by Ben Slay

1. Unpacking inequalities in Europe and Central Asia
Ben Slay, UNDP senior advisor
8 May 2015

2. Global inequality discourse:
Two dominant threads
• “Northern”: OECD countries (Picketty, Stiglitz)
– Impact of trade, financial globalization, demographics,
– Strong links to social inclusion
– Good data, can focus on wealth as well as income
• “Southern”: Developing
country focus (Humanity
Divided)
– Coverage of social
protection/services
– Progressive taxes
– Role of women

3. Neither focus is quite right for our
region’s programme countries
• Post-socialist legacies left well established
systems of social protection, services . . .
– But with growing gaps?
• Position of women better than in other
developing regions . . .
– But is progress being lost?
• Inequalities in our region
do seem to be important
– Apparent in national
consultations
– Maybe because people
aren’t used to them?

4. Regional inequality narratives
• Two common stories:
– Transition economies: “Paradise lost”
• Very low pre-1990 inequalities
• Huge post-1990 increases
• Result: (very) high levels of inequalities
– Turkey: “Traditional developing country profile”
• High levels of income inequality . . .
• . . . That are coming down
• Do the stories hold up? What do the data say?
– Transition economies—Yes, but:
• Choice of base year matters a lot
• Lots of national differences
– Turkey: Yes—but inequalities are still high
• Caveat: Data are imperfect, inconsistent

5. Western CIS, South Caucasus:
Do they fit the profile?
0.1
0.2
0.3
0.4
0.5
1981 1990 1993 1996 1999 2002 2005 2008 2010*
Armenia
Azerbaijan
Belarus
Georgia
Moldova
Ukraine
Income inequality: Gini coefficients
* 2010, or most recent year. Source: POVCALNET (internationally comparable data).

6. Turkey, Western Balkans:
Do they fit the profile?
0.2
0.3
0.4
0.5
1981 1990 1993 1996 1999 2002 2005 2008 2010*
Albania
BiH
FYRoM
Montenegro
Serbia
Turkey
* 2010, or most recent year. Source: POVCALNET (internationally comparable data).
Income inequality: Gini coefficients

7. Central Asia:
Does it fit the profile?
0.2
0.3
0.4
0.5
0.6
1981 1990 1993 1996 1999 2002 2005 2008 2010*
Kazakhstan
Kyrgyzstan
Tajikistan
Income inequality: Gini coefficients
Turkmenistan?
Uzbekistan?
* 2010, or most recent year. Source: POVCALNET (internationally comparable data).

8. Low levels of/reductions in income
inequality can help reduce poverty . . .
0.00
0.05
0.10
0.15
0.20
0.25
0.30
2002 2005 2008 2011
Poverty rate (%)
Gini coefficient
0.3
0.4
0.5
0.6
0.7
0.8
2002 2005 2008 2010
Poverty rate (%)
Gini coefficient
Poverty threshold: PPP$4.30/day. Source: POVCALNET (internationally comparable data).
Belarus Moldova

9. . . . While high/rising income
inequalities can make poverty worse
0.20
0.25
0.30
0.35
0.40
0.45
2002 2005 2008
Poverty rate (%)
Gini coefficient
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
2002 2005 2008 2010
Poverty rate (%)
Gini coefficient
Poverty threshold: PPP$4.30/day. Source: POVCALNET (internationally comparable data).
FYR Macedonia Georgia

10. Income inequality:
Some initial conclusions
• Serious inequality
concerns in:
–FYR Macedonia
–Georgia
–Albania
–Turkey
• Serious data questions
• After initial growth in inequalities (1990s),
many countries make progress

11. Initial conclusions (continued)
• Other countries seem to have been more
successful—Interpretation?
– Statistical anomalies? (Ukraine? Kazakhstan?)
– Do policies matter? (Belarus)
• Pro-poor growth often
goes with reductions in
inequality
• Need to go beyond
income inequality

12. Beyond income inequalities: UNDP’s
Inequality-adjusted HDI
7%
8% 9% 10%
11% 11% 12% 12%
14% 14% 15% 15% 16%
17%
18%
23% 23%
Source: UNDP Human Development Report Office (2012 data).
Human development losses due to inequalities
in per-capita GNI, education, life expectancy

13. Maybe what matters is exclusion?
(Especially from labour markets)
35%
40%
45%
50%
55%
60%
1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
BiH, FYRoM, MNE, SRB
Albania, Turkey
Western CIS
Caucasus
Central Asia
Share of population
aged 15 and above
that is employed
World Bank data, UNDP calculations (unweighted averages). 13

14. . . . Disaggregated by vulnerability
criteria (ethnicity)?
BiH FYRoM Serbia Montenegro Croatia Albania
62%
55%
43%
37% 36%
27%
54%
53%
49%
44%
65%
23%
29%
31%
23%
20%
14% 13%
Youth
Roma
National
Unemployment rates
for youth, Roma
Sources: ILO, national statistical offices, UNDP/EU/World Bank Roma vulnerability database. 2011 data.

15. Other “new poor” (“newly
vulnerable”)—Migrant households
42%
32%
25%
21%
14% 12%
Ratios of remittance
inflows to GDP (2013)
Kyrgyzstan: Income
poverty rates
Sources: National statistical offices, World Bank, IMF, CBR data; UNDP estimates.
2010 2011 2012 2013
34%
37%
38%
37%
40%
43%
45%
44%
W/ remittances
W/out remittances

16. Data review: Some conclusions
– But long lags affect
internationally
comparable income
inequality data
• Reducing income
inequalities matters
for reducing poverty
• Need to go beyond
income inequalities
– Post-2015 indicators
to underpin the SDGs
• Better data needed for many inequality indicators
– Especially for non-income inequalities

17. Dialog on inequalities “takeaways”
• Pluses:
– Strong interest from national, regional partners
– Empirically: income poverty and inequality seem
to move together in programme countries
• Minuses:
– Significant measurement issues:
• Data gaps (quality, quantity)
• Low awareness of new indicators (e.g., Palma ratios)
– How to measure non-income inequalities?
– Except for gender programming, not many
“inequality projects”
– Conflation of inequality, poverty?

18. From regional “Dialog” to “Human
Development Report” on inequalities
• Strengthen
inequalities
programming
• Strengthen UN
regional inequalities
“brand”
– Link to regional social
protection platform? 18
• Better connect region with global inequality
narratives—and vice-versa

19. “Process, not just a publication”
• RHDR to serve as platform for:
– Continuation of UN post-2015
advocacy around inequalities
– Project development
– Dissemination of inequalities-
related content, knowledge
• Strong use of social media,
innovation opportunities
• Inequality-related SDGs
(targets, indicators) to be
cross-cutting thread
• Country case studies included19

20. Programming questions
• “Stand alone” versus “mainstreaming” inequality
programming?
– Gender parallel
– When does the “inequality lens” add value?
• Socio-economic versus spatial inequalities
– When is area-based/regional/local development
programming about reducing (spatial) inequalities?
• Do national data support programming to
address inequalities?
– Could this be new programming area?
– How strong is government interest?
• How to best link to SDGs?

21. Thanks!