Foreign Direct Investment and Foreign Aid as Factors of Growth

Running Head: FDI AND FOREIGN AID AS GROWTH FACTORS 1
Foreign Direct Investment and Foreign Aid as Factors of Growth:
A Literature Review
Nicolas Vander Meer
University of Florida

FDI AND FOREIGN AID AS GROWTH FACTORS 2
I. INTRODUCTION
In development economics, research is often focused on the factors that induce economic
growth in developing nations. While the potential growth factors are numerous and varied, a bulk
of the research has been conducted on the effects of foreign direct investment (FDI) and foreign
aid on growth, as these two factors are generally perceived as the primary channels through
which growth may be affected. This paper provides a review of some of the most prominent
literature regarding the roles of FDI and foreign aid in accelerating growth in developing nations
as well as literature concerning the relationship between foreign aid and FDI. The structure of
this paper is as follows: Section II discusses the literature pertaining to the effect of FDI on
growth. In Section III, the role of foreign aid in affecting growth is examined, Section IV
discusses the relationship between foreign aid and FDI, and Section V concludes.
II. FDI AS A FACTOR OF GROWTH
The role of FDI as a factor of growth has long been in question, as it was once a common
belief that FDI increases competition among similar industries in the recipient country to the
point of driving domestic firms out of the market and consequently reducing overall welfare in
the recipient country. While the net effect of FDI is still debated and oftentimes ambiguous,
more recent literature starting in the 1990s has shown that FDI inflows can actually instigate
economic growth in the recipient country in various ways. The most significant and widely
believed channels through which FDI can positively affect growth are through productivity

spillover and knowledge transfer externalities and supply chain linkages, which will be further
developed later in this paper.
It is imperative to denote the type of FDI discussed in this paper. Of the two types,
market-seeking FDI is FDI with the intention of selling the produced goods in the host
country, thus rendering FDI decisions dependent on domestic demand for the products. Non-
market seeking FDI output is produced in the host country but sold abroad and is therefore
dependent on the relative ease of exporting in the host country. Because non-market seeking
FDI is more likely to flow to smaller, poorer countries than market-seeking FDI, the type of
FDI used for the purposes of this paper is non-market seeking (Asiedu 2001).
While the focus of this section is the ways in which FDI can affect growth, it is important
to briefly discuss the determinants of FDI. Upon reviewing the sections of the relevant literature
that discuss determinants of FDI, the specific determinants of FDI inflows vary from paper to
paper. However, the most significant determinants common among the literature include real
GDP per capita, political stability, openness to trade, and existing infrastructure development. In
fact, according to Asiedu (2001), openness, infrastructure, and return on investment accounted
for 60 percent of the variability in FDI-GDP ratio that was used as the metric for growth in the
71-country study, which provides evidence in support of the claim that openness to trade and
infrastructure development are strong determinants of FDI. The main determinants of FDI are
important to note because the levels of determinants can indirectly affect by how much FDI
impacts growth, which will be expounded upon later in this section.

FDI can have a positive impact on domestic growth through the aforementioned channels
of productivity externalities and supply chain linkages. Both channels create similar positive
feedback loops that may result in more FDI inflows to the recipient country. The first of these
positive feedback loops is the productivity feedback loop. De Mello (1997) argues that if we
assume there are diminishing returns to capital inputs, the recipient country would ultimately
regress to its steady state, implying that there would be no long-term effect of FDI on growth.
However, de Mello goes on to claim that long-term growth as a result of FDI is possible if there
are permanent technological shocks or transfers to the recipient country’s production. These
technological transfers’ impact on the domestic firms is greater the larger the difference in
production-related technology between the foreign firm and the domestic firms. The basic idea
behind these technology-transfer induced feedback loops is that the technology used by the
foreign firm would spill over to domestic firms in related industries, hence increasing their
marginal product of capital and total factor productivity (TFP). The domestic firms would then
become more efficient in production, reducing their costs and increasing their profits, allowing
the firms to be more competitive domestically and globally and to grow. The enhanced growth
subsequently makes the recipient country more attractive to FDI, and the process repeats itself,
establishing a positive feedback loop as a result of a technology shock from FDI inflows. De
Mello (1997) also discusses how knowledge transfers are similar to technology transfers in their
effects on growth. De Mello references one of his previous studies to describe the various forms
of knowledge transfers, namely labor training, skill acquisition and diffusion, as well as
managerial and organizational practices. Parallel to technology transfers, knowledge transfers
increase TFP but do so by increasing the domestic firms’ marginal product of labor, as the
knowledge transfers increase the human capital stock in the recipient country. After this point,

the knowledge transfers establish the same positive feedback loop as technology transfers,
ultimately making the recipient country more attractive to FDI. The overall productivity increase
stemming from the various externalities can be large enough to offset the diminishing returns to
marginal product of capital in the long run, which is why de Mello (1997) expects that
knowledge and technology transfers will be the most significant channels through which FDI
enhances growth in the recipient country.
Supply chain linkages cause the second type of positive feedback loop, which will
hereinafter be referred to as the supply chain feedback loop. Markusen and Venables (1998) use
a model in their research that focuses on the foreign firm responsible for the FDI being a final
goods producer, which implies that the supply chain feedback loop from their model centers
around backward linkages. Markusen and Venables (1998) argue that while the flow of FDI to
the final goods sector of the recipient country would have an initial competition effect that could
drive some domestic final goods producers out of the market, the foreign firm’s presence would
increase the demand for intermediate goods, which would, in turn, expand domestic intermediate
goods production. Depending on the relative strengths of the competition effect and the
backward linkages, the expansion of intermediate goods production alone could increase GDP
and make the recipient country more attractive to FDI, thus continuing the positive feedback
loop. Both of these results would, in theory, have a positive welfare effect on the recipient
country.
The above feedback loops call into question the directional causality of the relationship
between FDI and growth, as growth seems to attract FDI that subsequently instigates growth, and

so forth. The answers to that question are varied and ambiguous, as de Mello (1997) claims that
growth may cause FDI in cases where the determinants of FDI have a strong association with
growth; however, de Mello (1997) also argues that FDI may cause growth if the FDI
determinants are already in place, but the growth rate increases only after FDI inflows are
present. Hansen and Rand (2005) made a stronger, less ambiguous claim in which they found
strong causal effects in the long run of FDI on GDP levels when changing the explanatory
variable to mean changes in the ratio of FDI to gross capital formation (FDI/GCF). This change
in their model arose as a counter to Carkovic et al. (2002), who found no robust impact on
growth using a relationship between FDI ratio and the log-level of GDP. More specifically, the
study’s empirical results indicate that a one-percentage point increase in the mean of FDI/GCF
causes, on average, a 2.25 percent increase in the GDP level, an effect to which they liken the
effect of a domestic savings ratio of 20 percent. Hansen and Rand (2005) also found that GDP
Granger-causes FDI, but they did not find any long-run effect of GDP on the FDI ratio. In
regards to FDI determinants, they found that, on average, FDI has a significant long-run impact
on GDP, regardless of the level of determinants.
It must be acknowledged that while the productivity and supply chain feedback loops
appear to be plausible and apparent, their net effects are reliant on externalities stemming from
the flow of FDI, and because externalities are inherently difficult to measure, the results from the
relevant studies are somewhat limited. Similarly, the directional causality appears to be
ambiguous and difficult to measure as well. It is for these reasons that the externalities of FDI
and direction causality between FDI and growth should be researched further.

From the current literature on FDI as a factor of growth in recipient countries, it seems
that the main channels through which FDI can affect growth are through technology and
knowledge transfers that lead to changes in TFP and supply chain linkages that expand domestic
production. Both of these effects could, in theory, contribute to positive feedback loops that have
a positive welfare effect on the recipient country.
III. FOREIGN AID AS A FACTOR OF GROWTH
Since the beginning of the twentieth century, the use of foreign aid has been increasingly
widespread as a tool to show good faith to an ally, solidarity in the face of military conflict and
natural disasters, and to foster strategic relationships among nations. The most public and well-
known form of foreign aid has been granted in the wake of extreme natural disasters, often
donated in large sums in order to mitigate the death tolls and displacements of affected people.
However, for the purposes of this paper, the form of foreign aid that will be discussed is the year-
to-year foreign aid that is granted with the functional intent to reduce poverty and improving
common human development indicators (HDIs) in the recipient country by fostering economic
development. While older research claimed that foreign aid had no significant impact on
economic growth, more recent literature has argued that aid can positively affect growth in
theory. In practice, however, aid has not been alleviating poverty as it was intended to because it
is being widely misallocated.
In a widely cited paper relating aid effectiveness to varying types of political regimes,
Boone (1996) argues that foreign aid has had insignificant effects on both economic growth and

HDIs such as infant mortality and primary schooling ratios regardless of the nature of the
political regime. However, studies since then have countered Boone’s claim through introducing
conditional explanatory variables in their models and by expanding the dependent variable
metrics used to measure the effect of aid on economic growth and HDIs.
By relating the existing economic policies of the recipient country to aid effectiveness,
Burnside and Dollar (1997) arrived at an interesting conclusion. In their model, they determined
that the three economic policies that should have substantial weight are the budget surplus,
inflation, and a trade openness dummy. From these three policies, they created a policy index
with which they interacted foreign aid. They found that aid has a robust, positive impact on
economic growth when aid inflows coincide with levels on the policy index that equate to
“good” policy, which means that foreign aid effectiveness increases with good policy.
Quantitatively, by using a benchmark distortionary economy with zero aid inflows and with a
growth rate of 0.2%, they found that better policy without aid would increase the growth rate by
0.15 percentage points; aid inflows without better policy would increase the growth rate by 0.6
percentage points; and when aid inflows were combined with better policy, they found there to
be an increase in the growth rate of 1.15 percentage points. The combination of aid inflows and
better policy appears to have a synergistic effect in that the summed effects of isolated better
policy and isolated aid inflows, 0.75 percentage points, is less than the effect of the two factors in
combination. Their findings also support the converse being true: the effectiveness of good
policy increases with foreign aid inflows. Their results are also consistent with Boone (1996) in
that aid was found to have an insignificant impact on growth when policy was average. It is
crucial to note that to account for potential endogeneity in their model and to test for the separate

impacts of aid and policy on growth, Burnside and Dollar (1997) made identifying assumptions
about the exogenous determinants of aid, policy, and growth and used two-stage least squares
regressions to estimate simultaneous equations for growth, aid, and policy. The authors also used
numerous variables of which Aid/GDP is a function as instruments in another two-stage least
squares regression so as to highly reduce the correlation of Aid/GDP with the growth
regression’s error term, which allows the regression to use a portion of Aid/GDP that is not
explained by the other growth regression variables.
Furthermore, Burnside and Dollar (1997) made aid receipts and their policy index
endogenous in separate equations in order to estimate the relationship between aid receipts and
policy and vice versa. Through this, they found the effect of aid on the policy index to be
insignificantly different from zero, indicating that the amount of aid inflows does not
significantly alter the recipient countries’ policies. However, the coefficients in their regression
where aid receipts are endogenous indicate that a one-standard deviation improvement in the
policy index would increase aid receipts by approximately one fifth of the mean level of total
foreign aid. This result implies that donors tend to grant more foreign aid to countries with better
economic policies. Burnside and Dollar’s results are consistent with other literature that holds
that the interaction of aid and policy is both statistically highly significant and economically
substantial (Collier and Dollar 2002).
Broadening the metric of aid effectiveness to include the impact on social sectors has also
provided evidence that supports the claim that aid positively impacts growth. In one model of 78
developing countries from 1970 to 2007 that included various HDIs, aid was found to have a

positive, statistically significant effect on growth and HDIs. Upon disaggregating the results, an
average annual aid inflow equal to 5% of the recipient country’s GDP would be expected to
increase the average annual rate of economic growth by approximately 1.5 percentage points,
reduce poverty by approximately 15 percentage points, and reduce infant mortality by 14 in
every 1,000 births (Arndt, Jones, and Tarp 2014).
With recent literature indicating that aid can indeed have positive effects on growth and
poverty in recipient countries, it begs the question of why much of the foreign aid literature
claims that aid has not significantly contributed to growth in practice. The answer, it seems, lies
in the misallocation of aid, both by donors and by recipient governments.
In a paper that addresses the misallocation of aid, Collier and Dollar (2002) propose a
poverty-efficient aid allocation equilibrium. In their analytical framework, they assume that
donors allocate aid to maximize poverty reduction and that donors have no influence on the
distribution of aid within the recipient country, meaning the donors can only affect poverty by
raising aggregate income. They identify the poverty-efficient equilibrium as the allocation of aid
for which the marginal impact of an additional million U.S. dollars in aid is equalized across aid-
receiving countries. In their model, Collier and Dollar (2002) compare the actual allocation of
aid to the poverty-efficient allocation by first estimating the effect on poverty of the actual
allocation. To do this, they estimate the effect of eliminating the current flows of aid for one
year, which they found would increase the headcount poverty metric in the sample countries by
10 million people. This indicates that aid as it is actually being allocated lifts approximately 10
million people out of poverty per year. They then run a model where the amount of aid currently

being allocated is re-allocated using their poverty-efficient equilibrium, which resulted in an
additional 9.1 million people being lifted out of poverty. Their model implies that if aid were
allocated efficiently to maximize poverty reduction, it would increase poverty reduction by
almost double the current amount. It must be noted, however, that there is a relatively high
degree of uncertainty about these estimates, but the estimates are so large that the authors are
confident that aid effectiveness could be augmented if aid were allocated efficiently, as in their
model (Collier and Dollar 2002).
Revisiting Burnside and Dollar (1997), two important findings in their research indicate
that government consumption has no robust association with growth and that donor interest
variables have more explanatory power for aid allocation than the aforementioned policy index.
These findings serve as important premises because when the authors disaggregated aid into
bilateral and multilateral aid and interacted them with government consumption and donor
interest variables, they found that while multilateral aid has no association with either, bilateral
aid has a large positive association with government consumption and is associated with donor
interests. Burnside and Dollar (2001) use these associations to argue that bilateral trade is not
consistently allocated to countries with the good policies that promote economic growth with
aid; rather, oftentimes aid is allocated to allies or countries of strategic interest that might have
distortionary policies and use the aid to complement government consumption, which they
showed is not associated with economic growth. This dominant allocation might contribute to the
reason why aid has not reached its potential in poverty reduction. The authors offer a solution to
this issue: the allocation of aid should be based on the quality of the recipients’ policies, which

would increase the mean growth rate in their sample of poor countries from 1.10% to 1.44%
(Burnside and Dollar 2001).
Similar to Burnside and Dollar’s (2001) finding of aid being heavily allocated to
countries who might have economic policies that do not provide a supportive environment for
aid, one of the primary reasons for which Boone (1996) argues that aid does not impact
economic growth is also the misallocation of aid, specifically the allocation of aid within the
recipient country. In his framework, Boone (1996) assumes that poverty is caused or enhanced
by distortionary policies introduced by politicians. From this, he argues that aid does not promote
economic development because the political regimes that implement the distortionary policies
are not incentivized to improve their policies if they are constantly receiving aid flows. These
distortionary policies were primarily found to distribute the received aid more heavily to the
wealthy political elite, and the people in poverty for whom the aid was intended only receive a
small fraction of the benefits from aid. This improper within-country allocation of aid also
dilutes the positive effects on HDIs. In fact, Boone (1996) estimated that this government failure
in his sample results in infant mortality being reduced only by 2 to 3% for aid flows equal to
10% of GNP. If the same amount of aid were allocated to the poorest 60% of the population,
infant mortality could be reduced by 20% (Boone 1996).
It is imperative to discuss, albeit briefly, the sustainability of foreign aid. If donors can
develop methods of allocating and implementing aid that maximize the sustainability of its
effectiveness, aid could have an increased effect on long-run growth. One paper calculated that
aid has diminishing returns that begin to set in once aid flows reach 20% of the recipient

country’s GDP (Feeny and Fry 2014). From this identification of diminishing returns, the same
authors found that foreign aid’s impact on growth has a half-life of 2 years. In other words, half
of the addition to growth with which aid is associated is experienced within the first 2 years of
receiving the aid. They then disaggregated foreign aid into aid grants and aid loans and found
that aid loans have a longer half-life of 3 years compared to the 2-year half-life of aid grants.
Finally, they found that aid, as a whole, was more sustainable in good policy environments,
which is not surprising given Burnside and Dollar’s (1997) contributions (Feeny and Fry 2014).
It is clear from the literature that foreign aid has the potential to positively impact growth,
but aid is being systemically misallocated and is therefore not achieving the growth effect that it
should. More research should be focused on determining the most productive forms of aid and
the most efficient allocation in order to reform aid policy to bolster economic growth in aid-
receiving developing countries.
IV. THE RELATIONSHIP BETWEEN FOREIGN AID AND FDI
After examining the separate potential effects of FDI and foreign aid on growth, it is
natural to then attempt to determine if there is a relationship between foreign aid and FDI,
namely, whether foreign aid has a positive effect on FDI flows. While the literature on this
relationship is not extensive, there are a few studies that shed some light on the possible
relationship between foreign aid and FDI. From these studies, it is possible to conclude that the
composition and implementation method of aid, as well as political and macroeconomic
conditions in the recipient countries contribute to a positive relationship between aid and FDI.

There have been studies regarding the aid-FDI relationship that use aggregate aid and
provide ambiguous results. Selaya and Sunesen (2012) disaggregate aid in their model into aid
for complementary factors of production, aidA, and aid in the form of physical capital, aidK and
hypothesized that aidK would crowd out FDI in physical capital one-for-one and that aidA has an
ambiguous net effect on FDI. The ambiguity of the net effect of aidA arises from aidA increasing
the marginal product of capital in the recipient country, which attracts FDI but also increases
domestic income and savings, which tend to crowd out foreign investment. Using a first-
difference GMM estimator and a system GMM estimator on their panel data consisting of 99
countries during the 1970-2001 period, they estimated that in the short-run, $1 of aidK crowds
out on average $0.84 of FDI per capita, while $1 of aidA attracts on average $1.09 of FDI per
capita. However, the short-run effects do not carry much weight in terms of policy implications
if the effects cannot be sustained in the long-run, which is why Selaya and Sunesen (2012) go on
to run similar regressions in order to estimate the long-run effects of aidA and aidK on FDI by
assuming that the FDI level per capita is equal in every period in their dynamic model. With this
new assumption, they estimated that at the median levels of each aid component, $1 of aidK
crowds out on average $1.61 of FDI per capita in the long run, and $1 of aidA attracts on average
$1.98 of FDI per capita. These results, coupled with a Hansen test of overidentification not
rejecting the null hypothesis that the instruments as a group used in both GMM estimations
appear as exogenous provide strong evidence in support of their hypothesis. The authors then
conducted a Wald test to determine the combined effect of aidK and aidA at the median. The
Wald test estimated that this combined effect attracts on average $0.19 of FDI per capita. This
result implies that on average, the complementary effect of aidA outweighs the substitution effect

of aidK. To test for robustness, the authors expanded the model’s definition of aidA to include
Technical Cooperation Grants, added idiosyncratic country-specific investment risk, and
included omitted variables in the form of other determinants of FDI and found that the
coefficients on aidK and aidA were smaller but still had the same effects as in their original
estimations. From these results, the authors conclude that the composition of aid is crucial in
determining aid’s overall effect on FDI. This is a logical conclusion given that the net combined
effect of aidK and aidA is only slightly positive; a shift towards a marginally larger amount of
aidK relative to aidA could result in a slightly negative effect on FDI.
In a study conducted to determine if there is a positive effect of bilateral aid on FDI flows
from the aid donor to the aid recipient, dubbed a “vanguard effect,” Kimura and Todo (2009) use
source-recipient country pairs instead of aggregate donors and recipients. They also disaggregate
aid into aid for infrastructure, both physical and social, and aid for other purposes, which is
mostly donated to support the recipient countries’ federal budgets or to assist in debt relief. To
simplify their model, they limit their donors to the top five donor countries, namely France,
Germany, Japan, the United Kingdom, and the United States and limit their recipients to middle-
and low-income countries, as per the World Bank’s 1990 classification. Even with these
limitations on sample size, the sample still ends up consisting of 227 country pairs from the
period 1985-2002, and the total amount of foreign aid in the sample is equal to 42% of the total
aid flows from donors to the entire world. Due to potential biases that arise from country pair-
specific fixed effects and endogeneity concerns, Kimura and Todo (2009) employ system GMM
estimation in which they use second lagged regressors as instruments for their model’s first-
differenced equation. Since these lagged regressors are predetermined, they should not be

correlated with the contemporaneous error term. The authors also apply the two-step method to
the system GMM estimation in order to obtain larger efficiency. In their first models, Kimura
and Todo (2009) assume that there are no differences in the size of aid effects on FDI across
donor countries. In these models, they found that there are no significant vanguard effects for
any of the donor countries. However, once they relaxed the aforementioned assumption and
interacted aid from specific donors with a dummy variable for each respective donor country,
they found that Japan has a significantly positive vanguard effect. Japan was also the only donor
to have a robust vanguard effect for both aggregated aid and aid for infrastructure. Using Japan’s
aid and FDI flows to East Asia, the authors estimated that 6% of Japanese FDI flows to East Asia
are attributable to the presence of Japanese aid. However, Kimura and Todo (2009) note that
there might be non-stationarity biases in their system GMM estimations, so they subsequently
employ a dynamic GMM estimation using a shorter panel that is based on three-year averages of
all the variables to further alleviate these biases. This robustness check, along with using a sub-
sample of countries that are defined as lower-middle- or low-income countries, resulted in the
Japanese vanguard effect coefficient being more than twice as large as its coefficient for the
whole sample. The authors conclude from this that Japanese aid has a stronger vanguard effect
when donated to poorer countries.
Kimura and Todo’s (2009) results beg the question of why Japanese aid has such a
pronounced vanguard effect while the other four donors’ aid does not. The authors assert that a
general vanguard effect could stem from the possibility that in the presence of foreign aid,
recipient country business environment information can be relayed exclusively to donor country
firms; the fact that the donor country’s government provides the aid can reduce the donor

country’s firms’ subjective perception of the recipient country’s investment risk; and the fact that
aid flows may transplant the donor country’s specific business practices and institutions into the
recipient country. With regards to Japanese aid’s vanguard effect, the authors assert that it likely
to be a result of the close interaction between Japan’s public and private sectors, which implies
that it is primarily due to the second general reason above.
Karakaplan et al. (2005) examined the effect of political and macroeconomic conditions
in the recipient countries on the efficacy of foreign aid in attracting FDI. Using unbalanced panel
data from 97 countries during the period 1960-2004, they hypothesized that aid has a positive
effect on FDI flows only in cases of good governance and financial market development and not
necessarily otherwise. To correct for potential biases and endogeneity, the authors used moving
averages of all explanatory variables and dynamic GMM estimation after first-differencing their
primary equation to eliminate fixed effects. Their estimations pass the Sargan test for instrument
validity and the m2 test for lack of second order serial correlation, both of which add to the
authors’ confidence in their estimates. In their models of interest, Karakaplan et al. (2005)
developed standardized indices of governance, of which there are six, and financial market
development (FMD), of which there are three. They interacted both aid and lagged FDI with
each of these indices separately and found that aid and lagged FDI had significantly positive
coefficients when interacted with all six governance indices and all three FMD indices. In the
same regressions, aid and lagged FDI by themselves had significantly negative coefficients,
which lends support to their hypothesis.

In the culmination of their models, Karakaplan et al. (2005) interacted aid and lagged FDI
with both the governance and FMD indices and found that all of the interaction coefficients were
significantly positive, although some were less significant than in the previous models. This
result implies that financial market development contributes to an environment that attracts FDI
above the positive effect of good governance; however, it must be noted that the increased
multicollinearity among the right-hand side variables could possibly account for the loss of
significance of some of the interaction terms. The authors further checked for robustness by
controlling for world variables, namely the world growth rate and the average inflation of the G5
countries, a check that yielded virtually the same results.
While the above studies provide support for a positive relationship between aid flows and
FDI, the proposed relationships seem to be predicated on certain conditions, both regarding the
components of the aid itself and regarding the environmental conditions of the donors and
recipients.
V. CONCLUSION
From the relevant literature, conclusions can be made about the effects of FDI and
foreign aid on growth and the relationship between foreign aid and FDI. While once widely
believed to hinder growth, more recent literature has supported the positive effects of FDI on
growth through positive externalities and feedback loops in the recipient country. The literature
on foreign aid has shown in multiple cases that aid can have a positive effect on growth when it
is efficiently allocated to and within recipient countries; however, the current allocation of aid

has proven to be inefficient and is not promoting growth as it is intended to do. Finally, the
relationship between foreign aid and FDI appears to be positive, although this effect seems to be
dependent upon the composition of the donated aid and the macroeconomic and political
environments of both the donor and recipient countries. One of the main concerns in
development economics is lack of reliable and complete data, primarily from developing
countries for which consistent records have not been kept. In order to continue to make strides in
development economics and make accurate policy decisions, it is imperative to acquire more
reliable, balanced data from the economies of interest. With more balanced and complete data,
further research could and should be conducted regarding methods to achieve efficient aid
allocation and examining the possible roles of microeconomic factors in affecting growth.

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