GDP ratio.
The result of the study has shown that the debt-to-GDP ratio and the user cost of
capital played a significant role in the fall of private investment. The latter declined from 17.2% in 1979-1980 to 13.3% in 1986-1988. The increase of debt-to GDP ratio from 47% in 1979-1980 to 70% in 1986-1988 explains over 70% ofthe drop in private investment rate. Thus growing uncertainty of future policy reversals and higher expected future taxes to service the external debt played an important role in the decline of private investment in Morocco in the eighties. The sharp rise of the user cost of capital from 0 to 24% over the same period also explains up to 50% of the
slump of private investment.
Cohen's (1993) results on the correlation between developing countries (LDCs) debt
and private investment in the 1980s showed that the level of stock of debt does not appear to have much power to explain the slowdown of investment in developing
countries during the 1980s. It is the actual flows ofnet transfers that matter. He found
that the actual service of debt crowded out private investment. Similar results were
found by Degefe (1992) in the framework of Ethiopian experience from 1964 to 1980.
Elbadawi et al. (1997) empirically investigated the debt overhang disincentive and crowding out effects, using panel data for a cross section of 53 low and lower middle-income countries over the period of 1970-1990. The model used the investment-to-GDP ratio as a function ofthe rate ofgrowth, the debt service-to-export ratio, the
debt-to-revenue ratio, the inflation rate, the illiteracy rate which proxies for the level of human capital underdevelopment, and the lagged value of the rate of investment. The estimate results suggested that the coefficients for debt service-to-export and the debt-to-revenue variableswere significantat the 1 % level. The debtservice ratio had a
significant negative coefficient. This result supports the crowding out effect on private
investment.
Deshpande (1997) examined experience of 13 highly indebted countries from 1971 to 1992. He found a significantly negative relationship (1%) between external debt levels and investment. He argued that debt overhang impacts on private investment through
two channels. The first channel was a direct disincentive effect, which implies a fear from appropriation of funds invested for debt servicing. The second channel is indirect effect via adjustment measures undertaken to face debt servicing difficulties such as import cuts and decreased public sector investment. Hjertholm et al. (1998) found the
same results, using panel data for a cross section of 53 low and lower-middle income countries over the period between 1970-1999.
UNECA (1998) investigated the dynamic effectsof external debt on private investment
rates in Africa. The paper shows how once a country passes the critical threshold of external debt accumulation, negative effects on private investment develop. The empirical findings show that most African countries could not reverse this trend over the period 1970-1994. The paper argues that the external debt problem in Africa led to private investment slump. Another finding of UNECA (1998) is that the channel
through
which the debt overhang affects private investment starts at a computed threshold of 33.5% of debt accumulation to GDP. The study further points out that "in addition to the debt accumulated, the debt-service burden which crowds out domestic45
expenditure needed for supporting productive capacity further compounds the problem, making it difficultto stimulate investment and subsequently reducing growth".
Sahr (1998) examined the debt overhang effect on private investment in Sierra Leone from 1970 to 1995. Private investment was defined as a function of external debt to GDP ratio (DOD), debt service to export ratio (DSR), public sector investment (PUI) in percentage of GDP, real GDP growth (RG), credit to private sector (CD) as a ratio of GDP, foreign direct investment (FDI), and domestic inflation as a percentage of
consumerprice index (CPI). Itwaswritten as:
PVI, =f(DOD,DSR,PUI, RG, CD, FDI, INF)
Sahr (1998) concluded that debt overhang accounted for a large share of private
investmentslowdown over the period of study as hypothesized. The coefficient of debt service ratio was negatively significant at 1 percent confidence level. And a
percentage increase in debt service payment causes private investment to decline by
0.23 percentage points.
Were (2001) assessed the impact of external debt on private investment in Kenya for
the period of 1970-1999. The findings showed that current debt flows stimulate private investment while past debt flows deter investment. He also suggested that current debt service ratio crowds out private investment at a statistical significance of 12%.
However variation in past debt service ratio was found to positively affect private investment at 3 percentage points. In addition the study revealed that public investment crowds in private investment though the coefficient is not statistically significant.
Empirical studies always include public investment in private investment with respect
to external debt models for developing countries and most have found evidence in support in crowding in effect. Blejer and Khan (1984) estimated investment model in which the crowding-in infrastructural component ofpublic investment is captured by its time trend and the crowding-out effect by the deviation from this trend. They found a
significant positive coefficient on the former and a significant negative coefficient on
the latter suggesting that the non-infrastructural component of public investment has crowding out effect (Balassa, 1988 and Lauman, 1990).
Oshikoya (1994), in a regression analysis of private investment in 4 low and 4 middle-income African countries over 1970s and 1980s, found that public investment is
positively related to private investment in both groups but with a stronger complementary impact in the middle-income countries. At the same time, the study
revealed that debt service ratio negatively affects private investment as well as the macroeconomic uncertainty and instability during the 1980s. Cardoso (1993) reported negative effects of real exchange rate and terms oftrade on private investment. These results were corroborated by Faruquee (1992) and Hadjimichael and Ghura (1995) in
their study for Sub-Saharan Africa. Aizenman and Marion (1999) highlighted the
similar results in a sample of 46 developing countries over the period of 1970 and 1992. The same was revealed by Mbanga and Sikod (2001) in Cameroon.
Servén (1997) studied Sub-Saharan African private investment over the period
between 1980 and 1990 and found that there was a robust negative effect of
macroeconomic uncertainty on private investment particularly when macroeconomic uncertainty is measured by the real exchange rate. In 1998, Servén extended the
study on a large data series covering 94 developing countries overthe period between
1970 and 1995 and found similar results. These have been corroborated by Patillo (1998) in herstudy on Ghana.
Finally, various studies use proxies for political instability, and find them significant
(Bleaney,
1993; Schneider and Frey, 1985). However, in the few models where the latter have been used, their explanatory power is limited. Forexample, Servén (1998) included seven indicators of political instability in an investment model for developingcountries. None of which were statistically significant and only three of them had the expected negative sign. Hadjimichael and Ghura (1995) also found a positive and
statistically insignificant association between private investment and political liberty for
32developing countries during the period 1986-1992.
47
In conclusion, most of the empirical studies show negative effects of external debt overhang on private investment. Studies that show favorable effects of external debt
are rare. They include World Bank (1988) study for the period 1980-86 and Chowdhurry (1994) for Bangladesh, Indonesia and South Korea. The above empirical
literature reviewed suggests results that are quite revealing. More interestingly there is
need to apply similar findings to the Rwanda's economy. The present study attempts
to assess the impact ofexternal debt on private investment in Rwanda over the period
1970-2000.
Chapter
4 METHODOLOGY
AND REGRESSIONANALYSIS4.1
Methodology
4.1.1 Theoretical Framework
The sharp fall in investment rate in many developing countriesfollowing the debt crisis
of the early 1980s led to the inclusion of debt burden as a key deterrent of private
investment. The debt overhang is expected todiscourage domestic private and foreign
investors as they anticipate confiscatorytaxes to finance the debt service in the future,
hence makingthe expected returns on projects uncertain.
Most of the above highlighted empirical studies have shown negative association
between private investment and debt overhang. The model specifications used are based on the neoclassical framework: Oshikoya (1994), Schmidt-Hebbel and Muller (1991), Elbadawi et al. (1997), Sahr (1998), Blejer and Khan (1984) and Were (2001).
Debt overhang is proxied by variables such as debt stock and debt-service ratio. Other explanatory variables include annual growth rate, per capita GDP growth rate, debt
service ratio, real exchange rate, inflation rate, terms of trade, credit to private sector, lagged private investment and public investment.
This paper adopts the methodology used by the studies mentioned above to assess the effect of external debt on private investment in Rwanda. The model is specified as:
PRI=
#0+ci\DSR+ Q2PUI+ agOT+ct^IR+ct^EDT
+@$Y-\+&jCPS+ ci$PRI_i
+ct^POST+
s(-) (+) (-) (-) (+) (+) (+) é (+)
where:
DSR PRI EDT
= Private investmentas a ratio ofGDP
= Stock of external debt
= Debtservice as a ratio ofexportearnings
49
PUI = Public investment as a ratio of GDP TOT = Terms of tradeto capture external shocks POST = Dummy variable for political stability
GPRI (-1) = Private investment lagged one period to reflect the effect of past investment
IR = Interest rate reflects the lending rate (long-term) of commercial
banks
Y-i = Real GDP lagged byone period
CPS = Credit to private sector (% ofGDP) provided by the banking
sector
e = Errorterm
One has to be cautious on the relationship between public and private investment. In developing countries including Rwanda, private investment and public investment may be related although there is uncertainty about whether public sector investment raises
orlowers private investment (Chhiber A. and M. Dailami, 1993; Amin, 1998, Blejer and Khan, 1984).
Public investment can cause crowding-out effects when it utilizes scarce financial
resources that would otherwise be available to the private sector. In addition, crowding
out can occur if public sector investment produces marketable goods that compete with private output. Furthermore, the financing of publicsector investment by issuance
of debt or through taxes will lower the resources available to the private sector and thus depress private investment activity.
Yet, public investment related to the development of infrastructure (roads, telecommunications, etc) and provision of public goods can be complementary to private investment. Such public investment enhances the possibilities for private investment and increases the demand for private sector output. Consequently, it can
be argued that the marginal productivity of private investment is enhanced by public investment.
4.1.2 Data Sources
The study covers the period between 1970 and 2000. Most of the data used in
regression analysis are obtained from the CD-ROM of the World Bank Africa Database, 2002. These are gross private investment and public investment,
debt-service-ratio to exports, the stock ofexternal debt, and the terms of trade. The interest rate (IR) is collected from the IMF, international financial statistics yearbook (2002).
Credit to private sector is obtained from CD ROM of the World Bank, World Development Indicators, 2002.
Adummy variable is introduced to accommodate political stability in the model. To this effect, the years 1973, 1990 and 1994 are defined as the periods of instability and
were valued 0 and the rest 1. The macroeconomic data are provided and graphed in
the appendix.
Investment data should be interpreted with some caution, and private investment more
so. In the present study, private investment is computed as the difference between
gross domestic investment and public investment, because time series are only
available for gross domestic investment and public investment. Public investment is
defined as all investment activities that are not carried out by the private sector. Since private investment is produced as a residual, investment variables might contain not only private investment's own measurement errors, but also errors from the
measurement of public investment aswell.
Lagged private investment (PRI-1) reflects the effect of past investment. Theory suggests that the investment decisions today largely depend on the past investment output.
4.1.3 Time Series Properties
In empirical analysis, working with non-stationary time series data leads to spurious
results from which inference may not be meaningful. Therefore, it is necessaryto first
test for stationarity of the variables. To this effect, the conventional Philip-Perron (PP)
51
test is used and the choice is guided by its robustness over the Augmented
Dickey-Fuller (ADF). Variables are said stationary when they are integrated of order zero (in level). They are not stationary when differenced once ortwice (integrated of order 1 or
2).
Table 7: PP Unit RootTestResults
Variable PPteststatistic 5% critical value Order ofintegration
PRI 4.262914 3.5670 HO)
DSR 8.130693 3.5731 H1)
PUI 5.348813 3.5731 H1)
TOT 3.677280 3.5670 H0)
IR 5.822869 3.5731 Hi)
EDT 6.597897 3.5731 Hi)
Y 5.048870 3.5731 Hi)
CPS 5.658513 3.5796 l~(2)
POST 5.890519 3.5670 HO)
The results show that DSR, EDT, IR and PUI are stationary after differencing them
once. They are integrated oforder 1. The rest are stationary.
Differencing variables to achieve stationarity leads to loss of their long-run properties.
We then need to test whether non-stationary variables bearcointegration relationship.
The theory of cointegration implies that if there is a long run relationship between 2 or more non-stationary variables, deviation from this long run path are stationary.
Johansen test was used and table (8) confirms the existence of two cointegrating equations.
Table 8: JohansenCointegration Test
Likelihood 5 Percent 1 Percent Hypothesized Eigenvalue Ratio Critical Value Critical Value No. ofCE(s)
0.857175 199.9669 131.70 143.09 None **
0.818316 143.5291 102.14 111.01 At most 1 **
0.725344 94.07000 76.07 84.45 At most 2**
0.643833 56.59521 53.12 60.16 At most 3 *
0.387817 26.65686 34.91 41.07 At most 4
0.295241 12.42585 19.96 24.60 At most 5
0.075571 2.278782 9.24 12.97 At most 6
=— —= — —
*(**)
denotesrejection of the hypothesis
at5% significance level
L.R. testindicates 4 cointegrating equations at 5% significance level
4.2
Regression Analysis and Interpretation of the Results
The model is estimated using OLS and time series data cover 1970-2000. The
econometrics computer package, EVIEWS, version 3.0 is used for the estimation.
Results are reported in table 9.
Table 9: EstimationResults
DependentVariable: PRI
Method: LeastSquares Date: 05/30/03 Time: 16:58 Sample(adjusted): 1971 2000
Included observations: 30 afteradjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
C
0.906815 Mean dependentvar 3.920333 0.864882 S.D.dependentvar 3.745747 1.376875 Akaike info criterion 3.738712 37.91571 Schwarz criterion 4.205778 -46.08068 F-statistic 21.62530 2.525687 Prob(F-stati sti c) 0.000000
53
The results in table 9 are reproduced in linearform as:
PRI = 3.81 -0.10DSR-0.47PUI + 7.76*1O^EDT -2.20*10"7TOT- 0.04IR+
(-1.99) (-2.95) (3.66) (-2.34)
(-0.28)
0.004CPS + 1.04*10-9Y-i -0.13PRI-1 + 1.56POST (0.53) (1.27) (-0.98)
(1.39)
(.) stands fort-statistic
As we can read across table 9, variables DSR, PUI, TOT and EDT are found significant. The rest are not
significant. All the variables have their hypothesized
sign, except PUI and
PRI(-1).
If the debt-service to exports ratio (DSR) increases
of 10%, private investment will
decrease to -10%. When there is an increase of 10% in PUI, and IR,
private
investment will deteriorate of -4.7% and -0.4% respectively. If credit to private
sector is provided at 100% level,