Department of Economics
& a Ph.D Scholar, Ebonyi State University, Abakaliki, Nigeria
Journal E-mails: martinslibrary1@gmail.com
Abstract
This study employs Error
Correction Mechanism (ECM) to investigate the speed of adjustment between
capital flight and economic growth in Nigeria using annual time series data
spanning from 1970 – 2010.
The analysis begins with testing the stationary status
of the time series by employing Augmented Dickey Fuller to confirm the number
of integrating order. The study harvested that for most of the periods, capital
flight estimates had positive sign, indicating that residents consistently took
capital out of Nigeria. The study further documented that exchange rate
volatility and high inflation rate are the
important means through which capital flight is effected in Nigeria,
with evidences that confirmed the existence of financial revolving door
relationship between capital flight and external indebtedness in Nigeria. The
study, therefore, submits inter-alia repatriating of
flight capital to boost the growth initiatives with selective controls on
capital outflow, changes in Nigeria tax laws, and a bias toward poor wages.
Keywords: Capital Flight, Economic Growth, Cointegration and Nigeria.
Section I
Introduction
One of the crucial economic maladies that confronted
most developing countries in the early 1970’s is the way capital flew from less
developed nations to advanced nations of the world. In Nigeria for instance,
London-based Tax Justice Network (TJN), recently, released a report totaling
Nigeria’s Real capital flight (from 1970 to 2010) at a whopping $233.9 billion
(Henry, 2012). However, they spoke against the backdrop of a report by the
London-based Tax Justice Network (TJN) that Nigeria’s capital flight and
investment income stood at $306.2 billion in 2011, about 40 times the nation’s
total external debt of $7.9 billion. Since 1970s, it appears that private
elites in developing world countries especially Nigeria, have been able to
accumulate at least $7.3tn-9.3tn of offshore wealth, even while many of their
public sectors were borrowing themselves into bankruptcy, enduring agonizing
structural adjustment and low growth, and holding fire sales for public assets.
Yet, Nigeria loses more than N2 trillion annually to
capital flight following inability of indigenous ship owners to fully
participate in crude oil exports (Okonjo-Iweala, 2012).
In a specific term, capital flight refers to any
illicit movement of capital away from a domestic to a foreign economy. It is
normally done in such manners that circumvent the regulatory purview of the
domestic authorities. In the contemporary literature of development economies,
there has been increasing attention to the notion of capital flight. Many
analysts have attributed sluggish economic growth and persistent balance of
payments deficits in most developing counties to capital flight (Ajayi, 1996).
Capital flight is particularly a serious and an important concern for a
developing country like Nigeria. First, capital is scarce in the developing
world, so capital flight contributes to worsening the capital scarcity problem.
In addition, it also restricts the capacity and ability of affected countries
to mobilize domestic resources and access foreign capital necessary to finance
economic growth and development.
Consequently, capital flight can contribute to the retardation of
economic growth and development of developing countries. Second, capital flight
can lead to a negative feedback. Because of the resulting tightening of capital
constraints and the possibility of being cut off from foreign capital, even
more capital flight could occur and consequently, progressive economic policies
become more difficult to implement and raising social conditions a heavier
burden to solve.
Thus, an estimate of capital flight at this period is important in order
to know the relationship existing between capital flight and growth in Nigeria. While many studies have been done on the
topic, very few of these studies have been undertaken in relation to investment
by Nigerians themselves. The studies of Ajayi (1996) which covered the period
between 1970 and 1989 need a revisit. The studies of Onwudoukit (2001) did not
provide any estimate and that of Lawanson (2007), was basically on capital
flight with no relation to any other economic variable. These studies provided
estimates to show the impact of trade misinvoicing or trade faking. The producer price compounded figures for
trade misinvoicing were put at $316,888 million ($316.9 billion) and $436,092.3
million ($436 billion) by Morgan Trust and World Bank respectively as at 2009
using the residual methods (World Bank, 2010). These figures are at variance
with Collier’s, and need some clarifications. However, none of these studies
empirically studied capital flight with its impacts on investment in the
domestic economy.
Therefore, the resurgence of capital flight in recent times is related to
a paradoxical situation of high accumulation of external debt by developing
countries on the one hand and the acquisition of foreign assets by the citizens
of the heavily indebted countries on the other. Consequently, interest is shown
on capital flight at the policy to bring about reversal of capital flight as an
opportunity not only to improve on the external liability situation of the
economy but also to promote growth in Nigeria.
The remaining part of this study is divided into four sections. Section
II deals with theoretical and empirical review. Section III highlights the
methodological issues, section IV presents and analyses the empirical evidence
while section V concludes the entire study.
Section II
Theoretical and Empirical Review
2.1 Theoretical Review
Capital flight theory, traditional, received
only a scanty attention until recently. However, one basic theory upon which
this study is leaned on is the portfolio theory of capital flight. Discussions on
portfolio theory elicits high level of esotericism, when they tend to become
dynamic, as static models and one country assumption can be easily understood
and assimilated. The considerations of currency, real effective exchange rates,
possibility of foreign or home bias, influences by the level of risks and real
rates of return makes matters to be slightly complicated (Tille and Wincoop,
2007). However, the portfolio approach to international flows and flights of
capital have been accepted as the most popular (Obstefeld, 2004). In the analyses
of many of the flights of capital investigated, the portfolio approaches seem
to have gained upper hand. The choice depends so much on the choice of
investors who choose where to hold their wealth, either at home or overseas.
The choice of either of these is influenced a lot by the risk and return trade
off and other considerations.
Kraay and Ventura (2003) in
their analyses, grouped the drivers of international portfolio flows into two
namely: portfolio growth and portfolio reallocation models. The portfolio
growth components are defined as increases in the national savings that lead to
capital outflows which equals to the rise in national savings times portfolio
share of foreign assets. The second one is active portfolio reallocation of
wealth across assets. Capital outflows that relate to portfolio reallocation
reflect a change in portfolio shares away from passive portfolio, since changes
in assets price affect portfolio shares without any asset trade- a dimension
known as passive portfolio management. The above scenario abandons the other
relative factors that are involved in the inflow and outflow on the exchange
rates and equity prices. An appreciable increase in the inflow of resources
will undoubtedly affect the real effective exchange rate and the prices of the
available equity stocks at home. This is the current position with our stock
market and the relationship with the rate of exchange in its bubble days at
about 2007 and 2008. This implies that the inflows of foreign portfolio funds
into the capital market need to be monitored in order for it not to result in
capital flight in the nearest future.
One significant aspect of this theory has been argued
by several studies. This argument has been seen in more than one way. So
important studies have been those of Lane and Millesi-Ferretti (2004) and
Obstfeld (2004) who have called for the continuous use of portfolio approach in
the explanation of countries of open economy dynamics. Though the studies of
Kouri (1976) and Dooley and Isard (1982) did not support the use of portfolio
approach initially with macroeconomic foundations as a result of lack of
empiricism, the campaign nevertheless received caution earlier in Obstfeld and
Rogoff (2002) as to where to draw the line in dynamic first order open economy
before the current position. In Deveruox and Saito (2006), it was found that
the existence of nominal bonds and the portfolio composition of net foreign
assets is an essential element and a significant cause of capital flows between
countries. When investors adjust their
gross positions in each currency’s bonds, countries can achieve an optimally
hedged change in their net foreign assets (or their capital account), thus
facilitating international capital flows.
2.2 Empirical Review
Not a few studies have been
conducted to investigate the link between capital flight and economic
performance in developing countries. However, the review undertaken here will
be selective rather than been exhaustive. For instance, Lensink, Hermes and Murinde (2009) in their cross-sectional examination
of the link between political risk and capital flight for a number of
developing countries concluded that no matter how capital flight is defined or
measured, political risk factors has a significant role to play in the
determination of capital flight where no other macroeconomic variables are
considered. Fatehi (1994) analyzed the impact of political disturbances on
capital flight in 17 Latin American countries. He utilized a stepwise multiple
regression analysis on data between 1950 and 1982. He concluded that political
disturbances in some of those countries have effects on capital flight from
these countries.
Forgha (2008) and Valeria
Gusarova (2009) studing Cameroon and some developing nations respectively
observed that capital flight adversely impact real economic growth. Beja (2006) notes that with capital flight
presents the possibility of cutting off a nation from external sources of
funds. Consequently, it becomes more difficult to implement economic policies,
and improving the social conditions of people also becomes more difficult. Ajilore (2010) and De Boyrie (2011) observed
that trade faking and mis-invoicing account majorly for capital flight in selected
African countries including Nigeria and hinder long-term economic growth. Ayadi
(2008) found interest differential and exchange rate depreciation significant
causes of capital flight in Nigeria and concluded that capital flight is
depriving Nigerian economy of substantial and critical financial resources
needed for investment and building of social capital among others. Kosarev
(2000) identified capital export as a normal economic phenomenon which does not
affect the economy significantly from global perspective, while capital flight
presents a danger and leads to the impoverishment of the economy.
Hermes and Lensink, (2000) and Lensink et al., (2000)
in their empirical studies discovered that political instability in Africa is
associated with greater capital flight whilst democracy and political freedom
tend to reduce the incidence of capital flight. These together with weaknesses
in the institutions for protecting property rights and incessant political
unrest and associated general sense of insecurity to life and property tend to
encourage capital flight in Nigeria. In his empirical study, Boyce (1992)
identified 2 sets of bi-directional causality between external debt and capital
flight, leading to the categorization of causal linkages into: debt driven
capital flight; debt-fuelled capital flight; flight-driven external borrowing;
and flight fuelled external borrowing. Contrary to the findings of Boyce, Ajayi
(1996) found no evidence of causal links (in any direction) between external
debt and capital flight. However, Collier et al. (2001), in a cross-sectional
study, which includes some African countries, found evidence of debt-fuelled
capital flight. Such finding is not surprising, as it must have been largely
influenced by the presence of non-African countries in the sample where evidence
of debt fuelled capital flight and flight-fuelled external borrowing had been
reported earlier. Another major determinant of capital flight is risk-adjusted
returns to investment. Certain studies have demonstrated a linkage between
risk-adjusted returns to investment and capital
flight.This is argued on the assumption that investors
attempt to maximize profits by diversifying their portfolios between foreign
and domestic investments based on the relative risk-adjusted rate of return
abroad and at home. Ndikumana and Boyce (2002 in their empirical studies, used
exchange rate volatility, interest rate differential between home and abroad,
and a host of survey-based measures of institutional investor risk perceptions
to explain the concept of risk-adjusted returns to investment. Ndikumana and
Boyce (2002) including Hermes and Lensink, (2009); Murinde et al., (1996);
Nyoni, (2008); Ng’eno, (2000), which used interest rates as an explanatory
variable in their models found no statistically significant relationship
between interest rates and capital flight. However Murinde, et al., (1996);
Hermes and Lensink, (2000) and Lensink et al., (2009), which used exchange rate
indicators as an indicator of risk-adjusted returns found some evidence of the
link between exchange rate overvaluation and capital flight in Nigeria.
Section III
Methodological Issue
3.1 Theoretical model
In order to capture the precise link between capital
flight and endogenous growth in Nigeria, this study adopts Vector
Autoregressive (VAR) model. The term autoregressive is due to the appearance of
the lagged value of the dependent variable on the right-hand side and the term
vector is due to the fact that we are dealing with a vector of two or more
variables. The model is based on two lags of each endogenous variable. In a VAR
model, each variable is in turn explained by its own lagged value, plus current
and present value of the remaining variables. The VAR model present all
variables as dependent variables which have the dynamic power to reflect impact
of random disturbance on the variables, thereby modeling every endogenous
variable in the system as a function of the lagged value of all the endogenous
variable in the system. The VAR model presented here is composed of seven
variables, namely: Economic growth (GDP), Capital flight (CAPFL), Exchange rate
(EXCHR), Inflation rate (INFR) External Debt Relief (EXDR), Foreign Direct
Investment (FDI) and Openness (OPEN). Thus, this study adopts a VAR model of
Abdul Majid (2007) as modified by the researcher as follows:
at = SAiat-1 + et
(3.1)
Where:
at = is a column vector of observation at
time t on all the variance in the model,
S = summation of exogenous variable at time t
Ai = x1-x7
at-1= lag of endogenous variable.
et = stochastic error term or innovation of
shocks.
Following the modeling approach of Abdul Majid (2007),
we can specify the model in its implicit functional form as:
GDPt = (CAPFL, EXCHR, INFR, EXDR, FDI,
OPEN)
(3.2)
Now, stating the model in an explicit stochastic form
gives;
GDPt = a0 + a1GDPt-1
+ a2CAPFL + a3EXCHR + a4INFR + a5EXDR
+ a6FDI + a7OPEN + Ut
(3.3)
Taking logarithms of both sides of the equation; we
have,
LogGDPt = a0 + a1logGDPt-1
+ a2logCAPFL + a3logEXCHR + a4logINFR + a5logEXDR
+ a6logFDI + a7logOPEN + Ut (3.4)
where ;
a0 = constant and a1 to a7
= coefficients,
logGDPt = log of real Gross Domestic
Product (GDP) growth in time t,
logGDPt-1 = log of lagged GDP in one year,
logCAPFL = log of Capital Flight,
logEXCHR = log of
Exchange Rate,
logINFR = log of Inflation Rate’
logEXDR = log of External Debt Relief,
logFDI = log of Foreign Direct Investment,
logOPEN = log of Openness of the economy (measures the
difference between export and import), and
Ut = Disturbance element.
Apriori, it is expected that a1>0, a2>0,
a3>0, a4<0, a5<0, a6>0,
a7>0.
3.2 Sources of
Data and Estimation Procedure
Data used for this study are mainly secondary in
nature and were sourced from the Statistical Bulletin of the Central Bank of
Nigeria (CBN) and Annual Abstract of Statistic of the Bureau of Statistics
(NBS). The Ordinary Least Square (OLS) technique is used to investigate the
link between capital flight and economic performance in Nigeria. Regression
model was adopted to know the effect of capital flight on growth in Nigeria
within the period under study. Also, coefficient of determination (R2),
T-statistic, F-statistic, and the Durbin Watson test were employed to evaluate
the significance of the estimated parameters of the regression model. The study
also attempted to test for the time series characteristics using Augmented
Dickey Fuller (ADF) Unit Root Test, Co integration and Error Correction
Modeling, all at 5 or 10 percent level of significance.
Section IV
Presentation and Analyses of Results
4.1 Presentation of Results
In presenting the empirical evidence, table 1 below
shows the output for absolute regression.
Table 1: Short-run Log Linear Result of Impact of Capital Flight and
Growth
Dependent Variable: LOG (GDP)
Method: Least Squares
Date: 10/28/12 Time:
19:09
Sample (adjusted): 1970 2010
Included observations: 40 after adjusting endpoints
Convergence achievement after 6 iterations
Variable
Coefficient Std. Error t-Statistic
Prob.
C 9.874991 0.203714 49.47489 0.0000
LOG(CAPFL)
163.9319 534.7683 0.306547 0.6782
LOG(EXCHR
-16.14560 7.842118 -2.058832 0.0584
LOG(INFR)
-0.026677 0.021395 -1.246858 0.6529
LOG(EXD)
0.858685 0.096694 8.880416 0.0000
LOG(FDI)
0.073393 0.031426 3.335451 0.0262
LOG(OPEN)
82.84605 37.28148 2.222177 0.0414
R-squared 0.945867 Mean
Dependent var. 113.3829
Adjusted R-squared 0.836793 S.D
Dependent var. 98.38592
S.E. of regression 0.182537 Akaite
info criterion 40.67834
Sum squared resid 0.281349 Schwarz
criterion 40.48392
Log likelihood -32.43872 F-Statistic 89.72738
Durbin-Watson stat 1.873416 Prob
(F-Statistic) 0.000000
Inverted MA Roots .89
Source : Extracted from
E-views package 6.0 Output
Recall that the specified model is
GDPt = a0 + a1CAPFL +
a2EXCHR + a3INFR + a4EXDR + a5FDI +
a6OPEN + Ut
Thus, using the absolute values of all the variables,
the estimated parameters of the short run regression model is:
GDP = 9.874991 + 163.9319CAPFL - 16.14560EXCHR - 0.026677INFR + 0.858685EXDR + 0.073393FDI
+ 82.84605OPEN
(3.5)
The estimated model shows that there exist positive
relationship between real GDP and the explanatory variables – capital flight,
external debt, foreign direct investment and openness of the economy. This
empirical evidence is in conformity with the theoretical expectation except
exchange rate and inflation rate which is expected to be growth retarding. The
estimated result revealed that a unit change in capital flight (CAPFL),
external debt (EXDR), foreign direct investment (FDI), and openness of the
economy (OPEN) will enhance economic activities in Nigeria by values of 163.93,
0.86, 0.07 and 82.85 percent respectively. Likewise, a one percent change in
exchange rate and inflation rate will retard growth by 16.15 and 0.02 percent
respectively. However, the t-statistic is used to test for individual
significance of the estimated parameters (a1, a2, a3,
a4, a5, a6). The result reveals that not all
the parameters estimated are significant (e.g. capital flight and inflation
rate), because their respective t-calculated values of 0.31 and 1.25 are less
than the t-tabulated value of 2.04. Therefore, the null hypothesis is accepted
in that sense. This suggests that capital flight and inflation rate have not
contributed to economic growth in Nigeria within the period under study. The
f-statistic is used to test for a simultaneous significance of all the
estimated parameters and the result showed that they are all simultaneously significant.
This is so because the f-calculated (89.73) is greater than the f-tabulated
(2.74). The Durbin-Watson test showed that there is little or no presence of
serial correlation in the residual as its value (1.87) is approximately equal
to 2. Overall, the Coefficient of Determination (R2) which measures
how well the sample regression line fits the data is considered quite high,
about (0.945867) or 95 percent. This implies that about 95 percent of the
regression model was explained by the explanatory power. Only, an infinitesimal
of 5 percent was unexplained.
Thus, the econometric analysis of the link between
capital flight and growth in Nigeria within the years under study have shown
that all the variables under investigation except exchange rate and inflation
rate have positive relationship with economic activities in Nigeria, but the
effect is insignificant. In conclusion however, the null hypothesis is accepted
which implies that capital flight and its proxies have no significant impact on
economic growth in Nigeria during the periods under review.
4.2 Unit Root Test Analysis
In the unit root test, the variables for our analysis
were subjected into a single type of unit root test (Augmented Dickey Fuller -
ADF), to determine whether there is a presence of unit root or the series are
stationary. Here, we investigated the time series characteristics of the
variables (GDP, CAPFL, EXCHR, INFR, EXDR, FDI and OPEN) of the model in this
study. For a brief, a variable is said to be stationary when it has no unit
root which is denoted in literature as I(0). A non-stationary variable can have
one or more unit roots and it is denoted by I(d), d is the number of unit roots
that the variable possesses and, by implication, the number of unit roots that
the variable must be differenced in order to make it stationary. Similarly, if
a time series has to be differenced twice (i.e. take the first difference of
the first differences) to make it stationary, we call such a time series integrated of order 2 - I(2).
As depicted in table 2 below, all the variables are
stationary at the first difference for each of the forms of estimation
excepting capital flight which is stationary at second difference for all the
three forms of the random walk model. This implies that all the variables of
interest are integrated of order one i.e.
I(1), excluding capital flight which is integrated of order two i.e. I(2). See the summary of the unit root
test as depicted in table 2 below:
Table 2: Summary
of Augmented Dickey-Fuller Unit Root Test
Variables 1% Critical
Value 5% Critical Value 10% Critical Value ADF T-Statistic Order
D(GDP) -3.6117 -2.939 -2.6080 -7.626370
I(1)
D(CAPFR) -3.6171 -2.9422 -2.6092
-4.349505 I(2)
D(EXCHR) -3.6117 -2.9399 -2.6080
-5.122170 I(1)
D(INFR) -3.6117
-2.9399 -2.6080
-6.728054 I(1)
D(EXD) -3.6117 -2.9399 -2.6080
-5.236530 I(1)
D(FDI)
-3.6117 -2.9399
-2.6080 -5.362451 I(1)
D(0PEN) -3.6117 -2.9399 -2.6080
-5.043710 I(1)
Source: Author’s Computation
4.3 Cointegration Test: Long-Run Analysis
So far, we have assumed that all the variables are of
the same order of integration i.e. I(2),
in order to carry out further tests. We then run an OLS regression of the
variables at levels and test for cointegration by testing that the residual is
I(1). This is the long run dynamic. The unit root test for the residual is
carried out as follows: Recall again, our specified model as,
GDPt = a0 + a1CAPFL +
a2EXCHR + a3INFR + a4EXDR + a5FDI +
a6OPEN + Ut
The residual series is generated from the estimated
model as shown below:
GDP = 9.874991 + 163.9319CAPFL - 16.14560EXCHR - 0.026677INFR + 0.858685EXDR +
0.073393FDI + 82.84605OPEN
Here, through transformation in residual, we have;
Ut = GDP – (9.874991 + 163.9319CAPFL - 16.14560EXCHR - 0.026677INFR+ 0.858685EXDR +
0.073393FDI + 82.84605OPEN) (3.6)
Thus, the ADF is used to test whether the residual is
stationary or non-stationary. Since the estimated
Ut are based on the estimated cointegrating parameters, a1, a2,
a3, a4,a5, a6, the ADF critical
significance values are not quite appropriate (Engle and Granger, 1987).
Therefore, the ADF test in the present context is otherwise known as Augmented
Engle-Granger (AEG) test. The result from the analysis revealed that the
residual Ut is stationaryat 5 percent and 10 percent critical level since the
tau value is more negative than the critical values; the null hypothesis of no
cointegration is therefore rejected (see table 3 below). In conclusion, the
residuals from the regression of the model of GDP on the exogenous variables as
specified above are integrated of order zero i.e. I(0); that is, they are
stationary. This implies that the regression is not spurious even though
individually the incorporated variables in the model are non stationary at
levels but all are stationary at first difference excluding capital flight that
is stationary at second difference. Hence, the estimated model shows the static
or long-run function of the relationship between capital flight and economic
growth in Nigeria.
Table 3: Johansen’s Cointegration Result for Model of GDP
Date: 10/28/12 Time:
09:17
Sample (adjusted): 1970
2010
Included observations:
40 after adjusting endpoints
Trend assumption: Linear
deterministic trend
Series: GDP CAPFL EXCHR INFR EXD FDI OPEN
Lags interval (in first differences):1 to 1
Unrestricted Cointegration Rank Test
Hypothesized Trace
5 percent 1 Percent
No. of CE(s) Eigenvalue Statistic Critical value Critical value
None**
0.968256 138.7621 120.33 137.28
At most 1 0.598161 126.3850 114.31 121.91
At most 2 0.856334 73.9359 64.59 93.53
At most 3 0.597324 42.7372 33.84 65.84
At most 4 0.315398 29.7283 22.98 46.27
At most 5 0.457239 34.7398 46.47 72.68
At Most 6 0.545762 42.9532 89.33 108.52
*(**) denotes rejection of the hypothesis at the
5%(1%) level
Trace test indicates 1 cointegrating equation(s) at
both 5% and 1% levels
Source: E-views Computer result
Table 4: Parsimonious Error Correction Model
Dependent Variable: D(LN(GDP)
Method: Least Squares
Date: 10/28/12 Time:
16:18
Sample (adjusted): 1970 2010
Included observation: 40 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob
C 0.038397 0.014075 2.728038 0.5612
D(LN(GDP(-1))) -0.065942 0.016049 -4.108824 0.0017
D(LN(GDP(-2))) 0.883840 1.905095 0.463935 0.5672
DLN(GDP(-3))) -0.330889 0.307877 -1.074743 0.2684
D(LN(CAPFL(-1))) 7.422882 3.312050 2.241174 0.0567
D(LN(CAPFL(-3) -0.563216 0.170897 -3.295652 0.0026
D(LN(CAPFL(-5))) -2.200368 12.43412 -0.176962 0.6203
D(LN(EXCHR))) 0.072310 0.017959 4.026336 0.0020
D(LN(EXCHR(-1))) 0.058776 0.024360 2.412846 0.3413
D(LN(EXCHR(-2)))
-0.022334 0.211316
-0.105690 0.3468
D(LN(EXCHR(-3))) 3.423728 2.345612 1.459631 0.5622
D(LN(INFR))) 0.283062 0.063248 4.475430 0.2683
D(LN(INFR(-1))) -2.325676 1.138334 -2.043052 0.0672
D(LN(INFR(-2))) -4.348634 2.386634 -1.822078 0.6689
D(LN(INFR(-3))) -0.338943 0.384367 -0.868686 0.3621
D(LN(EXD(-1)))) -2.364286 1.394683 -1.695214 0.0626
D(LN(EXD(-2))) 1.637266 0.421636 3.883127 0.3672
D(LN(EXD(-3))) 0.083682 0.025677 3.259026 0.3961
D(LN(FDI(-1))) 0.867623 0.373624 2.322182 0.0673
D(LN(FDI(-2))) -4.337463 1.856882 -2.335885 0.0000
D(LN(FDI(-3))) -0.720367 0.289243 -2.490525 0.7324
D(LN(OPEN(-1)) -0.328401 0.187384 -1.752556 0.0568
D(LN(OPEN(-3))) 5.326732 3.863742 1.378645 0.1682
D(LN(OPEN(-5))) 2.867287 0.934656 3.067745 0.0767
ECM(-1) -1.568358 0.066348 - 0.066348 0.0000
R-squared 0.996726 Mean dependent var. 0.038735
Adjusted R-Squared 0.898463 S.D. dependent var. 0.094636
S.E. of regression 0.367842 Akaike info criterion 17.67843
Sum squared resid 17.36384 Schwarz criterion 17.12685
Log likelihood -43.73925 F-Statistic 9.567283
Durbin-Watson Stat 2.489265 Prob. (F-Statistic) 0.000000
Source: E-views Computer result
4.4 Result of Error Correction Mechanism
The estimated result of Table 4 reports the initial over-parameterized
error correction of capital flight and economic activities in Nigeria. All the
variables were lagged equally in this model. The result of parsimonious model
as reported above indicates model parsimony. Thus, this result clearly shows a
well defined error correction term, and indicates a feedback of 157 percent of
the previous year’s disequilibrium from the long run capital flight elasticity
of economic activities in Nigeria. The implication of this result is that both capital
flight and its proxies maintained equilibrium with GDP through time. The
effects of these disequilibria error corrections is not only large, but also
have negative signs as expected. The strong significance of the coefficient of
ECMt-1 supports our earlier assertion that GDP indeed cointegrates
with capital flight in Nigeria.
However, we previously showed that all variables under consideration are
cointegrated at 5 percent and 10 percent critical level, i.e. there is a
long-run relationship among them. In the short-run, there may be disequilibrium
in which the model, i.e.
Ut = GDP – (9.874991 + 163.9319CAPFL - 16.14560EXCHR - 0.026677INFR+ 0.858685EXDR + 0.073393FDI
+ 82.84605OPEN ) (3.7)
is the “equilibrium error”.
Therefore, the error term is used to show the short-run behavior of real GDP to
its long-run values. We can now specify the ECM equation for this study as:
ΔGDPt = a0 + a1ΔCAPFLt + a2ΔEXCHRt + a3ΔINFRt + a4ΔEXDRt + a5ΔFDIt + a6ΔOPENt + a7ECMt-1 + Ԑt
(3.8)
Where;
Δ denotes the
difference operator; Ԑt is the random
error term, and
ECMt-1 = (GDP -
a0 – a1CAPFLt-1 – a2EXCHRt-1 – a3INFRt-1 – a4EXDRt-1 – a5FDIt-1 –
a6OPENt-1), that is, the
one-period lagged value of the error from the cointegrating regression. The ECMt-1
equation above states that ΔGDP
depends on change in the explanatory variables and also on equilibrium error
term that determines the short-run behavior of the model. The ECMt-1
equation is estimated through the use of E-view 6.0 and the result extracted
from the E-view output as reported in table 4 above. Since, ECMt-1 is
positive (i.e. GDP is above its equilibrium value), a7ECMt-1 will
need to be negative which will cause ΔGDPt
to be negative.
Therefore, leading GDPt to fall in period t.
Thus, the absolute value of a7 (1.000) decides how quickly the
equilibrium is restored i.e. Ut-1 is the mechanism that adjust to
the long-run equilibrium by a unit of any distortion that may occur in the
short-run. The estimated ECMt-1 equation above shows that the
short-run changes in all the exogenous variables have positive and significant
impact on the short-run changes in the endogenous variable GDP. Therefore, the
estimated parameters - a1 to
a6 are the short-run marginal effect on economic activities in
Nigeria.
Section V
Conclusion and Recommendation
5.1 Conclusion
Our major task in this paper is to investigate
empirically the impact of capital flight on economic performance in Nigeria
using recent econometric tool such as cointegration and Error Correction
Mechanism (ECM). First, we begin with the analysis of time series with
stochastic non-stationary components by analyzing the unit root properties of
the relevant series. The results clearly show that the tests fail to reject the
null hypothesis that these variables are stationary and they are, indeed,
integrated of order two, which is the highest order i.e. I(2).
Given the stationary status of the series, the cointegration equation
was estimated. The evidence, however, shows that capital flight, exchange rate,
inflation rate, external debt, foreign direct investment and measures of
openness cointegrate with the Gross Domestic Product (GDP) in Nigeria. It was
also evidence that neither exchange rate (EXCHR) nor inflation rate (INFR)
series seems to exert positively with the GDP series, rather increase in exchange
rate with regards to naira and inflation galloping encourages capital flight,
and consequently retard growth. The existence of one cointegrating linear
combination was, therefore, established which corresponds to a long run GDP
function with respect to exogenous variables under consideration. Based on
this, an error correction model was developed which was shown to be well
specified relative to its own information set and capable of parsimoniously
representing the data set.
Adopting a cointegrating and an error correction modeling strategy, the
relationship between Nigeria’s GDP and capital flight were analyzed through a
series of reduction from over parameterized model interrelating all the components
of the GDP models. Thus, the estimates presented in this study suggest that
high exchange rate and inflationary rate encourage capital flight, and hence
retard economic activities in Nigeria.
5.2 Policy Recommendations.
Emanating from the result, for capital flight and its various components
such as exchange rate, inflation rate, external debt relief, foreign direct
investment and openness of the economy to have significant impact on economic
growth in Nigeria, the following policy options are recommended: First, the huge estimates of capital flight suggest a huge
potential for capital flight reversals. Efforts must be made towards the design
and implementation of appropriate policy measures that would encourage flight
capital to return to the country. Better economic reforms that will encourage the
inflow of foreign capital should be made. The reform should thus be based on
the need to encourage growth, exchange rate reduction, single-digit inflation
and reverse the negative distributional effects of capital flight. Specific policies
might include repatriation of flight capital to boost the growth initiatives
with selective controls on capital outflow, changes in Nigeria tax laws, and a
bias toward poor wages. More generally, a new overall strategy that would
encourage Nigerians abroad to come back home and invest in the country is
required. Repatriate
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