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Aid, Poverty Reduction and the 'New Conditionality'

Author(s): Paul Mosley, John Hudson and Arjan VerschoorSource: The Economic Journal, Vol. 114, No. 496, Features (Jun., 2004), pp. F217-F243Published by: Wiley on behalf of the Royal Economic Society

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The EconomicJournal, 114 (June), F217-F243. ? Royal Economic Society 2004. Published by Blackwell

Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Maiden, MA02148, USA.

AID, POVERTY REDUCTION AND THE

'NEW CONDITIONALITY'*

Paul Mosley, ohn Hudson and Arjan Verschoor

The paper examines the effect of aid on poverty, ather than on economic growth. We devise a

'pro-poor (public) expenditure index', and present evidence that, together with inequality and

corruption, 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 in

punishing slippage on previous commitments, and keys aid disbursements to performance in

respect 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 (usually

interpreted as GDP growth) in the recipient country has been replaced by the

objective 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 the

level 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

policiesare in some sense

alreadyacceptable.' For those many who support the first of these developments, it is

important 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 ofJennifer 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 Kalwijfor helpful comments.

1 The World Bank's recent WorldDevelopmentReport2000/01 explains, in a section of Chapter 11entitled 'Making aid more effective in reducing poverty' how selectivity emerges from the failings of

conditionality: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 some

conditions, 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 as

binding, 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: or aid to be most effective at reducing poverty, it must be well targeted. If all aid money were

allocated 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).

[ F217 ]







F218 THE ECONOMIC JOURNAL [JUNE

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 the

quality of a country's policies and institutions for promoting growth (let alone

pro-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 further

simplifying 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 to

where 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 with

policies 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 and

within their own governments) and giving aid to bad-policy countries. The

emergenceof this donor's dilemma has

alreadyforced a number of donors into

2 Collier (1997) traces the origin of conditionality failure to donors being insufficiently motivated to

punish recipients for non-compliance.

? Royal Economic Society 2004







2004] AID, POVERTY AND 'NEW CONDITIONALITY' F219

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:

*

multiplelevels of commitment and withdrawal, rather than a

simple yes/nodecision on whether to give aid or not. Thus a 'very good' aid recipient will

receive programme aid for policy reform, accelerated debt relief under the

HIPC (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 an

alternative to leverage on government;5* the exercise of pressure which operates through social and political, as well as

economic, 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, further

the economic objectives of growth and poverty reduction (Whiteley, 2000;

Knack, 1999; Knack and Keefer, 1997), through a more long-term and

indirect route.

Any aid donor objective may thus be sought through three alternative methods:

selectivity; traditional 'ultimatum' conditionality; or new conditionality as above

described. 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 of

two 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 the

late 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 wasvirtuallya command economy until the early 1990s and were rewarded

when, 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 implementing

agreed 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 there

may need to be a process of 'trust building' during which the donor tolerates some slippage but after a

certain 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; aid

donors, aware of the government's weakness as a service provider, have happily used these NGOs as a

supplementary channel for aid flows.









Table 1

Optionsfor Increasingthe PovertyLeverageof Aid

Leverage of aid on the growth

ObjectiveInstrument

Leverageof aid on

growth elasticityof

povertyreduction

Selectivity (reallocation World Bank programming World Bank (2000, ch.11)between recipient models of 1960s and 1970s;

governments) more recently Burnside andDollar (2000) and Collier and

Dollar(2001, 2002)

Conditionality (pressure toreform policies of recipientgovernments):Old 'ultimatum' variety Traditional 'structural World Bank 1991-3

adjustment' approach'New' Bilateral donors in Uganda Bilateral donors especially in

1992-94 Uganda, Tanzania, Ethiopia,Mozambique 1994-2000

The World Bank's current official position, as we have already emphasised, is

sceptical of what 'ultimatum' conditionality may achieve. Reflecting this Collier

and Dollar (2001, 2002) derive a poverty-efficient allocation of aid that assumes

that donors have no influence whatsoever over recipients' policies. This has alwaysseemed unlikely as to an extent selectivity is no more than ex-post conditionality.6Thus they argue, although this position is slightly modified in the paper in this

symposium 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 of

the limitations of their 'benchmark allocation': donors' local knowledge, specialcircumstances (terms-of-trade shocks) and recipient characteristics not included

in their original analysis (inequality, corruption, the composition of government

expenditure) all represent legitimate reasons for deviations from the 'efficient'

allocation of aid proposed in their previous papers. Moreover, they cite studies

of 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 much

welcome these qualifications of their initial position but argue in this paper that

their combined force, when analysed properly and with a view to their effect

not on growth but on poverty, amounts not so much to cosmetic changes of

their benchmark allocation but rather to a radically altered approach to aid

allocation.

First of all, growth regressions, and a fortioriaid-growth regressions, are in their

infancy and face, among other challenges, the gigantic one of endogenising the

evolution of institutions (Easterly, 2002). Dalgaard et al. in this symposium makepioneering foraysin that direction but the variable that in their analysis proxies for

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

@ Royal Economic Society 2004








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 aid

effectiveness in the sense of reducing poverty, but also that some of these can be

and have been influenced by donors using a conditionality approach. Among the

variables that Collier and Dollar suggest as representing potentially fruitfulextensions of their analysis, we find in Section 1 that the composition of public

spending, inequality and corruption are especially relevant for increasing the

poverty leverage of aid. Moreover, we find in Section 2 that donors through a new

conditionality approach are capable of influencing the orientation of public

expenditures towards poverty reduction. As we have noted, Collier and Dollar

show themselves aware in this symposium of donor influence on recipient policy.However, they warn that it 'should not become a dominant consideration in aid

allocation', as the likely quantitative impact would be small in their opinion. In

that largely unsubstantiated phraselies 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 than

the Collier and Dollar approach - which is itself more effective than the current

allocation. 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 in

which the aid-recipient government, constrained by resource scarcity, uses aid as

an instrument in the pursuit of its own objectives; and then to use the reduced

form of that model as an estimating equation for aid effectiveness. Recent

controversy has centred on the economic and statistical significance of the

coefficients on aid and on aid interacted with policy in a growth equation(Burnside and Dollar (2000) versus Hansen and Tarp (2001) and most recently

Easterly 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 it

remains ambiguous.In this paper we enter the controversy on aid effectiveness from a different

angle, focussing on aid's ability to reduce poverty - which in recent years has been

the principal target variable for aid donors and many recipient countries (WorldBank, 2000). Analyticallywe 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 2):

dP OP OP_F2y

?yO2?O OPP

d= - + + + (1)

dAW A y A 0 AJ OAA

and whereas most of the debate (as exemplified by the two other papers in this

symposium) has focussed on the terms between square brackets, we focus on most

? RoyalEconomicSociety2004








of the remaining terms.7At 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 (particularlyin Africa and eastern Europe, it appears) and need to

be supplemented by policies which will increase the (absolute value of) the povertyelasticity (in reality this is more accurately referred to as the income elasticity:

alnP/alnY).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

(a•/aA ? 0) that influence poverty (aP/jla < 0).There are two steps in the argument. Firstwe 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 that

influence 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-based

country grades). They are regarded with some suspicion by commentators who

question country experts' ability to evaluate the quality of policies separately from

Table 2

Aid, Growth and Poverty Reduction by Region

(1) Poverty (2) Growth (3)=

(1)/(2)reduction GDP pc Poverty1990-99* 1990-99t reduction (5) - (1)/(4)

(percentage (percentage per unit (4) ODA/GNP Poverty reduction

Regions points/year) points/year) of growth (%), 1992t 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 3.77

Developing 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, t World Development Indicators (data arrays

used here are presented in Table A2).

7 aP/aA 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 country

performance to the CPIA score may suffer from a circularityproblem. We cannot

examine this satisfactorily,for the required data are not in the public domain (see

Dalgaard et al. in this symposium, for an extensive discussion of the CPIA index

and its problems).Considering that policy variables emerging from aid-growth regressions are

highly controversial, and recalling that, by consensus, aid's effectiveness is now

primarilyjudged 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 one

that we particularlywish to put under the microscope is the composition of public

expenditure, 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 majorityof people and can

be changed relatively quickly; and by contrast with exchange-rate policy, it can be

relatively easily adjusted to the requirements of particular potential beneficiary

groups (Van de Walle and Nead, 1995).Serious practical difficulties nevertheless arise when attempting to assess the

orientation towards poverty reduction of any given composition of public expen-diture: individual sectors differ in the balance of their direct and indirect effects

on 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) for

primary education and health spending, or anti-poor (Knight et al., 1996) for

militaryspending. 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 basic

needs, on benefit incidence, and so forth) and among development practitioners,have a reputation of being pro-poor: basic health care, primary education, water

and sanitation, rural roads and agriculturalextension services. Next, sector-specificpoverty elasticities are estimated and a composite policy indicator is constructed

that weighs sectoral outlays accordingly. Appendix A describes in some detail the

construction of the PPE index used in the present analysis. From the discussion

there it emerges that whereas poverty elasticities of public spending on other

sectors confirm an a prioriexpectation irrespective of which poverty measure is

used for estimation, the impact of public spending on health crucially depends on

the choice of performance indicator; we therefore below analyse the impact of

health spending separately.In addition to the PPE index, in our estimations we consider two additional

'handles' - inequality and corruption - which influence the poverty leverage of

aid, although the ability of donors to grasp and manipulate these handles in the

way that they appear to be able to control the PPE index (see Section 2) is much

more 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 and

Perotti, 1996). Corruption likewise exercises a downward influence on investment

and 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 public

spending, even on allegedly 'pro-poor' sectors, that reaches the poor: it affects

what is commonly called the benefit incidence of spending (Van de Walle, 1998;Castro-Leal et al., 1999; Sahn and Younger, 2000).

Notingthe

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 following

system, consisting of poverty, policy and aid equations, the structural parameters of

which are to be estimated using a GMM 3SLS estimator.

Pit = f ( Yit,Xit, nit) +elit (2)

Ait = f2(Nit, YitVit) + 82it (3)

f=Yi f3(Yit, Ait,kit) + Eg3it. (4)

The specification of the equations is standard and largely drawn from the

literature. The poverty equations (2) are estimated both using a poverty head-

count ratio and alternatively infant mortality as the dependent variable (two of

the well-publicised OECD Millennium Development Goals), and regressed on

income per capita (Yi,) 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 (Dit) index is determined using

values of coefficients on individual spending indicators in a version of (2) de-fined in Appendix A. The aid equation (3) relates the share of Overseas

Development Aid (ODA) in GNP to the well-documented small-country bias in

aid allocation through the inclusion of population size N, and includes a vector

V 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 includes

various good policy variables to allow for the possibility that good policy attracts

aid. 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 etal. (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 that

PPE and health spending increase with income per capita (in other words, are

'luxury goods'), and includes a vector k of control variables.10The 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 infant

mortality equation, respectively, and on aid in the PPE equation, have the

expected sign and are significant. Table 3 presents the results of estimating

equations simultaneously, using a GMM 3SLS estimator. Columns 1 to 5 reportcoefficients on all pertinent variables apart from corruption; because of the

limited overlap between headcount poverty and corruption data points, which

would have resulted in a substantial loss of degrees of freedom, we report in

columns 6 to 8 a smaller system of equations that includes (absence of) cor-

ruption as an independentvariable. We discuss the

povertyand aid

equationshere; the results obtained for the policy equations form part of the discussion of

the next Section.

Most importantly, we find that the PPE index is comfortably significant (with the

correct 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 growth

elasticity 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. Their

assumption amounts to saying that 1% extra growth always reduces poverty by an

extra 2%, whereas our findings suggest that, on average and controlling for other

variables, 2%growth reduces poverty by less than 1%.12 Not only the magnitude of

this 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 of

growth and PPE on poverty,'3 we estimated the following poverty equation within

the 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 service

provision.11 The same is true for most other coefficients, with the exception of the trend term in the infant

mortality 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 of

poverty 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 34

countries 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.








Table 3

Aid, Pro-poorExpenditureand Poverty(3SLS)

In Poverty In Pro-poor In Healtheadcount In Infant In Aid expenditure spending($1/day) t mortalityt (ODA/GNP)T (PPE)? (% of GN

Constant 5.496*** 8.089*** 12.146*** -4.446*** -6.539**

(4.19) (16.91) (6.43) (2.80) (2.49)In GNPpc -0.479** -0.457*** 0.602*** 0.809*

(2.62) (6.57) (3.19) (2.61)Pro-poor expenditure (PPE)? -0.740**

(2.31)Public health spending (% of GNP)

(-0.223***(5.34)

Gini? 0.046***

(3.26)Absence of corruption

Ln(aid)tlow-incoment 0.420** 0.191(2.18) (1.434)

Ln(population) -0.946*** -0.126***(7.45) (3.51)

Colony11 0.395(1.15)

Islam?? 0.807*(1.89)

Macro-policyttt 0.096**(2.69)

Openttt -0.517(1.65)

In (infant mortality)t 1.257***(3.80)

Trend -0.011 0.040**(0.96) (2.57)

R2 0.30 0.77 0.52 0.47 0.20N 67 67 67 67 67

t-statistics n parentheses. * significant at 10%, ** at 5%, and *** at 1%.Notes and data sources: data set is a pooledfor details see Gomanee et al. (2003); tWorld Bank Poverty Monitoring Database;I World Development IndicatorsWorldDevelopmentReport various issues), construction described in Appendix A; ? WIDER inequality database;for GDP pc < 1,422; ??Colony = 1 for ex-colonies of Britain and France; ??Islam = 1 for Islamic countries; t i(1995) indicator of openness of the economy.

?

0i

0

0S,

0

0t








LPOVi= (5.849 - 0.867LGDDPCi - 2.690PPE + 0.251GINIi) x (1 - 0.0112GINIi). (5)(1.59) (1.70) (2.70) (1.64) (4.34)

The significance of coefficients on the variables in the other equations is

unaffected.In the

poverty equation,coefficients on income

per capitaand PPE

retain 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 an

inverse function of inequality - that is, the greater the degree of inequality, the

lower will be the poverty elasticity coefficient (cc):

ci = 0.867 -0.0112GINIi

(6a)

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

Si= (2.690 - 0.0112 GINI)PPEi (6b)

Our estimates imply that for high inequality countries in the definition ofHanmer and Naschold (2000), that is countries with a GINI coefficient of 43% or

higher, the partial growth elasticity of poverty reduction is 0.45 or lower - a value

very similar to the one they obtain for this group of countries (0.34).14 We make

use of the result that crucial poverty elasticities are affected by inequality in our

aid-allocation simulations in Section 3. The aid equation itself, reported on in

column 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 with

infant mortality). Although outside our main focus, an intriguing finding is that

when 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 of

growth, public spending priorities, inequality and corruption determine poverty in

our model - all of which represent channels for aid to impact on poverty once

donors have found a technique to influence those. Here of course lies the main

difficulty: donors may not yet - given the present state of research - be in a

position to exercise much influence on growth, inequality and corruption.15 We

will demonstrate in the next Section that their prospects may be much better in

the area ofattempting

to influencepublic spending priorities.

Inparticular,

we

must 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 at

all' - 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 is

alwaysthe 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, a

limitation which is implicit in many cross section analyses. In this case data limitations are substantial

and effectively prevent more substantial time series analyses. In any case, the latter themselves face the

problem 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 health

expenditure and other variables and to reduce PPE expenditure.









entire World Bank (footnote 2 above).16 On what basis can we claim that this

proposition is incorrect?

2. The Influence of Aid onPro-poor Policy

Of the various ways in which governments may seek to influence poverty, we focus

in this Section on the PPE (pro-poor expenditure) index, on the grounds that it is

relatively easy for resource-constrained governments to influence - easier than

other public instruments of redistribution such as tax progressivity, and certainlyeasier than corruption17 and inequality, which are the other catalytic variables

revealed as significant by Table 3. The question now to be tackled is whether aid

donors can in turn influence those governments. The PPE equations in Table 3

suggest that financial aid has influenced public spending in a pro-poor direction,but

onlyin countries with a GDP

per capitabelow a critical

threshold.'sWe return

to this result below, towards the end of this Section, but first look at recent case

study evidence and examine our data set in more detail, so as to be able to

understand the forces behind this crucial finding.Recent case studies suggest that for aid to bring about increased pro-poor

spending, 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 of

recipient country governments of donors' advice on budgetary priorities has been

documented 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 between

the cases mentioned:

(i) Debt cancellation in return for pro-poor expenditure changes. The four

countries mentioned were among the earliest and largest beneficiaries from

HIPC 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 ante

policy conditionality since the origin of the Bretton Woods system in 1945 and shows no sign of

abandoning it.17 Knack (2000) argues that aid has historically tended to encourage corruption. See also the paper

by 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 easier

to monitor;

(iii) Social funds (White, 2002) under which donors made funds available for

labour-intensive public works projects in priority sectors - a very effective

device 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-rein

conditionality, documented in the introduction, has reduced the likelihood

of a retreat by both parties to ultimatum positions from which there is little

scope 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 trust

was built up but later increased pressure on recipients to meet increasingly

jointly agreed-upon targets. This tendency was often combined with invita-

tions torecipients

to frame their own definition ofpro-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, in

which governments find it expedient to use aid money to pursue goals with

broad-based political appeal - which are consonant with poverty reduction

(the achievement of universal primary education in Uganda is a classic

example) - 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-Dollar

proposition of donors having no influence and, as a consequence, there have beenmore opportunities to turn potential into actual aid leverage. Needless to say, this

process did not alwaysrun smoothly and exactly how this process has occurred, in

the places it has, is not alwayscompletely clear but one key element in it appears to

be that, in those places, aid donors made themselves a political as well as a

financial asset to recipients - in part through a negotiating style characteristic of a

new cluster of donor influence, which we have termed 'new conditionality'. The

combined impact of cases such as these appears to be strong enough for an overall

relationship 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, from

which we may observe the experience of some other countries for which we have

data about changes over time (but no case study evidence) where a virtuous circle

of the kind documented above may be operating: Ghana, Kenya (sic) and Nepal. A

tight relationship between aid and PPE in the sense of episodes of changing aid

coinciding with a changing PPE index is indeed confined to the subset of low-

income countries, ii, following the World Bank (2000) definition. Bycontrast, even

19 The definition adopted by the Uganda Ministry of Finance in 1994 is in many ways more

sophisticated than our PPE index. For a general discussion of the PRSP process, see Stewart (2003).o0In an election on December 27, 2002 the former president Daniel Arap Moi stood down and

KANU, 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.









Hun92 Hun917Hun90

Bu192B0wa92A19*0

a87Les91

V0 *B Bra8l

19 ( o

s

Eth88

0 000

*o0

Eth88

Ve•n3 CsBr

93 j

Cos850500

Nic93.-- 48 * Zim8 0

S ols. PPE= 3.728 - 0.006 In(ODA/GNP) Ken93 0

(t) (26.611)(-0.083) 0* - Gha9m86

o -* oo o

...

0??

.

en8

Nep93*

.oo.

o Boo

,

??O??? 2

0.

Gha81

ols : PPE=1 648+03671n(ODA/GNP) Mad91

O Nep8sO Mad9

*,, 0 ,

5 -4 -3 -2 -1 0 1 2 3

S -1

In(ODA/GNP)

Fig. 1. Aid and PPE

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 scores

appear to respond in low-income countries but not in middle-income countries to

increases or decreases in aid inflows.

We suggest that there are two factors at work. First, in poorer countries, the

scope for fungibility is less, for the simple reason that where aid is paying for most

of the public sector's investment budget, recipients have very limited ability to

switch into types of spending not desired by aid donors. Second, we have seen that

donors 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 potential

leverage, is greatest. For illustrative purposes, we have estimated the link from aid

to PPE in OLS regressions separately for both groups of countries, so as to be able

to add best-fit lines to the data in the Figure. These lines illustrate the point made

properly 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 contend

therefore that, whateverconditionality may

ormay

not have achieved in relation to

other policy variables,it is achieving something in relation to spending priorities of

governments in low-income countries. The relationship between aid and pro-poor

expenditures, illustrated in Figure 1, is established econometrically in Table 3 and

validated by case study evidence. It should therefore not be seen as merely a

? RoyalEconomicSociety2004








statistical artefact but as constituting a genuine process of influence, even if the

process is not universal, nor completely understood. It is plausible that neglect of

those possibilities for 'new conditionality' that do exist, as proposed by Collier and

Dollar, leaves a reservoir of poverty-reducing opportunities untapped. In the next

Section we quantify the unrealised potential of an aid allocation rule that rejectsconditionality and exclusively practices selectivity. It is also important to stress that

our empirical analysis only covers a limited form of new conditionality and as it has

been in evidence in the past. The more rigorous form we propose which explicitlylinks aid receipts to good policies along several dimensions, which also have in

built flexibility to suit the specific realities of the country may be expected to

change 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 be

limited 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 direct

the allocation of aid rather than only a selectivity approach, given that aid is

able to influence the expenditure mix;

(ii) we work with estimated poverty impacts (of PPE and of income per capita)

that varyas a function of recipient countries' characteristics.The findings presented in Sections 1 and 2 obviously have a bearing on the

optimal distribution of international aid. In this Section we obtain an order of

magnitude of the difference that new conditionality can make by deriving an aid

allocation formula with key elasticities based on our estimation results, and by

working through a number of scenarios. Collier and Dollar (2001) (CD from now

on) approach the problem of aid optimisation by holding constant the elasticity of

poverty reduction with respect to income, and assuming that aid has no impact on

policy. We approach the problem by allowing poverty elasticities to varyas 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 of

public expenditures as a discontinuous function of aid. We set up the basic problemin the same waythat CD set up theirs, modifying terms where appropriate given our

different assumptions. Formally, the optimisation problem facing donors who aim

to maximise the poverty reduction impact of their collective overseas aid budget

(given a small-country bias in aid allocation) can be represented as

MaxGioihiNiNi-# + ihiNiNi-# (7)

subject to

SAiyiNi-A









where the right hand side of the constraint relates to a fixed aid budget, G denotes

growth, a the growth elasticity of poverty reduction (with respect to GDP per

capita), h the poverty headcount ratio, N population size, / the small-countrybias

in aid allocation, 92PPE, y = ah/APPE, A aid, y per capita income, and i a country

superscript (for ease of comparison, we adopt CD's notation in this Section).Solving the Lagrangean yields

hi(G ?ihyi) y

)Nifl. (8)

+ aY,

Subscript a denotes the partial derivative with respect to aid, and 2 the shadow

value of aid (with the understanding that poverty reduction is in effect valued more

in countries with a smaller population; for a precise explanation, see CD, p. 1792). In

order to be able to isolate the effects due to tracking a route for aid via the structure

of 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 effects

fairly with CD's calculations, we work with their Ga function for the response of

growth to aid (for a critique, see Dalgaard et al.'s contribution to this symposium):

G= =0.185Pi

- 0.072A1 (9)

Pi denotes a country's CPIA score, which, as will be recalled, is based on World

Bank country experts' assessment of the quality of institutions and policies for

promoting broad-based growth. Inserting (9) into (8) and solving for aid gives the

country-by-countryaid allocation that maximises donors'

objectivefunction

(7):

A = 2.6P0.07

Ni007a

(10)

The first two terms on the right-hand-side of (10) capture in a nutshell the case

for being selective in aid allocation as made by CD: CPIA scores, poverty and

population (with the growth elasticity of poverty reduction held constant),determine jointly where the marginal benefit of aid is highest and thence where

the marginal aid dollar should go. The third term represents part of our addition.

Itrepresents

anaspect

of newconditionality:

thehigher

thepoverty leverage

of

aid,21 the greater the extent to which donors will be rewarded for attempting to

persuade recipients to re-orient public spending towards poverty reduction.

Scenario1 (selectivity)22

0.07al \ Z

Vi-ci

=.'Vi.

This is selectivity CD-style, ignoring in the aid-allocation rule, variation in the

growth elasticity of poverty reduction.

21 To be precise, aid's impact on PPEtimes PPE's impact on poverty relative to the growth elasticityof

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









Scenario2 (proper new conditionality, with y allowed to varywith respect to PPE)

A = 2.6Pz Na i-?p

A-0.07- 0.07-ai

'

Qi = f(yAi)

i=

fj(ni, Fi)

where I denotes corruption. In this scenario the aid-allocation rule is optimal:

selectivity and new conditionality jointly realise donors' objectives, with the former

noting that inequality F lowers the growth elasticity of poverty reduction, and the

latter noting that corruption and inequality worsen the benefit incidence of public

expenditure items that have the potential of being poverty-reducing. This is, of

course the ideal, but lack of full information on corruption limits the subsequentanalysis to linking y 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. We

feel this is likely to be misleading given the number of assumptions that have been

made and in any case also depends upon the dynamics of poverty incidence fol-

lowing, semi-endogenous, demographicand

policy changesand also the differ-

ential ability of economies to absorb substantial changes in aid allocations, an

analysis of which is beyond the scope of this paper. In calculating optimal aid

allocations we need information on the CPIA index. This we do not have, but we

do 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.23We also fail

to have information on PPE for all the countries in the sample and where this is

not 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's

analysis 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 2. 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 both

inequality 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 receives

almost 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 the

public domain that is not possible.









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 the

regional averages reported by CD.CPIA-basedgrade: Very good = 4.5,

Good = 4.0, Moderate = 3.5, Poor = 2.5,

Very poor = 1.95K2 PPE score Computation described in Appendix 1

(missing values replaced with predictedvalue)

fa 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 = 0.48Vi in the allocation rule in 1;

ai = f(T) (6a) in the allocation rule in 2Y PPE elasticity of poverty reduction yi

= f(F,H) (6b) in the allocation rule in 2

2 Shadow price of aid Determined endogenously in order to

equate simulated and actual aid

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

fighting corruption.In the second place, and as noted previously, any conclusions which are drawn

critically depend on assumptions about poverty elasticities. In the simulations

presented, these are assumed to be stable functions of inequality. In common with

Dalgaard 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 event

of the very large inter-country allocations of aid proposed in these simulations,such functions may well collapse, because of absorptive capacity problems (the

difficulty 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 percentage

points of GDP (or $330 million) as proposed by CD, or 8.4 percentage points of

GDP 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 in

this paper, at least in the short run, let alone with the even more optimistic one

assumed by CD. In the third place, any allocation method based on parametersobtained in cross-country regressions is bound to make mistakes in individual

cases, because local knowledge is ignored (which corresponds with the variation

left unexplained by the regressions). In the fourth place, as noted towardsthe end

of Section 2, middle-income countries may suffer unduly in our approach because

those future opportunities for new conditionality that may exist also in richer

developingcountries can

bydefinition not be observed in statistical

patternsin

data about the past.The combined force of these qualifying comments is such that the figures in

Table A3, still less the amount of poverty reduction that can be computed from

them, cannot be taken at face value. But since they weigh equally heavily on









scenario 1 and 2, and since our purpose in this Section was never more than to

obtain an order of magnitude of the difference new conditionality can make to the

CD allocation rule, the differences between the scenarios should be revealing. The

countries 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 actual

level, 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 under

scenario 2 it would receive under scenario 1. Overall, we estimate that the amount

of poverty reduction that our point estimates of PPE and growth elasticities imply is

12% higher under scenario 2 than under scenario 1.

Although this estimate of the size of the reservoir of poverty-reducing oppor-

tunities that the CDallocation

rule leaves untappedis the best available to

us,it

should be interpreted with some caution. Because of data limitations we have

worked 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 here

because of the possibilities we have noted for extending the approach to more

countries and more areas of pro-poor policy; and selectivity may perform better

than 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 their

policy 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 considerable

underestimate as to what can be achieved by targeting aid. The form of new

conditionality we have included in this paper is focused on the impact of aid

induced policy reform within a narrow area. In practice and in future there need

be no such limitations.

4. Conclusions and Policy Implications

Fifteen years ago, in its comprehensive review Twenty-FiveYears of DevelopmentCo-operation, the OECD's Development Assistance Committee (DAC) concluded

that 'the most troubling shortcoming of development aid has been its limited

measurable contribution to the reduction - as distinguished from the relief - of

extreme poverty, especially in rural areas' (World Bank, 1990, p. 127). Partly in

response to this shortcoming, the OECD in 1996 announced a range of Inter-

national Development Targets, the centrepiece of which is a halving of the

proportion of the population in extreme poverty by 2015, which constitute a

focus for the strategies of most of the main donors. Our objective here has been

totry

and understand whatthey

can dothrough

aid, which of course isonly

one

of the policy instruments available to them (de Haan, 2003), to increase their

impact on poverty.In common with Collier and Dollar, we feel that inter-country reallocations of

aid 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', we

maintain 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 of

good micro and macro policies as well as income distribution and GDP per

capita are more effective than ones which tend to ignore income distribution

and the potential for impacting upon microeconomic policies. We believe that

the better performance of new conditionality that we simulate above derives from

a real phenomenon, which we have sought to illustrate through the analysis of

this paper. That reality is that the possibilities for conditionality, in that majorityof LDCs where economic policy is not ideal, continue to be significant and

utilisedby

aiddonors,

albeitnowadays

in a more subtleform than

the 'ultima-

tum' form favoured in the 1980s and the very early 1990s; in particular donors

can take advantage, through Poverty Reduction Strategy Papers and otherwise,of the possibilities opened up by the adoption of a more pro-poor public

expenditure mix.

Thus, this puts us somewhat in between the Collier and Dollar view of the world

and 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 it

implies that perceptions of good policy and, hence, aid allocation rules, need to be

conditioned 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 the

Dalgaard, 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 also

acknowledge the contribution of Collier, Dollar and associates in focusing on the

potentially differential impact aid might have dependent upon the recipient

country's characteristics. But we feel that their usage of good policy, first in

focusing on macro-polices and subsequently by a simple agglomeration of differ-ing, and possibly conflicating, policies is unsatisfactory.What further differentiates

our paper from both the other contributions is our focus directly on poverty,while

assuming an effect of aid on economic growth;whilst other papers have focused on

the 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 variables

which have a demonstrable ability to reduce poverty. We focus in particular on

what we call the pro-poor expenditure index, which several LDC governments have

foundrelatively easy

tomanipulate

and which aid, in its turn, has shown itsabilityto influence. Further research in defining this link more precisely (for example, in

illustrating the positive role played by specific types of agricultural expenditure,and the negative role played by arms expenditure) is required before we can be

exact about the form which poverty-conscious restructuring of public expenditure









should take. In the interim, the PPEindex represents one rule of thumb which can

be used as a basis for new conditionality.

Universityof Sheffield

Universityof BathUniversityof 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 and

among 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 these

spending data are available on a sufficiently comprehensive scale: the more disaggregated

the expenditure item, the less readily information about it can be obtained. Spending datafor education (including primary) and health care (including basic) are recorded in

UNESCO statistical yearbooks, and IMF GovernmentFinance Statistics (GFS) yearbooks,

respectively. For spending on other pro-poor sectors, we have had had to use proxies: water

and sanitation is included in the WorldDevelopmentReport'ssocial 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 generate

greater labour-market benefits;(b) some expenditures provide more services for low-income consumers (and in some

cases 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 manner

described by Collier and Dollar (in this symposium). The idea of designing a 'povertysensitive' pattern of public expenditures has been often articulated (notably by Ferroni and

Kanbur (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 two

cover only one channel of impact (and we only have data for a few countries), whereas the

last 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 (Morrisseyand Verschoor, 2002; Rock, 2003). These are: primaryhealth and

education, agricultural research and extension, rural water and sanitation.2. A benefit incidence approach (covering effect (b)) - the definition of 'pro-poor

expenditure' as those sectors whose output, on the evidence of household budgetsurveys, is consumed by the poor. Sahn and Younger (2000), drawing on household

budget surveys in eight low-income African countries, have assessed the extent to









which different public expenditures fall on low-income groups. They conclude that

expenditures 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 do

well 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. For

education, health and 'social sector' expenditures, only, they find that the impact of

increasing the share of public spending dedicated to those expenditure sectors is

poverty-reducing.4. A regression approach - the definition of 'pro-poor expenditure' as those sectors

where expenditure exhibits correlation with poverty reduction (Gomanee et al.,2003). We estimate the following poverty equation in which spending indicators for

which we have an a prioripreference, as indicated above, are candidate explanatoryvariables.

OLSpoverty quation (R2 = 0.660, N= 67). t

Dependent: og ($1/day povertyheadcountratio)(Constant) 12.4 3.46

Log (GDP per capita) -1.05 -2.33

Log (education expenditure/GDP) -1.86 -2.43

Log (healthexpenditure/GDP) 1.84 3.17

Log (housing and amenities* expenditure/GDP) -0.96 3.21

Log (agriculture expenditure/GDP) -0.43 2.17

*Includes water and sanitation and social security.

We may classify as follows (Table Al) the findings of each of these four methods in

relation to the sectors in which expansion of expenditures is likely to be poverty-reducing:

Table Al

'PovertyElasticity' of Components of Public Expenditure: Summary of Findings

Methodologies

'Single-channel' methodologies 'Comprehensive' methodologies

Benefit Labour ntensityComponents of expenditure incidence (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-increasingeffect

throughthe

methodologystated.

From Table Al it is very clear that educational and 'social' (in the old World Bank's

classification, 'housing and amenities') expenditure belong in any pro-poor expenditureindex. Health and agricultural expenditure are ambiguous. Health very strongly 'refuses to









behave' in our poverty regression, in spite of Sahn and Younger's weakly positive results on

benefit incidence, and in the last cut we decided to omit it and consider spending on health

separately in an infant mortality equation in the body of the paper. Agriculture has the right

sign 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 to

poverty reduction from the early 1990s onwards. Thus our PPE index includes indicators of

spending on the following individual sectors: agriculture, housing, education, water and

sanitation, and social security. It is computed as follows (with weights on individual

spending indicators obtained from the poverty regression reported above):PPE = 0.431 (log of agriculture as %GDP) + 0.964(log housing, water, sanitation and

social 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

Poverty reduction 1990-99* Growth GDP pc 1990-99t

Country (percentage points/year) (percentage points/year) ODA/GNP (%), 1992t

Cote d'Ivoire 0.76 -0.79943 3.91

Ethiopia -0.11 1.42051 2.9Ghana -2.04 1.60523 2.04

Kenya 0.27 -0.67812 1.91Lesotho 1.83 2.18699 3.09

Madagascar 0.85 -0.98976 2.84Niger 0.38 -1.671 2.97

Nigeria 7.23 0.30524 0.19

Senegal -4.78 0.78919 4.03Tanzania -14.33 0.43716 4.46

Uganda -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 0.16

Philippines -0.7 0.70238 0.36Thailand -1.57 4.13094 0.2

Algeria -0.08 -0.21871 0.22

Jordan -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 -0.03DominicanRep. -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 0.46

Peru 1.3 1.44112 0.37Venezuela 0.56 0.25406 0.02

Bulgaria 0 -1.73432 0.46CzechRep. 0 0.17993 0.11Estonia 0.69 -0.36676 0.91









Table A2

Continued

Poverty reduction 1990-99* Growth GDP pc 1990-99t

Country (percentage points/year) (percentage points/year)ODA/GNP (%), 1992t

Hungary 1.75 0.84074 0.26

KyrgyzRep. -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 0.26

Bangladesh 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, tWorld Development Indicators.

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

Table A3

EfficientDistributionof 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 4.40

Cape 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 5.10

Congo DR 2.60 2.44 0.41 2.00

Congo Rep 5.07 9.74 8.86 4.60Cote d'Ivoire 5.02 4.64 3.91 5.50

Equat. 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 7.10

Kenya 4.15 12.22 1.91 5.30Lesotho 7.31 5.30 3.09 8.10

Madagascar 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 0.00

Mozambique 6.77 8.54 9.21 8.00Namibia 1.27 0.00 2.27 3.70

Niger 5.46 6.64 2.97 6.60

Nigeria 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 6.70

Togo 5.58 5.50 2.33 6.00

Uganda 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 0.00

Fiji 0.00 0.00 1.33 2.10Indonesia 0.00 0.00 0.16 0.00Korea 0.00 0.00 -0.02 0.00

Laos 4.91 6.20 5.73 6.60Malaysia 0.00 0.00 -0.20 0.00

Mongolia 6.96 7.19 4.34 6.90

Papua N.G. 2.21 0.10 2.87 3.20

Philippines 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 4.00

Algeria 0.00 0.00 0.22 0.00

Egypt 0.00 0.00 1.31 0.00

Jordan 0.00 0.00 3.26 0.00Morocco 0.00 0.00 0.70 0.00

Tunisia 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 3.50

Guyana 5.72 3.94 6.96 7.90Haiti 4.08 3.95 4.51 5.50

Honduras 5.65 2.54 2.82 6.70Jamaica 0.00 0.00 0.66 0.00Mexico 0.00 0.00 0.04 0.00

Nicaragua 4.91 3.57 10.21 6.60Panama 1.02 0.00 0.46 0.00

Paraguay 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 0.00

Uruguay 0.00 0.00 0.20 0.00Venezuala 0.00 0.00 0.02 0.00

Azerbaijan 1.37 1.43 0.93 4.00

Belarus 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 2.80

Hungary 0.00 0.00 0.26 0.00









Table A3

Continued


Kazakhstan 0.00 0.00 0.23 0.00KyrgyzRep. 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 2.00

Tajikistan 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 0.00

Bangladesh 4.84 5.04 1.02 6.50India 0.00 0.00 0.13 1.00Maldives 6.24 5.23 3.77 8.60

Nepal 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

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