Practice and Concept of Multidimensioanl Poverty Analysis

7/27/2019 Practice and Concept of Multidimensioanl Poverty Analysis

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MULTIDIMENSIONAL POVERTY ANALYSIS

APPROACH CONCEPT AND PRACTICE

Multidimensional poverty analysis aimed at gaining an understanding of the local

situation in its wider context, including social, economic, physical and financial

aspects, unlike the other economic poverty analysis which tries to explain the

situation on the context of economics direction leaving the other face of poverty.

Consequently that approach makes poverty analysis a sided approach

A useful literature was provided from the contributions of the following on

multidimensional poverty. (Praag and Carbonell, 2005; Kamanou, 2005; Anup,

2010; Foster, Greer and Thorbecke, 2010; Sabina and Foster, 2011; Rodrogo,

2011; Alkaire and Foster, 2011a; Alkire and Foster 2011b; Hatzimasoura and

Bennet, 2011; Gaston 2012). Their effort to justify the need for multidimensional

poverty is clear. Most of them observed that poverty analysis is tilted on economic

perspective while leaving other aspect. This is quite a complete movement away

from traditional one-dimensional to multidimensional poverty measurement

particularly with respect to the identification step.

There were consensus that poverty depends on many variables not just income,

expenditure or wealth. Longevity, low education poor health status, insecurity, low

self confidence or powerlessness, absence of right such as freedom are the most

obvious variable. Most of them if not all are ordinal variables in nature they are

rank based on specific criteria. Also an Individual cannot be said is poor

completely or not since He/ she can be poor in some dimension and not poor in

another dimension. Hence it is good to measure poverty as degree or intensity of

being in poverty rather than classifying them into poor and non poor. Even the

most widely used model for income poverty analysis, the FGT (Foster Greer and


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Thorbike) which was developed 25 years ago now change perspective try to

capture multidimensional poverty using ordinal variables. Generally the

subjectivity of poverty depends on the individual, views, principles and feelings to

the entire situation. The concept of Attitude Belief and Opinion had beenassessed in many behavioral research using either ordinal or Categorical

measurement scales. The major problems with the ordinal and categorical variable

is that most such data at first look researchers feel embarrassed, totally random

difficult and inappropriate in most inferential data analysis. Recent advances in

computer solve these problem now many classification, reduction and pattern,

relationship detection software are available to which uses both ordinal and

categorical variables objectively.

Obviously, it is a clear fact those who are experiencing poverty usually have a

perspective on the sources, forms, nature and intensities of their various

deprivation and deficit that is entirely different from that of the analyst studying a

particular indicator, especially absolute income or food poverty line. These insights

remain silent and invisible. Notwithstanding the fact that the methodology of self

perception based approaches to poverty identification and analysis is not without

its own problems the relevance of such subjective information cannot be

overlooked. Hence require a new insight into how to capture the perspective of the

affected the way they experience it directly. This can be done through

multidimensional approach to capture data. But must transform to reflect the way

analysis can be possible. This transformation are done by Composite or latent

variable measurement. This process is one of many methods of solving the

voluminous nature of poverty indicators which were thought to be a problem

before. Recent work tries to use the multidimensional indicators but reduced and

transformed them in a form more liable to further analysis.


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Multidimensional poverty is complex interrelated problems and mostly two way

dependency relationships exist, variable interacting between themselves. This

makes multidimensional poverty analysis intricate task, therefore for ease of

investigation some statistical applications had to be imploded. Pattern recognition,

classification, examining relations are the common approach to multidimensional

poverty analysis. The pattern recognition is the process of finding general relations

in a set of data, and forms the core of many disciplines, from neural networks to

so-called syntactical pattern recognition.

Measurement of relationship and pattern detection in multidimensional poverty is

done with multivariate analysis technique which comprise of factor analysis,

principal component, multidimensional scaling, cluster analysis conjoint analysis,

discriminant analysis, and correspondence analysis. These techniques prove

beneficial in their application and solve, most of the problems experience using

regression.

None the less, the use of multivariate statistical technique in multidimensional

poverty analysis increased significantly. Generally in multidimensional poverty

analysis number of variables exhibit interrelationship between themselves hence

the need for knowledge of the structure patterns of the variables. Factor,

component, cluster analysis techniques were suitable for analyzing the patterns of

complex, multidimensional relationship encountered in research. They can be

utilized to examine the underlying pattern or relationship for large number of

variables and to determine whether information can be condense or summarized,

classified, grouped, or segregate into a smaller set of factors or components. Karl


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and Irini, 2001; Koutsyiannis, 2001; Manly, 2005 Ken, 2006; Bengt and Tihomir,

2011; all suggested principal component or factor analysis for data reduction

without losing the essential features of the indicators as well as capturing ordinal,

categorical, and converting them to metric variables usable for analysis. The

approach helps to avoid multicolinearity problem, identification and simultaneous

equation bias and the effect of aggregation problem common to many macro

economics problem.

But multiple correspondence analysis approach was used to measure

multidimensional poverty by Herve, 2007; Assellin (2009) in Senegal;

Abdeljaouad and Paolo (2012) in Morocco. The method is comprehensive

embracing every aspect of human life at the same time transforming and serving

the objective of data reduction, pattern recognition and relationship exploration.

The multiple correspondence analyses were principally used for data that were

categorical or nominal unlike the factorial and principal component method whichuses both. Although Rencher (2002); Landau(2004), Hair (2010), all believe that

principal component and factor analysis are good in data reduction for both metric

or non metric data

In Multidimensional poverty it is common to encountered situations that can best

resolve by defining groups of homogenous variables, or individuals suffering from

poverty, their level or even their behaviors. The ultimate strategic options based to

identifying groups within the populations such as segmentation and classification

would not be possible without an objective methodology. Cluster analysis is the

best approach to perform such. The techniques of cluster analysis have been


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extensively applied to data in many fields, such as medicine, psychiatry, sociology,

criminology, anthropology, archaeology, geology, geography, remote sensing,

market research, economics, taxonomy of plant and animal, soil classification and

engineering. The method use in searching for natural structure among the

observation based on multivariate profile. Cluster analysis group individuals or

objects into clusters so that objects in the same cluster are more similar to one

another than they are to objects in other cluster. The attempt is to maximize the

homogeneity of the objects within the cluster while also maximizing the

heterogeneity between the clusters. Therefore this work will used the approach in

searching for groups and classes of multidimensional poverty in the area as one ofthe analytical tools.

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Practice and Concept of Multidimensioanl Poverty Analysis