Illicit Trade of Natural Resources and Economic Growth in Nigeria

Illicit Trade of Natural Resources and Economic Growth in Nigeria

Ishaku Rimamanung Nyiputen & Adeyemo Olulayo Elizabeth

Department of Economics, faculty of social science, Federal University Wukari Taraba State, Nigeria

DOI: https://dx.doi.org/10.47772/IJRISS.2025.903SEDU0591

Received: 18 September 2025; Accepted: 04 October 2025; Published: 29 October 2025

ABSTRACT

Natural resources, both renewable and non-renewable, and ecosystem services are a part of the real wealth of nations. They are the natural capital out of which other forms of capital are made. They contribute towards fiscal revenue, income, and poverty reduction. Sectors related to natural resources use provide jobs and are often the basis of livelihoods in poorer communities.  Owing to this fundamental importance of natural resources, they must be managed sustainably. Government plays the essential role in putting into place policies that ensure that resources contribute to the long-term economic development of nations, and not only to short-term revenue generation, high-quality institutions in the present, and planning for the future. This study investigated the impact of illicit trade of natural resources and economic growth in Nigeria.  The data involved for the study were growth rate of gross domestic product (GDPR), shadow economy as a share of GDP (SDGDP), formal economy as a share of GDP (FMEGDP), corruption perception (CORR), and government effectiveness (GOVEF). This research’s analysis proceeded from the descriptive statistics which involved a summary statistic of the quantitative behaviour of the variables included in the model. Furthermore, the Augmented Dickey Fuller (ADF) unit root test was conducted to verify the stationarity of the study’s data.

on Nigeria economic growth. The research was carried out under the assumption that natural resources deemed to impact level of economic growth of Nigeria The study made use of Secondary data from 1991 to 2021. Given the empirical findings in this study, it was concluded that the size of the adverse effect generated from the illicit trade in natural resources, corresponds to the substantial enhancing effect of the formal sector on the growth of the Nigerian economy. Specifically, revenues generated in the illicit trade of resources significantly and adversely affect the growth of the Nigerian economy.

Keyword: Illicit trade, Natural Resources and Economics’ growth

The topic of illicit commerce is still divisive. Globally, people are involved in black market operations on a daily basis. These folks typically go underground to avoid paying taxes and to save money because goods and services are much less expensive there. Increased illegal trade operations worry policymakers because they erode the foundations of social security and taxes. Because of this, they may base their poor policy choices on inaccurate official data, which would have the effect of crowding out official economic activity (Schneider and Enste, 2000).These unregistered operations have a negative impact on the country’s income (Ogunc and Yilmaz, 2000.

However, Since colonial authority ended in 1960, Nigeria’s economic trajectory has experienced substantial changes. Nigeria sought to go from an economy that was mostly based on agriculture, industry, and services to one that is self-sustaining and somewhat varied. But once oil was discovered, the economy went through a significant decline in the 1980s, which was made worse by declining oil prices and output. During this time, the real gross national product (GNP) per capita decreased. This trend continued until the 1990s, when rising oil prices accompanied a slow but steady increase in illegal commerce. (Omodero 2019).

Nigeria’s illegal economy is projected to be worth $1,168 billion at GDP purchasing parity power level, or 57.7% of the country’s GDP, according to the Quarterly Informal Economic Survey (QIES) by World Economics, London. According to the African Development Bank (2022), the real GDP growth rate had fluctuations, falling from 7% in 2000 to 2.7% in 2015, then to 1.6 % in 2016, before increasing to 0.8 % in 2017 and 1.9 % in 2018. The growth rate peaked in the first half of 2019 at 2.0%. Projections show an average growth of 3.2 percent between 2022 and the first quarter of 2024, but they are vulnerable to negative factors like additional drops in oil output and increased levels of insecurity.

Nigeria’s oil revenue was initially meant to fund significant investments in social services and infrastructure, but it has fallen short of those goals. Instead, the nation has struggled with a lack of investment opportunities, a decline in living standards, transportation logistical issues, and widespread insecurity brought on by illicit trade practices like oil theft and illegal bunkering. These problems have mostly impacted young job prospects, the standard of healthcare and education, and national security in general (African Development Bank, 2022).the question is that, is there any effort made by government of the day to reduce such menace?  However this study intended to provide solution to the following questions. How does illicit trade of natural resources revenue affect the economic growth of Nigeria? Does formal natural resources revenue affect the economic growth of Nigeria?

In other to provide answers to the above questions, the study wish to examine the following specifics objectives:  to examine the effect of illicit trade of natural resources revenue on economic growth in Nigeria , while the second specific objectives is to examine the effect of formal natural resources revenue on economic growth.  Also two hypothesis will be tested at the end of the analysis. That is i. There is no significant effect of illicit trade of natural resources revenue on economic growth of Nigeria. ii. There is no significant effect of formal natural resources revenue on economic growth of Nigeria.

LITERATURE REVIEW

Illicit trade concepts

The production or distribution of an item or service that a legislature considers illegal is known as illicit commerce. It covers trade that is categorically forbidden in other legal systems. Illegal trade operations continue to be a common economic situation worldwide, and they are considerably more common in poorer nations where they are seen as the main source of food for the majority of people. Despite not being formally recognized, these activities significantly increase the country’s revenue (Ogunc and Yilmaz, 2000). In their research, Chen and Schneider (2018) developed a widely accepted definition of the illicit trade: it is all illegal, unofficial, unregistered, and criminal economic activity and money that goes toward the publicly reported Gross National Product.

Nigeria illicit trade areas

The majority of Nigeria’s illicit commerce is related to the country’s wildlife, timber, fisheries, minerals, and oil industries.

Trafficking in wildlife: This includes poaching and the smuggling of goods like tiger bones, ivory, rhino horn, and ivory. The World Economic Forum and the United Nations Environment Programme estimate that the illegal wildlife trade is worth between US$7 billion and US$23 billion worldwide (ANRC, 2016). Illegal wildlife trade is a global problem, but because of the sharp rise in media coverage and the poaching of elephants and rhinos, Africa receives the majority of the attention from across the world. The increasing quantity of animal parts being poached and traded, such as rhinoceros horns and elephant tusks, highlights the complexity of the issue facing wildlife management and conservation strategies. There is a clear correlation between the scope of this illegal activity and corruption.

Illicit trade’s root causes

Schneider (2011) believed that the following factors contributed to the rise of the shadow economy or illicit trade:

Social Security and taxes

Due to their increased labour costs in the official sector, taxes and social security contributions are major contributors to the creation of the shadow economy. The greater the discrepancies between the after-tax incomes from employment and the total cost of labour in the official economy. Numerous studies have shown compelling evidence linking the tax system to the shadow economy. Shadow economies are often smaller in nations with low tax rates, minimal laws and regulations, and a strong legal system. Microeconomic and macroeconomic modelling studies based on data for multiple countries indicate that rising restrictions in the official labour market wages rates play a role, as does the growing burden of taxes and social security payments, as the main drivers behind the size and growth of the shadow economy (Schneider, 2011).

Illicit trade’s effects on social and economic growth

1. It results in acts of terrorism or political violence: Conflict and criminality, like the one in Nigeria’s Niger Delta, have been driven by minerals, oil-related conflicts, and fishing-related piracy. Wildlife wardens are regularly murdered as a result of crimes related to the trade in wildlife.
2. It is closely related to development in that poverty can encourage illegal mining while, at the same time, economic growth makes luxury goods more accessible to a wider range of individuals.
3. It affects residents’ sustainability in a number of ways.
4. It may worsen to the point where the earth’s environment is destroyed, harming the world permanently. Illegal mining has harmed the ecosystem as a result of illicit commerce. It has harmed biodiversity by upsetting ecosystems, particularly in the mining, oil, and forestry industries. There are problems with health and safety resulting from mining waste disposal and environmental degradation.
5. Because the resources that should have been utilized to address the critical status of the economy are unavailable, it undermines essential governmental programs.

Natural resource concepts

Natural resources are the basic materials and energy sources that humans use, including soil, sand, wind, water, metals, gasoline, and everything in between. Although manufactured goods like concrete, plastic, sheet metal, textiles, microchips, and energy are not natural resources, they are unquestionably produced from them (Bertola, 2000). These are the resources that are created naturally by the environment and without the help of people. Natural resources include things like soil, stone, sunshine, air, plants, animals, and fossil fuels. Natural resources are those that exist (on the earth) independently of human activity, which is relevant to our subject.

Theories of economic growth  

The improvement or rise in the market value of the commodities and services generated by an economy throughout a fiscal year after accounting for inflation is known as economic growth. Real GDP, or real gross domestic product, growth is typically measured by statisticians as a percentage rate of rise (Dreze and Sen, 2002). A rise in the production of products and services within a certain time period relative to a prior period is known as economic growth. It can be calculated in real terms (adjusted to account for inflation) or nominal terms. Although other metrics are occasionally employed, gross national product (GNP) or gross domestic product (GDP) is the traditional measure of aggregate economic growth (Sala-I-Martain, 2006). Growth is often computed in real terms, or terms adjusted for inflation, in order to remove the impact of inflation on the pricing of items produced.

Modernization/Dualistic Theory

The first person to advocate for this viewpoint was the French philosopher Rene Descartes (1591-1650). This included unofficial, non-formal, marginal enterprises that give the impoverished a means of subsistence and function as a safety net during difficult times. This theory explains the role of the dual labour market, which is separated into four categories: primary, secondary, informal, and illegal (Doeringer, 1971; Saint-Paul, 1997). Regular wage occupations that are subject to taxes and regulations make up the main sector. Jobs with less security and less regulation than main labour, such as lower-paying positions in the service industry, make up the secondary sector. People who run their own small businesses on a cash-only or unregulated basis, as well as those who labour off-books for employers, are examples of informal workers.

Theory of globalization and structuralism

In line with the structuralist/globalization idea, which Edward Titchenerin and Ferdinand de Saussure first proposed in 1857. Subordinated economic units, or micro companies, should be used to study the informal sector. These workers lower labour and input costs, which boosts the competitiveness of major capitalist enterprises. The structuralist approach, in contrast to the other schools, views the informal sector as a characteristic of a capitalistic growth. The informal economy’s intrinsic link to the formal economy is one of its distinguishing features (Routh, 2011). This theory, which is based on Wallerstein’s (2007) world systems model, depicts the duality that exists between an informal periphery of low wage, low productivity economic activity and a formal core of high wage, high productivity economic activity within or among nations (Godfrey, 2011). According to Maloney (1999), employees in the so-called “informal sector,” who work in small businesses or for themselves, engage in labour-intensive tasks without benefits related to job security.

Legalist/neoliberal theory

The hypothesis was first put out by Friedrich Hayek, Milton Friedman, Karl Popper, George Stigler, and Ludwig Von Mises. Micro-enterprises that opt to operate informally to save money, time, and effort on official registration make up the informal sector. A key component of this theory is the idea that slower development is caused by poorer productivity, less investments, an ineffective tax system, a low degree of technological adoption, and challenges with macroeconomic policy. According to De Soto, as long as government processes remain onerous and expensive, small-scale producers would persist in operating unofficially (Aldersdale, 2006). According to Godfrey (2011), legalists and dualists are different in that the former view informality as essentially resulting from institutional preferences and arrangements, while the latter regard it as a result of the deep structure of economic roots (i.e., the marginal value of labour supplies).

Empirical Review

An empirical study of illicit trade  

Using the Error Correction Model of Multiple Indications Multiple Causes (EMIMIC) model, Schneider and Buehn (2011) examined the factors that contributed to the underground economy in thirty-nine (39) highly developed Organization Economic Co-Operation and Development countries between 1999 and 2010. They discovered that the main causes of the underground economy were high tax burdens, government regulations, and unemployment. On the other hand, Ogbuabor and Malaolu (2013) used the Error Correction Model of Multiple Indications Multiple Causes (EMIMIC) model to examine the size and causes of the illicit economy in Nigeria from 1970 to 2010. They found that the main causes of the illicit economy in Nigeria are government regulations, unemployment, a high tax burden, and the rate of inflation. Using a linear regression model, Manole (2014) examined the influence of the underground economy on the Romanian economy from 1999 to 2012 and came to the conclusion that the GDP of Romania was significantly harmed by the underground economy.

Elgin and Birinci (2005) found that both small and big shadow economies had a link with minimal growth in GDP per capita in their study on the effects of illegal economies on economic growth of 161 nations from 1950 to 2010 using panel data. Using panel data from 150 countries, Kireeriko and Nevozorova (2015) examined the effects of the shadow economy on the level and quality of life between 1999 and 2007. They found that while the quality of life, as measured by long, healthy lives, education, and other factors, declined as the shadow economy grew.

Additionally, Schneider et al. (2015) used the official GDP percentage to measure the extent of the shadow economy in 28 European Union nations from 2003 to 2014. They discovered that the shadow economy had grown to 22.6% in 2003 and then dropped to 18% in 2014. Using synthetic index data from the Romanian shadow economy, Zaman and Goschan (2015) examined the influence of the shadow economy on the country’s economic development from 1999 to 2012 and came to the conclusion that there could be a long-term, continuous link between the shadow and formal economies. According to Favour (2016), the size of the illicit economy had a negative impact on the amount of tax revenue that the government collected in most Latin American countries and the Organization for Economic Co-Operation and Development. This was determined using the Multiple Indicators Multiple Causes (MIMIC) and Generalized Moment Method (GMM) approach between 1985 and 2005.

Formal economy empirical

Natural resource management has a substantial impact on the environmental livelihood of Ghanaians, according to John (2015), who used OLS to analyze the environmental livelihood and natural resource management in the lower Volta Basin of Ghana from 1995 to 2005. Furthermore, Karl (2013) studied the importance of natural resources using the VAR model of Central Asia and Eastern Europe from 1995 to 2005 and found that these resources had a major economic impact on the lives of the research area’s population. Furthermore, Santhira (2018) used ARDL to study the relationship between natural resources and economic growth in South Africa and discovered a strong correlation between the two. However, Peter (2014) found that natural resources had a beneficial influence on sustainable economic growth through his research employing OLS in Ghana to study natural resource management and sustainable development. However, Warner (2019), controlling for institutional quality in FDI and economic growth in Nigeria from 1984 to 2018 ARDL, found no evidence of a positive association between FDI and economic growth.

Furthermore, Levine and Kennet (1992) used the EBA approach to investigate the link between economic growth and income from 1975 to 1990 and found a strong negative association between the two. Furthermore, Bengoa and Sanches (2003) used panel data to study the relationship between economic freedom, growth, and foreign direct investment in Latin American nations between 1970 and 1999. They found that the countries with higher indexes had higher growth rates and FDI inflows. Using the GMM technique, Levina (2011) examined the link between foreign direct investment (FDI), economic freedom, and economic growth from 1981 to 2000. She discovered that all three variables had favourable impact on economic growth.

Research gap

The assertion made by Anwar et al. (2017) that self-employment may drive people to understate (undisclosed) their revenue (wages and salaries) is untrue since he failed to include any examples of civilized locations where people register their enterprises and report their income to the government. In more civilized sections of Nigeria, some self-employed individuals register their firms, engage in trade, hold regular meetings, and as a consequence, they reveal their revenue and pay taxes on time. According to Medina et al. (2017) and Remeikiene et al. (2018), the national statistic approach is appropriate for estimating the size of the illicit economy because it yields accurate results when expenditure is subtracted from income. Additionally, since individuals working in the informal sector are unable to conceal their expenses in the same way that they conceal their income to avoid taxes, the claims made about their income are untrue because they have neglected certain expenses that do not occur in the same nation. When we deduct expenses from income, we are unable to obtain an accurate picture of the illicit trade because some investments and expenses are made abroad, some relatives and friends who benefit from illicit income do so as well, and it is impossible to connect or trace illicit income to individuals who reside outside of the nation.

Additionally, some so-called non-governmental organizations (NGOs) involved in illegal commerce actively promote certain business entities and spend lavishly, with no record of their expenditures.

Furthermore, the neo-classical theory of convergence’s premise that any increase in illicit trade results in a decrease in the formal economy and vice versa is untrue because it ignored some individuals who engage in both licit and illicit trade. In fact, those individuals must have learned how to conduct their business, as well as what tools are needed, how to market it, and how much to charge, from the licit trade, so they must engage in licit trade during the day and illicit trade at night or during their breaks. Furthermore, none of the several writers who have conducted extensive study on the illegal economy and natural resources (crude oil) including one that was closely related have combined the two topics in their empirical reviews of the literature. The gap has been found and filled by this research project.

RESEARCH METHODOLOGY

The research was done in Nigeria. A quantitative data analysis method called Dynamic Ordinary Least Squares (DOLS) was used in this work. Descriptive statistics were also used in the study to determine the traits and behaviour of the data that were part of the model. It employed the Hansen-stability test for co-integration, the Augmented Dickey Fuller (ADF) and Philips-Perron unit root tests, and additional robustness checks using a variety of residual and stability tests.

Research design

This study was conducted using a quantitative research design. Evaluating the scope, drivers, and economic impact of illegal trade on Nigeria’s economic growth was the goal of a quantitative study.

Sources of data

A quantitative study design technique was used, utilizing data sets obtained from the Federal Inland Revenue Service (FIRS), World Bank Development Indicator, and Governance Index between 1991 and 2022, to assess the extent, drivers, and implications of illicit trade on economic growth in Nigeria.

Method of data analysis

A quantitative data analysis method called Dynamic Ordinary Least Squares (DOLS) was used in this paper. Descriptive statistics were also used in the study to determine the traits and behaviour of the data that were part of the model. It employed the Hansen-stability test for cointegration, the Augmented Dickey Fuller (ADF) and Philips-Perron for the unit root test, and additional robustness checks using a variety of residual and stability tests.

Inferential and descriptive statistics

Prior to testing the variables, the descriptive and inferential statistics were crucial preliminary tests. In order to determine the nature of the variables and if their mean, median, maximum, minimum, and standard deviation were normally distributed, a descriptive statistics test was conducted. The Jarque-Bera probability values determine whether the null hypothesis should be accepted or rejected. On the other hand, we accept the null hypothesis that the variables were normally distributed and suited for estimation if the value of the Jarque-Bera probability value is greater than 0.5.

Unit Root Test

The stationarity of the variables included in the study was evaluated during the estimate technique stage. By using the Augmented Dickey Fuller (ADF) unit root test, which was proposed by Dickey and Fuller in (1981), it is possible to ascertain the sequence in which the data series integrate. The purpose of this test was to determine the variables’ long-term characteristics. If the time series are determined to be stationary, it indicates that their variance, means, and covariance remain constant throughout time, indicating that the analysis’s conclusion was trustworthy and capable of forecasting the economy’s future activity. The following models were used to run the ADF test:

 …………………………………………. (1)

 ……………………………………. (2)

Where n is the ideal number of delays in the development variable and e_t is the stochastic variable, Y is a data series, t is a linear time trend, ∆ is the first difference operator,  is constant, and so on. In the meanwhile, the series has a one-unit root and is of integrated order one if the ADF result fails to reject the test in levels but rejects the test in the first difference. Furthermore, it suggests that the series has two unit roots and is of integrated order two if the test fails to reject the test in levels and at first difference but rejects it in second differences

Model Details

The following is the functional form of the model that illustrates the connection between Nigeria’s economic development, the formal economy, and illegal trade:

where: GDPR is gross domestic product’s growth rate, which proxy for the growth of the  economy; SDGDP is shadow/illicit trade as a share of GDP, and FMEGDP is formal economy as a share of GDP. Equation 3.1 shows the relationship between illicit trade and economic growth, while equation 3.2 the association between the formal economy and economic growth.

The mathematical forms of the model are further expressed as:

where: CORR represents corruption perception and GOVEF is government effectiveness; and both are used as control variables in the model. Equation 3.3 shows the relationship between illicit trade and economic growth, while equation 3.4 is the association between the formal economy and economic growth.

A further representation of the model in an econometric form gives:

Where:   to  and  to  are coefficients to be estimated and  and  are error terms.

Transforming equations 3.5 and 3.6 into DOLS model yields:

where:  is a vector of all explanatory variables; Z^/ is a subset of I(1) series of Y; denote leads of the first difference of the regressors; Δ is the first difference operator, and represents lags of the first difference of the backwards. The link between illegal commerce and economic growth is depicted in equation 3.7, while the relationship between the formal economy and economic growth is represented in equation 3.8.

Leads and lags are incorporated into the DOLS architecture to assist in managing endogeneity and autocorrelation problems.

Rationale for the Model

Dynamic Ordinary Least Square (DOLS) was used to solve the endogeneity problem by incorporating the lead and lags of the first distinct endogenous vector in order to meet the study’s research objectives. It may be used with small sample sizes and is resistant to the auto-correlation issue. Because the typical OLS approach does not account for second-order asymptotic bias and serial correlation, the regression results generated by DOLS are more resilient than those produced by OLS. Moreover, the DOLS estimator’s flexibility allows the framework to support both I(0) and I(1) variables, whereas the FMOLS estimator requires that all series be I(1) integrated.

DATA PRESENTATION AND ANALYSIS

Data Presentation

Appendix 8 of this report contains the data utilized for this analysis. The growth rate of the gross domestic product (GDPR), the formal and shadow economies as a percentage of GDP (FMEGDP and SDGDP), the perception of corruption (CORR), and the efficiency of the government (GOVEF) were the data used in the study. The analysis of this study started with the descriptive statistics, which comprised a summary statistic of the quantitative behaviour of the model’s variables. To further confirm the stationarity of the study’s data, the Augmented Dickey Fuller (ADF) unit root test was performed.

Characteristic Statistics

Table 4.1 summarizes the descriptive data used in this investigation. According to the table, Nigeria’s average GDP growth rate over the research period was almost 4.08%. This figure is higher than the emerging nations’ average yearly growth rate of 3.5% (WDI, 2023). Furthermore, the legal sector accounts for 41.5% of the nation’s GDP, while the shadow economy makes for 58.5%. According to these figures, the official sector’s contributions to the nation’s GDP profile are surpassed by those of the shadow economy. Additionally, the nation’s mean corruption perception score is -1.1, indicating a lack of strong institutions to curb the growth of corrupt activities there. Similarly, the average impression measure for government efficacy is around -0.99. This number indicates that the nation’s level of government effectiveness has not been particularly high during the studied period.

Table 4.1 further provided a supplementary representation of the kurtosis distribution for each variable. Pre-requisitely, the series’ kurtosis was considered normal if its distribution value was 3, somewhat normal if it was greater than 3 (leptokurtic), and less normal or negative excess kurtosis if it was less than 3 (platypurtic). Table 4.1 shows that every variable is leptokurtic since their kurtosis is greater than 3. In addition, the Jarque-Bera data for the variables demonstrate that the formal economy, the illicit/shadow economy , and the GDP growth rate do indeed follow a normal distribution. On the other hand, there was a non-normal distribution for government effectiveness and corruption. This suggests that the research variables could be non-stationary series, and it is thus strongly warranted to test for the unit root of each variable.

Table 4.1: Descriptive Statistics Result

Note: **, and *** indicates significance at 5%, and 1% respectively.

Source: Author’s estimated output.

Unit Root Test

Before engaging in any economic analysis, a critical step in the process involves ascertaining the level of stationarity of the data being employed. This will help to guide the researcher in any empirical conclusion that is to be taken. This research applied the ADF unit root test in determining the stationarity level of the variables used in the analysis. The unit root tests outcomes are given in Table 4.2. Three test criteria were applied for critical unit root analysis and they include test with constant, with constant and trend, and without constant and trend.

Presented in Table 4.2 is the ADF unit root outcome and it demonstrates that GDP growth rate, illicit economy, formal economy, corruption, and governance effectiveness were not stationary in their level form. However, a further probe for stationarity showed all the variables to be overwhelmingly first difference stationary series.

Table 4.2: ADF unit root test result

	ADF Test at Level					ADF Test at 1st Difference
	With Constant	With Constant & Trend	Without Constant & Trend			With Constant	With Constant & Trend	Without Constant & Trend
	-2.874*	-2.810	-1.210			-7.605***	-5.493***	-7.742***
	-0.615	-0.398	0.794			-3.013**	-7.019***	-2.948***
	-0.615	-0.398	-0.939			-3.013**	-7.019***	-2.948***
	-1.918	-4.447	0.558			-4.782***	-5.036***	-4.832***
	-1.543	-2.508	0.033			-16.037***	-15.772***	-16.314***

Where *, ** and *** indicates significance at 10%, 5% and 1%, respectively.

Source: Author’s estimated output.

Cointegration test result

After justifying the stationary state of the variables, the next step wass to use Johansen (1988) full information maximum likelihood to test for cointegration. The Cointegration test results are presented in Tables 4.3 and 4.4. The cointegration test results as shown in Table 4.3 and 4.4, indicates that the trace test as well as the maximum-eigenvalue supports the presence of a long termnexus between the variables at the 0.05 significance level. Thus, the maximum eigenvalue statistics and the trace test rejects the null hypothesis at 0.05 level of no cointegration; stating otherwise, that there exist one cointegrating vector, suggesting a linear model with intercept but no trend.

Table 4.3: Unrestricted Cointegration Rank Test (Trace)

Trace test indicates 1 cointegrating equation at the 0.05 level of significance.

*, Denotes rejection of the hypothesis at the 0.05 level of significance

**,  represents Mackinnon-Haug-Michelis (1999) P-values.

Source: Author’s Estimated Result.

Table 4.4: Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Max- eigenvalue test indicates 1 cointegrating equation at the 0.05 level of significance.

*, Denotes rejection of the hypothesis at the 0.05 level of significance

**, Represents Mackinnon-Haug-Michelis (1999) P-values

Source: Author’s Estimated Result.

DOLS estimated output

Effect of illicit trade on Nigeria economy

Captured in Table 4.5 is the estimated output for the illicit trade effect on the growth of the economy. The result demonstrates that illicit trade has a significant adverse effect on the economic growth of Nigeria. Specifically, a unit rise in illicit trade will lead to a 0.19-unit decline in the growth of the economy. The result is statistically significant at the 10% level. Similarly, the corruption coefficient indicates a significant negative effect on the economy. Evidence from Table 4.5 revealed that a unit increases in the level of corruption will significantly depreciate the economy’s growth by 10 units at the 10% significance level. However, estimate for government effectiveness shows an insignificant positive effect on the economy. A unit rise in government effectiveness will lead to an insignificant 6.14-units decline in the economy.

Table 4.5: Illicit economy’s regression output

Variable	Coefficient	Std. Error	t-statistic	Prob.
	-0.243	0.139	-1.740	0.097*
	-12.145	6.826	-1.779	0.090*
	14.759	11.025	1.339	0.1957
	21.018	12.502	1.681	0.108
	0.57 0.38

Note: * denote significance at 10% level.

Source: Author’s estimated result.

Effect of formal economy on Nigeria economic growth

Represented in Table 4.6 is the estimated output for the effect of the formal economy on economic growth of Nigeria. The result demonstrates that the formal economy has a significant positive effect on the economic growth of Nigeria. Specifically, a unit rise in the formal economy will lead to a 0.19-unit increase in the growth of the economy. The result is statistically significant at 10% level. Similarly, the corruption coefficient indicates a significant adverse effect on the economy. Evidence from Table 4.5 reveals that a unit increase in the level of corruption will significantly depreciate the economic growth by 10 units at the 10% significance level. However, estimate for government effectiveness shows an insignificant positive effect on the economy. A unit rise in government effectiveness will lead to 6.14-units increase in the growth of the economy; however, the increase is statistically insignificant due to failure of some other issues such as administrative bottlenecks, selective implementations, inadequate funding of implementation process and so on.

Table 4.6: Formal economy regression output

Variable	Coefficient	Std. Error	t-statistic	Prob.
	0.190	0.096	1.979	0.058*
	-10.710	5.316	-2.015	0.054*
	6.144	4.721	1.301	0.204
	-9.649	4.805	-2.008	0.055*
	0.32 0.24

Note: * denote significance at 10% level.

Source: Author’s estimated result

Hypothesis Testing

H0_1: There is no significant effect of illicit trade on the economic growth of Nigeria

For the purpose of hypothesis testing, the T-test approach was applied for this study. As captured in Table 4.5, the long run coefficient for illicit trade (-0.740) is significant at 10% statistical level. Accordingly, the t-statistic confidence interval at 0.10 was used for hypothesis testing. Hence, if the calculated T-value falls within the ± 1.65  confidence interval (i.e., -1.65 ≥ T-value ≥ 1.65) the null hypothesis is rejected while the alternative hypothesis upheld. Given the output in Table 4.5, the T-value for the coefficient was revealed as -1.740, which is lower than the rule of thumb value of -1.65. Consequently, the null hypothesis is invalidated and the alternative hypothesis that there is a significant effect of illicit trade on the growth of the economy is sustained.

H0_2: There is no significant effect of the formal economy on the economic growth of Nigeria

As represented in Table 4.6, the long run coefficient for the formal economy (0.190) is significant at 10% statistical level. Accordingly, the t-statistic confidence interval at 0.10 was also used for hypothesis testing. Hence, if the calculated T-value falls within the ± 1.65 confidence interval, (i.e., -1.65 ≥ T-value ≥ 1.65) the null hypothesis is rejected while the alternative hypothesis upheld. Given the output in Table 4.5, the T-value for the coefficient is shown as 1.979, which is above the rule of thumb value of ± 1.65. Accordingly, the null hypothesis is invalidated and the alternative hypothesis that there is a significant effect of the formal economy on the growth of the Nigeria economy is upheld.

Post Estimation Tests

The following diagnostic tests were carried out to ensure that the model is good fit and reliable for policy recommendations in Nigeria. Thus, in Table 4.7, the normality results for the estimated equations demonstrated that the JB-statistics (X²) of 0.235 and 0.086 were statistically insignificant given their probability value which was greater than 1%, 5%, and 10% significance levels. Thus, this implies the acceptance of the null hypothesis that the residuals in the equation were normally distributed.

Similarly, Table 4.7 shows the result from the Breusch-Godfrey (BG) general test of serial-correlation. The result expressed that the test statistic value of 0.374 and 5.829 were statistically insignificant at 1%, 5%, and 10% significance levels given their probability value. The implication of this was that the study does not reject the null hypothesis of no serial correlation. Hence, there was no serial correlation associated with the equations.

In addition, the test for Heteroskedasticity in the residuals showed that the statistics for the equations (5.532 and 1.024) are also statistically insignificant based on their probability values exceeding the statistically insignificant 1%, 5% and 10% levels. Thus implying that the null hypothesis of no constant variance of residuals was rejected for the equations, thereby confirming the homoskedastic nature of the residuals in the estimated outputs.

Also, a stability tests for DOLS equations’ parameters was conducted by using the cumulative sum of recursive residuals (CUSUM) and the cumulative sum of squares (CUSUMQ) of recursive residuals respectively. The CUSUM statistics plots in Appendix 4 and 7 reveals that the critical bounds fall within the 5% level of statistical significance. Similarly, the CUSUMQ plots in appendix 4 and 7 lie within the critical bounds at the 5% significance level. Thus, this signifies that the DOLS estimates are reliable and consistent. Stated differently, the study parameters are stable since the cumulative sum and cumulative sum square (blue lines) does not fall outside the area between the two critical bounds (red lines) in the analysis. Hence, the derived outcomes from the study analysis are considered consistent and reliable for policy recommendations.

Table 4.7.1: Diagnostics Analysis

Source: Author’s estimated result.

Discussion of Findings

Based on the empirical evidences contained in Tables 4.5 and 4.6, this research discussed the outcome of the results by adopting an objective-based approach. Interestingly, the empirical outcome in both tables suggested that the magnitude of negative effect generated from the illicit trade in natural resources, corresponded to the significant positive effect of the formal sector on the growth of Nigeria economy. However, the significance of these effects were discussed as presented below.

Objective 1: Examine the effect of illicit trade of natural resources revenue on economic growth in Nigeria.

Table 4.5 had demonstrated that illicit trade has an adverse effect on the growth of the  Nigeria economy. This outcome aligned with the findings of Manole (2014), Schneider et al. (2017) and Omodaro (2019) that the growth of illicit trade in the resources of a country poses significant detrimental effect on the growth of the nation’s economy. It is noteworthy stating that rising conflicts, waste, greed, fluctuating growth, and over-consumption have encouraged the rise of illicit markets in Nigeria. These illicit trades in legal goods–comprising the stealing and diversion of products, and the adulteration, counterfeiting, and production of substandard goods. Legal and illicit products often pass-through the same supply chains and often sold by the same vendors. Cargo ships and aircraft may convey an illicit commodity in one direction, and a legal one on the return trip.

The  Illicit trade in natural wealth is known to be initiated, enabled, and protected by a wide range of players, ranging from violent groups (such as armed bandits and militants) in different parts of the country to unethical corporations and corrupt officials at all levels of government. In Nigeria, particularly, criminal organizations play a critical role in encouraging illicit trade, exploiting vulnerabilities throughout supply chains (including regulatory and legislative lapses), and governance and market failures. Another modus operandi of illicit trading involves situations whereby legal products are diverted–wittingly or unwittingly–by criminal elements with the aid or cover provided by corrupt government officials. Hence, given the continuous rise of activities in the hidden economy, significant amount of revenue needed by the government in form of taxes and rents will be diverted into wrong hands. Also, the legitimate industrious’ businesses will be undermined. The implications of these effects is a substantial constrain on the growth of Nigeria economy. However, Table 4.5 further reveals that corruption has a higher adverse effects on the growth of the economy giving the thriving significance of the informal sector. This effect is expected since the illicit sector will only continue to expand due to growth in corrupt tendencies which fuels the existence of sectors.

Objective 2: Examine the effect of formal natural resources revenue on economic growth in Nigeria.

Result in Table 4.6 showed that the growth of the formal economy contributes positively to the growth of Nigeria economy. This result is in line with the findings of Peter (2014) and Santhira (2018) who reported a positive effect of the formal sector on economic growth. The enhancing effect of the formal sector is plausible given that revenue from the formal economy aid in the provision of economic infrastructure such as roads that connects raw materials to their destined markets; power grids needed to boost and reduce cost of production, health and educational centres to promote human capital development, etc. Also, the more the revenue from these licit economic activities, the better the government can contribute to the development of sectors that are less productive to the growth of the economy. In fact, government at all levels relies on revenue from this sector to pursue their annual budget in Nigeria. Consequently, the upward trajectory of the economy (i.e., GDP growth) depends on the successive income yields of the formal economy.

However, Table 4.6 also showed that corruption diminishes economic growth of the formal economy. This constraining behaviour of corruption occurs through various channels such as capital flight, misappropriation of public funds and nepotism in the location of government projects. Nevertheless, government effectiveness in tackling this menace and further propel growth is demonstrated to be insignificant. This outcome could be due to weakness of government institutions  to effectively regulate the various sectors of the economy by misallocating resources. Specifically, challenges such as poorly targeted investments, macroeconomic instability and excessive bureaucracy can hinder government effectiveness from spurring economic growth in Nigeria.

SUMMARY, CONCLUSION AND RECOMMENDATIONS

Summary

This study examined the effect of illicit trade of natural resources revenue on Nigeria economic growth between 1991 and 2021. Five theories were considered by this research and they were the dualistic, structuralistic, neoliberal, post-structuralistic, and management theories. Variables adopted for the study included shadow economy as a share of GDP, formal economy as a share of GDP, corruption perception index, and government effectiveness. These data were derived from the World Bank Development Indicator, World Bank Governance Index, and Federal Inland Revenue Service. The data were subjected to the Augmented Dickey Fuller (ADF) unit root test technique for stationarity determination.

In addition, the Johansen cointegration procedure was used to ascertain the long-run relationship between the independent variables, and the study findings were estimated through the DOLS modelling approach. Diagnostic tests were conducted including normality, serial correlation, and heteroscedasticity. They were used to determine the reliability of the research’s estimates. A stability test through the cumulative sum and cumulative sum squared approaches was also carried-out; and their result confirmed that the study model is stable and can be used for policy recommendations.

Conclusion

Given the empirical findings in this study, it is concluded that the size of the adverse effect generated from the illicit trade in natural resources, corresponds to the substantial enhancing effect of the formal sector on the growth of the economy. Specifically, revenues generated in the illicit trade of resources significantly and adversely affects the growth of Nigeria economy. This is because, given the continuous rise of activities in the hidden economy, supported by the level of corruption and weak government effectiveness in the country, a significant amount of revenue needed by the government in form of taxes and rents are diverted into wrong sources, which can substantially constrain the growth of the  economy.  However, the more the growth in the revenue generated from the formal economy, the higher the growth of the economy. This is because government at all levels rely on revenue from this sector to pursue their annual budget in Nigeria. Consequently, the upward trajectory of the  economy (i.e., GDP growth) depends on the successive revenue yields of the formal economy.

Recommendations

The study put forward the following recommendations as a way of tackling the economic loss suffered due to illicit trade in natural resources, and how to further boost revenue derived from the formal sector for the growth of the Nigeria economy.

To curb the significant negative effects of illicit economy on the economic growth, product tracking is essential as well as tracing based on cost-effective digitization. Also, the effective control and supervision of the free-trade zones are critical. Having properly equipped and motivated law-enforcement agencies, guided by a clear mandate to crackdown on illicit trade in natural resources is also required. In addition, having a transnational legal cooperation that promotes effective exchange of information between national law-enforcement agencies and judicial bodies to adequately investigate, and offer punitive measures that can further deter the illegal cross-border trading of natural resources.

Furthermore, the government can enhance the contribution of the formal sector by improving on the formalization of different economic enterprises; thereby ensuring higher productivity and improved market penetrations for businesses operating in the economy. By embarking on such measure will guarantee the sustainability and fair competition of the formal sector in the domestic and international markets. Also, improvement in institutional quality including reducing corruption and boosting government effectiveness, can further enhance the performance of the formal sector and its contribution to economic growth in the country.

Contribution to the body of Knowledge

This research has empirically determined the effect of illicit and formal trade in natural resources on the growth of Nigeria economy. Furthermore, it has been able to show that the adverse effect of illicit trade of natural resources equals the positive effect from the formal sector on the economic growth of Nigeria.

Limitations/ Constraints of the study

One major constrain of this study is the inadequacy of empirical literature in the area of the illicit trade in natural resources on the growth of the economy. Also, the unavailability of adequate data on specific natural resources such as gas, coal, oil, marine resources, and agricultural products for Nigeria, limited the scope of variables adopted for the study

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