Natural resources, financial development and institutional

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Resources Policy

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Natural resources, financial development and institutional quality in Africa:Is there a resource curse?

Richard Adjei Dwumfour⁎, Matthew Ntow-Gyamfi

Department of Finance, University of Ghana Business School, Bank of Ghana, Accra

A R T I C L E I N F O

JEL classification:E44N57P48Q32Q33

Keywords:Natural resource curseFinancial developmentBanking stabilityInstitutional qualityAfrica

A B S T R A C T

We examine the resource curse hypothesis in the relationship between natural resources, financial development(FD) and institutional quality of 38 African countries from 2000 to 2012. We use the Arellano-Bond GeneralizedMethod of Moments (GMM) estimator in our estimations. Our findings reveal that 32% of African countries aredependent on resource rents ranging from a low of 12.5% (in Zambia) to 52.6% (in Angola) share of GDP. Wefurther find that in Africa, the impact of natural resource rents (Rents) on financial development is ambiguous. Itlargely depends on the FD indicator used. The resource curse is seen when a Z-score is used as FD indicator.Particularly, we find that the resource curse in FD is seen in Sub-Saharan Africa, Low Income Countries andMiddle Income countries but not in the North African region. The impact of Rents on credit however is positivein all regions. In these relationships the quality of institutions can reduce the negative impact of Rents on FD.Our findings are robust to the use of different indicators of FD and institutional quality.

1. Introduction

Evidence has shown that Africa's economy is consistently growingfaster than those of the rest of the developing world (Chuhan-Pole et al.,2013). Chuhan-Pole et al. (2013) indicates that Sub-Saharan Africa's(SSA) economy has grown more than the rest of the developing world.They show that the SSA economy expanded at an estimated 4.6% peryear during 1999–2010 (5.2% excluding South Africa). This exceeds bymore than 0.9% the average annual growth rate of the rest of the de-veloping world (excluding China) and in fact three of the world's 10fastest-growing countries have been in Sub-Saharan Africa. Despite thisgrowth turnaround, the growth impact of natural resource revenues inAfrica remains mixed. A large literature exists on whether natural re-source revenues are a curse or blessing to economic growth/develop-ment (Allcott and Keniston, 2014; Carmignani, 2013; Aragon and Rud,2013; Frankel, 2012; Van der Ploeg, 2011; Rosser, 2006; Bulte et al.,2005; Sala-iMartin and Subramanian, 2013; Auty, 2001). While dif-ferent causal channels by which a resource curse might operate havebeen identified in these studies, most of these studies have focused oneconomic development. Most of these studies suggest that on averageresource-rich countries fare worse in terms of economic progress thanresource-poor countries while few other studies find evidence contra-dicting the resource curse (Sala-i-Martin et al., 2004; Wright and

Czelusta, 2004). For instance, when the resource curse is discussed inAfrica, the example of Nigeria is often mentioned. Even though Nigeriahas enjoyed huge oil windfalls since the late 1960s, its per capita GDP(in purchasing power parity terms) is among the lowest in the worldand was even lower in 2000 than it was in 1970 (Sala-i-Martin andSubramanian, 2013). Similar evidence is also shown in Angola which isrich in diamonds. On the other hand, considering the diamond-richBotswana, average incomes are more than four times higher in Bots-wana than in Angola and property rights are considerably stronger too.This is hardly surprising as Botswana has been one of the fastestgrowing countries in the last 40 years while Angola has been plaguedby civil wars most of the time (Acemoglu et al., 2003). In most of theseprevious studies, little attention has been given to financial develop-ment which is a key sector that can also experience the resource curse.This imbalance in literature is surprising given that it is now widelyaccepted that financial development constitutes a potentially importantmechanism for long run growth (Levine, 2003; Demetriades andAndrianova, 2004; Goodhart, 2004) hence the need to examine theimpact of natural resources on this sector.

First, we focus on whether natural resources are a curse or blessingfor the financial sector in terms of stability or development of thesector. Indeed, for the private sector to thrive, there needs to be a stableand robust financial sector that supports private sector growth through

https://doi.org/10.1016/j.resourpol.2018.08.012Received 26 November 2017; Received in revised form 16 July 2018; Accepted 16 August 2018

⁎ Corresponding author.E-mail address: [email protected] (R.A. Dwumfour).

Resources Policy 59 (2018) 411–426

Available online 31 August 20180301-4207/ © 2018 Elsevier Ltd. All rights reserved.

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finance. Banks or deposit taking institutions need deposits to survive astheir core aim is to lend surplus funds to deficit units. One major sectorthat can drive banks’ deposit mobilisation to give loans/credit to othersis the extractive or natural resource sector. Countries that are blessedwith natural resources like oil, gas and minerals among others canleverage these resources to attract private capital flows into thecountry. These capital flows are channelled through the financial sectorthereby having the potential of causing financial sector development.Africa holds the largest mineral industry in the world. This resourcewealth when properly harnessed could help develop the financial sectorin various ways both directly and indirectly. Ideally, when countries arerich in natural resources, banks can have increased deposits and savingsthrough which credit can increase to spur investments. This phenom-enon could stimulate growth in the banking sector and the economy asa whole. Sadly, earlier studies have observed that resource-rich coun-tries in Africa have had slower growth. These countries are said tosuffer from what Auty (1993) coined a “resource curse”.

Second, where countries are more dependent on their resourcesector, banks may tend to suffer stability issues because of the in-stability in the extractive sector. For instance, the global fall in prices ofminerals, world metals and oil from 2014 left most oil dependentcountries like Nigeria in distress. The global increase in oil supply andthe low demand for oil due to slow growth in China and Europe causedprices of oil to fall. Some multinationals retrenched some of their in-vestments, decommissioning some of their rigs and cutting back onexploration and production. These developments came up in the wakeof the heat of resource price volatilities.

Third, the resource curse concept reveals the rent-seeking beha-viours of public officials put in charge of the economy. These officialsinstead of channelling resource revenues to productive ventures for theeconomy have rather channelled the monies to their pockets. Evenmore unfortunate is that these officials hold offshore accounts and in-vest in foreign countries who benefit from these monies. It is commonto read of corrupt officials being arrested in some countries where de-mocracy and accountability is strengthened. These developments meanthat where institutions are strengthened, resource revenues could beproperly channelled. Thus, theory suggests that an adverse effect ofresource revenue is less severe in the presence of good institutions(Mehlum et al., 2006a). That is, countries which have strong institu-tions at the time of resource development—low corruption, strongproperty rights protection, strong rule of law, and effective bureau-cracy—benefit from their resource wealth; but those with weak in-stitutions suffer the curse (Mehlum et al., 2006a, 2006b; Robinsonet al., 2006). The endowment hypothesis introduced by Acemoglu et al.(2001) acknowledges the importance of strong institutions for financialdevelopment. In simple terms, the strength of institutions in a countrycan be a determining factor for the impact of natural resource wealth onfinancial development. This natural resource wealth-financial devel-opment nexus with the intermediary role of institutional quality hasreceived little attention in literature. Hence, the core aim of this study isto revisit this thesis and explore the relationship properly in Africa.Through this, we examine the role that institutional strength plays inthis relationship. In this regard, we examine various dimensions of in-stitutions that have the potential to influence the impact of naturalresources on financial development. These core areas of institutions arevoice & accountability, political stability & non-violence, governmenteffectiveness, regulatory burden, rule of law and control of corruption.

Fourth, a vast majority of empirical analysis relies on one or twoindicators of natural resource wealth like rents from mineral, forest, oilor gas to provide insights into the relationship between resources andeconomic development (Weszkalnys, 2009). Evidence of a more com-prehensive measure of natural resource rents in examining the resourcecurse thesis remains scarce in literature. We follow Dwumfour et al.(2017) and develop a weighted average of resource rents using fourindicators from minerals, forest, oil and gas to examine this resourcecurse in Africa.

Fifth, it is important to note that most of the previous studies on theresource curse have been done either for developing countries, countrycase studies or Africa as a whole without taking into consideration re-gional differences or levels of development (income groups) in theserelationships. Indeed, the resource abundance and dependence in eachregion or income group differ. As such, the level and dynamics of howtheir financial sectors operate and respond to the extractive sector andtheir institutional framework may differ. Other studies like that ofFleming et al. (2015) contribute to the resource curse literature bydiscussing its theoretical causes across scale (regional vs. national ef-fects). In their discussion, they show evidence of the local economicimpacts of resources extraction across regions of a country. In thisstudy, we examine the resource curse/blessing in the context of dif-ferent regions and income groups in the continent. We do this by in-cluding regional dummies (Sub-Saharan Africa – SSA and North Africa –NA) and Income groups (Low Income Countries, Lower Middle Incomecountries and Middle Income countries) to examine whether there is aresource curse in these regions and income groups.

Sixth, as indicated by Badeeb et al. (2017) in their literature surveyof the resource curse thesis, future studies on the resource curse need tocarefully address issues of endogeneity in measures of resource de-pendence. Therefore, the estimation technique that addresses the issueof endogeneity is relevant. While Brunnschweiler and Bulte (2008)argue that the resource concept may be a red herring, Van der Ploegand Poelhekke (2009) argue that the Brunnschweiler and Bulte study isflawed by weak instruments, omitted variable bias, a violation of theexclusion restriction, and a misspecified model. In the light of thesearguments, this study uses the Generalized Method of Moments (GMM)approach to address the endogeneity issues to test the resource curseconcept. Also, it is important to note that financial development maypersist, hence the choice of our estimation technique is relevant. TheGMM technique accounts for these issues.

The rest of the study is arranged as follows: Section 2 gives a briefreview of the relevant literature. Section 3 deals with the methodologyadopted for the study. Section 4 focuses on data description and trends.Section 5 deals with the empirical results and discussion of findingswhereas Section 6 concludes and gives some policy recommendations.

2. Theoretical and empirical review

It is imperative to consider the development channels throughwhich natural resource wealth can affect countries’ development paths.Van der Ploeg (2011) defines a resource rent as economic profits ac-cruing after paying out factors of production and taking into accountthe opportunity costs of production – essentially above marginal costs.While these returns from resources can be very high, the dynamics ofhow these gains can be efficiently used to improve growth and mitigatepoverty may vary across countries and time (Van der Ploeg, 2011). Thetraditional orthodox view of natural resource wealth as a spur togrowth or development has been challenged by numerous empiricalstudies in the past. This brought about the well-known concept of a“resource curse” as indicated by Auty (1993). Evidence has shown thatresource rich countries have experienced lagged growth, high conflicts,higher income inequalities, and slower progress in financial reforms,trade liberalization, diversification and improvement in business cli-mate. The various forms of resource curse that countries have experi-enced have included the “Dutch disease”, rent seeking, neglect ofeducation, and a weak institutional framework among others. Keyamong these explained in the literature is the Dutch disease.

This Dutch disease is a phenomenon which refers to the apprecia-tion of the real exchange rate: the result of inflation arising from thespending of revenues especially on non-tradeable goods and high ex-pectations of capital inflows (Humphreys et al., 2007; Rodriguez andSachs, 1999; Auty, 1993). This leads to an appreciation of the nominalexchange rate as the domestic currency attracts higher demand. Theseresource-rich countries tend to be vulnerable to the Dutch Disease

R.A. Dwumfour, M. Ntow-Gyamfi Resources Policy 59 (2018) 411–426

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because they rely heavily on the export of natural resources such as oilfor foreign exchange and tend not to pay attention to other industriessuch as manufacturing. This leads to de-industrialisation (Sachs andWarner, 1995). Moreover, as indicated earlier, as the currencies ofthese countries appreciate in value relative to other currencies, theirexports become expensive, and thus, uncompetitive. Dependence onnatural resource wealth, therefore, debilitates other export-led activ-ities of the economy, particularly manufacturing. These economies areprone to structural stagnation and have little, if any, diversification. Acountry which is Dutch Diseased may experience stagnated growthbecause it ‘can damage the growth process by crowding out exportactivities that otherwise have the potential to grow rapidly’ (Collier,2007, p. 40; Humphreys et al., 2007, p. 5). This usually leads to acontraction in the non-oil, gas, or mineral traded sector. Countries thatfall within this ‘disease’ thus suffer low long term growth resulting fromthe crowding out of other sectors of the economy (Gylfason, 2004). Asindicated by Sarraf and Jiwanji (2001), in most cases, Dutch Diseasehas taken on a much wider meaning and is usually meant to encompassall of the negative macroeconomic effects associated with “resourcecurses”.

Another form of the resource curse is rent seeking behavior of elitesor government officials that otherwise could be put to more productiveor profitable ventures. Generally, the resource curse can be explained asan inverse relationship between natural resource wealth and develop-ment of a nation. Various indicators that have been used to measure theresource curse have been economic growth/development, incidence ofcivil war, poverty and inequality among others.

The nexus between natural resources and financial development hasreceived little attention. In this regard, we posit that a country escapesfrom the resource curse when natural resource abundance helps de-velop the financial sector. First, when a country has abundant re-sources, the banking sector could have increased liquidity through in-creased deposit mobilisation from private and public sectors and taxreceipts from government. Therefore, all else being equal, one mightexpect more bank credit to firms and households, and more developedfinancial markets in resource-rich countries. On the other hand, therelationship between natural resources and FD could be moderated byinstitutional quality, especially those responsible for regulating andenforcing contracts between creditors and debtors (La Porta et al.,2000; Djankov et al., 2007). Thus, in the absence of strong institutions,creditors find it difficult to enforce contracts, and debtors may havelittle incentive to repay their debt. As a result private investors andbanks might be reluctant to borrow or lend in the first place, even whenhighly liquid. Thus, theory suggests that an adverse effect of resourcerevenue is less severe in the presence of good institutions (Mehlumet al., 2006a, 2006b). The role of strong institutions has been empha-sised to be crucial to the development impact of resource rents whilemitigating any adverse effects (Boschini et al., 2007). Again, the eco-nomic choices of ruling elites especially in resource rich countries canbe highly influenced by the strength of the political institutions in thecountry (Acemoglu and Robinson, 2012). Examples of strong institu-tions are where there is rule of law, proper checks on corruption, low/no violence and where citizens have strong voice to check the gov-ernment. The study therefore posits that, when institutions are strongand well regulated, natural resource wealth can help develop the fi-nancial sector.

Empirically, Bhattacharyya and Hodler (2014) study whether nat-ural resource revenues hinder financial development, and what rolepolitical institutions are playing in this process. The authors find that,resource rents are negatively associated with financial development incountries with weak political institutions, but this negative effect de-creases in absolute value and eventually vanishes as the quality of po-litical institutions improves. Thus, the authors conclude that resource-rich countries with weak political institutions are likely to be financiallyunderdeveloped. Beck (2012) shows that banks in resource-richeconomies are more liquid but give fewer loans to firms. The study

posits that these firms use less external finance, and a smaller share ofthem use bank loans even though their demand for credit is similar tothe demand of firms elsewhere. The author argues that these findingspoint toward a supply constraint and suggests that there could be afinancial resource curse. Murphy et al. (1993) show that resource rev-enues increases rent seeking behavior and waste. Torvik (2002), positsthat a natural resource bonanza could shift productive entrepreneurs torent seeking. This could lessen growth if the extra income from theresource revenues is less than the reduction in income due to decline inproductivity.

Robinson et al. (2006) develop a political economy model whichshows that the impact of a “resource boom” crucially depends on thequality of the political institutions, and in particular the degree of cli-entelism in the public sector. Countries with worse-quality institutionsare more likely to suffer from a resource curse. Atkinson and Hamilton(2003) show that natural resource abundance may have negative effectson development when weak institutions allow resource profits to bespent in government consumption rather than investment, especially incountries with low levels of genuine saving. Girma and Shortland(2008) and Huang (2010) find positive effects of better political in-stitutions on financial development, and Bhattacharyya (2013) findsthat democratization leads to a more market-based financial system. LaPorta et al. (1998) find that British common law countries are likely tohave a better developed legal system which promotes financial devel-opment. Herger et al. (2008) suggest that colonial history mainly affectstoday's financial development through its effect on political institutions.Guiso et al. (2004) in contrast argue that social capital and informalrules that govern social interaction play a crucial role in financial de-velopment. Huang (2010) studies the effect of political institutions onfinancial development using a panel dataset of 90 developed and de-veloping countries between 1960–99. The study reveals a positive effectof institutional improvement on financial development at least in theshort-run, particularly for lower income countries. Other studies(Pagano and Volpin, 2001; Rajan and Zingales, 2003; Beck et al., 2003)also highlight the role of political intervention and institutions in fi-nancial development. Huang (2009) studies what forces lead govern-ment to undertake reforms to enhance financial development and findsthat the extent of democracy is one of the significant forces. La Portaet al., (1997, 1998) have argued that the origins of the legal codesubstantially influence the treatment of creditors and shareholders, andthe efficiency of contract enforcement. Researchers such as Mehlumet al. (2006b) and Mavrotas et al. (2011) argue that institutions aredecisive for determining whether resource revenues bring curse orblessing. Consistent with this view, Torvik (2009) and Sarmidi et al.(2014) argue that a good institutional framework dissipates the nega-tive effects of natural resource endowments on growth. However, in asmaller vein, Brunnschweiler (2008) does not find that institutions playa significant causal role in the resource curse outcome.

From the literature, it emerges that the financial development ef-fects of natural resource abundance could also depend on institutionalquality variables included in the analysis: there may in fact only be acurse when natural resource wealth occurs together with low qualityinstitutions. The current study draws from this previous literature andmakes a new contribution to the resource curse literature. We examinethe resource curse in Africa and its sub-regions from the perspective ofFD. We further examine the resource curse in Low and Middle incomecountries in Africa to understand whether curse/blessing effects vary byincome status. In these relationships, we examine the moderating roleof institutional quality.

3. Methodology

3.1. Dataset

Data is sourced from Global Financial development Database(GFDD), World Governance Indicators (WGI) and World Development


413

Indicators (WDI) of the World Bank. As indicated by Krishnan (2011),the Global Financial Development Database (GFDD) provides an ex-tensive dataset of financial system characteristics. The variables used tomeasure institutional quality are taken from a World Bank WorldGovernance Indicators dataset covering six different dimensions ofgovernance from 1996 onwards (Kaufmann et al., 2010; also includedin the World Development Indicators). The dataset reports aggregateand individual governance indicators for over 200 countries and terri-tories. Its six indicators are Voice and Accountability, Political Stabilityand Absence of Violence, Government Effectiveness, RegulatoryQuality, Rule of Law, and Control of Corruption. These indicators are allpositively correlated among each other, as well as with measures ofinstitutional quality used in the growth literature (Acemoglu et al.,2005; Knack and Keefer, 1995; La Porta et al., 1999). Due to this, thesesix indicators are used separately in our regressions. The WDI dataset isthe primary World Bank collection of development indicators, compiledfrom officially-recognized international sources which presents themost current and accurate global development data available, and in-cludes national, regional and global estimates (World Bank, 2017). Themain advantages of the World Bank measures lie in their objectivityprovided by a very broad survey sample which includes and adds to thesources for earlier indicators and the excellent country coverage. Eventhough earlier data are available for the majority of countries, thesample is restricted to the 2000–2012 period, in line with the avail-ability of other indicators during the study period. In all we use countrylevel data for 38 African countries.

3.2. The impact of natural resources on financial development (FD)

To address the main hypothesis of the study we specify the modelsbelow to understand the impact of natural resource wealth on financialdevelopment.

= + + + +

+ + + + + +

−Devpt α Devpt α Rents α INST α NIM α Boone

α CONCEN α Education α Inflation δ μ εit i t it it it it

it it it i t it

1 , 1 2 3 4 5

6 7 8

(1)

Here, Devptit is the dependent variable for either financial stability ordomestic credit provided to the private sector (Credit) as a ratio of GDPfor country i at time t. Schwab (2011) in the Financial DevelopmentReport indicates financial stability and banking financial services asmeasures of financial development. In this study, we choose Z-score as ameasure of financial stability and domestic credit to private sector as ashare of GDP as a measure of banking financial services. All these twoare indicators of financial development and our data is sourced from theGFDD. Again, our preference for a stability is anchored in the fact that,irrespective of how successful the banking sector is, stability that en-sures long-run survival is crucial for banks to continue to run as a goingconcern. Thus, banks need to be stable.

Zscoreit captures the probability of default of a country's bankingsystem. Z-score compares the buffer of a country's banking system(capitalization and returns) with the volatility of those returns.

It is estimated as:

+ROA

sd ROA( )

EquityAssets

(2)

ROA is return on assets, Sd (ROA) is the standard deviation of ROA.Data is collected from the GFDD. In the GFDD's calculations, ROA,equity, and assets are country-level aggregate figures are calculatedfrom underlying bank-by-bank unconsolidated data from Bankscope.Thus Z-score is the number of standard deviations by which a bank'sreturn on assets has to fall for the bank to become insolvent. Z-scorecompares the buffer of a country's commercial banking system (capi-talization and returns) with the volatility of those returns. Higher Z-score values show higher stability.

Creditit refers to financial resources provided to the private sector,

such as through loans, purchases of nonequity securities, and tradecredits and other accounts receivable, that establish a claim for re-payment. For some countries these claims include credit to public en-terprises. This is expressed as a ratio of GDP. Data is collected from theGFDD. This ratio suggests that a country is financially underdevelopedif there is little credit available for the private sector relative to the sizeof its economy.

NIMit is the net Interest margin of country i at time t. we expect apositive relationship between NIM and development indicating thathigher margins improve stability and banking development.

Booneit is the Boone indicator which measures the level of compe-tition in the financial sector. The more negative the Boone indicator,the higher is the degree of competition because the effect of realloca-tion is stronger. An increase in the Boone indicator implies a dete-rioration of the competitive conduct of financial intermediaries.

CONCENi t, is the assets of five largest commercial banks as a share oftotal commercial banking assets of country i at time t.

Rentsit is the natural resource indicator of country i at time t. Ourmeasure of natural resources is broad and follows Dwumfour et al.(2017). In terms of contribution to GDP, this measure employs the totalvalue weighted of four resource rents including oil rents, forest rents,gas rents, and mineral rents as a proportion of GDP. We use this in-dicator for various reasons. First, this is important as it tells us about thetype of natural resource rents that may affect financial development.Second, this indicator takes into consideration the more importantsources rents for a particular country. Third, by measuring resourcerents, this measure is a good proxy for resource revenues that can po-tentially be appropriated by political leaders. Fourth, it is fairly widelyavailable across countries. Therefore we are able to minimize the risk ofsample selection bias. It also provides a reasonably long time dimen-sion.

We follow the formula below:

∑==

Rents W Jitj

j i t i t1

4

, , ,(3)

Rentsit is the total value weighted rents as a share of GDP of countryi at time t. Wjit is the weight of resource rent j (forest, oil, gas, andmineral rent), thus the value of the particular rent divided by the totalrents of country i at time t. Ji t, is the resource rent j (forest, oil, gas, ormineral rent) of country i at time t. j is the number of resource rentincluded in the calculation. Based on the literature on the resourcecurse, we expect an ambiguous sign.

INSTit is an institutional quality index which we obtain from theWorld Governance Indicators which includes, voice & accountability(Voice), political stability & non-violence (Political), government ef-fectiveness (Government), regulatory burden (RegQuality), rule of law(Law) and control of corruption (Corruption). Higher values indicatebetter or strong institutional quality. We expect a positive sign in-dicating strong institutions improve banking sector development.

Educationit is education proxied by a primary enrolment ratio. Weexpect a positive relationship as we postulate that the more educatedthe public, the more they are likely to understand complex financialinformation thereby increasing access and participation in bankingservices such as credit hence improving development.

Inflationit is the log of average Consumer Price Index (CPI) per yearof country i at time t. δi is the country fixed effect and μt a time fixedeffect. By using country fixed effects, we indirectly control for legalorigin, colonial history, and social capital, and thereby ensure that ourresults cannot be driven by these well-established determinants of fi-nancial development. εit is the error term.

Interaction terms are included to examine the moderating role ofinstitutions in the relationship of natural resources and financial de-velopment. We also include regional dummies (SSA and NA) as well asdummies for LIC and MIC to examine whether the resource curseconcept varies by region or income status.


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3.3. Estimation technique

As indicated by Badeeb et al. (2017) in their literature survey of theresource curse thesis, future studies on the resource curse need tocarefully address issues of endogeneity in measures of resource de-pendence. We thus use the GMM-SYS which addresses endogeneity is-sues in our models. The structure of our model gives rise to auto-correlations as well as correlation between the country fixed effects andthe error. This problem of endogeneity is corrected using the differencegeneralized method of moments (GMM) of Arellano and Bond (1991)which uses the first difference of the explanatory variables to deal withthe fixed effects and their lagged values as instruments. However sev-eral problems may arise from the difference estimator. In particular,using the first difference of the explanatory variables to deal with thefixed effects and their lagged values as instruments, when the ex-planatory variables are persistent over time, can result in weak in-struments for the regression equation in first differences (Blundell andBond, 1998 and Alonso-Borrego and Arellano, 1999). Instrumentweakness influences the asymptotic and small-sample performance ofthe difference estimator. Asymptotically, the variance of the coeffi-cients rises. In small samples, weak instruments can bias the coefficientsand thus the difference GMM has been found to have low predictiveability. To reduce the potential biases and imprecision associated withthe usual estimator, we use a new estimator that combines in a systemthe regression in differences with the regression in levels.

We use the system-GMM (GMM-SYS) estimator which is a productof the work done by Blundell and Bond (1998). The two-step system-GMM (GMM-SYS) estimator which helps us to address the possibleendogeneity issues associated with the various determinants of fi-nancial development. For instance, the introduction of lagged financialdevelopment ( −Devpti t, 1) is necessary because it is likely that a countrythat has shown high financial development in the past is more prone tobe subject to higher financial development in the following period. Thelags of the industry specific variables are treated as endogenous andhence used as instruments. Since this reduces the number of observa-tions and power of regressions we employ the collapsing method ofHoltz-Eakin et al. (1988) to limit the number of instruments. We test theinstrument validity by using Hansen's J statistic of over-identifyingrestrictions. The Hansen's J statistic is used in place of the Sargan test ofover-identifying restrictions because of its consistency in the presenceof autocorrelation and heteroscedasticity (Neanidis and Varvarigos,2009; Roodman, 2007). The Hansen J statistic tests the null hypothesisthat the over-identifying restrictions are valid. The test results for themodels do not reject the null hypothesis of valid instruments (becauseprob> χ2 are all greater than 0.05). We make sure we check whetherdeeper lags of the instrumented variables are correlated with deeperlags of the disturbances. We use the Arellano and Bond (1991) AR (1) &AR (2) tests for first and second order serial autocorrelation. Forsystem-GMM we only check for the absence of second order serial au-tocorrelation.

4. Descriptive statistics and data summary

We present general summary statistics of all variables. We alsopresent a summary of the data as per country and year.

4.1. Descriptive statistics

Table 1 describes the data used in the study. The Z-score displays awide variation of financial sector stability for countries across Africa(−12.02 to 66.1). This shows that a while there are some countriesshowing some decent levels of financial sector stability, others arehighly unstable with Z-score value far in excess of 60%. The maximumZ-score value recorded on the continent for the period under study was66.1%. Credit which shows the total credit to the private sector bybanks as a percentage of GDP also recorded a mean of 24.28%. Higher

values of credit to the private sector indicate increased levels of fi-nancial development. With a standard deviation in excess of the mean(SD = 26%), it is clear that financial sector development levels acrossthe continent is speckled. The natural resource wealth (Rents) share ofGDP recorded an average of 11.39% of GDP and a minimum value of0.00%. This shows that though the African continent is touted as aresource rich continent, there are some pockets of countries that are notendowed with these resources. Therefore in interpreting the narrativeof Africa as resource rich continent, caution must be taken.

All the institutional quality variables have negative mean valuesshowing weak institutional strength in the continent. These indicatorsare usually set from approximately −2.5 (weak) to 2.5 (strong) to in-dicate institutional quality. The variables for measuring institutionalstrength are the six dimensions of governance: Voice, Political,Government, RegQuality, Law and Corruption. The revelation by thesummary statistics is not surprising as Africa continues to contend withweak institutional quality levels. It is worth noting that Governmentwas seen to be the weakest institutional quality measure with thelowest mean score.

4.2. Regional and Income group trends in main variables of interest

Here, we study the trends of the main variables (Z-score, Credit andRents) of interest in the study to know how they changed over time. Inthis regard, we compare the various regions and income groups so as tounderstand the similarities or otherwise amongst them. In Fig. 1, wecompare SSA with NA regions. We see that the NA region has hadhigher levels of stability (Z-score) than the SSA region. Even during the2008/2009 global financial crises period, the NA region saw an increasein Z-score compared to the decline in the SSA region. In terms of Credit,the NA has higher Credit levels than SA counterparts. Interestingly,looking at the trend for Rents, we see that the NA region had higherRents than SSA up until after 2009 when SSA overtook the NA region.In Fig. 2, we compare Low Income Countries (LIC) with Middle Income

Table 1Descriptive statistics.

Variable Source Obs. Mean Std. Dev. Min Max

IndustryZ-score GFDD 486 11.1580 8.9668 −12.0200 66.1000Credit GFDD 478 0.2428 0.2600 0.0020 1.6013NIM GFDD 485 0.0655 0.0362 0.0062 0.3924Boone GFDD 471 −0.0571 0.0919 −1.0200 0.3900CONCEN GFDD 360 0.8706 0.1342 0.3342 1ResourceRents WDI* 486 0.1139 0.1323 0.0000 0.6969InstitutionsCorruption WGI 456 −0.5218 0.5610 −1.5661 1.2497RegQuality WGI 456 −0.4848 0.5310 −2.1102 0.9837Law WGI 456 −0.5414 0.5879 −1.9475 1.0567Voice WGI 456 −0.5512 0.6986 −1.9356 1.0004Government WGI 456 −0.5719 0.5691 −1.9606 0.9342Political WGI 456 −0.4376 0.8945 −2.6600 1.1900MacroeconomicPER WDI 399 101.4859 21.1691 33.6478 164.8584Inflation WDI 493 −0.2818 0.3952 −3.5370 0.5174

Z-score: is the z-score variable, Credit: total credit provided by banks to theprivate sector as a share of GDP, NIM: Net Interest Margin, Boone: the booneindicator as a measure of competition, CONCEN: is the assets of five largestbanks in the country, Rents: is our measure of natural resource rents,Corruption: is the corruption index, RegQuality: is a regulatory quality variable,Law: is a rule of law variable, Voice: is the voice and accountability variable,Government: measures government effectiveness, Political: political stabilityand absence of violence/terrorism, PER: is the primary enrolment ratio,Inflation: is the natural logarithm of the Consumer Price Index (CPI), GFDD:Global Financial Development Database, WDI: World Development Indicators,WDI*: is calculated based on data from WDI, see Eq. (3) for explanation, WGI:World Governance Indicators (WGI).


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Countries (MIC). It can be noticed that the MIC group has higher Z-score and Credit than the LIC over the years. A similar trend was ob-served for the Rents except for 2010 and 2011.

4.3. Mean analysis by country

Here, we show the mean statistics of the main variables of interestper country to have an overview of each country's characteristics. FromTable 2, it can be seen that Libya has the highest Z-score (Mean =36.41) in Africa considering the 38 countries used. This is not surprisingas the country is positioned at 23rd country in terms of Credit eventhough they have the 2nd highest Rents (Mean = 46.69%) on the

continent after Angola (Rents = 52.59%). Mauritania is the 3rd countryin terms of Z-score and 4th in terms of Rents but 10th in terms of Credit.Just a cursory look at this shows the country is well positioned in thecontinent in terms of achieving strong scores across these 3 indicators.Morocco as well is very well positioned. The country places 2nd and 4thin terms of Z-score and Credit respectively. This indicates the country isdoing something right in terms of managing its Credit and Z-score eventhough the country's resource rents are just 1.85% of GDP, placing themin 33rd rank. Similar observation can be made for Namibia. Two otherinteresting countries to look at are South Africa and Nigeria, the top 2largest economies in Africa in GDP terms. South Africa, though has oneof the lowest Rents (1.14%, 35th rank), it is the topmost country when

Fig. 1. Trends across Sub-Saharan Africa (SSA) and North Africa (NA).

Fig. 2. Trends across income groups (LIC-low income countries, MIC-middle income countries).


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it comes to Credit (Mean = 138%, rank 1st). The country is placed inthe 8th position in terms of Z-score. Nigeria presents an opposite case toSouth Africa. The country is 5th in terms of Rents, yet places last interms of Z-score (Mean = 1.17, rank = 38th). The interesting revela-tion is that the county places 16th in terms of Credit (Mean = 17.88%).These comparisons and analysis helps throw some light on the positionof each country in terms of these fundamental sectors of the economy.

It can also be observed that more than half of the sampled countriesfall below the mean Z-score (11.1580). Thus, most of these countriesneed to improve their banking market stability to be more competitivein the continent. With regards to Credit, only 9 countries of the 38 placeabove the mean Credit (24.28%). This shows the remaining countrieslag behind in terms of financial sector development in the continent.Much effort needs to be put in place by governments to develop thefinancial sector through the provision of credit. As a measure of re-source dependence, the International Monetary Fund (IMF) takes theaverage share over multiple years of a country's resource revenues overtotal revenues (Barma et al., 2012). The IMF defines a country as re-source dependent if this measure is greater than 25 per cent (Badeebet al., 2017). Considering our measure of Rents, from Table 2, five (5)countries in the sample namely, Angola, Gabon, Libya, Mauritania, andNigeria can be said to be resource dependent having mean Rents above25%. Again, from Table 2, the average Rents for the region over theperiod is 11.39% share of GDP. Based on our measure of Rents, thisstudy defines a country to be resource dependent if this measure isabove 11%. Based on this, 12 out of the 38 countries place above thisaverage. This would mean Algeria, Burundi, Congo D.R, Ethiopia,

Sudan, Uganda and Zambia would add to the earlier 5 countries asbeing resource dependent. Thus, we can indicate that 32% of countriesin Africa are resource dependent. These countries must thus managetheir natural resources well to escape the resource curse in financialdevelopment.

4.4. Test of multicollinearity

The correlation matrix of Table 3 is used to identify any possibilityof pairwise multicollinearity among the independent variables. Con-sidering all the independent variables, all the correlation coefficientsexcept the institutional variables have an absolute value of less than 0.6indicating that their inclusion will not present any problem of multi-collinearity since they are less than 0.70 (Kennedy, 2008). The in-stitutional variables are used separately in all the estimations.

5. Empirical results and discussion

The aim of the study is to examine the impact of natural resourcewealth on financial development in Africa. We use two measures offinancial development, namely Z-score as a measure of stability andtotal credit provided to the private sector by banks as a share of GDP(Credit). We employ the two-step SYS-GMM. All estimation diagnosticspoint to a significant fit of the regression models. A test of the validityof the results as indicated by the Arrellano-Bond test, AR (2), and theHansen J tests suggests that all the regressions are valid. The estimatesindicate that the lag of the dependent variable is significant in all the

Table 2Summary of means by country.

Country Z-score Rank Country Credit Rank Country Rents Rank

Libya 36.4108 1st South Africa 1.3837 1st Angola 0.5259 1stMorocco 36.1061 2nd Mauritius 0.7614 2nd Libya 0.4669 2ndMauritania 22.8108 3rd Tunisia 0.6313 3rd Gabon 0.4222 3rdCape Verde 22.5520 4th Morocco 0.5440 4th Mauritania 0.2691 4thNamibia 19.5100 5th Cape Verde 0.4695 5th Nigeria 0.2667 5thMauritius 18.7085 6th Namibia 0.4663 6th Congo, D.R. 0.2329 6thSudan 17.9115 7th Egypt 0.4510 7th Burundi 0.2322 7thSouth Africa 15.8177 8th Kenya 0.2610 8th Ethiopia 0.1885 8thTunisia 15.0792 9th Djibouti 0.2585 9th Sudan 0.1654 9thDjibouti 13.8615 10th Mauritania 0.2322 10th Algeria 0.1636 10thEgypt 13.6523 11th Senegal 0.2284 11th Uganda 0.1324 11thAlgeria 13.0892 12th Botswana 0.2271 12th Zambia 0.1250 12thBenin 12.1238 13th Ethiopia 0.2081 13th Sierra Leone 0.0910 13thKenya 11.9639 14th Togo 0.1930 14th Niger 0.0796 14thAngola 11.6339 15th Mali 0.1813 15th Malawi 0.0779 15thBurundi 11.5931 16th Nigeria 0.1788 16th Mozambique 0.0771 16thCameroon 9.8338 17th Burundi 0.1681 17th Egypt, 0.0769 17thTanzania 9.6046 18th Benin 0.1638 18th Cameroon 0.0714 18thGabon 9.5169 19th Burkina Faso 0.1602 19th Burkina Faso 0.0687 19thEthiopia 9.3685 20th Mozambique 0.1509 20th Ghana 0.0660 20thMali 9.2454 21st Cote d′Ivoire 0.1499 21st Togo 0.0628 21stMalawi 9.1800 22nd Ghana 0.1402 22nd Rwanda 0.0606 22ndNiger 8.9377 23rd Libya 0.1217 23rd Mali 0.0603 23rdBotswana 8.4215 24th Algeria 0.1211 24th Madagascar 0.0554 24thCote d′Ivoire 7.6354 25th Gambia, The 0.1195 25th Benin 0.0455 25thGambia, The 7.0623 26th Malawi 0.1115 26th Tanzania 0.0449 26thBurkina Faso 6.7385 27th Cameroon 0.1058 27th Gambia, The 0.0394 27thSenegal 6.5708 28th Rwanda 0.1054 28th Kenya 0.0353 28thSierra Leone 5.8100 29th Uganda 0.1036 29th Botswana 0.0304 29thGhana 5.7215 30th Angola 0.1017 30th Tunisia 0.0279 30thCongo, D.R. 4.8331 31st Madagascar 0.1004 31st Cote d′Ivoire 0.0275 31stTogo 4.5062 32nd Gabon 0.1002 32nd Senegal 0.0220 32ndRwanda 4.4885 33rd Tanzania 0.0960 33rd Morocco 0.0185 33rdMadagascar 3.5185 34th Sudan 0.0916 34th Namibia 0.0149 34thMozambique 2.2308 35th Zambia 0.0913 35th South Africa 0.0114 35thZambia 2.0800 36th Niger 0.0870 36th Djibouti 0.0107 36thUganda 1.7208 37th Sierra Leone 0.0454 37th Cape Verde 0.0044 37thNigeria 1.1677 38th Congo, D. R. 0.0255 38th Mauritius 0.0001 38thTotal 11.1580 Total 0.2428 Total 0.1139

All variables are as defined earlier.


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regressions. This indicates that previous year's development affectscurrent year development, justifying the dynamic nature of this re-lationship hence the use of sys-GMM.

5.1. Impact of natural resource rents on FD

Using Z-score as the dependent variable, the results are fairly con-sistent over the different estimations. From Table 4.0, the main variableof interest, Rents, has a significant negative impact on Z-score. We finda strong impact of Rents on Z-score varying across all models except formodel 3 where the impact is small when RegQuality is used as an in-stitutional quality variable. These variations are observed in all theother models, suggesting the differing impact of Rents on Z-score in thepresence of different institutional quality variables. These results in-dicate that resource rents reduce stability of the banking sector, sug-gesting a resource curse. This is consistent in all the estimations. Amongthe institutional quality variables, Law reduces Z-score while RegQu-ality, Government and Political stability variables have a positive im-pact on Z-score. The negative impact of Law could be attributed to some

shortcuts that some firms or individuals may want to capitalise on incircumventing the right procedures particularly in terms of contractenforcement, and property rights among the other general rules of thecountry. This could happen when the rules of countries with extractivesectors benefit the few elites groups ignoring private property rights,proper law enforcement, compromised police and courts. When thishappens, these institutions hinder financial development. This maysupport the settler mortality hypothesis of Acemoglu et al. (2001) andextended by Beck et al. (2003). The positive impact of RegQualityshows that when governments implement sound policies that supportprivate sector development, the financial sector also develops throughstability.

Again, the positive impact of Government also show that whenpublic and civil services are strong and independent from politicalpressures these institutions can positively impact financial sector de-velopment. Also, in countries where there is political stability and ab-sence of violence/terrorism (Political), the financial sector can operatesoundly and thus develop. In all these estimations, the magnitude of theimpact of institutional quality variables on Z-score varies. RegQuality

Table 3Pairwise correlation matrix.

NIM Boone CONCEN Rents Corruption RegQuality Law Voice Gov. Political Education Inflation

NIM 1Boone −0.1298*** 1CONCEN −0.0955* 0.1785*** 1Rents −0.0179 0.2282*** 0.0706 1Corruption −0.1127** −0.1968*** 0.1009* −0.4841*** 1RegQuality −0.0768 −0.1899*** −0.1054* −0.5431*** 0.8095*** 1Law −0.1321*** −0.1836*** −0.0205 −0.4803*** 0.8731*** 0.8555*** 1Voice 0.0855* −0.0981** −0.0001 −0.4945*** 0.6877*** 0.7491*** 0.7376*** 1Gov. −0.1493*** −0.1872*** −0.0971* −0.4825*** 0.8674*** 0.8801*** 0.8909*** 0.6851*** 1Political −0.0696 −0.1847*** 0.1538*** −0.2690*** 0.6712*** 0.6343*** 0.7479*** 0.6301*** 0.606*** 1PER 0.1188** 0.0162 −0.0678 0.1041** 0.1047** 0.0741 0.1246** 0.0015 0.1165** 0.1272** 1Inflation −0.3218*** 0.0573 −0.0585 −0.1367*** 0.1101** 0.1773*** 0.1578*** 0.0856* 0.0975** 0.1844*** 0.1911*** 1

* Significance at 10%.** Significance at 5%.*** Significance at 1%.

Table 4.0Impact of natural resource on financial development (Z-score).

Variable (1) (2) (3) (4) (5) (6)

L. Z-score 0.7624*** (0.0698) 0.7081*** (0.0416) 0.7470*** (0.0542) 0.7897*** (0.0706) 0.7844*** (0.0636) 0.7777*** (0.0741)Banking sectorNIM 24.0072*** (7.1913) 34.2336*** (4.2123) 21.3119*** (3.0254) 28.7013*** (5.6478) 20.5822*** (7.1234) 25.0130*** (6.6949)Boone −8.7889 (5.3363) −7.6133*** (2.5311) −12.1586** (5.0291) −7.0982* (3.6804) −6.9107 (4.9709) −3.8480 (3.1162)Concentration 2.4904* (1.4828) 0.8999 (1.3989) 3.5280*** (0.5827) 2.9873* (1.5211) 2.7459** (1.2554) 2.7128* (1.6192)Natural resourcesRents −15.6651*** (3.3157) −14.8782*** (1.5626) −4.4455** (2.2411) −14.3274*** (2.7423) −14.1791*** (3.4712) −12.8311*** (3.1986)Institutional qualityCorruption 0.3242 (0.4639)Law −1.9274*** (0.2316)RegQuality 2.9762*** (0.3115)Voice −0.1503 (0.3145)Government 1.2487** (0.6013)Political 0.6230** (0.2769)Macroeconomic variablesEducation (PER) 0.0207** (0.0084) 0.01467*** (0.0054) 0.0218*** (0.0052) 0.0214*** (0.0075) 0.0149*** (0.0075) 0.01529*** (0.0089)Inflation 2.3530*** (0.3641) 2.6079*** (0.2531) 2.4955*** (0.2200) 2.5786*** (0.3238) 2.4283*** (0.3376) 2.1120*** (0.3307)Country effect YES YES YES YES YES YESYear effect YES YES YES YES YES YESNo. of Obs. 254 254 254 254 254 254No. of countries included 36 36 36 36 36 36No. of instruments 69 69 69 69 69 69AR(2): P-value 0.436 0.388 0.382 0.423 0.418 0.425Hansen J: P-value 0.427 0.557 0.293 0.474 0.419 0.509

* Significance at 10%.** Significance at 5%.*** Significance at 1%. Standard errors are in parenthesis.


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has the most profound impact on Z-score. These findings show thesignificant role that each institutional quality variable plays in financialdevelopment. On the control variables, NIM has a significant positiveimpact on Z-score. Boone shows a significant negative impact on Z-score indicating that more competition may support financial devel-opment. Concentration has a positive impact on z-score supporting theconcentration-stability view. The results also largely show that themore educated the public, the better it is for financial development.Thus, the more educated a public the better for them to understand thefinancial services provided. Educated people also are able to relativelyeasily access loans/credit well since they are more likely to be wellorganised to access formal credit/loans.

Using Credit as the dependent variable, the results are fairly con-sistent over the different estimations. From Table 4.1, the main variableof interest, Rents, has a positive impact on Credit unlike in Table 4when the impact on Z-score was negative. This indicates that resourcewealth improves the development of the banking sector through theCredit channel. This is not surprising as indeed the financial systemshould serve as an important absorption tool for windfall gains, arisingfrom natural resource rents (Beck and Poelhekke, 2017). Thus, highernatural resource rents in these countries normally result in higher de-posit funding for a country's banking system if they are saved domes-tically and thus can also lead to higher loan demands. While this is goodfor financial development, care must be taken as a country whose fi-nancial sector is highly dependent on resource rents resulting in highlevels of credit as a share of GDP, may experience decline in economicgrowth when there is a major shock in the financial sector resultingfrom loan defaults or indirect shocks from the resource sector; which wedo not investigate in this study. We find that the positive impact ofRents on Credit is consistent in all the estimations. These results showmarginal differences in the degree of impact of Rents on Credit in allestimations. The highest impact of Rents on Credit is seen when Gov-ernment is used as an institutional quality variable.

Interestingly, our results show different impact of Rents on Z-score(negative) and Credit (positive). A careful analysis however shows acompletely plausible relationship over here. We posit that increasedcredit (not quality credit per se) relative to GDP has the tendency of

reducing asset quality; thereby threatening banking sector stability.With an upsurge in natural resource rent, financial institutions mayenjoy high deposits and thus increase in the supply of loanable funds.An increase in the supply of loanable funds may reduce the quality ofcredit process creating moral hazards and adverse selection; reducingasset quality and creating banking sector instability.

Among the institutional quality variables, only Government andPolitical variables had an impact on Credit but a negative one. Theseeffects are small even though they are significant at 1%. As explainedearlier, these institutional frameworks may protect the interest of elitegroups, restrict participation in the political system, and so on. Thiswould be an obstacle to financial development. Rajan and Zingales(2003) emphasise the role that interest groups can play, especially theincumbent industrial firms and the domestic financial sector, in theprocess of financial development. The provision of credit to the privatesector could thus be hindered. This may not be surprising as the de-scriptive statistics revealed the banking sector in the continent to becontrolled by a few big banks who may be protecting their interest. Thiswas shown in the results as Concentration also has a strong significantnegative impact on Credit. Of the other control variables, NIM has asignificant positive impact on Credit. Boone strongly shows a significantnegative impact on Credit indicating that more competition supportsfinancial development. Inflation had a significant negative impact onCredit as expected.

5.2. Impact of natural resource rents on FD with interactions

Here, having the earlier results that show resource rents hinderstability and the ambiguous impact of the institutional variables on fi-nancial development, we interact Rents and those institutional vari-ables to understand the role of strong institutions in the financial de-velopment impact of Rents. We follow Model 4. The results are shownin Table 5. From the Table, only estimations (15) and (18) have sig-nificant effects of Rents, INST and the interaction terms. With estima-tion (15), Rents and the interaction term have a significant negativeimpact on financial development. Government however has a sig-nificant positive impact. Even though the interaction term (Rents x

Table 4.1Impact of natural resource on financial development (total credit/GDP).

Variable (7) (8) (9) (10) (11) (12)

L. Credit 1.0812*** (0.0023) 1.0787*** (0.0028) 1.0781*** (0.0047) 1.0784*** (0.0035) 1.0810*** (0.0051) 1.0917*** (0.0057)Banking sectorNIM 0.6014*** (0.0471) 0.6165*** (0.5382) 0.5956*** (0.0693) 0.7194*** (0.0640) 0.6337*** (0.0932) 0.7021*** (0.0488)Boone −0.2323*** (0.253) −0.2180*** (0.0292) −0.2331*** (0.0314) −0.1929*** (0.0469) −0.2052*** (0.0382) −0.2922*** (0.0317)Concentration −0.2200*** (0.0103) −0.2203*** (0.0111) −0.2446*** (0.0227) −0.2316*** (0.0190) −0.2268*** (0.0157) −0.2317*** (0.0091)Natural resourcesRents 0.3618*** (0.0198) 0.3710*** (0.02115) 0.3825*** (0.0237) 0.3513*** (0.0192) 0.3857*** (0.0238) 0.2696*** (0.0134)Institutional qualityCorruption −0.0018 (0.0027)Law −0.0038 (0.0030)RegQuality 0.0142 (0.0104)Voice −0.0186*** (0.0047)Government 0.0050 (0.0060)Political −0.0302*** (0.0134)Macroeconomic variablesEducation (PER) 0.0001* (0.0000) 0.0001 (0.0001) 0.0000 (0.0000) 0.0000 (0.0001) 0.0000 (0.0001) −0.0001 (0.0001)Inflation −0.0563*** (0.0025) −0.0565*** (0.0026) −0.0587*** (0.0041) −0.0479*** (0.0041) −0.0542*** (0.0032) −0.0494*** (0.0051)Country effect YES YES YES YES YES YESYear effect YES YES YES YES YES YESNo. of Obs. 244 244 244 244 244 244No. of countries included 35 35 35 35 35 35No. of instruments 67 67 67 67 67 67AR(2): P-value 0.528 0.525 0.536 0.516 0.531 0.509Hansen J: P-value 0.341 0.369 0.619 0.404 0.571 0.361

** Significance at 5%.* Significance at 10%.*** Significance at 1%. Standard errors are in parenthesis.


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Government) has a negative impact, the magnitude of the impact isreduced compared to the direct negative impact of Rents on FD. Thisshows that, in countries where institutions are strong, the negativeimpact of Rents on development is reduced. From estimation (18)however, the interaction term (Rents x Voice) has a significant positiveimpact on Credit. Even though the impact is positive, the magnitude issmall compared to the direct positive impact of Rents on Credit. This

indicates that without a proper institutional framework, the positiveimpact of Rents on Credit is reduced.

5.3. Impact of natural resource rents on FD in African sub-regions

Here, after obtaining the results for the whole continent, we esti-mate the regressions by including dummies for SSA and NA regions. The

Table 5Impact of natural resource on financial development with interactions with institutional quality.

Variable Z-score(13) Z-score(14) Z-score (15) Z-score(16) Credit (17) Credit (18)

L. Dep. Variable 0.7967*** (0.0763) 0.7442*** (0.0577) 0.6845*** (0.0584) 0.5746*** (0.0432) 1.0906*** (0.0061) 1.0785*** (0.0057)NIM 24.3978** (10.1937) 21.8308*** (4.7773) 30.8875*** (6.2747) 0.6651 (3.8309) 0.6994*** (0.0345) 0.7951*** (0.0665)Boone −3.5665 (5.6336) −12.6470** (6.2697) −9.3422* (4.8789) −29.9640*** (4.7984) −0.1851*** (0.0391) −0.1971*** (0.0494)Concentration 1.1116 (1.5001) 3.5692*** (0.9042) 2.6617** (1.2766) 5.0382*** (1.2262) −0.2466*** (0.0100) −0.2234*** (0.0177)Rents 5.9371 (12.4978) −7.7509* (4.5054) −29.6873**** (4.3101) −0.1557 (2.9932) 0.3086*** (0.0291) 0.3884*** (0.0487)Law −3.7989*** (0.9225)Rents × Law 24.3991* (13.6017)RegQuality 2.9381*** (0.7459)Rents × RegQuality −1.3899 (6.5596)Government 2.2360*** (0.4236)Rents × Government −18.6536*** (5.7123)Political −1.4426*** (0.3075) −0.0056 (0.0074)Rents × Political 14.8834*** (2.4869) −0.0663*** (0.0239)Voice −0.0232*** (0.0053)Rents × Voice 0.0854* (0.0458)Education (PER) 0.0018 (0.114) 0.0227*** (0.0052) 0.0286*** (0.0047) 0.0231*** (0.0080) −0.0000 (0.0001) 0.0001 (0.0001)Inflation 1.3653** (0.6257) 2.5312*** (0.3288) 2.8579*** (0.2380) 2.0048*** (0.3043) −0.0559*** (0.0058) −0.0459*** (0.0047)Country effect YES YES YES YES YES YESYear effect YES YES YES YES YES YESNo. of Obs. 254 254 254 254 244 244No. of countries included 36 36 36 36 35 35No. of instruments 69 69 69 69 67 67AR(2): P-value 0.443 0.396 0.405 0.714 0.474 0.509Hansen J: P-value 0.346 0.268 0.377 0.258 0.716 0.453


Table 6Impact of natural resource on financial development in African regions (SSA & NA)-Z-score.

Variable (19) (20) (21) (22) (23) (24)

L. Z-score 0.4060*** (0.0380) 0.5279*** (0.0297) 0.3441*** (0.0288) 0.4408*** (0.0320) 0.3915*** (0.0224) 0.4505*** (0.0396)NIM 22.9053*** (2.4468) 12.1696*** (4.3306) 6.3264 (4.2393) 26.6896*** (4.1023) 21.5862*** (1.6061) 14.0718*** (3.0406)Boone −21.2690*** (5.4444) −25.6945*** (1.3475) −32.7985*** (3.8605) −16.5945*** (4.9576) −32.0508*** (2.5183) −27.7995*** (4.0213)Concentration 0.9072 (0.7720) 2.7983*** (0.8370) 2.0225*** (0.60640) −0.0691 (0.9635) 1.8275*** (0.4625) 2.6221*** (0.7724)Rents × SSA −16.1169*** (0.7511) −17.7338*** (0.9831) −10.5940*** (1.2809) −16.4067*** (0.6300) −19.6037*** (1.1698) −16.1744*** (0.5703)Rents × NA 48.1498*** (5.5059) 38.5451*** (2.1910) 59.5396*** (3.6728) 48.5255*** (4.4978) 41.0533*** (3.3807) 40.6152*** (3.9333)Corruption × SSA 0.0500 (0.1675)Corruption × NA 3.9286*** (0.8412)Law × SSA −1.2185*** (0.1243)Law × NA 1.3109 (3.1085)RegQuality × SSA 1.7864*** (0.3366)RegQuality × NA −5.6750*** (0.8749)Government × SSA −0.1972 (0.1530)Government × NA 5.0041*** (1.1215)Political × SSA −0.8759*** (0.1807)Political × NA 2.6262*** (0.1486)Voice × SSA 0.6782*** (0.2177)Voice × NA −2.7138*** (0.5793)Education (PER) 0.0219*** (0.0064) 0.0228*** (0.0025) 0.0183*** (0.0050) 0.0179*** (0.0042) 0.0236*** (0.0034) 0.0282*** (0.0049)Inflation 2.2908*** (0.2817) 2.3027*** (0.1685) 1.2439*** (0.2454) 2.2310*** (0.2519) 2.6852*** (0.1486) 2.2209*** (0.2306)Country effect YES YES YES YES YES YESYear effect YES YES YES YES YES YESNo. of Obs. 254 254 254 254 254 254No. of countries included 36 36 36 36 36 36No. of instruments 69 69 69 69 69 69AR(2): P-value 0.417 0.454 0.604 0.354 0.684 0.543Hansen J: P-value 0.336 0.330 0.321 0.426 0.404 0.220



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Table 7Impact of natural resource on financial development in African regions (SSA & NA)-Z-score.

Variable (25) (26) (27) (28) (29)

L.Z-score 0.3789*** (0.0388) 0.3514*** (0.0270) 0.4703*** (0.0312) 0.4388*** (0.0447) 0.5244*** (0.0446)NIM 21.3966*** (3.7330) 14.6782*** (4.0709) 27.4144*** (4.4555) 24.4323*** (3.4959) 15.9822*** (6.0821)Boone −24.0186*** (3.7330) −34.5102*** (3.8213) −12.9370*** (3.7522) −21.6362*** (5.3730) −16.6190*** (4.8509)Concentration −1.1161* (0.6557) 0.9994 (0.7553) −0.4873 (0.9050) 0.6662 (0.7784) 1.3432 (1.1839)Rents × SSA 3.5683 (3.7447) −3.0452 (3.1754) −16.6958*** (3.7812) −16.8337*** (2.5567) −12.7337*** (3.1241)Rents × NA 112.4211*** (9.8137) −1.7867 (23.1697) 93.3971*** (6.7072) 72.0553*** (17.0849) 111.8253*** (16.4441)Corruption × SSA −0.8775*** (0.1717)Corruption × NA 2.9761*** (0.2380)RegQuality × SSA 0.9981*** (0.3025)RegQuality × NA 2.0896 (2.9439)Government × SSA −0.1708 (0.3305)Government × NA 5.1266*** (1.0986)Political × SSA −1.0643* (0.5717)Political × NA 5.7313*** (1.7904)Voice × SSA 0.5921* (0.3152)Voice × NA −4.7510*** (0.5472)Rents × Corrupt × SSA 23.5980*** (4.5831)Rents × Corrupt × NA 47.9331*** (6.0749)Rents × RegQuality × SSA 9.7552** (3.8624)Rents × RegQuality × NA −58.3205*** (22.6951)Rents × Government × SSA −0.4658 (3.8422)Rents × Government × NA 30.2322*** (5.3464)Rents × Political × SSA 2.1167 (2.4985)Rents × Political × NA −23.7891** (10.8653)Rents × Voice × SSA 3.2263 (3.3651)Rents × Voice × NA 32.0039*** (7.4058)Education (PER) 0.0174*** (0.0043) 0.0228*** (0.0066) 0.0141*** (0.0053) 0.0118** (0.0049) 0.0149** (0.0058)Inflation 0.7675*** (0.2446) 1.3350*** (0.3208) 2.0558*** (0.2272) 2.4128*** (0.3603) 1.7892*** (0.3032)No. of Obs. 254 254 254 254 254No. of countries included 36 36 36 36 36No. of instruments 69 69 69 69 69AR(2): P-value 0.718 0.700 0.331 0.351 0.307Hansen J: P-value 0.426 0.391 0.397 0.207 0.148

* Significance at 10%.** Significance at 5%.*** Significance at 1%. Standard errors are in parenthesis. Estimations include country effect and year effect.

Table 8Impact of natural resource on financial development in African regions (SSA & NA)-credit.

Variable (30) (31) (32) (33) (34) (35)

L. Credit 1.0878*** (0.0072) 1.1004*** (0.0175) 1.1044*** (0.0177) 1.0785*** (0.0223) 1.1083*** (0.0180) 1.0991*** (0.0193)NIM 0.6394*** (0.0848) 0.7632*** (0.0472) 0.7075*** (0.1006) 0.6560*** (0.0964) 0.7288*** (0.0585) 0.8708*** (0.0574)Boone −0.2307*** (0.0438) −0.2146*** (0.0356) −0.2188*** (0.0350) −0.2127*** (0.0446) −0.2455*** (0.0302) −0.2145*** (0.0380)Concentration −0.2220*** (0.0125) −0.2188*** (0.0139) −0.2270*** (0.0162) −0.2196*** (0.0139) −0.2419*** (0.0118) −0.2150*** (0.0150)Rents × SSA 0.4098*** (0.0258) 0.3890*** (0.0326) 0.4201*** (0.0285) 0.4108*** (0.0286) 0.3432*** (0.0363) 0.3984*** (0.0337)Rents × NA 0.1346*** (0.0445) 0.1424*** (0.0287) 0.0808* (0.0465) 0.1312*** (0.0254) 0.2028*** (0.0513) 0.1336*** (0.0276)Corruption × SSA −0.0000 (0.0061)Corruption × NA −0.0292*** (0.0068)Law × SSA −0.0161*** (0.0048)Law × NA 0.0052 (0.0227)RegQuality × SSA 0.0000 (0.0082)RegQuality × NA 0.0664*** (0.0051)Government × SSA 0.0031 (0.0076)Government × NA −0.0072 (0.0097)Political × SSA −0.0261*** (0.0069)Political × NA −0.0077 (0.0073)Voice × SSA −0.0213*** (0.0049)Voice × NA −0.0025 (0.0135)Education (PER) 0.0000 (0.0001) 0.0002* (0.0001) 0.0001 (0.0001) 0.0000 (0.0001) −0.0001 (0.0001) 0.0002 (0.0001)Inflation −0.0556*** (0.0028) −0.0547*** (0.0053) −0.0578*** (0.0058) −0.0538*** (0.0034) −0.0522*** (0.0063) −0.0443*** (0.0057)Country effect YES YES YES YES YES YESYear effect YES YES YES YES YES YESNo. of Obs. 244 244 244 244 244 244No. of countries included 35 35 35 35 35 36No. of instruments 67 67 67 67 67 67AR(2): P-value 0.491 0.496 0.493 0.490 0.488 0.489Hansen J: P-value 0.671 0.422 0.573 0.543 0.527 0.566

** Significance at 5%.* Significance at 10%.*** Significance at 1%. Standard errors are in parenthesis.


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results are presented in Tables 6–9. The results from Table 6 are in-teresting. We find that in Africa, the resource curse is seen in the SSAregion while for the NA region, Rents has a positive direct impact onfinancial development (Z-score). Thus, in the SSA region, increases inresource wealth reduces financial development as measured by Z-score.This may be because the more dependent countries are on their naturalresource wealth for their deposits and savings, the more likely thestability of the banking sector is shaken by changes in resource rents.This is not surprising as the earlier results for the continent showed. Thecase for the NA region was however different as the resource wealth ofthe region is seen to improve stability. The impact of institutionalquality variables on financial development is ambiguous depending onthe indicator used. Thus, while some institutional quality indicatorsimprove financial development, others are hindrances as indicated inthe earlier results. Based on these results, the interaction of these in-stitutional variables and resources are examined in Table 7. Here, thestudy shows that only the NA region saw institutional quality variables(Corruption, Government and Voice) that ensured resources improvefinancial development. Only the interaction of Rents and Political (Es-timation 28) showed a negative impact on development. This maysuggest that without proper political stability in the NA region, naturalresources will have an adverse impact on financial development.

From Tables 8 and 9 when credit is used as a financial developmentindicator, the results show that Rents have a positive impact on fi-nancial development in both regions. Thus, higher resource rents inboth regions promote financial development. The impact of the in-stitutional quality variables on financial development is ambiguous. Inthe SSA region, institutional quality variables (Corruption, Political and

Voice) have a significant negative impact on financial development. Inthe NA region, corruption reduces credit while RegQuality has a posi-tive impact on credit. These results show that strong institutional fra-meworks that check corruption reduce the credit provided to the pri-vate sector while proper regulatory quality helps improve the creditprovision in the region. The interaction terms in Table 9 show that aproper regulatory framework helps resource rents to improve credit inthe NA region even though the magnitude of the direct impact of Rentsis higher. However, the interaction term (Rents × Voice × SSA) inTable 9 show that the negative impact of Voice on credit in the SSAerodes the positive impact of Rents on Credit. This indicate that withouta proper institutional framework, the positive development impact ofresource rents on financial development is eroded.

5.4. Impact of natural resource rents on FD in African income groups

Here we investigate income level differences in the relationshipbetween natural resources and financial development. The results arepresented in Tables 10–13. From Table 10, the results generally showthat the impact of Rents on Z-score in both the LIC and MIC groups isnegative. Thus, regardless of the development status of a country, re-source rents still hinder financial development. The results also showthat RegQuality improves financial development in both LIC and MICgroups. The government indicator also has a positive impact on Z-scorein the MIC group. Only Law shows a negative impact on financial de-velopment in both LIC and MIC. In Table 11, when we interact Rentswith the institutional quality variables, the study shows that with theLIC group, proper rule of law in the management of resource rents are

Table 9Impact of natural resource on financial development in African regions (SSA & NA)-credit.

Variable (36) (37) (38) (39) (40)

L. Credit 1.1657*** (0.0239) 1.0167*** (0.0221) 1.0691*** (0.0079) 1.1114*** (0.0238) 1.0986*** (0.0238)NIM 0.4065*** (0.1135) 0.7371*** (0.0644) 0.6624*** (0.0886) 0.7430*** (0.0493) 0.8689*** (0.0481)Boone −0.2965*** (0.0522) −0.1780*** (0.0477) −0.2147*** (0.0295) −0.2399*** (0.0570) −0.1923*** (0.0289)Concentration −0.1769*** (0.0193) −0.1809*** (0.0225) −0.1802*** (0.0321) −0.2383*** (0.0142) −0.2222*** (0.0132)Rents × SSA 0.1230 (0.1070) 0.1769 (0.1178) 0.2555*** (0.0881) 0.2908*** (0.0679) 0.2742*** (0.0468)Rents × NA −0.8402*** (0.1505) 1.3632*** (0.1733) 0.3417*** (0.1107) 0.2426 (0.2093) 0.2722 (0.3133)Corruption × SSA 0.0421* (0.0234)Corruption × NA −0.0108 (0.0139)Law × SSA 0.0007 (0.0119)Law × NA 0.0435** (0.0176)RegQuality × SSA 0.0238 (0.0171)RegQuality × NA 0.0289* (0.0161)Political × SSA −0.0138 (0.0100)Political × NA −0.0042 (0.0297)Voice × SSA −0.0155** (0.0063)Voice × NA −0.0122 (0.0173)Rents × Corrupt × SSA −0.4544*** (0.1572)Rents × Corrupt × NA −0.8228*** (0.1285)Rents × Law × SSA −0.2320* (0.1344)Rents × Law × NA 1.5182*** (0.2131)Rents × RegQuality × SSA −0.2236* (0.1232)Rents × RegQuality × NA 0.3053*** (0.0976)Rents × Political × SSA −0.0590 (0.0442)Rents × Political × NA −0.0300 (0.1528)Rents × Voice × SSA −0.1861*** (0.0624)Rents × Voice × NA 0.0610 (0.1346)Education (PER) 0.0003 (0.0002) 0.0001 (0.0002) 0.0002* (0.0001) −0.0000 (0.0001) 0.0001 (0.0001)Inflation −0.0550*** (0.0082) −0.0320*** (0.0108) −0.0408*** (0.0095) −0.0508*** (0.0074) −0.0419*** (0.0046)Country effect YES YES YES YES YESYear effect YES YES YES YES YESNo. of Obs. 244 244 244 244 244No. of countries included 35 35 35 35 35No. of instruments 67 67 67 67 67AR(2): P-value 0.515 0.566 0.505 0.482 0.498Hansen J: P-value 0.273 0.252 0.380 0.436 0.325



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able to improve Z-score. In the MIC model (Model 37), while Law had asignificant negative impact on Z-score, the impact of Rents was notsignificant. The interaction term (Law × Rents × MIC) however issignificantly positive. This shows that rule of law has the potential to

improve the resource impact on financial development. The study(Model 38) finds that without proper regulatory quality, the impact ofresource rents on financial development is reduced.

Using Credit as the financial development indicator, Table 12 show

Table 10Impact of natural resource on financial development (Z-score) in African income levels.

Variable (30) (31) (32) (33) (34) (35)

L.Z-score 0.8291*** (9.02) 0.7981*** (19.26) 0.7775*** (11.21) 0.7507*** (8.94) 0.8186*** (8.17) 0.7717*** (8.88)NIM 20.4437*** (2.65) 31.8440*** (6.39) 19.1348*** (4.42) 26.2480*** (4.17) 26.7905*** (3.96) 31.2416*** (6.34)Boone −7.3528 (−1.49) −4.5916 (−1.59) −12.3018* (−1.92) −12.9704** (−2.39) −4.9749 (−1.04) −9.9526** (−1.99)Concentration 1.4037 (0.77) 2.1606 (1.36) 3.4228*** (5.12) 2.3022 (1.55) 1.7462 (1.07) 2.4745 (1.54)Rents × LIC −14.2304*** (−3.07) −12.1167*** (−4.29) −2.7266 (−1.07) −20.7914*** (−4.80) −15.5631*** (−3.57) −18.7032*** (−5.77)Rents × MIC −13.2829*** (−3.64) −15.2220*** (−9.96) −7.4061** (−2.26) −12.7897*** (−3.23) −12.6347*** (−3.55) −14.3496*** (−4.32)Corruption × LIC 0.5483 (1.05)Corruption × MIC −0.4457 (−1.19)Law × LIC −2.3385*** (−7.28)Law × MIC −3.6500*** (−7.19)RegQuality × LIC 3.1657*** (11.32)RegQuality × MIC 2.2840*** (3.56)Government × LIC 0.7198 (1.19)Government × MIC 1.3931*** (3.34)Political × LIC 0.4439 (1.09)Political × MIC 0.2241 (0.37)Voice × LIC 0.2121 (0.44)Voice × MIC 0.3920 (0.59)Education (PER) 0.0101 (1.08) 0.0048 (0.76) 0.0218*** (4.66) 0.0184* (1.91) 0.0130 (1.20) 0.0217*** (2.71)Inflation 1.8435*** (0.3160) 2.5245*** (9.25) 2.3001*** (8.71) 2.4121*** (7.18) 1.9594*** (5.88) 2.3680*** (6.33)Country effect YES YES YES YES YES YESYear effect YES YES YES YES YES YESNo. of Obs. 254 254 254 254 254 254No. of countries included 36 36 36 36 36 36No. of instruments 69 69 69 69 69 69AR(2): P-value 0.417 0.346 0.402 0.435 0.409 0.421Hansen J: P-value 0.411 0.397 0.237 0.384 0.369 0.387


Table 11Impact of natural resource on financial development (Z-score) in African income levels with interactions.

Variable (37) (38) (39)

L.Z-score 0.8401*** (12.96) 0.70083*** (10.51) 0.6946*** (10.25)NIM 32.4224*** (3.97) 26.1601*** (3.96) 26.9491*** (4.69)Boone −18.7466*** (−2.79) −20.7157** (−2.52) −19.7217*** (−3.13)Concentration −3.7550* (−1.82) 0.5899 (0.39) 0.0616 (0.06)Rents × LIC 24.7766* (1.68) −5.2700 (−0.69) 8.8148 (0.71)Rents × MIC 13.6567 (1.34) −23.8161*** (−4.23) −18.3026** (−2.32)Law × LIC −5.3780*** (−5.44)Law × MIC −3.6529*** (4.41)RegQuality × LIC 1.9728 (1.48)RegQuality × MIC 3.4401*** (3.46)Government × LIC −0.6719 (−0.62)Government × MIC 1.1879* (1.83)Rents × Law × LIC 64.5553** (4.41)Rents × Law × MIC 27.6694** (2.40)Rents × RegQuality × LIC 10.8118 (1.07)Rents × RegQuality × MIC −32.9411*** (−3.66)Rents × Government × LIC 39.8778*** (2.68)Rents × Government × MIC −14.1692 (−1.57)Education (PER) 0.0359** (2.20) 0.0176 (1.31) 0.0148 (1.31)Inflation −0.2066 (−0.25) 2.1626*** (4.28) 2.2826*** (7.14)Country effect YES YES YESYear effect NO YES YESNo. of Obs. 254 254 254No. of countries included 36 36 36No. of instruments 68 69 69AR(2): P-value 0.806 0.535 0.506Hansen J: P-value 0.336 0.338 0.299



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Table 12Impact of natural resource on financial development (credit) in African income levels.

Variable (40) (41) (42) (43) (44) (45)

L.Credit 1.0910*** (160.48) 1.0932*** (105.18) 1.0764*** (79.01) 1.0775*** (82.61) 1.0957*** (16893) 1.0896*** (134.85)NIM 0.7103*** (11.98) 0.7492*** (11.90) 0.6701*** (11.75) 0.7082*** (12.08) 0.6564*** (10.98) 0.7582*** (9.23)Boone −0.1788*** (−3.56) −0.2258*** (−3.92) −0.1891*** (−3.07) −0.1831*** (−2.84) −0.6564*** (−14.04) −0.2181*** (−3.52)Concentration −0.2453*** (−15.22) −0.2269*** (−9.68) −0.2548*** (−13.06) −0.2124*** (−11.20) −0.2276*** (−14.54) −0.2260*** (−9.44)Rents × LIC 0.1727*** (3.08) 0.2404*** (3.46) 0.1001 (1.54) 0.2500*** (3.96) 0.0660 (1.02) 0.3048*** (4.00)Rents × MIC 0.3615*** (14.63) 0.3761*** (7.46) 0.3287*** (8.30) 0.3594*** (8.98) 0.2326*** (12.46) 0.3251*** (8.30)Corruption × LIC −0.0335*** (−6.06)Corruption × MIC 0.0015 (0.48)Law × LIC −0.0129 (−1.38)Law × MIC 0.0097 (0.70)RegQuality × LIC −0.0180** (−2.03)RegQuality × MIC 0.0318*** (2.97)Government × LIC −0.0118 (−1.42)Government × MIC 0.0172** (0.49)Political × LIC −0.0403*** (−7.75)Political × MIC −0.0194*** (−5.22)Voice × LIC −0.0148* (−1.85)Voice × MIC −0.0065 (−0.66)Education (PER) 0.0000 (0.03) 0.0001 (0.47) 0.0003** (2.05) 0.0001 (0.49) 0.0000 (0.30) 0.0001 (0.30)Inflation −0.0569*** (−12.45) −0.0564*** (−9.02) −0.0564*** (−9.10) −0.0484*** (−8.53) −0.0561*** (−9.07) −0.0505*** (−6.31)Country effect YES YES YES YES YES YESYear effect YES YES YES YES YES YESNo. of Obs. 244 244 244 244 244 244No. of countries included 35 35 35 35 35 35No. of instruments 67 67 67 67 67 67AR(2): P-value 0.625 0.619 0.654 0.553 0.588 0.531Hansen J: P-value 0.576 0.347 0.368 0.307 0.321 0.379


Table 13Impact of natural resource on financial development (credit) in African income levels with interactions.

Variable (46) (47) (48) (49)

L. Credit 1.0928*** (85.80) 1.1089*** (41.50) 1.0907*** (99.59) 1.0904*** (126.23)NIM 0.5702*** (5.49) 0.7425*** (10.80) 0.5817*** (5.18) 0.7348*** (14.10)Boone −0.1556*** (−2.76) −0.2894*** (−3.07) −0.2051** (−2.38) −0.3005*** (−4.98)Concentration −0.2055*** (−9.91) −0.2495*** (−5.94) −0.1901*** (−7.52) −0.2698*** (−10.90)Rents × LIC 0.0005 (0.00) 0.2368*** (2.01) 0.0624 (0.69) 0.3551*** (7.23)Rents × MIC 0.3421*** (3.79) 0.7261*** (6.67) 0.2237*** (6.71) 0.3560*** (7.41)Corruption × LIC −0.0154 (−1.48)Corruption × MIC 0.0066 (0.67)RegQuality × LIC −0.0434 (−1.55)RegQuality × MIC −0.0027 (−0.17)Political × LIC −0.0196** (−2.25)Political × MIC −0.0155 (−1.34)Voice × LIC −0.0359*** (−2.93)Voice × MIC 0.0075 (0.60)Rents × Corrupt × LIC −0.2828* (−1.94)Rents × Corrupt × MIC −0.0035 (−0.02)Rents × RegQuality × LIC 0.1667 (0.74)Rents × RegQuality × MIC 0.5669*** (4.21)Rents × Political × LIC −0.1830*** (0.0624)Rents × Political × MIC −0.0116 (−0.32)Rents × Voice × LIC 0.4181*** (2.88)Rents × Voice × MIC 0.0450 (0.99)Education (PER) −0.0000 (−0.31) 0.0003 (1.33) 0.0001 (0.63) 0.0002 (1.15)Inflation −0.0496*** (−7.45) −0.0660*** (−6.16) −0.0497*** (−5.58) −0.0623*** (−7.83)Country effect YES YES YES YESYear effect YES YES YES YESNo. of Obs. 244 244 244 244No. of countries included 35 35 35 35No. of instruments 67 67 67 67AR(2): P-value 0.657 0.641 0.362 0.686Hansen J: P-value 0.476 0.337 0.277 0.347



424

similar results for the resource rents-financial development relationshipas found earlier. The results generally show a positive impact of Rentson Credit in both LIC and MIC groups. The study finds an ambiguousimpact of institutional quality on Credit. These institutional qualityvariables are interacted with Rents and the results are shown inTable 13. From the table, we find that only Model 48 shows significanteffects for Rents, Voice and interaction term (Rents × Voice × LIC) inthe LIC group. The interaction term is positive, indicating that the po-sitive impact of resource rents on financial development is increasedwhen there is strong voice and accountability in the country.

6. Conclusion and policy recommendations

The development of the financial sector of every nation is critical toits economic development. In Africa for instance, the banking sector ismost critical to the development of the general financial market asbanks constitute the major part of the financial sector. Again, depositsand savings are major sources of funds for banks’ survival. In this re-gard, having the world's major deposits of natural resources, Africashould experience higher liquidity in their banking sector through in-creased deposits and savings from resource wealth. Thus, traditionally,it is expected that the higher the natural resource wealth of a nation,the more development of the financial sector is expected. Even though anumber of contrary research findings allude to a resource curse, in mostAfrican nations with regards to economic growth and development,little is known about the natural resource-financial development nexus.This study fills this gap.

The results show that 32% of African countries are dependent onresource rents ranging from a low of 12.5–52.6% share of GDP. TheGMM estimations reveal that in Africa as a whole, the impact of re-source rents on financial development is ambiguous depending on thefinancial development indicator used. A resource curse in financialdevelopment is seen when Z-score stability is used as an indicator offinancial development. Thus, resource rents in Africa threaten stabilityof the banking sector. Particularly, we find that the negative impact ofresource rents on financial development is seen in the SSA region, LIC,and MIC group but not in the NA region. Central banks, especially thosein SSA region, LIC and MIC groups should be concerned with the sta-bility of their banking sectors in response to resource wealth in thecountry. Mindful of the liberalization of the banking sector, we proposea limit to the loan exposure of banks to the natural resource sector.Through this banks should also monitor and regulate their risk ex-posures to the extractive sector.

In contrast, the impact of resource rents on credit use is positive inall regions and groups. While this is good for development, care shouldbe taken by financial institutions so as not to be overly- dependent onthe sector to affect stability. As explained earlier, countries that arehighly dependent on resource rents with high levels of Credit as a shareof GDP, are susceptible to a fall in economic growth at the behest ofshocks in the financial sector resulting from instability of the sectorthrough loan defaults or indirect shocks from the resource sector. In thisregard, banks/financial institutions should limit their loan exposure tothe natural resource sector. Likewise, there is the need for diversifica-tion of deposit sources such that too much concentration is not placedon the natural resource sector. The central banks should play a keysupervisory role in this regard; probably through moral suasion. Wealso find that in these relationships, the quality of institutions play amajor role. Governments are therefore encouraged to build strong in-stitutions that check corruption, ensure the rule of law, protect in-vestors and make government officials more accountable to the people.The results also show that competition is necessary for financial de-velopment. Less competition in the financial sector does not augur wellfor the development of the sector. Central banks are thus encouraged tospur competition in the financial sector. The results also show that themore educated the public, the better it is for the development of thefinancial sector. Thus, one way that governments can help develop the

financial sector is by investing and improving the education of the ci-tizens. This will be killing two birds with one stone.

Acknowledgement

We would like to thank the Editor, David A. Fleming-Muñoz, andanonymous referees for helpful comments and suggestions. The usualdisclaimer applies.

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Natural resources, financial development and institutional