Unpacking inequalities in Europe and Central Asia

Ben Slay, UNDP senior advisor

8 May 2015

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

Neither focus is quite right for our region

• 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?

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?

1981 1990 1993 1996 1999 2002 2005 2008 2010*0.1

0.2

0.3

0.4

0.5

Armenia

Azerbaijan

Belarus

Georgia

Moldova

UkraineIncome inequality: Gini coefficients

* 2010, or most recent year. Source: POVCALNET (internationally comparable data).

Turkey, Western Balkans: Do they fit the profile?

1981 1990 1993 1996 1999 2002 2005 2008 2010*0.2

0.3

0.4

0.5

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?

1981 1990 1993 1996 1999 2002 2005 2008 2010*0.2

0.3

0.4

0.5

0.6Kazakhstan

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 . . .

2002 2005 2008 2011-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Poverty rate (%)

Gini coefficient

2002 2005 2008 20100.3

0.4

0.5

0.6

0.7

0.8

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

2002 2005 20080.20

0.25

0.30

0.35

0.40

0.45

Poverty rate (%)

Gini coefficient

2002 2005 2008 20100.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

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

Montenegro

Belarus

Ukraine

Serb

ia

Armenia

Azerb

aijan BiH

Moldova

Kazakh

stan

Albania

FYRoM

Georgia

Uzbekis

tan

Kyrgyzs

tan

Tajikis

tan

Turkey

World

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

12

Maybe what matters is exclusion? (Especially from labour markets)

35%

40%

45%

50%

55%

60%

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).

. . . 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/ remittancesW/out remittances

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

Dialog on inequalities “takeaways”• Pluses:– Strong interest from national, regional partners– Empirically: income poverty and inequality move

together in our 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?

17

From “Dialog on Inequalities”to “Inequalities RHDR”

• Strengthen regional, national programming in inequalities

• Build a UN(DP) regional inequalities “brand”

• Better connect region with global inequality narratives—and vice-versa

18

“Process, not just a publication”• RHDR to serve as platform for: – Project development– Dissemination of inequalities-

related content, knowledge• Strong use of social media, innovation

opportunities

• Inequality-related SDGs (especially targets, indicators) to be cross-cutting thread

• Country case studies included– Co-financed by COs, IRH– Expressions of interest received to

date from 8 COs

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?

Thanks!