1

Aid and State Formation in Africa: What the Rich World Cannot DoODI, London, May 22, 2006

Nancy BirdsallPresidentCenter for Global DevelopmentWashington, D.C.

2

Outline

Part I: The donors’ dilemma: three decades of massive aid to still-poor countries, mostly in SSA

Part II: The IPT and the aid-institutions paradox: aid is not helping and may even by hurting

Part III: What donors can and cannot do about poverty and state failure in SSA

3

Part I: The donors’ dilemma

A large set of countries remains poor (20% of more of the population living on a $1 a day or less)

And have received massive amounts of aid(10% of GDP or more)

Aid intensity varies among these poor countries,but most are in Sub-Saharan Africa

O An “institutional poverty” trap?

4

Net official development assistance

Poverty headcount ratio at $1 a day

(% of population, PPP)

Real GDP per capita

2003 1996-20021 2002Bangladesh 3 36 382Burkina Faso 11 45 243Burundi 38 55 103Cambodia 12 34 303El Salvador 1 31 2,128Ethiopia 23 23 109Ghana 12 45 267Honduras 6 21 922India <1 35 478Kenya 3 23 341Lao PDR 14 26 343Lesotho 7 36 518Madagascar 10 61 218Malawi 29 42 154Mauritania 22 26 363Moldova 5 22 346Mongolia 20 27 406Mozambique 24 38 243Nepal 8 39 239Nicaragua 20 45 769Niger 17 61 174Rwanda 20 52 259Senegal 7 22 467Uganda 15 85 271Zambia 13 64 342Notes:1. Latest year available for the period 1996-2002.

Source: WDI (2005).

Countries with 20% or more of the population living on $1 a day or less

2. Data on the share of the population living on $1 a day or less is unavailable for 16 low-income Sub-Saharan African countries: Benin, Mali, CAF, Chad, Congo, Republic, Cote d'Ivoire, Eritrea, Guinea, Sierra Leone, Tanzania, Togo, Congo, Dem. Republic, Sudan, Liberia and Somalia.

5

Aid dependency varies among these poor countries,but most are in Sub-Saharan Africa

Net official development assistance greater than 10% of GDP

Share of population living on less than $1 a day greater than 20%

Real GDP per capita $1000 or less

2003 1996-20021 2002Bangladesh No Yes YesBurkina Faso Yes Yes YesBurundi Yes Yes YesCambodia Yes Yes YesEl Salvador No Yes NoEthiopia Yes Yes YesGhana Yes Yes YesHonduras No Yes YesIndia No Yes YesKenya No Yes YesLao PDR Yes Yes YesLesotho No Yes YesMadagascar Yes Yes YesMalawi Yes Yes YesMauritania Yes Yes YesMoldova No Yes YesMongolia Yes Yes YesMozambique Yes Yes YesNepal No Yes YesNicaragua Yes Yes YesNiger Yes Yes YesRwanda Yes Yes YesSenegal No Yes YesUganda Yes Yes YesZambia Yes Yes YesNotes:1. Latest year available for the period 1996-2002.

Source: WDI (2005).

Countries with 20% or more of the population living on $1 a day or less

2. Data on the share of the population living on $1 a day or less is unavailable for 16 low-income Sub-Saharan African countries: Benin, Mali, CAF, Chad, Congo, Republic, Cote d'Ivoire, Eritrea, Guinea, Sierra Leone, Tanzania, Togo, Congo, Dem. Republic, Sudan, Liberia and Somalia.3. Sub-Saharan African countries in bold.

6

Defining the institutional poverty trap: What an institutional poverty trap is not

Not a Sachs-type poverty trap

Growth acceleration1 Growth acceleration1

Angola Malawi YesBenin Mali YesBurkina Faso MauritaniaBurundi MozambiqueCameroon NigerCentral African Republic Nigeria YesChad Yes Rwanda YesCongo, Democratic, Rep. SenegalCongo, Republic Yes Sierra LeoneCote d'Ivoire SomaliaEritrea SudanEthiopia TanzaniaGhana Yes TogoGuinea Uganda YesKenya ZambiaLiberia Zimbabwe YesMadagascarNote:

Source: Hausmann, Pritchett, and Rodrik (2004).

1. Growth accelerations are defined as periods of GDP per capita growth equal to or greater than 3.5 percent per year sustained for 8 years or longer, growth in the current period exceeds growth in the previous periods by at least 2 percent, and post-growth output is greater than pre-acceleration growth (Hausmann et al., 2004).

7

Convergence and divergence

All Sub-Saharan Africa

Latin America & Caribbean

Asia Poorest 1/3rd

Middle 1/3rd

Richest 1/3rd

Percentage of Countries 90% 76% 93% 100% 92% 79% 97%

All Sub-Saharan

AfricaLatin America &

Caribbean Asia Poorest

1/3rd Middle 1/3rd

Richest 1/3rd

Percentage of Countries 94% 100% 89% 81% 100% 92% 97%

Country observations 125 42 28 16 37 38 37

number of years. Divergence is defined by having lower average growth rate than US. Growth calculations made from the Penn World Tables v6.1. Countries with less than 20 years of available GDP data are not included in this table. Observation counts by income trecile do not sum to 125 because 13 countries have growth series that begin after 1960.

Source: Jones and Olker (2005).

Income in 1960 Region

Notes: Convergence is defined by whether country has average growth rate that is higher than US growth over indicated

Convergence over 10-year Period

Divergence over 10-year Period

Income in 1960Region

8

The best and worst 10-year average growth rates within countries

Source: Reproduced from Jones and Olker (2005).

9

Growth spurts are not pure recoveryincome after best 10 year growth episode relative to prior GDP peak

Source: Reproduced from Jones and Olker (2005).

10

Basic facts of growth and poverty do not support notion of poverty trap defined as a “persistent low-level equilibrium”

(Berg et. al.) Poor countries are not a persistently well-defined group

Easterly (2005): growth rates are not statistically lower in poor countries; income levels are not stationary

There is lots of movement across quintiles of countries, including growth successes and growth disasters

11

02

46

810

US

$ p.

c. in

com

e, in

thou

sand

s

1960 1970 1980 1990 2000

Botswana ChinaCongo_Republic_of IndiaIndonesia LesothoNepal Pakistan

Improvements Starting At The First Quintile

Growth Successes...

Source: Reproduced from Berg and Leite (2006).

12

Growth Successes...

05

1015

20U

S$

p.c.

inco

me,

in th

ousa

nds

1960 1970 1980 1990 2000

Cape Verde Dominican_RepublicEgypt GrenadaKorea MoroccoSyria TaiwanThailand

Improvements Starting At The Second Quintile

Source: Reproduced from Berg and Leite (2006).

13

.2.4

.6.8

11.

2U

S$

p.c.

inco

me,

in th

ousa

nds

1960 1970 1980 1990 2000

Burkina_Faso BurundiCongo_Dem_Rep EthiopiaGuinea_Bissau MalawiMali RwandaTanzania Uganda

No Change From First Quintile

Maybe there are traps for a subset of countries, e.g. tropical landlocked countries

Source: Reproduced from Berg and Leite (2006).

14

But even tropical landlocked countries in SSA have had growth accelerations (adapted from Berg et. al.)

15

The real problem: Growth accelerations in SSA have not led to autonomous sustained growth (“take-offs”)

Africa’s problem is more duration of growth spells (Berg et. al.) There are growth reversals

Sounds more like an institutional poverty trap than a conventional low “savings” poverty trap

16

Some growth reversals ...

.51

1.5

22.

5U

S$

p.c.

inco

me,

in th

ousa

nds

1960 1970 1980 1990 2000

Benin Cental_African_RepublicChad MadagascarMozambique NigerNigeria Sierra_LeoneZambia

Negative Change Ending At First Quintile

Source: Reproduced from Berg and Leite (2006).

17

Defining the institutional poverty trap: What an institutional poverty trap is not

Not a debt trap: aid transfers have financed debt payments

Source: Birdsall, Claessens, and Diwan (2003).

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

1977 1980 1983 1986 1989 1992 1995 1998

Net

tran

sfer

s (%

of G

DP

)

Low debt Multi low Multi high

18

Defining the institutional poverty trap: What an institutional poverty trap is not

Not a simple “corruption” problem, or lack of democracy

(East Asian tigers in the 1960s and 1970s; Indonesia 1970s through 1997; Vietnam and China 1990s to 2005. All these have had decade-long or more growth)

19

Defining the institutional poverty trap: What an institutional poverty trap is not

Not a Sachs-type poverty trap

Not a debt trap per se

Not a simple “corruption” problem, or lack of democracy

20

What the institutional poverty trap is: some inadequate definitions

Vicious circle in which poor institutions impede sustainable growth which undermines building of sound institutions

The absence of a “developmental state” a la Leftwich): Lack of effective state institutions that generate predictable, credible and clear rules of the game that enable markets to operate and support investment, invention, efficiency and thus economic growth

The absence of at least one of two characteristics: an “autonomous state” (from interest groups; East Asia) with capable civil service, or sufficient direct “accountability” (India, free press, democratic institutions)

21

Ex ante efforts at “measuring” institutions have not (yet) been particularly successful

Sub-Saharan African low-income countries as a group scored better on the ICRG measure of institutional quality in 1985 than other low-income countries, but have fared worse on growth

Good “institutions” are by definition stable and credible, but some countries’ ICRG indices fell more than 44 percent between 1985 and 1997

MCA eligibility and CPIA scores are not consistent, nor are Freedom House, ICRG and CPIA scores with other measures of “capacity”, “legitimacy” etc.

22

Sub-Saharan African countries as a group scored better on the ICRG in 1985 than other low-income countries

ICRG index1985

ICRG index1985

Cote d'Ivoire 5.00 Papua New Guinea 5.28Kenya 4.56 India 4.50Niger 4.40 Vietnam 4.16Mozambique 4.30 Pakistan 3.40Cameroon 4.20 Myanmar 3.26Sierra Leone 4.20 Nicaragua 3.14Burkina Faso 3.80 Haiti 2.40Malawi 3.80 Guinea-Bissau 2.40Senegal 3.80 Bangladesh 1.92Tanzania 3.72Ethiopia 3.60Madagascar 3.60Zimbabwe 3.60Guinea 3.48Angola 3.40Togo 3.40Zambia 3.40Congo, Republic 3.20Somalia 3.20Liberia 2.70Ghana 2.56Mali 2.42Uganda 2.40Sudan 2.20Congo, Dem. Rep. 2.06Sub-Saharan African low income countries 3.48 Other low income countries 3.38

Middle-income countries 3.66

Note:

Source: PRS Group Researcher Dataset (2004).

The version of the Institutional Country Risk Guide (ICRG) index used here has five components: corruption, rule of law, bureaucratic quality, repudiation of government contracts and expropriation risk.

The World Bank defines 58 countries as low income. 34 of these for which data are available are shown above.

23

Sub-Saharan African countries as a group scored better on the ICRG in 1985 than other low-income countries

ICRG index 1985

Sub-Saharan African low-income countries 3.48

Other low-income countries 3.38

Middle-income countries 3.66

Note:

Source: PRS Group Researcher Dataset (2004).

The World Bank defines 58 countries as low income. 34 of these for which data are available are shown above.The version of the Institutional Country Risk Guide (ICRG) index used here has five components: corruption, rule of law, bureaucratic quality, repudiation of government contracts and expropriation risk.

24

Though some countries’ ICRG indices rose between 1985 and 1997…ICRG index

1985ICRG index

1995ICRG index

1997Angola 3.40 3.98 4.78Burkina Faso* 3.80 4.00 4.18Cameroon 4.20 5.00 5.12Congo, Dem. Rep. 2.06 1.90 2.16Congo, Republic 3.20 4.20 4.58Cote d'Ivoire 5.00 4.80 4.40Ethiopia 3.60 4.56 4.94Ghana* 2.56 5.28 5.40Guinea 3.48 4.40 4.42Kenya** 4.56 5.40 5.40Liberia 2.70 1.40 1.52Madagascar* 3.60 3.78 3.60Malawi** 3.80 4.54 5.20Mali* 2.42 2.90 2.80Mozambique* 4.30 4.88 4.98Niger 4.40 3.76 3.66Senegal* 3.80 4.08 4.00Sierra Leone 4.20 2.40 3.22Somalia 3.20 1.72 1.80Sudan 2.20 3.00 2.68Tanzania* 3.72 5.48 5.20Togo 3.40 4.00 4.00Uganda** 2.40 3.90 4.60Zambia** 3.40 4.38 4.60Zimbabwe 3.60 5.38 4.94Sub-Saharan African low income countries 3.59 3.84 4.01

Middle income countries 3.66 5.48 5.52

Note:

Source: PRS Group Researcher Dataset (2004).

* indicates MCA eligible countries, ** indicates MCA threshold country.The World Bank defines 58 countries as low income. 34 of these for which data are available are shown above.The version of the Institutional Country Risk Guide (ICRG) index used here has five components: corruption, rule of law, bureaucratic quality, repudiation of government contracts and expropriation risk.

25

“Bureaucratic quality” increased little over the same periodBureaucratic quality

1985Bureaucratic quality

1995Bureaucratic quality

1997Angola 3.00 2.70 3.70Burkina Faso* 3.00 2.00 2.00Cameroon 4.00 4.00 4.00Congo, Dem. Rep. 1.30 2.00 2.00Congo, Rep. 2.00 2.00 2.00Cote d'Ivoire 4.00 4.00 4.00Ethiopia 1.00 1.60 2.00Ghana* 1.80 4.00 4.00Guinea 1.20 2.00 2.00Kenya** 3.80 4.00 4.00Liberia 1.00 1.00 1.00Madagascar* 3.00 2.00 2.00Malawi** 2.00 2.00 2.00Mali* 1.10 1.00 1.00Mozambique* 3.00 3.00 3.00Niger 4.00 2.00 2.00Senegal* 3.00 3.00 3.00Sierra Leone 3.00 1.00 1.90Somalia 2.00 1.00 1.00Sudan 1.00 2.00 2.00Tanzania* 1.00 2.00 2.00Togo 2.00 2.00 2.00Uganda** 1.00 2.00 2.00Zambia** 2.00 2.00 2.00Zimbabwe 4.00 4.00 4.00Sub-Saharan African low income countries 2.33 2.33 2.42

Middle-income countries 2.61 3.03 3.06

Note:

Source: PRS Group Researcher Dataset (2004).

* indicates MCA eligible countries, ** indicates MCA threshold country.Bureaucratic quality is one of the sub-indices of the Institutional Country Risk Guide (ICRG).

26

MCA eligibility and CPIA scores are not consistent,

Eliminated from MCA by corruption criteria

Countries actually selected for the MCA

CPIA rankingby quintile 2002

AlbaniaBangladeshMalawiMoldovaMozambique

----Mozambique

22333

Missed MCA by one indicator(out of 16)

Countries actually selected for the MCA

BeninBurkina FasoGeorgiaIndiaMaliMauritaniaSao Tome and PrincipeTogo

Benin-Georgia-Mali---

22412155

Additional countries selected for the MCA

Cape VerdeVanuatu

14

Sources: Radelet, Steve (2003) “Challenging Foreign Aid,” The Center for Global Development; and International Development Association (2004) “Allocating IDA Funds based on Performance. Fourth Annual Report on IDA’s Country Assessment and Allocation Process”.

27

…nor are CPIA scores with other measures of “capacity”, “legitimacy” etc.

Top two quintiles of CPIA and Security Gap

Top two quintiles of CPIA and Legitimacy Gap

Top two quintiles of CPIA and Capacity Gap

Senegal Vietnam Bhutan

Sri Lanka Pakistan India

Uganda Rwanda Mauritania

Indonesia Senegal

Nepal Burkina Faso

Rwanda Indonesia

Mali

PakistanNote:

Sources: International Development Association (2004); Center for Global Development (2004).

The security gap measures conflict in low-income countries 1998-2003, with major war defined as at least 1000 battle-related deaths in any given year over the period. Intermediate war is defined as any conflict with at least 25, but less than 1000 battle-related deaths in any given year and an accumulated total of at least 1000 battle-related deaths over 1998-2003. Minor war classified as any conflict with at least 25 battle-related deaths in any given year and less than 1000 battle-related deaths over the period. The capacity gap is based on immunization rates, and the legitimacy gap on the "voice and accountability" sub-index from the Kaufmann, Kraay, and Zoido-Lobaton governance index.

The CPIA (Country and Policy Institutional Assessment) index is the World Bank's internal scoring system of IDA countries' instititutional capacity.

28

Institutional quality ex ante does not seem to be associated with a subsequent growth acceleration; if anything growth in SSA raises (the measure of) institutional quality

Institutional quality before and after growth accelerations by region, 1970s-1990s

Freedom House indexbefore growth acceleration2

Freedom House index after growth acceleration3

Growth-accelerating countries1

Sub-Saharan Africa 5.7 4.8

South-Asia 4.1 4.6

East Asia 4.0 4.5

Latin America 4.1 3.9

Middle East and North Africa 6.1 5.9

Sub-Saharan Africa 5.6 5.5

Latin America 3.7 3.2Notes:

3. Freedom House index 5 years after the initial year of growth acceleration.

Sources: Freedom House (2005); Hausmann, Pritchett, and Rodrik (2004); author's calculations.

(1=highest degree of freedom, 7=lowest degree of freedom)

4. For the non-growth accelerating countries the year before acceleration is 1973 and for after 1979. These years were chosen based on the decades with most growth accelerations for the growth-acclerating countries and available data.

1. Growth accelerations are defined as periods of GDP per capita growth equal to or greater than 3.5 percent per year sustained for 8 years or longer, growth in the current period exceeds growth in the previous periods by at least 2 percent, and post-growth output is greater than pre-acceleration growth (Hausmann et al., 2004).2. The Freedom House index is based on two components: political rights and civil liberties. Data for the year before the initial year of growth acceleration.

Non-growth accelerating countries4

29

What characteristics makes a country more likely to be in an institutional poverty trap?

Natural resources (exception: Botswana)

Low natural openness (landlocked, non-trading neighbors)

Primary commodity dependent – subject to terms of trade shocks

Historically high inequality; and small non-state/SOE-dependent middle class

High levels of prebendalism

Civil service pay low

30

Natural resource rich countries have lower enrollment and literacy rates

Mean Median Mean MedianResource Poor 28.5 26 56.4 61.3Resource Rich 25.3 19.5 52.2 53.2Difference 2.8 6.5 4.2 8.1Resource Poor 39.5 40.5 64.7 72.5Resource Rich 35.7 34 60.8 63.4Difference 3.8 6.5 3.9 9.1

Note: Categorization of countries taken from Auty, 1997.

Education and Resource Abundance

Source: Birdsall, Pinckney and Sabot. 2001. “Natural Resources, Human Capital and Growth.” In Resource Abundance and Economic Development, Ed. Richard Auty.

1975

1985

(percent)Adult LiteracySecondary Enrollment

(percent)

31

The wrong asset: Open, globalizing countries dependent on commodity prices have not grown

-2%

-1%

-1%

0%

1%

1%

2%

2%

Ave

rage

ann

ual g

row

th ra

te o

f rea

l GD

P p

er c

apita

(mea

n, p

erce

nt)

Least commodity dependent countries

Most commodity dependent countries

1980s

1980s

1990s

1990s

Source: Birdsall and Hamoudi (2002) “Commodity Dependence, Trade, and Growth: When “Openness” is Not Enough.”

32

Variable HR p HR p HR p HR p

Log inflationInitial level 1.03 0.05 1.01 0.60Change within spell 1.03 0.04 1.01 0.55

Log depreciation of the official exchange rateInitial level 1.02 0.05Change within spell 1.02 0.04

Fiscal balance (change within spell) 0.96 0.28Primary education (change within spell) 0.35 0.09Gini coefficient 1.05 0.04 1.06 0.00 1.09 0.00 1.06 0.02Terms of trade

contemporaneous 0.99 0.66 1.00 0.78 1.00 0.93 1.00 0.79lag 0.97 0.04 0.97 0.05 0.99 0.59 0.98 0.19N

Log inflationInitial level 1.04 0.02 1.04 0.06Change within spell 1.03 0.03 1.03 0.09

Log depreciation of the official exchange rateInitial level 1.02 0.07Change within spell 1.02 0.11

Fiscal balance (change within spell) 0.92 0.08Primary education (change within spell) 0.52 0.41Gini coefficient 1.08 0.01 1.10 0.00 1.17 0.00 1.07 0.04Terms of trade

contemporaneous 1.00 0.97 1.00 0.97 0.99 0.82 1.01 0.71lag 0.96 0.06 0.96 0.02 0.98 0.52 0.97 0.10N

Table 10. Summary Regression Results 1/

53 47 38 41

3 4

1/ Survival time regressions based on spells sample in Definition (1), minimum insterstitiary period (h) of 5 and growth cutoff (g) of 2 percent. Regressions also control for initial income per capita, which generally has a HR > 1 (not always statistically significant)

1 2Model No.

(Spells sample for p = 0.5)

(Spells sample for p = 0.25)

33 30 21 27

Source: Reproduced from Berg and Leite (2006).

33Source: Reproduced from Berg and Leite (2006).

ModelNo. Variable HR p HR p HR p HR p

1 Political ParticipationInitial level 1.00 0.97 1.02 0.67 1.03 0.44 1.01 0.77Change within spell 1.03 0.41 1.02 0.58 1.04 0.32 1.01 0.79N

2 DemocracyInitial level 1.00 0.98 1.10 0.25 1.08 0.26 1.04 0.68Change within spell 1.03 0.65 1.07 0.41 1.09 0.20 1.03 0.72N

3 Constraints on ExecutiveInitial level 1.04 0.69 1.15 0.30 1.10 0.43 1.07 0.68Change within spell 1.14 0.18 1.16 0.24 1.18 0.12 1.11 0.45N

4 Income InequalityInitial level 1.09 0.00 1.13 0.00 1.08 0.00 1.20 0.00Change within spell 0.99 0.84 1.00 0.97 1.01 0.86 1.04 0.54N

5 Income Inequality 1.07 0.00 1.11 0.00 1.09 0.00 1.16 0.00N

6 Ethnic heterogeneity 1.00 0.81 0.99 0.26 1.00 0.61 0.99 0.35N

2/ Includes growth spells following an initial upbreak even when per capita growth already exceeded 2 percent.3/ Excludes growth spells following an initial upbreak when per capita growth already exceeded 2 percent.

Table 5. Duration Regressors: Institutional Variables and Inequality 1/

56 34 45 28

71

33

41 60

p = 0.5 p = 0.25

50 31

34

1/ Survival time regressions based on breaks with minimum insterstitiary period (h) of 5 and growth cutoff (g) of 2 percent. All models include terms of trade shocks and initial income per capita as controls.

67 41 56 34

43 26 36 20

Spells Definition 2 3/Spells Definition 1 2/

61 38

p = 0.5 p = 0.25

66 40 55

34

Inequality is high in all developing countries

Income inequality by region 1995-1996

Sub-Saharan Africa 48.7

Latin America & Caribbean 47.5

East Asia 41.4

South Asia 47.1Note:

Source: Galbraith and Kum (2003).Income inequality is measured by the estimated household income inequality index (EHII).

35

But in Africa, the middle strata get a smaller piece of the pie …Income share of middle

strata1

1995-20002

(percent)

Income share of middle strata1

1995-2000(percent)

Income share of middle strata1

2000(percent)

Botswana 36.7 Argentina 40.5 Austria 56.0Burkina Faso 18.6 Bolivia 33.4 Belgium 52.0Cameroon 34.2 Brazil 31.9 Denmark 53.1Central African Rep. 29.8 Bulgaria 41.0 Finland 53.4Ethiopia 37.4 Chile 32.7 France 54.0Ghana 40.5 Colombia 36.1 Germany 55.0Guinea 23.0 Costa Rica 41.6 Ireland 92.0Madagascar 33.3 Dominican Republic 42.1 Italy 51.1Malawi 33.0 Ecuador 36.6 Korea 53.5Mali 23.8 Egypt, Arab Rep. 36.9 Luxembourg 53.2Senegal 26.1 El Salvador 40.1 Norway 51.7Uganda 36.4 Guatemala 33.8 Portugal 51.0Zambia 29.0 Honduras 40.2 Spain 52.0Zimbabwe 23.3 Jamaica 39.5 Sweden 51.4

Mexico 38.4 United Kingdom 50.2Panama 36.2 United States 46.8Sri Lanka 42.4Thailand 36.1Uruguay 45.1Venezuela 45.1Peru 39.8Botswana 40.7China 52.3Indonesia 46.3Malaysia 40.5Paraguay 39.2Philippines 40.8

Sub-Saharan African low income countries 29.9 Middle income

countries 39.6 High income OECD countries 54.8

Notes:1. Middle strata defined as the three middle quintiles of the population.2. Latest year available for period 1995-2000.Sources: WIID 2a; author's calculations.

36

But in Africa, the middle strata get a smaller piece of the pie

Income share of middle strata1

1995-20002

(percent)

Sub-Saharan African low income countries 29.9

Middle income countries 39.6

High income OECD countries 54.8

Notes:1. Middle strata defined as the three middle quintiles of the population.2. Latest year available for period 1995-2000.Sources: WIID 2a; author's calculations.

37

Prevalent prebendalism (which is worse for growth than clientelism)

Prebendalism “refers to the handing out of prebends, in which individuals are given public offices in order for them to benefit from personal access to state resources.”

(van de Walle, 2005, p. 20)

“President Mobuto Sese Seko of Zaire famously commanded his ministers to enrich themselves but ‘not to steal too much’.”

(van de Walle, 2005, p. 21)

38

Civil service pay is low

In many Sub-Saharan African countries the real value of civil servant wages has declined by 50-70% since the 1970s (Lindauer and Nunberg, 1994).

In the late 1990s a mid-level economist in Kenya could make $250 per month working for the goverment, compared to $3,000-$6,000 if working for an NGO or a donor program (Brautigan, 2000).

39

What characteristics are associated with our intuition that a country is in an institutional poverty trap?

Natural resources

Low natural openness2

Primary commodity dependent

High inequality Low non-trade tax revenue

Long duration of heads of

state3

Burkina Faso X X X XXBurundi X X X X XCongo, Dem. Rep. X X X X XEthiopia X X X X XGambia X X X XGhana X X XGuinea-Bissau X X XMalawi X X X XMauritania X X X X XXMozambique X XNiger X X XRwanda X X X XSierra Leone X X X XUganda X X X X XXZambia X X X X XNote:1. Countries with natural resource rents equal to or greater than 5 percent of GDP.2. Landlocked countries or countries with some sea access but surrounded by non-trading neighbors.3. Current head of state in power for 10 years or more. XX indicates in power for more than 15 years.Sources: WDI (2005); WIID 2.0a; GFS (2005), van de Walle (2005).

Institutional poverty trap characteristics of Sub-Saharan African countries that receive more than 10% of GDP in aid

40

Conclusion Part I

Many low-income countries are probably suffering from the institutional poverty trap, even when they are growing

But the ex post definition and multiple symptoms make it hard to identify the institutional poverty trap ex ante, let alone pin down its causes

And we do not know how to help countries escape this trap since it is mainly about politics and power-sharing

Next: Are we making things worse when we try to help?

41

Part I: The donors’ dilemma

Part II: Country-based aid is not helping and is probably hurting

Part III: What donors can and cannot do

42

Part II: Country-based aid is not helping and is probably hurting:

Dutch disease and “competitiveness”

Government revenue

Accountability

Donor fragmentation and poaching

The NGO “bypass” issue

Technical assistance

The Washington Consensus, a.k.a. Policy autonomy and missed opportunities

The “exit” issue

Volatility

43

Dutch disease

44

Government revenueSub-Saharan Africa still relies on trade taxes

Taxes on international trade, % of total tax revenue

2002-2003Sub-Saharan Africa 27.6East Asia and Pacific 9.1Latin America 5.3South Asia 19.4High-income OECD 0.7Note: Tax revenue excludes grants.Sources: WB Africa Database (2002); WDI (2005).

45

Government revenue: Many low-income and lower middle-income countries could increase tax revenue

BLR

BLR

BEN

BTN BTN BTNBTN

BTNBOL BOL

BOL

BOL

BRA

BRABRABRABRA

BGR

BGRBGR

BFABFA

BFABFABFA BFABDIBDI

BDI BDI

BDI BDIBDI

CMRCMR

CMR

CMR

CMR

CMRCMR

TCD

TCDTCD

TCD

COLCOLCOLCOLCOLCOLCOL

COGCOG

COG

COGCOG

CIVCIV CIV

CIVCIV

DOMDOMDOMDOMDOM

DOMDOM

EGY EGY

EGY

EGYEGY

EGY

EGY

ETHETH ETH

ETH

GMBGMB

GMB GMB

GMB

GTMGTMGTM

GUY

GUY

GUY

GUY

HTIHTIHTIHNDHNDHNDINDINDINDINDINDINDIND

IDN

IDN

IDN

IDNIDNIDNIDN

IRNIRNIRNIRNIRNIRN

IRN

JAMJAM

JAM

JAM

LSOLSO

LSO

LSO

LSOLSO

LSOLBRLBR

LBRLBR

LBRMDG

MDGMDG

MDGMDG MDGMDV

MDVMDVMDVMLIMLI MLI MLI

MLIMRT MRT

MNG

MNGMARMARMARMARMAR

MARNAM

NAMNAM

NPLNPL NPLNPL NPLNPL

NPL

NICNIC

NIC

NIC

NICNIC

PAKPAKPAKPAKPAKPAK

PAK

PRYPRYPRYPRYPRYPRY

PERPERPER

PERPER

PERPER

RWARWA

RWARWA

RWA

SENSEN

SENSEN

ZAFZAF

LKA LKALKA

LKALKA

LKALKASUR

SURSUR

SYRSYR

SYR

SYR

SYRSYRSYR

THATHATHATHA

THATHATHA

TGOTGO

TGO

TONTONTON

TUN

TUNTUNTUNTUN

TUN

TUN

TURTUR

TUR

TUR

VUT

VUT VUTVUT

YEM

YEM

YEM

ZMBZMBZMB

ZMB

ZMB

ZWE

ZWEZWEZWE ZWE

ZWE

010

2030

40Ta

x sh

are

excl

udin

g tra

de ta

xes

(per

cent

of G

DP

)

0 10 20 30 40 50Aid share (percent of GNI)

(4 year averages, percent)Aid and tax shares in low and lower middle income countries 1972-1999

Source: Moss, Pettersson, and van de Walle (2005).

46

Accountability

47

Donor Fragmentation and Bureaucratic Qualityin Sub-Saharan Africa

Source: Reproduced from Knack and Rahman (2004).

48

The NGO “bypass” issue

49

Technical assistance

“Expatriate personnel working for aid agencies and NGOs rarely are required to pay local income taxes. At one point in Tanzania, the total for government wages and salaries (which are taxed) was $100 million, while the salary bill for technical assistants supplied under aid programs (and not taxed) was $200 million.”

(Berg, 1993 cited in Brautigam and Knack, 2004, p. 262)

50

The Washington Consensus, a.k.a. Policy autonomy and missed opportunities

51

The “exit” issue

Number of Adjustment Loans to the 20 Countries with Most Adjustment Loans Over the Period 1980-1999.

14-19 loans Niger, Zambia, Madagascar, Togo, Malawi, Mali, Mauritania, Kenya, Bolivia, Philippines, Jamaica, Bangladesh

20-25 loans

26-30 loans

Senegal, Uganda, Mexico, Morocco, Pakistan

Côte d’Ivoire, Ghana, Argentina

Out of these countries, only Bangladesh, Pakistan and Uganda achieved annual per capita growth rates above 2% over the period from their first adjustment loan to 1999.

Notes: These are IMF and World Bank adjustment loans. The average number of adjustment loans for these countries over the period is 19 compared to the average of 7 for all developing countries.

Source: Easterly (2002) “What Did Structural Adjustment Adjust? The Association of Policies and Growth with Repeated IMF and World Bank Adjustment Loans.” Center for Global Development Working Paper 11.

52

Volatility

Source: Reproduced from Bulir and Haman (2006).

Volatility of aid flows by country, 1975-2003

53

The resulting risk of doubling country-based aid

Aid intensity under “Big Push” scenarios

Source: Moss and Subramanian (2005).

54

Conclusion Part II

55

Part I: The donors’ dilemma

Part II: Country-based aid is not helping and is probably hurting

Part III: What donors can and cannot do

56

A. Humility and regret: Living with the institutional poverty trap of most African countries

Eliminate debt more expeditiously Provide aid in grant form until per capita income exceeds $500 But only through government budgets and only with some

matching funds from government revenue Set specific, measurable, time-bound goals a lá MDGs for all

country-based aid Increase share of aid going through multilaterals End policy and process conditionality, instead finance programs

on the basis of results More impact evaluation Exit countries where head of state stays in office beyond 10-12

years

57

B. Beyond country aid

EITI Advocate and support direct distribution of proceeds of

natural resources Global warming Trade and TRIPS Making markets for vaccines; Green Revolution for Africa International migration

58

Conclusion

59

60

61

62

63