Multidimensional poverty measurement for EU-SILC

countries

Sabina Alkire, Mauricio Apablaza, Euijin Jung

UNECE meeting, Geneva May 6, 2015

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1. Background2. Methodology3. Three possible Measures4. Results

a. M0 , H , A

b. Dimensional breakdownc. Dynamic Analysesd. Decomposition

5. Recommendations for EU-SILC survey

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

2. Create deprivation profiles per person

3. Identify who is poor

e.g. if score > 34%

1

2

3

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.

M0 = H × A

3. Experimental measures

3 measures constructed

Units of identification and of analysis: individual 16+

Four, Five, and Six Dimensions:1. Health2. Education3. Living Environment4. Living Standards (all EU-2020 indicators

not below)5. Material Deprivation6. 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

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

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Dimension Variable  Respondent is not deprived if: M1 M2 M3EU 2020 AROP The 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

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Dimension Variable  Respondent is not deprived if: M1 M2 M3Education Education The respondent has completed primary

education 1/4 1/5 1/6

Environment Noise The respondent lives in a household with low noise from neighbourhood or from the street 1/16 1/20 1/24

Pollution The respondent lives in a household with low pollution, grime or other environmental problems

1/16 1/20 1/24

Crime 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

1/16 1/20 1/24

Health 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

Morbidity The 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

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Measures 1-3: Weighting Structure

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Measures 1-3: Weights & Poverty cutoff k

26% 21

%

34%

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Table 3: Correlations (Cramers’ V) across uncensored deprivation

headcount ratios

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q-joble

ss

s mat dep

education

noisepolluti

oncrim

ehousi

nghealth

chr. illness

morbidity

u.m. need

sAROP 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 0.15s 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 0.30pollutio

n 1.00 0.38 0.24 0.19 0.37 0.19 0.23crime 1.00 0.24 0.17 0.37 0.18 0.20housin

g 1.00 0.24 0.37 0.21 0.28health 1.00 0.91 0.65 0.22

chr illness 1.00 0.93 0.50morbid

ity 1.00 0.16um

needs                     1.00

Table 4: Redundancy values across uncensored deprivation headcount

ratios

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q-jobles

s

sev. mat dep

education

noise

pollution

crime

housing

health

chr. illnes

s

morbidity

u.m. need

s

AROP 0.27 0.22 0.09 0.03 0.010.03 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 0.410.25 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 0.05housing 1 0.07 0.04 0.04 0.08health 1 0.42 0.55 0.11chr.

illness 1 0.39 0.1morbidi

ty 1 0.08u.m.

needs                     1

Redundancy: ratio of percentage deprived in both indicators to lower of the two total

deprivation headcount ratios

Figure 2: Adjusted Headcount Ratio (M0) by poverty cut-off 2006-2009-2012

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Measure 1 Measure 2 Measure 3M0 M0 M0

k k k

Poverty reduced 2006-12, but not necessarily significantly

Figure 1: Measure 1 Adjusted Headcount Ratio (M 0) by poverty cut-off

2006-2009-2012

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2006 2009 2012M0 M0 M0

k k k

Southern Europe is always poorest k=1-40%.

Figure 4: Dimensional Breakdown SILC selected countries 2006-2009-2012

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Headcount ratio: 4-43% M1 5-39% M2 1-18% M3

Figure 5: Dimensional Decomposition Measure 1 k=26% by country (2009)

ranked from poorest

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Figure 6: Dimensional Decomposition Measure 2 k=21% by country (2009),

ranked from poorest

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Figure 7: Dimensional Decomposition Measure 3 k=34% by country (2009),

ranked from poorest

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Figure 8: Raw and Censored Headcount Ratios Measure 3 k=34% for Norway,

Hungary and Portugal (2009)

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Figure 10: Adjusted Headcount Ratio for all Measures by country (2006-

2012)

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Measure 1 k=26%

Measure 2 k=21%

Measure 3 k=34%

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

1

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

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

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Figure 14b: Contributions to National Multidimensional Poverty (M0) by Gender

2012 (Measure 1)

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Figure 16a: Aggregate Multidimensional Poverty (M0) by

Gender and Year Measure 2

27Women have higher deprivations overall in education and health

Figure 16b: Multidimensional Poverty (M0) by Gender and country Measure 1

(A)

28Women always have higher deprivations in education and health

Figure 16b: Multidimensional Poverty (M0) by Gender and country Measure 1

(B)

29Here there are exceptions. For ed: DE, SE, IS, and NO.

Figure 17a: Percentage contributions to Multidimensional Poverty (M0) by age and

year Measure 1 (A)

30Youth contribution highest in UK; NO 2012; Elder

high

Figure 17a: Percentage contributions to Multidimensional Poverty (M0) by age and

year Measure 1 (B)

31France has distinctively high elder poverty 65+

Figure 17b: Percentage contributions to Multidimensional Poverty (M0) by Age,

Dimension and Year Measure 1

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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-school 1 SKIP YEARSPrimary ETC

Education YEARS (Adult and child above 5) 

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 activities for a period of time? 

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.

1. Background

Changes from previous draft

Three new measuresChanged indicator definitionsStandard errorsRegistry data countries includedProposals for EU-SILC survey design

Comparable questions on Education, Health, and Living Environment.

New: dimensional breakdownThe 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.

2. Methodology1. 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 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

Figure 3: Headcount ratio and intensity SILC selected countries 2006-2009-

2012

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Measure 1 k=26%

Measure 2 k=21%

Measure 3 k=34%

Figure 9: Changes in the adjusted headcount ratio M0 by region over time

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Measure 1 k=26%

Measure 2 k=21%

Measure 3 k=34%M0 M0 M0

k k k

Figure 14a: Contributions to National Multidimensional Poverty (M0) by Gender

2006 (Measure 1)

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

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