CAPITAL FLIGHT AND REVENUE GENERATION OF NIGERIA
Egbere Michael Ikechukwu,PhD, Emengini, S.E,PhD &Prof. Okafor R
Department of accountancy, University of Nigeria
The quest for sufficient capital in developing countries especially in Nigeria to restore balance in the government budget and the attendant improvement in the quality of life of the populace has taken its toll on the war against corruption in most of the developing countries. The devastating effect of this capital flight on revenue generation in Nigeria has not only aggravates the shortage of resources for development, it indirectly leads to a decline in domestic investments as well as a reduction in the revenue generation of the governments. Currently the revenue generation has not been able to reflect the economic reality on ground in Nigeria. Hence, this study seeks to examine the issues of capital flight and revenue generation of Nigeria. The study adopted the ex-post facto research design. The source of data was purely from secondary sources as the data were collected from Central Bank of Nigeria. The study was a time series from 1994-2016. The dependent variable is revenue generation proxied by total federally generated revenue while the independent variable is capital flight whose surrogates is depicted by ratio of over invoicing to capital flight (ROIKFT), ratio of under invoicing to capital flight (RUIKFT), ratio of debt servicing to capital flight (RDSCFT), ratio of exchange growth rate (EXGR) and Inflation rate (INFLA).The statistical tools adopted in the study were: Unit Root Test, Ordinary Least Square, Co integration Test and Granger Causality Test. In testing the hypotheses, Error Correction Model (ECM) was employed, at 5% level of significance (p0, ?2>0, ?3>0, ?4>0, ?5>0
It is expected that the sign of the coefficient of ROIKFT, RUIKFT, RDSCFT, EXGR) and INFLA Should be Negative. This is because an increase in any of these variables leads lead to a decrease in Total-federally generated revenue.
Method of Data Analysis
The data collected were analyzed using basic statistical tool just Multiple- Linear Regression was employed to test the hypotheses with aid of the E-Views 7 Statistical Software. The following analyses were carried out on this study. The statistical tools adopted in the study were: Unit Root Test, Ordinary Least Square, Co integration Test and Granger Causality Test. In testing the hypotheses, Error Correction Model (ECM) was employed, at 5% level of significance (p