Political Corruption, Income Inequality and Poverty in Nigeria

Lucas B. Ojo¹, Amadi Chibuike Eusebius², Okoh Johnson Ifeanyi³, Timothy Ayomitunde Aderemi⁴

Abstract: The aim of this study is to examine the relationship between political corruption, income inequality and poverty between 2000 and 2019 in Nigeria. The study utilized secondary data from World Bank Development Indicators, Transparency International and the National Bureau of Statistics in Nigeria, and fully modified ordinary least square and Granger causality were employed to address the objective of the study. Consequently, the findings that emerged in this work could be enunciated thus; political corruption and income inequality have a significant negative relationship. However, corruption and poverty head count have an insignificant direct relationship. In the same vein, corruption and exchange rate have a significant positive relationship. In addition, there is a unidirectional causality which runs from political corruption to income inequality. Similarly, political corruption Granger causes exchange rate. Also, there is one-way feedback relationship flowing from exchange rate to inflation rate. However, no feedback relationship exists between political corruption and income inequality in one hand, and political corruption and exchange rate on the other hand. In a nutshell, it could be concluded that political corruption is responsible for the rise in poverty level in Nigeria, Also, political corruption Granger causes income inequality in Nigeria. Against this backdrop, the study makes the following recommendations to the policy makers in Nigeria that any time the goal of the policy makers is to reduce poverty and inequality of income among the Nigerians, policy that will address political corruption should be of the priority. In the same line, ant graft institutions such as EFCC and ICPC should be strengthened in order to achieve their primary goals of curbing corruption in Nigeria.

Keywords: Political Corruption; Inequality; Income; Poverty and Nigeria

1. Introduction

The issues surrounding corruption, income inequality and poverty have been a serious subject of debate in developing countries (Ajisafe, 2016; Ikubaje, 2014; Obazee, 2014; Rothstein & Holberg, 2011; Eichengreen & Gupta, 2011). The trio are regarded as an undisputed colossal disaster in any country. In many developing economies, income inequality and poverty in which they are experiencing currently could not be totally detached from the corrupt practices of their past and present public office holders. However, in the case of Nigeria, poverty is multidimensional, ranging from lack of access to public goods and services, illiteracy and exploitation, insecurity of food, life and property, bad governance and poor income (Aye, 2013).

Nigeria is one of the few oil rich countries in the world, yet majority of its population wallow in abject poverty. Whereas, a small group of politicians and elites enjoys the nation`s ever-expanding wealth. In Nigeria, distribution of resources is skewed in favour of the rich. The exorbitant cost of governance reinforces income inequality and poverty because the Nigerian law makers are one of the highest paid in the universe, with basic annual salary of $118,000, in which their huge various allowances could not be accounted for. This basic salary is 63 times bigger than GDP per capita of the country. In the same vein, management of public resources are subjected to the whip and caprices of the political office holders. This has facilitated exponential rate of political corruption in Nigeria. From 1960 to 2005, the Economic and Financial Crimes Commission (EFCC) reported that, public office holders had stolen over $20 trillion from the Nigerian treasury. These stolen funds are bigger than GDP of USA in 2012. Against this backdrop, empirical investigation into the nexus between political corruption, income inequality and poverty in Nigeria is highly imperative. Therefore, it is very urgent to embark on extensive research on how political factors, especially political corruption drives extreme income inequality and poverty in Nigeria

1.2. Brief Statement of the Problem

In the past few decades, the gap between the poor and the rich has been continuously growing in an alarming rate in Nigeria. Despite the fact that Nigeria is the biggest economy in Africa, 6^th highest exporter of crude oil in the world, yet the country is the headquarters of poverty (Aderemi et al. 2020; Adebayo, 2018; WDI, 2018). Before Nigeria slipped into economy recession in 2015, it is paradoxical that poverty and inequality were growing rapidly in the presence of economic prosperity in the country. For instance, in the period of 2000s, the Nigerian economy grew above 7% on average. Nevertheless, people living below national poverty line rose in 2004 from 69 million to 112 million in 2010, which is 67.3% increment in less than a decade. In the same vein, income inequality measured by the Gini Index rose in 2003 from 40% to 43% in 2009 (NBS, 2012).

However, corruption has been identified as one of the principal factors orchestrating poverty, income inequality and underdevelopment in an economy (Ellis, 2015; Gupta et al., 2002; Li, Xu & Zou, 2000). In the mist of rising poverty level and income inequality in Nigeria, corruption rate continues to expand exponentially. And this has received a global attention in the recent times. In 2013, Nigeria was ranked the third most corrupt country in ECOWAS sub region and 144^th out 177 countries, and moved to 148^th out of 180 countries in the globe (Transparency Int`l., 2018). This above scenario has sparked off the issues of concerned among various scholars and policy makers whether political corruption provokes income inequality and poverty in Nigeria. These issues of concern require a very urgent empirical investigation in order to mitigate their possible danger on the economy and humanity in Nigeria. In view of the above, the study raise these research questions. What are the channels of transmission mechanism through which political corruption stimulates income inequality and poverty in Nigeria? What are the effects of political corruption on income inequality and poverty in Nigeria?

Despite the fact that Nigeria is blessed with both abundant natural and human resources, income inequality and poverty have been on a perpetual increase. Meanwhile, the Nigerian society is bewildered with wrong policy formulation and implementation due to corruption. Against this backdrop, this study will move the frontiers of knowledge by examining the nexus between political corruption, income inequality and poverty in Nigeria. Hence, the relevant recommendations with respect to the reduction in income inequality and poverty, and improvement in good governance in Nigeria, and by extension, Africa could be an eye opener for the achievement of both SDGs and 2063 African Agenda.

2. Literature Review

Over the years, various studies have been carried out in examining the nexus between corruption, inequality and poverty. It is imperative to present the findings of past empirical studies in this section of the paper.

In a study carried out by Ajisafe (2016), an Autoregressive Distributed Lag (ARDL) model was used to analyse how corruption and poverty were related in Nigeria over the period of 1986 to 2014. The study concluded by establishing that corruption caused a devastating effect to citizens` welfare through reduction of health and education expenditures, including other social services thereby leading to a rise in poverty level in the country. In another study that focuses on a panel analysis of 97 developing economies, Vahideh, Zakariah and Hesam (2010) applied a Granger causality approach to examine a dynamic relationship between corruption and poverty from 1997 to 2006. It was argued in the study that two-way feedback existed between poverty and corruption in the study. While applying both indirect and direct measures,

In a panel analysis of ten Asian countries, Chiung-Ju Huang (2012) used a vector error correction in appraise the link between corruption, income inequality and economic growth. The study discovered that economic growth was not retarded by corruption. It was also revealed in the study that a direct link existed between economic growth and inequality of income.

Estefania (2010) examined the connection between corruption and poverty in 18 countries in Latin America. The author reinforced that a significant inverse link existed between corruption and the level of poverty.

Osabohien et al. (2020) employed fixed and random effect methods to analysis the relationship between social protection programmes, poverty and inequality in Africa between 2000 and 2017. The authors submitted that provision of social protection had a negative impact on poverty and inequality simultaneously. Mohammad (2013) used Autoregressive distributed lag model (ARDL) to assess linkage between income inequality, corruption and economic growth. It was discovered from the work that a long run convergence existed between income inequality, corruption and economic growth. It was also discovered that corruption led to a negative impact on the growth of the Nigerian economy.

However, Ellis (2012) posited the primary cause of poverty was not corruption but if facilitated poverty via a decrease in public services in terms of quality and quantity of education, health, housing etc. which the masses enjoy.

In the same vein, study carried out by Action Aid Report (2015) in which nexus between corruption and poverty was evaluated in Nigeria with the aid of content analysis. It was discovered from the study a strong correlation existed between poverty and corruption in the country. Also, the corruption perception index of Nigeria was very high and the reverse was the case of the Human Development Index. Eichengreen and Gupta (2011) employed fixed and random effects approach to assess the linkage between corruption, economic growth and poverty in 72 countries. The finding from the study argued that per capita income was reduced due to corruption. Economic growth of poor countries were more afflicted by corruption than mixed countries. Age and Wokekoro (2012) applied ordinary least square to investigate the relationship between corruption and sustainable economic development in Nigeria. The authors asserted that the major contributors of corruption in Nigeria are lack of transparency, high level of poverty and unemployment, weak institutional system, weak legal system and high rate of political interferences in ant graft agencies in the country. In another related work, Adenike (2013) assessed how corruption affects the growth of the Nigerian economy between 1980 and 2009 with the application of regression analysis alongside Granger causality test. The findings from the study posited that corruption per worker had a direct relationship with output per worker. Whereas, the reverse is its relationship with capital expenditure, educational expenditure and investment from private sector. Also, a unidirectional causality runs from output worker to corruption per worker.

Consequently, Wickberg (2012) submitted that corruption orchestrated poverty via the influence it reverberated on income, distribution of resources and accessibility of services. In another perspective, Justesen and Bjornskov (2014) enunciated that the poor were much more vulnerable to offer bribes to officials of the government before obtaining services from them. The authors argued further that poverty led to a rise in the frequency of which individuals encounter demands for bribes in the process of getting services from government officials, especially in urban centres.

Conclusively, past studies have shown various impacts of corruption on economy. However, its impacts on poverty and income inequality still remain a bone of contention Nigeria in the recent times. Hence, the importance of this work.

3. Methodology and Data

This study adopted an ex-post facto research design. This research design is considered appropriate for this study because its main interest is to explore the viable relationship and describe how the explanatory variable predicts variation in the dependent variable. The study covered the period of 2000 to 2019. It is should be stressed that 2000 was adopted as the base year because uninterrupted democracy started in 1999 in Nigeria. Also, the study utilized quantitative data for its empirical analysis. To be explicit, Poverty head count was used to measure poverty index. These data were extracted from the World Bank Development Indicators (WDI) for Nigeria. In the same vein, corruption index provided by International Country Risk Guide will be used as proxy for control of corruption, and sourced from the Freedom House now known as Transparency International. Income inequality data were equally obtained from the National Bureau of statistics in Nigeria.

3.1. Empirical Model

In estimating the relationship between the variables of interest in this work, the relevant model to achieve this could be adopted from the works of Ajisafe (2016), Adeleye and Eboagu (2019), Matthew et al. (2019) and Osabohien et al. (2020) by eliminating some variables that are not relevant to this study.

Political Corruption = F (Income Inequality, Poverty) (1)

PCR = F (INQ, PTY, EXR, INF) (2)

Linearizing model (2) in log form is expressed as follows:

PCR = γ1+ γ2INQ+ γ3PTY+ γ4EXR+ γ5INF+u (3)

It is important to state that PCR is used to denote political corruption, and is measured by corruption perception index. INQ is used to proxy income inequality, and is measured by Gini index. PTY is used to capture poverty level, and is measured by poverty head count at $1.25 PPP 2005. EXR is official exchange rate in Nigeria and INF is inflation rate, measured by consumer price index, u is error term which captures other variables outside the model. t ranges from 2000 to 2018. The apriori expectation of the model could be stated as follows γ2, γ3, γ4 and γ5 >0.

3.2. Discussion of Results

Table 1. Descriptive Statistics of Variables of Interest

Source: Authors’ Work (2020)

Descriptive statistics of data help to show the distribution of data series over time. This study examined the relationship of variables of interest over the periods of 19 years. Political corruption proxied by corruption perception index has a minimum value of 1.4 and maximum value of 2.8. The mean value of the data is greater than its standard deviation. That means these data are moderately dispersed from its mean. The data are however skewed towards negative side and Kurtosis value of less than 3. In the same vein, income inequality data possess a similar property as that of political corruption data.

Consequently, poverty head count, exchange rate and inflation rate data are moderate dispersed from their mean values because they the value of their standard deviations are less than their mean value. Meanwhile, these data are positively skewed with poverty head count, exchange rate, inflation rate having Kurtosis values of 3.047970, 5.038727 and 1.903503 respectively. It could be therefore deduced from the above that these data have some elements of symmetrical distribution. It could be used for further econometric analysis.

Table 2. Unit Root Tests

Source: Authors’ Work (2020)

The above table presents estimated results of both the standard Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests which were used to examine the stationarity properties of the data. Carrying out this test becomes imperative because presence of unit root in data could inform spurious regression which could cause the policy implication of study to be biased. The above results imply that the data are mixture of I(0), I(1) and I(2) which could be spelt out explicitly as follows; political corruption and income inequality data are not stationary in their original form, while poverty head count and inflation rate data show otherwise. It is only exchange rate data that is stationary after second differencing.

Table 3. Test for Cointegration (Johansen Cointegration Test Trace Statistics &Maximum Eigenvalue)

Source: Authors’ Work (2020)

While examining the long run relationship between corruption, income inequality and poverty in Nigeria, this study applied a multivariate cointegration technique developed by Johansen and Juselius (1990), and the results were presented in the above table. It could be inferred from the table that four (4) cointegration equations existed among the variables of interested as indicated by both the trace statistics and the maximal eigenvalue statistics. Therefore, a long run equilibrium relationship existed between political corruption, income inequality and poverty in Nigeria.

Table 4. Relationship between Corruption, Income Inequality and Poverty

Dependent Variable: Corruption

Method: Fully Modified Least Squares (FMOLS)

Source: Authors’ work (2020)

The results from the estimation of the model were shown in the table above. It could be deduced that variables such poverty head count and exchange rate followed the aprori expectation while other variables like income inequality and inflation rate showed otherwise. Similarly, the model is relatively good for the analysis due to its R-Squared which shows that 61% of the variation in the dependent variable was explained by the set of the explanatory variables, and thereby leaving 39% unexplained as a result of random error. Consequently, corruption and income inequality have a negative relationship which is significant at 10 percent level of significance. A unit change in the corruption level reduces income inequality by 39% in Nigeria. However, corruption and poverty head count have a direct relationship in Nigeria. Though, the relationship is not significant at 10% level of significance. This finding is in tandem with the submissions of Ajisafe (2016) and Wickberg (2012) in related studies. Meanwhile, the relationship is not significant at 10 percent level of significance. A unit change in corruption level brings about an increment in the poverty rate by 0.37% in the country. Corruption and exchange rate have a significant positive relationship. A unit change in corruption level leads to 0.37% rise in exchange rate in Nigeria. Whereas, corruption and inflation rate have a significant inverse relationship. A unit change in corruption level reduces inflation rate by 5.9% in Nigeria.

Table 5. Feedback Relationship between the Variables of Interest

Source: Authors’ work

While examining the field back relationship between the principal variables of interest, this paper employed Granger causality technique with the following results; there is a unidirectional causality which runs from political corruption to income inequality in Nigeria. This implies that political corruption stimulates inequality of income among the Nigerian populace. Similarly, political corruption Granger causes exchange rate in the country. This implies that the persistent fall in Nigerian currency vis-à-vis dollar could be attributed to the political corruption in Nigeria. Also, there is one way feedback relationship flowing from exchange rate to inflation rate in Nigeria. However, no feedback relationship exists between political corruption and income inequality in one hand, and political corruption and exchange rate on the other hand.

4. Conclusion and Recommendation

Investigation into the relationship between political corruption, income inequality and poverty between 2000 and 2019 has been carried out in this study within the framework of fully modified ordinary least square and Granger causality. Consequently, the findings that emerged in this work could be enunciated thus; political corruption and income inequality have a significant negative relationship in Nigeria. This shows that political corruption does not stimulate income inequality in Nigeria However, corruption and poverty head count have an insignificant direct relationship in Nigeria. This implies that the current level of poverty in Nigeria was motivated by political corruption in the country. In the same vein, corruption and exchange rate have a significant positive relationship. Political corruption could also be attributed to the persistent fall in Naira vis-à-vis dollar in the foreign exchange market. In addition, there is a unidirectional causality which runs from political corruption to income inequality in Nigeria. This implies that political corruption stimulates inequality of income among the Nigerian populace. Similarly, political corruption Granger causes exchange rate in the country. This implies that the persistent fall in Nigerian currency vis-à-vis dollar could be attributed to the political corruption in Nigeria. Also, there is one way feedback relationship flowing from exchange rate to inflation rate in Nigeria. However, no feedback relationship exists between political corruption and income inequality in one hand, and political corruption and exchange rate on the other hand. In a nutshell, it could be concluded that political corruption is responsible for the rise in poverty level in Nigeria, Also, political corruption Granger causes income inequality in Nigeria. Against this backdrop, the study makes the following recommendations to the policy makers in Nigeria that any time the goal of the policy makers is to reduce poverty and inequality of income among the Nigerians, policy that will address political corruption should be effectively implemented. In the same line, ant graft institutions such as EFCC and ICPC should be strengthened in order to achieve their primary goals of curbing corruption in Nigeria.

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