Multidimensional poverty measurement for
EU-SILC countries
Sabina Alkire, Mauricio Apablaza, Euijin Jung
UNECE meeting, Geneva May 6, 2015
1. Background
2. Methodology
3. Three possible Measures
4. Results
a. M0 , H , A
b. Dimensional breakdown
c. Dynamic Analyses
d. Decomposition
5. Recommendations for EU-SILC survey
2
1. Background
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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
1. Select Dimensions, Indicators,
Weights, and Cutoffs
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:
M0 = 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.
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
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
Dimension Variable
EU 2020 AROP
Respondent is not deprived if:
M1 M2 M3
The respondent’s equivalized disposable income is
1/12 1/10 1/6
above 60 per cent of the national median
QuasiThe respondent lives in household where the ratio of
Joblessness 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
1/12 1/10 1/6
same household members theoretically could have
worked in the same period is higher than 0.2
Severe
The respondent has at least six of the following: the
material
ability to make ends meet; to afford one week of
deprivation 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
8
Dimension
Education
Variable
Education
Environment Noise
Pollution
Crime
Respondent is not deprived if:
The respondent has completed primary
education
The respondent lives in a household with low
noise from neighbourhood or from the street
The respondent lives in a household with low
pollution, grime or other environmental
problems
The respondent lives in a household with low
crime, violence or vandalism in the area
M2
M3
1/4
1/5
1/6
1/16 1/20 1/24
1/16 1/20 1/24
1/16 1/20 1/24
Housing
Health
The respondent lives in a household with no
leaking roof, damp walls, rot in window frames
or floor
Health
The respondent considers her own health as fair
or above
Chronic
The respondent has no chronic illness or longIllness
term condition
Morbidity The respondent has no limitations due to health
problems
Unmet Med. The respondent does not report unmet medical
Needs
needs
M1
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
9
Measures 1-3: Weighting Structure
10
Measures 1-3: Weights & Poverty cutoff k
34%
26%
21%
11
12
Table 3: Correlations (Cramers’ V) across uncensored
deprivation headcount ratios
u.m.
needs
0.23
0.15
0.20
0.16
0.30
0.23
0.20
0.28
0.22
0.50
0.16
1.00
q-jobless s mat dep education noise pollution crime housing health chr. illness morbidity
AROP 0.44
q-jobless 1.00
s mat dep
education
noise
pollution
crime
housing
health
chr illness
morbidity
um needs
0.45
0.30
1.00
0.23
0.19
0.22
1.00
0.24
0.26
0.30
0.20
1.00
0.16
0.18
0.22
0.15
0.61
1.00
0.18
0.20
0.22
0.13
0.46
0.38
1.00
0.25
0.23
0.40
0.21
0.32
0.24
0.24
1.00
0.23
0.20
0.23
0.34
0.25
0.19
0.17
0.24
1.00
0.36
0.45
0.41
0.48
0.36
0.37
0.37
0.37
0.91
1.00
0.21
0.20
0.15
0.28
0.25
0.19
0.18
0.21
0.65
0.93
1.00
13
Table 4: Redundancy values across uncensored
deprivation headcount ratios
q-jobless
AROP
0.27
q-jobless
1
sev. mat dep
education
noise
pollution
crime
housing
health
chr. illness
morbidity
u.m. needs
sev. mat
education noise pollution crime
dep
0.22
0.09 0.03 0.01 0.03
0.18
0.06 0.04 0.02 0.05
1
0.07 0.06 0.05 0.06
1
-0.01 -0.01 -0.01
1
0.41 0.25
1
0.25
1
housing health
0.1
0.07
0.18
0.06
0.12
0.1
0.09
1
0.07
0.11
0.12
0.19
0.03
0.03
0.03
0.07
1
chr.
illness
0.03
0.09
0.05
0.14
0.04
0.05
0.05
0.04
0.42
1
morbidity
0.05
0.1
0.07
0.12
0.03
0.03
0.03
0.04
0.55
0.39
1
u.m.
needs
0.06
0.05
0.14
0.02
0.05
0.05
0.05
0.08
0.11
0.1
0.08
1
Redundancy: ratio of percentage deprived in both indicators to
lower of the two total deprivation headcount ratios
14
Figure 2: Adjusted Headcount Ratio (M0) by poverty
cut-off 2006-2009-2012
Measure 1
Measure 2
M0
Measure 3
M0
M0
k
k
Poverty reduced 2006-12, but not necessarily significantly15
k
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
0.18
Figure 10: Adjusted Headcount Ratio for all
Measures by country (2006-2012)
0.16
0.14
Measure 1 k=26%
Measure 2 k=21%
Measure 3 k=34%
0.12
0.10
0.08
0.06
0.04
0.02
0.00
2006
2007
2008
2009
2010
2011
2012
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 11: Poverty contributions by country,
population-weighted Measure 1
23
Figure 12: Bubble graph of changes Measure 1 by H
and A 2006-2009-2012
24
Figure 13: Multidimensional Poverty (M0) by
Measure, Gender and Year
25
Figure 14b: Contributions to National Multidimensional
Poverty (M0) by Gender 2012 (Measure 1)
26
Figure 16a: Aggregate Multidimensional Poverty
(M0) by Gender and Year Measure 2
Women have higher deprivations overall in education and health
27
Figure 16b: Multidimensional Poverty (M0) by
Gender and country Measure 1 (A)
Women always have higher deprivations in education and health
28
Figure 16b: Multidimensional Poverty (M0) by
Gender and country Measure 1 (B)
Here there are exceptions. For ed: DE, SE, IS, and NO.
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Figure 17a: Percentage contributions to Multidimensional
Poverty (M0) by age and year Measure 1 (A)
Youth contribution highest in UK; NO 2012; Elder high
30
Figure 17a: Percentage contributions to Multidimensional
Poverty (M0) by age and year Measure 1 (B)
France has distinctively high elder poverty 65+
31
Figure 17b: Percentage contributions to Multidimensional
Poverty (M0) by Age, Dimension and Year Measure 1
32
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
In Summary
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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
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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.
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.
2. Methodology
1.
2.
3.
4.
5.
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
Figure 3: Headcount ratio and intensity SILC
selected countries 2006-2009-2012
Measure 1 k=26%
Measure 2 k=21%
Measure 3 k=34%
40
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
41
Figure 14a: Contributions to National Multidimensional
Poverty (M0) by Gender 2006 (Measure 1)
42
Figure 15: Gender Decomposition of M0 by Country
2006 and 2012 (Measure 3)
43