Nigerian Governance Challenge: Exploring the Role of Natural Resource Rents

Abstract

Nigeria is a glaring example of a country where weak public institutions are pervasive in spite of its huge natural resource wealth. The presence of natural resource abundance has exacerbated the overwhelming development challenge in the economy. While the upshot of most empirical findings of the resource impact covers how the growth path is determined through the channel of institutions, the question as to why resource rents often fail to stimulate improved governance is more critical than ever. Hence, the study examines the effect of natural resource rents on the quality of governance in Nigeria for the period 1984–2017, using ARDL bounds test approach, Dynamic Least Squares (DOLS), and Granger Causality test based on Vector Error Correction Model (VECM). Results reveal that natural resource rents have an insignificant effect on governance indicators in the long-run as well in the short-run, suggesting that natural resource windfalls have a shallow effect on the development of good governance. However, further evidence indicates that pervasive institutional gaps in Nigeria could be stimulated or caused by the overdependence on natural resource rents and entrenched mismanagement tendencies. Thus, the study suggests that maintaining strong political commitment, curtailing overdependence on natural resources, and ensuring sound management of natural resource wealth are central for improved governance.

Keywords

Governance natural resource rents sustainable development ARDL Nigeria

Introduction

The literature on the role of natural resources is immense with the conjecture that the presence of natural resource structures the policy framework in a way that affects governance effectiveness. Although the principal cause of sub-Saharan African (SSA) vicious cycle of underdevelopment is weak governance (fiscal) measures (Mauro, 1995), the prevailing notion has been that resource abundance could raise the rate of investments and imports, and thereby lead to improved economic outcomes, strengthening governance, and a decline in fiscal deficits (Franke et al., 2009). Resource rents could be used for the development of projects (such as provision of health facilities, infrastructure, and education), which, in turn, engender economic progress. Measuring in fiscal terms, resource-rich countries have a generally positive effect on state capacity (Thies, 2010). For instance, Morrison (2009) supports the view that non-tax revenue induces social spending in autocracies. Thus, natural resource windfalls are among the factors emphasized in the literature that influences public sector performance in an economy (Ross, 2014; Werger, 2009).

In contrast, Collier and Hoeffler (2009) stress that overtime, institutional checks, and balances are eroded by resource rents, while Nurkse (1958) argues that resource export industries are susceptible to the vicissitudes of the world market which give rise to an unstable macroeconomic environment through the transfer of such effect to the domestic economy. More specifically, Sala-i-Martin and Subramanian (2008) underscore the adverse effect of resource income wastage on institutional measures in Nigeria. Rents from natural resources could create disincentives for leaders to focus on tax collection (Besley & Persson, 2010; Knack, 2008). Anchoring on the destabilizing effect of resource rents on the political system, resource rents, and good governance have been viewed to be inversely related (Hendrix & Noland, 2014). In most resource rent-dependent countries, people are marginalized from politics, as the state attains financial independent status, the role of taxation is constrained and becomes insignificant in financing the public projects. Accordingly, the insignificant contribution of the electorates to the provision of public goods may lead to passive dispositions toward the affairs of the state, and thus the increased lack of accountability and transparency in governance cycle. By downplaying the centrality of a well-functioning tax system, compared with resource-poor ones, resource-rich countries are at higher institutional risks (Bornhorst, Gupta & Thornton, 2009; Bjorvatn & Farzanegan, 2015). For resource-rich countries with all the concomitant development issues, there is nothing in these arguments that denies that natural resource abundance exacerbates economic mismanagement which strains the institutional capacity to the limit.

The presence and ubiquity of administrative laxity in Africa, especially in Nigeria, continue to spark debate about the perennial disruption of the economic structure and quality of institutions. For instance, Nigeria, in spite of the enormous amount of money generated from the crude oil proceeds, the country suffers from widespread poor governance and weak public institutions. While resource-rich countries like Botswana and Canada are better ranked in terms of improved institutional capacity, Nigeria has often been at the forefront of countries with poor governance indicators (Dinh & Dinh, 2016).¹

In addition, many resource-rich countries are characterized with high corruption and weak governance. For example, Angola, Equatorial Guinea, Democratic Republic of Congo, Chad, Papua New Guinea among others.

The abiding political competition and feeble institutions could be attributed to overwhelming attraction and dispositions of Nigeria’s government (leaders) to the collection of resource rents at the expense of wealth creation. The evasion of rent-seeking behavior and high level of political commitment crucial for improved governance and long-term sustainable economic development are undermined by ingrained corruption, financing rebels, and imbued recklessness.

Following the empirical postulation of Olsson and Fors (2004), huge resource wealth is the typical tool that fuels increased institutional deficits and lack of viable policy in resource-dependent economies. However, while the literature substantially assesses how the quality of institutions could shape the effect of natural resources on economic growth (Collier & Gunning, 1999; Robinson et al., 2005; Ross et al., 2012), it falls short when analyzing how the state of institutional indicators is determined by resource rents. For example, Sala-i-Martin and Subramanian (2008) seem to have been the only well-known study that exclusively explores the effect of resource income mismanagement on Nigerian economic structures with the use of descriptive approach. Although considering the scant empirical evidence, this study differs in some respect following the adoption of ARDL (autoregressive distributed lag) bounds test technique and Granger Causality test based on Vector Error Correction Model (VECM) with a focus on the role of natural resource rents in institutional development process. Furthermore, anchoring on the notion that since institutional obstacles remain unassailable in countries where the rule of law is absent but with entrenched unaccountable governing systems, shortcomings in governance and unsustainable practices which have been the operational rules and basic political features in Nigeria have engendered the need for informed discussion. This is informed by the growing concern as to why resource rents often fail to stimulate improved governance and development sustainability for the country. Moreover, considering the current development challenge, Nigeria appears to be more resource-dependent, which makes this study more central than ever.

Over the years, the natural resource-governance-growth effect has been given significant attention in research, and many policy prescriptions have been made. Nonetheless, the main thrust of this study is not to form another argument on how economic growth is affected by natural resources through the channel of institutions in Nigeria, but to address the fundamental institutional measures that could be significantly influenced by natural resource rents. Hence, in order to offer an alternate set of remedies with high capacity to mitigate the unsustainable institutional practices, the study’s main objective is to explore the long-run and short-run effect of resource rents on the quality of governance in Nigeria. For this purpose, the study is structured as follows: The following section focuses on the literature review with a detailed analysis of Nigerian governance challenges. This is followed by the method of the study, data description and econometric techniques adopted, and the following sections present the results and the discussion, and finally the concluding remarks.

Literature Review

Theoretical Linkage

The central role of natural resource production in a resource-dependent country poses a variety of challenges that can affect the operational efficiency of all sectors in such economies. Theoretically, it has been stressed that resource rents could influence the structure of the economy, and in turn, adversely affect the political setting. This is supported by the Dutch disease theory. The proponents of this school of thought state that spending on both tradable (such as agriculture and manufacturing) and non-tradable (such as real-estate and services) goods are increased by positive shocks in resource rents. In most countries, especially developing ones, resources are often moved to the non-tradable sector from the tradable (Ross, 1999). In the long-run, this de-industrialization process impairs economic growth,²

Although the main focus of this work is not to examine the relationship between resource dependence and economic performance, based on empirical evidence, the work of Sachs and Warner (1995) has been identified as the first scholarly study that establishes the negative effects of resource dependence on the economy. Subsequent studies also confirm the veracity of the adverse effect of resource dependence (Badeeb et al., 2017; Gylfason, 1999, 2001).

and thus exacerbates the unemployment rate escalation. The resulting high rates of unemployment give rise to political unrest, and destabilization of the political system which harms the quality of governance (Bjorvatn & Farzanegan, 2015). Alternatively, the theory stresses that natural resource abundance often crowds out both public and private investment in other sectors, thereby inducing smaller tax bases. With reduced tax bases, peoples’ interest in the policies of government becomes weak, hence it undermines the importance of good governance across levels (Farzanegan & Markwardt, 2009; van Wijenbergen, 1984).³

For detailed elucidations and discussion on the crowding-out effects, see Sachs and Warner (2001), Wu et al. (2018), and Namazi and Mohammadi (2018).

Furthermore, expositions on the link between large natural resource rents and economic growth and development are built on numerous divergent views among economists. Conflicting arguments have emerged on whether economic performance is enhanced or impeded in economies with huge natural resource rents. Wantchekon (1999), Smith (2004), and Herb (2005) show the absence of common consensus in their respective studies. Many scholars establish that resource abundance is a curse rather than a blessing for most developing economies (Auty, 1993; Gelb, 1988). On the other hand, a few scholars fail to support this argument, as they emphasize that resource curse phenomenon does not exist (Davis, 1995). As a consequence, aside from the Dutch disease theory, some other theories have been developed. These theories include export instability theory, linkage theory, two-gap and three-gap model, booming sectors’ theory. Basically, the emergence of these theories was compelled by the need to ascertain how resource windfalls could be translated toward enhanced growth and development (Ogunleye, 2008). Nonetheless, in all, the linkage theory offers the most appropriate theoretical basis for explaining the nexus between resource wealth and economic development (Hirschman, 1981). More specifically, the theory mainly asserts that in relation to social, political, and economic contexts, dominance activity of the system is better stimulated by certain conducive characteristics. The interaction effect between a leading sector and other sectors can be grouped into production linkage, consumption linkage, and fiscal linkage (Ogunleye, 2008). In light of the operational features of Nigerian economy, in addition to Dutch disease theory, the fiscal linkage theory which is more crucial for establishing conceptual relation between natural resource sector and public sector performance will inform the theoretical foundation for this study.

Empirical Evidence

Looking at the leading research work in literature, studies on the link between natural resources and economic growth taking into account the institutional role have gained prominence among scholars. A few of these studies include Isham et al. (2005), Hodler (2006), Bhattacharyya and Hodler (2010), Bjorvatn et al. (2012), and El Anshasy and Katsaiti (2013). However, in the resource curse literature, the argument on how economic growth is influenced by natural resources through the channel of institutions has remained the subject of debate over the years. Considering Botswana’s experience in terms of economic development compared to other resource-rich countries in Africa, the country’s better economic performance seems to have been linked to her relatively fair institutional quality. This has necessitated increased attention accorded to the quality of institutions, which has been identified to be central for the success made by Botswana in terms of natural resource rent management and growth rates (Corrigan, 2014). Given the empirical postulations on the negative relationship between resource rents and government effectiveness (Hendrix & Noland, 2014), feeble institutions are responsible for slow economic growth in African countries⁴

On the other hand, according to Robinson et al. (2005), strong institutions can change a resource-curse to sociopolitical and economic blessings. In this case, economic growth may be enhanced by natural resource rents. However, studies into the underlying causes of the paradox of plenty suggest that there is a strong association between natural resources, weak institutions, political instability, corruption, revenue mismanagement, poverty and economic stagnation (Adams et al., 2017).

(Collier & Gunning, 1999). Essentially, Isham et al. (2005) affirm that apart from the significant effect of institutional measures on the growth of the economy, the resource abundance of each country is a key determining factor. Mehlum et al. (2006) also corroborate the view that in the natural resource context, institutions are particularly vital. Nonetheless, most of these studies do not exclusively assess how institutional quality is undermined or affected by resource rents in Africa. Dominance arguments often center on resource-governance-growth, resource-growth, and institution-growth nexus.

The role of natural resources in shaping the effectiveness of governance has only been examined by few authors. Accordingly, Arezki and Brückner (2011) argue that corruption and political instability could be escalated by natural resources in countries. However, they also stress that civil rights and liberties might improve due to the presence of natural resources at the same time. In the work of Awadallah and Adeel (2011), it is emphasized that given the rising incentive and capacity of autocratic leaders to hold on to power, a country’s transition to democracy may be adversely affected by natural resource rents, as leaders are well prepared to use any means to avoid handing overpower if they are compelled to follow democratic process. It is further established by Ross et al. (2012) that oil impedes democratization and, as it imbues the culture of patronage in political elite, rents from oil could exacerbate political instability. According to Arezki and Nabli (2012), the decision on developmental policies and political outcomes could also be determined by natural resources. There is high tendency for natural resources to induce corruption and rent-seeking behavior among the elite who are often eager to exploit natural resource rents (Torvik, 2002). In resource-rich states, public demand for democratic accountability and transparency is shown to be weakened by the overreliance of authoritarian regimes on resource rents at the expense of tax revenues (Badeeb et al., 2017; Ross, 2001). Recently, using the two-stage least squares (2SLS) method, Henri (2019) identified some institutional and economic indicators that were more adversely affected by natural resource rents in Africa. Based on the results, corruption is affected the most, followed by the problem related to rule of law or justice, inefficient public administrations, bad regulation, the absence of voice and accountability, and political instability.

More specifically, in Nigeria’s case, the link to the new way of natural resource curse following the paradoxical act of multinational companies has been explored. For instance, Amaeshi et al. (2014) investigate why and how multinational companies still dictate the space in the enactment of critical and responsible business practices in Nigeria. In such contexts, they posit that responsible business practices are always tailored toward some corporate social responsibility mechanisms. Although multinational oil companies (e.g. Shell, BP, and Statoil) now support the notion that they can greatly influence the quality of governance, there has been no drastic change in the view of non-involvement in the affairs of government (Frynas, 2010). Furthermore, the Extractive Industries Transparency Initiative (EITI) membership has not played any significant role in reducing corruption scores (Kasekende et al., 2016). Based on earlier findings, in relation to focus on the natural resource curse, the mismanagement of natural resource abundance would exacerbate the incidence of weak governance in Nigeria (Sala-i-Martin & Subramanian, 2008).

In light of the preceding empirical expositions, while natural resource literature is popular, regarding Nigeria, only a few studies have identified that governance effectiveness could be enhanced or hampered by natural resources. It is obvious that the dominant debate which has been whether resources are a curse or blessing are mostly based on the interaction effect of natural resource abundance and institutions on economic growth. However, the emphasis on the magnitude of the effect of natural resource rents on institutional indicators (such as rule of law, democratic accountability, bureaucratic quality, and government effectiveness, etc.), in the context of Nigeria, has been an empirical conundrum to academic researchers. In fact, this is somewhat engendered by the growing skepticism on the extent of governance vulnerability to the development in the natural resource sector in most resource-rich economies.

Mismanagement of Natural Resource Rents and Challenges of Governance in Nigeria

The lack of effective management of natural resource windfalls has been a major threat to Nigerian progress. Given the elusiveness of meaningful social, economic, and political development in Nigeria in spite of abundant natural resources, a crucial element of the quest for Nigeria’s greatness is sustainable resource management. The common trend running through all these concerns is that the existence of natural resource abundance should generate revenue which could be translated into improved growth and development. Thus, revenues accruing to the economy should provide capital in the form of foreign exchange and overcoming what is seen as key barriers to economic progress and good governance. The development theories, especially the requirement for a “big-push” (Rosenstein-Rodan, 1961), and capital constraint (Lewis, 1955; Rostow, 1960) largely corroborate this assertion. Nigeria’s case represents evidence of how abundant natural resources are influencing governance structures, and the possibility of the outbreak of conflicts. Indeed, these issues associated with governance are fundamental determinants of the intensiveness of resource curse (Ploeg, 2008; United Nations Development Programme, 2011).

Over the years, in Nigeria, there have been appealing pictures of returns (rents) from natural resources (see Figure 1) but there are a myriad of problems facing the country such as poverty, insecurity, infrastructural decay, unemployment, ethno-religious crises, kidnapping, and many more. These problems could be ascribed to poor institutional capacity exacerbated by ineffective management of natural resources. Specifically, following World Bank (2015) report, the natural resource sector (especially oil sector) accounts for 90 percent of the country’s export revenue and 41 percent of its GDP (gross domestic product). It also provides 95 percent of foreign exchange earnings and about 65 percent of government’s budgetary revenue. It is evident that Nigeria’s consumption, investment, and governance operations are heavily dependent on natural resource revenues (oil). These point to the fact that the natural resource sector plays a significant role in driving the efficiency of other sectors of the economy, while its proper management is critical for maintaining strong public institutions. Nigeria depends much on commodity export (crude oil) for its survival, therefore, the country has been hit hard by the commodity price slump of 2015 (Nigeria is one of the countries that is worst hit by the slump in Africa). Coping with low commodity prices has been a great challenge for the government, as the country has one of the lowest tax-to-GDP ratios in the world (Akwagyiram, 2019). This is reflective of the assertion that rents from natural resources could create disincentives for leaders to focus on tax collection (Besley & Persson, 2010). As a consequence, in Africa’s biggest economy (Nigeria), GDP per capita growth rates had recorded a downward trend in recent years (see Figure 2).

Figure 1.

Source: Authors’ estimates based on data from World Development Indicator (WDI), World Bank (2018).

Figure 2.

Source: Authors’ estimates based on data from World Development Indicator (WDI), World Bank (2018).

Nigeria’s state reveals that the persistent failure of political leaders to significantly maintain good governance is contributing to pervasive corruption and social crisis in Africa’s most populous country. The current ruling government seems to have failed in their promises for improving governance as the officials are too preoccupied with the enrichment of their pockets at the expense of the poor. This has led to a dire state of poverty in the country (currently Nigeria has the highest number of extremely poor people in the world—Brookings Institution (2018). Indeed, many believe that corruption is pervasive in their government. Nigeria ranks 144 out of the 175 surveyed countries on corruption (Corruption Perceptions Index). This implies that Nigeria is one of the most corrupt nations in 2018—Transparency International (2018). Over the years, the corruption index for Nigeria has been persistently low (see Figure 3). Billions of US dollars are being stashed in foreign bank accounts on a daily basis from the spoils of systemic corruption. Natural resource rents are not used for the enhancement of effective governance at all levels. Based on the World Governance Indicator, World Bank (2017), there are low levels of voice and accountability, political stability, government effectiveness, regulatory quality, rule of law and control of corruption (see Table 1).⁵

Estimates of governance indicators range from approximately −2.5 (weak) to 2.5 (strong) governance performance.

Hence, it is imperative to initiate and espouse to measures that could aid high levels of political accountability and transparency and natural resource windfalls’ impact on institutional effectiveness.

Table 1.

Estimate of Governance Indicator

Governance Indicator

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Voice and accountability

−0.87

−0.62

−0.76

−0.78

−0.85

−0.78

−0.71

−0.70

−0.69

−0.59

−0.37

−0.30

Political stability

−1.67

−2.03

−2.01

−1.86

−2.00

−2.21

−1.96

−2.04

−2.09

−2.13

−1.93

−1.85

Government effectiveness

−0.89

−0.97

−1.04

−0.98

−1.21

−1.17

−1.10

−1.00

−0.99

−1.19

−0.96

−1.09

Regulatory quality

−0.76

−0.91

−0.89

−0.8

−0.75

−0.73

−0.68

−0.71

−0.66

−0.82

−0.85

−0.92

Rule of law

−1.35

−1.07

−1.04

−1.15

−1.16

−1.18

−1.15

−1.11

−1.05

−0.96

−1.05

Control of corruption

−1.16

−1.12

−1.05

−0.89

−1.03

−1.05

−1.17

−1.22

−1.27

−1.08

−1.04

Source: The Worldwide Governance Indicators (WGI), World Bank (2017).

Figure 3.

Source: Authors’ estimates based on data from Transparency International (2018).

Methodology

Model Specification and Data Description

The study assesses the relationship between natural resource rents and the quality of governance by incorporating economic growth, unemployment rate, and inflation into governance demand function. Based on Bjorvatn and Farzanegan (2015), high rates of unemployment could lead to increased social vices, and hence political unrest and poor governance. With this assertion, the level of unemployment in a country matters in institutional quality function. Economic growth and inflation represent the macroeconomic indicators capable of influencing the performance of public institutions (Franke et al., 2009). In the study, three institutional indicators (bureaucratic quality, democratic accountability, and rule of law) are used as the dependent variables with different models for each indicator.⁶

Since these variables are highly related, their inclusion in the same model could lead to bias outcomes.

Accordingly, Model 1 represents the use of bureaucratic quality as the institutional measure, Model 2 covers the use of democratic accountability, and Model 3 represents the incorporation of the rule of law only. The selection of scope is determined by the availability of institutional data. On the basis of aforementioned theoretical linkage (Farzanegan & Markwardt, 2009; Ross, 1999; van Wijenbergen, 1984), the general form of governance function is modeled as follows:

G O V_{t} = f (R E S_{t}, G D P_{t}, E M P_{t}, I N F_{t})

(1)

where GOV, RES, GDP, EMP, and INF indicate governance indicators (bureaucratic quality, democratic accountability, and rule of law), natural resource rents (constant 2010 US$), GDP per capita (constant 2010 US$), unemployment rate and inflation (constant 2010 US$), respectively. These variables are measured and described in Table 2. For the normalization of the series (Ahmed et al., 2016), only natural resource rents and GDP per capita are transformed into natural-log, since other variables are given in rates. Equation (1) can be rewritten in an econometric form as;

G O V_{t} = α + R E S_{t}, G D P_{t}, E M P_{t}, I N F_{t} + μ_{t}

(2)

Table 2.

Data Description and Definition.

Variable	Measurement and Definition	Source
Dependent variable
Bureaucratic quality	It accounts for perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote the quality of governance in public sector (original scale: 6 points)	International Country Risk Guide (2018 edition)
Democratic accountability	It captures perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote accountability and transparency (original scale: 6 points)	International Country Risk Guide (2018 edition)
Rule of law	It measures perceptions of the extent to which agents have confidence in and abide by the rules of the society, and in particular, the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence (original scale: 6 points)	International Country Risk Guide (2018 edition)
Independent variable
Natural resource rents	The sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents	World Development Indicators (2018 edition)
Unemployment rate	It is defined as the share of the labor force that is without work but available for and seeking employment	World Development Indicators (2018 edition)
GDP per capita	Gross domestic product divided by midyear population	World Development Indicators (2018 edition)
Inflation	It represents the annual percentage change in the cost to the average consumer of getting a basket of goods and services that can be fixed or changed at defined intervals, such as annually	World Development Indicators (2018 edition)

Source: International Country Risk Guide (2018 edition) and World Development Indicators (2018 edition).

Econometric Techniques

ARDL Bounds Test Approach to Cointegration

In the study, the ARDL bounds testing approach to cointegration developed by Pesaran et al. (2001) is employed to assess the long-run and short-run association between resource rents and governance indicators. This cointegration test by Pesaran et al. (2001) has numerous advantages over other cointegration techniques which include Ganger causality of Engle and Granger (1987), Johansen and Juselius (1990), and Johansen (1991) cointegration test.⁷

These techniques are only applicable with a unique or specific order of integration.

Regarding the order of integration, this approach is applicable whether the variables are viewed to be stationary at I(1) or I(0) but should not be I(2) or above. For capturing the data-generating process using a general-to-specific modeling framework, ARDL bounds testing empirical model makes use of an adequate number of lags (Laurenceson & Chai, 2003). Through a simple linear transformation, the dynamic Unrestricted Error Correction Model (UECM) could be obtained from the ARDL bounds testing. This technique offers reliable empirical results and is also used for small samples. Without losing any long-run information, the UECM combines the long-run equilibrium with the short-run dynamics. This method recognizes the cointegrating vectors that are due to multiple cointegrating vectors that occur in the empirical model. Under the ARDL framework, a different optimal number of lags for various variables to compute the ARDL F-statistic for ascertaining whether cointegration exists between the variables can be used. The estimated models for the ARDL bounds testing approach to cointegration is stated as follows:

G O V_{t} = α_{1} + ω_{1} G O V_{t -1} + ω_{2} R E S_{t -1} + ω_{3} G D P_{t -1} + ω_{4} E M P_{t -1} + ω_{5} I N F_{T-1} + \sum_{i = 0}^{n} β_{1} Δ G O V_{t -1} + \sum_{i - 0}^{n} β_{2} Δ R E S_{t -1} + \sum_{i - 0}^{n} β_{3} Δ G D P_{t -1} + \sum_{i - 0}^{n} β_{4} Δ E M P_{t -1}, + \sum_{i - 0}^{n} β_{5} Δ I N F_{t-1} + μ_{t}

(3)

where Δ indicates the differenced operator. ω₁,…,ω₅ are long-run estimates. β₁,…,β₅ are short-run estimates while α₁ and μ₁ represent the constant and error term, respectively. The appropriate lag length of the series is chosen based on the Akaike information criterion (AIC). ARDL F-test gives different F-statistic at different lag orders. To calculate the ARDL F-statistic, the null hypothesis of no cointegration for all models is formed as H₀: β₁=β₂=β₃=β₄=β₅=0. The alternative hypothesis of the existence of cointegration is H₁: β₁≠β₂≠β₃≠β₄≠β₅≠0. Based on the rule of thumb, if the ARDL F-statistic exceeds the upper critical bound (UCB), there is existence of cointegration between the variables. On the other hand, if the lower critical bound (LCB) is more than F-statistic, there is no cointegration between the series. There will be inconclusive decision when the F-statistic is in-between UCB and LCB. Since the confirmation of the existence of cointegration would lead to Error Correction Model (ECM) formulation in the short-run dynamics form (Pesaran et al., 2001); ECM model is given as:

G O V_{t -1} = α_{1} + \sum_{i - 1}^{n} β_{1} Δ G O V_{t -1} + \sum_{i - 0}^{n} β_{2} Δ R E S_{t -1} + \sum_{i - 0}^{n} β_{3} Δ G D P_{t -1} + \sum_{i - 0}^{n} β_{4} Δ E M P_{t -1}, + \sum_{i - 0}^{n} β_{5} Δ I N F_{t-1} + \partial_{i} E C T_{t - 1} + μ_{t}

(4)

The speed of adjustment parameters is represented by ∂ _i . Based on the rule of thumb, these parameters are expected to be negative (−) and significant.

Furthermore, given the significance of stability test, CUSUM (Cumulative Sum) and CUSUMSQ (Cumulative Sum of Squares) will be performed for estimated ECM in order to ascertain whether the long-run and short-run relationship found is stable for the entire study period. The CUSUM and the CUSUMSQ tests developed by Brown et al. (1975) are used. Unlike the Chow test that requires break points to be specified, these techniques can be used even if the structural break point is not known. While the CUSUMSQ employs the use of squared recursive residuals, the CUSUM test, based on the first (n) observations, uses the CUSUM of recursive residuals, and it is updated in a recursive form and plotted against the break point.

Granger Causality Test Based on Vector Error Correction Model (VECM)

The presence of a long-run association suggests that there is need to identify a causal link between natural resource rents and quality of governance. If variables are cointegrated, Engle and Granger (1987) argued that there should be causality between the variables in at-least from one direction. Hence, VECM Granger causality is applied, which shows a causal relationship between the variables in the long-run as well as in the short-run. This empirical approach is helpful in designing comprehensive policy measures. The VECM Granger causality model is specified as follows:

1 - M [\begin{matrix} G O V_{t} \\ R E S_{t} \end{matrix}] = [\begin{matrix} α_{1} \\ α_{2} \end{matrix}] + [\begin{matrix} α_{11 i} & α_{12 i} \\ α_{21 i} & α_{22 i} \end{matrix}] X [\begin{matrix} G O V_{t - 1} \\ R E S_{t - 1} \end{matrix}] + \dots + [\begin{matrix} α_{11 i} & α_{12 i} \\ α_{21 i} & α_{22 i} \end{matrix}] X [\begin{matrix} G O V_{t - 1} \\ R E S_{t - 1} \end{matrix}] + [\begin{matrix} γ \\ δ \end{matrix}] E C T_{t - 1} + [\begin{matrix} μ_{1 t} \\ μ_{2 t} \end{matrix}]

(5)

where 1−M is the differenced operator, the lagged correction term obtained from the long-run equation is represented by ETC_t-1. Long-run causality is confirmed by the statistical significance of ETC_t-1. The significant relationship in first difference of the variables gives the direction of short-run causality. Hence, under the framework of the Wald test, the joint x² statistic for the first differenced lagged explanatory variables is employed to test the direction of short-run causality between the variables. For example,α_12i≠0∀_i indicates that natural resource rents Granger cause institutional indicators, while if α_12i≠0∀_i, institutional indicators Granger cause natural resource rents.

Empirical Results and Discussion

Summary Statistics and Pairwise Correlation Coefficient

Statistical features of the variables are presented in Table 3. Compared to governance indicators (bureaucratic quality, democratic accountability, and rule of law), the analysis depicts the existence of relatively higher average values for GDP per capita, resource rents, inflation and unemployment rate, respectively. This indicates the low level of governance quality during the sample period (1984–2017). Following the Jarque–Bera (JB) test of normality,⁸

The Jarque–Bera (JB) test is computed based on the following formula: JB=N(S2/6+(K-2/24) where N = sample size; S = skewness coefficient, K = kurtosis coefficient. For a distributed series with normality, S = 0 and K = 3. Thus, the JB test of normality is used to test for the joint hypothesis that S and K are 0 and 3, respectively.

only the skewness for the rule of law among the institutional variables nears zero (0), while for other variables, it is unemployment rate(s). In case of kurtosis, only bureaucratic quality is found to be close to three (3). These results show that the majority of the series are not normally distributed. Similarly, in all, the probability of 5 percent is not attained except for democratic accountability and inflation. In addition, the pairwise correlation coefficient reported in Table 4 reveals that natural resource rents are negatively correlated with all the governance indicators, suggesting that an inverse association exists between resource rents and the quality of governance.

Table 3.

Summary Statistics

	BQUALITY	DACC	LAW	RES	GDP	EMP	INF
Mean	1.17	2.86	2.02	27.86	1,756.04	10.67	19.72
Median	1.00	3.02	2.00	28.38	1,512.72	12.05	12.54
Maximum	2.00	3.79	3.00	63.55	2,563.09	21.10	72.84
Minimum	0.00	0.50	1.00	4.60	1,323.50	1.90	5.38
Std. Dev.	0.53	0.85	0.72	13.22	446.30	6.30	18.10
Skewness	0.19	−1.39	0.06	0.36	0.65	0.01	1.64
Kurtosis	3.05	3.98	2.87	3.27	1.81	1.43	4.34
Jarque–Bera	0.21	12.36	1.85	0.85	4.41	3.50	17.79
Probability	0.90	0.00	0.40	0.65	0.11	0.07	0.000
Sum	39.83	97.25	68.67	947.20	59,705.38	362.90	670.49
Sum Sq. Dev.	9.31	23.99	17.19	5,767.88	65,729,03	1,308.39	10,806.23
Observations	34	34	34	34	34	34	34

Source: Authors’ estimates based on Eviews 14.

Notes: Bureaucratic quality is represented by BQUALITY; Democratic accountability is represented by DACC; Rule of law is represented by LAW; Resource rents are represented by RES; GDP per capita is represented by GDP; Unemployment rate is represented by EMP; and Inflation is represented by INF.

Table 4.

Pairwise Correlation Coefficient.

Variable	BQUALITY	DACC	LAW	RES	GDP	EMP	INF
BQUALITY	1.00
DACC	0.34*	1.00
LAW	0.81	0.50	1.00
RES	−0.59*	−0.50*	−0.17	1.00
GDP	−0.22	0.58*	−0.07	0.74	1.00
EMP	−0.48	0.71	−0.02	−0.71*	0.74*	1.00
INF	−0.62*	−0.17*	0.25	0.51*	−0.36	−0.53*	1.00

Source: Authors’ estimates based on Eviews 14.

Note: * represents correlation coefficients significant at the 5 percent level or better.

Unit Root Test

Table 5 shows the results of the augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests of stationarity for the series both at levels and first difference. Accordingly, it could be deduced that all the variables are integrated of order one, i.e. I(1). None is found to be I(0). Given the stationarity of the variables at their first difference, the test for the certainty of the existence of cointegration (F-bounds test) is performed and presented in Table 6. The test indicates that there exists a long-run relationship between the explanatory variables and governance indicators (dependent variables) across models, as the computed F-statistic exceeds the upper bounds critical values in all the models (1, 2, and 3) at 5 percent significance level. Using Pesaran et al. (2001), the null hypothesis of no cointegration is rejected. On the test of stability, Figure 4 reports the plots of CUSUM and CUSUMSQ, and it shows that they fall within the 5 percent critical bound, hence, offering evidence that there is no case of structural instability for the estimated parameters of the models.

Moreover, with the presence of I(1) variables in all, the robustness of the findings could be better enhanced with the use of Dynamic Least Squares (DOLS). Since this approach is mostly applicable when all variables in the model are I(1), coupled with its superiority over Fully Modified Least Squares (FMOLS) and Canonical Cointegrating Regression (CCR) (Kao & Chiang, 2000), it is employed for robustness checks of the estimates. For further justification of the use of DOLS, two cointegration tests are conducted which include Engle–Granger and Hansen Parameter Instability test. The results of these tests, which are reported in the Appendix, confirm the existence of cointegration among the series. In order to gauge the plausible effect of resource rents on the Nigerian public institutions, Equation (2) is used for the estimation of DOLS cointegration regression.

Table 5.

Unit Root Test

Variable	Augmented Dickey–Fuller (ADF)			Phillips–Perron (PP)
Variable	Level	First Difference	Status	Level	First Difference	Status
BQUALITY	−2.02 (0.27)	−4.70*** (0.00)	I(1)	−2.79* (0.07)	−6.67*** (0.00)	I(1)
DACC	−2.27 (0.19)	−5.53*** (0.00)	I(1)	−2.18 (0.22)	−8.96*** (0.00)	I(1)
LAW	−2.49 (0.19)	−3.49** (0.02)	I(1)	−1.88 (0.34)	−3.43** (0.02)	I(1)
RES	0.96 (0.99)	−5.80*** (0.00)	I(1)	−0.40 (0.89)	−5.85** (0.00)	I(1)
GDP	−0.22 (0.92)	−3.81** (0.01)	I(1)	−0.17 (0.93)	−3.74** (0.00)	I(1)
EMP	−1.2 (0.66)	−5.03*** (0.00)	I(1)	−1.11 (0.70)	−6.04*** (0.00)	I(1)
INF	−1.50 (0.52)	−4.64*** (0.00)	I(1)	−2.52 (0.11)	−6.83*** (0.00)	I(1)

Source: Authors’ estimates based on Eviews 14.

Notes: *** represents 1 percent, ** represents 5 percent, and * represents 10 percent. Values in parentheses are probability values, whereas the ones with no parentheses are t-statistical values. The critical values of both augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) techniques are (−3.679322), (−2.967767), and (−2.622989) at 1 percent, 5 percent, and 10 percent, respectively.

Table 6.

F-Bounds Test for Cointegration

Test Statistic	Value	K
F-statistic (Model 1)—BQAULITY(2, 1, 0, 1, 0)	4.41	4
F-statistic (Model 2)—DACC(1, 0, 0, 1, 2)	4.84	4
F-statistic (Model 3)—LAW(1, 0, 1, 2, 0)	6.10	4
Significance	I(0) lower bound	I(1) upper bound
1%	3.74	5.06
5%	2.86	4.01
10%	2.45	3.52

Source: Authors’ estimates based on Eviews 14.

Figure 4.

Source: Authors’ estimates based on Eviews 14.

ARDL Long-Run and Short-Run Estimates

The ARDL estimates of both long-run and short-run association between the variables are presented in Table 7. Based on the results, there seems to be no evidence in favor of the significant effect of natural resource rents on all the governance indicators used in the long-run. The results entirely depict the insignificant effect of resource rents on the quality of institutions in Models 1, 2, and 3, respectively, indicating that in the institutional development process, natural resources rents play a shallow role. Following these insignificant estimated parameters, the quality of governance is not substantially induced by the flows of resource wealth. The findings clearly buttress the notion that the effectiveness of institutional measures is not strongly enhanced by the proceeds from the natural resource sector. In terms of offering critical support for addressing the governance problems or the severity of institutional crises in Nigeria, these results imply that rising natural resource wealth has no substantial influence. This study aligns with the argument that in most resource-dependent countries, like Nigeria, natural resource abundance could contribute to pervasive institutional deficits in these economies. The link between natural resource rents and the low level of governance measures can be elucidated by the destabilizing effect of huge resource windfalls on the political system and policy framework (Bjorvatn & Farzanegan, 2015; Mahdavy, 1970). Based on this, huge resource windfalls have made the Nigerian government not to have initiated sound policy reforms or measures that can stimulate the building of strong institutions for improved performance. In relation to this assertion, since Nigeria is a resource-rich country with highly ethnical fractionalized groups, increased resource rents have put the country at higher risk of political instability, poor governance, and weak public institutions over the years (Henri, 2019; Montalvo & Reynal-Querol, 2005). Regarding the short-run, the insignificant effect of resource rents is confirmed in the same Table 7. By and large, these findings corroborate the view on the prevailing governance crises in resource-dependent countries. In these countries, the entrenchment of corruption and rent-seeking among political leaders are well pronounced due to the weak link between resource rents and the development of good institutions (Dinh & Dinh, 2016).

Table 7.

ARDL Long-Run and Short-Run Estimates

Long-run estimates
Variable	Model 1—BQUALITY	Model 2—DACC	Model 3—LAW
RES	−0.31 (−1.02)	−0.25 (−0.63)	−2.14 (−1.35)
GDP	1.05* (1.72)	0.75* (1.61)	1.47** (2.14)
EMP	−0.01 (−1.15)	−0.07** (−2.39)	−0.47 (−1.45)
INF	0.03** (2.34)	−0.02 (−1.09)	−0.05* (−2.38)
Short-run estimates
Constant	−3.16** (−2.06)	−2.02*** (−4.13)	−1.44 (−3.67)
ECT	−0.37*** (−5.09)	−0.51*** (−4.08)	−0.13*** (−6.01)
∆RES	−0.11 (−0.32)	−0.13 (−0.63)	−0.27 (−1.13)
∆GDP	0.39 (0.99)	0.38 (0.59)	1.56** (3.89)
∆EMP	−0.003** (2.12)	−0.09 (3.83)	−0.06*** (−3.89)
∆INF	0.011** (4.05)	0.01** (2.86)	−0.002** (−1.73)
Diagnostic test
Durbin–Watson	2.04	1.95	1.97
Breusch–Godfrey serial correlation test	0.89	0.81	0.44
Ramsey reset test	0.49	0.36	0.52
Normality test	0.23	0.37	0.21

Source: Authors’ estimates based on Eviews 14.

Notes: Values in parentheses are t-values. ***, **, and * indicate significance at 1 percent, 5 percent, and 10 percent, respectively.

Focusing on the control variables (GDP, unemployment rate, and inflation), in Table 7, the results indicate that the effect of unemployment rates is negative and significant in Model 2 in the long-run, while it is significant in Model 1 and 3 in the short-run. In other words, unemployment rates give rise to decreasing governance quality and weak policy framework. This implies that since a large chunk of the population is unemployed, public resources that could be used to finance entrenched strong institutions and the effective management of the economies are diverted to the settlement of political and civil unrest caused by high unemployment rates. Theoretically, this would engender worse economic performance, and in turn harms institutional quality (Bjorvatn & Farzanegan, 2015). On GDP per capita, the results reveal that GDP positively affects the quality of governance although not significant in all the models in the short-run. This supports the conjecture that better economic management could strengthen policy framework and thereby facilitates improved governance. However, due to pervasive wasteful and reckless tendencies in the public sector, increased growth may not translate to enhanced social welfare and accelerated development which can potentially curb the growing rate of bad governance. This empirical postulation is somewhat related to consumption linkage, production linkage, and fiscal linkage (Ogunleye, 2008). Also, although not significant in Model 2 in the long-run, overall, the findings show that inflation has substantial influence on the level of governance in the country. In all, the speed of adjustment estimated parameters representing the ECT lie between 0.13 and 0.50 and are significantly negative at 1 percent level of significance which is in tandem with theoretical expectation.

Granger Causality Test (VECM) and DOLS Analysis

Table 8 presents the analysis of the causal link between resource rents and governance indicators used. Based on the table, resource rents Granger cause bureaucratic quality, democratic accountability, and rule of law in both long-run and short-run in Models 1, 2, and 3, respectively. This relationship represents a large causal effect of natural resource wealth on the quality of institutions, suggesting that resource rents can influence the condition and state of public institutions in the country. The overdependence on natural resources (especially oil) could be the main reason for this. On the other hand, with the exemption of Models 2 and 3, institutional quality (bureaucratic quality) does not Granger causes resource rents in Model 1 in the long-run, but overall no causality from governance indicators in the short-run. This implies that in the short-run there is unidirectional causality running from resource rents to the quality of institutions, while this only holds for bureaucratic quality (Model 1) in the long-run. However, between resource rents and two other institutional measures (democratic accountability and rule of law) bi-directional causality exists in the long-run. Furthermore, in Table 9, the results of the DOLS are somewhat analogous to the findings obtained in the previous section (Table 7) in relation to the long-run effect regarding the key variables (natural resource rents and governance indicators). In sum, low levels of institutional measures and performance are somewhat caused by huge natural resource rents. This confirms the prevalence of deepening institutional defects and pallid governance roles in Nigeria, which are symptoms of resource-dependent economies (Bornhorst et al., 2009; Olsson & Fors, 2004).

Table 8.

Granger Causality Results Based on VECM

Model Lag	Resource Rents-Led Governance			Governance-Led Resource Rents
	Variable	Short-Runª	ECTb	Variable	Short-Runª	ECTb
Model (1)2	RES	7.02***	−0.03*** (−4.35)	BQUALITY	1.41	−0.72 (−0.29)
	GDP	1.83		GDP	0.61
	EMP	4.69**		EMP	0.37
	INF	1.50		INF
Model (2)2	RES	5.43**	−0.14** (−3.42)	DACC	1.30	−0.26** (−2.19)
	GDP	1.55		GDP	2.77*
	EMP	3.23*		EMP	3.24*
	INF	0.10		INF
Model (3)2	RES	4.91**	−0.15*** (−4.24)	LAW	1.01	−0.04* (−1.73)
	GDP	1.62		GDP	0.19
	EMP	4.15**		EMP	0.27
	INF	0.12		INF	0.31

Source: Authors’ estimates based on Eviews 14.

Notes: ªThe Wald statistic is reported. It tests the joint significance of the lagged values of the variables, which follow x2 distribution. bValues in parenthesis represent t-statistic. ***, **, and * indicate the level of significance at 1 percent, 5 percent, and 10 percent, respectively.

Table 9.

Dynamic Least Squares (DOLS)

Variable	Model 1—BQUALITY	Model 2—DACC	Model 3—LAW
Constant	−1.33* (−1.74)	−1.21 (−0.71)	3.53 (0.38)
RES	−0.32 (−0.98)	−0.34 (−0.48)	−1.33 (−0.56)
GDP	1.13* (1.87)	1.40** (2.34)	1.12* (1.61)
EMP	−0.05 (−1.50)	−0.03 (−1.23)	−0.19** (2.92)
INF	0.01** (2.90)	−0.03* (−1.73)	0.01 (0.66)

Source: Authors’ estimates based on Eviews 14.

Notes: Values in parentheses are t-values. ** and * indicate significance at 5 percent and 10 percent, respectively.

Concluding Remarks

Dominant arguments in the literature have been that resource-dependent countries typically suffer from resource governance constraints and lack of shared national strategies. Nigeria is a prominent example of a country with the pervasiveness of weak public institutions in spite of its huge resource wealth. The presence of natural resource abundance has exacerbated the overwhelming development challenge in the economy. While the upshot of most empirical findings of the resource impact covered how the growth path is determined through the channel of institutions, the question as to why resource rents often fail to stimulate improved governance is more critical than ever. Hence, the main thrust of the study is to examine the long-run and short-run effect of natural resource rents on the quality of governance in Nigeria for the period 1984–2017, using ARDL bounds test approach and Granger Causality test based on VECM. Given the use of three (3) institutional indicators (bureaucratic quality, democratic accountability, and rule of law), models were specified in accordance with these measures.

The analysis confirms that the growing incidence of bad governance and weak institutions is caused by the inability of the government to focus on the development of institutions for enhanced economic development due to entrenched mismanagement tendencies and overreliance on natural resource wealth. With the insignificant effect of resource rents in the long-run as well as in the short-run, it is affirmed that huge natural resource rents have no substantial support for the building of good institutions. Indeed, an increase in resource rents has a weak influence on governance improvement, but huge resource wealth may exacerbate the ineffectiveness of bureaucratic quality, democratic accountability, and rule of law. With the main interest on the long-term causal relationship, findings indicate that there is unidirectional causality running from resource rents to bureaucratic quality, while for democratic accountability and rule of law, there is bi-directional causality between resource rents and governance quality. This suggests that low levels of institutional indicators and huge natural resource rents are highly correlated. Hence, the pervasive institutional gap in Nigeria could be stimulated or caused by the overdependence on natural resources and mismanagement of resource rents (especially oil) coupled with the destabilizing effect of huge resource windfalls on the political system and policy framework. It is also demonstrated that a resource-rich country (such as Nigeria) with highly ethnical fractionalized groups, and increased resource rents could put the country at higher risk of political instability, poor governance, and decrepit institutions. Thus, this accounts for the ingrained corruption, rent-seeking, wasteful, and reckless tendencies in the public sector.

In all, empirical evidence largely supports the assertion that shortcomings in governance and unsustainable practices are engendered by institutional obstacles posed from natural resource windfalls. Furthermore, it is confirmed that resource rents could shape the fundamental institutional measures in Nigeria. Therefore, the study suggests that maintaining strong political commitment, curtailing overdependence on natural resources and the evasion of rent-seeking in the public sector are central for improved governance and sustainable economic development.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Fisayo Fagbemi

Appendix

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