Unpacking Inequalities in Europe and Central Asia

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’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?

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

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

• Serious inequality concerns in:– FYR Macedonia–Georgia–Albania– Turkey

• Serious data questions• After initial growth in inequalities (1990s),

many countries make progress

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

Beyond income inequalities: UNDP’s Inequality-adjusted HDI

Montenegro

Belarus

Ukraine

Serbia

Armenia

Azerbaijan BiH

Moldova

Kazakh

stan

Albania

FYRoM

Georgia

Uzbekis

tan

Kyrgyzs

tan

Tajikis

tanTurke

yWorld

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%

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 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?

• 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 included

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!