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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
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
ALKIRE-FOSTER MULTIDIMENSIONAL
POVERTY METHOD
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$
Multidimensional poverty
Multidimensional poverty

Headcount H

Average Deprivation among Poor A

MPI = H x A
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
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
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
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
Multiple deprivation
•

Unidimensional
– Variable – income
100

•

Multidimensional

– Variables – income, employment,
access to water, …
100

y

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

36

24

125

500

16

500

250

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
GLOBAL MULTIDIMENSIONAL
POVERTY INDEX
Dimensions and Indicators of MPI
MPI Poor and Income Poor

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

OPHI, Multidimensional Poverty Index 2013
Turkey MPI: Deprivation in each
Indicator

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

OPHI, Turkey Country Briefing, 2013
MPI in Turkey Regions
Inequalities matter
Inequality of Opportunities
matters most

E. Molina et al. Outcomes, Opportunity and Development , WPS 6735
MULTIDIMENSIONAL WELLBEING:
SOCIAL EXCLUSION INDEX
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
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
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
Profiles of exclusion
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
Secondary Source Contextualization

Administrative
datasets

Survey

Statistical
databases
Local economy matters
Serbia: Social Exclusion and
Health
Serbia: Social Exclusion and Education
Serbia: Social Exclusion and
Employment
QUALITATIVE AND QUANTITATIVE
DATA NEXUS: ‘MICRONARRATIVES’
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
How it works?
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libero condimentum
condimentum ut at
neque.

Past

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pellentesque porttitor
lacus, eget venenatis
ipsum eleifend sit
amet.

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
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
Cooperation

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

Corruption
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
Questions for discussion
• Do Turkey need national MPI?
• What are relevant dimensions and indicators?
• What could be a data source for index?
Mihail Peleah
Human Development Programme and Research Officer
UNDP Bratislava Regional Center
mihail.peleah@undp.org

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Measuring Poverty Beyond Income

  • 1. Multidimensional Human Poverty “New Approaches in Poverty Measurement”, Ankara University, Ankara, Turkey, 20 February 2014 Mihail Peleah Human Development Officer, UNDP BRC
  • 2. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vivamus quis sem gravida, viverra tortor non, dignissim quam. Mauris ullamcorper suscipit vestibulum. Pellentesque commodo turpis at viverra pharetra. Nullam molestie quam sed mattis egestas. Vivamus justo sapien, fermentum et pulvinar id, scelerisque at nulla. 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
  • 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$
  • 6.
  • 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 • Multidimensional – Variables – income, employment, access to water, … 100 y 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 36 24 125 500 16 500 250 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
  • 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
  • 22. Inequality of Opportunities matters most E. Molina et al. Outcomes, Opportunity and Development , WPS 6735
  • 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
  • 27.
  • 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
  • 33. Serbia: Social Exclusion and Education
  • 34. Serbia: Social Exclusion and Employment
  • 35. QUALITATIVE AND QUANTITATIVE DATA NEXUS: ‘MICRONARRATIVES’
  • 36.
  • 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? Vestibulum nec libero at libero condimentum condimentum ut at neque. Past Maecenas pellentesque porttitor lacus, eget venenatis ipsum eleifend sit amet. 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