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

2. 2 1. Background 2. Methodology 3. Three possible Measures 4. Results a.M 0, H, A b.Dimensional breakdown c.Dynamic Analyses d.Decomposition 5. 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 2.Create deprivation profiles per person 3.Identify who is poor e.g. if score > 34% 1 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) Intensity of people’s deprivation ~ the average share of dimensions in which poore people are deprived A. M 0 = H × A

6. 3. Experimental measures  3 measures constructed  Units of identification and of analysis: individual 16+  Four, Five, and Six Dimensions: 1.Health 2.Education 3.Living Environment 4.Living Standards (all EU-2020 indicators not below) 5.Material Deprivation 6.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 8 DimensionVariable Respondent is not deprived if:M1M2M3 EU 2020AROPThe respondent’s equivalized disposable income is above 60 per cent of the national median 1/12 1/10 1/6 Quasi- Joblessness The respondent lives in household where the ratio of the total number of months that all - household members aged 16-59 have worked during the income reference year and the total number of months the same household members theoretically could have worked in the same period is higher than 0.2 1/12 1/10 1/6 Severe material deprivation The respondent has at least six of the following: the ability to make ends meet; to afford one week of holidays; a meal with meat, chicken, fish or vegi equivalent; to face unexpected expenses; and, to keep home adequately warm. Or the respondent has a car, a colour TV, a washing machine, and a telephone. 1/12 1/5 1/6

9. 9 DimensionVariable Respondent is not deprived if:M1M2M3 Education The respondent has completed primary education 1/4 1/5 1/6 EnvironmentNoiseThe respondent lives in a household with low noise from neighbourhood or from the street 1/16 1/20 1/24 PollutionThe respondent lives in a household with low pollution, grime or other environmental problems 1/16 1/20 1/24 CrimeThe respondent lives in a household with low crime, violence or vandalism in the area 1/16 1/20 1/24 HousingThe respondent lives in a household with no leaking roof, damp walls, rot in window frames or floor 1/16 1/20 1/24 Health The respondent considers her own health as fair or above 1/16 1/20 1/24 Chronic Illness The respondent has no chronic illness or long- term condition 1/16 1/20 1/24 MorbidityThe respondent has no limitations due to health problems 1/16 1/20 1/24 Unmet Med. Needs The respondent does not report unmet medical needs 1/16 1/20 1/24

10. 10 Measures 1-3: Weighting Structure

11. 11 Measures 1-3: Weights & Poverty cutoff k 26% 21% 34%

12. 12

13. Table 3: Correlations (Cramers’ V) across uncensored deprivation headcount ratios 13 q-joblesss mat depeducationnoisepollutioncrimehousinghealthchr. illnessmorbidity u.m. needs AROP0.440.450.230.240.160.180.250.230.360.210.23 q-jobless1.000.300.190.260.180.200.230.200.450.200.15 s mat dep1.000.220.300.22 0.400.230.410.150.20 education1.000.200.150.130.210.340.480.280.16 noise1.000.610.460.320.250.360.250.30 pollution1.000.380.240.190.370.190.23 crime1.000.240.170.370.180.20 housing1.000.240.370.210.28 health1.000.910.650.22 chr illness1.000.930.50 morbidity1.000.16 um needs 1.00

14. Table 4: Redundancy values across uncensored deprivation headcount ratios 14 q-jobless sev. mat dep educationnoisepollutioncrimehousinghealth chr. illness morbidity u.m. needs AROP0.270.220.090.030.010.030.10.070.030.050.06 q-jobless10.180.060.040.020.050.070.110.090.10.05 sev. mat dep10.070.060.050.060.180.120.050.070.14 education1-0.01 0.060.190.140.120.02 noise10.410.250.120.030.040.030.05 pollution10.250.10.030.050.030.05 crime10.090.030.050.030.05 housing10.070.04 0.08 health10.420.550.11 chr. illness10.390.1 morbidity10.08 u.m. needs 1 Redundancy: ratio of percentage deprived in both indicators to lower of the two total deprivation headcount ratios

15. Figure 2: Adjusted Headcount Ratio (M 0 ) by poverty cut-off 2006-2009-2012 15 Measure 1 Measure 2 Measure 3 M0M0 M0M0 M0M0 kk k Poverty reduced 2006-12, but not necessarily significantly

16. Figure 1: Measure 1 Adjusted Headcount Ratio (M­ 0 ) by poverty cut-off 2006-2009-2012 16 2006 2009 2012 M0M0 M0M0 M0M0 k k k Southern Europe is always poorest k=1-40%.

17. Figure 4: Dimensional Breakdown SILC selected countries 2006-2009-2012 17 Headcount ratio: 4-43% M1 5-39% M2 1-18% M3

18. Figure 5: Dimensional Decomposition Measure 1 k=26% by country (2009) ranked from poorest 18

19. Figure 6: Dimensional Decomposition Measure 2 k=21% by country (2009), ranked from poorest 19

20. Figure 7: Dimensional Decomposition Measure 3 k=34% by country (2009), ranked from poorest 20

21. Figure 8: Raw and Censored Headcount Ratios Measure 3 k=34% for Norway, Hungary and Portugal (2009) 21

22. Figure 10: Adjusted Headcount Ratio for all Measures by country (2006-2012) 22 Measure 1 k=26%Measure 2 k=21%Measure 3 k=34%

23. Figure 11: Poverty contributions by country, population-weighted Measure 1 23

24. Figure 12: Bubble graph of changes Measure 1 by H and A 2006-2009-2012 24

25. Figure 13: Multidimensional Poverty (M 0 ) by Measure, Gender and Year 25

26. Figure 14b: Contributions to National Multidimensional Poverty (M 0 ) by Gender 2012 (Measure 1) 26

27. Figure 16a: Aggregate Multidimensional Poverty (M 0 ) by Gender and Year Measure 2 27 Women have higher deprivations overall in education and health

28. Figure 16b: Multidimensional Poverty (M 0 ) by Gender and country Measure 1 (A) 28 Women always have higher deprivations in education and health

29. Figure 16b: Multidimensional Poverty (M 0 ) by Gender and country Measure 1 (B) 29 Here there are exceptions. For ed: DE, SE, IS, and NO.

30. Figure 17a: Percentage contributions to Multidimensional Poverty (M 0 ) by age and year Measure 1 (A) 30 Youth contribution highest in UK; NO 2012; Elder high

31. Figure 17a: Percentage contributions to Multidimensional Poverty (M 0 ) by age and year Measure 1 (B) 31 France has distinctively high elder poverty 65+

32. Figure 17b: Percentage contributions to Multidimensional Poverty (M 0 ) by Age, Dimension and Year Measure 1 32

33. 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. Education LEVEL (Adult and Child above 5) Circle the appropriate ISCED code What is the highest level of school (NAME) has attended? Pre-school1  SKIP YEARS Primary ETC Education YEARS (Adult and child above 5) What is the highest grade (NAME) completed at this level?

34. 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.

35. 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) PROPERTY In the last 12 months, did someone steal or try to steal something you or a member of your household owns, whether it was in your dwelling, or was outside (like vehicles), or whether it damaged your home or property? How many times in the last year did this happen? What is the value of the property that was stolen or damaged? PERSON In the past year, were you or a member of your household attacked or forcibly assaulted whether without any weapon, or whether by someone with a gun, knife, bomb or another instrument? This may have occurred inside or outside your home. How many times in the last year did this happen? Did anyone die in any of these incidents? In the worst incident were you or anyone else seriously injured and could not continue their normal activities for a period of time?

36. 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.

37. 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.

38. 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.

39. 2. Methodology 1.Select Dimensions, Indicators and Values 2.Apply Deprivation cutoffs for each indicator 3.Create weighted deprivation score per person 4.Apply a poverty cutoff to identify who is poor 5.Aggregate information about poverty in a measure We use Alkire Foster M 0 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

40. Figure 3: Headcount ratio and intensity SILC selected countries 2006-2009-2012 40 Measure 1 k=26%Measure 2 k=21%Measure 3 k=34%

41. Figure 9: Changes in the adjusted headcount ratio M 0 by region over time 41 Measure 1 k=26%Measure 2 k=21%Measure 3 k=34% M0M0 M0M0 M0M0 k k k

42. Figure 14a: Contributions to National Multidimensional Poverty (M 0 ) by Gender 2006 (Measure 1) 42

43. Figure 15: Gender Decomposition of M 0 by Country 2006 and 2012 (Measure 3) 43