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ASSESSING AID: A MANIFESTO FOR AID IN THE 21ST CENTURY?

ROBERT LENSINK University of Groningen (b.w.lensink@eco.rug.nl)

and

HOWARD WHITE* Institute of Development Studies, University of Sussex (h.white@ids.ac.uk)

ABSTRACT

The World Bank report Assessing Aid argues that aid can have positive effects ongrowth and infant mortality, but only when “good policies” are being followed bythe recipient. It follows, especially since aid is fungible, and so cannot be targetted toparticular uses, that donors should focus their aid on low income countries with goodpolicies (i.e. apply greater selectivity). This paper explores a number of weaknessesin these arguments. The growth regressions are not robust, so that different resultscan be obtained with relatively minor variations in model specification. In particularthe argument that aid only works when policies are right is not supported in otherstudies - and even the World Bank’s evidence can be interpreted as saying policieswork better when supported by aid inflows. The choice of which policies are “goodpolicies” is also problematic, and the analysis in the report ignores the likelypresence of threshold effects and other non-linearities; others would anyhow proposea different set of “right policies” especially if the focus is poverty reduction ratherthan growth. The importance of fungibility may be over-stated so that donors in factcan target poverty-reduction activities, suggesting that the selectivity rules proposedin Assessing Aid are misleading. Even if the report’s proposals are to be accepted itis silent on a number of important issues - such as whether to use the level or changein the index - that face the aid manager in practice.

1. INTRODUCTION

By 1992 developing countries were receiving $60 billion a year in aid from the main donor

countries, reflecting a steady rise in aid flows over the preceding three decades. The growth of aid

then faltered, the nominal total falling for three consecutive years (the first time this has

happened), falling to $48 billion by 1997.1 This decline in aid may in part be a manifestation of

* The authors are both also External Fellows of CREDIT, University of Nottingham. This note isbased on contributions made by the authors to a discussion of Assessing Aid at DGIS (TheHague) and we acknowledge the ideas of other contributors to that discussion. Useful commentshave also been received by an anonymous referee. Parts of this paper are based on work carriedout by Howard White for Sida. The usual disclaimer applies.

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frequently proclaimed aid fatigue, which has in fact plagued aid throughout its history (see White,

1999a), based on a feeling that aid doesn’t really work that well. A new World Bank report,

Assessing Aid, seeks to challenge this view, arguing that “aid has at times been a spectacular

success” (World Bank, 1998: 1). However there is an important caveat to this statement, namely

that “aid works in a good policy environment” (ibid.: 2), whereas it has no impact, or is even

harmful, when policies are wrong.

It is not our intention to cast doubt upon the report’s finding that aid can play a positive

role in promoting development, a position with which we have sympathy. However, a review of

the econometric evidence in the report casts doubt on its policy recommendations. Part 2

discusses the main econometrics in the report (and associated background papers) and in Part 3

we turn to aid management issues. Part 4 concludes.

2. THE ECONOMETRIC EVIDENCE

The conclusion that aid should be given to low income countries with good policies is built upon

three arguments. The first is based on growth regressions which show that aid can have a positive

effect on growth, but only when policies are right. This view is buttressed by evidence that aid is

fungible, so that donor attempts to target aid are fruitless - hence if aid is to be well spent it must

be given to governments who can be trusted to do the right thing. Finally, since the object of aid

is to reduce poverty, a calculation is performed to show that the maximum impact will be

obtained by focusing on countries with high poverty but also with good policies. We consider

each of these three arguments in turn.

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Aid and growth

One of the main basis for Assessing Aid’s claims that aid works only when policies are right is a

regression of growth (g) on aid (A), policy (P), an interactive term (the product of A and P) and

some other variables (X). That is:

g A P AP X= + + + + +β β β β β ε1 2 3 4 5 (1)

This equation may be re-written as:

g P A P X= + + + + +β β β β β ε1 2 4 3 5( ) (2)

The regression results, presented in Appendix 1 of the report and more fully in the paper

of Burnside and Dollar (1997), show that β2 is insignificant, whereas β4 is significantly

positive. These results are thus taken to show that aid can affect growth, but only when policies

are right.

Our main comments on these results can be categorised as problems in general about

cross-country growth regressions, and more specific concerns over the policy variable. But first

we note that it is also an issue of interpretation, since equation (1) can equally well be re-written

as:

g A A P X= + + + + +β β β β β ε1 2 3 4 5( ) (3)

This formulation can be interpreted as saying that policies work better if supported by aid

inflows. There is no way from the results of choosing between interpretations (2) and (3), and

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there are indeed reasons to believe aid flows may indeed assist reform (see White 1999b), so the

emphasis the report places on an equation (2) interpretation may be misplaced.2

The use and abuse of cross-country regressions

Over many years there has been a sizeable literature, given a boost by the widely-cited

paper of Barro (1991), in which economic growth in a cross-section of countries is regressed on a

group of explanatory variables. Unfortunately, theory does not provide clear guidance as to the

explanatory variables which should be taken into account. Moreover, it is far from clear how to

accurately measure several important concepts identified in the theoretical growth literature in

general; this point applies particularly for some variables used in the policy index applied in

Assessing Aid (this issue is discussed more extensively below). A very large number of

explanatory variables have been tested for a significant role in explaining economic growth.

Depending upon the aim of the study, and the insights or beliefs of the author, different

explanatory variables are included in the regression equation and different variables are found to

be significant. A clear drawback of this approach is that almost any explanatory variable could be

shown to have a significant effect on economic growth, though the relationship may result from

common causalities or spurious regressions. This problem may be particularly acute when no

other variables are included in the regression which are closely related to the variable(s) under

consideration. Renelt illustrates this point in his review of the literature, stating that “about 50

separate independent variables are included in at least one study and most are shown to have

statistically significant partial correlation with growth” (1991: 17).

Following from this point is that growth regressions may suffer from omitted variable

bias, which implies that the model yields inconsistent estimators. 3 Only in the case where the

omitted variable is uncorrelated with all the included independent variables will there be no bias,

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but such a case is highly unlikely. Levine and Renelt (1992) and King and Levine (1993) find that

only a few variables - such as the investment share, the secondary school enrolment rate, the

initial level of income, and different financial indicators - robustly affect economic growth. The

regressions estimated in Assessing Aid omit all of these variables, except for the initial GDP per

capita and a measure for financial depth. Moreover, the coefficients for both initial GDP per

capita and financial depth are insignificant in the regression used to determine the weights for the

index of economic management (see below). This fact suggests that the data are not supporting

the underlying growth model, so that one can seriously doubt the relevance of the additional

variables included in the regression, such as the variables used in the policy index.

Pooling data across countries (and across different periods) assumes that the productivity

of aid is constant (except that the interactive term allows it to vary by policy regime). Yet it

seems obvious that the productivity of aid will vary from place to place (and in the same place at

different points in time), if only because “aid” takes many different forms - emergency assistance,

building schools and roads, overseas training, programme aid etc. - all of which will have a rather

different impact on growth and with a rather different lag structure. It is also possible that the

effectiveness of aid is affected by the stability of the donor-recipient relationship. Indeed, Lensink

and Morrisey (1999) show that the impact of aid on economic growth is primarily determined by

the stability of the aid flows, and not by the level of aid per se. Tests of parameter stability in

cross-country regressions are frequently rejected: the Burnside and Dollar results providing one

example of this since their results excluding middle-income countries are rather different (aid is

significant, but negative, and none of the policy variables are significant - though the aid times

policy term is significant, perhaps further supporting the view that aid is necessary for policy

effectiveness in low-income countries)

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The implication of these points is that one should be at the least extremely cautious in

concluding that a particular variable has a positive growth effect when it is found to be significant

in a cross-country growth regression (see Kuper et al., 1996; and Lensink and Kuper, 1998). It

often appears that the results are not robust. A regression coefficient is said to be robust if it does

not change too greatly as either model specification or sample are changed. Therefore, in most

recent growth regressions it is common to apply stability checks on the explanatory variables in

others to test for the robustness (e.g. Levine and Renault, 1992; and Sala-i-Martin, 1997). Yet,

Burnside and Dollar present only a limited range of robustness tests, casting doubts on the

reliability of their results. As already noted, the results for low-income countries are somewhat

different. More significantly, Hansen and Tarp (1999) using a data set consisting of the same

variables (although not an identical data set as this is not in the public domain) were unable to

replicate the results. Rather they find that aid can be significant, but that the aid-policy term never

is. Similar results are reported in Lensink and White (1999a).

A further problem is that a single equation is being estimated for what is of course a

system of several equations. At best we have reduced form estimation, with no clear idea of the

channels for aid’s impact, but of course there may also be simultaneity bias in the coefficients.

For example, aid may allow a non-inflationary increase in government spending. Public

investment financed in this way is itself an increment to the capital stock which can enhance

growth, but which may further crowd-in private investment or increase its productivity. That is

aid can promote growth by increasing either or both the quality or quantity of investment. The

way in which the analysis is performed does not allow us to break down these effects. In our

opinion, much deeper work on the way how aid affects the different variables in the growth

regressions, and how these variables in turn affect aid, especially those variables included in the

policy measure, is necessary before something reasonable can be said about the aid-policy-growth

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relationship. This calls for much more emphasis on estimating structural models, and less on the

reduced form regressions on which the reports’ main conclusions are based.

Burnside and Dollar (1997) do consider that aid may be endogenous and so instrument

for aid. But the use of instruments compounds the problem of fragility, since the coefficient on

aid is very sensitive to the instrument chosen (Lensink and White, 1999a). In fact, as argued long

ago by Mosley (1980), it is unlikely that aid is endogenous with respect to growth. It is more

likely that some of the other regressors – such as inflation – are endogenous, so that instruments

should more properly have been used for these variables.

Three conclusions emerge from this analysis. First, the conclusion that aid only works

when policies are right does not stand up in the face of the evidence. The case for aid in fact

seems stronger, although a large number of caveats surround such findings. Second, the results

give little insight into how aid (or which aid) works, which is clearly a crucial concern for policy

makers. Third, the set of policies discussed in the context of aid effectiveness is a fairly narrow

range, and does not reflect the full package of World Bank policy recommendations. It is to this

issue of policies that we now turn.

The policy index and the choice of policies

The regressions use a policy index which is calculated from inflation, budget surplus,

Sachs-Warner trade openness index and institutional quality. There are problems here concerning

what you put in the index, what you leave out, and how you combine them.

Let us begin with the individual components of the index. We have already mentioned

that there are severe measurement problems for many variables used in growth regressions. This

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holds for all variables, but in particular for trade openness. There is a battery of measures of

openness that have been employed in alternative growth regressions. Pritchett (1996) describes

four main types of trade policy indicators. There is no obvious preference for one or another trade

measure since they all refer to different elements of trade policy and hence the choice depends on

what one exactly wants to measure. There would be no problem of interpretation concerning the

effects of trade openness on growth when the different measures appear to be correlated to a

reasonable degree. Unfortunately, Pritchett (1996) shows that the different measures are entirely

uncorrelated, which implies that the observed impact of openness on growth will depend on

which measure is selected. This casts serious doubts on the reliability of the empirical evidence

on the (positive) effects of outward orientation on economic growth. Indeed, Sala-i-Martin (1997)

shows that almost none of the measures of openness proposed in the literature has a robust impact

on growth. The study of Pritchett (1996) also suggests that different components of trade policy

may have different effects on economic growth and that a country’s openness ranking entirely

depends on the trade measure used. Indeed, the degree of outward orientation is not itself a

measure of policy, but may be achieved in various ways, either through liberalisation or export

subsidies. Different policies are likely to be the right ones at different points of time: an economy

which would benefit from greater liberalisation will not, however, grow as a result of liberalising

from one day to the next. Yet, the World Bank report emphasises the importance of outward

oriented policies without making clear what is actually meant by it. In Assessing Aid outward

orientation is measured by using the Sachs-Warner (1995) trade index. This measure combines

different elements of trade policy, some of which are individually described in the Pritchett paper.

More specifically, the trade measure used is a zero-one dummy which classifies a country as

being closed when the average tariffs on machinery and materials are above 40%, the black

market premium is above 20% and when the government strongly intervenes in the sector of

tradeable goods. The use of trade measure based on a combination of different trade policies

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makes it even more difficult to gain insights in the trade-growth relationship. Therefore, the

approach adopted in the report can offer no insights in trade policy issues of great policy

relevance, such as how and what type of trade policies do affect economic growth (or as to which

are most likely to make aid more effective).

Inflation and the budget surplus are also problematic components of a policy index for

several reasons. First, inflation is not in itself a measure of policy and a budget surplus can be

improved by lowering spending or raising revenue, which may be expected to have rather

different results. Second, and more importantly, the nature and extent of their relationship with

growth is disputed. Indeed, in the Burnside and Dollar paper the budget surplus variable is

insignificant in the growth regression used to determine the weights for the index (this procedure

is discussed below), and this finding is in line with other recent research which finds the budget

surplus not to be a significant determinant of growth (see, for example, Levine and Renelt, 1992).

An insignificant coefficient for the budget surplus implies that the weight for this variable in the

economic management index should be zero. Yet, in Assessing Aid, the budget surplus has a large

impact on the policy index. Other literature also shows that, whilst high inflation is bad for

growth, the same is not so of low inflation, and that trying to reduce already low inflation can

actually be bad for growth. To put this point more technically, inflation should enter the growth

equation in a non-linear manner (probably with a quadratic term). This point is of great current

policy relevance, with IMF-backed programmes requiring a target inflation rate of 5 per cent in

low growth economies.

The institutional quality index is based on a measure of Knack and Keefer (1995) which

is based on the security of property rights and the efficiency of government bureaucracy; i.e. it is

a composite of different aspects of governance. This is an area in which there are a growing

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number of available indicators. It is of course very plausible that government efficiency affects

the efficiency of aid – the fact the Burnside and Dollar (1997: note 67) find otherwise suggests

this is an area requiring further research.

Whilst Assessing Aid talks of the importance of “good policies” the four measures

included in fact leave out a large number of the policy changes which are typically included in

World Bank-supported reforms, such as liberalisation of financial, foreign exchange and

agricultural markets, privatisation and various tax reforms (such as the introduction of VAT).

Thus as much attention should be paid to what is missing as to what is there. The report does not

provide evidence in favour of World Bank style reform.4 Indeed, the report seems to underplay

the move toward recognising a greater role for the state, which was of course recognised in the

1997 World Development Report. Both theory and experience suggest that the state can play an

important role in the development process, and it would be most helpful to try to identify what

that role should be in particular contexts.

Even if we agree on which variables to be included there are problems in the construction

of a single index. As already noted, taking a linear combination suggests a linear relationship

between each of the policy variables and growth, which is almost certainly not the case.

Theoretical analyses often suggest the existence of non-linear relationships, such as threshold

effects and inverted U curves, between explanatory variables and economic growth. This refers to

almost all variables, but in particular to inflation. There may even be complementarities between

the different variables, that is some policies work better if implemented in conjunction with other

policies, requiring interactive terms in addition to non-linear ones.

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Even if a linear combination is taken to be appropriate there remains the problem of the

appropriate weights to be used. A first problem is that the weights may differ by country or

country group, as shown to be the case by Berthelemy and Varoudakis (1996) who show that a

policy of trade openness has favourable effects only in countries with a developed financial

system. Already a decade ago, Helleiner (1986) suggested that a positive relationship between

export orientation and economic growth only exists for relatively developed countries. This

finding implies that the effects of trade openness on economic growth vary between countries.

There is no reason why this should not also be the case for the other variables used to construct

the policy index. Implicitly this argument is indeed confirmed by the study of Burnside and

Dollar who show that the coefficients of the individual components of the policy indicator differ

considerably for the entire group of countries and the data set excluding the Middle Income

Countries. Yet the policy index used in the World Bank report is based on an estimate for the

entire data set, not allowing for differences by country group.

Furthermore, if the weights applied do not accurately capture the relative contribution of

the different policies to growth then an inappropriate restriction is being imposed on the data. The

weights are often taken as the coefficients from a multiple regression of growth on the various

components of the index. However, such a regression suffers from omitted variable bias - i.e. the

coefficients are wrong - as other determinants of growth are excluded. Of course all determinants

could be included, but then the full growth model is being estimated, and there is nothing to be

gained by then constructing a policy index and re-estimating the equation. Assessing Aid falls

between these two stools (which is not satisfactory either) since the weights come from a multiple

regression including some, but not all, of the non-policy determinants of growth (the aid terms are

omitted).

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Finally, even if all the above mentioned problems are set aside, there remains the problem

that a significant interactive term between the policy index and aid does not imply that individual

components of the policy index, if interacted with aid, would have a significant coefficient. In the

Burnside and Dollar background study the lack of information is given as the reason why aid is

not simultaneously interacted with several policies. This might be so, but in order to obtain better

insights into the channels by which aid can be made more effective, we would have liked to see

more evidence on the significance of the coefficients of the individual components interacted

with aid. It might, for instance, be the case that better institutional quality would make aid more

efficient, whereas a more outward oriented policy is not that important, or not relevant at all. In

fact Burnside and Dollar suggest the opposite may be the case as they show that an interaction

term between institutional quality and aid is insignificant (1997: note 6), although in Assessing

Aid institutional quality is one of the components of the index. The main conclusion of this

discussion is that, without more information on the robustness of the policy index, and its

components, we may debate the relevance of the policy index.

Is aid fungible?

The second reason why policy matters according to the Assessing Aid report is that aid is

fungible. This means that project aid does not finance the sectors to which it is attached, so that

“government commitment to particular sectors is more important than targetting aid” (World

Bank, 1998: 69). If governments are not committed then donors should consider not giving aid:

“where donors and governments do not agree on the allocation of expenditures and spending is

not likely to be effective [as policies are bad], the best approach is to reduce financing and

increase support for policy dialogue and institution building” (ibid: 61). These are strong

conclusions. How firm are the foundations on which they rest?

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Aid is said to be fungible if the marginal increment in expenditure in response to an aid

inflow is not the expenditure toward which the aid was targeted. This is not a question of

corruption but the fact that, if government would have supported that activity anyway, then the

inflow of aid funds frees up the government’s own resources to be spent on other activities. The

idea that aid is fungible, which has been around at least since the seminal contribution of Singer

(1965), would seem to suggest that donor attempts to direct aid to projects are misguided.

However, the importance of fungibility (which undoubtedly exists) can be over-stated for at least

two reasons. First, there may be a “quality effect” from donor involvement in the project so that it

is better with the donor than it would have been without it. Second there are limits to fungibility,

since the government may well not have devoted its own resources to the donor-financed activity,

either as it has very few resources of its own or because donor and recipient preferences vary.

Ultimately of course the extent of fungibility is an empirical matter. However, existing

empirical studies give a somewhat mixed picture, and the background paper by Feyzioglu et al.

(1998) does not clarify it. There are two strands to this literature.5 The larger of the two strands

uses a model first estimated by Heller (1975) in which government minimises a loss function

containing target values of various policy variables. This model has been applied to both cross-

section data sets and time series. In general some fungibility is found, but the extent varies.

However, there are serious problems in both the model and its estimation (White, 1994). The

second approach is an atheoretical model used by Pack and Pack (1990 and 1993) which

regresses sectoral spending on a range of variables including aid for that sector. This is the

approach used by Feyzioglu et al., though it shares some problems with the first approach. At the

theoretical one problem is the partial nature of the analysis, so that no allowance is made for

feedback effects through aid’s impact on the level of economic activity.

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At the empirical level, such analysis is complicated by the fact that there are no data

available for the sectoral allocation of aid. Such data are available in some countries, or may be

constructed at the county level, although there remains a problem of not knowing how much aid

is going through the budget. Feyzioglu et al. get round this problem by using data for

concessionary loans only; i.e. their analysis excludes grants, which constitute the vast bulk of

bilateral aid. It is therefore difficult to have much confidence that their results tell us anything

about aid in general. As already indicated, there is likely to be fungibility but its extent will vary

across time and space. We do not, however, necessarily wish to question the view that donors

should give some preference to governments who allocate their own resources in a

developmentally-oriented way, as it seems clear that they provide a more fertile environment for

successful aid.

Allocating aid

The main message of Assessing Aid is that aid works when policies are right. The implication for

donors is that aid should go countries with “good policies”, which the report claims is not the

case: “poor countries with good policies should get more aid than ones with mediocre policies -

but in fact they get less” (World Bank, 1998: 23). The background paper by Collier and Dollar

(1998) supporting this analysis proposes an aid allocation algorithm which will maximise aid’s

impact on poverty. How sound is this analysis?6

Collier and Dollar set up an optimisation problem whereby aid is allocated to have its

maximum poverty reducing effect subject to the aid budget constraint. The change in poverty is

given by the product of growth and the a poverty elasticity.7 Aid enters the model since growth

depends on aid and the interaction of aid and policy regime (i.e. a Burnside-Dollar growth model

as discussed above). There are several problems with this procedure. First, are the weaknesses of

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the underlying growth regression which we have discussed above. One weakness is the lack of

robustness. In Lensink and White (1999b) we show that very different “optimal aid allocations”

can be obtained with slight variations in the model. We also argue that which are “the right

policies” is likely to diverge even more strongly from the World Bank’s menu if the focus is on

poverty reduction rather than only growth. Second, is the idea that aid can affect poverty only

through its effect on growth. As we elaborate below in our discussion of infant mortality, aid may

also directly reduce poverty. Furthermore the link between growth and poverty depends on

distribution. Aid can affect distribution directly, or may change the elasticity so that growth has a

greater poverty reducing impact.

The analysis discussed so far focuses entirely on income-poverty, whereas it is

commonly recognised that a number of other measures should be used as indicators of poverty.

Whilst there are many aspects of poverty it is common to settle on social indicators to capture

non-income aspects. In this respect Assessing Aid also reports results for the impact of aid on

infant mortality which are very similar to those for growth - there is no impact when policies are

poor but is when they are good: “with good management an extra one per cent of GDP in aid

leads to a decline in infant mortality of 0.9 per cent. In contrast, if a country has poor

management, there is no marginal impact from aid” (World Bank, 1998: 39). However, many

similar comments may be made about the regression of infant mortality as we already made about

the growth regressions, such as the lack of robustness of the results. In addition, the analysis

assumes that aid can only reduce infant mortality by increasing growth, rather than by, say,

supporting mother and child health. Two counter-arguments may be made in defence of the

Assessing Aid approach: first that aid is fungible and second that (as has been argued in recent

World Bank research, notably Filmer and Pritchett, 1997) health expenditure has had only a small

impact on infant mortality. But both these responses are flawed, as shown by the greatly

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increased immunisation coverage in Africa during the 1980s as a result of the Expanded

Programme of Immunization (EPI). Several studies (e.g. Cornia and Mwabu, 1997) have found

that immunization has a significant impact on mortality and Hanmer et al. (1999) show

immunization to be one of the robust determinants of infant and child mortality, as are several

other indicators of health sector inputs. The fact that aid, by supporting health expenditures, can

directly reduce infant mortality without relying on growth effects, undermines the emphasis

placed in Assessing Aid on the fact that policies must be right in order to give aid. This is one

example of the fact that the analysis in Assessing Aid tells us little about what forms of aid are

appropriate and can mislead as to which countries should be given aid. Hence the report provides

little guidance to aid managers. It is to this issue we now turn.

3. AID MANAGEMENT ISSUES

As the main output of a substantial programme of research on aid effectiveness it may be

imagined that Assessing Aid would provide a fairly comprehensive set of proposals for improving

aid. In fact, the recommendations, whilst strong, are quite limited, and we argue here, of rather

limited practical value.

How selectively should aid be allocated?

The recommendation that aid should be focused on countries with good policies is a

statement of the policy of selectivity, which has been discussed amongst donors over the last few

years. Many donors give aid to a large number of countries, and have not distinguished between

those in which aid may work and those in which it may not. Assessing Aid thus comes down

firmly on the side of those who say that practices should change so that aid is concentrated on

fewer countries. But the concept of partnership, which has been embraced by many donors in the

second half of the 1990s (see, for example, White Papers from Canada, Sweden and the United

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Kingdom) has already reached this position, and donors are working with a more nuanced

understanding of selectivity. Donors have expressed the desire to enter into partnerships with

developing countries who share their developmental objectives (which may be broadly

summarised as poverty reduction, market-led growth, gender equality and environmental

sustainability)8, with the implication that those not so inclined will receive less (no?) aid.

However, donors quickly realised that the only countries sharing their developmental objectives

were other donors (as one Swedish official remarked, “if we really follow these criteria we will

have to give all our aid to Norway”), and so some refinement of the concept was needed.9

However, the research from the Assessing Aid project has set some donors (Denmark and the

Netherlands are examples of which we are aware) back in their thinking with high-level

(ministerial) interventions to restrict allocation based on a policy index.

The reality of partnership will surely be that there are a range of relationships - it is the

type of aid which should be determined by a government’s policy stance, not the decision

whether or not to give aid at all. To say otherwise is to say that there is no way that poor people

can be helped by aid in countries with bad governments - and this is indeed the view expressed in

some World Bank publications, but it is not the attitude of donors who will remain under pressure

to continue aiding people in such circumstances. There has thus been a more pragmatic stance,

one that, for example, allowed aid to NGOs to be given to South Africa under apartheid, and such

practices will no doubt continue10 (in the case of the Netherlands, NGOs are indeed expected to

play this role, whilst official aid will be restricted to countries with good policy).11 The position

taken in Assessing Aid is that it is ideas, rather than money, that matter when the policy

environment is poor, a position which implies giving aid in some form - but most donors would

argue that a wider range of interventions remains both feasible and justified.

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Once these points are accepted then the allocation of aid will display some bias away

from those countries having good policies. There is of course another argument why this should

be so, which is that the countries that have good policies and are growing well may have less

need of aid, being able to mobilise savings and call on private international flows. Our arguments

are not intended to downplay the danger of reinforcing bad policies by bailing them out with aid,

but, as recognised in Assessing Aid, the link between aid and policy change is a far from simple

one. Just as aid has not been very successful at buying good policies, it cannot be shown that aid

necessarily finances bad ones.

Applying selectivity

The first problem in applying selectivity is the basis on which countries should be

selected. Assessing Aid proposes that countries be selected according to how they score on a

policy index. But we showed above that there are fundamental problems in both the policies

proposed and their combination in an index. And if donors want to allocate aid to maximise the

impact on poverty rather then growth then the divergence with the policies proposed by Assessing

Aid is likely to be still greater than suggested by our discussion of growth. Government’s

commitment to poverty reduction, as evidenced by its own expenditure priorities, and the

existence of targetted spending such as school feeding programmes and labour-intensive public

works, should surely be part of the policy index.

Even if it is accepted that donors should allocate aid according to a World Bank-style

policy index problems remain. The first is the decision rule to be applied: is there some threshold

value for the index as a whole, or do some (or all ) of its components have individual thresholds?

If the former, then a country can get away with mass torture provided it has a very liberal trade

and exchange regime. But if the latter, a country’s reform may be jeopardised by falling behind in

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just one area of the program. Either way, weights have to be applied to the different bits of the

index - how is this to be done? We discussed above how this many be done econometrically for

the purposes of estimating growth, but these may not be the same weights as a donor would like

to apply (who may value some components more highly than others). A third problem is how to

allow for exogenous shocks which may knock policy off course through no fault of the

government. Finally, should aid be allocated according to the level of the policy index or its

change (i.e. should we give aid to a country with a good, but worsening, policy index, rather than

one with a poor, but improving, index)? None of these issues, which are very real practical

problems facing an aid administrator attempting to take up the World Bank’s recommendations in

allocating aid by the policy stance of potential recipients, are confronted in the report.

As explained above, there are limits to the extent to which bilateral donors either can or

wish to allocate their aid according to a policy index. But the World Bank has allocated IDA

resources in a manner such as this for some time and has recently constructed a new policy index

used for these purposes. One can imagine that both the construction of this index and (especially

since it contains a number of subjective judgements) the levels accorded to particular countries

would be an issue attracting some debate. But they have not for the simple fact that this

information is not in the public domain. Despite great advances in openness during the 1990s, the

World Bank can still lack transparency in important respects, of which this is one example. This

point clearly matters as, as we have discussed, there is not agreement as to which policies are the

right ones.

4. CONCLUSIONS

Assessing Aid argues that aid can work, but only when policies are right. Hence, it is argued,

donors should concentrate their resources on low income countries with good policies. We

20

endorse this positive assessment of aid’s role, as much of aid’s bad press is either unsubstantiated

or a result of dubious analysis. But we are less certain of how good a guide is to donor practice.

This lack of certainty arises both because the report is not very concrete on the policy

recommendations and that we do not agree with all the directions in which it is pointing. The

finding that aid only works when there are good policies may be criticised on a number of

grounds, including the rather narrow range of items identified as being “good policies”. The

report does not provide empirical support for the broad gamut of liberalisation which typically

constitutes a World Bank reform programme. It may sound a rather technical point to say that the

construction of the policy index does not allow for non-linearities: but this is a crucial policy

issue for low growth economies applying a cash budget, which may well be described as a matter

of life and death. Donors would thus be well advised to avoid taking the report as being a

vindication of either support for market-based reform or too great a selectivity in the allocation of

aid.

21

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26

NOTES

1 Data from OECD web-site (www.oecd.org/dac).

2In Appendix 1 of the report it is recognised that the results also imply that policies are more

effective when supported by aid inflows: “the positive coefficient on the interactive term also

means that policy improvements are more potent if a country is receiving aid”. The coefficient

may indeed be spread over the two interpretations (thus lessening the impact of policies on aid

effectiveness) or may be wholly attributed to either interpretation. But the coefficient cannot be

fully attributed to both effects (which is implicitly done in the report’s presentations of how

policy affects aid effectiveness).

3 This point is elaborated in White (1998: Chapter 2).

4 This is not to say that such reforms are misguided. Reviews of adjustment policies (e.g. Lensink,

1996 and White, 1996) suggest, as one would expect, that some policies work better then others

and that some indicators - notably private investment and agricultural output - have responded

less well than others.

5 The authors of Assessing Aid seem only to be aware of the second, smaller literature,

6 The arguments here are elaborated in another paper (Lensink and White, 1999b).

7 The elasticity gives the percentage reduction in the poverty headcount for a one per cent

increase in real GDP.

8 Assessing Aid only addresses the issue of market-led growth, although the authors would claim

it also addresses poverty reduction. They cannot claim, however, that it addresses either gender or

environmental concerns since these issues, which are central to the policy statements of most aid

donors - including the World Bank - are not considered in the report.

9 Bilateral donors have had to modify this concept for two reasons. That discussed here is that

27

few, if any, developing countries meet the ideals set out for “full partnership”. A second reason is

that donors also fall short of their ideals, with political and commercial pressures on the aid

budget which distort aid allocations.

10 In this vein, one study qualifies its endorsement of the relationship between aid effectiveness

and policy environment as follows: “this is not to say that foreign aid never benefits a country

that is pursuing counterproductive economic policies. Child immunization programs, for

example, are likely to benefit a developing country regardless of its economic policies…” (CBO,

1997: xiv).

11 Specifically the bilateral budget disbursed through official bilateral channels will go to 19

countries based on the policy index, whereas the amount for NGOs (about one third the former

amount) is intended to go to other countries.