Multiple Deprivation, Severity and Latent Sub-Groups Advantages of Factor Mixture Modelling for Analysing Material Deprivation

• Published in: Social indicators research Vol. 131; no. 2; pp. 681 - 700
• Main Author: Catalan, Hector E. Najera
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Science + Business Media 01.03.2017; Springer Netherlands; Springer Nature B.V
• Subjects: Deprivation; Disadvantaged; Durable goods; Economic Factors; Health care access; Health services; Housing; Human Geography; Indexes; Latent class analysis; Microeconomics; Population; Poverty; Public Health; Quality of Life Research; Relative deprivation; Residential patterns; Sanitation; Social research; Social Sciences; Sociology; Studies; United Kingdom > UK; United States > US; California; Deprivation; Severity; Poverty; Factor mixture model; Latent class analysis
• ISSN: 0303-8300; 1573-0921
• DOI: 10.1007/s11205-016-1272-y

Material deprivation is represented in different forms and manifestations. Two individuals with the same deprivation score (i.e. number of deprivations), for instance, are likely to be unable to afford or access entirely or partially different sets of goods and services, while one individual may fail to purchase clothes and consumer durables and another one may lack access to healthcare and be deprived of adequate housing. As such, the number of possible patterns or combinations of multiple deprivation become increasingly complex for a higher number of indicators. Given this difficulty, there is interest in poverty research in understanding multiple deprivation, as this analysis might lead to the identification of meaningful population sub-groups that could be the subjects of specific policies. This article applies a factor mixture model (FMM) to a real dataset and discusses its conceptual and empirical advantages and disadvantages with respect to other methods that have been used in poverty research. The exercise suggests that FMM is based on more sensible assumptions (i.e. deprivation covary within each class), provides valuable information with which to understand multiple deprivation and is useful to understand severity of deprivation and the additive properties of deprivation indicators.