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Simplified Jackknife Variance Estimates for Fuzzy Measures of Multidimensional Poverty
Author(s) -
Betti Gianni,
Gagliardi Francesca,
Verma Vijay
Publication year - 2018
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/insr.12219
Subject(s) - jackknife resampling , econometrics , variance (accounting) , statistics , context (archaeology) , sample (material) , poverty , fuzzy logic , standard error , representation (politics) , sampling (signal processing) , estimation , mathematics , computer science , economics , geography , artificial intelligence , accounting , estimator , chemistry , archaeology , filter (signal processing) , chromatography , management , politics , economic growth , political science , law , computer vision
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.