A comparative analysis of the effect of aid and microfinance on growth1
Maricruz Lacalle-Calderón
Applied Economics Department
Faculty of Economics and Business
Universidad Autónoma de Madrid
Avda. Francisco Tomás y Valiente 5
28049 Madrid (Spain)
Telephone: +34 914972895
Email: maicu.lacalle@uam.es
Coro Chasco
Applied Economics
Universidad Autónoma de Madrid
Facultad de Ciencias Económicas y Empresariales, Módulo 14
Avda. Francisco Tomás y Valiente 5
28049 Madrid (Spain)
Telephone: +34 914975266
Email: coro.chasco@uam.es
Javier Alfonso-Gil
Applied Economics Department
Faculty of Economics and Business
Universidad Autónoma de Madrid
Avda. Francisco Tomás y Valiente 5
28049 Madrid (Spain)
Telephone: +34 914973434
Email: javier.alfonso@uam.es
Isabel Neira
Econometrics Department
Faculty of Economics and Business
Universidad de Santiago de Compostela
Av.Xoan XXIII s/n
15704 Santiago de Compostela (Spain)
Telephone: +34 981 566287
Email: isabel.neira@usc.es
Maricruz Lacalle‐Calderón and Coro Chasco acknowledge financial support from the 8th UAM‐Santander Bank
Project. Coro Chasco also thanks the Spanish Ministry of Education and Science (Grant No. ECO2012‐36032‐C03‐
01).
1
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A comparative analysis of the effect of aid and microfinance on growth
Abstract
The objective of this paper is to study the effect of official development aid (ODA) and
microfinance (MF) on economic growth and compare the results. To achieve this objective, we
identify and analyze the transmission mechanisms from ODA and microfinance to growth. We then
study the dynamics of ODA and microfinance on growth, using an unbalanced panel of 67 countries
for the period 2001-2011. After country and time effects are accounted for in a dynamic panel data
model, our results show that microfinance has a positive and statistically significant effect on
economic growth through private investment, while ODA has no effect on growth. This conclusion
suggests that deploying resources in the microfinance industry will foster economic development in
poor countries.
Key Words: Microfinance; Official Development Aid; Economic Growth; Panel Data.
JEL classification codes: O20; O40; G23; C23
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I. Introduction
Foreign aid in the form of Official Development Aid (hereafter ODA) reached a record amount of
180.1 billion USD in 2010 from all donor countries, totaling 4.2 trillion USD over the past 50 years
(OECD 2013a). There is still substantial controversy, however, concerning ODA’s expected impact
on growth in receiving countries (Easterly 2008 and Gibson et al. 2009).
In recent decades, microfinance (MF) has emerged as a complementary approach to
financing development. MF is a bottom-up financial tool developed in a businesslike way and with
a sustainable focus. The gross loan portfolio of the worldwide microfinance industry reached
64.769 billion USD in 2011 (MIX Market 2011). As in the case of ODA, there is some uncertainty
as to the impact of microcredit on growth (Hulme 2007, Dichter 2007, Bateman 2010, Roodman
2012 and Duflo et al. 2013).
The objective of this paper is to study the effect of ODA and MF on economic growth and
compare the results. There is an extensive empirical literature on the effect of aidi on growth, but
this is not the case for microfinance. Although both ODA and MF are funding tools for
development and have very similar objectives, their characteristics and dynamics are not the same.
The channel by which ODA affects growth may, therefore, be different than that of microfinance.
Empirical analysis must explain and address both transmission mechanisms. We therefore test the
indirect impact of ODA on growth through a two-step panel data model following applications
similar to those used in Gomanee et al. (2005). We repeat this procedure for microfinance to ensure
proper comparison of the funding tools.
To compare the effects of these two instruments on economic growth, we conduct the
analysis for an unbalanced panel of 67 countries, all of which are ODA recipients and have MF
activity (and can therefore provide data on both tools). The results show a positive and statistically
significant association between MF and economic growth through private investment, but not
between ODA and growth.
This paper contributes to the literature by: a) exploring the macroeconomic effects of
microfinance, b) focusing on the comparative effects of aid and microfinance on growth, and c)
determining whether the transmission mechanisms from these tools to growth are the same.
The rest of the paper is organized as follows. Section 2 analyzes theoretically how and why
ODA may impact economic growth (i.e., transmission mechanisms) and summarizes the most
important literature on the effectiveness of ODA. Section 3 analyzes how and why microfinance
may affect growth. In Section 4, a panel data model is used to explore the dynamics of ODA and
MF on economic growth. We also perform a battery of sensitivity checks to confirm the robustness
of our results with different specifications and estimation strategies. Section 5 presents the results
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derived from comparing the impacts of ODA and MF on economic growth. Section 6 presents the
conclusions.
II. Foreign aid and economic growth
According to the definition provided by the OECD’s Development Assistance Committee (DAC),
ODA represents those flows to recipient countries and multilateral institutions provided by official
agencies (including state and local governments or their executive agencies) and administered
fundamentally for the promotion of the economic development and welfare of developing countries.
To constitute ODA, these flows must be concessional in character and include a grant element of at
least 25% (OECD 2013b). ODA is considered a top-down financing tool because it flows from
governments or international agencies to governments. It is basically a one-wayii financial flow that
operates in large-scale development projects to address the shortages in capital goods in poor
countries, where the insufficiency of domestic savings leads to weak investmentsiii. In economic
theory, several authors have identified capital formation shortages as a significant impediment to
development, arguing that aid could be a significant determinant for these investments (Lewis 1954,
Burnside and Dollar 2000, Hansen and Tarp 2001, among others). The basic transmission
mechanisms from ODA to growth were presented by Chenery and Strout (1966) in their two-gap
model. First, aid was needed to close the savings-investment gap, known as the internal gap.
Second, they also argued that aid was needed to fill the external gap, that is, the difference between
imports and exports. Poor countries not only lack sufficient internal resources but often have low
and volatile export earnings. They thus lack the resources to finance importation of the capital
goods, technology, and intermediate goods (primarily fuel) needed for growth. Aid, in the form of
hard currency, could also fill the foreign exchange gap (Gomanee et al. 2005).
Finally, since ODA flows fundamentally to governments, it could encourage public
spending and consumption and thus affect growth negatively. Several authors have argued that,
rather than fostering growth-promoting investment or improving human development indicators,
large free aid flows increase the size of government, resulting in excessive and unsustainable
government consumption and public deficit (Boone 1996, Burnside and Dollar 2000, Moss et al.
2008).
Over the past 60 years, the question of whether aid has a macroeconomic impact on growth
has been studied from a variety of methodological approaches. We can define two main but
opposing positions on the effectiveness of aid, although the evidence is still ambiguous and the
debate continues (Easterly 2008 and Sachs 2011). Some studies have obtained positive results for
ODA’s influence on growth (Levy 1988), while others show negative results (Mosley 1980, Mosley
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et al. 1987, Boone 1996). In the new century, Burnside and Dollar (2000) found that aid works in
“good policy environments”, an argument that has influenced the World Bank’s view that aid
boosts economic growth, reduces poverty, and improves social indicators in good policy
environments (Easterly 2003). Hansen and Tarp (2001), Collier and Dollar (2002), and Sachs
(2004) obtain similar results supporting the effectiveness of aid. Instead, Easterly et al. (2004),
Lensink and White (2001), Banerjee et al. (2006), and Rajan and Subramanian (2008) determine
that aid does not affect growth at all. Moreover, Djankov et al. (2006 and 2008) finds that foreign
aid has a negative impact on democracy and economic growth in developing countries. It must be
said that these studies have significant problems, such as the erratic nature of the concept of aid,
poor data quality, the low ratio of aid to GDP in most recipient countries, the problem of
endogeneity, and the use of weak instruments in econometric models (Tarp 2006).
Recently, two meta-analyses have been performed with contrary results. Doucouliagos and
Paldam (2011) find that aid is ineffective in promoting growth, while Mekasha and Tarp (2013) find
that the effect of aid on growth is positive and statistically significant. Based on an extensive review
of the literature, one could conclude that, although the expected impact of aid on growth is dubious,
the entire development community, from Sachs to Easterly, believes foreign aid is needed to
stimulate progress. ODA should be rethought (Easterly 2008) but not rejected. Indeed, foreign aid
has been effective where projects have had narrow and monitorable goals that meet the needs of the
poor directly (Schultz 2004, Kremer and Miguel 2007, Banerjee and He 2008, Banerjee and Duflo
2011, Karlan and Appel 2011).
An appropriate framework to study empirically whether aid has any impact on growth
should thus consider explicitly the three transmission mechanisms shown above –investment,
imports, and government consumption– treating the data and econometric models properly
(Chenery and Strout 1966 and Gomanee et al. 2005). The following sections of this paper will
perform such a study.
III. Microfinance and economic growth
Microfinance is a recent bottom-up approach to financing development that focuses mainly on the
individual. It was born in response to the lack of access to financial services experienced by
millions of people worldwide who are excluded from the formal financial system (Helms 2006).
The World Bank estimates that 2.7 billion people globally have no access to formal financial
services (Chaia et al. 2009 and World Bank 2011) and must rely on informal financial services that
may be more costly and less reliable (CGAP 2010).
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Microfinance offers small-scale loans, savings accounts, insurance, housing loans, and other
financial services to the poor (CGAP 2009), providing the possibility of overcoming these
constraints. It helps the poor to improve their financial security, allows them to take advantage of
new business opportunities, and enables many to expand and diversify their economic activities and
increase their incomes (Robinson 2001 and Armendariz and Morduch 2010). Further, the potential
of MF not only opens new possibilities to those excluded but provides them with the social network
and institutional capital created in the process of providing MF (Matin et al. 2007).
According to the most recent report of the Microcredit Summit Campaign (Reed 2013),
microcredit increased exponentially from 1997 to 2010, from 7.6 million poorest clientsiv receiving
microcredit at the end of 1997 to 137 million at the end of 2010. In 2010, the total numberv of
clients reached by the 3652 microfinance institutions reporting to the Summit was 205 million.
According to data from the MIX Market (2011), the gross loan portfolio of the 1115 worldwide
microfinance institutions reporting data to this institution totaled 64.769 billion USD.
Based on all of these characteristics, how might microfinance affect growth? We begin by
recognizing that economic activity or economic growth is impossible without financing (Hermes
and Meesters 2011 and Roodman 2012). By analogy the argument by Schumpeter (1934) that
financial intermediation spurs growth by identifying and funding productive investments,
microfinance funds productive microenterprises for the poor through funding of investment in
physical capital (Okurut et al. 2005).
Microfinance is not limited to small scale loans. It also includes access to saving accounts
for the poor and unbanked, mobilizing savings and channeling these savings into microentrepreneurs’ productive investments. Traditionally, private banks thought poor people could not
save, but several case studies have reported this is not true. Poor people can and do save (Robinson
2001). Microfinance institutions thus help to achieve financial deepening. They fulfill an unmet
need for financial intermediation by receiving savings from poor people and using those savings to
fund productive microbusiness that would otherwise be unfeasible. Without microfinance services,
thousands of unbankable entrepreneurs could not invest and create or maintain labor, income, and
consumption. Khandker (2005) and Mahjaeen (2008) report that microfinance appears to play a
very significant role in increasing income and consumption. Based on these arguments, the
transmission mechanism from microfinance to growth may occur primarily through productive
private investment and secondarily through private consumption (Okurut et al. 2005, Khandker
2005 and Mahjaeen 2008).
Caution should be exercised, however, in accepting this conclusion, since microfinance is
still an immature and unproven tool in some areas (Dichter 2007). Cases of over-indebtedness and
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repayment problems with microcredit in India in 2010—and previously in other countries, such as
Bolivia, Bosnia-Herzegovina, and Morocco—have proven that microcredit can also be a debt trap
(Maes and Reed 2012). Access to financial services may improve the well-being of many poor
people, but the process is not automatic (Roodman 2012). After thorough research, Roodman
(2012) finds little evidence that the microfinance movement has lived up to its claims of achieving
development or reducing poverty in the last thirty years.
In recent decades, several studies have reported that well-managed microfinance institutions
have been successful in smoothing consumption, creating jobs and income, and improving women’s
empowerment and education (Pitt and Khandker 1998, Khandker 2005, Khandker et al. 2013,
Karlan and Ziman 2010, Karlan and Apple 2011, among others). Microfinance increases freedom,
since it gives the poor a new way to navigate tricky financial currents (Sen 1999). Moreover, five
randomized control trials (RCTs) published recently confirm some positive microeconomic
outcomes of microfinance (Crépon et al. 2011, Attanasio et al. 2011, Augsburg et al. 2012,
Angelucci et al. 2013 and Duflo et al. 2013).vi
At the macro level, some recently published papers study how microfinance affects
macroeconomic activity. Among them, Buera et al. (2012) and Ahlin and Jiang (2008) propose two
theoretical models to study the potential of MF to generate a gradual process of development (the
former) and its potential to create a redistributive effect on the overall economy in a country (the
latter). Imai et al. (2012) study the impact of MF on national poverty levels. Finally, Ahlin et al.
(2011) and Hermes and Meesters (2011) investigate the relationship between macroeconomic
conditions and the performance of microfinance institutions. Still, almost no empirical research
studies the macroeconomic effect of MF on economic growth. To our knowledge, Sodokin and
Donou-Aeonsou (2010) is the only empirical study. It measures the impact of the joint effort
between banks and MF to promote economic growth in the West African Monetary Union region.
To sum up, the transmission mechanisms from microfinance to growth may occur primarily
through productive private investment and secondarily through private consumption, since
microcredit activity is basically credit to create or maintain microenterprises, as well as to increase
or maintain income and consumption.
IV. Empirical analysis
Our empirical analysis focuses on the effect of ODA and MF on growth, taking into account the
transmission mechanisms explained in the previous sections. ODA may affect growth through
public investment, imports, and government consumption spending. The effect of ODA on growth
is thus considered to be indirect. Exactly the same dynamism occurs with MF, which may affect
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growth indirectly via private investment and/or private consumption. All of these indirect impacts
of ODA and microfinance on growth will be tested in the following pages.
Data
The dataset used in this paper to analyze the relationship of ODA and MF to economic growth was
obtained by combining a variety of databases, as shown in Table 1. The variable records were
matched by country and year. After the exclusion of some records that could not be matched, the
final dataset is composed of an unbalanced panel of 67 countriesvii for the period 2001-2011. The
availability of data for MF limits the analysis to this period.
------- TABLE 1 ------Main model
The base specification is a dynamic panel data model of the logarithm of real GDP PPP (gdpit), as
in Sodokin and Donou-Adonsou (2010). The model is specified as follows:
gdpit gdpi,t 1 pit tit cit i t vit
(1)
Initial GDP growth (gdpi,t1) is used to control for persistence of economic development
over time. The measures of the principal variables, ODA and MF, are designated by p. Two GDP
panel data models will thus be specified from Equation (1), depending on the main variable
considered: total net ODA or microcredit gross loan portfolio.
We incorporate two vectors of other groups of variables. The vector of transmission
variables (t) includes private and public investment, private and public consumption, and total
imports. Following Gomanee et al. (2005), Acemoglu (2009), and Compton et al. (2011), the vector
of the control variables (c) considers a wide array of socioeconomic variables conditioning
economic growth (e.g., labor force, malnutrition prevalence, credits, inflation, interest, and real
exchange rates), as well as other political indicators (democracy, role of law, and control of
corruption). All variables are specified in log form, except the rule of law and control of corruption
indexes and the inflation and interest rate variables.
The term i captures the unobservable time-invariant country heterogeneity, since we allow
for the presence of country effects; t represents time effects; α, β, , and are parameters to be
estimated; and vit is the error term, capturing all other omitted factors, with E[vit]=0 for all i and t.
We estimate dynamic panel data model (1) using the consistent General Method of Moments
(GMM) estimator proposed by Arellano and Bond (1991), because this method allows for
unobserved country-specific effects, measurement errors, and endogeneity problems, not only of the
lagged dependent variable but also of any other regressor (Arndt et al. 2010). Causality from ODA
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and MF to economic growth could run both ways. MF could affect economic growth, but economic
growth could also have a positive impact on microfinance institutions’ performance (Ahlin et al.
2011 and Hermes and Meesters 2011). We must test for the same effect in the case of ODA, as in
Asiedu and Nandwa (2007) and Hansen and Tarp (2001). Thus, neither ODA nor MF can be
considered a priori as exogenous to economic growth in Equation (1).
Arellano and Bond’s “difference GMM” estimator uses lagged levels of first difference of
variables as instruments to treat endogeneity accurately in both the lagged dependent variable and
the other explanatory variables. This method identifies how many lags of the dependent variable,
the predetermined variables, and the endogenous variables are valid instruments and how to
combine these lagged levels with first differences of the strictly exogenous variables into a
potentially large instrument matrix. We employ the so-called “two-step” difference GMM
estimator, which allows for heteroskedasticity in the error terms. Additionally, the Windmeijer biascorrected robust VCE has been computed, since non-robust results for inference on the coefficients
tend to be severely biased downward (see Arellano and Bond 1991 and Windmeijer 2005 for
details). Hence, if we time difference Equation (1), we obtain the following specification:
gdpit gdpi,t 1 pit tit cit t vit
(2)
where the fixed country effectsi are removed by time differencing.
This estimator requires that there be no autocorrelation in the idiosyncratic errors (vit). When
the idiosyncratic errors are independently and identically distributed (i.i.d.), the first-differenced
errors (vit) are first-order serially correlated. Nevertheless, serial correlation in the first-differenced
errors at an order higher than 1 is not allowed, since this would imply that the moment conditions
used in the model are not valid. For this reason, we have calculated the Arellano–Bond test for firstand second-order autocorrelation in the first-differenced errors of the specified models. We have
also computed the Sargan test derived by Arellano and Bond (1991), which contrasts the null
hypothesis that the overidentifying restrictions are valid. Rejecting this null hypothesis would
indicate that we need to reconsider either our model or our instruments.
Estimation of the transmission mechanisms for ODA and MF
As discussed above, three potential transmission variables are considered for aid: public investment
(gcfpub), government consumption (cpubl), and imports (impor). For MF, these variables are
private investment (gcfpriv) and private consumption (cpriv). In a first step, we test whether these
variables are indeed transmission mechanisms for the effect of ODA/MF on economic growth. We
do this simply to determine whether the coefficients of the ODA/MF variable are significant in a
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dynamic panel data regression of ODA/MF–and other explanatory variables–for each particular
transmission variable. These models are estimated using a two-step GMM, as in Equation (2). The
formal models are the following:
a) Transmission mechanisms for ODA:
gcfpubit gcfpubi,t 1 oda it 1credit it 2tot i,t 1 t vit
cpublit cpubli,t 1 oda it 1taxit 2densit t vit
impor impor oda expor tot inf v
it
i,t 1
it
1
it
2
i,t 1
3
i,t 1
t
it
(3)
To account for the dependence of public investment (gcfpub), we include its own one-period
lag, bank private credit (credit), one-period lagged net barter terms of trade index (tot), as well as
ODA. Government consumption spending (cpubl) is explained by its own time lag and by both
domestic and foreign sources of government revenue, such as tax revenue (tax), population density
(dens), and ODA. Finally, imports (impor) are considered to be determined by their own time lag,
one-period lagged inflation rate (inf), one-period lagged net barter terms of trade index (tot), exports
(expor), and ODA.
b) Transmission mechanisms for MF:
gcfpriv it gcfpriv i,t 1 mfit 1credit it 2 infi,t 1 3demoit t v it
cpriv it cpriv i,t 1 mfit 1credit it 2interi,t 1 3infi,t 1 t vit
(4)
Private investment (gcfpriv) is explained by its own time lag, bank private credits (credit),
one-period lagged inflation rate (inf), a democracy index (demo), and MF. Private expenditure
(cpriv) is determined by its own time lag, bank private credits (credit), one-period lagged interest
rate (inter), one-period lagged inflation rate (inf), and MF.
V. Econometric results: the impact of ODA and MF on economic growth
The estimation results of the models in Equations (3) and (4) are presented in Table 2. Recall that
our aim is to determine whether ODA and MF are significant determinants of cross-country
variations in the level of the transmission variables under consideration.
First, we perform the Durbin and Wu-Hausman tests to investigate whether ODA and MF
are exogenous (null hypothesis) in each of the models in which they appear. This involves
comparing the results of OLS and IV regressions using the Sargan test for validity of instruments.viii
As shown in Table 2, these tests clearly fail to reject the null hypothesis that the exogenous
regressors (ODA/MF) and error terms are uncorrelated. Consequently, our sample provides no
evidence of the need to use instruments for either ODA or MF.
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The Arellano-Bond serial autocorrelation tests also obtain the expected outcomes in all
cases: they are always significant for a first order (t–1) but no longer significant for the second
order. The Sargan specification test always accepts the null hypothesis of validity of the
overidentifying restrictions, indicating good specification of both model and instruments.
The results displayed in Table 2 show coefficient estimates with the expected signs, but the
results are not always statistically significant. In fact, the first important result is that we cannot
obtain evidence of a relevant effect of ODA on either of its potential transmission variables, while
MF is found to be highly significant in explaining both private investment and private consumption.
On average, an increase of 1 percentage point in MF raises private investment and private
expenditure by about 0.09% and 0.06%, respectively.
------- TABLE 2 ------The results suggest that there are no mediating mechanisms for ODA to impact on economic
growth, while private investment and consumption are significant transmission mechanisms for MF
Therefore in this last case, it is necessary to consider the ‘double-counting’ problem to avoid MF
being counted more than once.
In a second step, we contrast the role of the transmission variables in a GDP growth
equation in which ODA and MF have not yet been considered. Although aid is not found to be a
significant determinant of public investment (gcfpubl), government consumption (cpubl), and
imports (impor) in our sample, we include these key variables in the growth equation. The results
are presented in the second column of Table 3. Of the full set of explanatory variables included in
Table 1, only private investment (gcfpriv), imports (impor), the net barter terms of trade index (tot),
and the democracy index (demo), together with the one-period lag of the dependent variable, are
found to be statistically significant for GDP growth and to show the expected signs. Thus, neither
the variables (private/public) consumption nor public investment can be considered relevant
determinants of economic growth for our country sample, although imports are.
Steps 1 and 2 reach the following conclusions: 1) ODA has no significant transmission
mediations on income growth; 2) MF has two significant transmission mechanisms that impact
economic growth, private investment, and private consumption; 3) of these last two significant
mediations, only private investment has been found to have a relevant impact on growth.
------- TABLE 3 ------In order to quantify the impact of ODA and MF on income growth, we thus include these
variables in the previously estimated growth model. In the third column of Table 3, we contrast to
what extent ODA has –at least– a direct impact on GDP, what must also be rejected due to the nonsignificant level of its corresponding coefficient. In the fourth column, we have estimated Equation
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(2) with MF and the original private investment variable (gcfpriv). The coefficient of MF is low
(0.0032) and only significant at the 80% confidence level. Hence it is not possible to prove a direct
effect of MF on GDP growth. Nevertheless, since MF does affect private investment, which, in turn,
has a relevant impact on growth, we are not taking into account the aforementioned doublecounting problem. Specifically, the total effect of MF in growth is spread out across the coefficients
on the transmission variable (gcfpriv) and MF, and the net effect of MF cannot be disentangled.
Double counting problems can be solved as in Gomanee et al. (2005), who recommend the
use of the generated residual variable procedure proposed by Pagan (1984) to build the part of each
transmission variable that is not attributed to ODA or MF. This method consists of constructing a
new regressor for each transmission variable from the residuals of a supplementary bivariate
regression of the original transmission variable on ODA or MF, as is appropriate. This method
captures transmission from ODA/MF to their corresponding transmission variables. It also enables
us to identify that portion of the effect on growth produced by the relevant transmission mechanism
that is not directly due to ODA/MF, avoiding problems of double counting and omitted variables.
In our case, since only MF has a significant transmission mechanism to growth through
private investment, we generate a residual variable for this mediator from a bivariate regression of
private investment on MF (gcfprires). This new variable corresponds to the portion of private
investment not explained by MF.ix Inclusion of both regressors (gcfprires–instead of the original
gcfpriv– and MF) in Equation (2), as well as the other significant explanatory variables, leads to the
results presented in the fifth column of Table 3. Both the private investment generated and the MF
variables show very significant estimators in this equation, as do the other regressors. This result
demonstrates that the MF coefficient in a regression including the original investment term will be
an underestimate of the true effect of MF on growth. Namely, there is a significant transmission
mechanism from MF to economic growth via private investment.
To test for robustness, a dynamic panel data model is estimated for ODA and MF as the only
explanatory variables (with the one-period lagged dependent variable) of growth. Again, only MF
obtains a highly significant estimator for this sample, while ODA remains unconnected to growth.
We conclude this section by affirming that ODA is not found to be a significant determinant
of public investment, public consumption, or imports, since none of the coefficients of these three
variables is statistically significant (see Figure 1). Although the import variable is a relevant
determinant of economic growth, ODA has no effect on economic growth since it does not have any
previous effect on imports. This result differs from that of Gomanee et al. (2005), who find the
transmission mechanism working from aid to investment, and from investment to growth.
------- FIGURE 1 ------12
The results for microfinance (columns 4 to 5 of Table 2) provide strong evidence that
microfinance is associated with its transmission mechanisms. Microfinance is a significant
determinant of private investment and private consumption. As we see in the fourth column of
Table 2, all coefficients are significant and have the expected sign. Private investment depends
basically on access to financial resources. Following Okurut et al. (2005), credit may increase
production capacities through funding of investment on capital. Financial resources come primarily
from the banking sector in developed countries, but in poor and developing countries (like those in
our sample), financial resources for financing private investment come from both formal and
informal financial intermediaries. The results in Table 2 show the importance of these two variables
(credit and MF). The microcredit gross loan portfolio coefficient is positive and highly significant.
Credit is also positive and significant. This means that microfinance increases private investment in
the countries in the sample. As one might expect, inflation affects private investment negatively. Its
coefficient is negative and significant. Finally, private investment may occur in a stable and
democratic environment. As to private consumption (column 5, Table 2), again, microfinance and
credit has positive and statistically significant coefficients. This result is similar to that of Khandker
(2005) and Mahjabeen (2008), who find that microfinance is highly significant in increasing
household consumption. The other coefficients in this column are as expected. An increase in the
inflation rate or in the interest rate reduces private consumption. All of these estimation results are
as expected and fit economic and microfinance theory quite well.
Although MF is a relevant determinant of both private investment and private consumption,
as mentioned above, only the former is found to be statistically significant for GDP growth (see
Figure 1).
Again, our primary and most important result is that the effect of ODA on growth is not
statistically significant, while microfinance presents a positive and statistically significant
association with economic growth (Table 3). This finding agrees with Buera et al. (2012) and Ahlin
and Jiang (2008), who also detect positive effects of MF on economic activity, as well as with the
most recent aid-growth literature, which does not find a significant effect of aid on growth (Mosley
et al. 1987, Boone 1996, Djankov et al. 2006 and 2008, Rajan and Subramanian 2008 and
Doucouliagos and Paldam 2011).
VI. Conclusions
The impact of ODA on growth has been studied for a long time, but there is still substantial
controversy concerning its effects on growth. Microfinance, in contrast, is a newer tool, and almost
no empirical studies have been performed to test its impact on growth. Our paper has studied and
13
compared the effect of these two tools on economic growth and finds that there may be different
transmission mechanisms from these tools to growth. ODA would affect growth indirectly through
public investment, government consumption, and imports, whereas microfinance would impact
growth through private investment and private consumption.
We performed an empirical analysis to test these hypotheses completely using dynamic
panel data models of 67 countries for the period 2001-2011. The results of the empirical analysis
show that ODA has no effect on any of its potential transmission variables: public investment,
public consumption, and imports. There are no mediating mechanisms for ODA to impact growth.
In contrast, microfinance shows two significant transmission mechanisms to impact economic
growth: private investment and private consumption, and, of these two significant mediations, only
private investment has been found to have a relevant impact on growth.
Therefore, ODA has no impact on growth, and microfinance has a significant impact on
growth, but only through private investment. Based on the empirical results obtained, the policy
recommendation for the international community is to allocate resources to the microfinance
industry in order to provide more options for the economically active poor and to enable them to
choose their own path.
The terms Aid and ODA will be used interchangeably in this paper.
ODA flows include grants and concessional loans whose grant component is at least 25% (OECD 2013). The grant
component of ODA has increased over the years. At present, almost all aid flows are grants. The average
repayment rate of ODA (the difference between gross and net ODA) from 1960 to 2010 has been only 9.42%.
iii Investment should be understood not only as fixed capital but also as the improvement of human skills and
institutional change.
iv
According to the Summit Report, “poorest clients” are those people living on less than $1.25 a day, adjusted for PPP.
v
All microcredit clients, not only the poorest.
vi
These RCTs obtain mixed results but prove that microfinance does not cause harm. Duflo et al. (2013) concludes that
some favorable impacts result from the poor having direct access to credit but that these impacts are not especially
strong. The other four RCTs (Crépon et al. 2011, Attanasio et al. 2011, Augsburg et.al. 2012, and Angelucci et al.
2013) obtain more positive results on decrease in expenditure on “temptation goods,” growth of self-employment
activities, and increase in business revenues and profits.
vii The sample comprises all countries that are ODA recipients and that have an MF sector, that is, all countries for
which there are data on ODA and microfinance. Other countries for which there is no data for ODA, microfinance, or
even GDP have been eliminated. Zimbabwe, Democratic Republic of the Congo, Lao P.D.R., and Micronesia Fed. State
have been excluded because their data is unreliable. Kosovo and Thailand have also been excluded because they have
not received ODA in net terms in last five years. Any other exclusion is due to missing observations on microfinance
data. The base sample is composed of 67 countries (see Appendix 1).
viii
ODA has been instrumented by its own time lag, rule of law (rol), and malnutrition prevalence (malnut) as a proxy
for poverty, plus the purely exogenous variables in each regression. MF has been instrumented by its own time lag plus
private credits, as in Sodokin and Donou-Adonsou (2010), and the purely exogenous variables in each regression. The
determination coefficient values for the regressions of ODA/MF on their corresponding instruments are all above 0.87
and 0.92, respectively.
ix
Although ODA is not statistically significant in its corresponding first-step equations, we have also generated new
residual variables for ODA’s potential transmission mechanisms: public investment (gcfpures), government expenditure
(cpubres), and imports (impores). Nonetheless, as stated above, only import is a significant regressor in the growth
equation, although it was not found to be relevant as a mediator for ODA. The generated residual variables have been
i
ii
14
estimated from the following bivariate regressions: variable gfpures from gfpubl=13.85+0.38·oda, R2=0.04; variable
cpubres from cpubl=17.70+0.24·oda, R2=0.02; variable impores from import=19.68+0.20·oda, R2=0.02; variable
gfprires from gfpriv=18.00+0.25·mf, R2=0.10; and variable cprires from cpriv=20.34+0.22·mf, R2=0.11.
15
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19
APPENDIX 1
Countries in the sample
Albania
Argentina
Armenia
Azerbaijan
Bangladesh
Benin
Bolivia
Bosnia and Herzegovina
Brazil
Cambodia
Cameroon
Colombia
Congo, Rep.
Costa Rica
Chile
China
Dominican Republic
Ecuador
Egypt, Arab Rep.
El Salvador
Ethiopia
Georgia
Ghana
Guatemala
Guinea
Haiti
Honduras
India
Indonesia
Jordan
Kazakhstan
Kenya
Kyrgyz Republic
Lebanon
Madagascar
Malawi
Mali
Mexico
Moldova
Mongolia
Morocco
Mozambique
Nepal
Nicaragua
Niger
Nigeria
Paraguay
Peru
Philippines
Rwanda
Senegal
Sierra Leone
South Africa
Sri Lanka
Swaziland
Syrian Arab Republic
Tajikistan
Tanzania
Togo
Tunisia
Turkey
Uganda
Uzbekistan
Venezuela, RB
Vietnam
Yemen, Rep.
Zambia
20
TABLES:
Table 1 Variable list specification, 67 countries, and period 2001-2011
Variable Description
Dependent variable:
gdp
Real GDP, PPP (constant 2005)
Units
Source
Internat. $
World Development Indicators
a) Persistence in economic development over time
gdpl1
GDP (constant 2005) in the preceding year
Internat. $
World Development Indicators
b) Main variables
oda
Total Net ODA
mf
Gross Loan Portfolio on Microfinance
Internat. $
Internat. $
OECD (DAC database)
MIX Market
Internat. $
Internat. $
Internat. $
Internat. $
Internat. $
World Development Indicators
World Development Indicators
World Development Indicators
World Development Indicators
World Development Indicators
Internat. $
Internat. $
%
Internat. $
%
Index
Index
Internat. $
% of GDP
Years
# persons
# persons
Persons/Km2
%
010
2.5 to 2.5
2.5 to 2.5
IFSIMF
IFSIMF
World Development Indicators
World Development Indicators
World Development Indicators
World Development Indicators
World Development Indicators
World Development Indicators
World Development Indicators
Barro and Lee database
International Labor Organization
Penn Word Tables
Penn Word Tables and GIS*
World Development Indicators
Polity IV
World Development Indicators
World Development Indicators
Explanatory variables:
c) Transmission variables
gcfpriv
gcfpubl
cpriv
cpubl
impor
Private investment
Public investment
Private final consumption expenditure
Govern final consumption expenditure
Imports of goods and services (constant 2005)
d) Control variables
credit
depos
inf
tax
inter
er
tot
expor
open
edu
lf
popu
dens
malnut
demo
rol
corrupt
Bank private credit
Deposit money bank assets
Inflation rate (consumer price index)
Tax revenue
Real interest rate
Real effective exchange rate index (2005=100)
Net barter terms of trade index (2000=100)
Exports
Openness at 2005 constant prices
Total years of schooling
Labor force (economically active population)
Total population
Population density
Malnutrition prevalence (% of children under 5)
Democracy index
Rule of law
Control of corruption
* Developed by the authors with a Geographical Information System (GIS)
21
Table 2 Estimation results of the transmission mechanisms for ODA and MF(1), 67 countries, and period
2001-2011
Dependent variables:
oda
mf
lagged dependent variable
credit
tax
inf (lagged)
inter(lagged)
tot (lagged)
expor
lf
dens
demo
Arell.-Bond
1storder
autocor. test
2ndorder
Sargan specification test
Endogeneity
Durbin
tests for
W-Haus.
oda and mf
Sargan
ODA transmission equations (Eq. 3)
Public
Government
investment
consumption
Imports
(gcfpubl)
(cpubl)
(impor)
0.0570
0.0193
0.0083
0.3416***
0.6270***
0.3200***
0.3888***
0.2010***
0.0016***
0.2850***
0.1795***
0.4668***
0.6549**
*
0.6077
0.0123*
2.0679**
2.4951***
1.7120*
0.2331
0.5245
0.42681
46.8469
46.1080
49.1945
0.6264
2.0501
0.2875
0.1496
1.5084
0.1697
5.3637
0.1544
0.7722
MF transmission equations (Eq. 4)
Private
Private
investment
consumption
(gcfpriv)
(cpriv)
0.0878***
0.0614***
0.6244***
0.5843***
*
0.2157
0.1226**
0.0055*
0.0019**
0.0018***
0.0170**
2.4995**
1.7698*
-0.67261
0.87741
48.8144
47.9938
0.1433
0.8416
0.1410
0.8289
0.5516
0.9010
(1)
All variables are expressed in log form, except the inflation and interest rate variables. *** Significant at pvalue<0.01; ** significant at p<0.5; * significant at p<0.1.
Table 3 Estimation results of the economic growth models for ODA and MF(1), 67 countries, and period
2001-2011
Dependent variable: GDP
growth (gdp)
oda
mf
gdpl1
gcfpriv
gcfprires
impor
tot
demo
Arellano-Bond
1st order
autocorr. test
2nd order
Sargan specification test
Endogeneity Durbin
test for
W-Hausman
ODA/MF
Sargan
Initial
growth
model
ODAgrowth
model
-
0.8310***
0.0189**
0.1111***
0.0422**
0.0045**
2.3080**
0.4934
51.7735
-
0.0019
0.8420***
0.0197**
0.1114***
0.0042*
2.3473**
0.4473
51.1756
2.2029
1.0202
4.5673
MFgrowth
model
0.0032
0.8091***
0.0189**
0.1123***
0.0428**
0.0043*
2.2630**
0.5207
53.3160
-
MF
correctedgrowth
model
0.0078**
0.8091***
0.0185**
0.1123***
0.0427**
0.0042*
2.2630**
0.5207
0.6813
0.6696
0.9665
Robust checks
ODAgrowth
0.0058
0.9942***
-
MFgrowth
0.0115***
0.9280***
-
*** significant at p-value < 0.01; ** significant at p < 0.5; * significant at p < 0.1. (1) All variables are expressed in log
form, except the inflation and interest rate variables.
22
FIGURES:
Figure 1. Model estimation results of transmission mechanisms of ODA and MF on GDP growth
Public
investment
Government
expenditure
ODA
ˆ2 0.1123
Imports
ˆ1 0.0185
ˆ 0.0878
Private
investment
MF
ˆ 0.0614
GDP
growth
ˆ 0.0078
**
Private
consumption
Direct effect
Indirect effect
Developed by the authors
23
V i e w
p u b l i c a t i o n
s t a t s