Christopher, T. Whelan*, Brian Nolan** and Bertrand Maître*** *School of Sociology and Geary Institute, University College Dublin & School of Sociology & Social Policy, Queen’s University Belfast ** College of Human Sciences, University College Dublin *** Economic and Social Research Institute, Dublin

Introduction Increasing focus on multidimensional approaches to poverty & social exclusion Variety of increasingly sophisticated analytic strategies Application of the Alkire & Foster multidimensional headcount approach Framed in a development rather than a rich country context Apply to EU-SILC 2009 Data

The Alkire & Foster Approach Framework for multidimensional poverty, counting poor & measure of extent of poverty (Bourguignon & Chakravarty, 2003) Axiomatic properties Limitations of counting approach – union & intersection Alkire & Foster dual cut-off approach Deprivation cut off for individual dimensions Poverty cut-of for number of dimensions – “breadth” of deprivation

The Alkire & Foster Approach (ii) Transition between identification and aggregation can be understood as involving a progression of matrices The achievement matrix Y shows the outcome for - n persons on d dimensions The deprivation matrix replaces each entry in Y that is below the deprivation cut-off with 0. The censored deprivation matrix multiplies each row in the deprivation matrix by the identification function. If the person is multi-dimensionally poor i.e. above the cut-off point the row remains unchanged. If not it is replaced with 0s. Information on non-poor has no effect of measurement

The Adjusted Head Count Ratio The Adjusted Head Count Ratio (AHCR) is the mean of the censored deprivation matrix. AHCR has a potential range of values going from 0 to 1.Where no one in the population experiences any deprivation it has a value of 0. Where everyone is deprived on all dimensions it takes on a value of 1. The headcount H is the proportion of people who are multi- dimensionally poor The intensity A is the average deprivation share among the poor H*A=AHCR AHCR properties includes decomposability in terms of dimensions & sub-groups

Data and Measures EU-SILC 2009, 28 countries Dimensions of deprivation: Basic (absence of meal, clothes, leisure activity, home heating, etc) Consumption (PC, car, internet) Health HRP (health status, restricted activities, chronic illness) Neighbourhood environment (presence of litters, pollution, crime/violence etc...) Cronbach’s alpha 0.85 (basic) to 0.64 (neighbourhood env) Use of prevalence weights and normalised score-0(no deprivation) to 1 (deprived all items). At Risk of Poverty (60% median income) Macro variables Gini & Gross Income Per capita

Multidimensional Poverty by Country, EU-SILC 2009

Decomposition of the Adjusted Head Count Ratio by Dimension by Country, EU-SILC 2009 (%)

Adjusted Head Count Ratio by Social Class and Country, EU-SILC 2009 Higher Professional & Managerial Lower Professional & Managerial Intermediate & Lower Supv Small Employer & Self-employ Farmers Lower services & Clerical & technical Routine & Never Worked Norway Netherlands Denmark Germany UK Ireland Italy Greece Czech Republic Estonia Hungary Bulgaria

Adjusted Head Count Ratio by Social Class and Country, EU-SILC 2009

Mean Adjusted Head Count Social Exclusion Ratio by Age Group by Country EU-SILC 2009

Multilevel Analysis of Multidimensional Poverty, EU-SILC 2009 Hierarchical multilevel regressions (AHCR dep variable) a. Empty model (ICC:10.8%) b. Households & HRP characteristics (social class, education...) *Reduc in, country var (1.9%), indiv var (10.6%), tot var (9.2%) c. Macro-economic variables (GNDH & GINI) *GINI not sig * Reduc in, country var (67.9%), indiv var(0%), tot var (16.8%) d. Interaction of b. with GNDH *more pronounced effects of socio-eco disadvantages at lower level of GNDH * Reduc in, country var (71.0%), indiv var(11.7%), tot var (18.2%)

Conclusion (i) Limitations of union & intersection approaches AHCR approach provides a middle ground Censoring central Identifies a non-trivial minority as poor in each country. Size of poor group varies systematically with average income per capita but is not related to Gini Main source of variation head count rather than intensity In less affluent countries basic & consumption deprivation play a more prominent role while in more affluent countries health & income poverty dominate

Conclusion (ii) Systematic variation by socio-economic group. Impact of social class is stronger in low income countries. Age group effects vary by country Limitations of EU Poverty Target Approach. Diversity of profiles captured by EU measure Employing the Alkire & Foster Approach makes it possible that the implications of crucial choices in relation to dimensions, thresholds and weighting can be assessed in a consistent and transparent fashion.