aid and state formation in africa: what the rich world cannot do odi, london, may 22, 2006
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Aid and State Formation in Africa: What the Rich World Cannot Do ODI, London, May 22, 2006. Nancy Birdsall President Center for Global Development Washington, D.C. Outline. Part I: The donors’ dilemma: three decades of massive aid to still-poor countries, mostly in SSA - PowerPoint PPT PresentationTRANSCRIPT
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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.
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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).
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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
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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
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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).
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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
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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)
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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)
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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.
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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.
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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.
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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.
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“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).
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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”.
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…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.
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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
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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
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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)
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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.”
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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
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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