3.0 Research methodology

In this section data concerning the economic growth and development of various African countries: Nigeria and Rwanda among others are collected utilizing retrieval. This research majorly used secondary data that was retrieved from online sources like the World Bank website and the above countries various statistical websites, e.g. Rwanda bureau of statistics website. Also, retrieval of various economic journals was done. In order to analyze the collected data after retrieval from the various economic journals and websites, regression analysis was used to regress the dependent variable economic growth, GDP per capita-(Y variable) on the dependent variables x1- corruption), x2(conflict), X3(conflict* corruption) and X4 – other factors using the following regression model-

Y= β₀ + β₁X₁+ β₂X₂ + β₃X₃+ β₄X₄ +µ; where µ is the error term. In addition as far as determination of the relationship between conflict and corruption individually with GDP is done by the use of correlation to come up with the negativity or positivity of the respective relationships. To present the above data in a manner that it can be understood with ease and the trend as far as economic growth and the two factors are concerned, line graph is used so that the trend can be seen with ease without any difficulty. Besides, tabulation of descriptive statistics concerning corruption and conflict in the African nations of concern is used so that an overview of the two variables can be looked at and understood within a twinkle of an eye even by the layman.

In this section, I critically looked at various aspects or positions of economic performance as well as multiple indicators of conflict and corruption. Though, this information was found concerning what aspects of economic performance are mostly affected by this issue of conflict and corruption. Selection of variables that measure economic performance was done; for example, unemployment rates, gross domestic product, foreign direct investment, the rate of industrialization and the per capita income. Corruption Perception Index was used to measure corruption. The index is the combination of various indicators and is typically a measure of how a specific country that is corrupt is perceived to be. It is crucial to note that a Conflict Index was created as there is no published concerning conflict exists and this will be analogous to the Corruption Perception Index. Regarding Conflict Index, it was up to various indicators like mortality rates from battle, the period of conflict, and the number of times they occur and many more.

 All the above parameters were retrieved and tabulated for the sake of analysis of data. Excel data tool was used for the proposes of study to come up with the following impressive results and findings.

4.0 Analysis of Results and Findings

Data on Nigeria corruption index

DATE	GDP per capita annual growth	VALUE OF CPI	CHANGE IN  CPI	GDP	GDP GROWTH	GDP per capita in $
2018	2.9%	27.00	0.00%	397,270M	1.9%	2,028
2017	-9.6%	27.00	-3.57%	376,361M	0.8%	1972
2016	-20.0%	28.00	7.69%	405,442M	-1.6%	2180
2015	-15.4%	26.00	-3.70%	493,841M	2.7%	2726
2014	7.5%	27.00	8.00%	568,496M	6.3%	3223
2013	7.2%	25.00	-7.41%	514,965M	5.4%	2998
2012	8.3%	27.00	10.23%	460,952M	4.3%	2798
2011	9.2%	24.49	2.06%	414,095M	4.9%	2583
2010	20.8%	24.00	-4.00%	369,062M	11.3%	2365
2009	-12.3%	25.00	-7.41%	297,458M	8.4%	1959
2008	22.6%	27.00	22.73%	330,260M	7.2%	2234
2007	14.6%	22.00		262,215M	7.3%	1823
2006	27.8%	22		222,791M	6.7%	1591
2005	26.7%	19		169,645M	7.0%	1245
2004	23.2%	10		130,345M	10.4%	983
2003	6.6%	14		102,935M	9.5%	798
2002	25.1%	16		93,983M	14.6%	748
2001	4.9%	10		73,128M	6.7%	598
2000	14.8%	12		67,824M	5.5%	570
1999	-73.3%	16		57,477M	0.5%	496

 

DESCRIPTIVE STATISTICS of the above information:         GDP pc % growt

     CPI value             GDP                GDP growth   GDP

                                                                                                                                        Per capita

	MEAN4.58%	21.4245		290,427	5.99%	1,796
MEDIAN	7.90%	24.245		313,859	6.50%	1,966
MODE	#N/A	27		#N/A	0.067	#N/A

Table 2

Scatter diagram showing the correlation between CPI and GDP per capita

Figure 1

Line graph showing time in years and GDP in millions

Figure 2

Bar graph of GDP per capita, time in years and corruption perception index

Figure 3

Correlation between time and GDP per capita

Figure 4

REGRESSION RESULT

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.871783
R Square	0.760006
Adjusted R Square	0.746673
Standard Error	439.2935
Observations	20
ANOVA
	df	SS	MS	F	Significance F
Regression	1	11000130	11000130	57.00175	5.53E-07
Residual	18	3473618	192978.8
Total	19	14473748
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	-831.22	361.5642	-2.29895	0.033699	-1590.84	-71.6014	-1590.84	-71.6014
X Variable 1	122.6222	16.24146	7.549951	5.53E-07	88.50017	156.7443	88.50017	156.7443
Table 3

Figure 5

2.0 Data on the relationship between conflict and economic growth in Rwanda

Date	GDP Per-capita	GDP P.C annual growth IN %	UNEMPLOYMENT RATE IN %	FDI into Rwanda in million $
2018	773	1.3	0.98	–
2017	763	5.1	0.96	366.2
2016	726	-0.4	1.02	342.3
2015	730	1.0	1.1	379.8
2014	723	2.5	1.18	458.9
2013	705	1.2	1.16	257.6
2012	697	9.5	1.12	255.0
2011	636	10.2	1.08	119.1
2010	577	4.1	1.06	250.5
2009	555	8.3	0.96	118.7
2008	512	23.1	0.6	102.3
2007	416	18.8	0.76	82.3
2006	350	19.4	0.84	30.6
2005	293	21.7	0.96	8.0
2004	241	11.9	0.97	7.7
2003	215	7.6	0.96	4.7
2002	200	-4.3	0.88	1.5
2001	209	-8.8	0.8	18.5
2000	229	-12.7	0.78	8.1
1999	263	-16	0.72	1.7
1998	313	-2.1	0.64	7.1
1997	319	28.5	0.62	2.6
1996	248	9.6	0.56	2.2
1995	227	-31.4	0.52	2.0
1994	206	2.8	0.48	0.0
1993	301	6.1	0.4	5.8
1992	293	-28.4	0.32	5.5
1991	276	-6.6	0.3	4.6
1990	386	1.0	0.28	7.7

 

The descriptive statistics are

               GDP Per capita                 GDP P.C annual               

      Growth IN %                      UNEMPLOYMENT

                                                                                                          RATE IN %                         FDI into Rwanda in                                                                                                                                                                                million $

	426.9655		3.281481		0.830741		109.1808
median	319		4.1		0.88		13.3
mode	293		#N/A		0.96		#N/A

Table 5

The above descriptive statistics show that on average the GDP per capita of Rwanda over the years is 426.9655 million USD, average per capita growth in percentage is 3.281481%, average rate of unemployment at 0.830741 while that of FDI inflow is 109.1808 million USD. The most frequent GDP per capita over the years in Rwanda is 293 million USD, that of GDP P.C growth at 4.1%, MOST regular unemployment rate at 0.88% and 13.33 million USD  FDI inflow. Just by looking at the above table 2, we can observe even before any analysis the effect that conflict and stability can have on the per capita income of a nation. In 1994 at the time when there was a lot of conflict in Rwanda due to the famous genocide that took place in Rwanda in that year,  the country’s GDP went down to  206 million USD; the lowest awhile FDIO inflow was at 0.0. In the preceding year, 1995, the rate of GDP growth went down to -31.4%

The following scatter diagram, show the correlation that exists between GDP P.C annual growth IN % and the unemployment rate in %

Figure 6

 The following scatter diagram shows the correlation between FDI inflow and GDP growth rate%

Figure 7

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.848511
R Square	0.719971
Adjusted R Square	0.7096
Standard Error	112.2078
Observations	29
ANOVA
	df	SS	MS	F	Significance F
Regression	1	874021.1	874021.1	69.41862	6.1E-09
Residual	27	339945.8	12590.59
Total	28	1213967
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	-41155.5	4990.872	-8.24616	7.47E-09	-51396	-30915.1	-51396	-30915.1
X Variable 1	20.74975	2.490434	8.331784	6.1E-09	15.63981	25.8597	15.63981	25.8597
Table 6

Figure 8

 Line graph of Rwanda’s GDP against the number of years

Figure 9

Corruption perception index of Democratic Republic of Congo

year	CPI	GDP
2018	29	568,496M
2017	25	514,965M
2016	27.8	460,952M
2015	24	414,095M
2014	25	369,062M
2013	23	297,458M
2012	22.5	330,260M

FROM THE TABLE ABOVE, there is sufficient evidence that the year when CPI was high, the GDP went low, and when CPI was low, the GDP went higher.

Scatter diagram showing the correlation of the above

The above shows that there is a

5.0 DISCUSSION OF RESULTS

Based on the Nigeria data

The descriptive statistics on table 2 shows that on average, the Nigeria GDP growth rate is 4.58%. The corruption perception index averagely over the years is 21.424, and average real GDP is 290,427 million USD. The GDP growth over the years is 5.99% on average, while the GDP per capita is 1976$.  The most frequent CPI over the years examined is 27 while the most prevalent real GDP growth is 0.067%

From the scatter diagram on figure 1 Showing the correlation between CPI and GDP per capita, the equation Y= 86.68 x where y represent GDP and x- CPI, there exists a positive correlation between corruption perception index and GDP per capita

Hypothesis

H0: there is NO significant correlation between CPI and GDP per capita

H1: there is a significant correlation between CPI and GDP per capita

Test statistics

R2= 0.689

Using the P-value = 0.00001

 Since the p-value < 0.05, we conclude that the result is significant at 5% level of significance. Therefore there exists a significant positive correlation between Corruption perception index and GDP per capita, and therefore, the model y= 86.68 X can be used to predict values of GDP in Nigeria. R2 values show that 68.9 % of the variations in GDP is explained by the variations in Corruption perception index while the remaining 31.1 % of the variation in the model is explained by other factors, not in the model. Hence this model is a good fit. From this, we can conclude that Nigeria government should put more resources on fighting corruption so that the value of CPI can continue to go up so that its GDP can go up and the per capita income of its citizens can also rise higher.

 From the line graph, figure 2 of per capita income and time in years, we can see that right from 1999, the level of GDP per capita has been rising as time goes by, this is an indication that there have been efforts by the successive governments of Nigeria to fight corruption hence making the level of GDP per capita to go higher as the years advance. This shows that advancement in technology and borrowing a leaf from more advanced and developed nations have encouraged the Nigerian government to invest more on the people and distribute its resources equally across the entire country and best of all; they seem to have introduced some serious criminal charges on those leaders that are involved in corruption. The above is in line with Agbiboa, 2013 findings.

 Based on the correlation results of time and GDP in figure 4

Y=0.895x

R2= 0.010

H0: there is no relationship between time in years and GDP

H1: there is a significant relationship between years and GDP

 Alpha= 0.05

Test statistics

p-Value = 0.666264

The above result is not significant at p<0.05 since the value of p is > the significance level, 0.05

 Though from the correlation equation, there is a positive relationship between GDP and time, the correlation is not statistically significant, and therefore we conclude that the equation above should not be used to predict the future values of GDP based on time and GDP.

Based on the regression results in table 3 above,

Y= -831.22+122.6222X and the p-value for x =5.53E-07

HO: the two variable are not related

H1: the two variables are significantly related

P-value = 5.53E-07

Since p value< 0.05, we conclude that the two variables are statistically significant and say that the relationship between corruption perception index and per capita income is significant and also conclude that the regression model that was used is a good model hence it can be used to predict future values of GDP per capita of Nigeria. This is also supported by the R²=0.760006 which is above 0.5 showing that 76% of the variation in the model is explained by factors in the model while 24 % of the variations re-explained by other factors, not in the model.

Since the residual plots are randomly distributed, it shows that the line is a good fit and the model is appropriate for the data hence this model can be used in predicting future values of the dependent variable.

The above findings Some scholars believe that Africa is not that so corrupt than the rest world; rather, they indicate that corruption in Africa is more transparent. Research indicates that widespread poverty is an issue that has led to rampant corruption in Africa. With many people including government officials having little or no access some of the most basic needs, it is hard for most to people resist any help from anyone including those involved in criminal activities (Justino, 2009).It is also argued that the idea of limited government regulation gives room for corruption acts in Arica.  In many parts of the continent, there are no bank accounts or any online records, and because of that, it is easy for officials to receive any favor-returns in the form of money. In that case, there is little that anyone could do to prove guilty of officials who accepted a bribe. Africa countries lack strict laws according to research (Okafor, 2017). In general，many

According to Jorji A Nwogu and Victor Ushahemba, 2016, Corruption is a global issue, and there is no single nation in the entire globe that is completely free from it. Corruption is has been seen as a political, cultural, and individual problem and even though it is s global problem, these two authors argue that it is a recurring problem in the African nations. For example in Nigeria, that is one of the most populated countries in Africa and is a very critical producer of oil. This should make it one of the world’s top nations as far as economic development, living standards and welfare of citizens is concerned. On the contrary, these two authors put it clear that Nigeria is one of the countries still struggling with high unemployment rate; it is mired still with high rate of inequality among other issues of underdevelopment.( Nwogu, and Ijirshar, 2016) The above makes the idea that Nigeria is well endowed with enough human and material resources as that are very critical for national development and advancement to be baseless and with no evidence to prove it out. According to Jorji and victor, 2016 all the above is due to the fact that there are widespread cases of public funds misappropriation and misappropriation of assets by corrupt elites that take advantage of the illiterate public citizens and non-strict government to misappropriate the funds hence making economic development, raising standards of livings and equality as far as resource distribution is concerned to be an impossible dream. Corruption has also made development that should have been realized long time ago to be done at a plodding pace. Nigeria has an abysmal score as far as transparency international corruption perception index is concerned. In 2014, Nigeria had a score of 27 which was a 2 points increase from the previous 2013 score on scale of 0-100 ; 0( most corrupt) and 100( least corrupt making the country to be ranked 38^th is among the world’s most corrupt countries and  136^th out of the 175 countries that were assessed.(Jorji and victor, 2016)

According to Collier, 2006. the idea of corruption is directly linked to conflict; in that corruption leads to rising of conflicts. This is something that is experienced in countries in Africa. Communities’ fighting others simply because of issues related to elections has affected the economy of Africa as a whole. Researchers indicate that corruption and conflicts are hazards to economic growth (Arezki, and Gylfason, 2013)

According to Le Billon, 2003, Conflict and economic growth are two antagonizing things in the sense that one only thrives where the other is in its lowest even though there are some nations that do well when the others are at war, but these are only third party nations and not the actual nations at war (one of the reason is that they may take advantage of the war to offer the services that the countries at war were offering before).According to Lopez and Wodon, 2005, the economy of Rwanda subsided in the year1994- 1995 during the time which the nation was mired by conflict. At this same time, Juvenal Habyrimana regime was at the verge of being overthrown, and this increased conflict in the entire nation since most of the citizens loved him. According to Lopez, and Wodon, 2005, before this dark period, Rwanda under President Juvenal Habyriman was doing very well, and its economy was booming with most of its citizens having the best living standards as compared to other African countries.( Ikejiaku, 2009)The war and instability brought about a lot of distraction since they could not even extract oil that used to better their economy at peace hence proving beyond doubt that conflict is a serious enemy of development and should not be given a chance in any state be it developing or developed. (Verwimp, 2005)

One of the major reasons as to why I have decided to focus on this area for my project is basically that there is no literature among the above and many others not discussed here that has been done in comparing quantitatively the negative effect or impact that conflict and corruption on economic performance for Africa. Hence making me dig deep and find out the negative impact that both corruption and conflict has on African developing economies in terms of qualitative comparison as far as these two factors are concerned.

Based on the findings of Rwanda data

 The correlation between GDP P.C annual growth rate in % and the unemployment rate shows that( on figure 6:

Y=0.057X and R2= -0.04

The above equation shows that there is a positive correlation between the unemployment rate and GDP growth rate. Since the value of R2 is a negative number, we can’t get the value of r hence getting the p-value is difficult. From the TR2 value alone, we can conclude that the model is not a good fit, and therefore we can’t use it to predict the future values of the dependent variable. The absolute value of  R² is less than o.5, and it shows that only 4% of the variations in the model is explained by factors in the model while a whole 96% of the variations in the model are explained by other factors, not in the model. This is a very poor model

The correlation between GDP % annual growth rate and FDI inflow into Rwanda shows that 9( figure 7)

Y=2.4x; R2= -0.51

H0: there is no significant relationship between FDI and GDP growth rate

H1: there is a significant relationship between FDI inflow and GDP growth rate.

Test statistics

R2= -0.51

 Since the absolute value of 2 is more than 0.5, we say that 51% of the variation in the model(GDP growth) is explained by FDI Inflow. Therefore we conclude that the relationship between GDP growth rate and FDI inflow is significantly positive, and therefore its s clear indication that  when the inflow of FDI into a country in our case Rwanda goes up, the rate of DGP growth also increases and therefore the more peaceful a country is the more growth and development it will enjoy.  Conflict brings a lot of distraction and fear among foreign investors since they are afraid that whenever there is conflict in a nation where they have invested in.  There are higher chances that their investments will be destroyed and therefore leading them to make losses. This explains the reason as to why there was 0.00 FDI in Rwanda in 1994 since at this particular time, there was no favorable environment in Rwanda for business and therefore there was no foreign investor that wanted to risk their capital by investing in an unstable nation mired with war and contestant unrest.  The above is in line with what Serneels, and Verpoorten, 2015 found out in their work.

Based on Rwanda’s data the regression result shows that (from table 6 above)

Y= -41155.5+ 20.7495+4993.36

The p-value for x-intercept is 6.1 E^-09

H0: there is NO significant relationship between GDP % annual growth and FDI inflow

H1: there is a significant relationship between GDP % annual growth and FDI inflow

Test statistics

P-value= 6.1 E^-09

Since p-value < 0.05, we reject H0 and conclude that there is a significant relationship between GDP % annual growth and FDI inflow. This shows and confirms that peace is essential in attracting foreign investors in the case of Rwanda.  From the table+9. We can see that in the periods when there was conflict in Rwanda, 1994 -1995, there was very low GDP and the rate of FDI inflow was very low with 0 in 1994.

Since the residual plots are randomly distributed, it shows that the line is a good fit and the model is appropriate for the data hence this model can be used in predicting future values of the dependent variable.

6.0 Recommendations

For any country especially in the developing parts of the world to develop and advance, they need to try as much as possible to maintain peace at all costs if at all they want to achieve economic development.  We have seen that whenever conflict arises, the economy of the affected nation is jeopardized and brought to a standstill since things cannot operate as usual. Unemployment rate goes up at this time, FDI reduces, and GDP goes down. So for all these to be avoided, developing nations should make sure that they develop robust strategies to prevent the rise of conflict and those that can help them stop the dispute as fast as possible whenever it arises.

Governments in the developing countries need to come up with strategic measures to curb corruption since it derails development agendas of any hopeful young economy. They need to draft laws that severely condemn the act of fraud, for example, jail terms for the affected individuals and forever burn from being in any public office once someone is found guilty of corruption. This will make public officials in these countries understand that government offices are meant to serve the public and not to enrich themselves.

7.0 Limitations of the research

Resource constraint- there was no enough financial resources and enough time to enable me to travel to more African nations and collect raw data. The above made the scope of the study to be a bit narrow. There are chances that the secondary data that I used  may have been biased since it was data that was collected by some other researchers for their own designed motives hence the accuracy of the data and its reliability was not 100%