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Measuring Poverty as a Fuzzy and Multidimensional Concept: Theory and Evidence from the United Kingdom

Kim, Sung-Geun (2012) Measuring Poverty as a Fuzzy and Multidimensional Concept: Theory and Evidence from the United Kingdom. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Previous research shows that poor people define poverty not only in material terms, but also in psychological and social terms, though it has been consistently characterized by economic resources in social sciences. Using a method based on `fuzzy-set' theory can be uniquely placed to answer the question as it allows us not only to tackle the problem of arbitrary poverty line, but also to integrate multiple dimensions into one index in an intuitive way. It can avoid the problem of poverty line entirely by introducing the concept of `membership function' which represents a degree of inclusion in a fuzzy subgroup poor.

I therefore argue that the fuzzy measures of poverty can be a strong multidimensional alternative for the measures centered around income. To support the argument, two crucial
points are clarfied. Firstly, the difference between traditional measures and the fuzzy measures needs to be discussed further since the discussions on the new measures so far lean more toward the fresh insights from the measures, so that the distinction in policy-relevant information has not been emphasized enough. From the comparison, I present that the fuzzy measures can provide a richer description of the social phenomenon, enabling a
more acceptable distinction between different sub-populations. Secondly, how the measures behave statistically should be considered in depth because one of the most frequent critiques for poverty measurements is that present methods depend too much on arbitrary decisions
like setting a poverty line. Utilizing a Monte Carlo simulation, I find that the measures (Totally Fuzzy, Totally Fuzzy and Relative, and Integrated Fuzzy and Relative) acknowledge two points quite well: (i) poverty is a multidimensional concept, and (ii) the `poor' and `non-poor' are not two mutually exclusive sets and the distinction can be `fuzzy'. It also turns out that the sampling distribution of the fuzzy measures is well-behaved, and they are robust to arbitrary choice in the estimation as well as reliable with relatively small sample size. Besides, I show that they are robust to measurement errors. Finally, I investigate the identification performance of each measure and show that the measures have a strong consistency.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairPicard, Louispicard@pitt.eduPICARD
Committee CoChairMaertens , Annemiemaertens@pitt.eduMAERTENS
Committee MemberMihriban, Finkelmfinkel@pitt.eduMFINKEL
Committee MemberReynolds, Angelaamf50@pitt.eduAMF50
Committee MemberStone, Clementcas@pitt.eduCAS
Date: 27 September 2012
Date Type: Publication
Defense Date: 3 August 2012
Approval Date: 27 September 2012
Submission Date: 9 August 2012
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 221
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public and International Affairs > Public and International Affairs
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: poverty, fuzzy, multidimensional, measurement, capability, simulation, Monte Carlo method, Bootstrap.
Date Deposited: 27 Sep 2012 17:01
Last Modified: 15 Nov 2016 14:01


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