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Data to capture those at risk of exclusion Mihail Peleah UNDP Istanbul Regional Hub Promoting Job Rich and Sustainable Growth in Armenia, Azerbaijan, Belarus, Georgia, Moldova and Ukraine, Tbilisi, Georgia, 29 October 2015

Data to capture those at risk of exclusion

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Page 1: Data to capture those at risk of exclusion

Data to capture those at risk of exclusion

Mihail PeleahUNDP Istanbul Regional Hub

Promoting Job Rich and Sustainable Growth in Armenia,

Azerbaijan, Belarus, Georgia, Moldova and Ukraine,

Tbilisi, Georgia, 29 October 2015

Page 2: Data to capture those at risk of exclusion

Essentially, all models are wrong, but some are useful.

(George E. P. Box)

Page 3: Data to capture those at risk of exclusion

Income Inequality Gini 32 Income Inequality Gini 33

Social inequality 29 Social inequality 7

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Availability – Status – Perception

Ukraine, 2010

63.7

8.124.6

Page 5: Data to capture those at risk of exclusion

Azerbaijan Armenia Belarus Georgia Moldova Ukraine Singapore0

10

20

30

40

50

60Income inequalityAverage call for income equalityActive call for income equality

Page 6: Data to capture those at risk of exclusion

Own calculations based on Regional Human Development Report, 2011

Lee Kwan Yew “[our goal is] to give every citizen a stake in the country and its future.”

Page 7: Data to capture those at risk of exclusion
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Drivers of social exclusionSpecific local conditions

Social exclusion status

Individual characteristics

interact with

in context of

…and result inStructures and institutions; values and behavior patterns; policiesDrivers 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

Predominant industry, single or multiple employment opportunities, local infrastructures, history of violent conflict or environmental disaster

of the individual in three dimensions—exclusion from economic life, social services, and civic and social participation

Social Exclusion Chain

Page 9: Data to capture those at risk of exclusion

Different combinations of individual risks and local context results in different levels of social exclusion

Average

Capital or economic center

Small town

Village

Average risk of exclusion in the region hides significant territorial differences…

Individual vulnerabilities (like disability) interact with local

conditions and amplify exclusion

Disabled doesn’t mean automatically excluded!

Local conditions matter

Source: RHDR “Beyond Transition: Toward Inclusive Societies”, 2011

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Corruption: Supply and Tolerance

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Attitudes matter

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Cycles of exclusion

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Secondary Source Contextualization

Survey

Administrative datasets

Statistical databases

Page 18: Data to capture those at risk of exclusion

Local economy matters

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Serbia: Social Exclusion andHealth

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Serbia: Social Exclusion and Education

Page 21: Data to capture those at risk of exclusion

Serbia: Social Exclusion and Employment

Page 22: Data to capture those at risk of exclusion

Different combinations of individual risks, drivers and local context results in 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

Inclusion gain due to education

Inclusion gain due to business environment

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Thanks to Borko Vulikic [email protected] for this case

Cooperation

Competition Corruption

Page 25: Data to capture those at risk of exclusion

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 [email protected] for this case

Page 26: Data to capture those at risk of exclusion

Mihail PeleahUNDP Istanbul Regional [email protected]

Thank you!