ESTIMATING THE GROWTH IMPACT OF AID IN THRESHOLD REGRESSIONS:

ARE AID FLOWS EXCESSIVE OR INSUFFICIENT?

Sarantis Kalyvitisa, Thanasis Stengosb, and Irene Vlachakic

January 2010 Abstract: Existing empirical studies and policy reports claim that foreign aid flows have not succeeded in boosting economic growth in the recipient countries. The present note examines whether there exists an aid threshold that determines the growth impact of foreign aid. We use a threshold regression methodology to estimate growth specifications and the associated aid thresholds in a sample of 42 aid recipients covering the period 1970-2000. Our findings indicate that there is a threshold level of aid above which its growth impact becomes positive. Keywords: growth, aid, threshold regression, endogeneity. JEL classification: F35, O4, C2. Acknowledgements: We thank Zeb Aurangzeb for excellent research assistance.

a Corresponding author: Department of International and European Economic Studies, Athens University of Economics and Business, Patission Str. 76, Athens 10434, Greece. Tel: (+30210) – 8203174. Fax: (+30210) – 8203137. e-mail: skalyvitis@aueb.gr b Department of Economics, University of Guelph, Guelph, Ontario N1G 2W1, Canada. e-mail: tstengos@uoguelph.ca c Department of International and European Economic Studies, Athens University of Economics and Business, Patission Str. 76, Athens 10434, Greece. e-mail: vlachaki@aueb.gr

1. Introduction

A persistent issue in development economics involves the failure of foreign aid in terms of boosting

economic growth in the recipients.1 Yet, some empirical studies are more favorable to a positive impact of

aid on growth under the assumption of diminishing returns. In particular, Hadjimichael et al. (1995),

Lensink and White (2001), Dalgaard and Hansen (2001), and Clemens et al. (2004) have found positive

but decreasing marginal returns to aid flows by introducing an aid-squared term. Hansen and Tarp (2000,

2001) have controlled for both decreasing marginal returns and a potential synergy effect between aid and

policy, in the spirit of Burnside and Dollar (2000) who had claimed that aid contributes positively to

growth only in good policy environments. They show that the inclusion of decreasing returns in aid flows

is important and renders the interaction effect of aid with policy insignificant. Alvi et al. (2008) adopt a

non-parametric approach to allow for a flexible functional form of aid-nonlinearities and also provide

results in favor of diminishing returns. On the flip side, and viewing the growth-aid nexus from a policy

perspective, several reports have highlighted the role of insufficient, rather than excessive, aid flows in

explaining the poor growth results of recipients. Perhaps the most prominent manifestation of this claim

involves the attainment of the Millennium Development Goals (MDGs), which require substantial

additional funding in terms of foreign aid flows to the developing world.2 For instance, Zedillo et al.

(2001) estimate that roughly $50 billion a year in additional aid will be required to achieve the MDGs in

all developing countries. Devarajan et al. (2002) have estimated a figure in the range of 40 to 70 billion

USD in required increased assistance per year, which roughly represent a doubling of official aid flows

over 2000 levels. The Commission for Africa (2005) calls for an additional 25 billion USD per year in aid

to African countries by 2010, with a further 25 billion USD a year to be implemented by 2015.

The present note attempts to re-examine the growth impact of aid by asking the following question.

1 For detailed reviews and evidence see Kanbur (2006), Doucougliagos and Paldam (2008), and the papers cited therein. 2 In July 2005, the G-8 agreed to double foreign aid to Africa, from $25 billion a year to $50 billion to finance the “big push”, required for African countries to get out of the poverty trap though a large aid-financed increase in investment. Similarly, the European Commission (2005) issued an EU Strategy for Africa in which increased aid was required to achieve a large increase in growth. See also United Nations (2006) for a detailed review on related estimates for Africa.

1

Are aid flows to developing or underdeveloped countries excessive or insufficient? In other words, we aim

at answering whether there exists a threshold in foreign aid flows, above which the growth impact of aid

changes critically. To this end, we use the threshold regression model developed by Hansen (2000) and

Caner and Hansen (2004) to estimate variants of standard growth specifications with aid thresholds in a

cross section of 42 aid recipients covering the period 1970-2000. Our central finding is that there is a

threshold level of aid above which the growth impact of aid becomes unambiguously positive. In

particular, we find that low values of aid flows as percentage of the recipients’ GDP exert a negative or

insignificant effect on growth. However, the growth impact of aid becomes positive for countries in which

aid flows exceed a critical threshold. This result is robust to a number of specifications, the data

frequency, and to the endogeneity of institutions in aid recipients, a point that has been forcefully raised

by Acemoglu et al. (2001) and has since then been a benchmark in assessing the growth effect of aid in

developing countries.

Our results obviously coincide with recent calls for a major scaling up of aid to help poor countries

achieve the Millennium Development Goals. In particular, Sachs et al. (2004) and Sachs (2005) have put

forward an idea that goes back to Rostow (1960), according to which poor countries are stuck in low-

savings poverty traps and that a major intervention (‘big push’) is required to eliminate poverty. One

simplifying idea behind these calls is that investment is inadequate due to low savings (triggered for

instance by the needs for subsistence consumption) and productivity in developing countries. Hence these

countries will converge to a low-growth equilibrium, a situation that is aggravated under credit market

imperfections. Alternatively, potential non-convexities in the production process, like increasing returns

on infrastructural capital or threshold effects in human capital, suggest that a large aid-induced rise in

domestic investment would have a strong long-run growth impact. In this vein, aid recipients could benefit

from a massive inflow of aid oriented towards sufficient savings and capital accumulation to break free

from the poverty trap.

It is noteworthy that the actual experiences and associated empirical evidence have not provided

overwhelming support for these mechanisms up to now. Although Azariadis and Stachurski (2005) have

2

noted that generally poverty-trap models seem to be lacking testable quantitative implications, some

studies have attempted to investigate their predictions in the context of aid flows. Easterly (2006) claims

that the stylized facts are not consistent with a low-income poverty trap due to insufficient aid, as growth

is lower in aid-intensive countries than in similar developing countries that get little aid, whereas aid has

risen over time as a percent of income in Africa, but Africa’s growth rate has fallen over time. Kraay and

Raddatz (2007) have recently tested whether the savings and increasing returns patterns predicted by

poverty-trap models are supported by the data. They show that there is no supporting evidence either in

the behavior of saving and per capita income, or technological nonconvexities, in favor of a poverty trap.

The authors also fail to find evidence for the existence of a high-growth high-equilibrium that countries

might be able to attain with appropriately large aid inflows.

Against such a background, the results presented in the current paper seem to offer, for the first time,

some compelling evidence that large-scale aid flows can have a growth impact over the long run. This

empirical regularity is supported by the recent findings of Herzer and Morrissey (2009) who have

established that there is substantial heterogeneity in the output effects of aid among 59 recipients: although

the estimated long-run effect of aid is negative, it is found that the effect is positive in about one third of

the countries examined. Indeed, the United Nations (2006) report has mentioned several stories of aid

successes, which resulted in boosting domestic investment and growth over the last decades. For instance,

the East Asian miracle economies, notably the Republic of Korea and Taiwan Province of China, received

enormous amounts of aid during the initial and early stages of their development.3 In Africa, both

Botswana and Mauritius received very large amounts of aid at key strategic moments in their development

as, earlier, did Tunisia.4 These examples indicate that, despite the often-cited failure of aid in boosting

development in recipients, large amounts of well-targeted aid can, in conjunction with other factors,

3 The nearly 6 billion USD in US economic aid to South Korea between 1946 and 1978 was only marginally lower than its total aid to all of Africa in the same period (6.9 billion USD). A similar pattern was found in Taiwan Province of China, where although its big push began on the back of a greater degree of domestic resource mobilization, aid still accounted for nearly 40 per cent of gross domestic capital formation in the 1950s and was over 4 billion USD between 1949 and 1967 with per capita aid being higher than that to Korea. 4 Botswana had initially a very high aid to GDP ratio, which dropped sharply thanks to a sustained period of rapid growth, whereas a similar picture can be found in Mauritius.

3

produce remarkable success stories in terms of growth.

The rest of the paper is structured as follows. Section 2 briefly outlines the empirical methodology and

describes the specification utilized and the dataset. Section 3 presents the empirical results and section 4

concludes the paper.

2. Empirical methodology and data

The threshold regression model treats the sample split value (threshold parameter) as unknown by

internally sorting the data on the basis of some threshold determinants into groups of observations, each of

which obeys the same model. The threshold regression approach is parsimonious, but also allows for

increased flexibility in functional form and it is not as susceptible to the curse of dimensionality problems

as nonparametric methods. Chan (1993) showed that the asymptotic distribution of the threshold estimate

is a function of a compound Poisson process. This distribution is too complicated for inference as it

depends on nuisance parameters. Using a concentrated least squares (TR-CLS) approach, Hansen (2000)

developed a more useful asymptotic distribution theory for estimating both the threshold parameter

estimate and the regression slope coefficients in a cross-section of observations. This sample-splitting

methodology has the advantage of endogenously determining the threshold, as opposed to simple

parametric approaches that set the threshold exogenously.5

In particular, assume that 1{ , , , }ni i i iy x q u = is strictly stationary, ergodic and ρ-mixing, and that

Eui |F i−1 0, where yi is the dependent variable (growth), xi is a p×1 vector of covariates (including

aid), and qi is a threshold variable (aid). Consider then the following threshold regression with threshold

for aid:

1 1i iy x uβ′= + i , qi ≤ γ (1)

5 Masanjala and Papageorgiou (2004) point out that the exogeneity assumption in determining the threshold effect is, in fact, constrained to the estimation of dynamic linear panel data models, which is not the case in the context of the present analysis.

4

2 2i iy x uβ′= + i , qi ≤ γ (2) Equations (1) and (2) describe the relationship between the variables of interest in each of the two regimes

with γ being the sample split (aid threshold). Note that qi is observed but the sample split is unknown. Τhe

variance covariance matrix of the errors 1 2( , )i iu u ′ has the following properties: Eu1i,u2i 0,

Eu1i2 1

2 0, Eu2i2 2

2 0. In general, if the model involves exogenous slope variables then

estimation is based on Concentrated Least Squares (Hansen, 2000). In turn, the heteroskedasticity-

consistent Lagrange Multiplier (LM) test introduced by Hansen (1996) is used to verify whether there is

indeed evidence of a sample split; the null hypothesis of the test is that there is no threshold effect and the

corresponding p-values are computed by a bootstrap analog. Using a similar set of assumptions, Caner and

Hansen (2004) study the case of endogeneity in the slope variables and propose a concentrated two stage

least squares estimator (IVTR-C2SLS) for the threshold parameter and a GMM estimator for the slope

parameters.

To estimate equations (1) and (2) we use data from 42 aid-recipient countries over the period 1970-

2000.6 Since our emphasis is on the long-run growth impact of aggregate aid without the inclusion of

country fixed effects that traditionally help capture the impact of worldwide business cycles, we estimate

long-run horizon cross-country regressions using alternatively whole-period and ten-year averages, rather

than four-year averages as is common in a strand of the relevant literature. Hence, growth volatility, which

is far higher in poorer countries (Pritchett, 2000), and cyclical factors are unlikely to affect our estimates.

In turn, we follow the empirical specification adopted by Dalgaard et al. (2004, Table 3), which allows for

a parsimonious representation of the long-run growth equation with aid as one of the determinants.

However, we also experiment with additional potential control variables to assess the robustness of our

results. Data come from the World Bank database unless otherwise specified. The dependent variable of

6 The countries included are: Burkina Faso, Bolivia, Brazil, Botswana, Chile, Côte d'Ivoire, Cameroon, Congo Rep., Colombia, Costa Rica, Dominican Republic, Ecuador, Egypt Arab Rep., Ethiopia, Ghana, Gambia, Guinea-Bissau, Guatemala, Honduras, Indonesia, Jamaica, Kenya, South Africa, Sri Lanka, Madagascar, Mexico, Mali, Malawi, Malaysia, Niger, Nicaragua, Peru, Philippines, Paraguay, Singapore, Sierra Leone, El Salvador, Thailand, Trinidad and Tobago, Uganda, Venezuela, and Yemen.

5

the estimated regressions is the average growth rate of real per capita GDP. To capture convergence

effects the logarithm of initial GDP per capita in constant 1985 dollars (source: Heston et al., 2006) is

included as a control variable. Dalgaard et al. (2004) have established that the growth impact of aid is far

smaller in the tropical region. In line with these authors, the importance of (non-political) structural

characteristics on aid effectiveness is assessed using the fraction of a country’s area that is in the tropics

(source: Gallup and Sachs, 1999). A measure of institutional quality that captures security of property

rights and efficiency of the government bureaucracy also enters growth regressions; data are drawn from

Knack and Keefer (1995).7 Turning to macroeconomic policy variables, and in line with Burnside and

Dollar (2000), Dalgaard et al. (2004) use the budget surplus as a percentage of GDP to capture fiscal

policy, inflation, and the revised trade openness dummy variable introduced by Sachs and Warner (1995)

and updated by Easterly et al. (2004) and Wacziarg and Welch (2008).8

Regarding data on aid flows, we employ in some benchmark regressions ordinary data for aid flows

measured by Effective Development Assistance (EDA) as a percentage of real GDP (constant 1985

dollars) drawn from Roodman (2007).9 However, a prevalent criticism of aid-growth regressions involves

the likely endogeneity of aid as is often argued that donors might reward countries that have used aid well

in the past or, conversely, help countries that have experienced natural disasters, thus inducing a spurious

correlation between aid and growth. To avoid endogeneity-induced problems and account solely for the

exogenous component of aid, we use the recently developed approach by Rajan and Subramanian (2008)

to instrument aid. This approach focuses merely on bilateral aid flows and donor-specific, rather than

recipient-specific, characteristics by starting from the bilateral donor-recipient relationship and

aggregating up to construct endogeneity-free data. In particular, the instrumentation strategy by Rajan and

Subramanian (2008) picks instruments directly at the level of the donor, rather than the recipient, country

7 The dummies for East Asia and Sub-Saharan Africa, which are routinely included in empirical growth specifications, cannot be identified in a threshold regression framework. 8 Sachs and Warner (1995) define closed economies as those that have average tariffs on machinery and materials above 40%, or a black market premium above 20%, or pervasive government control of key tradables. 9 Another standard measure of aid is Official Development Assistance (ODA) provided by the OECD and the World Bank. However, as also pointed out by Dalgaard and Hansen (2001), EDA and ODA are highly correlated, so switching from one definition to the other is not expected to affect our results significantly.

6

and hence precludes any direct association of excluded instruments with growth rates. The authors model

the supply of aid based on the bilateral (donor-recipient) relationship and then aggregate up the predicted

values of aid received from each donor over all donors in order to get a precise measure of aid flows as a

percentage of the recipient’s GDP. The choice of aid instruments relies on two assumptions: the first

assumption is that the greater the extent of historic relationships between a donor and a recipient the more

likely that a donor will want to give aid. This idea introduces colonial links and common language in the

aid supply regression. The second assumption is that donors are more likely to want to give aid the more

they expect to have influence over the recipient. Thus, the relative size of donor and recipient, and also the

interaction terms between relative size and colonial links are included in the set of instruments to construct

the exogenous part of aid flows.10

To assess the relationship between actual and fitted aid for the period 1970–2000 we regress actual aid

on fitted aid and the full set of growth covariates. We find that the relationship between actual and fitted

aid is strong, with a t-statistic that exceeds 6. Figure 1, which depicts the residuals from regressions of

actual and fitted aid on the growth covariates, indicates that the correlation coefficient is high and

statistically significant, reaching almost 0.65. Thus, we can safely infer that, as in Rajan and Subramanian

(2008), fitted aid contains a great amount of information about actual aid flows, which cannot be

attributed to growth factors that affect both variables simultaneously.

An important issue in the present context involves the endogeneity of institutional quality in a growth

regression since more developed countries can simply afford better institutions, or both growth and

institutions might be affected by the same factors. In this vein, Acemoglu et al. (2001) have used

European settler mortality as a source of exogenous variation in institutions. During colonization in the

previous three centuries, Europeans pursued different policies depending on the mortality rate faced by the

settlers. Specifically, Europeans were more likely to set up extractive institutions when faced with high

mortality, and it is possible that the differences in institutions have persisted to create differences in

institutional qualities across countries in the late twentieth century. Following this rationale, we adopt the

10 See Rajan and Subramanian (2008) for more details on the instrumentation strategy.

7

same approach to address the likely endogeneity of institutions and we use data on settler mortality

obtained from Acemoglu et al. (2001). These data correspond to estimates of mortality rates (expressed in

logarithms) faced by European soldiers, bishops and sailors in the colonies in the 17th, 18th and 19th

centuries.11 However, the settler mortality instrument is available for a subset of countries that were

colonized, and this reduces our sample to 36 countries.

Table 1 summarizes the descriptive statistics of the variables at hand. Actual aid flows for the 42 aid

recipients analyzed here have on average been 1.5% of their GDP ranging between nearly zero and almost

7%. In contrast, the estimated exogenous component of aid amounts to almost 5% of the recipients’ GDP

and varies widely ranging between -4.4% and 30%. The annual growth rate of the recipients’ real per

capita GDP hardly reaches 1.4%, but in some extreme cases it may exceed 7%. Institutional quality is at

medium levels and inflation is on average 1% with a maximum value of almost 7%. Average budget

surplus is close to zero and almost half of the recipient counties have closed economies, according to the

definition of Sachs and Werner (1995). Ninety per cent of the recipients’ land is located in the tropics.

Descriptive statistics for settler mortality implies that in ex-colonies almost one-quarter of the living

settlers would die per annum due to unfavourable disease environment.12

3. Empirical findings

In this section we present the empirical results based on the methodology developed in the previous

section. For comparison purposes, we also report results from OLS and 2SLS regressions. The first four

columns of Table 2 report the results when ordinary aid data are used to assess the growth impact of aid.

Although this dataset for aid is not purged from endogeneity, we nevertheless report our findings to allow

for comparisons of our estimates with the rest of the literature. Column (1) presents the estimates of a 11 Estimates of mortality rates correspond to potential settler mortality, measured in terms of deaths per annum per 1,000 “mean strength” (raw mortality numbers are adjusted to what they would be if a force of 1,000 living people were kept in place for a whole year, e.g., it is possible for this number to exceed 1,000 in episodes of extreme mortality as those who die are replaced with new arrivals). For more details on the construction of the mortality rate index, see Acemoglu et al. (2001). 12 The extreme value of this variable corresponds to Mali, for which estimated mortality rates exceed 1,000, i.e. all living settlers and also new-born settlers are expected to die in one-year period. Another case with estimated mortality rates above 1,000 is Gambia for which the index reaches 1470.

8

pooled OLS regression that confirm some standard results from the literature. The control variables have

the expected signs although the majority of them are statistically insignificant. In particular, institutional

quality, budget surplus and trade openness are positively signed, as expected, but only the latter variable is

statistically significant at 1% level. Initial income appears with a negative and statistically significant sign,

thus confirming the standard convergence hypothesis. In accordance with Dalgaard et al. (2004), inflation

and the percent of a country’s land located in the tropics enter with negative signs, but are found to be

insignificant. Interestingly for our purposes, the coefficient on aid is negative and statistically

insignificant, thus confirming the broad picture from the empirical literature on the aid-growth nexus.

To detect any non-linearities in the aid-growth relationship we follow the standard empirical strategy

and we augment the linear growth regression of column (1) by adding an aid-squared term. The inclusion

of this additional variable improves OLS estimation whereas the effects of the control variables remain

intact (see column 2 of Table 2). Aid now exerts a statistically significant negative effect on growth rates,

but this adverse effect is progressively weakened as aid increases, as indicated by the statistically

significant positive coefficient of the squared term. Thus, in face of ordinary aid data one finds significant

evidence that the marginal growth effect of aid is not uniform across recipients, but it is differentiated

according to the amount received.

Given the aforementioned evidence in favour of aid non-linearities, in columns (3) and (4) we move

on to estimate threshold regressions using the same dataset with ordinary aid data. The control variables

have the expected signs and are now mostly significant. In particular, the threshold estimates show that in

major aid recipients (i.e. countries above the threshold) the fraction of land in tropics exerts a largely

negative climate effect on growth and the budget surplus affects growth positively. For both subgroups of

countries trade openness retains its positive significance and the standard convergence hypothesis is

empirically validated. Regarding aid, the threshold regressions indicate the coefficient on aid is

insignificant or significantly negative below this threshold, whereas it becomes positive, but is always

insignificant, for values above the threshold. However, the threshold is found to be insignificant. The

heteroskedasticity-consistent Lagrange-multiplier (LM) test obtained in Hansen (1996), as reported in the

9

lower part of Table 2, indicates that no aid threshold exists when ordinary aid data are employed.

The right panel of Table 2 highlights the main point of the paper. In particular, in columns (5)-(10) we

report estimates with endogeneity-free aid data. First, column (5) reports the results from OLS regression.

Again, the control variables appear with the expected signs and trade openness, budget surplus and initial

income exert a statistically significant growth effect. The OLS coefficient on aid is statistically

insignificant, although now it is positively signed. As in the case of ordinary aid data, we let a quadratic

term of endogeneity-free aid enter an alternative growth regression in order to detect non-constant

marginal effects of aid (column 6). The results remain virtually the same, but the coefficient of aid

becomes now negative (although it is still insignificant), whereas the statistically significant positive

coefficient of the squared term bears evidence that the reverse growth effect of aid is moderated at higher

aid levels. Thus, we move on to threshold regressions with endogeneity-free aid. The picture here changes

starkly: there is an aid threshold below which the sign of the estimated aid impact is negative and

statistically insignificant, but above which the corresponding coefficient becomes positive and statistically

significant. This result provides prima facie evidence that the adverse growth impact of aid typically

reported in the literature is driven by low aid flows, whereas it is reversed in countries with large aid

inflows. In columns (9) and (10) of Table 2 we perform a similar exercise with ten-year averages, which

may give an indication of the robustness of the aforementioned results in the medium-run horizon. Our

unbalanced sample of 42 countries now consists of 114 observations with endogeneity-free aid data

obtained in a similar manner as before.13 The main picture survives and, in fact, is now more striking. The

estimated aid coefficient above this threshold remains positive and statistically significant, whereas the

corresponding ones below the threshold is found to be negative and statistically significant. In accordance

with these findings, the values of the LM test indicate that one can safely reject the null hypothesis of no

threshold at 1% significance level. Thus, we get significant evidence in favour of the existence of an aid

threshold effect when both cross-sectional and panel data are employed and when endogeneity of aid is

controlled for. Regarding the rest of the controls, we find that, as in column (1), the fraction of land in the

13 We do not report results from the corresponding OLS regression, since they are similar to those of column (5).

10

tropics turns out a growth deterrent in major aid recipients, whereas budget surplus exerts an adverse

effect for countries below the aid threshold and a positive effect for those above the threshold.

We next address the potential endogeneity of institutions discussed in the previous section by using

settler mortality data as instruments. Due to data availability our sample is now reduced to 36

observations.14 Column (1) in Table 3 reports results from the Two-Stage Least Squares regression and it

is evident that the instrumentation of institutions does not affect the growth impact of aid. The coefficient

of aid turns out negative and insignificant validating the findings of the literature. However, when we

account for the presence of a threshold in the effect of aid the picture is again different; as can be readily

seen in columns (2) and (3) of Table 3, there is a threshold for aid above which the growth impact of aid is

positive and statistically significant, whereas it is insignificant below the threshold.15 Thus, we confirm

that the presence of an aid threshold for its positive growth impact is not affected by the endogeneity of

institutions. We also address the endogeneity of institutions using 10-year averages in a similar way as the

one outlined for the whole-period averages; consequently, our sample now consists of 101 observations.

The results are reported in columns (4) and (5) of Table 3. Although the threshold regressions indicate that

the coefficient of aid above the threshold is now insignificant, its sign changes again from significantly

negative to positive above the threshold.

As a final step, to eliminate the possibility of omitted-variable bias and test the robustness of our main

result to the inclusion of additional explanatory variables we follow the recent study by Alvi et al. (2008)

and we augment the model by first adding ethnic fractionalization to our benchmark regression. Ethnic

fractionalization denotes the probability that two individuals will belong to different ethnic groups and the

data correspond to 1960 values provided by Easterly and Levine (1997). Columns (1)-(2) and (5)-(6) of

Table 4 report the results for both exogenous and endogenous institutions. Ethnic fractionalization has a

negative effect on growth in high aid-recipient countries. Our main result persists across the estimated

14 Botswana, Guinea-Bissau, Malawi, Philippines, Thailand, and Yemen are excluded due to missing observations. 15 We note that the budget deficit is not included in specifications (2)-(3) because it is highly correlated with institutional quality in high aid recipients, which caused numerical problems in the cross section regression with endogenous institutions.

11

specifications. The coefficient on aid is found to be negative below the threshold and significantly positive

above the threshold. Following Alvi et al. (2008), we also replicate estimation using money supply as a

share of GDP as a control variable (source: World Bank). Again the regressions corroborate our evidence

on the positive growth impact of aid for high aid recipients.16

4. Concluding remarks

One of the major issues in international development is the failure of aid to boost growth in recipient

countries. The negative or, at best, insignificant growth effect of aid supported by almost all studies lies in

the central assumption that the relationship between aid and growth is uniform across countries. Using a

data-driven threshold regression approach, this paper aimed at investigating whether the growth impact of

aid changes beyond a critical threshold. We showed that in standard OLS and Two-Stage Least Squares

regressions aid is found to be ineffective in enhancing growth in recipient countries. However, when a

threshold regression approach is used we found that high aid flows affect growth positively.

The paper thus belongs to the newer generation of empirical studies that have attempted to investigate

heterogeneous policy effects on growth. For instance, Kourtellos et al. (2007) have recently shown that

countries that belong to a growth regime characterized by levels of ethnolinguistic fractionalization above

a threshold value experience a negative partial relationship between aid and growth, while those belonging

to the regime with fractionalization below the threshold do not experience any growth effects from aid.

The authors also find that countries in the regime with higher levels of ethnolinguistic fractionalization

experience, on average, lower growth rates than countries in the lower fractionalization regime.

Investigating threshold effects of aid in conjunction with other variables, like ethnolinguistic

fractionalization, seems therefore to offer a promising route for future research.

16 We also experimented with the following additional variables. We introduced assassinations and an interaction term with ethnic fractionalization, but both variables turned out insignificant, whereas the coefficient of aid was not substantially affected. Also, following Clemens et al. (2004) we augmented the model by adding the logarithm of life expectancy at birth in 1970. However, the estimation of this specification generated numerical problems because, although initial log life expectancy is not very strongly correlated with initial log GDP (the correlation coefficient is around 0.7), for high aid levels the correlation between these variables reaches 0.95.

12

We close this note with a word of caution. Given that most recipients are below the estimated

threshold, our evidence provides some indications why aid flows have been insufficient in terms of

exerting a large effect on the growth of recipients. Hence, the evidence seems to favor the view that a

substantial increase of aid flows is required in order for aid to be effective in terms of growth.

Nevertheless, the present approach cannot identify the generating mechanisms and channels through

which this effect takes place, but only aims at highlighting a robust empirical fact that warrants further

exploration. Recently, Minoiu and Reddy (2010) find that developmental aid has a positive and large

effect on growth, while non-developmental aid is mostly growth-neutral. Ouattara and Strobl (2008) have

shown that the negative growth effect of aid comes mainly from financial program aid, whereas project

aid affects growth positively but with diminishing returns. In this spirit, investigating the role of various

aid forms on growth through a more refined analysis warrants further investigation.

13

References

Acemoglu D., S. Johnson and J. A. Robinson (2001), “The colonial origins of comparative development: an empirical investigation”, American Economic Review 91(5): 1369-1401.

Alvi E., D. Mukherjee and E.K. Shukralla (2008), “Aid, policies, and growth in developing countries: a new look at the empirics”, Southern Economic Journal, 74(3): 693-706

Azariadis C. and J. Stachurski (2005), “Poverty traps”, in P. Aghion and S. Durlauf (eds.), Handbook of Economic Growth, North Holland: Amsterdam.

Burnside C. and Dollar (2000), “Aid, policies and growth”, American Economic Review 90(4): 847-868.

Caner M. and B. Hansen (2004), “Instrumental variable estimation of a threshold model”, Econometric Theory 20(5): 813-843.

Chan K.S. (1993), “Consistency and limiting distribution of the least squares estimator of a threshold autoregressive model”, The Annals of Statistics 21(1): 520-533.

Clemens M.A., R. Bhavnani and S. Radelet (2004) “Counting chickens when they hatch: The short-term effect of aid on growth”, Center for Global Development Working Paper 44: Washington.

Commission for Africa (2005), Our Common Interest: Report Of The Commission For Africa, http://www.commissionforafrica.org.

Dalgaard C. and H. Hansen (2001), “On aid, growth and good policies”, Journal of Development Studies 37(6): 17–41.

Dalgaard C., H. Hansen and F. Tarp (2004), “On the empirics of foreign aid and growth”, Economic Journal 114(496): F191–216.

Devarajan S., M. Miller and Eric V. Swanson (2002), “Goals for development: history, prospects and costs”, World Bank Policy Research Working Paper No. 2819.

Doucougliagos H. and M. Paldam (2008), “Aid effectiveness on growth: a meta study”, European Journal of Political Economy 24(1), 1–24.

Easterly W. and R. Levine (1997), “Africa’s growth tragedy: policies and ethnic divisions”, Quarterly Journal of Economics 112(4): 1203–50.

Easterly W. (2006), “Reliving the 1950s: the big push, poverty traps, and takeoffs in economic development”, Journal of Economic Growth, 11: 289–318

Easterly W., R. Levine and D. Roodman (2004), “Aid, policies and growth: a comment”, American Economic Review 94(3): 774-780.

European Commission (2005), EU Strategy for Africa: Towards a Euro-African pact to accelerate Africa’s development, European Parliament and the European Economic and Social Committee: Brussels.

Gallup J.L. and J.D. Sachs (1999), “Geography and economic development”, in: B. Pleskovic and Stiglitz J.E. (eds.), Annual World Bank Conference on Development Economics, World Bank: Washington DC.

Hadjimichael M.T., D. Ghura, M. Mühleisen, R. Nord, and E. M. Uçer (1995), “Sub-Saharan Africa: growth, savings, and investment, 1986-93”, Occasional Paper 118, International Monetary Fund.

Hansen B.E. (1996), “Inference when a nuisance parameter is not identified under the null hypothesis”, Econometrica 64(2), 413-430.

Hansen, B.E. (2000), “Sample splitting and threshold estimation”, Econometrica 68(3): 575–604.

Hansen H. and F. Tarp (2000), “Aid effectiveness disputed”, Journal of International Development 12(3): 375–98.

Hansen H. and F. Tarp (2001), “Aid and growth regressions”, Journal of Development Economics 64(2): 547–70.

Herzer D. and O. Morrissey (2009), “The long-run effect of aid on domestic output”, CREDIT Research Paper 09/01, University of Nottingham.

Heston A, R. Summers and B. Aten (2006), Penn World Tables Version 6.2, Center for International Comparisons of Production, Income and Prices, University of Pennsylvania.

Kanbur R. (2006), “The economics of international aid”, in S. Kolm and J.M. Ythier (eds.), Handbook of the Economics of Giving, Altruism and Reciprocity, Elsevier: Amsterdam.

Knack S. and P. Keefer (1995), “Institutions and economic performance: cross-country tests using alternative institutional measures”, Economics and Politics 7(3): 207-227.

Kraay A. and C. Raddatz (2007), “Poverty traps, aid, and growth”, Journal of Development Economics, 82: 315–347.

Kourtellos A, C.M. Tan, X. Zhang (2007), “Is the relationship between aid and economic growth nonlinear?”, Journal of Macroeconomics 29(3): 515–540.

Lensink R. and H. White (2001), “Are there negative returns to aid?”, Journal of Development Studies

15

37(6): 42–65.

Masanjala W.H. and C. Papageorgiou (2004), “The Solow model with CES technology: nonlinearities and parameter heterogeneity”, Journal of Applied Econometrics 19(2): 171-201.

Minoiu C. and S.G. Reddy (2010), “Development aid and economic growth: A positive long-run relation”, Quarterly Review of Economics and Finance, 50(1): 27-39.

Ouattara B. and E. Strobl (2008), “Aid, policy and growth: does aid modality matter?”, Review of World Economics, 144(2): 347-365.

Pritchett L. (2000), “Understanding patterns of economic growth: searching for Hills among Plateaus, Mountains and Plains”, World Bank Economic Review 14(2): 221-250.

Rajan R.G. and A. Subramanian (2008), “Aid and growth: what does the cross-country evidence really show?”, Review of Economics and Statistics 90(4): 643-665.

Roodman D. (2007), “The anarchy of numbers: aid, development, and cross-country empirics”, World Bank Economic Review 21(2): 255-277.

Rostow W. (1960), The Stages of Economic Growth: A Noncommunist Manifesto, Cambridge University Press: Cambridge.

Sachs J. (2005), Investing in Development: A Practical Plan to Achieve the Millenium Development Goals, UN Millenium Project: New York.

Sachs J. and A. Warner (1995), “Economic reform and the process of global integration”, Brookings Papers on Economic Activity 1: 1-118.

Sachs J., J.W. McArthur, G. Schmidt-Traub, M. Kruk, C. Bahadur, M. Faye, G. McCord (2004), “Ending Africa's poverty trap”, Brookings Papers on Economic Activity 1:117-240.

United Nations (2006), Doubling Aid: Making the ‘Big Push’ Work, New York.

Wacziarg R. and K.H. Welch (2008), “Trade liberalization and growth: new evidence”, The World Bank Economic Review, 22(2): 187-231.

Zedillo E., A.Y Al-Hamad, D. Bryer D, M. Chinery-Hesse, J. Delors, R. Grynspan, A.Y. Livshit, A.M. Osman, R. Rubin, M. Singh and M. Son (2001), Recommendations of the Highlevel Panel on Financing for Development, UN General Assembly, New York: United Nations.

16

Table 1. Descriptive statistics (42 countries)

Mean Std. dev. Minimum Maximum

Real per capita GDP growth 1.39 1.91 -1.93 7.13

Aid (EDA) 1.42 1.50 -0.01 6.73

Aid (endogeneity-free data) 4.77 5.10 -4.39 29.99

Institutional quality 4.62 1.53 2.50 8.94

Fraction of land in tropics 0.90 0.25 0.04 1.00

Budget surplus -0.04 0.04 -0.22 0.05

Initial GDP per capita (log) 7.26 0.74 5.69 8.96

Inflation 0.20 0.20 0.03 0.91

Sachs-Warner openness 0.45 0.26 0.13 1.00

Settler mortality rates* 284.42 533.38 15.50 2940.00

Note: Descriptive statistics for settler mortality rates correspond to a sub-sample of 36 aid-recipients for which data are available.

17

18

Table 2. Growth OLS and aid threshold regressions

ordinary aid endogeneity-free aid

OLS TR OLS TR TR ≤ 1.37 >1.37 ≤3.32 >3.32 ≤ 6.18 < 6.18 CI = [1.29, 1.87] CI = [2.81, 6.48] CI = [5.32, 8.10]

Dependent variable: per capita growth (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Aid -0.33 (0.32)

-1.20** (0.50)

-1.21**(0.53)

0.15 (0.29)

0.05 (0.05)

-0.05 (0.07)

-0.06 (0.06)

0.25*** (0.06)

-0.26*** (0.09)

0.08** (0.04)

Institutional quality 0.20 (0.14)

0.18 (0.14)

0.35***(0.11)

-0.02 (0.32)

0.11 (0.16)

0.15 (0.16)

-0.18* (0.11)

0.02 (0.21)

0.10 (0.16)

1.06*** (0.29)

Fraction of land in tropics -1.12 (1.19)

-1.13 (1.35)

1.06* (0.60)

-7.26*** (1.58)

-1.51 (1.22)

-1.37 (1.28)

-0.71 (0.44)

-1.50 (2.06)

-0.83 (0.85)

-19.51***(5.53)

Budget surplus 7.86 (10.50)

13.01 (10.68)

3.86 (6.21)

18.30* (10.16)

17.26** (8.64)

22.22** (10.65)

-29.38***(4.50)

41.17***(10.71)

-0.08 (0.36)

0.99** (0.42)

Initial GDP per capita (log) -0.93** (0.40)

-1.31***(0.35)

-1.28***(0.41)

-2.54*** (0.91)

-0.56* (0.30)

-0.59* (0.31)

-0.15 (0.10)

-0.79** (0.37)

-2.61 (7.19)

-11.24 (9.40)

Inflation -0.45 (1.26)

-0.19 (1.14)

-0.62 (0.87)

3.78 (2.50)

-0.41 (1.53)

-0.16 (1.60)

-0.55 (0.41)

2.12 (1.61)

-2.73*** (0.63)

-3.84***(0.85)

Sachs-Warner openness 3.68***(1.03)

3.19*** (0.93)

2.44***(0.86)

4.28*** (1.33)

4.08*** (0.96)

3.61*** (0.94)

5.75*** (0.49)

3.16*** (0.82)

0.71 (0.48)

-0.84 (0.73)

Aid Squared 0.16** (0.07) 0.01*

(0.00)

R-squared 0.54 0.58 - - 0.53 0.55 - -

F-statistic on joint-significance (Prob) 7.69 (0.00)

12.72 (0.00) - - 5.81

(0.00) 5.94

(0.00) - -

LM test for no threshold: Bootstrap P-value - - 0.25 - - 0.00 0.01

No of countries 42 42 42 42 42 42 42

No. of observations 42 42 24 18 42 16 26 78 36

Notes: All regressions include a constant. Robust standard errors are in parentheses. *** denotes significance at 1%, ** at 5%, and * at 10%. TR and CI denote Hansen (2000) Threshold Regression and the 95% Confidence Interval. Variables in columns (1)-(8) are averaged over 1970-2000 and in columns (9)-(10) are 10-year averages. For the heteroscedasticity-consistent Lagrange-multiplier (LM) test for no threshold the null hypothesis is that there is no threshold effect (Hansen, 1996).

19

Table 3. Growth 2SLS and aid threshold regressions: endogenous institutions

2SLS TR TR ≤ 5.41 > 5.41 ≤ 4.87 > 4.87

CI = [5.28, 6.49]] CI = [0.14, 6.00]

Dependent variable: Real per capita GDP growth (1) (2) (3) (4) (5)

Endogeneity-free Aid -0.17 (0.15)

-0.15 (0.11)

0.38** (0.16)

-0.29** (0.13)

0.74 (0.69)

Institutional quality 1.53 (1.06)

1.00 (2.75)

1.55*** (0.30)

0.69 (0.79)

3.84 (3.96)

Fraction of land in tropics 1.14 (2.16)

-0.02 (3.50)

-59.41*** (15.88)

0.03 (0.98)

-28.68 (24.86)

Budget surplus -7.50 (20.57) - - -12.60

(8.06) -48.63 (63.15)

Initial GDP per capita (log) -1.06 (0.69)

-1.53 (2.55)

0.36 (0.31)

-0.55 (0.84)

-0.17 (1.15)

Inflation 0.12 (1.45)

1.73 (8.13)

-4.71*** (1.00)

-2.95*** (0.65)

-5.68 (3.74)

Sachs-Warner openness 1.82 (2.15)

3.03 (2.37)

-2.33 (1.89)

0.43 (0.72)

-0.96 (2.26)

F-statistic on joint-significance (Prob)

1.68 (0.16) - -

No of countries 36 36 36

No. of observations 36 15 21 58 43

Notes: For the Two-Stage Least Squares estimation heteroscendasticity-consistent robust standard errors are reported. In columns (2)-(5) the Caner and Hansen (2004) regressions are used and a heteroskedasticity corrected asymptotic 95% confidence interval for the threshold estimate is computed using a quadratic polynomial as in Hansen (2000). Institutional quality is instrumented using log settler mortality as in Acemoglu et al. (2001). See also Table 2.

Table 4. Aid threshold regressions: robustness tests exogenous institutions endogenous institutions ≤ 5.76 > 5.76 ≤ 3.32 > 3.32 ≤ 6.37 > 6.37 ≤ 6.37 > 6.37

CI = [2.82, 6.49] CI = [2.82, 6.49] CI = [5.28, 6.49] CI = [5.28, 6.49]

Dependent variable: Real per capita GDP growth (1) (2) (3) (4) (5) (6) (7) (8)

Endogeneity-free Aid -0.18*** (0.07)

0.43* (0.25)

-0.08 (0.06)

0.27***(0.06)

-0.16 (0.24)

0.58*** (0.15)

-0.22 (0.92)

0.44 (0.29)

Institutional quality -0.03 (0.13)

0.16 (0.42)

-0.21* (0.11)

-0.05 (0.25)

1.61 (6.13)

0.67* (0.36)

2.02 (20.26)

1.18 (0.79)

Fraction of land in tropics -1.20* (0.73)

-10.76***(3.18)

-0.48 (0.42)

-2.10 (2.37)

0.92 (8.29)

-70.47*** (9.38)

0.89 (19.21)

-69.11***(14.29)

Budget surplus -2.41 (10.04)

35.67***(12.76)

-26.91***(2.67)

42.36***(10.50) - - - -

Initial GDP per capita (log) -0.51 (0.35)

-0.48* (0.25)

-0.12 (0.10)

-1.00**(0.45)

-2.39 (5.84)

0.59 (0.41)

-2.14 (13.60)

0.12 (1.20)

Inflation -1.92 (1.34)

-1.13 (2.04)

0.06 (0.60)

2.59 (2.03)

4.64 (22.44)

-6.11*** (0.96)

4.56 (55.49)

-4.19***(0.46)

Sachs-Warner openness 3.25*** (0.85)

1.56 (1.86)

5.68*** (0.49)

2.41* (1.33)

3.38 (2.48)

1.93 (2.39)

2.56 (10.53)

-4.38 (8.62)

Ethnic fractionalization 0.25 (0.74)

-3.70***(0.45) -2.52

(7.31) -3.42** (1.53)

M2 0.02 (0.01)

0.03 (0.04) -0.02

(0.33) 0.08

(0.07)

LM test for no threshold: Bootstrap P-value 0.09 0.01 - -

No of countries 41 42 36 36

No. of observations 28 13 16 26 25 11 25 11

Notes: See Tables 2 and 3.

20

Figure 1. Conditional relationship between Actual and Fitted Aid, 1970-2000

BFA

BOL

BRA

BWA

CHL

CIV

CMR COGCOL

CRI

DOMECU

EGY

ETH

GHA

GMB

GNB

GTM

HND

IDN

JAM

KEN

LKA

MDGMEX

MLI

MWI

MYS

NERNIC

PER

PHL

PRY

SGP

SLE

SLV

THA

TTO

UGA

VEN

YEM

ZAF

-2-1

01

2Re

sidua

ls of

Act

ual A

id (%

of G

DP)

-5 0 5 10 15Residuals of Fitted Aid (% of GDP)

Note: The figure plots the first-stage relationship between actual and fitted aid, conditional on all the covariates that enter the second-stage growth regression.

21