income inequalities and beyond in europe and central asia
TRANSCRIPT
Dialogue on Inequalities 21-22 January 2015 - Istanbul, Turkey
Income Inequalities and Beyond
In Europe and Central Asia
Ben Slay
UNDP Senior Advisor
What’s this presentation about?
– Income inequalities– Non-income inequalities
• What don’t they show?• Some conclusions
– Inequalities have risen, but still relatively low
– They need to be: • Disaggregated• Monitored
– Some countries are of particular concern
• What do the regional inequality data show?
Income inequality: What do the regional data show?
• 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?– 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
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).
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
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).
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
. . . 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
Income inequality: Some initial conclusions
– FYR Macedonia– Georgia– Albania– Turkey
• Other countries seem to have been more successful– Statistical anomalies? – Or do policies matter?
• Pro-poor growth often goes with reductions in inequality
• Need to go beyond income inequality
• Serious data questions
• Inequality concerns seem particularly pressing in:
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
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). 11
. . . 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.
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
Some conclusions
– But long lags affect even internationally comparable income inequality data
• Reducing income inequalities seems to matter for reducing poverty
• Need to go beyond income inequalities– Post-2015 indicators to
underpin the SDGs
• Better data are needed for many inequality indicators– Especially for non-income inequalities
Dialogue on Inequalities 21-22 January 2015 - Istanbul, Turkey
Thank you very much!