1 / 43

Multidimensional poverty measurement for EU-SILC countries

Multidimensional poverty measurement for EU-SILC countries Sabina Alkire, Mauricio Apablaza, Euijin Jung UNECE meeting, Geneva May 6, 2015. Background Methodology Three possible Measures Results M 0 , H , A Dimensional breakdown Dynamic Analyses Decomposition

burla
Download Presentation

Multidimensional poverty measurement for EU-SILC countries

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Multidimensional poverty measurement for EU-SILC countries Sabina Alkire, Mauricio Apablaza, Euijin Jung UNECE meeting, Geneva May 6, 2015

  2. Background • Methodology • Three possible Measures • Results • M0 , H , A • Dimensional breakdown • Dynamic Analyses • Decomposition • Recommendations for EU-SILC survey

  3. 1. Background • Long tradition of counting measures • Severe Material Deprivation Indicator • EU-2020 • Whelan Nolan Maitre (2014) • This paper: seeks to illustrate the kinds of analyses that could be possible by implementing an AF methodology using limited variables across cross-sectional data 2006-2012.

  4. Counting-based Identification 1 • Select Dimensions, Indicators, Weights, and Cutoffs • Create deprivation profiles per person • Identify who is poor e.g. if score > 34% 2 3

  5. FGT-based Aggregation Poverty measure is the product of two components: 1) Prevalence ~ the percentage of people who are poor, or the headcount ratio H. 2) Intensityof people’s deprivation ~ the average share of dimensions in which poore people are deprived A. M0 = H × A

  6. 3. Experimental measures • 3 measures constructed • Units of identification and of analysis: individual 16+ • Four, Five, and Six Dimensions: • Health • Education • Living Environment • Living Standards (all EU-2020 indicators not below) • Material Deprivation • Quasi Joblessness • Countries aggregated if data covers 6 waves 2006-12

  7. 3. Experimental measures • Indicators: 12 • Same in all measures • Health: 4, Env: 4; Educ: 1, EU-2020: 3 • Weights: Differ for each measure • 1: EU-2020 as one dimension; equal weights • 2: EU-2020 = [AROP + QJ] and [Severe Mat Dep] • 3: EU-2020: one dimension each • Poverty Cutoffs: Strictly more than 1 (1,2) or 2 (3) Ds. • 26% in measure 1, 21% in measure 2; 34% in M 3

  8. Table 5: Dimensions, Indicators and Weights for Measures (M) 1, 2 and 3

  9. Measures 1-3: Weighting Structure

  10. Measures 1-3: Weights & Poverty cutoff k 34% 26% 21%

  11. Table 3: Correlations (Cramers’ V) across uncensored deprivation headcount ratios

  12. Table 4: Redundancy values across uncensored deprivation headcount ratios Redundancy: ratio of percentage deprived in both indicators to lower of the two total deprivation headcount ratios

  13. Figure 2: Adjusted Headcount Ratio (M0) by poverty cut-off 2006-2009-2012 Measure 1 Measure 2 Measure 3 M0 M0 M0 k k k Poverty reduced 2006-12, but not necessarily significantly

  14. Figure 1: Measure 1 Adjusted Headcount Ratio (M­0) by poverty cut-off 2006-2009-2012 2006 2009 2012 M0 M0 M0 k k k Southern Europe is always poorest k=1-40%.

  15. Figure 4: Dimensional Breakdown SILC selected countries 2006-2009-2012 Headcount ratio: 4-43% M1 5-39% M2 1-18% M3

  16. Figure 5: Dimensional Decomposition Measure 1 k=26% by country (2009) ranked from poorest

  17. Figure 6: Dimensional Decomposition Measure 2 k=21% by country (2009), ranked from poorest

  18. Figure 7: Dimensional Decomposition Measure 3 k=34% by country (2009), ranked from poorest

  19. Figure 8: Raw and Censored Headcount Ratios Measure 3 k=34% for Norway, Hungary and Portugal (2009)

  20. Figure 10: Adjusted Headcount Ratio for all Measures by country (2006-2012) Measure 1 k=26% Measure 2 k=21% Measure 3 k=34%

  21. Figure 11: Poverty contributions by country, population-weighted Measure 1

  22. Figure 12: Bubble graph of changes Measure 1 by H and A 2006-2009-2012

  23. Figure 13: Multidimensional Poverty (M0) by Measure, Gender and Year

  24. Figure 14b: Contributions to National Multidimensional Poverty (M0) by Gender 2012 (Measure 1)

  25. Figure 16a: Aggregate Multidimensional Poverty (M0) by Gender and Year Measure 2 Women have higher deprivations overall in education and health

  26. Figure 16b: Multidimensional Poverty (M0) by Gender and country Measure 1 (A) Women always have higher deprivations in education and health

  27. Figure 16b: Multidimensional Poverty (M0) by Gender and country Measure 1 (B) Here there are exceptions. For ed: DE, SE, IS, and NO.

  28. Figure 17a: Percentage contributions to Multidimensional Poverty (M0) by age and year Measure 1 (A) Youth contribution highest in UK; NO 2012; Elder high

  29. Figure 17a: Percentage contributions to Multidimensional Poverty (M0) by age and year Measure 1 (B) France has distinctively high elder poverty 65+

  30. Figure 17b: Percentage contributions to Multidimensional Poverty (M0) by Age, Dimension and Year Measure 1

  31. Recommendations for EU-SILC survey questions • Highest ISCED level of schooling attained : levels do not have the same number of years across countries or; or, at times, across age cohorts or subnational regions. Recommendation: supplement with the number of years of schooling completed, to facilitate comparisons.

  32. Recommendations for EU-SILC survey • Self-Assessed Health: cutoff points may be differently defined according to age, gender, culture, language, health knowledge or aspirations, making comparisons difficult. Recommendation: replace with objective indicators, or with more focused self-report on health functionings (mppn.org) – or health states.

  33. Recommendations for EU-SILC survey • Perception of Crime: responses have been documented to be inversely related to objective incidents of violence. Recommendation: replace with reported violence against person or property in last 12 months and the severity of that violence (mppn.org)

  34. In Summary • Constructs 3 Multidimensional Poverty measures • Report poverty, headcount and intensity • Compares these on aggregate 2006-2012 • Decomposes by regions, countries – across time. • Analyses decomposition by dimension • Analyses changes over time by H and A • Decomposes results by gender • Decomposes results by age category • Recommends gathering comparable social indicators • Purpose: illustrates a measurement methodology and the analyses it can generate.

  35. 1. Background • Changes from previous draft • Three new measures • Changed indicator definitions • Standard errors • Registry data countries included • Proposals for EU-SILC survey design • Comparable questions on Education, Health, and Living Environment.

  36. New: dimensional breakdown The poverty measure is also the sum of the weighted ‘censored headcounts’ of each indicator Censored Headcount for dimension j: The percentage of the population that is identified as poor, and is deprived in indicator j.

  37. 2. Methodology • Select Dimensions, Indicators and Values • Apply Deprivation cutoffs for each indicator • Create weighted deprivation score per person • Apply a poverty cutoff to identify who is poor • Aggregate information about poverty in a measure We use Alkire Foster M0 measure Reflects prevalence (H), intensity (A) Key Properties for analysis:subgroup decomposability, dimensional monotonicity, dimensional breakdown (post-identification), ordinality. Alkire, Sabina and James Foster J. of Public Economics 2011

  38. Figure 3: Headcount ratio and intensity SILC selected countries 2006-2009-2012 Measure 1 k=26% Measure 2 k=21% Measure 3 k=34%

  39. Figure 9: Changes in the adjusted headcount ratio M0 by region over time Measure 1 k=26% Measure 2 k=21% Measure 3 k=34% M0 M0 M0 k k k

  40. Figure 14a: Contributions to National Multidimensional Poverty (M0) by Gender 2006 (Measure 1)

  41. Figure 15: Gender Decomposition of M0 by Country 2006 and 2012 (Measure 3)

More Related