Multidimensional Human Poverty “New Approaches in Poverty Measurement”, Ankara University, Ankara, Turkey, 20 February 2014 Mihail Peleah Human Development Officer, UNDP BRC

1. Multidimensional
Human Poverty
“New Approaches in Poverty Measurement”, Ankara
University, Ankara, Turkey, 20 February 2014
Mihail Peleah
Human Development Officer, UNDP BRC

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http://fotky.sme.sk/fotka/299648/ciganska-osada-muranska-dlha-luka

3. Going beyond monetary poverty:
where?
• Multidimensional
wellbeing
Alkire-Foster Method
• Connecting data
Secondary Sources
Contextualization
• Qualitative and
Quantitative data nexus
‘Micronarratives’
Global MPI
National MPIs
Social Exclusion Index

4. ALKIRE-FOSTER MULTIDIMENSIONAL
POVERTY METHOD

5. Poverty in one dimension
• Selection of welfare
function
• Obtaining welfare function
distribution
• Selecting poverty line
• Calculating poverty
headcount (H)
X%
– plus poverty gap (FGT1),
poverty intensity (FGT2)
– plus sensitivity analysis
Y%
Y$
X$

7. Multidimensional poverty

8. Multidimensional poverty
Headcount H
Average Deprivation among Poor A
MPI = H x A

9. Multidimensional poverty:
setting up
•
•
•
•
Unit of analysis: individual? household?
Select dimensions
Select indicators for dimensions
Double poverty line method: first, deprivation in
each indicator; second, number of simultaneous
deprivations for poverty status
– ‘Deprivation line’ in each dimension
– ‘Poverty line’ for multidimensional poverty
• Weighting
– Within dimension
– Between dimensions

10. OPHI: Missing Dimensions
• Employment
– both formal and informal employment, with particular emphasis on quality of
employment
• Empowerment or Agency
– the ability to advance goals one values or has reason to value
• Physical safety
– Security from violence to property and person, as well as perceived violence
• The ability to go about without shame
– emphasize importance of dignity, respect and freedom from humilation
• Meaning and value
– emphasize meaning, satisfaction and their determinants
http://www.ophi.org.uk

11. MPI: Data Source
• Data hungry—all variables for one household / person
• Existing Survey(s)
– Global MPI: Demographic and Health Surveys, Multiple
Indicator Cluster Surveys, World Health Surveys and
National Surveys.
• New Survey
– Regional Social Exclusion Index: 6 countries
• Combination
– Moldova HBS: Core Questionnaire plus Additional Module
– Ukraine HBS: Additional Modules, once in 1-2 years
• Matching data

12. Multidimensional poverty:
counting
• H – poverty headcount
– Sensitive to frequencies
– Not sensitive to breadth
• A – average share of deprivations
– Sensitive to breadth
• M0=H•A – multidimensional poverty index
– Sensitive to frequencies
– Sensitive to breadth

13. Multiple deprivation
•
Unidimensional
– Variable – income
100
y
•
Multidimensional
– Variables – income, employment,
access to water, …
100 16
250
500
y
125
– Poverty line
z 190
–  Deprivation vector
–  Gap vector
– Headcount ratio – how many are
“poor”
– Gap – how far from poverty line
8
250 40
9
500 36 24
125 38 12
– Poverty lines – first cutoffs
z
190
35 12
–  Deprivation matrix
–  Gap matrix
– Second cut off k
•
•
•
Deprived in one dimension
Deprived in all dimensions
Deprived in more than k dimensions
– H – headcount rate
– A – average breath of deprivation
– M0 – adjusted headcount rate

14. GLOBAL MULTIDIMENSIONAL
POVERTY INDEX

15. Dimensions and Indicators of MPI

16. MPI Poor and Income Poor
OPHI, Multidimensional Poverty Index 2013

17. Changes in MPI: Incidence and Intensity
OPHI, Multidimensional Poverty Index 2013

18. Turkey MPI: Deprivation in each
Indicator
OPHI, Turkey Country Briefing, 2013

19. Turkey MPI: Urban – Rural disparities
OPHI, Turkey Country Briefing, 2013

20. MPI in Turkey Regions

21. Inequalities matter

22. Inequality of Opportunities
matters most
E. Molina et al. Outcomes, Opportunity and Development , WPS 6735

23. MULTIDIMENSIONAL WELLBEING:
SOCIAL EXCLUSION INDEX

24. Why this index?
• Objective and multidimensional
measure of social exclusion
• Measures status of exclusion or non-exclusion,
rather than ‘perceptions’ or ‘risks’
• Applicable for Europe and Central Asia region
• Useful for policymaking

25. Social Exclusion Chain
Individual characteristics
interact
with
Drivers of social exclusion
Structures and institutions; values and
behavior patterns; policies
Drivers are external factors, influenced by
legacies, that either speed up or slow
down the process of individual
vulnerabilities turning into social exclusion
(social exclusion risk factors),
like poor education, disability,
minority status
in context of
Specific local conditions
Predominant industry, single or multiple
employment opportunities, local
infrastructures, history of violent conflict
or environmental disaster
…and result in
Social exclusion status
of the individual in three dimensions—
exclusion from economic life, social
services, and civic and social
participation

26. Construction of index
• Based on Alkire-Foster (2009)
• Exclusion is overlapping deprivations
• 3 areas of exclusion, 24 indicators, 8 per area
–Economic exclusion
–Exclusion from social services
–Exclusion from civic and
political participation
• Threshold is 9 out of 24 possible deprivations, but
other possible thresholds tested

28. Profiles of exclusion

29. Different combinations of individual risks, drivers and
local context produce different levels of social exclusion
If you are young person, with low education, living in village, with single company—you face
high risk of exclusion
…and secondary education doesn’t help much in these conditions…
+
…while vibrant business environment makes a lot of difference
+
…economic centers offer more opportunities (even with low education)
+
+ + +
…and much more if you are educated
Source: RHDR ―Beyond Transition: Toward Inclusive Societies‖, 2011

30. Secondary Source Contextualization
Administrative
datasets
Survey
Statistical
databases

31. Local economy matters

32. Serbia: Social Exclusion and
Health

33. Serbia: Social Exclusion and Education

34. Serbia: Social Exclusion and
Employment

35. QUALITATIVE AND QUANTITATIVE
DATA NEXUS: ‘MICRONARRATIVES’

37. Why the ‘micronarratives’?
• QxQ—combine the best from Qualitative and
Quantitative research
• Zoom in and Zoom out—see the pattern and
investigate a case study
• Suitable for all stage of policy process:
–
–
–
–
Understanding
Planning
Implementation
Monitoring and Evaluation
• Could be a tool of empowerment and trust
building

38. How it works?
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Present
People tell stories
about the topic and tag
them against some
questions
Future
When we get more and more
stories patterns start
emerge
• easy to catch by human eye, hard
to compute
• less sensitive to number of
stories, more sensitive to topic
Patterns and stories help
identify issues, solutions
and actions
and create feedback
loops, involving people in
solutions and monitoring

39. Where we implement it?
•
•
•
•
Montenegro — Environmentally protected areas
Montenegro — Civil service and civilians
Serbia — Roma people
Belarus, Russia, Ukraine — Chernobyl-affected
areas
• Belarus — people with disabilities
• Georgia, Kyrgyzstan — youth perception of
development
• UNDP — internal business processes

40. Cooperation
Competition
Thanks to Borko Vulikic borko.vulikic@undp.org for this case
Corruption

41. Jeeps and Sheeps
• Cluster of stories re:
–
–
–
–
Communities
Revenues
Source of incomes
Limitations of income or business activities
• Dig into the stories
–
–
–
–
Jeep tours were organized to protected areas
…but they raise dust clouds
…which spoil milk, the raw material for cheese
…cheese is major local commodity and source of incomes
• Solution
– Move jeep trail 500 m from village
Thanks to Borko Vulikic borko.vulikic@undp.org for this case

42. Questions for discussion
• Do Turkey need national MPI?
• What are relevant dimensions and indicators?
• What could be a data source for index?

43. Mihail Peleah
Human Development Programme and Research Officer
UNDP Bratislava Regional Center
mihail.peleah@undp.org