AID, POVERTY REDUCTION AND THE ‘NEW CONDITIONALITY’siteresources.worldbank.org/INTPRS1/Resources/383606... · 2005-01-25 · AID, POVERTY REDUCTION AND THE ‘NEW CONDITIONALITY’

AID, POVERTY REDUCTION AND THE‘NEW CONDITIONALITY’*

Paul Mosley, John Hudson and Arjan Verschoor

The paper examines the effect of aid on poverty, rather than on economic growth. We devise a‘pro-poor (public) expenditure index’, and present evidence that, together with inequality andcorruption, this is a key determinant of the aid’s poverty leverage. After presenting empiricalevidence which suggests a positive leverage of aid donors on pro-poor expenditure, we arguefor the development of conditionality in a new form, which gives greater flexibility to donors inpunishing slippage on previous commitments, and keys aid disbursements to performance inrespect of policy variables which governments can influence in a pro-poor direction.

In two fundamental ways, the landscape of aid policy has changed in the last half-dozen years. At the level of ends, the basic objective of development (usuallyinterpreted as GDP growth) in the recipient country has been replaced by theobjective of poverty reduction (World Bank, 2000), so that for most donors growthin the developing world is only valuable if it can be construed as pro-poor. At thelevel of means, policy conditionality, until very recently seen as the main instru-ment for increasing the effectiveness of aid, has been dramatically thrown over-board and replaced with a concept of selectivity, in which aid agreements are onlyconcluded with those countries whose policies are in some sense alreadyacceptable.1 For those many who support the first of these developments, it isimportant to know whether the second points a reliable route to achieving it.To discover whether it does is the principal objective of this paper.

The proposition that conditionality should be abandoned in favour of selec-tivity derives principally from the well-known finding of Burnside and Dollar(2000) that aid is only effective where policies are good, yet has no ability to

* This work was carried out under a DFID research programme (R 7617) on ‘Maximising the povertyleverage of aid’. The research assistance of Jennifer Mbabazi and especially Karuna Gomanee was vital,and many thanks are due to them for help with computation and data collection. Thanks are due toAdriaan Kalwij for helpful comments.

1 The World Bank’s recent World Development Report 2000/01 explains, in a section of Chapter 11entitled ‘Making aid more effective in reducing poverty’ how selectivity emerges from the failings ofconditionality:Studies in the 1990s (presumably Burnside and Dollar, 2000) showed little systematic relationshipbetween conditionality and policy changes, though case studies do find positive effects under someconditions, especially where conditionality supports the hand of reforming groups. The dynamics be-tween aid donors and recipients explain why conditionality fails. Recipients do not see the conditions asbinding, and most donors are reluctant to stop giving aid when conditions are not met. As a result,compliance with conditions tends to be low, while the release rate of loan tranches remains high. Thusaid has often continued to flow despite the continuation of bad policies.Selectivity: For aid to be most effective at reducing poverty, it must be well targeted. If all aid money wereallocated on the basis of high poverty rates and reasonably effective policies and institutions, a recentstudy (Collier and Dollar, 1999) estimates, even today’s small aid flows could lift 19 million people outof poverty each year – almost twice the estimated 10 million now being helped…In addition to targetingpoverty, donors should allocate aid on the basis of the policy environment. Aid has been shown to beeffective in promoting growth and poverty reduction in poor countries with sound economic policiesand sound institutions – ineffective where these are lacking. (World Bank, 2000, pp. 193–6).

The Economic Journal, 114 (June), F217–F243. � Royal Economic Society 2004. Published by BlackwellPublishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

[ F217 ]

influence those policies. However, there are two important reasons to hesitatebefore accepting this conclusion. Firstly, Burnside and Dollar’s findings havebeen challenged on econometric grounds, most sharply by Hansen and Tarp(2001) and most recently by Easterly et al. (2003), who argue that aid effective-ness is invariant with respect to the Burnside and Dollar indicator of good policy.Secondly, whatever one’s judgement may be of the balance of the econometricevidence on the relationship between aid and the Burnside and Dollar indicator,the indicator itself hardly convinces in its ability to capture comprehensively thequality of a country’s policies and institutions for promoting growth (let alonepro-poor growth). It comprises only two macro-economic variables, at least two ofwhich (inflation and budget deficit) are more readily interpreted as correlates ofthe growth process rather than independent causes of growth. Collier and Dollarin this symposium express very similar doubts about the reliability of the Burn-side and Dollar indicator, for which reason they now work with a much morecomprehensive World Bank measure of good policies and institutions. Whatremains to be tested is whether aid is able, through policy conditionality,to influence this measure – or those components of it which are particularlyconducive to poverty reduction.

The World Bank’s current official position is nonetheless sceptical of what‘ultimatum’ conditionality may achieve.2 Collier and Dollar (2001, 2002) takethat position to its logical extreme: they derive a poverty-efficient allocation ofaid that assumes that donors have no influence whatsoever over recipients’policies, in the process (presumably for ease of computation) adding the furthersimplifying assumption that the growth elasticity of poverty reduction is a uni-versal constant, thereby in effect advocating aid’s impact on growth as the onlychannel through which it impacts on poverty. Taken at face value, their specificadaptation of the World Bank’s current official position implies that, in order toachieve maximum poverty reduction impact, donors need do no more thanallocate aid on the twin criteria of recipients’ existing economic policies andlevels of poverty.

It is important to realise that the Collier-Dollar approach to selectivity rede-fines good policy as a relative concept: the marginal aid dollar should flow towhere its effectiveness is highest, under the joint influence of existing policiesand levels of poverty, not necessarily to where it is high. In practice, particularlyin Africa, where the majority of poor countries are to be found but there are stillnot many governments who yet practise ‘good policies’ in the World Bank sense,this principle leads to the selection of countries for unconditional aid withpolicies that donors are reluctant to let fester. Donor aid administrations wishingto practise selectivity feel forced to choose between underspending their budget(and thus losing influence both within their developing-country partners andwithin their own governments) and giving aid to bad-policy countries. Theemergence of this donor’s dilemma has already forced a number of donors into

2 Collier (1997) traces the origin of conditionality failure to donors being insufficiently motivated topunish recipients for non-compliance.

F218 [ J U N ET H E E C O N O M I C J O U R N A L

� Royal Economic Society 2004

reconsidering conditionality in a new form, usually under a new and euphemisticname such as ‘engagement with poor performance’.3

The principal features of this third way, we shall argue, are:

• multiple levels of commitment and withdrawal, rather than a simple yes/nodecision on whether to give aid or not. Thus a ‘very good’ aid recipient willreceive programme aid for policy reform, accelerated debt relief under theHIPC (Highly Indebted Poor Countries) initiative, and social-sector aid(education, health, and rural infrastructure); a ‘moderate to poor’ recipientonly the last of these; and only a ‘ hopeless’ recipient nothing at all.4

• the active design of alternatives to government-to-government provision,especially NGOs but also in some cases the private sector, which provides analternative to leverage on government;5

• the exercise of pressure which operates through social and political, as well aseconomic, channels. It is now widely believed that aid money invested inconflict prevention, promoting democracy and equal citizenship and fight-ing corruption will help build social capital and thus and otherwise, furtherthe economic objectives of growth and poverty reduction (Whiteley, 2000;Knack, 1999; Knack and Keefer, 1997), through a more long-term andindirect route.

Any aid donor objective may thus be sought through three alternative methods:selectivity; traditional ‘ultimatum’ conditionality; or new conditionality as abovedescribed. A range of options for the specific donor objective of reducing povertyhas been proposed over the last thirty years, which may be represented as com-binations of one of these aid modalities and an attempt to reduce poverty in one oftwo ways: either by increasing the growth rate, or by enhancing poor people’sparticipation in growth (Table 1).

3 A DFID workshop on conditionality with this title was held at IDS, University of Sussex, on 4 July2000, and a further one-day conference on ‘Dealing with Poor Performance’ was held at the RoyalInstitute for International Affairs, London, on 29 November 2000. Two outstanding examples of the useof ‘new conditionality’ in practice are Uganda, where donors persisted with generous aid funding in thelate 1980s and early 1990s in spite of President Museveni’s strong initial opposition to the two cardinalWorld Bank principles of exchange rate flexibility and avoidance of export taxation, and Ethiopia,where donors indulged what was virtually a command economy until the early 1990s and were rewardedwhen, between 1994–6, President Zenawi embarked on a set of widespread price and expenditurereforms. For more detail see Mosley and Hudson (2002), Morrissey and Verschoor (2002) and Rock(2003).

4 More formally one could link future aid disbursements to previous performance in implementingagreed policy reform through a linear or non-linear decision or punishment rule. Poor performancewould be penalised, releasing more aid money for those countries who have performed better. Providedthis decision rule is known, it should induce a greater implementation of agreed policy reform, yet fewcountries will be totally excluded from aid and the policy dialogue process. The key factor is that therule is known and that the donor adheres strictly to the rule. However, it is possible that initially theremay need to be a process of ‘trust building’ during which the donor tolerates some slippage but after acertain amount of time the full rigor of the rule will need to be implemented.

5 In Kenya, Zambia and Bangladesh (to take three examples of ‘moderate to poor’ aid recipients)NGOs have long been involved in the provision of primary health, basic education and adult literacy,agricultural extension, small-business finance, and a number of traditional government functions; aiddonors, aware of the government’s weakness as a service provider, have happily used these NGOs as asupplementary channel for aid flows.

2004] F219A I D , P O V E R T Y A N D ‘ N E W C O N D I T I O N A L I T Y ’


The World Bank’s current official position, as we have already emphasised, issceptical of what ‘ultimatum’ conditionality may achieve. Reflecting this Collierand Dollar (2001, 2002) derive a poverty-efficient allocation of aid that assumesthat donors have no influence whatsoever over recipients’ policies. This has alwaysseemed unlikely as to an extent selectivity is no more than ex-post conditionality.6

Thus they argue, although this position is slightly modified in the paper in thissymposium that, in order to achieve maximum poverty reduction impact, donorsneed do no more than allocate aid on the twin criteria of recipients’ existingeconomic policies and levels of poverty.

To their credit, in this symposium Collier and Dollar show great awareness ofthe limitations of their ‘benchmark allocation’: donors’ local knowledge, specialcircumstances (terms-of-trade shocks) and recipient characteristics not includedin their original analysis (inequality, corruption, the composition of governmentexpenditure) all represent legitimate reasons for deviations from the ‘efficient’allocation of aid proposed in their previous papers. Moreover, they cite studiesof cases in which a careful sequencing of aid finance, together with charac-teristics of donors’ ‘style of relationship’ with recipients, has induced policyreform – a recognition of the reality of new conditionality. We very muchwelcome these qualifications of their initial position but argue in this paper thattheir combined force, when analysed properly and with a view to their effectnot on growth but on poverty, amounts not so much to cosmetic changes oftheir benchmark allocation but rather to a radically altered approach to aidallocation.

First of all, growth regressions, and a fortiori aid-growth regressions, are in theirinfancy and face, among other challenges, the gigantic one of endogenising theevolution of institutions (Easterly, 2002). Dalgaard et al. in this symposium makepioneering forays in that direction but the variable that in their analysis proxies for

Table 1

Options for Increasing the Poverty Leverage of Aid

Objective Instrument Leverage of aid on growthLeverage of aid on the growthelasticity of poverty reduction

Selectivity (reallocationbetween recipientgovernments)

World Bank programmingmodels of 1960s and 1970s;more recently Burnside andDollar (2000) and Collier andDollar(2001, 2002)

World Bank (2000, ch.11)

Conditionality (pressure toreform policies of recipientgovernments):Old ‘ultimatum’ variety Traditional ‘structural

adjustment’ approachWorld Bank 1991–3

‘New’ Bilateral donors in Uganda1992–94

Bilateral donors especially inUganda, Tanzania, Ethiopia,Mozambique 1994–2000

6 That is selectivity provides an incentive to follow good policies prior to receiving aid.



institutions (the tropics) is too broad to provide specific guidance for aid alloca-tion. For that reason, we propose not only that particular variables raise aideffectiveness in the sense of reducing poverty, but also that some of these can beand have been influenced by donors using a conditionality approach. Among thevariables that Collier and Dollar suggest as representing potentially fruitfulextensions of their analysis, we find in Section 1 that the composition of publicspending, inequality and corruption are especially relevant for increasing thepoverty leverage of aid. Moreover, we find in Section 2 that donors through a newconditionality approach are capable of influencing the orientation of publicexpenditures towards poverty reduction. As we have noted, Collier and Dollarshow themselves aware in this symposium of donor influence on recipient policy.However, they warn that it ‘should not become a dominant consideration in aidallocation’, as the likely quantitative impact would be small in their opinion. Inthat largely unsubstantiated phrase lies the crux of what remains of our disag-reement with them. In Section 3 we use the estimation results presented in Sec-tions 1 and 2 to compare the impact on poverty reduction of new conditionalityand selectivity through simulating a number of plausible scenarios and find evi-dence that our approach would render aid some 12% more effective thanthe Collier and Dollar approach – which is itself more effective than the currentallocation. Section 4 highlights the implications for policy.

1. Channels of Aid Impact on Poverty

The standard approach to estimating the effectiveness of aid (Mosley et al. 1987;Boone, 1996; Burnside and Dollar, 2000) has been to construct a model inwhich the aid-recipient government, constrained by resource scarcity, uses aid asan instrument in the pursuit of its own objectives; and then to use the reducedform of that model as an estimating equation for aid effectiveness. Recentcontroversy has centred on the economic and statistical significance of thecoefficients on aid and on aid interacted with policy in a growth equation(Burnside and Dollar (2000) versus Hansen and Tarp (2001) and most recentlyEasterly et al. (2003)). Our own view, as described in Hudson and Mosley (2001),is that aid effectiveness, in the sense of raising growth rates, has experienced anupward step-jump since the 1980s but that the role of policy in increasing itremains ambiguous.

In this paper we enter the controversy on aid effectiveness from a differentangle, focussing on aid’s ability to reduce poverty – which in recent years has beenthe principal target variable for aid donors and many recipient countries (WorldBank, 2000). Analytically we may distinguish the total impact of aid (A) on poverty(P) as a combination of its direct effect, its effect on growth or GNP per capita (y)plus its effect on policy (the vector X):

dP

dA¼ @P

@Aþ @P

@y

@y

@Aþ @y

@X@X@A

� �þ @P

@X@X@A

ð1Þ

and whereas most of the debate (as exemplified by the two other papers in thissymposium) has focussed on the terms between square brackets, we focus on most



of the remaining terms.7 At a superficial glance, there are two problems to resolveat the empirical level (Table 2). The first is that, even if aid policies wereto succeed in increasing growth, they would not necessarily on their own be able toreduce poverty (particularly in Africa and eastern Europe, it appears) and need tobe supplemented by policies which will increase the (absolute value of) the povertyelasticity (in reality this is more accurately referred to as the income elasticity:¶lnP/¶lnY ).The second is that relating aid to poverty reduction currently revealslittle correlation, which leaves the burden of proof on those who wish to argue thatthis overall pattern hides cases in which aid has been able to influence policies(¶X/¶A „ 0) that influence poverty (¶P/¶X < 0).

There are two steps in the argument. First we need to identify the terms in the‘policy vector’ which are capable of influencing poverty through aid, which is themain business of this Section, and next we need to define more precisely how thatinfluence will be exercised, which will be considered in Sections 2 and 3. In theaid/growth regressions of Burnside and Dollar (2000) there are just three policyvariables – budget deficit, inflation and openness – a number which in the analysisof Collier and Dollar (2001, 2002) has grown to twenty, summarised in a singleindex: the World Bank Country Policy and Institutional Assessment (CPIA) score.CPIA scores capture the quality of institutions and policies for promoting broad-based growth as perceived by country experts, and are confidential to the WorldBank (although occasionally tantalising hints are given in the form of CPIA-basedcountry grades). They are regarded with some suspicion by commentators whoquestion country experts’ ability to evaluate the quality of policies separately from

Table 2

Aid, Growth and Poverty Reduction by Region

Regions

(1) Povertyreduction1990–99*

(percentagepoints/year)

(2) GrowthGDP pc

1990–99�

(percentagepoints/year)

(3) ¼ (1)/(2)Poverty

reductionper unit

of growth(4) ODA/GNP

(%), 1992�

(5) ¼ (1)/(4)Poverty reduction

per unit of aid

Sub-Sah. Africa )1.02 0.47 )2.17 6.92 )0.15E. Asia and Pacific 1.05 7.18 0.15 0.34 3.09M. East/N. Africa 0.23 0.66 0.35 1.23 0.19L. America/Car. 0.23 1.23 0.19 0.30 0.77E. Europe/C. Asia )0.68 0.13 )5.23 2.17 )0.31S. Asia 2.50 3.33 0.75 0.66 3.77Developing World 0.92 3.81 0.24 1.45 0.63

Note: Calculations are based on annualised reductions in country poverty headcount percentages (usingas the poverty line either $1/day or a national one, depending on data availability) using populationshares as weights.Data sources: *World Bank Poverty Monitoring Database, � World Development Indicators (data arraysused here are presented in Table A2).

7 ¶P/¶A represents aid that circumvents the government (‘working around government’ in Collierand Dollar’s contribution to this symposium). We do not consider this term in this paper as our focus ison ‘working with government’.



a country’s actual performance. For that reason, any analysis that relates countryperformance to the CPIA score may suffer from a circularity problem. We cannotexamine this satisfactorily, for the required data are not in the public domain (seeDalgaard et al. in this symposium, for an extensive discussion of the CPIA indexand its problems).

Considering that policy variables emerging from aid-growth regressions arehighly controversial, and recalling that, by consensus, aid’s effectiveness is nowprimarily judged in terms of its poverty-reducing impact, there is a clear, urgentand practical need for the identification of comparatively simple policy instru-ments that are capable of reducing poverty at any given level of growth. The onethat we particularly wish to put under the microscope is the composition of publicexpenditure, which is arguably easier to manipulate in the interests of the poorthan most alternative policy variables. By contrast with other instruments of gov-ernment economic policy, it is quick-acting, wide-ranging and selective: by contrastwith direct taxation, it impacts on the livelihoods of the majority of people and canbe changed relatively quickly; and by contrast with exchange-rate policy, it can berelatively easily adjusted to the requirements of particular potential beneficiarygroups (Van de Walle and Nead, 1995).

Serious practical difficulties nevertheless arise when attempting to assess theorientation towards poverty reduction of any given composition of public expen-diture: individual sectors differ in the balance of their direct and indirect effectson poverty reduction as well as in their overall impact (Ferroni and Kanbur, 1991).Because of these difficulties, previous research efforts have not yielded compre-hensive estimates of the pro-poor content of public spending – only partial studieswhich indicate that certain components are pro-poor, (Gupta et al., 1999) forprimary education and health spending, or anti-poor (Knight et al., 1996) formilitary spending. Two of the present authors, with others, have developed a rangeof methodologies for devising one overall measure of pro-poor (public) expen-diture, called the PPE index (Gomanee et al., 2003). The general procedureconsists of two stages. First, sectors are identified that, from the literature (on basicneeds, on benefit incidence, and so forth) and among development practitioners,have a reputation of being pro-poor: basic health care, primary education, waterand sanitation, rural roads and agricultural extension services. Next, sector-specificpoverty elasticities are estimated and a composite policy indicator is constructedthat weighs sectoral outlays accordingly. Appendix A describes in some detail theconstruction of the PPE index used in the present analysis. From the discussionthere it emerges that whereas poverty elasticities of public spending on othersectors confirm an a priori expectation irrespective of which poverty measure isused for estimation, the impact of public spending on health crucially depends onthe choice of performance indicator; we therefore below analyse the impact ofhealth spending separately.

In addition to the PPE index, in our estimations we consider two additional‘handles’ – inequality and corruption – which influence the poverty leverage ofaid, although the ability of donors to grasp and manipulate these handles in theway that they appear to be able to control the PPE index (see Section 2) is muchmore open to debate. A well-established result in the literature is that inequality



exercises downward pressure on the extent to which growth benefits the poor(Hanmer and Naschold (2000) and references therein), as well as on growth itself.It operates through four channels in particular: by reducing levels of social capitaland trust, by increasing the likelihood of civil conflict, by depressing demand for(and hence production of) goods and services at the bottom end of the incomescale, and by reducing investment (Nafziger and Auvinen, 2002; Alesina andPerotti, 1996). Corruption likewise exercises a downward influence on investmentand productivity, mediated in part through an increase in the cost of doingbusiness and in part through a depletion of social capital (World Bank, 1997).Most importantly for our purposes, corruption is likely to affect the share of publicspending, even on allegedly ‘pro-poor’ sectors, that reaches the poor: it affectswhat is commonly called the benefit incidence of spending (Van de Walle, 1998;Castro-Leal et al., 1999; Sahn and Younger, 2000).

Noting the strong possibility that poverty, aid and PPE are simultaneouslydetermined (e.g. aid may be related negatively to poverty in the impact equationbut positively in the allocation equation; and so forth), we specify the followingsystem, consisting of poverty, policy and aid equations, the structural parameters ofwhich are to be estimated using a GMM 3SLS estimator.

Pit ¼ f1ðYit ;Xit ;XitÞ þ e1it ð2Þ

Ait ¼ f2ðNit ;Yit ;VitÞ þ e2it ð3Þ

Xit ¼ f3ðYit ;Ait ; kitÞ þ e3it : ð4Þ

The specification of the equations is standard and largely drawn from theliterature. The poverty equations (2) are estimated both using a poverty head-count ratio and alternatively infant mortality as the dependent variable (two ofthe well-publicised OECD Millennium Development Goals), and regressed onincome per capita (Yit) and a range of other variables denoted by the vector X,which comprises inequality, corruption and a combination of public spending indi-cators.8 Analogous to Burnside and Dollar’s (2000, p. 851) method of con-structing a composite policy indicator, the PPE (Xit) index is determined usingvalues of coefficients on individual spending indicators in a version of (2) de-fined in Appendix A. The aid equation (3) relates the share of OverseasDevelopment Aid (ODA) in GNP to the well-documented small-country bias inaid allocation through the inclusion of population size N, and includes a vectorV of other relevant variables (infant mortality representing a perceived financingneed, colonialisation9 and Islam dummy variables representing donors’ strategicinterest in or affinity with a recipient country, and so forth). It also includesvarious good policy variables to allow for the possibility that good policy attractsaid. The policy equation (4) examines the extent to which aid alters the struc-

8 The exact combination will depend upon whether the dependent variable is the poverty headcountindex or infant mortality.

9 The potential influence of colonialisation on developing countries’ performance has been noted byseveral authors, e.g. Acemoglu et al. (2001) and Bertocchi and Canova (2002). In our analysis this role isfocused on the greater potential for aid to flow to ex-colonies.



ture of public expenditures in a pro-poor direction, reflects the possibility thatPPE and health spending increase with income per capita (in other words, are‘luxury goods’), and includes a vector k of control variables.10 The exact struc-ture of the system is evident from Table 3.

As a first step we estimated equations individually, using OLS. Crucial coeffi-cients, notably on PPE and health spending in the headcount poverty and infantmortality equation, respectively, and on aid in the PPE equation, have theexpected sign and are significant.11 Table 3 presents the results of estimatingequations simultaneously, using a GMM 3SLS estimator. Columns 1 to 5 reportcoefficients on all pertinent variables apart from corruption; because of thelimited overlap between headcount poverty and corruption data points, whichwould have resulted in a substantial loss of degrees of freedom, we report incolumns 6 to 8 a smaller system of equations that includes (absence of) cor-ruption as an independent variable. We discuss the poverty and aid equationshere; the results obtained for the policy equations form part of the discussion ofthe next Section.

Most importantly, we find that the PPE index is comfortably significant (with thecorrect sign) as a determinant of headcount poverty, whereas health spendingcomes into its own as a determinant of infant mortality. Inequality and corruptionalso appear with the expected signs and are strongly significant. When inequalityand public spending priorities are included in the poverty equation, the growthelasticity of headcount poverty reduction becomes 0.48 and that of infant mortality0.46. This result casts doubt on Collier and Dollar’s (2001, 2002 and in this sym-posium) confident assumption that this elasticity is a universal constant of 2. Theirassumption amounts to saying that 1% extra growth always reduces poverty by anextra 2%, whereas our findings suggest that, on average and controlling for othervariables, 2% growth reduces poverty by less than 1%.12 Not only the magnitude ofthis poverty elasticity, but also the assumption that it is constant requires modifi-cation. To allow for the possibility that inequality as proxied by the GINI coeffi-cient impacts both directly on poverty and indirectly by limiting the impact ofgrowth and PPE on poverty,13 we estimated the following poverty equation withinthe full system estimation framework.

10 In the event only population, in the health expenditure equation, entered the final equationstructure. This was to reflect the possibility that there might be economies of scale in public serviceprovision.

11 The same is true for most other coefficients, with the exception of the trend term in the infantmortality equation, the macro-policy variable in the aid equation and the trend and aid terms in thehealth spending equation.

12 Although it is important to bear in mind that we speak here of a partial elasticity. Taking accountof the impact that growth has on poverty reduction through raising PPE yields a full growth elasticity ofpoverty reduction of 0.92; still considerably lower than 2. The limitations of cross-section analysis mustalso be borne in mind. Our data set, described in Gomanee et al. (2003), is a pooled sample of 34countries for the period 1980–2000: identification is therefore only partly based on within-countrychanges over time and for the remaining part on cross-section variation.

13 Intuitively it is apparent that the impact of growth on poverty, e.g. in shifting the income distri-bution to the right, will depend upon the proportion of people in the neighbourhood immediately tothe left of the critical level of income defining poverty. This in turn will be linked to the distribution ofincome.



Tab

le3

Aid

,P

ro-p

oor

Exp

endi

ture

and

Pov

erty

(3SL

S)

lnP

ove

rty

hea

dco

un

t($

1/d

ay)�

lnIn

fan

tm

ort

alit

y�ln

Aid

(OD

A/

GN

P)�

lnP

ro-p

oo

rex

pen

dit

ure

(PP

E)§

lnH

ealt

hsp

end

ing

(%o

fG

NP

)�ln

Infa

nt

mo

rtal

ity�

lnP

ro-p

oo

rex

pen

dit

ure

(PP

E)§

lnH

ealt

hsp

end

ing

(%o

fG

NP

)�

Co

nst

ant

5.49

6***

8.08

9***

12.1

46**

*)

4.44

6***

)6.

539*

*7.

827*

**)

3.49

4***

)1.

902

(4.1

9)(1

6.91

)(6

.43)

(2.8

0)(2

.49)

(14.

53)

(3.2

4)(1

.20)

lnG

NP

pc�

)0.

479*

*)

0.45

7***

0.60

2***

0.80

9**

)0.

357*

**0.

433*

**0.

557*

*(2

.62)

(6.5

7)(3

.19)

(2.6

1)(4

.57)

(3.1

5)(2

.91)

Pro

-po

or

exp

end

itu

re(P

PE

)§)

0.74

0**

(2.3

1)P

ub

lic

hea

lth

spen

din

g(%

of

GN

P)�

)0.

223*

**)

0.18

8***

(5.3

4)(5

.04)

Gin

i–0.

046*

**(3

.26)

Ab

sen

ceo

fco

rru

pti

on��

)0.

141*

**0.

116*

**0.

011

(4.4

1)(3

.04)

(0.2

3)L

n(a

id)�

low

-inco

me�

�0.

420*

*0.

191

0.28

0**

0.20

4(2

.18)

(1.4

34)

(2.7

4)(1

.51)

Ln

(po

pu

lati

on

)�)

0.94

6***

)0.

126*

**)

0.12

8***

(7.4

5)(3

.51)

(3.5

2)C

olo

ny§

§0.

395

(1.1

5)Is

lam

––

0.80

7*(1

.89)

Mac

ro-p

oli

cy��

0.09

6**

(2.6

9)O

pen

��

)0.

517

(1.6

5)ln

(in

fan

tm

ort

alit

y)�

1.25

7***

(3.8

0)T

ren

d)

0.01

10.

040*

*)

0.02

2*0.

012

(0.9

6)(2

.57)

(2.0

1)(0

.81)

R2

0.30

0.77

0.52

0.47

0.20

N67

6767

6767

5757

57

t-st

atis

tics

inp

aren

thes

es.*

sign

ifica

nt

at10

%,*

*at

5%,a

nd

***

at1%

.No

tes

and

dat

aso

urc

es:d

ata

set

isa

po

ole

dsa

mp

leo

f34

cou

ntr

ies

for

the

per

iod

1980

–200

0;fo

rd

etai

lsse

eG

om

anee

etal

.(20

03);

�W

orl

dB

ank

Po

vert

yM

on

ito

rin

gD

atab

ase;

�W

orl

dD

evel

op

men

tIn

dic

ato

rs(C

D-R

OM

);§

IMF

Gov

ern

men

tFi

nan

ceSt

atis

tics

and

Wor

ldD

evel

opm

ent

Rep

ort

(var

iou

sis

sues

),co

nst

ruct

ion

des

crib

edin

Ap

pen

dix

A;–

WID

ER

ineq

ual

ity

dat

abas

e;��

Tra

nsp

aren

cyIn

tern

atio

nal

;��

low

-inco

me¼

1fo

rG

DP

pc

<1,

422;

§§

Co

lon

y¼

1fo

rex

-co

lon

ies

of

Bri

tain

and

Fra

nce

;––

Isla

m¼

1fo

rIs

lam

icco

un

trie

s;��

infl

atio

nan

db

ud

get

defi

cit;

��

Sach

san

dW

arn

er(1

995)

ind

icat

or

of

op

enn

ess

of

the

eco

no

my.



LPOVi ¼ ð5:849(1.59)

� 0:867LGDDPCi

(1.70)� 2:690PPE

(2.70)þ 0:251GINIiÞ

(1.64)� ð1 � 0:0112GINIi

(4.34)Þ: ð5Þ

The significance of coefficients on the variables in the other equations isunaffected. In the poverty equation, coefficients on income per capita and PPEretain their previous significance, whilst the inequality term is significant at the 1%level. It follows that the growth elasticity of poverty reduction is not constant but aninverse function of inequality – that is, the greater the degree of inequality, thelower will be the poverty elasticity coefficient (a):

ai ¼ 0:867 � 0:0112GINIi ð6aÞ

and also the partial derivative of poverty with respect to PPE:

ci ¼ ð2:690 � 0:0112GINIiÞPPEi ð6bÞ

Our estimates imply that for high inequality countries in the definition ofHanmer and Naschold (2000), that is countries with a GINI coefficient of 43% orhigher, the partial growth elasticity of poverty reduction is 0.45 or lower – a valuevery similar to the one they obtain for this group of countries (0.34).14 We makeuse of the result that crucial poverty elasticities are affected by inequality in ouraid-allocation simulations in Section 3. The aid equation itself, reported on incolumn 3, behaves largely as expected. We find strong evidence for a small-countrybias and also find that aid is targeted towards a perceived need (as proxied withinfant mortality). Although outside our main focus, an intriguing finding is thatwhen we deconstruct the Burnside and Dollar indicator of good policy, we findthat good domestic policies attract aid whereas openness does not.

To summarise the evidence presented so far, we find that a combination ofgrowth, public spending priorities, inequality and corruption determine poverty inour model – all of which represent channels for aid to impact on poverty oncedonors have found a technique to influence those. Here of course lies the maindifficulty: donors may not yet – given the present state of research – be in aposition to exercise much influence on growth, inequality and corruption.15 Wewill demonstrate in the next Section that their prospects may be much better inthe area of attempting to influence public spending priorities. In particular, wemust justify the controversial statement that in this area ‘conditionality can work’.Collier and Dollar, in this symposium, have by contrast argued that ‘donors(should in most circumstances assume) that they have no influence on policy atall’ – an approach which, as we saw, has now extended into the rhetoric of the

14 These elasticity estimates are, of course, based on cross section regression results and thus, as isalways the case with cross section results, care needs to be taken before accepting too readily that theyare an accurate reflection of the elasticity in any given country over time. This is, as we have said, alimitation which is implicit in many cross section analyses. In this case data limitations are substantialand effectively prevent more substantial time series analyses. In any case, the latter themselves face theproblem that the elasticity may itself be changing over time.

15 The impact of corruption is to significantly increase infant mortality for a given level of healthexpenditure and other variables and to reduce PPE expenditure.



entire World Bank (footnote 2 above).16 On what basis can we claim that thisproposition is incorrect?

2. The Influence of Aid on Pro-poor Policy

Of the various ways in which governments may seek to influence poverty, we focusin this Section on the PPE (pro-poor expenditure) index, on the grounds that it isrelatively easy for resource-constrained governments to influence – easier thanother public instruments of redistribution such as tax progressivity, and certainlyeasier than corruption17 and inequality, which are the other catalytic variablesrevealed as significant by Table 3. The question now to be tackled is whether aiddonors can in turn influence those governments. The PPE equations in Table 3suggest that financial aid has influenced public spending in a pro-poor direction,but only in countries with a GDP per capita below a critical threshold.18 We returnto this result below, towards the end of this Section, but first look at recent casestudy evidence and examine our data set in more detail, so as to be able tounderstand the forces behind this crucial finding.

Recent case studies suggest that for aid to bring about increased pro-poorspending, policy dialogue has to come into its own: donors must negotiate skilfully,co-ordinate their demands, and be in it for the long haul. The process leadingfrom reluctance through a cautious to a whole-hearted embrace on the part ofrecipient country governments of donors’ advice on budgetary priorities has beendocumented for Ethiopia (Rock, 2003), Mozambique (Mosley, 2002), Tanzania(Morrissey, 2001) and Uganda (Morrissey and Verschoor, 2002). In those cases,the sequence was from a building up of trust between donor and recipient through‘constructive agreement to disagree’, to the donor being invited to provide tech-nical and financial support in implementation of a poverty action plan whose spirithad been agreed within government, to an eventual upward shift in PPE – verydifferent from old conditionality. There are some remarkable similarities betweenthe cases mentioned:

(i) Debt cancellation in return for pro-poor expenditure changes. The fourcountries mentioned were among the earliest and largest beneficiaries fromHIPC debt cancellations (Mozambique is the largest of all);

(ii) New instruments of budgetary control. Examples include spending targets,volume targets and required matching of counterpart funds spending by

16 We emphasise the World Bank. The International Monetary Fund has been practising ex antepolicy conditionality since the origin of the Bretton Woods system in 1945 and shows no sign ofabandoning it.

17 Knack (2000) argues that aid has historically tended to encourage corruption. See also the paperby Collier and Dollar in this symposium.

18 The ‘switch point’ of recipient income (determined inductively) at which aid ceases to haveinfluence on the pro-poor content of public expenditure is $1,422. The coefficients on the remainingterms in the PPE and health spending regressions are largely as expected: a higher income per capitatends to promote a larger share in the economy of pro-poor sectors (they are indeed ‘luxury goods’),and the absence of corruption, which may proxy for a government that ‘cares for its people’, isassociated with more spending on behalf of the poorest.



government, all of which made recurrent expenditure conditionalities easierto monitor;

(iii) Social funds (White, 2002) under which donors made funds available forlabour-intensive public works projects in priority sectors – a very effectivedevice for increasing the PPE index;

(iv) New conditionality. Not only have the instruments of aid policy been chan-ging, but so has its style. The transition from ‘old’ to ‘new’ loose-reinconditionality, documented in the introduction, has reduced the likelihoodof a retreat by both parties to ultimatum positions from which there is littlescope for a donor to influence a recipient’s policy stance. In these four cases,donors tended to initially tolerate short-term deviations from targets as trustwas built up but later increased pressure on recipients to meet increasinglyjointly agreed-upon targets. This tendency was often combined with invita-tions to recipients to frame their own definition of pro-poor expenditurewithin the general context of Poverty Reduction Strategy Papers (PRSPs).19

(v) Parallel to this development, a new politics appears to have taken root, inwhich governments find it expedient to use aid money to pursue goals withbroad-based political appeal – which are consonant with poverty reduction(the achievement of universal primary education in Uganda is a classicexample) – rather than to placate a more narrow group of powerful rent-holders, as in Zimbabwe and (until recently?) Kenya.20

In all of these cases a virtuous circle operated that contradicts the Collier-Dollarproposition of donors having no influence and, as a consequence, there have beenmore opportunities to turn potential into actual aid leverage. Needless to say, thisprocess did not always run smoothly and exactly how this process has occurred, inthe places it has, is not always completely clear but one key element in it appears tobe that, in those places, aid donors made themselves a political as well as afinancial asset to recipients – in part through a negotiating style characteristic of anew cluster of donor influence, which we have termed ‘new conditionality’. Thecombined impact of cases such as these appears to be strong enough for an overallrelationship to register within our system estimation between aid and PPE(Table 3), at least in low-income countries.

But if, on this evidence, ‘conditionality works’, why does it only work in low-income countries? Figure 1 provides a scatter of the aid-PPE relationship, fromwhich we may observe the experience of some other countries for which we havedata about changes over time (but no case study evidence) where a virtuous circleof the kind documented above may be operating: Ghana, Kenya (sic) and Nepal. Atight relationship between aid and PPE in the sense of episodes of changing aidcoinciding with a changing PPE index is indeed confined to the subset of low-income countries, li, following the World Bank (2000) definition. By contrast, even

19 The definition adopted by the Uganda Ministry of Finance in 1994 is in many ways moresophisticated than our PPE index. For a general discussion of the PRSP process, see Stewart (2003).

20 In an election on December 27, 2002 the former president Daniel Arap Moi stood down andKANU, which had governed the country for forty years since independence, was defeated by the newly-formed Rainbow Coalition under the leadership of the former finance minister Mwai Kibaki.



though values of the PPE index are considerably higher in middle-income, mi,countries than in low-income countries (manifestly evident in Figure 1 whereclosed dots denote middle-income and open dots low-income countries), changesin aid do not appear to have much effect on changes in PPE scores: PPE scoresappear to respond in low-income countries but not in middle-income countries toincreases or decreases in aid inflows.

We suggest that there are two factors at work. First, in poorer countries, thescope for fungibility is less, for the simple reason that where aid is paying for mostof the public sector’s investment budget, recipients have very limited ability toswitch into types of spending not desired by aid donors. Second, we have seen thatdonors have been using new instruments of conditionality, and it is in low-incomecountries that the share of aid in the economy, and for that reason its potentialleverage, is greatest. For illustrative purposes, we have estimated the link from aidto PPE in OLS regressions separately for both groups of countries, so as to be ableto add best-fit lines to the data in the Figure. These lines illustrate the point madeproperly within our system estimation (Table 3) that for countries with a per capitaincome below a threshold of about $1,450 does aid significantly influence recipi-ent governments’ spending priorities.

On the basis of both case study evidence and econometric analysis, we contendtherefore that, whatever conditionality may or may not have achieved in relation toother policy variables, it is achieving something in relation to spending priorities ofgovernments in low-income countries. The relationship between aid and pro-poorexpenditures, illustrated in Figure 1, is established econometrically in Table 3 andvalidated by case study evidence. It should therefore not be seen as merely a

–2

–1

0

1

2

3

4

5

6

7

8

–5 –4 –3 –2 –1 0 1 4

ln (ODA/GNP)

PPE

sco

re

limi

Les91

Nic93

Zim87

Gha81

Gha93

Nep81

Nep93

Zam86

Ken81

Ken93

Eth88

Zim89

Mad91

Mad93

Ven83

Hun90Hun92 Hun91

Jor91

Idn89

Bra81

Bra89

Bul91

Bul92

Cos85Cos93

ols : PPE = 3.728 – 0.006 ln (ODA/GNP)(t) (26.611) (–0.083)

ols : PPE=1.648+0.367ln(ODA/GNP)(t) (5.793) (2.514)

Bwa87Bwa92

2 3

Fig. 1. Aid and PPE



statistical artefact but as constituting a genuine process of influence, even if theprocess is not universal, nor completely understood. It is plausible that neglect ofthose possibilities for ‘new conditionality’ that do exist, as proposed by Collier andDollar, leaves a reservoir of poverty-reducing opportunities untapped. In the nextSection we quantify the unrealised potential of an aid allocation rule that rejectsconditionality and exclusively practices selectivity. It is also important to stress thatour empirical analysis only covers a limited form of new conditionality and as it hasbeen in evidence in the past. The more rigorous form we propose which explicitlylinks aid receipts to good policies along several dimensions, which also have inbuilt flexibility to suit the specific realities of the country may be expected tochange behaviour and induce greater adherence to good policies than in the past.Moreover there is no reason to expect that the success of such policies should belimited specifically to low per capita income countries as identified above.

3. Selectivity versus Conditionality

To recapitulate, the major differences between our approach and that of Collier-Dollar are:

(i) we leave open the possibility of using a new conditionality approach to directthe allocation of aid rather than only a selectivity approach, given that aid isable to influence the expenditure mix;

(ii) we work with estimated poverty impacts (of PPE and of income per capita)that vary as a function of recipient countries’ characteristics.

The findings presented in Sections 1 and 2 obviously have a bearing on theoptimal distribution of international aid. In this Section we obtain an order ofmagnitude of the difference that new conditionality can make by deriving an aidallocation formula with key elasticities based on our estimation results, and byworking through a number of scenarios. Collier and Dollar (2001) (CD from nowon) approach the problem of aid optimisation by holding constant the elasticity ofpoverty reduction with respect to income, and assuming that aid has no impact onpolicy. We approach the problem by allowing poverty elasticities to vary as a func-tion of corruption and inequality, by including the leverage of public expendituresas an argument in the poverty reduction function and by treating the structure ofpublic expenditures as a discontinuous function of aid. We set up the basic problemin the same way that CD set up theirs, modifying terms where appropriate given ourdifferent assumptions. Formally, the optimisation problem facing donors who aimto maximise the poverty reduction impact of their collective overseas aid budget(given a small-country bias in aid allocation) can be represented as

MaxX

i

GiaihiN iN i�b þ XicihiN iN i�b ð7Þ

subject to Xi

AiyiN i ¼ �A

Ai � 0



where the right hand side of the constraint relates to a fixed aid budget, G denotesgrowth, a the growth elasticity of poverty reduction (with respect to GDP percapita), h the poverty headcount ratio, N population size, b the small-country biasin aid allocation, X PPE, c ¼ ¶h/¶PPE, A aid, y per capita income, and i a countrysuperscript (for ease of comparison, we adopt CD’s notation in this Section).Solving the Lagrangean yields

Giaa

i þ Xiac

i� � hi

yi¼ kN ib: ð8Þ

Subscript a denotes the partial derivative with respect to aid, and k the shadowvalue of aid (with the understanding that poverty reduction is in effect valued morein countries with a smaller population; for a precise explanation, see CD, p. 1792). Inorder to be able to isolate the effects due to tracking a route for aid via the structureof public expenditures and to specifying elasticities of poverty reduction as a func-tion of corruption and income inequality; and to be able to compare these effectsfairly with CD’s calculations, we work with their Ga function for the response ofgrowth to aid (for a critique, see Dalgaard et al.’s contribution to this symposium):

Gia ¼ 0:185P i � 0:072Ai ð9Þ

Pi denotes a country’s CPIA score, which, as will be recalled, is based on WorldBank country experts’ assessment of the quality of institutions and policies forpromoting broad-based growth. Inserting (9) into (8) and solving for aid gives thecountry-by-country aid allocation that maximises donors’ objective function (7):

Ai ¼ 2:6P i � k0:07ai

N ib hi

yi

� ��1

þ Xiac

i

0:07ai: ð10Þ

The first two terms on the right-hand-side of (10) capture in a nutshell the casefor being selective in aid allocation as made by CD: CPIA scores, poverty andpopulation (with the growth elasticity of poverty reduction held constant),determine jointly where the marginal benefit of aid is highest and thence wherethe marginal aid dollar should go. The third term represents part of our addition.It represents an aspect of new conditionality: the higher the poverty leverage ofaid,21 the greater the extent to which donors will be rewarded for attempting topersuade recipients to re-orient public spending towards poverty reduction.

Scenario 1 (selectivity)22

Ai ¼ 2:6Pi � k0:07ai

N ib hi

yi

� ��1

;

ai ¼ a 8i:

This is selectivity CD-style, ignoring in the aid-allocation rule, variation in thegrowth elasticity of poverty reduction.

21 To be precise, aid’s impact on PPE times PPE’s impact on poverty relative to the growth elasticity ofpoverty reduction.

22 In all scenarios the formula the optimal aid allocation is assumed to have a lower bound of zero.



Scenario 2 (proper new conditionality, with c allowed to vary with respect to PPE)

Ai ¼ 2:6P i � k0:07ai

N ib hi

yi

� ��1

þ Xiac

i

0:07ai;

ai ¼ f ðCiÞXi

a ¼ f ðyi ;AiÞci ¼ f Pi ;Ci

� �;

where P denotes corruption. In this scenario the aid-allocation rule is optimal:selectivity and new conditionality jointly realise donors’ objectives, with the formernoting that inequality C lowers the growth elasticity of poverty reduction, and thelatter noting that corruption and inequality worsen the benefit incidence of publicexpenditure items that have the potential of being poverty-reducing. This is, ofcourse the ideal, but lack of full information on corruption limits the subsequentanalysis to linking c to inequality.

In the results which follow we

(i) calculate the optimal allocation of aid under the two different scenarios and(ii) hence calculate the impact on poverty reduction in relative terms.

We do not, as others have, present specific estimates of poverty reduction. Wefeel this is likely to be misleading given the number of assumptions that have beenmade and in any case also depends upon the dynamics of poverty incidence fol-lowing, semi-endogenous, demographic and policy changes and also the differ-ential ability of economies to absorb substantial changes in aid allocations, ananalysis of which is beyond the scope of this paper. In calculating optimal aidallocations we need information on the CPIA index. This we do not have, but wedo have (CD) qualitative information. We assume that very poor, poor, moderate,good and very good equate to 1.95. 2.5, 3.5, 4.0 and 4.5 respectively.23 We also failto have information on PPE for all the countries in the sample and where this isnot available we use predicted values based on the regression reported in Table 3.

In the Appendix (Table A3) we present, for all countries included in CD’sanalysis the distribution of aid in 1996 that results from the respective aid-allocation algorithms. In this algorithm total aid is constrained to be equal to theactual aid expenditure budget by adjustment of k. Scenario 1 is our approxi-mation of the CD rule. The results we obtain are very similar to theirs (CD, pp.1795–6). The correlation is 91%. In scenario 2, we take into account bothinequality and PPE in determining the optimal aid allocation. Several qualifyingcomments are required for a proper assessment of these figures, and before anyaid donor jumps to conclusions. In the first place, recipient governments’ anti-corruption stance has not been taken into account in any of the scenarios pre-sented. A country such as the Congo Republic, which in scenario 2 receivesalmost double the amount it receives in scenario 1, would see its share of the

23 These give reasonably close approximations to the regional values reported in CD. The practice isof course not ideal, it would be better to use the actual values, but as these are not currently in thepublic domain that is not possible.



total aid budget plummet again when note is taken of its very poor record offighting corruption.

In the second place, and as noted previously, any conclusions which are drawncritically depend on assumptions about poverty elasticities. In the simulationspresented, these are assumed to be stable functions of inequality. In common withDalgaard et al. in this symposium, who argue that excessively high rates of growthin foreign assistance may render it ineffective, we would surmise that in the eventof the very large inter-country allocations of aid proposed in these simulations,such functions may well collapse, because of absorptive capacity problems (thedifficulty experienced by Ethiopia in spending an increase in aid of less than$100 million of Live Aid money in the famine of 1984–5 will be recalled). Sup-posing that an immediate step-jump increase in aid to Ethiopia of 5.5 percentagepoints of GDP (or $330 million) as proposed by CD, or 8.4 percentage points ofGDP as in scenario 2 could be secured, it seems extremely unlikely that povertycould be made to respond to such an aid boost with the elasticities computed inthis paper, at least in the short run, let alone with the even more optimistic oneassumed by CD. In the third place, any allocation method based on parametersobtained in cross-country regressions is bound to make mistakes in individualcases, because local knowledge is ignored (which corresponds with the variationleft unexplained by the regressions). In the fourth place, as noted towards the endof Section 2, middle-income countries may suffer unduly in our approach becausethose future opportunities for new conditionality that may exist also in richerdeveloping countries can by definition not be observed in statistical patterns indata about the past.

The combined force of these qualifying comments is such that the figures inTable A3, still less the amount of poverty reduction that can be computed fromthem, cannot be taken at face value. But since they weigh equally heavily on

Table 4

Parameters Used in the Aid-allocation Simulations

Parameter Meaning Computed using:

P CPIA score Values determined inductively so as to obtainas close an approximation as possible to theregional averages reported by CD.

CPIA-based grade: Very good ¼ 4.5,Good ¼ 4.0, Moderate ¼ 3.5, Poor ¼ 2.5,Very poor ¼ 1.95

X PPE score Computation described in Appendix 1(missing values replaced with predictedvalue)

Xa Partial derivative of PPE w.r.t. aid Table 3 above (but set at zero in theallocation rule in 1 for all countries and in 2for middle-income countries)

a Growth elasticity of poverty reduction ai ¼ 0.48"i in the allocation rule in 1;ai ¼ f(C) (6a) in the allocation rule in 2

c PPE elasticity of poverty reduction ci ¼ f(C,P) (6b) in the allocation rule in 2k Shadow price of aid Determined endogenously in order to

equate simulated and actual aid



scenario 1 and 2, and since our purpose in this Section was never more than toobtain an order of magnitude of the difference new conditionality can make to theCD allocation rule, the differences between the scenarios should be revealing. Thecountries that benefit most from a move towards scenario 2 are low-income, low-inequality countries such as Mozambique: where CD propose a reduction in aidcompared to the actual allocation, scenario 2 virtually restores aid to its actuallevel, implying that the actual allocation is in fact more or less the correct one. Bythe same token, the countries that suffer most are middle-income, high-inequalitycountries such as Honduras, which receives approximately half the aid underscenario 2 it would receive under scenario 1. Overall, we estimate that the amountof poverty reduction that our point estimates of PPE and growth elasticities imply is12% higher under scenario 2 than under scenario 1.

Although this estimate of the size of the reservoir of poverty-reducing oppor-tunities that the CD allocation rule leaves untapped is the best available to us, itshould be interpreted with some caution. Because of data limitations we haveworked with imputed CPIA values and (in some cases) PPE scores. New condi-tionality may perform better in reality than we have been able to simulate herebecause of the possibilities we have noted for extending the approach to morecountries and more areas of pro-poor policy; and selectivity may perform betterthan simulated because, in a form of reversal of the Lucas critique, recipientcountries, once aware of the selection criteria inherent in the aid-allocation rule,are given an incentive to make themselves eligible for aid by improving theirpolicy environment (or at least those aspects of it on the basis of which theyknow donors allocate aid). In reality this 12%, although not an inconsiderablebenefit especially when it is 12% per annum, is likely to be a considerableunderestimate as to what can be achieved by targeting aid. The form of newconditionality we have included in this paper is focused on the impact of aidinduced policy reform within a narrow area. In practice and in future there needbe no such limitations.

4. Conclusions and Policy Implications

Fifteen years ago, in its comprehensive review Twenty-Five Years of DevelopmentCo-operation, the OECD’s Development Assistance Committee (DAC) concludedthat ‘the most troubling shortcoming of development aid has been its limitedmeasurable contribution to the reduction – as distinguished from the relief – ofextreme poverty, especially in rural areas’ (World Bank, 1990, p. 127). Partly inresponse to this shortcoming, the OECD in 1996 announced a range of Inter-national Development Targets, the centrepiece of which is a halving of theproportion of the population in extreme poverty by 2015, which constitute afocus for the strategies of most of the main donors. Our objective here has beento try and understand what they can do through aid, which of course is only oneof the policy instruments available to them (de Haan, 2003), to increase theirimpact on poverty.

In common with Collier and Dollar, we feel that inter-country reallocations ofaid could increase such poverty impact. Among the criteria that could form the



basis for such reallocations, we find corruption, inequality and the compositionof public expenditure to be particularly strongly associated with aid effectiveness.But whereas they reject the conditionality approach in favour of ‘selectivity’, wemaintain that conditionality – especially in what we define as its ‘new’ form –represents an important channel by which aid can reduce poverty. Taking somecaveats into account, our work suggests that aid allocations which take account ofgood micro and macro policies as well as income distribution and GDP percapita are more effective than ones which tend to ignore income distributionand the potential for impacting upon microeconomic policies. We believe thatthe better performance of new conditionality that we simulate above derives froma real phenomenon, which we have sought to illustrate through the analysis ofthis paper. That reality is that the possibilities for conditionality, in that majorityof LDCs where economic policy is not ideal, continue to be significant andutilised by aid donors, albeit nowadays in a more subtle form than the ‘ultima-tum’ form favoured in the 1980s and the very early 1990s; in particular donorscan take advantage, through Poverty Reduction Strategy Papers and otherwise,of the possibilities opened up by the adoption of a more pro-poor publicexpenditure mix.

Thus, this puts us somewhat in between the Collier and Dollar view of the worldand that of Dalgaard, Hansen and Tarp. We find the latter’s emphasis on geog-raphy to be interesting and potentially important. It is particularly important if itimplies that perceptions of good policy and, hence, aid allocation rules, need to beconditioned by factors such as geography. But equally, we feel that this is unlikelyto undermine completely the freedom of governments to pursue good/bad poli-cies and also the ability of aid to impact upon such policies, via new form condi-tionality or the ex-post effects of selectivity. We are also sympathetic to theDalgaard, Hansen and Tarp conclusion that too high growth rates of aid mayrender it ineffective and this needs to be taken into account when consideringshifts from current aid allocations to statically optimal ones. However, we alsoacknowledge the contribution of Collier, Dollar and associates in focusing on thepotentially differential impact aid might have dependent upon the recipientcountry’s characteristics. But we feel that their usage of good policy, first infocusing on macro-polices and subsequently by a simple agglomeration of differ-ing, and possibly conflicating, policies is unsatisfactory. What further differentiatesour paper from both the other contributions is our focus directly on poverty, whileassuming an effect of aid on economic growth; whilst other papers have focused onthe impact of aid on growth while taking the impact of growth on poverty as givenand automatic.

For credibility it is important that new conditionality be keyed to policy variableswhich have a demonstrable ability to reduce poverty. We focus in particular onwhat we call the pro-poor expenditure index, which several LDC governments havefound relatively easy to manipulate and which aid, in its turn, has shown its abilityto influence. Further research in defining this link more precisely (for example, inillustrating the positive role played by specific types of agricultural expenditure,and the negative role played by arms expenditure) is required before we can beexact about the form which poverty-conscious restructuring of public expenditure



should take. In the interim, the PPE index represents one rule of thumb which canbe used as a basis for new conditionality.

University of SheffieldUniversity of BathUniversity of Sheffield

Appendix A: Constructing the ‘Pro-poor Expenditure (PPE) Index’

The data set we use includes spending on those sectors that in the basic needs literature andamong development practitioners have the reputation of being pro-poor: education(especially primary education), health care (especially basic health care), water and sani-tation, agricultural research and extension, and rural roads (Verschoor, 2002). Not all thesespending data are available on a sufficiently comprehensive scale: the more disaggregatedthe expenditure item, the less readily information about it can be obtained. Spending datafor education (including primary) and health care (including basic) are recorded inUNESCO statistical yearbooks, and IMF Government Finance Statistics (GFS) yearbooks,respectively. For spending on other pro-poor sectors, we have had had to use proxies: waterand sanitation is included in the World Development Report’s ‘social services’, but this is a verybroad category; agricultural research and extension, and rural roads, we have had to proxywith the sector agriculture as a whole (IMF GFS).

We believe that the inter-sectoral mix of public expenditures may reduce poverty throughat least three channels:

(a) some expenditures are more intensive in the labour of the poor and hence generategreater labour-market benefits;

(b) some expenditures provide more services for low-income consumers (and in somecases generate externalities for them as well);

(c) some expenditures are better at generating social networks which are economicallybeneficial (‘social capital’).

Through all of these channels it is possible to reduce inequality by altering the expen-diture mix, and thereby very possibly to use it as a conflict prevention device in the mannerdescribed by Collier and Dollar (in this symposium). The idea of designing a ‘povertysensitive’ pattern of public expenditures has been often articulated (notably by Ferroni andKanbur (1991)), but to our knowledge such a pattern has not been empirically documen-ted. No approach is likely to be perfect because of the range of poverty impacts which areconceivable but the following ‘quick and dirty’ methods can be visualised. The first twocover only one channel of impact (and we only have data for a few countries), whereas thelast two are more general:

1. A labour-intensity approach – covering effect (a) – the definition of ‘pro-poor expen-diture’ as those expenditure sectors which are most labour-intensive. We know of nostatistical exercises which measure the propensity of different public expenditure sec-tors to take on low-income labour. However, the governments of the two most effectiveexercises in poverty reduction within low-income countries – Uganda and Ethiopia –prioritised the same expenditure sectors, explicitly on the grounds that they are labour-intensive (Morrissey and Verschoor, 2002; Rock, 2003). These are: primary health andeducation, agricultural research and extension, rural water and sanitation.

2. A benefit incidence approach (covering effect (b)) – the definition of ‘pro-poorexpenditure’ as those sectors whose output, on the evidence of household budgetsurveys, is consumed by the poor. Sahn and Younger (2000), drawing on householdbudget surveys in eight low-income African countries, have assessed the extent to



which different public expenditures fall on low-income groups. They conclude thatexpenditures on primary and secondary education (but not university education),and all types of healthcare, can be considered progressive and do reduce inequality.Nonetheless, they warn (p. 344), ‘expectations that social sector spending has asubstantial redistributive impact are misplaced’ and ‘African governments would dowell to consider how to better target their expenditures’.

3. A CGE approach – which can hope to trace comprehensively the effects of expen-diture on poverty through multiple channels of effect. For Uganda only, Chant et al.,(2003) have conducted simulations which examine the impact of different expendi-tures on poverty through all market channels, not just the two examined above. Foreducation, health and ‘social sector’ expenditures, only, they find that the impact ofincreasing the share of public spending dedicated to those expenditure sectors ispoverty-reducing.

4. A regression approach – the definition of ‘pro-poor expenditure’ as those sectorswhere expenditure exhibits correlation with poverty reduction (Gomanee et al.,2003). We estimate the following poverty equation in which spending indicators forwhich we have an a priori preference, as indicated above, are candidate explanatoryvariables.

OLS poverty equation (R2 ¼ 0.660, N ¼ 67).Dependent: log ($1/day poverty headcount ratio)

t

(Constant) 12.4 3.46Log (GDP per capita) )1.05 )2.33Log (education expenditure/GDP) )1.86 )2.43Log (health expenditure/GDP) 1.84 3.17Log (housing and amenities* expenditure/GDP) )0.96 3.21Log (agriculture expenditure/GDP) )0.43 2.17

*Includes water and sanitation and social security.

We may classify as follows (Table A1) the findings of each of these four methods inrelation to the sectors in which expansion of expenditures is likely to be poverty-reducing:

From Table A1 it is very clear that educational and ‘social’ (in the old World Bank’sclassification, ‘housing and amenities’) expenditure belong in any pro-poor expenditureindex. Health and agricultural expenditure are ambiguous. Health very strongly ‘refuses to

Table A1

‘Poverty Elasticity’ of Components of Public Expenditure: Summary of Findings

Components of expenditure

Methodologies

‘Single-channel’ methodologies ‘Comprehensive’ methodologies

Benefitincidence

Labour intensity(Ethiopia and Uganda) Regression CGE(Uganda only)

Educational expenditure + + + +Health expenditure + + ) +Agricultural expenditure (+) +‘Social’ expenditure + + +Military expenditure )

Notation: +, sector indicated has a significant poverty-reducing effect through the methodology stated;(+) sector indicated has a non-significant positive effect on poverty through the methodology stated;) sector indicated has a significant poverty-increasing effect through the methodology stated.



behave’ in our poverty regression, in spite of Sahn and Younger’s weakly positive results onbenefit incidence, and in the last cut we decided to omit it and consider spending on healthseparately in an infant mortality equation in the body of the paper. Agriculture has the rightsign but is insignificant in the regressions; we include it in the light of strong case-studyevidence, in particular from Uganda and Ethiopia (Morrissey and Verschoor, 2002; Rock,2003), that the prioritisation of agricultural spending made a very important difference topoverty reduction from the early 1990s onwards. Thus our PPE index includes indicators ofspending on the following individual sectors: agriculture, housing, education, water andsanitation, and social security. It is computed as follows (with weights on individualspending indicators obtained from the poverty regression reported above):

PPE ¼ 0.431 (log of agriculture as %GDP) + 0.964(log housing, water, sanitation andsocial security as %GDP) + 1.866(log education as % GDP)

This is the ‘pro-poor expenditure index’ used in the analysis of Table 3 and Fig. 1.

Appendix B: Aid, Growth and Poverty Reduction in the 1990s

Table A2

CountryPoverty reduction 1990–99*

(percentage points/year)Growth GDP pc 1990–99�(percentage points/year) ODA/GNP (%), 1992�

Cote d’Ivoire 0.76 )0.79943 3.91Ethiopia )0.11 1.42051 2.9Ghana )2.04 1.60523 2.04Kenya 0.27 )0.67812 1.91Lesotho 1.83 2.18699 3.09Madagascar 0.85 )0.98976 2.84Niger 0.38 )1.671 2.97Nigeria 7.23 0.30524 0.19Senegal )4.78 0.78919 4.03Tanzania )14.33 0.43716 4.46Uganda )2.12 3.29173 3.34Zambia 5.28 )1.99211 7.53Zimbabwe )0.9 )0.00421 1.45China )1.2 8.39801 0.06Indonesia )0.16 3.09375 0.16Philippines )0.7 0.70238 0.36Thailand )1.57 4.13094 0.2Algeria )0.08 )0.21871 0.22Jordan )0.55 0.43167 3.26Morocco )0.38 0.74084 0.7Tunisia )0.08 3.30509 0.29Brazil )0.89 0.63319 0.04Chile )0.86 4.78205 0.12Colombia 0.82 0.92055 0.1Costa Rica )1.33 2.72118 )0.03Dominican Rep. )0.65 3.09673 0.29Ecuador )0.66 )0.20308 0.44Guatemala )3.61 1.32058 0.51Honduras )0.6 0.13381 2.82Mexico 0.53 2.00036 0.04Panama )1.01 2.977 0.46Peru 1.3 1.44112 0.37Venezuela 0.56 0.25406 0.02Bulgaria 0 )1.73432 0.46Czech Rep. 0 0.17993 0.11Estonia 0.69 )0.36676 0.91



Table A2

Continued

CountryPoverty reduction 1990–99*

(percentage points/year)Growth GDP pc 1990–99�(percentage points/year) ODA/GNP (%), 1992�

Hungary 1.75 0.84074 0.26Kyrgyz Rep. )4.33 )3.83231 2.45Moldova 3.2 )8.34763 0.59Poland 0.68 3.58933 0.36Romania 0.56 )1.76076 0.21Turkmenistan 4.18 )5.06951 0.26Bangladesh 0.24 3.08564 1.02India )2.95 3.60203 0.13Pakistan )2.07 1.48289 0.41Sri Lanka )0.28 3.98201 1.16

Data sources: *World Bank Poverty Monitoring Database, �World Development Indicators.

Appendix C: Selectivity versus Conditionality: Detailed Country-levelData and Simulation Results

Table A3

Efficient Distribution of Aid (% of GDP) – various scenarios

C-D Optimal 1996 C-D (2001)

Scenario:Angola 1.20 0.25 2.45 0.60Benin 6.59 7.75 4.15 7.30Botswana 3.50 0.00 0.71 4.00Burkino F 6.48 8.32 4.11 6.90Burundi 5.72 9.07 5.31 5.30Cameroon 4.22 2.72 1.57 4.40Cape Verde 5.95 5.19 15.49 8.60C.A.F. 5.11 4.45 3.41 4.80Chad 5.37 6.18 5.07 6.70Comoros 5.25 4.96 4.49 5.10Congo DR 2.60 2.44 0.41 2.00Congo Rep 5.07 9.74 8.86 4.60Cote d’Ivoire 5.02 4.64 3.91 5.50Equat. Guinea 3.75 3.44 2.39 2.70Ethiopia 7.52 11.30 2.90 8.40Gabon 0.36 0.00 1.51 1.10Ghana 5.23 5.16 2.04 5.90Guinea 2.51 1.81 2.45 4.70Guinea-Bissau 5.86 5.44 15.67 7.10Kenya 4.15 12.22 1.91 5.30Lesotho 7.31 5.30 3.09 8.10Madagascar 5.27 5.76 2.84 6.30Malawi 7.00 11.14 7.09 8.00Mali 6.80 8.20 6.95 7.90Mauritania 5.89 5.67 6.15 7.20Mauritius 0.00 0.00 0.19 0.00Mozambique 6.77 8.54 9.21 8.00Namibia 1.27 0.00 2.27 3.70Niger 5.46 6.64 2.97 6.60Nigeria 2.71 0.98 0.19 3.60Rwanda 5.41 5.46 15.75 7.00



Table A3

Continued


Senegal 7.07 9.40 4.03 7.00Sierra Leone 5.64 4.95 8.11 6.30South Africa 0.00 0.00 0.13 0.00Swaziland 2.24 0.00 0.99 5.00Tanzania 5.87 6.60 4.46 6.70Togo 5.58 5.50 2.33 6.00Uganda 8.51 15.00 3.34 8.90Zambia 6.75 7.37 7.53 8.10Zimbabwe 2.48 0.00 1.45 4.30China 0.00 0.00 0.06 0.00Fiji 0.00 0.00 1.33 2.10Indonesia 0.00 0.00 0.16 0.00Korea 0.00 0.00 )0.02 0.00Laos 4.91 6.20 5.73 6.60Malaysia 0.00 0.00 )0.20 0.00Mongolia 6.96 7.19 4.34 6.90Papua N.G. 2.21 0.10 2.87 3.20Philippines 0.00 0.00 0.36 0.00Solomon Isl. 4.53 4.06 4.79 4.80Thailand 0.00 0.00 0.20 0.00Vanuatu 4.53 4.06 6.36 6.30Vietnam 2.93 2.53 0.78 4.00Algeria 0.00 0.00 0.22 0.00Egypt 0.00 0.00 1.31 0.00Jordan 0.00 0.00 3.26 0.00Morocco 0.00 0.00 0.70 0.00Tunisia 0.00 0.00 0.29 0.00Argentina 0.00 0.00 0.08 0.00Belize 3.89 2.94 1.87 5.40Brazil 0.00 0.00 0.04 0.00Chile 0.00 0.00 0.12 0.00Colombia 0.00 0.00 0.10 0.00Costa Rica 0.00 0.00 )0.03 0.00Dominican R. 0.00 0.00 0.29 0.00Ecuador 0.00 0.00 0.44 0.00El Salvador 2.20 0.00 1.94 5.70Guatemala 3.39 0.00 0.51 3.50Guyana 5.72 3.94 6.96 7.90Haiti 4.08 3.95 4.51 5.50Honduras 5.65 2.54 2.82 6.70Jamaica 0.00 0.00 0.66 0.00Mexico 0.00 0.00 0.04 0.00Nicaragua 4.91 3.57 10.21 6.60Panama 1.02 0.00 0.46 0.00Paraguay 0.00 0.00 0.56 0.00Peru 0.00 0.00 0.37 0.00St. Kitts 4.31 2.82 2.19 6.10St. Lucia 5.21 4.02 4.62 6.00Trinidad 0.00 0.00 0.19 0.00Uruguay 0.00 0.00 0.20 0.00Venezuala 0.00 0.00 0.02 0.00Azerbaijan 1.37 1.43 0.93 4.00Belarus 0.00 0.00 0.16 0.00Bulgaria 0.00 0.00 0.46 0.00Czech Rep. 0.00 0.00 0.11 0.00Estonia 0.00 0.00 0.91 2.80Hungary 0.00 0.00 0.26 0.00



References

Acemoglu, D., Johnson, S. and Robinson, J. A. (2001). ‘The colonial origins of comparative develop-ment: an empirical investigation’, American Economic Review, vol. 91(5), pp. 1369–401.

Alesina, A. and Perotti, R. (1996). ‘Income distribution, political instability, and investment’, EuropeanEconomic Review, vol. 40, pp. 1203–28.

Bertocchi, G. and Canova, F. (2002). ‘Did colonialisation matter for growth? An empirical investigationinto the historical causes of Africa’s underdevelopment’, European Economic Review, vol. 46(10),pp. 1851–71.

Boone, P. (1996). ‘Politics and the effectiveness of foreign aid’, European Economic Review, vol. 40(2),pp. 289–329.

Burnside, C. and Dollar, D. (2000). ‘Aid, policies and growth’, American Economic Review, vol. 90(4),pp. 847–68.

Castro-Leal, F., Dayton, J., Demery, L. and Mehra, K. (1999). ‘Public social spending in Africa: do thepoor benefit?’, World Bank Research Observer, vol. 14, pp. 49–72.

Chant, L., McDonald, S. and Morrissey, O. (2003). ‘Aid financing of health expenditure: a CGE analysisof the effect on the poor in Uganda’, mimeo, DFID Research Programme on Risk, Labour Marketsand Pro-Poor Growth.

Collier, P. (1997). ‘The failure of conditionality’, in (C. Gwin and J. Nelson, eds.), Perspectives on Aid andDevelopment, Washington DC: Overseas Development Council.

Collier, P. and Dollar, D. (1999). ‘Aid allocation and poverty reduction’, World Bank Policy ResearchWorking Paper 2041. Washington DC.: World Bank.

Collier, P. and Dollar, D. (2001). ‘Can the world cut poverty in half? How policy reform and effective aidcan meet international development goals’, World Development, vol. 29(11), pp. 1787–802.

Collier, P. and Dollar, D. (2002). ‘Aid allocation and poverty reduction’, European Economic Review,vol. 46(8), pp. 1475–500.

Collier, P. and Dollar, D. (2004) ‘Development effectiveness: what have we learnt?’, Economic Journal,vol. 114, pp. 244–71.

Dalgaard, C. J., Hansen, H. and Tarp, F. (2004). ‘On the empirics of foreign aid and growth’, Economic

Journal, vol. 114, pp. 191–216.De Haan, A. (2003). ‘Poverty reduction in north and south: an aid donor’s view’, in (P. Mosley and

E. Dowler, eds.), Poverty and Social Exclusion in North and South, ch. 4, London: Routledge.Easterly, W. (2002). The Elusive Quest for Growth – Economists’ Adventures and Misadventures in the Tropics,

Cambridge, MA: The MIT Press.Easterly, W., Levine, R. and Roadman, D. (2003). ‘New data, new doubts: revisiting ‘‘Aid, policies, and

growth’’’, Center for Global Development, Working Paper No. 26.

Table A3

Continued


Kazakhstan 0.00 0.00 0.23 0.00Kyrgyz Rep. 6.44 5.50 2.45 7.20Latvia 0.00 0.00 0.86 2.10Lithuania 0.00 0.00 0.54 0.00Moldova 0.00 0.00 0.59 3.30Poland 0.00 0.00 0.36 0.00Romania 0.00 0.00 0.21 0.00Russia 0.00 0.00 0.10 0.00Slovakia 0.00 0.00 0.35 2.00Tajikistan 4.33 4.27 2.12 4.90Turkmenistan 0.00 0.00 0.26 0.00Ukraine 0.00 0.00 0.33 0.00Uzbekistan 0.00 0.00 0.15 0.00Bangladesh 4.84 5.04 1.02 6.50India 0.00 0.00 0.13 1.00Maldives 6.24 5.23 3.77 8.60Nepal 4.29 5.48 1.70 5.20Pakistan 0.00 0.00 0.41 2.00Sri Lanka 0.00 0.00 1.16 2.10



Ferroni, M. and Kanbur, R. (1991). ‘Poverty conscious restructuring of public expenditures’, in(A. Chibber and S. Fischer, eds.), Economic Reform in Sub-Saharan Africa, pp. 90–105, Washington.,DC.: World Bank.

Gomanee, K., Morrissey, O., Mosley, P. and Verschoor, A. (2003). ‘Aid, pro-poor government spendingand welfare’, University of Nottingham: CREDIT Research Paper, no.03/03.

Gupta, S., Verhoeven, M. and Tiongson, E. (1999). ‘Does higher government spending buy betterresults in education and health care?’, Working Paper of the International Monetary Fund,WP/99/21.

Hanmer, L. and Naschold, F. (2000). ‘Attaining the international development targets: will growth beenough?’, Development Policy Review, vol. 18, pp. 11–36.

Hansen, H. and Tarp, F. (2001). ‘Aid and growth regressions’, Journal of Development Economics,vol. 64(2), pp. 547–70.

Hudson, J. and Mosley, P. (2001), ‘Aid policies and growth: in search of the Holy Grail’, Journal ofInternational Development, vol. 13, pp. 1023–38.

Knack, S. (1999). ‘Social capital, growth and poverty: a survey of cross-country experience’, World BankSocial Capital Initiative: Working Paper 7.

Knack, S. (2000). ‘Aid dependence and the quality of governance: cross-country empirical tests’,unpublished paper, Development Research Group, World Bank.

Knack, S. and Keefer, P. (1997). ‘Does social capital have an economic impact? A cross-country inves-tigation’, Quarterly Journal of Economics, vol. 112(4), pp. 1252–88.

Knight, M., Loayza, N. and Villanueva, D. (1996). ‘The peace dividend, military spending cuts andeconomic growth’, IMF Staff Papers, vol. 43(1), pp. 1–37.

Morrissey, O. (2001). ‘Pro-poor conditionality for aid and debt relief in East Africa’, Universities ofNottingham and Sheffield: DFID Pro–Poor Growth Programme occasional paper 3.

Morrissey, O. and Verschoor, A. ( 2002). ‘Is ownership a meaningful concept in policy reform? Policylearning and the evolution of pro-poor policies in Uganda’, mimeo, DFID Research Programmeon Risk, Labour Markets and Pro-Poor Growth, Sheffield, Nottingham, Cambridge and Open University.

Mosley, P. (2002). ‘Economic crisis, ‘‘political sustainability’’ and the international financial architec-ture’, unpublished paper, University of Sheffield.

Mosley, P. and Hudson, J. (2002). ‘Aid, poverty reduction and the new conditionality’, Universities ofNottingham and Sheffield: DFID Pro –Poor Growth Programme occasional paper 5.

Mosley, P., Hudson, J. and Horrell, S. (1987). ‘Aid, the public sector and the market in developingcountries’, Economic Journal, vol. 97, pp. 616–42.

Nafziger, E. W. and Auvinen, J. (2002). ‘Economic development, inequality, war, and state violence’,World Development, vol. 30(2), pp. 153–63.

Rock, J. (2003). ‘Donors, aid, policy learning and the evolution of pro-poor policies in Ethiopia’,mimeo, DFID Research Programme on Risk, Labour Markets and Pro-Poor Growth, Sheffield,Nottingham, Cambridge and Open University.

Sachs, J. and Warner, A. (1995). ‘Economic reform and the process of global integration’, BrookingsPapers on Economic Activity, no.1, pp. 1–118.

Sahn, D. and Younger, S. (2000). ‘Expenditure incidence in Africa: microeconomic evidence’, FiscalStudies, vol. 21, pp. 329–47.

Stewart, F. (2003). ‘Do PRSPs increase government ownership of poverty strategy?’, paper presented at aconference on The Impact on Globalisation on the Nation-State, Yale University, 25–27 April.

Van de Walle, D. (1998), ‘Assessing the welfare impacts of public spending’, World Development,vol. 26(3), pp. 365–79.

Van de Walle, D. and Nead, K. (eds.) (1995). Public Spending and the Poor: Theory and Evidence, Baltimoreand London: Johns Hopkins University Press for the World Bank.

Verschoor, A. (2002). ‘Aid and the poverty-sensitivity of the public sector budget’, DFID programme onpro-poor growth: Occasional paper 3.

White, H. (ed.) (2002). ‘Symposium on social funds’, Journal of International Development, vol. 14,pp. 605–66.

Whiteley, P. (2000). ‘Economic growth and social capital’, Political Studies, vol. 48, pp. 443–66.World Bank (1990). World Development Report 1990: Poverty, New York: Oxford University Press.World Bank (1997). World Development Report 1997: States and Markets, New York: Oxford University Press.World Bank (2000). World Development Report 2000/01: Attacking Poverty, New York: Oxford University

Press.



Documents

AID, POVERTY REDUCTION AND THE ‘NEW CONDITIONALITY’*siteresources.worldbank.org/INTPRS1/Resources/383606... · 2005-01-25 · AID, POVERTY REDUCTION AND THE ‘NEW CONDITIONALITY’*

AID, POVERTY REDUCTION AND THE ‘NEW CONDITIONALITY’siteresources.worldbank.org/INTPRS1/Resources/383606... · 2005-01-25 · AID, POVERTY REDUCTION AND THE ‘NEW CONDITIONALITY’