Aid, Policies, and Growth Reconsidered* **
Hirohisa Kohama
Graduate School of International Relations, University of Shizuoka,
Yasuyuki Sawada***
Graduate School of Economics, University of Tokyo,
and
Hisaki Kono
Graduate School of Economics, University of Tokyo
Revised: October 20, 2003
Abstract
Burnside and Dollar (2000) found that the impact of aid on growth of recipients is
positive with good policies. However, Easterly, Levine and Roodman (2003) showed
that this conditional linkage between aid and growth breaks down once data is extended
from 1970–93 to 1970–97. In this paper, we futher investigate the reason of these
seemingly inconsistent findings by decomposing their aid variable into different
components, i.e., loan and grant. By employing this augmented framework, we found
that the disappearence of the aid and growth nexus is generated by a combination of
different effects of loans and grants on economic growth. Interestingly, we found that
loans seem to have growth effects in the extended period whereas grants may be
effective in the shorter data. This may indicate a structural change in aid provisions in
the late 1990’s.
* A paper to be presented at an experts’ research workshop on "Quantifying the Impact
of Rich Countries' Policies on Poor Countries" at the Center for Global Development
(CGD), October 23-24, 2003.
**A part of this paper is financially supported by the Foundations for Advanced Studies
on International Development (FASID). We would like to thank Craig Burnside and
David Roodman for helping our replication of their results. We also thank Yujiro
Hayami and seminar participants at FASID for their constructive comments. Of course,
we are fully responsible for any remaining errors in this paper.
*** Corresponding author: Graduate School of Economics, University of Tokyo, 7-3-1,
Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan. Email: sawada@stanfordalumni.org
1
1. Introduction
How would aid affect growth? This question became the focal point of research
and policy on international aid. One of the most influential papers in this area is
Burnside and Dollar (2000), hereafter BD, which found that the impact of aid on growth
of recipients is positive with good fiscal, monetary and trade policies but has little effect
for recipients with poor policies.
Their findings suggest that aid would be more
effective if it were more systematically conditional on good policy responses of
recipients. The BD findings are practically important, too. In fact, their findings have
been used as a rationale for the selective provisions of foreign aid.
However, this conditional linkage between aid and poverty reduction through
economic growth breaks down once data is extended. Easterly, Levine and Roodman
(2003), hereafter ELR, augmented their data from 1970–93 to 1970–97, as well as
filling in missing data for the original period 1970–93. Then they find that there is no
significant relationship between the amount of aid and economic growth of the recipient
countries even after controlling for policy variables. In sum, the BD finding is not
robust once the data set is refined.
In this paper, we futher investigate the reason of this seemingly inconsistent
findings of BD and ELR by decomposing the aid variable into different components,
i.e., loan and grant. By employing the augmented BD-ELR framework, we found that
the disappearence of the aid and growth nexus is generated by combined different
effects of loans and grants on economic growth. Interestingly, we found that loans
seem to have growth effects in the extended period whereas grants may be effective in
the shorter data.
The reminder of this paper is organized as follows. In Section 2, we replicate
the BD and ELR findings. Section 3 presents our results by estimating the augmented
BD-ELR framework. In the final section, we summarizes concluding remarks
2
2. The Replication of the BD-ELR findings
Data
With respect to data, OECD defines Official Development Assistance (ODA) as
a net sum of grants, including tied to technical assistance, and highly concessional loans
with grant element of at least 25%. Yet, Chang et al. (1998) pointed that the net flow
nature of net ODA underestimated the aid content of disbursed flows by netting out
amortization payments. Also, 25% grant elements for concessionality of loan overrepresent of loans with high concessionality and under-represent loans with low
concessionality.
Hence, we employ the values of Loans and Grants for the 1975-95 period which
are extracted from the World Bank’s database, the Effective Development Aid (EDA)
compiled by Chang et al. (1998). Note that BD and ELR also employ the EDA data. In
order to construct the aid data set for 1970-74 and 1996-97, we took the following two
step procedure. First, by using the 1975-95 data set, we regress the loan variable from
the EDA data set on variables such as net loan, grant, and technical assistance. Then,
with the estimated regression coefficients, we have constructed loan data for 1970-74
and 1996-97. With respect to grant data, we employed the OECD (2001) data directly,
since grant data in EDA is the same as that in OECD (2001).
Note that EDA does not contain data on technical assistance (TA) since donors
benefit from payments received in return for the TA supplied, which may greatly reduce
the donor’s net financial cost. On the other hand, we also estimate the TA’s effect on
economic growth explicitly by using the OECD (2001) data as well.
All variables other than aid variables are the same as those of the ELR data set.
We exclude the outliers identified by ELR.
The Replications of the ELR specifications
By using the data set between 1970 and 1997, we can replicate the regression
results by ELR. The first three columns in Table 2, i.e., the specifications (1) and (2)
3
show our replications. Strictly speaking, the specification (1) is closely related to the
ELR’s results by using the full sample between 1970 and 1997. As we can see, the
coefficients on aid and aid-policy interaction are positive and negative, respectively, but
are both statistically insignificant. This replicates the results of ELR which found that
the BD results do not hold when we extend the analyses through to 1997.
We also conduct the 2SLS to cope with the possible bias generated by
endogeneity of the aid variables. The results, which are not reported in this paper but
available upon request, are also consistent with the ELR’s results.
The Replications of the BD-ELR specifications
Once we restrict the sample between 1970 and 1993, we obtain the replicated
regression results of BD and ELR. By the specifications (1) and (2) in Table 4, we have
shown the replication results. In the specification (2) of Table 4, the coefficient of the
aid-policy interaction variable is positive and statistically significant at the 95% level of
significance. This suggests that aid promotes economic growth if the recipient has
sound economic policies.
We also conduct the 2SLS to cope with the possible bias generated by
endogeneity of the aid variables. We do not report the results, which are also consistent
with the BD and ELR’s results.
3. Estimation Results of the Augmented BD-ELR Model
ELR criticized the BD findings since the results are not robust once they
extended the data. We hypothesize the two reasons for the seemingly inconsistency
between findings of BD and ELR. First, both BD and ELR utilized an aggregate
variable of foreign aid. Yet, the simple summation of grant and loan will be misleading,
since the way grant and loan work to generate economic returns are quite different.
Second, there may be a structural break in the 90’s with respect to the effectiveness of
foreign aid which leads to the ELR findings of ineffective aid on growth once they
extended the data set.
4
Aid Modality
In the specifications (3)-(8) of Table 2, we show the results which include the
effects of loans/GDP and grants/GDP on economic growth by using the extended data.
We found that the coefficients of loans are positive and statistically significant when we
include the loan-policy interactions. The coefficients of this interaction terms are all
negative and mostly significant. However, the total effect of loan on economic growth
will be positive in the relevant range, at least if we evaluate the total effect at the
average policy level, i.e., 1.423.
Coefficients of the loan-policy interactions are mostly negative in Table 2. This
means the impact of loans on the income growth is lower in the country with good
policies. This result is partly due to the characteristics of a policy variable. BD-ELR
policy variable is based on the regression results of income on fiscal surplus, inflation
rate, and openness. Considering the "flypaper" effect of aid money, new loans lead to
net increases of fiscal surplus. In fact, the existing studies on fungibility issues in
foreign aid suggest that there is a greater flypaper effect with concessional loans than
with grants [World Bank (1998), pp. 64-66]. Therefore, loan-policy interactions could
be regarded as the squared loan amounts. If this is a case, negative coefficients of loanpolicy interactions imply the decreasing marginal impact of loans on growth. This
interpretation is consistent with the findings of Burnside and Dollar (2002) and Collier
and Dollar (2002) which showed that there are diminishing returns of the impact of aid
on growth.
In the context of Japanese foreign aid, it has been commonly argued that the
requirements of loan payment prevent the recipients from investing in ineffective
projects and therefore impose discipline on project management. Accordingly, loans
are more cost-effective than grants. It is possible to understand this mechanism as an
analogy to the work requirements in workfare programs which can be used as selfsorting and incentive enhancement device [Besley and Coate (1992)]. Of course, there
is an opposite disincentive effect of loan particularly when the amount of outstanding
debt becomes excessive. This is known as the debt overhang problem in the sovereign
debt literature [Krugman (1988)]. Our results suggest that the former incentive effect
5
dominates the latter disincentive effect in the context of foreign aid.1
Interestingly, the coefficients on the technical assistance (TA) indicate that aid in
the form of TA enhance economic growth provided that policies of the recipient
countries are sufficiently sound [specifications (7) and (8) of Table 2]. For example, if
the level of the policy variable is above two, then the technical assistance will increase
per capita real GDP growth rate definitely.
1
To further investigate the effectiveness of loans, we also included the lagged variable of loans.
Loans are typically used for infrastructure projects so the impacts of loans realize with time lags.
The fact that data of loans are disbursement-based might also introduce the lagged effects. The
results which are not reported here are also consistent with the results of Table 2.
6
Structural Change in the 1990’s
Surprisingly, once we limit the sample between 1970 and 1993, the positive
effects of loans disappear. Alternatively, the coefficient on the grant-policy interaction
becomes statistically positively significant [specification (4) of Table 4]. This implies
that unless the policy variable is negative, aid in the form of grant will increase
economic growth rate.
The specification (8) of Table 4 indicates that the interaction term of squared
technical assistance (TA) and policy has a positive coefficient, showing that TA can
increase economic growth when policies of the recipient countries are sufficiently sound.
The change from the results of Table 2 to those of Table 4 implies that there may
be a structural change of the effectiveness of foreign aid in the late 1990s. In this period,
loans became more effective than grant in promoting economic growth. This change is
parallel to the shift of the focus in foreign aid from macroeconomic stabilization to
poverty reduction.
4. Discussions
In this paper, we augment the framework of BD and ELR, which investigate the
aid and growth nexus, by decomposing the aid variable into loan and grant. By
employing the augmented framework, we found that loans seem to have growth effects
in the extended period whereas grants may be effective in the shorter data. Technical
assistance might be growth-enhancing provided that policies of the recipient country are
appropriate. Our results suggest that the seemingly disappearence of the effectiveness
of aid on growth is generated since the ELR model combines different effects of loans
and grants.
Then the relevant question is: how can we implement the policy implications of
our empirical results? In reality, effective aid should satisfy two conditions. The first
condition is that the recipients utilize the received aid to enhance productivity of the
economy. Yet, a very influential paper by Boone (1996) found that aid has no effects on
investment and human development indicators, while aid does increase the size of
government.
A number of other studies such as Alesina and Weder (2002) also
7
concluded that the aid quantity does not alter the quality of policies of recipient
countries. Alesina and Weder (2002) found that an increase in aid is likely to increase
corruption, probably because an unexpected transfer will induce rent-seeking activities.
Second, donors should allocate foreign aid to the countries which can utilize
the aid effectively and efficiently. In fact, there is a substantial controversy over the
motivation behind aid provisions. Aid donor countries may be concerned with such
issues as mutual benefits, potential economic and political benefits for themselves,
poverty reduction, equity, and international security. Based on statistical tests of a
rigorous theoretical model of ODA, Trumbull and Wall (1994) found that foreign aid
allocations are determined by the needs of the recipients represented by infant morality
and political-civil rights. Sawada and Yamada (2003) applied the poverty targeting
framework of Besley and Kanbur (1988) based on the Foster=Greer=Thorbecke (1984)
poverty measure to investigate poverty reduction effects of aid provisions. They found
that in the late 1990s, grant allocations of Japan, the Netherlands, U.K., Canada,
Norway, and Sweden as well as aid allocations of mutilateral institutions, were
consistent with the necessary condition of optimal poverty targeting.
Yet, there are the recent studies which found that donor countries largely seem
to be motivated by strategic considerations, rather than the altruism or real needs of the
receving countries [Alesina and Dollar (2000)], confirming findings by Maizels and
Nissanke (1984). Collier and Dollar (2002) also support this view, finding that the
actual aid allocation is far from efficient in terms of poverty reduction. Moreover,
Aelsina and Weder (2002) documents that there is no evidence that donors allocate
more aid to less corrupt governments. Interestingly, Lahiri and Raimondos-Moller
(2000) argues that lobbying by ethinc groups in the donor country enhances aid
provisions to its country of origin.
2
These realistic aspects should be taken into account when we operationalize the
implications of the aid-growth regressions.
2
On the other hand, motivation for multilateral aid can be said to be more transparent.
Multilateral agencies are largely apolitical and more exclusively concerned with development
and/or poverty reduction [Cassen et al. (1994); Maizels and Nisanke (1984); Frey and Schneider
(1986); Sawada (1996); Sawada and Yamada (2003)].
8
References
Alesina, A. and Dollar, D. (2000) “Who Gives Foreign Aid to Whom and
Why?“ Journal of Economic Growth Vol. 5, 33-64 March.
Alesina, A. and Weder, B. (2002) “Do Corrupt Governments Receive less Foreign Aid?”
American Economic Review 92(4), 1126-1137.
Besley, T． and S． Coate (1992), “Workfare vs．Welfare: Incentive Arguments for
Work Requirements in Poverty Alleviation Programs,” American Economic Review
82(1), 29-261．
Besley, T. and Kanbur, R. (1988) “Food subsidies and poverty alleviation,” Economic
Journal Vol. 98, 701-719.
Boone, P. (1996) “Politics and the effectiveness of foreign aid,” European Economic
Review 40: 289-329.
Burnside, C. and Dollar, D. (2000) “Aid, Policies, and Growth,” American Economic
Review Vol.90, 847-868.
Burnside, C. and Dollar, D. (1998) “Aid, the Incentive Regime, and Poverty
Reduction,” Policy Research Working Paper 1937. World Bank, Development
Research Group, Washington, D.C..
Cassen, Robert and Associates (1994), Does Aid Work? - Report to an Intergovernmental Task Force, Second Edition, Oxford University Press.
Chang, Charles, C. and Eduardo Fernandez-Arias, and Luis Serven (1998), Measuring
Aid Flows: A New Approach,” Policy Research Working Paper # 2050, DECRG, the
World Bank.
Collier, P. and Dollar, D. (2002) “Aid Allocation and Poverty Reduction,” European
Economic Review 46, 1475-1500.
Dowling, J.M. and Heimenz, U. (1985) “Biases in the allocation of foreign aid: some
new evidence,” World Development Vol.13: 535-541.
Dudley, L. and Montmarquette, C. (1976) “A model of the supply of bilateral foreign
aid,” American Economic Review Vol.66:132-142.
Easterly, William, Ross Levine, and David Roodman (2003), “New Data, New Doubts:
Revisiting “Aid, Policies, and Growth,” Center for Global Development Working Paper
26．
Foster, J., Greer, J. and Thorbecke, E. (1984) “A class of decomposable poverty
9
measures,” Econometrica Vol52, 761-766,1984.
Kohama, H. (1995) “Japan's Development Cooperation and Economic Development in
Asia.” in Takatoshi Ito and A. O. Krueger, eds., Growth Theories in Light of the East
Asian Experience, University of Chicago Press, 201-226.
Krugman, P． R． (1988), “Financing vs． Forgiving a Debt Overhang,” Journal of
Development Economics 29, 253-268．
Lahiri, Sajal and Pascalis Raimondos-Møller (2000), “Lobbying by Ethnic Groups and
Aid Allocation,” Economic Journal 110, 62-79.
Maizels, A. and Nissanke, M. (1984) “Motivations for Aid to Developing Countries,”
World Development 12:879-900.
OECD (2001) International Development Statistics (CD-ROM).
Sawada,Y. (1996) “Aid and Poverty Alleviation: An International Comparison,” IDS
Bulletin 27, January 100-108.
Sawada, Y. and H. Yamada (2003), “Is Aid Allocation Consistent with Poverty
Indicators? A Cross-Donor Comparison,” mimeo, Faculty of Economics, University of
Tokyo.
Trumbull,W. N. and Wall, H. J. (1994) “Estimating Aid-Allocation Criteria with Panel
Data,” Economic Journal 104:876-882.
Wall, H. J. (1995) “The Allocation of Official Development Assistance,” Journal of
Policy Modeling 17(3): 307-314.
World Bank (1996), Annual Report, the World Bank.
World Bank (1998), Assessing Aid: What Works, What Doesn’t, and Why, the World
Bank.
World Bank (2001) World Development Indicators 2001. (CD-ROM).
World Bank (2002), Poverty Monitoring, the World Bank.
(http://www.worldbank.org/research/povmonitor/)
10
Table 1
Summary Statistics for the ELR Model
Sample period: 1970-1997
Variable name
GDP/capita growth
ln(initial GDP)
Policy
Ethnic Frac.
Assassinations
Ethnic*Assas.
Institution
M2/GDP
Aid
Loan
Grant
Technical Assistance
Obs
Mean
344
344
344
344
344
344
344
344
344
344
344
344
1.353
7.499
1.423
0.468
0.492
0.189
4.324
26.175
1.189
0.366
0.823
0.463
Std. Dev.
3.435
0.759
1.048
0.297
1.274
0.606
1.543
14.478
1.478
0.490
1.184
0.537
Min
Max
-12.693
5.429
-4.740
0.000
0.000
0.000
1.580
4.580
-0.049
-1.224
-0.002
0.009
10.076
9.339
3.720
0.900
11.500
7.360
8.140
120.308
7.312
2.382
6.696
2.831
11
Table 2. Reconsideration of the ELR Model
Sample period: 1970-1997
ln(initial GDP)
Policy
Ethnic Frac.
Assassinations
Ethnic*Assas.
Institution
M2/GDP
Sub-Saharan
Africa
East Asia
Aid
Replication of
ELR
(1)
(2)
-0.42
-0.37
(-1.12)
(-0.96)
1.04***
1.26***
(5.25)
(5.54)
-0.01
0.03
(-0.02)
(0.04)
-0.34
-0.37
(-1.34)
(-1.45)
0.12
0.20
(0.19)
(0.31)
0.32**
0.32***
(2.63)
(2.56)
0.00
0.00
(0.23)
(0.21)
-1.61*** -1.65***
(-2.92)
(-2.96)
1.35***
(2.62)
-0.04
(-0.25)
Aid*Policy
1.12**
(2.18)
0.24
(0.83)
-0.18
(-1.24)
Loan
(3)
-0.38
(-1.01)
1.05***
(5.34)
-0.02
(-0.03)
-0.32
(-1.27)
0.10
(0.16)
0.33***
(2.69)
0.00
(0.27)
-1.64***
(-2.95)
(4)
-0.38
(-0.98)
1.19***
(5.49)
0.09
(0.13)
-0.34
(-1.31)
0.15
(0.23)
0.33**
(2.60)
0.00
(0.06)
1.63***
(-2.93)
1.28**
(2.51)
(5)
-0.36
(-0.90)
1.17***
(5.17)
0.08
(0.12)
-0.33
(-1.27)
0.13
(0.21)
0.33**
(2.57)
0.00
(0.00)
1.62***
(-2.80)
1.32**
(2.48)
(6)
-0.39
(-1.02)
1.05***
(5.32)
-0.03
(-0.04)
-0.32
(-1.26)
0.10
(0.16)
0.33***
(2.68)
0.00
(0.26)
-1.60***
(-2.68)
(7)
-0.42
(-1.09)
1.07***
(5.00)
0.10
(0.15)
-0.34
(-1.31)
0.14
(0.22)
0.33***
(2.66)
0.00
(0.34)
-1.40**
(-2.32)
(8)
-0.52
(-1.35)
1.44***
(5.74)
-0.12
(-0.18)
-0.35
(-1.29)
0.14
(0.21)
0.28**
(2.27)
0.01
(0.48)
-1.09*
(-1.84)
1.34**
(2.60)
1.34**
(2.56)
0.96*
(1.82)
0.34
(0.66)
1.29*
(1.67)
-0.72*
(-1.72)
0.35
(0.65)
1.57*
(1.94)
-0.88**
(-2.03)
-0.14
(-0.73)
-0.18
(-0.44)
0.02
(0.11)
1.40*
(1.69)
-0.54
(-1.43)
-0.20
(-0.79)
-0.20
(-0.47)
0.06
(0.21)
0.00
(-0.05)
-0.13
(-0.50)
0.54
(0.96)
-0.40
(-1.38)
-0.08
(-0.12)
-2.25**
(-1.97)
1.25**
(2.17)
344
0.33
344
0.35
1.74*
(1.95)
-0.73*
(-1.70)
-0.25
(-1.01)
0.28
(0.52)
0.08
(0.18)
-0.06
(-0.88)
-2.08*
(-1.96)
-1.07
(-1.32)
1.02***
(3.18)
344
0.37
1.34***
(2.62)
Loan*Policy
Loan2*Policy
Grant
Grant*Policy
Grant2*Policy
Technical
Assistance(TA)
TA*Policy
TA2*Policy
Observations
R-squared
344
0.33
344
0.33
344
0.33
344
0.34
344
0.34
Notes: The dependent variable is real per capita GDP growth. We present t-statistics in the
parentheses.
* Significant at 10-percent level.
** Significant at 5-percent level.
12
Table 3
Summary Statistics for the BD-ELR Model
Sample period: 1970-1993
Variable
GDP/capita growth
ln(initial GDP)
Policy
Ethnic Frac.
Assassinations
Ethnic*Assas.
Institution
M2/GDP
Aid
Loan
Grant
Technical Assistance
Obs
Mean
268
268
268
268
268
268
268
268
268
268
268
268
1.328
7.535
1.346
0.459
0.465
0.181
4.396
24.210
1.027
0.366
0.662
0.438
Std. Dev.
3.477
0.711
1.280
0.300
1.265
0.614
1.519
11.393
1.285
0.486
0.952
0.521
Min
Max
-12.693
5.743
-3.910
0.000
0.000
0.000
1.580
4.580
-0.049
-1.224
-0.002
0.009
10.076
9.339
3.720
0.900
11.500
7.360
8.140
81.641
6.682
2.382
4.607
2.831
13
Table 4. Reconsideration of the BD-ELR Model
Sample period: 1970-1993
ln(initial GDP)
Policy
Ethnic Frac.
Assassinations
Ethnic*Assas.
Institution
M2/GDP
Sub-Saharan
Africa
East Asia
Aid
Aid*Policy
Replication of
BD-ELR
(1)
(2)
-0.04
-0.19
(-0.08)
(-0.41)
1.00***
0.70***
(5.73)
(3.86)
-0.53
-0.50
(-0.73)
(-0.66)
-0.51*
-0.54*
(-1.82)
(-1.69)
0.82
0.92*
(1.83)
(1.60)
0.34**
0.37***
(2.56)
(2.72)
0.02
0.02
(1.09)
(1.40)
-1.53**
-1.45**
(-2.28)
(-2.39)
0.79
1.17**
(1.34)
(1.98)
0.23
-0.20
(1.03)
(-0.64)
0.35**
(2.39)
Loan
(3)
-0.07
(-0.17)
0.99***
(5.66)
-0.53
(-0.71)
-0.56*
(-1.84)
0.93*
(1.84)
0.34**
(2.53)
0.02
(1.10)
-1.49**
(-2.37)
0.79
(1.33)
(4)
-0.19
(-0.40)
0.72***
(3.94)
-0.39
(-0.52)
-0.54*
(-1.75)
0.85
(1.65)
0.37***
(2.66)
0.02
(1.07)
-1.57**
(-2.47)
1.28**
(2.14)
(5)
-0.22
(-0.47)
0.78***
(4.25)
-0.51
(-0.67)
-0.53*
(-1.71)
0.84
(1.62)
0.33**
(2.37)
0.02
(1.17)
-1.42**
(-2.19)
1.13*
(1.89)
(6)
-0.08
(-0.17)
1.02***
(5.74)
-0.55
(-0.73)
-0.58*
(-1.89)
0.97*
(1.91)
0.34***
(2.63)
0.02
(1.02)
-1.18*
(-1.75)
0.72
(1.20)
(7)
-0.23
(-0.51)
0.71***
(3.79)
-0.42
(-0.55)
-0.55*
(-1.79)
0.87*
(1.68)
0.37***
(2.70)
0.02
(0.96)
-1.04
(-1.50)
1.25**
(2.08)
(8)
-0.34
(-0.71)
0.97***
(4.75)
-0.44
(-0.59)
-0.56*
(-1.81)
0.86*
(1.69)
0.32**
(2.34)
0.02
(0.88)
-0.92
(-1.37)
0.92
(1.54)
-0.07
(-0.13)
0.48
(0.59)
-0.19
(-0.57)
0.01
(0.02)
0.92
(1.03)
-0.39
(-1.06)
0.35
(1.18)
-0.40
(-0.85)
0.48***
(2.68)
0.56
(0.65)
-0.05
(-0.13)
-0.08
(-0.83)
-0.64
(-1.25)
0.10
(0.29)
0.16
(1.52)
0.60
(1.52)
0.19
(0.32)
0.23
(0.72)
-0.78
(-1.00)
-2.01
(-1.61)
0.68
(1.21)
268
0.39
268
0.41
1.10
(1.15)
-0.42
(-1.05)
-0.06
(-0.44)
-0.04
(-0.06)
0.71
(1.40)
-0.10
(-0.61)
-1.94
(-1.57)
-1.40
(-1.58)
0.98**
(2.38)
268
0.42
Loan*Policy
Loan2*Policy
Grant
Grant*Policy
Grant2*Policy
Technical
Assistance(TA)
TA*Policy
TA2*Policy
Observations
R-squared
268
0.38
268
0.39
268
0.38
268
0.40
268
0.40
Notes: The dependent variable is real per capita GDP growth. We present t-statistics in the
parentheses.
* Significant at 10-percent level.
** Significant at 5-percent level.
14