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Multidimensional Poverty Measurement in Europe: An Application of the Adjusted Headcount Approach

Multidimensional Poverty Measurement in Europe: An Application of the Adjusted Headcount Approach. 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

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Multidimensional Poverty Measurement in Europe: An Application of the Adjusted Headcount Approach

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  1. Multidimensional Poverty Measurement in Europe: An Application of the Adjusted Headcount Approach 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. Multidimensional Poverty by Country, EU-SILC 2009

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

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

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

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

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

  13. 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

  14. 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.

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