Abstract
This study investigates the empirical validity of the material deprivation indices (MDIs) using a partial criterion variable, namely UHCNIR (unmet health care need due to inadequate resources). This alternative approach helps to assess absolute validity (Type I and II errors) and sources of error in the measurement of poverty for a specific aspect of poverty (in this case inability to receive adequate health care due to affordability problems). A simple mismatch analysis identifies a sizable group, around 1% of the adult EU population, missed by MDIs despite being in UHCNIR. A majority of this 1% experiences not only UHCNIR but also multiple other deprivations, commonly reports having some difficulties making ends meet, and prevalently has a disability or a chronic health problem. The analysis reveals that MDIs miss specifically those “unhealthy poor” since these measures do not include a relevant item, and thus cannot adjust for different needs and costs in health care and account for the distinct poverty experiences of these people. Therefore, the main methodological assumption of MDIs, identifying the people in poverty with only a limited set of key deprivation indicators is not supported by this empirical analysis.
Highlights
There is widespread agreement on the need to broaden the analysis of poverty beyond income-based assessments
Based on the poverty definitions employed in relevant studies, ‘‘unmet health care need due to inadequate resources’’ is suggested as a suitable partial criterion variable for poverty
This latter group of 1% indicates the TIIE1 statistic: 1% of the population is in UHCNIR but not identified by the Severe material deprivation (SMD) index
Summary
There is widespread agreement on the need to broaden the analysis of poverty beyond income-based assessments. Studies usually focus only on construct validation and present ‘‘relative’’ evidence based on correlations These studies claim validity by relating the newly proposed MDIs to the existing poverty measures (e.g. income poverty measures or previous MDIs) and comparing their ability to (1) identify high poverty-risk groups (e.g. low social class, unemployed, lone parents) (Nolan and Whelan 1996; Layte et al 2001; Guio and Marlier 2013); (2) correlate most highly with the variables that are a priori expected to be associated with poverty (e.g. financial stress, psychological distress, low life satisfaction, bad health and various other welfare outcomes) (Nolan and Whelan 1996, 2011; Layte et al 2001; Hallerod and Larsson 2008; Hick 2014, 2016); (3) explain within and between country differences in variables such as financial stress (Whelan et al 2001; Whelan 2007; Whelan and Maıtre 2007, 2013); and (4) produce poverty rates consistent with social class and welfare regimes profiles (Nolan and Whelan 2011). The article concludes with a discussion of the results and their implications on poverty measurement
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