Download Foreign Aid and the Dutch Disease: Evidence

Foreign Aid and the Dutch Disease: Evidence from Sierra Leone
(For Presentation at the CSAE Conference 2014: Economic Development in Africa, Center for
the Study of African Economies, University of Oxford, Oxford, UK)
By Kelfala M. Kallon
Department of Economics
University of Northern Colorado
Greeley, CO 80639
(970) 351-2134
Email: [email protected]
Abstract: This paper investigated the long-run relationship between foreign aid, the real
exchange rate, the trade balance, and economic growth in Sierra Leone. It found no support for
the Dutch Disease hypothesis of an inverse relationship between foreign aid and economic
growth in aid-recipient countries. Supporting the Balassa-Samuelson hypothesis, it also found a
positive long-run relationship between labor productivity growth, the real exchange rate, the
trade balance, and economic growth. A key implication of these findings is that even though
foreign-aid is associated with long-run economic growth in Sierra Leone, its negative impact on
the real exchange rate lowers the real income of cash-crop farmers, which raises the level of
poverty and income inequality in the country. To reverse this outcome (of economic growth and
increased poverty and income inequality), Sierra Leonean authorities should be using foreign aid
resources and mineral rents to invest in the nation’s human capital stock and infrastructure,
thereby leading to improvements in labor productivity. Labor productivity growth would lead to
a real appreciation of the leone and economic growth. The stronger leone would then raise
producer incomes, thereby reducing the level of rural poverty and income inequality.
KEYWORDS: Foreign Aid; Dutch Disease; Balassa-Samuelson Hypothesis; Real Exchange
Rates; Sierra Leone
JEL CLASSIFICATIONS: F31, F35, O55.
1
I.
Introduction
The Dutch Disease hypothesis holds that when foreign aid is spent, even partially, on nontraded goods, the currency of the aid-recipient country appreciates in real terms. This then makes
its exports less competitive on world markets. As a result, exports fall and imports increase, thus
worsening the trade balance. The result is a fall in aggregate demand and, consequently,
equilibrium income (Van Wijnbergen, 1984, 1985).1 Additionally, as imports become relatively
less expensive following the real appreciation of the exchange rate, they displace domestically
produced import-competing goods, which can lead to deindustrialization.
Even if foreign aid causes a real appreciation of the recipient country’s currency, as
suggested by the Dutch Disease hypothesis, the conclusion that this adversely impacts its export
sector might not be true for all small open economies. Firstly, such economies are price takers on
world markets. Secondly, world market prices of primary commodities are set in the major
international currencies, not in those of the small open economies. Hence, the real exchange rate
does not impact the foreign-currency prices of their exports. For example, if the world-market
price of coffee is $500 per ton, Starbucks will pay $500 per ton for Sierra Leonean coffee
regardless of the leone-dollar real exchange rate. Therefore, the leone-dollar real exchange rate
will have no impact on Starbuck’s decision-making about how much Sierra Leonean coffee it
purchases.
Real currency appreciations however raise the real income of exporters and lower the
relative costs of imported inputs. This creates incentives for increased exports. For instance, at
any world market price of coffee, coffee farmers in Sierra Leone would receive more imported
1
In general, the negative impact on a country’s economy of anything that causes a huge inflow of foreign
currency can cause the Dutch Disease phenomenon. Thus, in addition to foreign aid, worker remittances and natural
resource exploitation can also inflict the Dutch Disease on a country.
2
goods per ton of coffee following a real appreciation of the leone. This exchange-rate-induced
real income effect would then create incentives for more coffee exports, not less. Furthermore,
on the cost side, a real exchange rate appreciation lowers the relative prices of imported inputs
(imported crude oil, fertilizer, and agricultural implements, for instance), which consequently
lowers production costs and increases per-unit profit. The reduced cost of imported inputs could
actually give the domestic import-competing industry a cost advantage, thereby increasing
production in the domestic import-competing sector. Therefore, contrary to the Dutch Disease
hypothesis, real exchange rate appreciations could actually improve the trade balance of a small
open economy.
However, small open economies that are dependent on foreign direct investment (FDI)
could be adversely affected by real exchange rate appreciations. This is because an appreciation
of the host country’s currency raises the relative price of domestic inputs, thereby making the
host country less attractive for foreign investment. Thus, if the supply elasticity of the FDIintensive export sector is greater than that of the small-farmer-produced export crops, which is
typically the case, a real exchange appreciation could reduce FDI flows, cause the trade balance
to deteriorate, and lower real output.
Finally, if the foreign-aid resources are expended on export-capacity expansion, the resultant
export-led boost to aggregate supply would lower the recipient country’s price level and raise its
real output. At the existing world export prices, the consequent increase in export volume would
increase export revenues and cause the domestic currency to appreciate. Domestic deflation, on
the contrary, would cause a real exchange rate depreciation. Finally, increases in domestic real
income would raise import demand and also cause a real depreciation of the domestic currency.
3
Table 1: Net ODA
As a Percentage of
Period
As a Percentage of GDP
Government Expenditures
1975-1979
3.1234
36.4956
1980-1989
8.7236
117.3996
1990-1999
18.2476
170.9770
2000-2005
34.6575
228.1825
1974-2005
15.5124
139.7453
Source: Author’s Calculations based on the World Development Indicators Dataset.
It should be clear from the above that the impact of foreign aid on the recipient country’s
real exchange rate of a small open economy and the effect of the latter on the trade balance and
real output cannot be determined a priori. Furthermore, because poverty reduction is a key
objective of foreign aid, the latter’s impact on the levels of poverty and income inequality is also
germane to the usefulness or lack thereof of foreign aid. Accordingly, this paper uses data from
Sierra Leone to empirically investigate the impact of foreign aid on the real exchange rate, the
trade balance, and real GDP growth. The implications of its findings on the levels of poverty and
income inequality in that country are also discussed.
The choice of Sierra Leone is justified by its historic dependence on foreign aid, which is
demonstrated in Table 1, which shows net ODA rising from a low 3.12 percent of GDP per year
between 1974 and 1979 to a high of 34.66 percent per year in the 2000-2005 period. For the
entire 1974-2005 period, net ODA constituted approximately 15.5 percent of GDP per year.
Data on net ODA as a percentage of government expenditures demonstrate the country’s aid
dependence even more glaringly. Constituting nearly 36.5 percent of government spending per
year in the 1974-1979 period, foreign aid as a percentage of government expenditures
skyrocketed to approximately 117.4 percent, 180 percent, and 228.2 percent in the 1980-1989,
4
1990-1999, and 2000-2005 periods respectively. For the entire 1974-2005 period, net ODA
accounted for approximately 139.75 percent of government spending per year.
The rest of the paper is organized as follows. The empirical literature on the Dutch Disease
phenomenon is surveyed in Section II. A model of the relationship between the real exchange
rate, the trade balance, and real output in small open economies is developed in Section III and
estimated in Section IV using Johansen’s maximum-likelihood procedure. Key findings of the
study and their policy implications are then presented in Section V.
II. A Survey of the Literature
While there have been several empirical studies on the determinants of the real exchange
rate in developing countries, those on the Dutch Disease in SSA have been scanty and have
produced mixed results. For example, Harvey (1992) found that Botswana’s diamond export
boom did not cause the Dutch Disease phenomenon in that country. However, Falck (1997)
found evidence of the phenomenon in Tanzania. This was contradicted by Nyoni (1998) and Li
and Rowe (2007). In West Africa, Sackey (2002) and Sanusi (2011) found support for the
phenomenon in Ghana while Ogun (1995) found no such effect in Nigeria.
In a cross-country study of twelve CFA Franc countries,2 Ouattara and Strobl (2008) found
no support for the phenomenon. In contrast, Adenauer and Vagassky (1998), Elbadawi (1999),
Elbadawi, Kaltani, and Soto (2009), and Fielding and Gibson (2012) found support for it among
a cross-section of SSA countries. Fielding and Gibson (2012) also found that “hard” fixed
exchange rate regimes have larger Dutch Disease effects than flexible exchange rate regimes.
In Asia, White and Wignaraja (1992) found that increases in aid and remittances caused a
real appreciation of the Sri Lankan currency. And on the general issue of private capital flows,
2
The CFA franc is a currency used by 12 formerly French colonies in West and Central Africa, whose value
was previously fixed against the French franc but is now fixed against the euro.
5
which is more important than foreign aid to Asia and Latin America, Athukorala and
Rajapatirana (2003) found increases in FDI associated with a depreciation of the real exchange
rate while other capital flows have the opposite effect. Meanwhile, Rajan and Subramanian
(2011) found support the Dutch Disease in a cross-section of developing countries. Finally,
worker remittances were shown to have Dutch Disease effects in Cape Verde (Falck and
Bourdet, 2006), Guatemala (Fuentes and Herrera, 2007), and Latin America in general (Lopez,
Molina, and Bussolo, 2007).
Like the empirical literature on the Dutch Disease, the empirical literature on the
relationship between foreign aid and real GDP growth is also quite mixed and unsettled. For
instance, in a meta-analysis of the existing literature at the time, Tsikata (1998) concluded that,
though contentious, the plurality of the evidence suggests an insignificant relationship between
aid and economic growth. Hansen and Tarp (2001) contrarily found the literature increasingly
being supportive of a positive relationship between the variables. Doucouliagos and Paldam
(2008) countered with a meta-analysis that again suggested the insignificance of the aideconomic growth relationship. Additionally, they concluded that the aid-economic growth
relationship becomes positive only when Asian countries are included in the sample. This
suggests that regional differences also matter. However, after reviewing the same studies in
Doucouliagos and Paldam (2008), Mekasha, and F. Tarp (2013) reported a positive relationship
between aid and economic growth. They further claimed that the insignificant findings by
Doucouliagos and Paldam (2008) had been based on the methodological error of assuming fixed
effects in the panel studies when the heterogeneity of recipient country characteristics make the
random effects assumption more appropriate.
6
From the experience of both post-World War II Europe and South Korea, we know that
foreign aid can foster economic growth if the capacity to use it effectively exists in the recipient
country or region. This suggests that cross-country studies of countries with different aid-using
capacities are bound to provoke more disagreement than agreement in view of the fact that they
lump together countries with different policies, histories, and capacities. Thus, individual country
studies are perhaps more appropriate because they are not polluted by the inclusion of countries
with disparate characteristics in the same sample.
The aid economic growth relationship also suffers from an endogeneity problem because if
poverty alleviation is a key objective of foreign aid, slow-growth countries would receive more
foreign aid than their high-growth counterparts. Also, the more an open economy, the more
foreign investment it is likely to attract. With foreign investment comes technology transfers,
which increase labor productivity, the real exchange rate, the trade balance, and real output.
Hence, the single-equation estimation techniques that have been used in previous studies suffer
from simultaneity bias. This calls for a multi-equation estimation in order to avoid that bias.
Finally, although Sierra Leone is hugely aid-dependent, I know of only one study (Kargbo,
2012) that has investigated the aid-economic-growth relationship in that country. That study
found a positive long-run relationship between foreign aid and economic growth in Sierra Leone.
Given the dependence of Sierra Leone on foreign aid, this study will be a valuable addition to
this virtually non-existent literature on a subject that is very crucial to the Sierra Leonean
economy.
III. The Empirical Model
A. The Long-Run Real Exchange Rate
The existing literature holds that the long run real exchange rate in developing economies
7
depends mainly on real variables—the terms-of-trade, the level and composition of real
government expenditures, trade and foreign-exchange controls, technical progress, market access
and institutional quality (De Gregorio and Wolf, 1994; Edwards, 1988; Li and Francis, 2007;
Elbadawi, 1994; Elbadawi and Soto, 1997; and Wong, 2009). The Dutch Disease hypothesis
adds foreign aid to that mix. Because how foreign aid impacts the real exchange rate has already
been discussed, the rest of this section will focus on its other determinants.
1.
Labor Productivity
Balassa (1964) and Samuelson (1964) demonstrated that labor productivity growth causes
the domestic currency to appreciate in real terms. Starting in the traded goods sector, labor
productivity growth initially causes a wage increase in that sector. Eventually, wages will increase
in the non-traded-goods sector also in order to equilibrate the aggregate labor market. This
increases production costs and the domestic price level. Consequently, the domestic currency
appreciates in real terms.
Labor productivity growth could also cause a real depreciation of the currency of a small
open economy if its citizens spend most of their productivity-induced income increases on
imported goods. Therefore, under the small-country assumption, labor productivity growth
would have an ambiguous effect on the real exchange rate.
2. Trade Openness
A positive relationship supposedly exists between trade openness, total factor productivity
growth, and economic growth (Barro, 1997; Dollar, 1992; Edwards, 1993 and 1998; Harrison,
1996; Mankiw, Romer, and Weil, 1992; and Yanikkaya, 2003). The resultant domestic
prosperity leads to increased imports, which then causes the domestic currency to depreciate in
both nominal and real terms.
8
Free trade involves exporting commodities in which a country’s firms have comparative
advantage and importing those in which they have a comparative disadvantage. Therefore, trade
openness should increase both imports and exports, thereby having an ambiguous effect on the
real exchange rate.
Trade openness also enhances a country’s export and/or import-substitution capacity
through technology transfers by multinational companies (MNCs). Under the small-country
assumption, the resultant export expansion and/or import reduction should cause an appreciation
of the domestic currency. The bottom line, therefore, is that the net effect of trade openness on
the real exchange rate of small open economies cannot be determined a priori.
3.
Foreign Incomes
Foreign income growth increases the demand for the domestic-economy’s exports, thereby
leading to a real appreciation of its currency. Additionally, economic prosperity in the developed
countries creates a favorable political climate for foreign aid and private capital flows to the
developing countries. Consequently, the increased demand for the domestic economy’s exports
and increased capital inflows should cause a real appreciation of the domestic currency.
4.
Government Expenditures
Government expenditures are by nature skewed toward the non-traded goods sector. Hence,
they initially raise wages and prices in that sector. The resultant reallocation of resources away
from the traded-goods to the non-traded-goods sector causes a reduction in exports, which causes
a real currency depreciation. Additionally, government spending is usually financed by monetary
expansion in much of Sub-Saharan Africa, which causes lower domestic interest rates, increased
capital outflows, and a real depreciation of the domestic currency. However, regardless of how it
is financed, government spending also raises aggregate demand and, consequently, the price
9
level and the interest rate. The resultant domestic inflation and capital inflows then causes a real
appreciation of the domestic currency. In other words, the net impact of government
expenditures on the real exchange rate cannot also be determined a priori.
5.
The Terms of Trade
Improvements in the terms of trade raise producer incomes in small open economies. This
incentivizes them to allocate productive resources to the export sector. Under the small-country
assumption, the increase in exports should cause a real appreciation of the domestic currency.
However, if domestic residents have a substantial taste bias for imported goods, improvements in
the terms of trade could disproportionately increase import demand, thereby causing a real
depreciation of the domestic currency. Consequently, the net impact of an improvement in the
terms of trade on the real exchange rate depends on the relative magnitudes of its supply-side
impact on export expansion and demand-side impact on imports.
Based on the above, the following is the long-run equilibrium behavioral equation for the
real exchange rate (R):3
?
?
?
+
?
?
 ?

R = R  NODA, LPROD, OPEN, GOV, YF, TOT, Z  ;


(1)
where NODA = net foreign aid flows; LPROD = labor productivity; OPEN = trade openness;
YF = foreign income; GOV = share of government expenditures in GDP, TOT = the terms of
trade, and Z = all other factors that impact the real exchange rate.4
3
A question mark above a variable denotes that its expected impact on the real exchange rate is ambiguous
while the plus denotes a positive impact.
4
This is a proxy for such things as the absence of tariffs against the country’s exports, the quality of its
institutions, its regulatory framework, the level and quality of its infrastructure, and the knowledge and skill base of
its workers.
10
B.
The Trade Balance
Neoclassical economics assumes the trade balance being positively related to foreign
incomes (YF) and inversely related to the real exchange rate (R) and domestic income (Y). The
above discussion however suggests that this may not hold for small open economies. On the one
hand, real exchange rate appreciations raise the per-unit profit of domestic producers by
increasing export revenues and lowering the domestic currency prices of imported inputs. This
should lead to export growth and an improvement in the trade balance. On the other hand, real
currency appreciations raise production costs in FDI-intensive activities, thereby making the
country relatively uncompetitive as a host for FDI. As a result, its exports suffer. Moreover,
imports become relatively cheaper when a country’s currency appreciates in reals terms.
Resultantly, imports increase, thereby putting downward pressure of the trade balance. Finally,
real currency appreciations also lower the domestic-currency prices of imported inputs, which
gives domestic producers of import-competing goods a cost advantage, thereby lowering
imports. Thus, the real exchange rate’s impact on the trade balance of small open economies is
ambiguous. Hence, the determinants of the former would also ambiguously impact the trade
balance, as suggested below in Equation (2):
?
?
?
?
?
?
−
? ? −
 ?

TB = F  R, YF, Y  = F  NODA, LPROD, OPEN, GOV, TOT, Z, YF, Y  ;




(2)
C. Real Output
In the Harrod-Domar growth model (Harrod, 1939; and Domar 1946), economic growth in
the developing countries is assumed to be inhibited by low savings, which limits investment in
both physical and human capital. Thus, foreign resources (in the form of aid, foreign investment
and loans) are necessary to close the savings gap. Solow’s growth model (Solow, 1956), on the
other hand, gave prominence to technical progress as the key driver of long-run economic
11
growth. However, technical progress was assumed to be exogenous. Therefore, regardless of
their domestic savings rates, developing countries could get new technology through
multinational corporations (MNCs)—which invest in the developing countries in order to take
advantage of their relatively higher rates of return on capital. Thus, even if foreign aid is not
available, economic growth would continue and convergence in living standards would occur if
free trade and free capital movements are allowed.
Finally, the endogenous growth model identified investment in knowledge, skills, and
infrastructure as the key determinants of technical progress, something that the Solow model
considered as exogenous. Thus, countries that invest in knowledge, infrastructure, and physical
capital grow faster than those that can’t or won’t. And because of low domestic savings,
developing countries can only adequately invest in these assets through foreign resources (aid,
technical assistance, loans and investment). This links foreign aid directly to economic growth in
the developing countries.
Rebelo’s AK model (Rebelo, 1991) best describes the aid-growth relationship in the spirit
of the endogenous growth model:5
Y = AK;
(3)
where A = total factor productivity and K = capital stock. Assuming that investment in a
developing country is mostly financed with foreign resources (foreign aid and foreign private
capital flows)—because of low or non-existent domestic saving—creates a direct relationship
between foreign resources and economic growth in developing countries. Furthermore, because
real exchange rate appreciations raise the cost of foreign investment in the host country, foreign
private capital flows are assumed to be inversely related to it. In sum, therefore, a developing
5
The AK model is a special class of the Cobb-Douglas production function in which the elasticities of output
are respectively 1 and zero for capital and labor.
12
country’s capital stock should depend positively on net foreign aid flows (NODA), negatively on
the real exchange rate, and positively on other exogenous factors (Z):
+
−
+
(4)
K = K(NODA, R, Z).
Substituting R in Equation (4) with the right-hand-side expression in Equation (1) yields the
following relationship between real output and its determinants:
− +
?
?
?
+
?
+
 +

 ?

Y = AK  NODA, R, Z  = AK  NODA, LPROD, OPEN, GOV, YF, TOT, Z  .




(5)
Equations (1), (2), and (5) can be restated in the following reduced-form long-run
equilibrium equations:
β11 R t -β12 NODAt -β13 LPRODt -β14 OPENt -β15 GOVt -β16 TOTt -β17 YFt -β18 Zt 06
β21 TBt -β22 NODAt -β23 LPRODt -β24 OPENt -β25 GOVt -β26 TOTt -β27 YFt -β28 Zt 0.7
β31 Yt -β32 NODAt -β33 LPRODt -β34 OPENt -β35 GOVt -β36 TOTt -β37 YFt -β38 Zt 0.8
The above equations represent a structural VAR model in which β11, β21, β31 are restricted to
1 and the coefficients of Rt, TBt, and Yt are restricted to zero in each other’s equation. If the
variables are cointegrated, the rank of their cointegrating matrix should be 3. In other words,
each βij in the last n-3 equations is assumed to be zero. Finally, under the Dutch Disease
hypothesis, β12 should be positive while β22 and β32 are negative. Additionally, the sign of each
βij in the real exchange rate equation should be the opposite of its corresponding coefficient in
the other two equations. Because labor productivity starts in the traded goods sector under the
Balassa-Sameulson hypothesis, it should also have a positive impact on net exports (the trade
balance) and equilibrium output in countries like Sierra Leone whose traded-goods activities are
purely export oriented. In other words, β13, β23, and β33 should be positive. However, under the
13
neoclassical assumptions of the Dutch Disease hypothesis, if β13 is positive, β23, and β33 should
be negative, and vice versa. Thus, the two hypotheses are mutually exclusive.
IV. Data, Estimation, and Results
A. Data
Annual data spanning 1974 and 2005 are used in the empirical analysis. The World Bank’s
World Development Indicators dataset is the source of net foreign aid flows as a proportion of
GDP. Data on labor productivity are from The Extended Penn World Tables (Marquetti, 2012).
Because there are no continuous data on Sierra Leone’s terms-of-trade for the entire sample
period, the terms of trade index for the Sub-Saharan Africa6 was used as a proxy. Meanwhile, the
index of industrial production for advanced economies was used as a proxy for foreign income.
Because of the lack of continuous time series on Z for Sierra Leone, it was not included in the
VAR. Finally, the January 2014 issue of the International Monetary Fund’s International
Financial Statistics is the source of data for the trade balance,7 unit values of SSA exports and
imports, government expenditures, real GDP, and the index of industrial production for advanced
economies. Finally, Darvas (2012) is the source of the data on the real effective exchange rate.8
In order to account for the impact of the civil war on the dependent variables, a war dummy
(WAR, which is equal to 1 for the 1991-2002 period and zero otherwise) was added to the model
6
This was calculated as the ratio of the unit value of SSA exports to the unit value of its imports)
7
Sierra Leone mostly ran trade deficits (interspersed with a handful of years of trade surpluses) during the
sample period. Therefore, the ratio of exports to imports was used as a proxy for the trade balance. Hence, the
difference of its log is the rate of growth of the trade deficit.
8
The real effective exchange rate was calculated as follows:
w ij
pe 
R i = ∏  i ij  ; where


j=1  p j 
t
n
pi
and
pj
denote Country I’s and Country J’s respective price levels;
eij
is the
bilateral spot exchange rate (defined as the units of Country J’s currency that can be exchanged for a unit of Country
I’s); and
wij is the ratio of the bilateral trade between the two countries. Therefore, an increase in Ri denotes a real
appreciation of Country I’s currency.
14
as a deterministic term. It is expected that its impact on the dependent variables will be negative.
Finally, all the variables were expressed in natural logarithms.
B. Estimation
Equations (6), (7), and (8) are best estimated by Johansen’s maximum-likelihood procedure
for cointegrated VAR models (Johansen, 1988) because it assumes that the variables are
endogenous. However, as Cheung and Lai (1993), Haug, (1996), Gredenhoff and Jacobson
(2001), and Toda (1995) have shown, the Johansen trace test suffers from small-sample bias. As
a remedy, Johansen (2002) developed a small-sample Bartlett correction. This has been shown to
improve the robustness of the Johansen procedure in small samples (Kurita, 2013). Version 2 of
Cointegration Analysis of Times Series (CATS), which is used in this study, has the Barlett
correction.9 Additionally, CATS has a multivariate stationarity test which is based on the
deterministic terms in the cointegrating relations. This makes it superior to the traditional
univariate unit-root tests.
1.
Rank Determination
When individual graphs of the variables were examined, some, but not all, of them show
trends. However, when they are graphed together, they resemble random-walk processes with
drifts. Therefore, if their linear combination is stationary, it is not likely to have a common trend.
Consequently, only a constant was assumed in the cointegrating space. Finally, based on the
Likelihood Ratio test, the Final Prediction Error Criterion, the Schwarz Criterion, and HannanQuinn Information Criterion, a one-period lag was chosen as optimal.10
9
CATS is a procedure in Estima’s Regression Analysis of Time Series (RATS).
10
In contrast, the Akaike Information Criterion identified 3. Monte Carlo simulations have shown that the use
of the likelihood ratio test together with information-criteria tests increase the chances of correctly choosing the
optimal lag (Hatemi-J and Kacker, 2009). This and the small sample size informed the decision on one lag.
15
Table 2: Rank Determination Statistics
NullHypothesis
r=0
Eigenvalue
0.9419
Uncorrected Trace
Trace
P-Value
281.5073
0.0000
Corrected Trace
Trace
P-Value
240.6473
0.0000
r=1
0.8569
193.3147
0.0000
170.1331
0.0008
r=2
0.7773
133.0337
0.0011
120.3208
0.0122
r=3
0.7111
86.4677
0.0245
80.2252
0.0704
r=4
0.5357
47.9792
0.2340
45.5837
0.3199
r=5
0.3922
24.1955
0.4528
23.4971
0.4949
r=6
0.2462
8.7615
0.5605
8.6816
0.5683
Table 2 reports the trace statistics for the cointegrating matrix. The corrected statistics are
smaller than their uncorrected counterparts. This is expected because the Bartlett Correction
imposes a penalty on small samples. The uncorrected trace test identified a rank of 4 at the 0.05
level while the corrected version identified 3. Consequently, a rank of 3 was chosen, meaning
that there are three long-run equilibrium relations between the endogenous variables, as
suggested by the structural model.
2.
The Estimated Long-Run Equilibrium Relationships
The estimated cointegrating vectors (β′) were normalized in terms of the dependent
variables in the structural equations (R, TB, and Y). Identifying restrictions were then placed on
the estimated cointegrating relations in consonance with the structural model. The estimated
coefficients and the absolute values of their t-statistics (in parenthesis), which are reported in
Table 3, show that the estimated coefficients of the trade openness variable are quite
insignificant in both the trade balance and real income equations. A zero-value restriction was
therefore placed on its coefficient in those equations. With a Chi-Square statistic of 0.1288 and 2
degrees of freedom, the null hypothesis could not be rejected. Thus the model was re-estimated
16
Table 3: Estimated Cointegrating Vectors (Unrestricted Model)
R Equation
TB Equation
Y Equation
Coefficient
t-Statistic
Coefficient
t-Statistic
Coefficient
t-Statistic
1.000
--
--
--
--
--
TB
--
--
1.0000
--
--
--
Y
--
--
--
--
1.0000
--
Constant
31.9954
5.5477a
-71.4266
7.5874a
-16.8093
21.7400a
NODA
0.9415
8.3604a
-1.9774
10.7578a
-0.1641
10.8728a
LPROD
-1.1859
-4.0282a
-0.7269
1.5127
-0.3300
8.3601a
OPEN
1.0650
7.3042a
-0.0961
0.4036
-0.0128
0.6546
GOV
-0.0954
-1.3141
0.3537
2.9839a
0.0131
1.3413
a
0.2166
1.7909
R
YF
-3.5402
-3.9237a
9.1191
6.1921
TOT
-2.3615
-3.1849a
8.0448
6.6471a
0.6123
6.1593a
WAR
-0.6764
-4.7586a
2.0020
8.6288a
0.2947
15.4659a
a=significant at the 0.01 level.
Table 4: Estimated Cointegrating Vectors (Restricted Model)
R
TB
Y
Coefficient
t-Statistic
Coefficient
t-Statistic
Coefficient
t-Statistic
1.000
--
--
--
--
--
TB
--
--
1.0000
--
--
--
Y
--
--
--
--
1.0000
--
Constant
32.6598
5.9474a
-71.9253
8.2502a
-16.8853
23.3760a
NODA
0.9476
8.5347a
-1.9672
10.8974a
-0.1630
10.8954a
LPROD
-1.2083
4.1964a
-0.7134
1.5320
-0.3278
8.4961a
OPEN
1.0125
11.2584a
--
--
--
--
GOV
-0.1068
1.5345
0.3729
3.3617a
0.0156
1.7001
YF
-3.6698
4.2410a
9.2821
6.7221a
0.2390
2.0889b
TOT
-2.3664
3.2594a
8.0198
6.8251a
0.6098
6.2631a
WAR
-0.7074
5.3700a
2.0374
9.9450a
0.2996
17.6495a
R
Chi-Square (2) = 0.1288; a=significant at the 0.01 level; b = significant at the 0.05 level.
17
Table 5: Residual Analysis
Test
Statistic
D.O.F
P-Value
LM(1)
62.2028
49
0.0975
LM(2)
49.5208
49
0.4523
Normality
22.3307
14
0.0721
LM(1)
835.9955
784
0.0965
LM(2)
1,604.1139
1,568
0.2572
Serial Correlation
ARCH
Figure 1: Actual and Fitted Dependent Variables
R
TB
5.8
Y
0.5
15.4
5.6
0.0
15.3
5.4
-0.5
5.2
-1.0
5.0
-1.5
4.8
-2.0
4.6
-2.5
4.4
1975
-3.0
1975
15.2
15.1
1980
1985
1990
Actual
1995
Fitted
2000
2005
15.0
14.9
1980
1985
1990
Actual
1995
2000
2005
14.8
1975
1980
1985
Fitted
1990
Actual
1995
2000
2005
Fitted
with the restrictions and the results are reported in Table 4. Although the magnitude of the
coefficients remained relatively unchanged, as expected, the t-statistics improved moderately. In
spite of this, the coefficient of labor productivity in the trade balance equation (β23) and those of
government expenditures in the real exchange rate and the real GDP equations (β15 and β35,
respectively) remained insignificant. Moreover, all the variables in the restricted model retained
their original signs. Finally, graphs of the estimated cointegrating relations are reported in the
appendix as Figures A1, A2, and A3. They show that the relations are stationary.
3.
Model Diagnosis
The Johansen maximum likelihood procedure used in this analysis assumes that the
residuals are NIID. Table 5 reports the results of the Breusch-Godfrey LM test for serial
18
correlation, the normality test, and the ARCH test for heteroskedasticity. They show that the null
hypotheses of the absence of serial correlation, normality, and homoscedasticity could not be
rejected at the 0.05 level. Hence, the residuals are NIID as assumed.
Also, CATS’ multivariate stationarity test rejected the null hypothesis of stationarity of all
the endogenous variables at the chosen rank of 3. The Augmented Dickey-Fuller (ADF) and
Phillip-Perron (PP) unit root tests also identified two of the endogenous variables (real GDP and
labor productivity) to be I(2) while the rest are I(1). However, only two endogenous variables
need to be integrated in the same order for cointegration to exist between a group of nonstationary endogenous variables (Dennis, 2006: 4). Therefore, together, these results confirm that
the endogenous variables are candidates for cointegration.11
Finally, how well a model predicts the endogenous variables is an important test of its
performance. Accordingly, actual and fitted values of the dependent variables from the restricted
model are graphed in Figure 1. They show the fitted values tracking their actual values very
closely.
12
This suggests that the estimated equilibrium relations from the restricted model are
quite robust.
3.
Interpretation of the Results
Contrary to the Dutch Disease hypothesis, the evidence shows foreign aid being associated
with a real depreciation of Sierra Leone’s currency (the leone), an improvement in its trade
balance, and economic growth. Thus, like Kargbo (2012), this study also concludes that foreign
aid promotes economic growth in Sierra Leone. Specifically, a percentage increase in foreign aid
11
The stationarity and unit root test results are available upon request.
12
Graphs of the other endogenous variables and their fitted values, which also show a remarkable fit, are
available from the author.
19
flows is associated with a 0.95 percent real depreciation of the leone, a 1.97 percent
improvement in the trade balance, and a 0.16 percent real GDP growth.
That real exchange rate has a positive long-run relationship with both the trade balance and
real output is supportive of the neoclassical view that real exchange rate depreciations increase
exports and decrease imports, thereby resulting in an improvement in the trade balance and
economic growth. This appears at face value to contradict the argument that the income effect of
real exchange rate changes on cash-crop exports in countries like Sierra Leone should cause the
trade balance to deteriorate following real currency depreciations. However, because coffee and
cocoa, the country’s major export crops are tree crops, which do not bear seeds for at least 5
years after they are planted, the response of farmers to real exchange rate changes is likely to be
relatively smaller than that of the FDI-intensive sector. Consequently, an aid-induced real
depreciation of the leone is likely to have lowered cash-crop exports by less than it increased
FDI-intensive exports. As a result, aggregate exports increased, causing economic growth and an
improvement in the trade balance.
Labor productivity growth is positively associated with the three dependent variables, as
suggested by the Balassa-Samuelson hypothesis. It is however not significant in the trade balance
equation. This suggests a taste bias for imported goods by workers in the FDI-intensive sector.
Therefore, as their incomes rise as a result of their increased productivity, their demand for
imports rises proportionately with the supply of FDI-intensive exports. As a result, the trade
balance remains unaffected. Finally, that labor productivity growth has a positive and significant
long-run relationship with real GDP, in spite of its lack of impact on the trade balance, suggests
that even though productivity growth might start initially in the traded-goods sector, it eventually
filters through the economy thereby leading to economy-wide output growth.
20
Trade openness was significant (negatively) in only the real exchange rate equation and not
the other two. As noted already, increased trade openness raises both exports and imports. It is
therefore not surprising that it had no significant impact on net exports and, consequently,
equilibrium income. The two effects might just have canceled each other out. That, in spite of
this, trade openness caused a real depreciation of the leone could be explained by movements in
the capital account following the reforms that made the economy more open. Specifically, the
abolition of controls on foreign exchange and capital flows might have caused net capital
outflows from Sierra Leone, consequently causing a real depreciation of the leone.
Like Kargbo (2012), no significant long-run relationship was found between government
spending and either the real exchange rate or the rate of economic growth. It was, however,
found to be negatively associated with the trade balance in the long run. This might be explained
by the fact that government spending is generally skewed in favor of the non-traded-goods
sector. The resultant reallocation of productive resources to that sector (away from the tradedgoods sector) might have caused the deterioration in the trade balance.
Foreign economic growth, meanwhile, was found to be positively related to the real
exchange rate and negatively to both the trade balance and real GDP. This is in congruence with
neoclassical theory, which holds that foreign economic growth increases exports. Without any
direct impact on imports, this should lead to an appreciation of the domestic currency. However,
if there is a significant consumption bias in favor of imported goods, the trade balance could
deteriorate if the foreign-income-induced increase in exports is outweighed by the exchange-rate
induced increase in imports. In that case, foreign income growth could also lead to a reduction in
domestic output. This seems to have been the case in Sierra Leone.
21
For its part, the terms-of-trade was found to be positively associated with a real appreciation
of the leone and negatively with both the trade balance and real output. Specifically, the leone
appreciated in real terms by approximately 2.37 percent for each percentage improvement in the
terms of trade. The income effect of improvements in the terms of trade raises both exports and
imports. Therefore, that the real exchange rate appreciated could be explained by the fact exports
rose faster than imports. However, if this were true, the trade balance would have improved. The
evidence, on the contrary, shows a significant negative relationship between the terms of trade
and the trade balance. This again suggests a high income elasticity of demand for imports among
Sierra Leoneans. As a result, increased producer incomes due to terms of trade improvements
raised imports in excess of exports, thereby causing a deterioration in the trade balance and a
reduction in the rate of economic growth.
Finally, the war years are associated with a 0.71 percent real appreciation of the leone. This
might be due to the massive wartime capital inflows due to operations by especially the United
Nations and the British during the latter half of the war. The war years were also found to be
negatively and significantly related to both the trade balance and economic growth. Obviously,
with domestic economic activities having almost ground to a halt amidst massive imports of
arms and food, this finding was expected.13
V. Conclusions and Policy Implications
A key finding of this study is that the Dutch Disease hypothesis does not hold for Sierra
Leone, a heavily foreign-aid dependent country. Therefore, in the case of Sierra Leone, the
answer to Easterly’s question “Can Foreign Aid Buy Growth?” (Easterly, 2003) is affirmative.
Even as it has promoted economic growth, has foreign aid achieved its core objectives of
13
This finding is contrary to the non-significant relationship found by Kargbo (2012) between war-time foreign
aid and economic growth found.
22
poverty reduction and human development in Sierra Leone? The finding of a negative
relationship between foreign aid and the real exchange rate suggests that the answer to this
question is negative. As noted earlier, there is a direct relationship between the real exchange
rate and the real incomes of the producers of export crops in countries like Sierra Leone. Thus,
foreign-aid-induced real depreciations of the leone reduce the real incomes of Sierra Leonean
cash-crop farmers, who constitute one of the poorest groups in the country. On the other hand,
they raise the prices of inputs in the FDI-intensive sector, much of which accrues to urban elites
and foreign owners of capital. Therefore, with over two-thirds of the Sierra Leonean population
engaged in rural agriculture, aid-induced reductions in rural incomes increases the levels of
poverty and income inequality in the country—even as it promotes long-run economic growth.
This is consistent with the finding that at least 44 percent of real GDP growth in the postindependence Sierra Leone has accrued to the owners of capital (Kallon, 2013).
Finally, the Dutch Disease hypothesis and other kindred justifications have led to policy
recommendations that, like their Asian counterparts, SSA countries should forego foreign aid
altogether (or be forced to do so by international donors) and rely more on FDI and loans in
order to foster economic growth.14 Contrarily, this study suggests that instead of foregoing
foreign aid, Sierra Leonean authorities should be investing foreign-aid resources to improve the
quantity and quality of the nation’s human capital stock and infrastructure (in especially rural
agriculture).15 This would raise the productivity of agricultural labor. Improvements in labor
productivity would cause the leone to appreciate in real terms, thereby raising rural incomes and,
thus, reducing rural poverty and income inequality. Therefore, if used in this way, foreign aid
14
For example, using corruption and government inefficiency to frame her argument, Moyo (2010)
recommends that foreign aid to Sub-Saharan African countries be replaced with loans.
15
Investments in education (both traditional and adult literacy), healthcare, and rural roads are ways to raise
labor productivity in rural agriculture.
23
can simultaneously achieve economic growth and poverty reduction in Sierra Leone. The
resultant economic growth should increase the country’s tax base, thereby eventually weaning it
off foreign aid.
References
Adenauer, I. and L. Vagassky (1998), “Aid and the Real Exchange Rate: Dutch Disease Effects
in African Countries”, Intereconomics, 33(4), 177-185.
Athurakola, P. and S. Rajapatinara (2003), “Capital Inflows and the Real Exchange Rate: A
Comparative Study of Asia and Latin America”, The World Economy 26(4), 613-637.
Balassa, B. (1964), “The Purchasing Power Parity Doctrine: A Reappraisal”, Journal of Political
Economy, 72(6), 584-596.
Barro, R. J. (1997). “Determinants of Economic Growth: A Cross-Country Empirical Study”,
Cambridge, MA: MIT Press.
Burnside, C. and D. Dollar, 2000. “Aid, Policies, and Growth.” American Economic Review
90(4): 847-68.
Cheung, Y.W. and K.S. Lai (1993). “Finite Sample Sizes of Johansen’s Likelihood Ratio Tests
for Cointegration,” Oxford Bulletin of Economics and Statistics 55: 313-332.
Darvas, Z. (2012), Real Effective Exchange Rates for 178 Countries: A New Database available at
http://www.bruegel.org/datasets/real-effective-exchange-rates-for-178-countries-a-newdatabase/
De Gregorio, J. and H. C. Wolf (1994), “Terms of Trade, Productivity, and the Real Exchange
Rate”, NBER Working Paper No. 4807. Washington, DC: National Bureau of Economic
Research.
Dennis, J. G. (2006), CATS in RATS: Cointegration of Time Series, Version 2. Evanston, IL:
Estima.
Dollar, D. (1992). “Outward-Oriented Developing Countries Really Do Grow More Rapidly:
Evidence from 95 LDCs, 1976-1985”, Economic Development and Cultural Change 40(3),
523-44.
Domar, E. (1946). "Capital Expansion, Rate of Growth, and Employment". Econometrica 14 (2):
137–147.
Doucouliagos, H. and M. Paldam (2013), “The Robust Result in Meta-Analysis of Aid
Effectiveness”, Journal of Development Studies 49(4): 584-87.
Doucouliagos, H. and M. Paldam (2008), “Aid Effectiveness on Growth: A Meta Study”,
European Journal of Political Economy 24(): 1-24.
24
Easterly, W., 2003. “Can Foreign Aid buy Growth?” Journal of Economic Perspectives 17(3):
23-48.
Edwards, S. (1988), “Real and Monetary Determinants of Real Exchange Rate Behavior”,
Journal of Development Economics, 29(3), 311-341.
Edwards, S. (1993), “Openness, Trade Liberalization, and Growth in Developing Countries”,
Journal of Economic Literature 31(3), 1358-1393.
Edwards, S. (1998). “Openness, Productivity and Growth: What Do We Really Know?” The
Economic Journal 108(447), 383-98.
Elbadawi, I. A. (1994), “Estimating Long-Run Equilibrium Real Exchange Rates”, in J.
Williamson (1994), Estimating Equilibrium Exchange Rates, Washington, DC: Institute for
International Economics.
Elbadawi, I. A. (1999), “External Aid: Help or Hindrance to Export Orientation in Africa?”
Journal of African Economies, 8(4), 578-616.
Elbadawi, I. A. and R. Soto (1997), “Real Exchange rates and Macroeconomic Adjustment in
Sub-Saharan Africa and Other Developing Countries”, Journal of African Economies 6(3),
74-120.
Elbadawi, I. A., L. Kaltani, and R. Soto (2009), “Aid, Real Exchange Rate Misalignment, and
Economic Performance in Sub-Saharan Africa”, available at http://www.economia.puc.cl/
docs/dt_368.pdf.
Falck, H. (1997), Aid and Economic Performance: The Case of Tanzania, Unpublished
Dissertation, Department of Economics, Lund University, Lund, Sweden.
Falck, H. and Y. Bourdet (2006), “Emigrants’ Remittances and the Dutch Disease in Cape
Verde”, International Economic Journal 20(3), 267-284.
Fielding, D. and F. Gibson (2012), “Aid and Dutch Disease in Sub-Saharan Africa”, Journal of
African Economies 22(1), 1-21.
Fuentes, J. C. J. and Herrera, J. C. C. (2007), “Emigrant Remittances and the Real Exchange
Rate in Guatemala: An Adjustment-Costs Story”, available at www.secmca.org/
InvestigacionesEconomicas/ForoInvestigadores/foro2/F2_06.pdf.
Gredenhoff, M. and T. Jacobson (2001), “Bootstrap Testing Linear Restrictions on Cointegrating
Vectors,” Journal of Business and Economics Statistics, 19(1), 63-72.
Hansen, H. and F. Tarp (2001), “Aid and Growth Regressions”, Journal of Development
Economics 64(2): 547-570.
Harrison, A. (1996). “Openness and Growth: A Time-Series, Cross-Country Analysis for
Developing Countries”, Journal of Development Economics, 48(2), 419-47.
Harrod, R. F. (1939). "An Essay in Dynamic Theory", The Economic Journal 49 (193): 14–33.
25
Harvey, C. (1992), “Botswana: Is the Economic Miracle Over?” Journal of African Economies,
1(3), 335-368.
Hatemi-J and R. S. Hacker (2009), “Can the LR Test be Helpful in Choosing the Optimal Lag
Order in the VAR Model when Information Criteria Suggest Different Lag Orders? Applied
Economics, 41(9), 1121-1125.
Haug, A. A. (1996). “Tests for Cointegration: A Monte-Carlo Comparison”, Journal of
Econometrics, 71(1-2), 89-115.
Johansen, S. (1988), “Statistical Analysis of Cointegrated Vectors”, Journal of Economic
Dynamics and Control, 12(2-3), 231-254.
Johansen, S. (2002). “A small Sample Correction of the Test for Cointegration Rank in the
Vector Autoregressive Model,” Econometrica, 70(5), 1929-1961.
Kallon, K. M. (2013). “Growth Empirics: Evidence from Sierra Leone”, African Development
Review 25(2), 215-230.
Kargbo, P. M., 2012. “Impact of Foreign Aid in Economic Growth in Sierra Leone.” World
Institute for Development Economics Research, Working Paper No. WP/2012/07 at
http://www.wider.unu.edu/publications/working-papers/2012/en_GB/wp2012-007/.
Kurita, T. (2013), “Exploring the Impact of Multivariate GARCH Innovations on Hypothesis
Testing for Cointegrating Vectors”, Communications in Statistics – Simulation and
Computation, 42(8), 1985-1800.
Li, Y. and F. Rowe (2007), “Aid inflows and the Real Effective Exchange Rate in Tanzania.”
World Bank Policy Research Working Paper No. 4456. Washington, DC: The World Bank.
Lopez, H., L. Molina, and M. Bussolo (2007), “Remittances and the Real Exchange Rate”,
World Bank Policy Research Working Papers No. 4213.
Mankiw, N. G., D. Romer, and D. N. Weil (1992), “A Contribution to the Empirics of Economic
Growth”, Quarterly Journal of Economics, 107(2), 407-437.
Marquetti, A. (2012), Extended Penn World Tables: Economic Growth Data Assembled from the
Penn World Tables and Other Sources at https://sites.google.com/a/newschool.edu/duncanfoley-homepage/home/EPWT.
Mekasha, T. J. and F. Tarp (2013), “Aid and Growth: What Meta Analysis Reveals”, Journal of
Development Studies 49(4):
Moyo, D. (2010), Dead Aid: Why Aid is not Working and How There is a Better Way for Africa.
London: Douglas & Mcintyre Ltd.
Nyoni, T. S. (1998), “Foreign Aid and Economic Performance in Tanzania”, World
Development, 26(7), 1235-1240.
Ogun, O. (1998). “Real Exchange Rate Movements and Export Growth: Nigeria, 1960-1999.”
AERC Research Paper No. 82, African Economic Research Consortium, Nairobi, Kenya.
26
Ouattara, B. and E. Strobl (2008). “Foreign Aid Inflows and the Real Exchange rate in the CFA
Franc Zone.” Économie Internationale, 4(116), 37-51.
Rajan, R. and A. Subramanian (2011), “Aid, Dutch Disease, and Manufacturing Growth”,
Journal of Development Economics, 94(1), 106-118.
Rebelo, S. (1991), “Long-Run Policy Analysis and Long-Run Growth”, Journal of Political
Economy 99(3): 500-521.
Sackey, H. (2002), “External Aid Inflows and the Real Exchange Rate in Ghana”, AERC
Research Paper No. 110, African Economic Research Consortium, Nairobi, Kenya.
Samuelson, P. (1964), “Theoretical Notes on Trade Problems”, Review of Economics and
Statistics, 46(2), 145-154.
Sanusi, A. R. (2011), “Foreign Aid Inflows and the Real Exchange Rate: Are there Dutch
Disease Effects in Ghana,” The IUP Journal of Financial Economics, 9(4), 28-59.
Solow, R. M. (1956), “A Contribution to the Theory of Economic Growth”, The Quarterly
Journal of Economics, 70(1), 65-94.
Toda, H. Y. (1995). “Finite Sample Performance of Likelihood Ration Tests for Cointegrating
Rank in Vector Autoregressions,” Econometric Theory, 11(5), 1015 – 1032.
Tsikata, T. M. (1998), “Aid Effectiveness: A Survey of the Recent Empirical Literature,” IMF
Paper on Policy Analysis and Assessment, 98/1. Washington, DC: IMF.
Van Wijnbergen, S. (1984), “The ‘Dutch Disease’: A Disease After All?” Economic Journal
94(373), 41-55.
Van Wijnbergen, S. (1985), Aid, Export Promotion and the Real Exchange Rate: An African
Dilemma. Center for Economic Policy Research, Discussion Paper No. 88.
White, H. and G. Wignaraja (1992). “Exchange rates, Trade Liberalization and Aid: The Sri
Lankan Experience”, World Development, 20(10), 1471-80.
Wong, H. T. (2009), “Real Exchange Rate and Terms of Trade: Some Empirical Evidence in
Malaysia, Singapore, and Thailand”, International Journal of Management Studies, 16(1),
229-259.
Yanikkaya, H. (2003). “Trade Openness and Economic Growth: A Cross-Country Empirical
Investigation”, Journal of Development Economics, 72 (1), 57-89.
27
Figure A1: The First Cointegrating Relation
Beta1'*Z1(t)
1.25
0.75
0.25
-0.25
-0.75
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
1993
1995
1997
1999
2001
2003
2005
Beta1'*R1(t)
1.00
0.75
0.50
0.25
0.00
-0.25
-0.50
-0.75
1975
1977
1979
1981
1983
1985
1987
1989
1991
Figure A2: The Second Cointegrating Relation
Beta2'*Z1(t)
1.5
0.5
-0.5
-1.5
-2.5
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
1993
1995
1997
1999
2001
2003
2005
Beta2'*R1(t)
1.5
0.5
-0.5
-1.5
-2.5
1975
1977
1979
1981
1983
1985
1987
1989
1991
Figure A3: The Third Cointegrating Relation
Beta3'*Z1(t)
0.3
0.2
0.1
0.0
-0.1
-0.2
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
1993
1995
1997
1999
2001
2003
2005
Beta3'*R1(t)
0.3
0.2
0.1
0.0
-0.1
-0.2
1975
1977
1979
1981
1983
1985
1987
1989
28
1991