. However, other measurement indicators such as basic amenities including food, clothing, medication, education, housing, employment, security etc. can be more useful and informative in a broad sense to define and understand poverty. Single monetary based indicator only cannot be able to capture the diversified range of phenomena contributing to poverty. Human Development Report (HDR) has re-estimated poverty in different ways apart from conventional measurement based on income indicator from 1997. It has been measured through Human Poverty Index (HPI) for the first instance and then succeeded by Multidimensional Poverty Index (MPI) in the year 2010. Human Development Report Office (HDRO) of UNDP and Oxford Poverty and Human Development Initiative (OPHI) of Oxford University collaborate designing this new multidimensional poverty index. From then on, OPHI computes and UNDP publishes the new global MPI every year. Furthermore, OPHI website displays sub-classification MPI indices of all countries divided into rural-urban, ethnicity or subnational breakdowns. More than one hundred developing countries are using MPI as an international measure of poverty incidence and intensity. MPI complements customary income based measurement through including acute deprivation that people face with reference to health, education and living standard. It is an assessment at individual poverty level [24, 25].
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Multidimensional Poverty in Indonesia: Trends, Interventions and Lesson Learned

Multidimensional Poverty in Indonesia: Trends, Interventions and Lesson Learned

rise unambiguously during periods of crisis or induced shocks. Second, long- term poverty alleviation and development strategies are not suitable as JPS programs during a crisis. Even programs such as a CCT might not be sufficient to act as a safety net to prevent poor and near-poor households from falling more deeply into poverty. The institutional structure developed through a CCT program, however, would support rapid appraisals and social interventions during crises. In particular, microcredit programs are not suitable as social protection programs. Not only are new microenterprises unlikely to be successful during a crisis, but also the hurried expansion of credit can undermine the slow, patient, and painstaking groundwork that successful microcredit programs require. Third, there is evidence that the current targeting system has a weak methodology, which is often the case with top- down programs like UCT, rather than programs that involve the community in one way or another, as CDD do. Therefore, it may be worth exploring ways to increase community participation rather than having a top-down targeting system. Fourth, the second lesson is in terms of coordination, both between central and local governments and between line ministries in the central government. The rules and regulations of a program must be well-designed and the duties of each ministry should be made explicit to avoid confusion. Fifth, given that Indonesia now adheres to a decentralized government system, the central government should refrain from implementing large-scale programs like the UCT, but rather play a more supporting role and let the local governments decide the best programs for their residents.
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Chronic health conditions and poverty: a cross-sectional study using a multidimensional poverty measure

Chronic health conditions and poverty: a cross-sectional study using a multidimensional poverty measure

Including health in a measure of poverty provides the opportunity for cross-portfolio responses to improving the living standards of disadvantaged members of society — with health being seen as a key contributor to low living standards, health interventions have the potential to be a direct policy response to improving living stan- dards alongside existing measures such as reform to social security arrangements. 26 However, different chronic health conditions are likely to have varying impacts on living standards, with some conditions more severely affecting living standards than others. ii This paper will look at the relationship between multidimen- sional poverty, measured using the Freedom Poverty Measure, and speci fi c chronic health conditions in the Australian population to determine which chronic health conditions are associated with multidimensional poverty (being disadvantaged in terms of income AND education or health), and as such their prevention or treatment should be targeted as a cross-portfolio concern.
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Analyzing Rural Poverty Considering Multidimensional Poverty Index: A Case Study in Jalma Union

Analyzing Rural Poverty Considering Multidimensional Poverty Index: A Case Study in Jalma Union

Abstract: The World has run on the way of prosperity in recent decades. Though it is great news for everyone, it has run up against many obstacles. Only a few number of people have grab this opportunity over the world. Most of the people are staying behind the scene. Poverty is one of the major concerns in the world. No country can overcome the curse of poverty. Within these Bangladesh is one of them, as a developing country in South Asia. The main obstacle to economic development is poverty. It is such an economic condition when one achieving a minimum living standard and losing the ability to purchase the essential living products as a result of very little income. Natural disasters such as cultural arbitrariness and aggression, population pressure, economic hardship, social and political problems, and foods, tidal waves, drought etc. create poverty. Over the world, it has measured by using some criteria and methods. Multidimensional Poverty index is one of them to measure the dimension of poverty. This paper provides new insights for the understanding, measurement, and analysis of multidimensional poverty in Jalma union, Khulna, Bangladesh. This paper makes an assessment of poverty scale in Jalma union using a comparative static approach.
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NEXUS BETWEEN POVERTY AND LIVELIHOOD DIVERSIFICATION AMONG RURAL HOUSEHOLDS IN NIGERIA:  A MULTIDIMENSIONAL POVERTY INDEX APPROACH

NEXUS BETWEEN POVERTY AND LIVELIHOOD DIVERSIFICATION AMONG RURAL HOUSEHOLDS IN NIGERIA: A MULTIDIMENSIONAL POVERTY INDEX APPROACH

increase their purchasing power to purchase basic necessities of life such as food, shelter, cloth, education and improved health care services, etc. which would translate to improvement in their standard of living, this result is in line with a similar research carried out by (12) , that found out that the number of livelihood diversification activities involved in by rural farming household heads had a negative relationship with their poverty level and it is statistically significant at 1% probability level (p<0.01), similarly, (13) found out that that a rise in the wages obtained from non-agricultural activities will reduce the poverty level of rural households by 0.6%. The coefficient of membership of cooperative society is negatively significant at 10% probability level (p<0.1), this shows that household heads that belongs to one or more cooperative societies are less likely to be poor compared to their counterparts that does not belong to cooperative societies, this is because cooperative membership popularizes market participation by making farmers cross fertilize ideas, experiences and affords access to sources of information regarding credit facilities, knowledge and skills with a view to improving their livelihood thereby stamping out poverty. However, as expected age has a negative relationship with poverty level though not significant, this implies that as the age of the household head increases there is likelihood that the household would be poor, similarly the coefficient of household size and marital status followed a-priori expectations.
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Multidimensional poverty, household environment and short-term morbidity in India

Multidimensional poverty, household environment and short-term morbidity in India

We have carried out the reliability and validity of multidimensional poverty esti- mates. The multidimensional poverty headcount ratio varies for different values of k (cut-off point) for India, suggesting the reliability of the estimates. It also holds true for the states of India (Fig. 3a), suggesting that the estimates of multi- dimensional poverty are robust. Also, the headcount ratio estimated based on the AF method was compared with estimates from Mishra and Shukla (multidimen- sional achievement index). At the national level, the multidimensional poverty headcount ratio is close to the multidimensional achievement index (Fig. 3b). The correlation coefficient between the two measures is found to be − 0.96 at the state level. As an external validation, the multidimensional poverty headcount ratio has also been validated with the Below Poverty Line (BPL) card and caste. Among households having a BPL card, 58.5% are multidimensional poor compared to 42.3% among households not having a BPL card, and the difference is statistically significant at p < 0.01. With respect to caste, about 73.7% among Scheduled Tribe were classified as multidimensional poor compared to 57% among Scheduled Caste, 48% among Other Backward Class and 34% among others.
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Multidimensional poverty and health: evidence from a nationwide survey in Japan

Multidimensional poverty and health: evidence from a nationwide survey in Japan

However, the association between multidimensional pov- erty and health has been largely understudied. Although many preceding studies have addressed how health is asso- ciated with particular poverty dimensions other than income, such as education [14], social protection [15], and housing conditions [16], the multidimensional pov- erty approach has not been fully applied to health. As one of the first attempts at addressing this issue, Callander, Shofield, and Shrestha [17] examined the association between multidimensional poverty and chronic health conditions using cross-sectional data from Australia. The authors showed that individuals with a chronic health con- dition were significantly more likely to be in multidimen- sional poverty than those without. They also observed that a substantial portion of those in multidimensional poverty suffered from a chronic health condition. In addition to Callander et al. [17], recent studies have paid increasing attention to the association between health and multidi- mensional aspects of social disadvantage and inequalities [18,19], although they did not specifically discuss multidi- mensional poverty.
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Poverty Measurement by Fuzzy MADM Approach

Poverty Measurement by Fuzzy MADM Approach

ABSTRACT: Poverty in a rural area is and is a leading problem to be evaluated. During the process of traditional evaluation, the consideration of the attributes and assuming their nature affect the algorithmic structure. It is not easy to mathematically represent them. A significant number of attempts have been proposed towards this problem. In this paper, a new approach is provided based on Multi Attribute Decision Making solution. A case study is also demonstrated to support the method’s applicability.

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