Multidimensional Poverty in Europe. A Longitudinal Perspective

Most analyses of multidimensional poverty use cross-sectional data. Consequently very little is known about multidimensional poverty dynamics at the micro-level. This paper uses panel data of the European Union Statistics on Income and Living Conditions (EU-SILC) for 19 countries over 2016–2020 to analyse a multidimensional poverty index broadly consistent with previous work using the same data. Technically, I build on previous research proposing analyses of transitions in multidimensional poverty and its deprivations to illuminate processes which result in deprivations to accumulate. Specifically, I test whether (multidimensional) poor people are (i) more likely to enter a new deprivation and (ii) less likely to leave an already experienced deprivation than comparable non-poor. I show that both hypotheses can be explored in a single model per deprivation and argue that estimating a linear model is sufficient for this purpose. I suggest and illustrate that differences or ratios of the respective conditional probabilities may be computed on an annual basis. The presented evidence lends support to both hypotheses, although I also find cross-country heterogeneity. The proposed analysis is applicable to rotating and short-run panels and is not limited to the analysis of multidimensional poverty. Moreover, routinely computations of the proposed measures may provide timely information for policy makers.

Speaker

 

 

Nicolai Suppa is a Research Associate at OPHI and a Juan de la Cierva Research Fellow at the Centre for Demographic Studies in Barcelona. At OPHI, he works on several research projects. Since 2018, he also co-leads the estimation of the global Multidimensional Poverty Index (global MPI), together with Usha Kanagaratnam and Sabina Alkire.