Aid to countries can be of two types, productive (or tied), and pure aid transfers. Productive aid is when aid is tied to public investment projects, and foreign aid given in the form of lump sum transfers is called pure aid. The different implications and the effectiveness that these may have on growth, has been of concern in many papers.
Recently, the focus has been on the volatility of the flow of foreign aid from donor countries to recipient countries i.e. aid has been variable and this in turn reduced its value. According to Homi Kharas (2008) at the Macro level, empirical evidence suggests that this volatility of ODA can negatively impact growth by influencing inflation, real exchange rates and fiscal policy. And at the Micro level, volatility can affect fiscal planning and the level and composition of investments. He observes in his paper that aid volatility is similar across regions, but differs substantially by donor. Inferring that donor policies contribute to the volatility, and that they should make reducing volatility a strong priority. He finds in his paper that the aid system is said to have generated the same negative shocks to developing countries, and with more frequency, as the two World Wars and the Great depression generated in the developed world.
Homi Kharas (2008) in his paper uses capital asset pricing model (CAPM) to measures the deadweight loss per dollar provided in aid, and finds that the cost of volatility of aid, rose steadily till 2000, and has since declined. The focus of his paper is on how aid volatility can be very costly, particularly in less developed countries with weak institutions. Despite this, and the benefits from reducing volatility of aid, and using it as a smoothing device, policymakers have been reluctant to take the problem of aid volatility seriously. According to him this behavior of the policymakers can be attributed to, firstly, the weak evidence between aid volatility and its negative impact on growth. Secondly, it is hard to differentiate between good volatility and bad volatility, like higher aid disbursement after a natural disaster or the ability to reduce aid to a corrupt government and increasing the aid to a reformist government is considered a good volatility. Thirdly, many policymakers dismiss estimated based on cross country empirical works, due to low robustness of results.
Kharas (2008) develops a measure of aid, called country programmable aid (CPA) which excludes from the total non-cash flow items like technical assistance, debt relief, food aid, and humanitarian assistance. It also subtracts on the interest payments made, to arrive at a true figure of cash flow received by the recipient government. The change in aid from donor i to recipient j at time t, Aijt can be explained by a constant term aij which defines the relationship between the donor-recipient countries, and an error term eijt :
Hence aid shocks may be defined as,
ΔAijt = aij + eijt
Summing this for all countries,
ΔAijt = ∑aij + eijt
The above equation gives the amount of aid each country receives over time. So the aid shock will be the difference between the actual amount of aid flows and the expected amount of aid flows, in each period.
He finds that the aid flows are highly volatile, with aid volatility five to six times as large as GDP volatility, and three times as large as export volatility. Also the measure of total aid cash flows, i.e. CPA is found to be more volatile than aid flows, despite the latter containing debt relief and humanitarian assistance, both of which are considered to be highly volatile.
He breaks down the volatility into subgroups: depending on geographic regions, degree of aid dependency, income level, and strength of state. Yet there is no statistically significant impact of these on the flow of aid.
Using CAPM to compute the value of the Global goodwill (Gj) in period t,
Gjt = E(Ajt+1) / (1 + E(rat+1))
Where, E(Ajt+1) is the expected value of the aid flows in period t+1, and E(rat+1) is the risk return that compensates for the volatility of aid.
E(ra) = E(rf) + Sσaj
Here S is the Sharpe ratio, and E(rf) is the expected value of the risk-free interest rate, and σaj is the standard deviation per unit of aid.
The certainty equivalent amount of aid CE(At+1) is,
CE(At+1) = ( 1+ rf) Gt
The deadweight loss (DWLt) is then calculated on these basis,
DWLjt = E(Ajt) - CE(Ajt) = E(Ajt) - ( Sσaj / 1+ rft + Sσaj )
Hence, DWL for recipient country j in time t, depends on three key variables. The DWL rises as the coefficient of variation of the aid flow goes up, as the market price of risk (Sharpe Ratio, S) goes up, and as the real risk-free interest rate falls. On an average it is found that countries lose about 2% of GDP due to aid volatility, and the most aid dependent countries lose about 7% of GDP. These are mostly the low income, weak state countries.
For a developing country, aid is usually uncoordinated and fragmented. This means that donors may support one sector one year, and move towards a different sector the next. This leads to duplication of analytical work and waste and overlap of activities as the recipient countries are unable to predict and plan resource flows.
Another channel for DWL arising out of aid volatility is due to its linkage with fiscal spending. The volatility in aid, leads to a volatility in fiscal spending, which leads to a volatility in the real exchange rate, and hence, slows growth. Presumably through the behavior of the exporters.
Also when aid takes the form of concessional credit, instead of grant it affects DWL through excessive debt buildup. The DWL arises from inefficient spending, as the cost of debt isn't fully internalized by the authorities.
Donors also contribute to DWL, and can minimize the losses in the following ways. They can give more aid to countries where total aid has tended to be more stable over time. They can try to run counter to the aid cycle. And they can try to reduce the volatility of the aid they give the recipient governments. However, studies have documented the donors tendency to "herd" implying that there's a high correlation between each donors aid flow and the total received by a country. Also donors promote harmonization which leads to a high correlation between their aid flows. Donors could also coordinate aid between them to smooth the aid volatility. And although donors are unwilling to make long term commitments to aid recipient countries, they could indicate amounts that they would support as a collective over the medium term to reduce aid volatility, which accounts for a loss of almost $16 billion at current aid levels.
BURNSIDE & DOLLAR
Burnside & Dollar (2004) find evidence that aid is effective in developing countries with sound institutions and policies, but has less or no effect in countries in which institutions and policies are poor and corrupt. This result stems from the fact that corrupt governments won't use aid wisely and donors will not be able to force it to change. This supports the recent trend towards greater "selectivity" - that is, giving relatively more aid to developing countries that have reasonably good institutions and policies.
They try to look at how much assistance is going to countries with differing institutional qualities, after controlling for per capita income and population. It is found that aid has a positive impact in developing countries with better than average institutions and policies, while aid had no significant effect in countries with average policies.
In the 1980s there was no significant relationship between ODA and IDA with institutional policies like democracy and rule of law. However, in the 1990s the picture changed, with a positive correlation of both ODA and IDA, and allocation towards countries with better institutions.
They then use the Kaufmann, Kraay and Zoido-Lobaton (KKZ) index for institutional quality. It is complied by standardizing and averaging all of the different institutional variables available in the second half of the 1990's. It gives a good measure of the extent to which a country's policies and institutions compliment its growth. The results support their earlier findings that aid leads to growth, conditional on institutions.
They establish that there are many types of information that are relevant when trying to formulate effective aid policies. Firstly, according to theory, for aid to have no impact on a low-income country, regardless of institution, would require a high degree of perfection in the international capital markets. This according to theory is implausible and hence, its quite plausible that aid would promote growth in poor countries that manage to put good institutions into place.
Secondly, it is broadly agreed that the Marshall Plan led to a faster growth in Europe, post WWII. This is an ideal example of the positive effects of aid on growth, conditioning on the quality of institutions in place. Also there are many case studies on aid to developing countries. They show that aid given to a highly corrupt government with distorted economic policies provides no lasting benefits. On the other hand, aid provided to reformist governments inevitably leads to a higher rate of growth.
Thirdly, they find evidence of dependency of aid effectiveness on institutions and policies from data on individual projects financed by aid. Overall they observe that projects are more likely to be successful in countries with growth-enhancing institutions and policies. Comparing the success of projects in South Korea, in the 1960's when it was highly dependent on aid. And the failure of Kenya, and Zimbabwe in the recent decades to achieve desired results from the investments, proves that sound institutions attract more aid.
Finally, they speculate that since, aid is increasingly being given to developing countries with relatively good institutions it may have a positive incentive effect. This may mean that more countries, to attract aid, might turn to reformist policies.
NEANIDIS & VARVARIGOS
The focus of their paper is on the volatility of aid, highlighting the additional repercussions emerging for the foreign aid-economic growth nexus when variability in aid is taken into consideration. Many studies have been concerned with the effects of different types of aid, and their different implications. Neanidis and Varvarigos take into consideration both aid disbursements and their volatility to map the overall effects of foreign aid. They cite Pallage & Robe (2001) who documented that aid is highly volatile, with an average volatility of about 25% in African countries, and 29.5% in non-African countries. Also they find that aid volatility occurs not only incase of emergency humanitarian aid, of which volatility is an intrinsic part. But rather, sector-specific project aid too tends to be quite volatile mostly due to policy failures.
They formulate a utility function U, defined as
U= E0∑βtln(Ct)
Here, E0 is the conditional expectations operator, and β is the discount factor.
It is assumed that each period a foreign donor provides an income transfer to the economy, of which a fraction is allocated to the private sector in the form of lump-sum income transfers. The remaining fraction is used to support the accumulation of productive public capital by co-financing its formation. Both of these types of foreign aid end up augmenting the available resources of the private sector. Lump-sum transfers do this directly, while co-financing of public capital investment leads to a reduction in the tax rate, owing to aid fungibility. This mitigates the "crowding out" effect through which privately produced output is transferred from the private to the public sector.
Neanidis and Varvarigos (2009; pp 451) state that the effects of an increase in aid volatility shows that the increase in investment, resulting from an expected reduction in aid, is more pronounced than the decrease in investment, resulting from an expected rise in aid of equal magnitude.
The methodology they use to test the effects of aid and its volatility on economic growth, results in a classification of aid into three types: short-impact aid, long-impact aid and humanitarian aid. Accordingly, short- and long-impact aid is expected to have a positive impact on growth, although with a time lag. Humanitarian aid is considered to be volatile as it is disbursed in times of emergency, like food aid, and reconstruction relief during and after natural disasters.
Their findings show that volatility of aid inhibits growth. Also, aid disbursements used for productive purposes have a positive effect on growth, while pure transfers reduce growth. Similarly, only volatility in productive aid hurts growth, while volatility in pure aid is associated with higher growth, hence, donors should ensure that aid provisions be the least erratic possible. They specifically find that, being situated in East Asia is related with higher growth rates, while being located in the tropical climate reduces growth.
CHAUVET & GUILLAUMONT
In the paper by Chauvet and Guillamont they show that aid, even though volatile, may not be as procyclical and destabilizing as often argued. Procyclicality of aid is when aid is disbursed during periods of high domestic output, and held back during contractionary periods. They measure procyclicality of aid with respect to exports, and not national income, as in previous studies. They justify this approach on two grounds; firstly, exports are most likely to be affected by exogenous shocks. And secondly, national income and fiscal revenues are more likely to be influenced by aid disbursements than exports.
If aid is volatile it may lead to macroeconomic instability, and then be itself a factor of vulnerability. This is particularly relevant for African countries which are vulnerable to external shocks. After controlling for traditional variables of income volatility, they find that the level of aid has a stabilizing effect on income, while its volatility has a destabilizing effect on income.
To determine whether aid is more pro- or contra cyclical they use the global adjustment method of cycle estimation, and find the coefficient of correlation between aid and export cycles. And although aid was found to be more procyclical with respect to exports in the 1970's and 1980's. They find that in the 1990's the total number of significant cases decreases, and the number of negative cases converges towards the number of positive cases. Although pro- or contra cyclicality is important, the volatility of aid too plays an important part to determine stability.
Stabilizing character of aid = Volatility of (X) - Volatility of (X+A)
Here the stabilizing nature of aid is measured as the difference between the volatility of exports and the volatility of exports plus aid. If the difference is positive, aid has a stabilizing effect on aid, and if negative, aid is seen as destabilizing with respect to exports.
Aid is of concern only if it is destabilizing, and hence, stability of aid depends on three aspects: its pro- or contra cyclicality, its degree of volatility, and its trend level. These three are measured with respect to exports.
However, since developing countries also face other kinds of shocks, like climatic instability etc. aid may have a dampening effect on these also. But to measure the stabilizing or destabilizing effect of aid with respect to all these shock variables is very difficult. So, they examine the extent to which multi-year income volatility has been influenced by the level and volatility of aid.
They form an income volatility equation, where aid and export volatilities are, respectively weighted, by the aid-to-GDP ration, and the export-to-GDP ratio.
