Reliability, Population Classification and Weighting in Multidimensional Poverty Measurement: A Monte Carlo Study

Héctor E. Nájera*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

13 Citations (Scopus)
276 Downloads (Pure)


In poverty measurement, differential weighting aims to take into account the unequal importance of the diverse dimensions and aspects of poverty and to add valuable information that improves the classification of the poor and the not-poor. This practice, however, is in contention with both classical test theory and modern measurement theories, which state that high reliability is a necessary condition for consistent population classification, while differential weighting is not so. The literature needs a clear numerical illustration of the relationship between high/low reliability and good/poor population classification to dissolve this tension and assist applied researchers in the assessment of multidimensional poverty indexes, using different reliability statistics. This paper uses a Monte Carlo study based on factor mixture models to draw up a series of uni-and multidimensional poverty measures with different reliabilities and predefined groups. The article shows that low reliability results in a high proportion of the poor group erroneously classified as part of the not poor group. Therefore, reliability inspections should be a systematic practice in poverty measurement. The article provides guidelines for interpreting the effects of unreliability upon adequate population classification and suggest that the classification error of current unreliable multidimensional indexes is above 10%.
Original languageEnglish
Number of pages24
JournalSocial Indicators Research
Early online date25 Jun 2018
Publication statusE-pub ahead of print - 25 Jun 2018


  • Deprivation
  • Poverty
  • Relative entropy
  • Reliability
  • Weighting

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