Acta Universitatis Danubius. Œconomica, Vol 14, No 4 (2018)
Macroeconomics and Monetary Economics
Re-examining the Casuality between Capital Flight and Foreign Direct Investmen in Nigeria
Abdullahi I. Bello1, Olaniyi A. Taiwo2. Adekunle O. Ahmed3
Abstract: The relationship between capital flight and Foreign Direct Investment (FDI) has generated continuous debate in literature. This study aims at providing quantitative analysis of cointegration and causality between capital flight and FDI in Nigeria from 1985 to 2015. The study employed secondary data which was obtained from Statistical bulletin of Central Bank of Nigeria and data base of World Bank.The data obtained were subjected to Units root test, Co-integration test and Pair–Wise test of Granger Causality. The findings of co-integration revealed that the estimated equation and the series are co-integrated. The Granger-Causality test shows that there is no bi-directional causality between FDI and Capital Flight in Nigeria.The study concludes that the success to curtail capital flight in Nigeria is to improve level of infrastructural facilities in the country which can facilitate increase in domestic investment and also attract FDI. It is recommended that enhancing investment environment by minimizing the obstacles to doing economic activities, and increasing the effort against international financial crime will help reduce capital flight and improve FDI in Nigeria.
Keywords: Capital flight; foreign direct investment; Causality; BOP; Nigeria
JEL Classification: B40; C10; F20; F21
1. Introduction
The empirical investigation into impact of foreign direct investment to the emancipation of any country has been debated quite persistently in the literature. Kant (1996) noted that this debate explained the channels in which FDI may help to boost growth in recipient countries particularly in developing countries. FDI flows into a country through stock of foreign debt, because when debt is significant, capital flight rises, capital flight also worsening and deepening the debt problems of the indebted countries.
However, capital flight observed in several developing countries indicates that scarce capital in these countries is fleeing to the developed countries, worsening their financing problems and making debt servicing more difficult and costly. Capital flight is a problem for developing countries where there is scarce-capital which usually reduces growth in this economy. It is also believed that if these funds can be used at home, they can be used to reduce the level of foreign indebtedness and the inherent liquidity bottlenecks in traversing the foreign-exchange constraint.
Furthermore, it is worrisome that capital flight from developing countries is an indication to foreign investors about the risks involved and lead to a decline in, or even cessation of private capital flows. (Schneider, 2003) The purgatory of capital through capital flight dampens the local tax base in developing economies but the working of FDI as showed by theoretical underpining to bring about economic growth and development has not had it impacts due to reoccuring effects of capital flight.
Turning to Nigeria, capital flight is more pronounced than it is elsewhere in other West Africa region. Nigeria is rated among the heavily-indebted countries where the problem of capital flight has been regarded uncontrolled. It is revealed that Nigeria capital flight has been significant over the years.
Table 1. Capital Flight Estimates of Nigeria 1970-2013 (US $ Million)
Year |
Total Estimates |
1985 |
1957 |
1986 |
6337 |
1987 |
1323 |
1988 |
670 |
1989 |
5297 |
1990 |
-1835 |
1991 |
-4360 |
1992 |
-3819 |
1993 |
5129 |
1994 |
5414 |
1995 |
316 |
1996 |
217 |
1997 |
3932 |
1998 |
4141 |
1999 |
-1448 |
2000 |
2124 |
2001 |
85 |
2002 |
8134 |
2003 |
4065 |
2004 |
-7764 |
2005 |
-14274 |
2006 |
-15218 |
2007 |
-10473 |
2008 |
2098 |
2009 |
8957 |
2010 |
20781 |
2011 |
18832 |
2012 |
30640 |
2013 |
39678 |
Computed by Authors, 2017
The picture painted above is quite direful. The mere size of capital outflows in relation to export earnings is clearly a source of concern because Capital Flight of this magnitude will continue to impede Nigeria’s development and poverty alleviation effort of government.
Furthermore, Upon this, policy observers, researcher and academicians have observed that foreign debt and capital flight (in most developing countries, Nigeria inclusive) accumulate simultaneously as in the case with private external borrowing guaranteed by governments (which escalate with capital flight). This off course has raise leading question whether FDI inflows in LDCs facilitate capital flight (as private foreign borrowings do), or do they, instead, mark a dwindle in capital flight or a return of flight capital to the resident developing countries? Or put more relevant to the Nigeria case which is the global focus for this study, has FDI resulted to a plough back of capital flight? And expressing the simultaneity more pronouncedly, has capital flight resulted in reducing FDI?
The primary objective of this empirical piece of work is to demonstrate quantitatively the cointegration and causality between capital flight and foreign direct investment in Nigeria through. The specific objective is to empirically determine if there is long run relationship causality between FDI and capital flight in Nigeria. Aside this, it provide the case for Nigeria for which limited adequate empirical work is available to the researcher on the interaction between FDI and capital flight to pattern with as at the time of this investigation. Thus, this study attempts to fill the gap.
Moreso, unlike the only previous work on the relationship between FDI and capital flight (i.e. Kant, 1996) which considers correlation analysis, this paper utilizes the econometric analysis. In particular, the granger-causality regression analysis will be used following adequate assessments of our data in order to suit the recent advancement in econometrics. The study depends on quantitative data which are available in historical forms. We employ time series data ranging from 1985 and 2015.
2. Literature Review
The empirical investigation into the implications of capital flight on FDI has received increasing attention from several studies. (Ajayi, 1995; Ayadi, 2008; Bakare, 2011; Beja, 2013; Oloye & Olatunji, 2015) During the period of the debt crises in the late 1970s and 1980s, a lot of attention was devoted to the study of the outflows of resident capital as a response to unhealthy domestic policies and political instability. Jimoh (1999) viewed capital flight to be generally believed as capital that is runing away. Essentially, capital flight from finance perspective is viewed as short-term speculative outflows from a country. This is taken to mean outflows that involve the acquisitions of assets oversea plus net errors and omission.
To ensure any economy remains economical and significant in a dynamic international economy, such economy requires substantial investment in modern technologies, equipment and contacts which presents an investment opportunity in the private equity space. (Javorcik, 2004) Reducing fled capital by means of injection either private equity or foreign direct investment is essential in enhancing efficiency and firm growth. (Bender & Ward, 2009) Aggravating the necessity for capital injection is the fact that the Zimbabwean working population depends on small enterprises for employment. The need for private equity finance to boost start-up firms and provide finance for leverage buyout transactions is therefore important. With limited access to bank debt due to insufficient trading history and the riskiness of cash flows, access to venture capital funds becomes pivotal. (Bender & Ward, 2009) Venture capital firms can be engines of new job creation and a source of innovation as they support growing and innovative companies. (Bertoni, Colombo & Grilli, 2013; Harris, Jenkinson & Kaplan, 2014)
The management style of multinational capitalists differs across countries as well, depending on the development of institutions. (Lerner & Tåg, 2013; Lerner et al., 2011) Venture capital firms are actively involved in managing their investments by way of getting board representation and involved in the day-to-day management issues. (Jaaskelainen, 2012) This makes private equity or foreign direct investment and venture capital-owned companies better managed compared to those company funded by other sources. (Bloom, Sadun & Van Reenen, 2015; Melusi & Mabutho, 2015)
Private equity funds vary among countries especially organizational form. In countries such as the United Kingdom and the United State, firms are said to be organized as limited partners which indirectly affect form of FDI, while in countries such as France and Germany, they have a structure that involves banks. (Lerner, Pierrakis, Collins & Bravo, 2011)
The literature has proffered concrete evidence that capital flight is indirectly influence by flows of capital particularly the flows of foreign capital in the mode of foreign debt into the recepient country. This contentious debate was highly discussed in literature especially in the obsequies of the debt crisis of the 1980s. Cuddington (1987) found in Mexico that 31 cents of each dollar of long current-term external loans to the government termed as capital flight in the same year. Earlier work on African countries show similar direction pointing to a close platform between capital flight and debt inflows. (Boyce & Ndikumana, 2015)
The statement by Kant (1996) points that the nexus between FDI and capital flight is undetermined a priori. The investment climate perspective, proved that capital flight is influenced by the risk-adjusted return dynamics between foreign and local assets. Under this view, capital flight is a pointer of higher returns to foreign asset relative to the local country. But as Lessard andWilliamson (1987) pointed out that the investment climate is not sufficient to explain continuos capital flight and FDI. If local assets are surrounded by foreign assets in rate of return, this is basis for both foreign and local investors. However, the investment climate suggest a negative relationship between capital flight and FDI, a lucrative investment environment would stimulate FDI but discouraging capital flight; in other words, it would encourage both FDI and domestic investment.
The discriminatory treatment perspection showed that capital flight is influenced by government laws and regulations that are strongly in favor of foreign investment. These may include preferential taxation such as tax holidays and priority given to foreign claims over resident claims in the scenario of a financial shortage. (Kant, 1996) Such preferential treatments would result in differential perceived or actual risk for home investment relative to FDI, which would stimulate capital flight. (Dooley, 1988; Eaton, 1987; Khan & Haque, 1985) Under those situation, high capital flight would lead to high FDI.
The third possibility has been ignored in the literature. The two perspectives above, showed that capital flight and FDI may move mutually or in the opposite direction due to a third factor that affects both. The nexus may not necessarily be direct. However, there is a tendency that FDI can actually cause capital flight directly, in a similar way as the debt-fueled capital flight. First, FDI generate new resources that can be flight out of the country in illicit channels, thus remaining unrecorded in the country’s Balance of Payments. This is a way in which FDI fueled capital flight. Second, ex ante, capital flight could be the true reason of FDI, in which case the host country serves as mere transit for unrecorded financial outflows, especially those destined to secrecy jurisdictions. This would be the case for capital flight-bound FDI.
The question then is, does FDI possible influencing factors that facilitate the capital flight link? Two possible ways is guarantee to explain this. First, local natural endowment attract FDI, which translate to financing capital flight. Second, as the natural resource sector is subject to corruption and rent seeking, FDI directed to natural resources is more susceptible to contribute to capital flight. This would show a direct relationship between capital flight and FDI very strong in countries that have both abundant natural resource endowment and corrupt institutions. The objective of this study is to show clearly the empirically the linkages between capital flight and FDI and sheds light on the role that FDI play in the leakages of scarce capital in the case of African countries (Nigeria particularly).
3. Research Methodology
In theory, the relationship that exist between capital flight and foreign direct investment (FDI) has remained traditionally independent of each other but recently emphasized has been placed on the interaction between them. More importantly, it has been argued that FDI is a component of capital flight measurability. However, since part of capital flight could also be used as investment in foreign countries thus capital flight is symbiotically also a crucial part of FDI. As such, the relationship between them is one of a mutual interdependence. For the purpose of this study, the Erbe and the World Bank broad was employed to measure capital flight, given by:
CF = ED + FDI + BOP + FR (4.1)
Where;
CF equal to capital flight, ED equal to external debt, FDI eqaul to FDI, CAS equal to current account surplus and FR equal to change in foreign reserves. Essentially, this measure is adopted for two reasons, first, it is the broadest capital flight measure that can be found in the literature and second, it takes change in gross external debt and net foreign direct investment as the sources of finance and subtracts current account deficit and building up of foreign reserves from it. As such, it possibly upholds our methodology of study which relates simultaneity of interactions between our variable of interests. According to Sims and Todd, if there is true simultaneity among a set of variables, they should be gauged on equal footing, and there should not be a priori difference between independent and dependent variables. Following this, our model for this empirical work is:
CF= F(FDI, BOP, RGDP, EXR, BM2) (4.2)
and in linear form with time subscript our equation above is restated as:
MCF = ψ0+ ψ1FDI + ψ2 BOP + ψ3 RGDP + ψ4 EXR + ψ5BM2 + Ω (4.2)
Where MCF = measure of capital flight (given by ED + FDI + BOP+ FR); FDI is foreign direct investment; BOP is balance of payment balance (deficit or surplus); RGDP is real gross domestic product; EXR is exchange rate; and BM2 is broad money supply and Ω is residuals for our equations. (This study sources secondary data from the Central Bank of Nigeria (CBN), Debt Management Office (DMO) and Nigerian Bureau of Statistics (NBS). The sample periods for estimation cover 1985 to 2015 consisting of quarterly series for each variable).
ψ0 ψ1 ψ2, ψ3, ψ4, are the parameter of the estimated equation while Ω is the error term.
The a priori expectation are as follow FDI>0, BOP<0, RGDP<0, EXR<0, and BM2>0
The
Granger
causality is used to test for the direction of causality between FDI
and capital flight in Nigeria. The Granger causality test equation
for capital flight granger cause FDI is specified as;
So also, the Granger causality test equationfor capital flight granger cause FDI is specified as
4. Data Analysis and Interpretation
4.1. Stationary Test
Table 2 is use to test the stationarity of the variables at level, the series (CF, FDI, EXR, BOP, GDP, and BM2) is exposed to unit root tests. The results are listed in the table below.
Table 2
Variables
|
Order
|
Included in Test Equation
|
ADF Test Statistic |
Mackinnon Critical Value |
GDP |
I(0) |
Trend & Intercept |
-2.9677 |
-1.2712 |
FDI |
I(0) |
Non |
-2.3677 |
-3.7017 |
EXR |
I(0) |
Trend & Intercept |
-2.4617 |
-0.4953 |
CF |
I(0) |
Intercept |
-2.2678 |
-3.7017 |
M2 |
I(0) |
Trend & Intercept |
-2.0588 |
-0.5875 |
BOP |
I(0) |
Intercept |
-2.9077 |
-1.5325 |
Source: Eviews output computed by the authors, 2017
The ADF results in table 2 shows that some of the variables are non stationary at level, i.e.I(0) at 5 percent confidence levels.
Table 3
Variables
|
Order
|
Included in Test Equation |
ADF Test Statistic |
Mackinnon Critical Value |
RGDP |
I(1) |
Trend & Intercept |
-2.9718 |
-7.9179 |
FDI |
I(1) |
Non |
-4.9762 |
-2.5955 |
EXR |
I(1) |
Trend & Intercept |
-2.9718 |
-5.1039 |
CF |
I(1) |
Intercept |
-3.9718 |
-2.1039 |
M2 |
I(1) |
Trend & Intercept |
-2.9980 |
-16.402 |
BOP |
I(1) |
Intercept |
-2.9718 |
-4.2262 |
Source: Eviews output computed by the authors, 2017
The ADF results in table 3 above shows that all the variables that are non stationary at level but became stationary at integration of order one, i.e.I(1) at 5 percent confidence levels.
Table 4
Unrestricted Cointegration Rank Test (Trace) |
|
||||
|
|
|
|
|
|
|
|
|
|
|
|
Hypothesized |
|
Trace |
0.05 |
|
|
No. of CE(s) |
Eigenvalue |
Statistic |
Critical Value |
Prob.** |
|
|
|
|
|
|
|
|
|
|
|
|
|
None * |
0.960946 |
172.0611 |
95.75366 |
0.0000 |
|
At most 1 * |
0.670374 |
84.50517 |
69.81889 |
0.0022 |
|
At most 2 * |
0.642068 |
54.54066 |
47.85613 |
0.0104 |
|
At most 3 |
0.400360 |
26.80052 |
29.79707 |
0.1066 |
|
At most 4 |
0.380659 |
12.99203 |
15.49471 |
0.1151 |
|
At most 5 |
0.002085 |
0.056356 |
3.841466 |
0.8123 |
|
|
|
|
|
|
|
|
|
|
|
|
|
Trace test indicates 3 cointegrating eqn(s) at the 0.05 level |
|||||
Unrestricted Cointegration Rank Test (Maximum Eigenvalue) |
|||||
|
|
|
|
|
|
|
|
|
|
|
|
Hypothesized |
|
Max-Eigen |
0.05 |
|
|
No. of CE(s) |
Eigenvalue |
Statistic |
Critical Value |
Prob.** |
|
|
|
|
|
|
|
|
|
|
|
|
|
None * |
0.960946 |
87.55596 |
40.07757 |
0.0000 |
|
At most 1 |
0.670374 |
29.96451 |
33.87687 |
0.1367 |
|
At most 2 * |
0.642068 |
27.74014 |
27.58434 |
0.0478 |
|
At most 3 |
0.400360 |
13.80849 |
21.13162 |
0.3810 |
|
At most 4 |
0.380659 |
12.93568 |
14.26460 |
0.0802 |
|
At most 5 |
0.002085 |
0.056356 |
3.841466 |
0.8123 |
|
|
|
|
|
|
|
|
|
|
|
|
|
Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level |
Since theTrace and Max-eigenvalue are not the same, the author make used of Trace test because it indicates 3 cointegration equation among variables of interest at 0.05 level
Table 5
|
|
|
|
|
|
|
|
|
|
Variable |
Coefficiet |
Std. Error |
t-Statistic |
Prob. |
|
|
|
|
|
|
|
|
|
|
C |
4.255166 |
2.362454 |
1.801164 |
0.0843 |
FDI |
0.145536 |
1.404223 |
0.103642 |
0.9183 |
EXCH |
0.127509 |
0.031608 |
0.237584 |
0.8142 |
M2 |
0.062119 |
0.000345 |
3.242290 |
0.0035 |
BOP |
-0.480288 |
0.132843 |
-3.615446 |
0.0014 |
GDPG |
-0.161895 |
0.170024 |
-0.952185 |
0.3505 |
|
|
|
|
|
|
|
|
|
|
R-squared |
0.783271 |
Mean dependent var |
3.378786 |
|
Adjusted R-squared |
0.649786 |
S.D. dependent var |
7.622073 |
|
S.E. of regression |
5.905087 |
Akaike info criterion |
6.566362 |
|
Sum squared resid |
836.8812 |
Schwarz criterion |
6.846601 |
|
Log likelihood |
-92.49543 |
Hannan-Quinn criter. |
6.656013 |
|
F-statistic |
4.863215 |
Durbin-Watson stat |
1.963753 |
|
Prob(F-statistic) |
0.003266 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Source: Eviews output computed by the authors, 2017
The estimated result in Table 5 above shows that a unit change in M2 will lead to 0.06 increase in capital flight. This conforms to a priori or theoretical postulation i.e increase in money supply will exarcebate capital flight. FDI conform to the apriori expectation but it is not significant. It shows that any unit increase in FDI will lead to 0.14 increase in capital flight i.e if FDI increases capital flight will increase too. The non significant of FDI is due to the fact that most FDI inflow into the country is based on oil sector, which make the sector more lucrative and under-develop other sector. The high concentration of capital in the oil sector makes capital flight highly traceable to the sector and lower the rate at which other sectors engage in capital flight. Exchange rate conform to apriori expectation but it is not significant. The non significant shows that there are other factors influencing capital flight in Nigeria such as corruption, inner-drive of political leader etc. RGDP is negatively related to capital flight i.e the higher the capital flight, the lower the RGDP of a country, it is significant at 5% level. The R2 (coefficient of determination) from the result is 0.78(78%) while adjusted R2 is 0.64 (64%). It shows that about 78% of systematic changes in the endogenous variable can be explained by changes in all independent variables. This is surely a good fit because only 22% systematic variation in CF is left unexplained by the model, which may be attributed to the disturbance term. The Durbin Watson value corrected which is 1.9637 implies that there is no presence of first-order positive or adverse autocorrelation. A test of general significance of the model shows that the general model is insignificant at 5% levels of significance. This indicates the entire slope coefficiently taken together is simultaneously insisgnificantly different from zero. One of the secondary objectives of this study is to examine the causality between capital flight and FDI. The causal relationship between these variables was carried out using Pair-Wise Granger causality test. The results are presented in table 6.
Table 6. Pair – Wise Granger Test Result
Null Hypothesis: |
Obs |
F-Statistic |
Prob. |
||
FDI does not Granger Cause CF |
28 |
0.68001 |
0.5165 |
||
CF does not Granger Cause FDI |
0.65678 |
0.5280 |
|||
BOP does not Granger Cause CF |
28 |
0.87643 |
0.0297 |
||
CF does not Granger Cause BOP |
4.10470 |
0.4299 |
|||
EXCH does not Granger Cause CF |
28 |
1.78320 |
0.0306 |
||
CF does not Granger Cause EXCH |
0.63423 |
0.5394 |
|||
M2 does not Granger Cause CF |
28 |
0.33872 |
0.7162 |
||
CF does not Granger Cause M2 |
0.19350 |
0.8254 |
|||
GDPG does not Granger Cause CF |
28 |
2.33047 |
0.1198 |
||
CF does not Granger Cause GDP |
0.46203 |
0.6357 |
|||
BOP does not Granger Cause FDI |
28 |
0.31491 |
0.7330 |
||
FDI does not Granger Cause BOP |
1.14975 |
0.0343 |
|||
EXCH does not Granger Cause FDI |
28 |
2.14889 |
0.1394 |
||
FDI does not Granger Cause EXCH |
0.56374 |
0.5767 |
|||
M2 does not Granger Cause FDI |
28 |
0.58404 |
0.5657 |
||
FDI does not Granger Cause M2 |
0.02600 |
0.9744 |
|||
GDPG does not Granger Cause FDI |
28 |
1.98328 |
0.1605 |
||
FDI does not Granger Cause GDP |
1.12611 |
0.3415 |
|||
EXCH does not Granger Cause BOP |
28 |
2.79627 |
0.0818 |
||
BOP does not Granger Cause EXCH |
0.50774 |
0.6084 |
|||
M2 does not Granger Cause BOP |
28 |
1.26067 |
0.3023 |
||
BOP does not Granger Cause M2 |
0.00844 |
0.9916 |
|||
GDPG does not Granger Cause BOP |
28 |
5.46122 |
0.0115 |
||
BOP does not Granger Cause GDP |
0.16713 |
0.8471 |
|||
M2 does not Granger Cause EXCH |
28 |
0.80111 |
0.4610 |
||
EXCH does not Granger Cause M2 |
0.39892 |
0.6756 |
|||
GDPG does not Granger Cause EXCH |
28 |
0.03676 |
0.9640 |
||
EXCH does not Granger Cause GDP |
1.76712 |
0.1932 |
|||
GDPG does not Granger Cause M2 |
28 |
0.25281 |
0.7787 |
||
M2 does not Granger Cause GDP |
0.30594 |
0.7394 |
Source: Eviews output computed by the authors, 2017
The Granger causality results displayed in figure 6 shows that there is non bi-directional between FDI and CF i.e according to this result, there is no Granger – Causality between FDI and CF in Nigeria, a unidirectional causality exists between BOP and CF; EXCH and CF at 5 per cent level of significance. It is also shown by the result that unidirectional causality exists between GDP and BOP i.e GDP granger cause BOP but BOP does not granger cause GDP.
However, the result shows non causal effect between FDI and CF. The existence of causal relationship as shown in Figure 4.5 can be summarized thus:
BOP→CF
EXCH→CF
4.2. Model Appropriateness Test
There are some features that these models should satisfy in order to be appropriate for policy consideration and implementation, the residuals must be normally distributed, absence of autocorrelation, no serial correlation, homoscedastic, etc. in this study these tests were conducted on the residuals to decide if these models are robust.
Table 7. Breusch-Godfrey Serial CorrelationLMTest
|
|
|
|
|
F-statistic |
0.254249 |
Prob. F(2,22) |
0.7777 |
|
Obs*R-squared |
0.677741 |
Prob. Chi-Square(2) |
0.7126 |
Source: Eviews output computed by the authors, 2017
Table 8. Heteroskedasticity Test:ARCH
F-statistic |
3.769433 |
Prob. F(1,27) |
0.6627 |
Obs*R-squared |
3.552668 |
Prob. Chi-Square(1) |
0.6294 |
Source: Eviews output computed by the authors, 2017
The Breusch-Godfrey serial correlation LM test shows a P-Value of 71% for the observed R2 which means we cannot reject null hypothesis that the residuals are not serially correlated. The Heteroscedasticity test also shows a P-Value of 62.9% for the observed R2 meaning that the null hypothesis that the residual has no ARCH effect cannot be rejected. All these tests confirm that the model is robust for policy consideration.
5. Conclusion and Recommendations
The paper seeks to assess the interaction between foreig FDI and capital flight in a developing country taking Nigeria as a case study. The study was motivated by the fact that both FDI and capital flight have increased tremendously in the country, for the periods studied, and this calls for attention to know whether there is any relationship between them given the contradictory effects of both and their attendant impacts on economic growth and development.
Essentially, our empirical work provides a direction of existing relationship between FDI, capital flight and their determining variables modelled. In effect, we observed that there is a direct relationship between FDI and capital flight. FDI seems to increase capital flight and vice versa. However, since parts of capital flight could also be used objectively or otherwise as foreign investment abroad thus capital flight is symbiotically also a crucial part of outflow of FDI.
This can be concluded that the success to curtail capital flight in Nigeria is to improve level of infrastructural facilities in the country and maintain a stable exchange rate in the country. Without the availability of these needed facilities, it would be risky for investors to come into any country and sustained their capital.
Finally, recommendations to enhancing investment environment by minimizing the obstacles to doing economic activities, and increasing the effort against international financial crime will help reduce capital flight and improve FDI in Nigeria. This is important because it will stimulate investment position of the country.
6. References
Ajayi, S.I. (2005). Managing capital flight: Issues and challenges. Paper presented at a seminar tittled; Capital flows and Economic Transformation in Nigeria, at the Central Bank of Nigeria’s 5th annual monetary policy conference. CBN Conference Hall, Abuja.
Ayadi, F.S. (2008). Econometric Analysis of Capital flight in Developing Countries. 8th Global Conference on Business and Economics. Florence, Italy, 12(13), pp. 18-49.
Bakare, M.O. (2011). Tax avoidance, Capital flight and Poverty in Nigeria: The Unpatriotic Collaboration of the Elite, the Multinational Corporations and the Accountants: Some evidence. Department of Management Studies. University of West Indies, Mona Kingston.
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1 University of Ilorin, Department of Finance, Nigeria, Address: 1515, Unilorin Staff Quarters, Amodu Bello Way, Ilorin, Nigeria, Corresponding author: ibrahimabdul2008@yahoo.com.
2, University of Ilorin, Fountain University, Osogbo, Nigeria, Address: Opp. Olomola Hospital, Along Agric Settlement Road, Oke-Osun, Osogbo, Nigeria, Tel.: of Accounting, Address: 1515, Unilorin Staff Quarters, Amodu Bello Way, Ilorin, Nigeria, E-mail: niyitaiwo03@yahoo.com.
3, Fountain University, Department of Accounting & Finance, Osogbo, Nigeria, Address: Opp. Olomola Hospital, Along Agric Settlement Road, Oke-Osun, Osogbo, Nigeria, Tel.: +2348037063960, E-mail: taahmed4real@yahoo.com.
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