Multidimensional
Human Poverty
“New Approaches in Poverty Measurement”, Ankara
University, Ankara, Turkey, 20 February 201...
Lorem ipsum dolor sit amet,
consectetur adipiscing elit. Vivamus
quis sem gravida, viverra tortor non,
dignissim quam. Mau...
Going beyond monetary poverty:
where?
• Multidimensional
wellbeing

Alkire-Foster Method

• Connecting data

Secondary S...
ALKIRE-FOSTER MULTIDIMENSIONAL
POVERTY METHOD
Poverty in one dimension
• Selection of welfare
function
• Obtaining welfare function
distribution
• Selecting poverty lin...
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...
OPHI: Missing Dimensions
• Employment
– both formal and informal employment, with particular emphasis on quality of
employ...
MPI: Data Source
• Data hungry—all variables for one household / person
• Existing Survey(s)
– Global MPI: Demographic and...
Multidimensional poverty:
counting
• H – poverty headcount
– Sensitive to frequencies
– Not sensitive to breadth

• A – av...
Multiple deprivation
•

Unidimensional
– Variable – income

100
y

•

Multidimensional

– Variables – income, employment,
...
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-exclusi...
Social Exclusion Chain
Individual characteristics

interact
with
Drivers of social exclusion
Structures and institutions; ...
Construction of index
• Based on Alkire-Foster (2009)
• Exclusion is overlapping deprivations
• 3 areas of exclusion, 24 i...
Profiles of exclusion
Different combinations of individual risks, drivers and
local context produce different levels of social exclusion
If you ...
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 t...
How it works?
Vestibulum nec libero at
libero condimentum
condimentum ut at
neque.

Past

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pellentesque porttitor
...
Where we implement it?
•
•
•
•

Montenegro — Environmentally protected areas
Montenegro — Civil service and civilians
Serb...
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 busines...
Questions for discussion
• Do Turkey need national MPI?
• What are relevant dimensions and indicators?
• What could be a d...
Mihail Peleah
Human Development Programme and Research Officer
UNDP Bratislava Regional Center
mihail.peleah@undp.org
Multidimensional Human Poverty
Multidimensional Human Poverty
Multidimensional Human Poverty
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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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Multidimensional Human Poverty

  1. 1. Multidimensional Human Poverty “New Approaches in Poverty Measurement”, Ankara University, Ankara, Turkey, 20 February 2014 Mihail Peleah Human Development Officer, UNDP BRC
  2. 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. 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. 4. ALKIRE-FOSTER MULTIDIMENSIONAL POVERTY METHOD
  5. 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. 6. Multidimensional poverty
  7. 7. Multidimensional poverty Headcount H Average Deprivation among Poor A MPI = H x A
  8. 8. 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
  9. 9. 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
  10. 10. 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
  11. 11. 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
  12. 12. 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
  13. 13. GLOBAL MULTIDIMENSIONAL POVERTY INDEX
  14. 14. Dimensions and Indicators of MPI
  15. 15. MPI Poor and Income Poor OPHI, Multidimensional Poverty Index 2013
  16. 16. Changes in MPI: Incidence and Intensity OPHI, Multidimensional Poverty Index 2013
  17. 17. Turkey MPI: Deprivation in each Indicator OPHI, Turkey Country Briefing, 2013
  18. 18. Turkey MPI: Urban – Rural disparities OPHI, Turkey Country Briefing, 2013
  19. 19. MPI in Turkey Regions
  20. 20. Inequalities matter
  21. 21. Inequality of Opportunities matters most E. Molina et al. Outcomes, Opportunity and Development , WPS 6735
  22. 22. MULTIDIMENSIONAL WELLBEING: SOCIAL EXCLUSION INDEX
  23. 23. 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
  24. 24. 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
  25. 25. 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
  26. 26. Profiles of exclusion
  27. 27. 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
  28. 28. Secondary Source Contextualization Administrative datasets Survey Statistical databases
  29. 29. Local economy matters
  30. 30. Serbia: Social Exclusion and Health
  31. 31. Serbia: Social Exclusion and Education
  32. 32. Serbia: Social Exclusion and Employment
  33. 33. QUALITATIVE AND QUANTITATIVE DATA NEXUS: ‘MICRONARRATIVES’
  34. 34. 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
  35. 35. 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
  36. 36. 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
  37. 37. Cooperation Competition Thanks to Borko Vulikic borko.vulikic@undp.org for this case Corruption
  38. 38. 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
  39. 39. Questions for discussion • Do Turkey need national MPI? • What are relevant dimensions and indicators? • What could be a data source for index?
  40. 40. Mihail Peleah Human Development Programme and Research Officer UNDP Bratislava Regional Center mihail.peleah@undp.org

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