Multidimensional poverty measurement for EU-SILC countries Sabina Alkire, Mauricio Apablaza, Euijin Jung OPHI Seminar, 17 Nov 2014
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 2
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.
Counting-based Identification 1. Select Dimensions, Indicators, Weights, and Cutoffs 1 2. Create deprivation profiles per person 2 3. Identify who is poor e.g. if score > 34% 3
FGT-based Aggregation Poverty measure is the product of two components: M 0 = H A 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.
Measures 1-3: Weighting Structure 1 2 3 AROP Quasi-Joblessness Severe mat. deprivation Education Noise Pollution Crime Housing Health Chronic Illness Morbidity Unmet Med. Needs 6
3. Experimental measures 3 measures constructed Units of identification and of analysis: individuals 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
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
Measures 1-3: Weights & Poverty cutoff k 26% 21% 34% 1 2 3 AROP Quasi-Joblessness Severe mat. deprivation Education Noise Pollution Crime Housing Health Chronic Illness Morbidity Unmet Med. Needs 9
Table 5: Dimensions, Indicators and Weights for Measures (M) 1, 2 and 3 Dimension Variable Respondent is not deprived if: M1 M2 M3 EU 2020 AROP The respondent s equivalized disposable income is 1/12 1/10 1/6 above 60 per cent of the national median 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 10
Dimension Variable Respondent is not deprived if: M1 M2 M3 Education Education The respondent has completed primary 1/4 1/5 1/6 education Environment Noise The respondent lives in a household with low noise from neighbourhood or from the street 1/16 1/20 1/24 Pollution Crime The respondent lives in a household with low pollution, grime or other environmental 1/16 1/20 1/24 problems The respondent lives in a household with low crime, violence or vandalism in the area 1/16 1/20 1/24 Housing The respondent lives in a household with no leaking roof, damp walls, rot in window frames or floor Health Health The respondent considers her own health as fair or above Chronic The respondent has no chronic illness or longterm Illness condition Morbidity The respondent has no limitations due to health Unmet Med. Needs problems The respondent does not report unmet medical needs 1/16 1/20 1/24 1/16 1/20 1/24 1/16 1/20 1/24 1/16 1/20 1/24 1/16 1/20 1/24 11
35 Uncensored Headcount Ratios (%) 30 25 20 15 10 5 0 2006 2012 12
Table 3: Correlations (Cramers V) across uncensored deprivation headcount ratios q-jobless s mat dep education noise pollution crime housing health chr. illness morbidity u.m. needs AROP 0.44 0.45 0.23 0.24 0.16 0.18 0.25 0.23 0.36 0.21 0.23 q-jobless 1.00 0.30 0.19 0.26 0.18 0.20 0.23 0.20 0.45 0.20 0.15 s mat dep 1.00 0.22 0.30 0.22 0.22 0.40 0.23 0.41 0.15 0.20 education 1.00 0.20 0.15 0.13 0.21 0.34 0.48 0.28 0.16 noise 1.00 0.61 0.46 0.32 0.25 0.36 0.25 0.30 pollution 1.00 0.38 0.24 0.19 0.37 0.19 0.23 crime 1.00 0.24 0.17 0.37 0.18 0.20 housing 1.00 0.24 0.37 0.21 0.28 health 1.00 0.91 0.65 0.22 chr illness 1.00 0.93 0.50 morbidity 1.00 0.16 um needs 1.00 13
Table 4: Redundancy values across uncensored deprivation headcount ratios sev. mat chr. u.m. q-jobless education noise pollution crime housing health morbidity dep illness needs AROP 0.27 0.22 0.09 0.03 0.01 0.03 0.1 0.07 0.03 0.05 0.06 q-jobless 1 0.18 0.06 0.04 0.02 0.05 0.07 0.11 0.09 0.1 0.05 sev. mat dep 1 0.07 0.06 0.05 0.06 0.18 0.12 0.05 0.07 0.14 education 1-0.01-0.01-0.01 0.06 0.19 0.14 0.12 0.02 noise 1 0.41 0.25 0.12 0.03 0.04 0.03 0.05 pollution 1 0.25 0.1 0.03 0.05 0.03 0.05 crime 1 0.09 0.03 0.05 0.03 0.05 housing 1 0.07 0.04 0.04 0.08 health 1 0.42 0.55 0.11 chr. illness 1 0.39 0.1 morbidity 1 0.08 u.m. needs 1 Redundancy: ratio of percentage deprived in both indicators to lower of the two total deprivation headcount ratios 14
Figure 2: Adjusted Headcount Ratio (M 0 ) by poverty cut-off 2006-2009-2012 Measure 1 Measure 2 Measure 3 M 0 M 0 M 0 k k k 15 Poverty reduced 2006-12, but not necessarily significantly
Figure 1: Measure 1 Adjusted Headcount Ratio (M0) by poverty cut-off 2006-2009-2012 M0 2006 M0 k 2009 M0 2012 k Southern Europe is always poorest k=1-40%. k 16
Figure 4: Dimensional Breakdown SILC selected countries 2006-2009-2012 Headcount ratio: 4-43% M1 5-39% M2 1-18% M3 17
Figure 5: Dimensional Decomposition Measure 1 k=26% by country (2009) ranked from poorest 18
Figure 6: Dimensional Decomposition Measure 2 k=21% by country (2009), ranked from poorest 19
Figure 7: Dimensional Decomposition Measure 3 k=34% by country (2009), ranked from poorest 20
Figure 8: Raw and Censored Headcount Ratios Measure 3 k=34% for Norway, Hungary and Portugal (2009) 21
Figure 10: Adjusted Headcount Ratio for all Measures by country (2006-2012) Measure 1 k=26% Measure 2 k=21% Measure 3 k=34% AT BE BG CH CY CZ DE DK EE EL ES FI FR HR HU IS IT LT LU LV MT NL NO PL PT RO SE SI SK UK IE 22
Figure 10: Adjusted Headcount Ratio for all Measures by country (2006-2012) Measure 1 k=26% Measure 2 k=21% Measure 3 k=34% Our commentator objected to decreases in poverty: I do not think that the NetSILC2 book can have a chapter that, contrary to other chapters, is showing that poverty decreases during the crisis, at least not without a very good explanation AT BE BG CH CY CZ DE DK EE EL ES FI FR HR HU IS IT LT LU LV MT NL NO PL PT RO SE SI SK UK IE 23
Composition of poverty 2006, 09, 12 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0 1 2 AROP Noise SR Health 3 4 QJ Pollution Chronic 5 6 SMD Crime Limitations 7 8 9 Education Housing Unmet H Needs 24
Figure 11: Poverty contributions by country, population-weighted Measure 1 25
Figure 12: Bubble graph of changes Measure 1 by H and A 2006-2009-2012 26
Figure 13: Multidimensional Poverty (M0) by Measure, Gender and Year 27
Figure 14b: Contributions to National Multidimensional Poverty (M0) by Gender 2012 (Measure 1) 28
Figure 16a: Aggregate Multidimensional Poverty (M0) by Gender and Year Measure 2 Women have higher deprivations overall in education and health 29
Figure 16b: Multidimensional Poverty (M0) by Gender and country Measure 1 (A) Women always have higher deprivations in education and health 30
Figure 16b: Multidimensional Poverty (M0) by Gender and country Measure 1 (B) Here there are exceptions. For ed: DE, SE, IS, and NO. 31
Figure 17a: Percentage contributions to Multidimensional Poverty (M0) by age and year Measure 1 (A) Youth contribution highest in UK; NO 2012; Elder high 32
Figure 17a: Percentage contributions to Multidimensional Poverty (M0) by age and year Measure 1 (B) France has distinctively high elder poverty 65+ 33
Figure 17b: Percentage contributions to Multidimensional Poverty (M0) by Age, Dimension and Year Measure 1 34
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) What is the highest level of school (NAME) has attended? Circle the appropriate ISCED code Pre-school Primary ETC Education YEARS (Adult and child above 5) 1 SKIP YEARS What is the highest grade (NAME) completed at this level?
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.
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
Observations for Individual Unit of Identification If Education indicator is level of completed schooling, it is completely a stock variable, impervious to policy. How capture livelong learning, skills, etc? Subjective health indicators to be avoided, but difficult issues will remain: Health cutoffs for the elderly same or scaled? Some indicators (quasi-worklessness) not so informative in retired households.
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.
Figure 9: Changes in the adjusted headcount ratio M0 by region over time Measure 1 k=26% Measure 2 k=21% M0 Measure 3 k=34% M0 k M0 k k 40