Kunofiwa Tsaurai¹

Abstract: The study investigated the impact of the complementarity between mining and financial development on economic growth in BRICS (Brazil, Russia, India, China, South Africa) using the dynamic generalized methods of moments (GMM) approach with panel data ranging from 1995 to 2018. Extensive empirical research on the role of either (1) mining on economic growth or (2) financial development on economic growth have been done and it appears that their positive influence on economic growth is no longer debatable and is now a conclusive matter. What is still inconclusive is the non-linear influence (revealed by Arezki and Gylfason. 2011) of either mining or financial development on economic growth. In other words, previous research wrongly assumed that mining has a direct linear influence on economic growth, a view which this study disagrees with. The non-linearity between mining and economic growth is the basis upon which this study is hinged on. Both mining and financial development were individually found to have had a significant positive impact on economic growth in BRICS. However, the study also observed that economic growth of BRICS was enhanced by the complementarity between mining and financial development, consistent with an argument put forward by Bakwena and Bodman (2010). BRICS countries are therefore urged to concurrently develop and implement policies targeted at improving mining sector operations and financial development in order to enhance economic growth. Future studies can investigate the various channels that enhances the mining sector’s influence on economic growth in BRICS.

Keywords: Mining Sector; Financial Development; Growth; BRICS; Panel Data

JEL Classification: B26

1. Introduction

This section discusses the background of the study, gaps found in the literature, contribution of the study towards literature and the structure of the rest of the paper.

1.1. Background of the Study and Gaps Found in the Literature

Theoretical literature argued that mineral resources are a backbone for economic growth in any country. This view was agreed to by theorists such as Singer (1950), Arezki et al (2013), Bhagwati (1958), Kalumbu (2014), Prebisch (1950), Tilton (2012), among others. Esfahani et al (2014) and Cavalcanti et al (2011) are some of the empirical studies which concurred that mining has a long-lasting positive influence on economic growth. Other empirical research concurred that natural resources such as minerals have significant long-term positive effect on economic growth, employment creation and poverty eradication if the right environment exists or prevails (Harvey, et al. 2017; Harvey, et al. 2010; Olakojo, 2015).

What is common is most of the studies on the influence of mining on economic growth is that they wrongly assume that the relationship between the two variables follow a linear format. Few studies such as Arezki and Gylfason (2011) and Bakwena and Bodman (2010) noted that the relationship between mining (natural resources) and economic growth follow a non-linear pattern. What is still not yet agreed is a list of variables which enhances mining’s ability to influence economic growth. It is the basis upon which this study investigated the effect of a complementarity between mining and financial development on economic growth in BRICS, in line with Bakwena and Bodman (2010) recommendation.

What is also clear in the existing empirical literature on the subject matter is that endogeneity issues and the dynamic characteristics of economic growth data were ignored. That is wrong because the econometric function which describes economic growth and its explanatory variables suffers from endogeneity problem, consistent with Rahman et al (2019). The fact that economic growth is affected by its own lag, consistent with Rahman et al (2019, p. 570) has not been given any attention in the literature on the influence of mining and economic growth. To the best of the author’s knowledge, no empirical study on the relationship between mining, financial development and economic growth within the BRICS context. This study seeks to address all these gaps in the literature.

1.2. Contribution of the Study

In conclusion, Bakwena and Bodman (2010) noted that future studies should examine the influence of financial development as a channel through which natural resources influence economic growth in development countries. This study seeks to address their suggestion by investigating the economic growth influence of the complementarity between mining and financial development in BRICS countries. The author is not aware of any prior empirical research that investigated the impact of the complementarity between mining and financial development on economic growth, let alone using BRICS as a unit of analysis. In other words, this study is the first of its kind to investigate such a phenomenon, more so in the context of BRICS. This study also addresses the endogeneity problem and the dynamic nature of the economic growth data, something totally ignored in previous similar empirical research on the subject matter.

1.3. Structure of the Paper

The remaining part of the paper is structured as follows: Section 2 focuses on the theoretical literature on the impact of mining on economic growth. Section 3 discusses the influence of mining on economic growth from an empirical literature point of view. Section 4 focuses on the literature that describes the relationship between financial development and economic growth. The impact of financial development on the mining sector is discussed in Section 5. Section 6 discusses the research methodology, data analysis and results interpretation. Section 7 summarizes the study, discusses policy implications and suggests future research.

2. Mining and Economic Growth –Theoretical Literature

Consistent with Singer (1950) and Prebisch (1950), the Prebisch-Singer hypothesis argues that a country that heavily relies on primary commodity exports such as minerals experience a rapid economic growth phase during the time when world commodity prices surge. The same hypothesis noted that the fall in prices and demand of the commodities in international markets lead to economic instability in that country, triggered by balance of trade deficit. Arezki et al (2013) supported the Prebisch-Singer hypothesis whilst a study done by Tilton (2012) contradicted the hypothesis. Contradicting the Prebisch-Singer hypothesis, Kaodor (1987) noted that a rise in prices of natural resource commodities is inflationary in nature hence impeding economic growth.

The immiserizing growth hypothesis developed by Bhagwati (1958) argued that economic growth of a country which over-depend on mineral commodity exports gets negatively affected by fluctuations in the international prices of the commodities especially if the terms of trade deteriorates. The scenario occurs if earlier growth triggered by a rise in commodity price increase is less than the extent of deterioration of the terms of trade.

A slight modification of the Prebisch-Singer hypothesis was done by Kalumbu (2014) who argued that countries which heavily depend on natural resources such as minerals experience negative economic growth when the natural resources depletes and begin experiencing balance of trade deficit.

The founder of the resource curse hypothesis known as Sachs and Warner (1995) noted that mineral commodity booms have a long run negative influence on the growth of the economy. Empirical research done by van der Ploeg (2011), Gylfason (2001) and Gylfason and Zoega (2006) produced results which tacitly supported the resource curse hypothesis. On the contrary, empirical studies by Cavalcanti et al (2011) and Esfahani et al (2014) found out that abundance of natural commodities is actually a blessing and not a curse to the economic prosperity of country.

According to Olakojo (2015), a rise in world commodity prices negatively affects economic growth of a country which is a net importer of the commodities (minerals). On the contrary, economic growth of a net exporter of commodities tends to increase in response to a rise in in world commodity prices especially if there exists investment expenditure by local firms, high levels of domestic consumption of domestic goods in the economy (Olakojo, 2015, pp. 11).

3. Mining and Economic Growth –Empirical Literature

Table 1. Empirical Literature on the Influence of Mining on Economic Growth

Source: Author Compilation

4. Impact of Financial Development on Economic Growth

Levine (1997) presented a theoretical framework showing how financial development affects economic growth. In summary, Levine (1997) noted that financial markets enhance economic growth through their ability to mobilise savings, efficiently allocate resources, facilitates risk management, exert corporate control and provide liquidity to enable ease of trading of goods and services.

According to Diamond and Dybvig (1983), the financial sector enables investors to have access to high return investment opportunities which normally are illiquid through pooling their liquidity risk. The view was supported by Pagano (1993) whose study noted that individuals are allowed to participate in unit trusts thus promoting diversification and risk sharing. This function of the financial sector enables the pooling together of more funds and channeling them towards economic growth of the country. Liquidity that is provided by the financial sector allows some of the financial assets (national certificates of deposits, shares, bankers’ acceptances) to be used as collateral security to allow productive firms and or projects to access funds (Levine, 1997; Osinubi, 1998).

Moreover, Schumpeter (1911) argued that financial sector is able to efficiently allocate resources to firms that are better place to meaningfully contribute towards economic growth through technological innovation and innovative products manufacturing. The same author argued that financial sector facilitates economic growth through risk diversification, savings pooling and efficiently allocating them to the sectors of the economy which are productive. Shaw (1973), McKinnon (1973), Goldsmith (1969) and Townsend (1983) agreed that information costs reduction, savings mobilization, risk management services, loan provision transaction costs reduction and efficient allocation of available financial resources to more productive projects are different ways through which economic growth is enhanced by financial sector development.

5. The Influence of Financial Development on the Mining Sector

Bakwena and Bodman (2010) noted that financial sector development enhances mining activities in the following ways: (1) It allows mining firms to easily access finance to purchase heavy equipment normally required for extraction activities in mining (2) It enables mining firms to list on local stock exchanges hence enabling mining firms to raise capital from the primary markets through selling shares, (3) listing mining firms enables them to access liquidity as and when they require it for their activities and (4) the financial sector enables mining companies to convert their expected export proceeds to liquidity through the discounting of letters of credit and or bankers’ acceptance, (5) financial sector helps the mining sector by providing research based information on relevant international commodity markets and (6) providing risk management financial products for the mining sector. The study therefore expects that the complementarity between mining and financial development enhances economic growth, not only in BRICS but in any economic grouping.

6. Research Methodology

This section has five sub-sections, namely data and its sources, general model specification, econometric model specification, pre-estimation diagnostics and lastly main data analysis, results presentation and interpretation.

6.1. Data and its Sources

Using BRICS (Brazil, Russia, India, China, South Africa) as a unit of analysis, this research employed the dynamic generalized methods of moments (GMM) econometric estimation technique. Panel data used ranges from 1995 to 2018. Whilst economic growth is the dependent variable, the independent variables used include mining, foreign direct investment, inflation, financial development, trade openness and savings. International Financial Statistics databases, Global Financial Indicators, United Nations Development Programme reports and World Bank Indicators are the reputable international public databases from which secondary data used was extracted.

6.2. General Model Specification

In line with similar empirical research done by Emara et al (2015) and Addison and Ghoshray (2014), the economic growth function is presented as follows:

GROWTH=f (MIN, FIN, FDI, OPEN, INFL, SAV) (1)

Where GROWTH, MIN, FIN, FDI, OPEN, INFL and SAV represents economic growth, mining, financial development, foreign direct investment, trade openness, inflation and savings respectively.

Majority of earlier empirical research work on the subject matter preferred to use similar independent variables, namely Nazlioglu and Soytas (2012), Arezki and Gylfason (2011), Camacho and Perez-Quiros (2014) and Cavalcanti et al (2015).

6.3. Econometric model specification

In econometric terms, equation 1 is transformed into equation 2.

Where represents the time invariant and unobserved country specific effect stands for the intercept term whilst Ɛ is the error term. is the vector of independent variables. Time and country is respectively represented by and subscripts.

Gross domestic product per capita, mineral rents (% of GDP), domestic credit by financial sector to GDP, net foreign direct investment (% of GDP), total trade (% of GDP), inflation consumer prices (annual %) and gross domestic savings (% of GDP) were the proxies used to measure economic growth, mining, financial development, foreign direct investment, trade openness, inflation and savings respectively. A significant positive co-efficient means that the mining and financial development complement each other in enhancing economic growth in BRICS.

6.4. The Influence of Control Variables on Economic Growth

This section discussed how each control variable influences economic growth, from a theoretical point of view (summarized in Table 2).

Table 2. Theory Intuition and Expected Sign(s)

Source: Author’s Compilation

6.5. Pre-estimation Diagnostics

This section presents and discusses the correlation results and trend analysis of key variables of BRICS.

Table 3. Correlation Results

Note: ***/**/* denotes statistical significance at the 1%/5%/10% level respectively.

Source: Author compilation from E-Views

The maximum correlation is between financial development and economic growth which is 43.79%. This is evidence that there is no multi-collinearity problem among all the variables used in this study, consistent with Stead (1996). As already observed in the literature, Table 2 shows the existence of a significant positive relationship between (1) mining and economic growth, (2) financial development and economic growth, (3) foreign direct investment and economic growth, (4) trade openness and economic growth and (5) savings and economic growth. In line with existing literature on inflation-growth nexus, a significant negative relationship was observed between inflation and economic growth (see Table 3).

Trend analysis (1995-2018) for key variables in the study such as economic growth, mining and financial development variables in BRICS is presented in Table 4.

Table 4. Economic Growth, Mining and Financial Development Trends in BRICS (1995-2018)

Source: Author’s own compilation

Brazil (US$7 166.08), Russia (US$7 202.08) and South Africa (US$5 157.39) had their GDP per capita greater than the overall mean GDP per capita of US$4 852.02 whilst India and China’s mean GDP per capita were less than the overall mean GDP per capita value. Brazil, Russia and India are outliers because their mean GDP per capita values deviated from the mean GDP per capita value (US$4 852.02) by a very wide margin. Countries whose mean mineral rights (% of GDP) were lower than the overall mean mineral rights value of 1.18% of GDP are Russia, India and China whilst Brazil and South Africa’s mean mineral rights (% of GDP) were higher than the overall mean mineral rights value. Considering the deviation between mean mineral rights of each BRICS country and the overall mean mineral rights for all country studied, outliers include India, China and South Africa. Brazil, Russia and India are the BRICS nations whose mean domestic credit by financial sector (% of GDP) were lower than the overall mean domestic credit by financial sector of 83.15% of GDP. China and South Africa’s mean domestic credit by financial sector were higher than the overall mean domestic credit by financial sector value of 83.15% of GDP. Russia, India and South Africa are outliers in this case because their mean domestic credit by financial sector (% of GD) deviated from the overall mean domestic credit by financial sector of 83.15% of GDP by quite a substantial margin. Before data analysis procedures could take place, all the variables used in the study were converted into natural logarithms in order to decisively do away from problems associated with outliers, multi-collinearity and data that does not follow a normal distribution pattern, consistent with Aye and Edoja (2017). The latter also noted that transforming data into natural logarithms before data analysis helps to avoid spurious results.

6.6. Panel Unit Root Tests

Levin, Lin and Chu (2002), Im, Pesaran and Shin (2003), Augmented Dicky Fuller (ADF) Fisher Chi Square and PP Fisher Chi Square tests were used to determine whether the data was stationary or not, stable or unstable, volatile or non-volatile.

Table 5. Panel Root Tests – Individual Intercept

Note: LLC, IPS, ADF and PP stands for Levin, Lin and Chu; Im, Pesaran and Shin; ADF Fisher Chi Square and PP Fisher Chi Square tests respectively. *, ** and *** denote 1%, 5% and 10% levels of significance, respectively.

Source: Author’s compilation - E-Views figures

The null hypothesis is that variables are stationary whilst the alternative hypothesis is that variables are non-stationary. At level, not all the variables’ probability values were significant. This means that not every variable was stationary at level. On the contrary, the probability values of all the variables used in the study were significant at first difference hence the null hypothesis which says that variables are stationary is not rejected.

6.7. Panel Co-Integration Tests

The existence of a long run relationship between and among the variables was tested using Kao (1999) panel co-integration procedure, in line with other empirical studies such as Okoroa and Chinweoke (2013). Odhiambo (2014) noted that if the variables used are co-integrated, it means that there is a long run relationship between and or among the variables studied.

Table 6. Results of Kao Co-Integration Tests

Source: Author Compilation

Where DCF stands for domestic credit by financial sector (% of GDP), SMC represents stock market capitalization (% of GDP) whilst DPD is outstanding domestic private debt securities (% of GDP). In all the three economic growth functions using different measures of financial development, the variables were found to be co-integrated (long run relationship among the variables used was established). The results of both panel unit root and co-integration tests allowed main data analysis (causality analysis) to happen, in line with Guisan (2014).

6.8. Main Data Analysis, Results Presentation and Interpretation

The dynamic GMM results of the economic growth function are presented in Table 7.

Table 7. Dynamic Generalised Methods of Moments (GMM) Results

***, ** and * denote 1%, 5% and 10% levels of significance, respectively.

Source: Author’s compilation from E-Views

Model 1 used domestic credit by financial sector (% of GDP), model 2 employed stock market capitalization ratio whilst outstanding domestic private debt securities to GDP was used in model 3 as measures of financial development.

Across all the three models, economic growth was found to have been positively and significantly influenced by its own lag, in line with Rahman et al (2019), whose study revealed that earlier economic growth had a significant positive influence on current economic growth in South Asia.

Mining sector was found to have had a significant positive impact on economic growth, consistent with earlier empirical studies such as Prebisch (1950) and Singer (1950) whose studies argued that countries which relies heavily on primary commodity exports like minerals experience a significant positive economic growth during the time when world commodity prices increase.

Financial development also had a significant positive effect on economic growth in all the three models. The results resonate with authors such as Levine (1997), McKinnon (1973), Shaw (1973), Townsend (1983) and Goldsmith (1969) whose studies argued that financial development improves economic growth through efficient resource allocation in the economy, mobilizing savings, exerting corporate control in the economy, liquidity provision to enable ease of trading of goods and services and facilitating risk management.

The study revealed that the complementarity between mining sector and financial development had a significant positive influence on economic growth in BRICS group of nations, consistent with researchers such as Bakwena and Bodman (2010). What is more striking is that the co-efficient size of the complementarity variable is larger than the co-efficient size of either mining or financial development variable. Such a result means that complementarity between mining and financial development enhanced economic growth in BRICS countries, in line with Bakwena and Bodman (2010) whose research argued that financial development could be a channel through which natural resources enhances economic growth especially in developing economies.

Foreign direct investment was found to have had a significant positive effect on economic growth in BRICS nations across all the three models, consistent with earlier researchers such as Romer (1986) who argued that that FDI brings in along with it human capital development, new skills, new technology and technical know-how, aspects which are key inputs into the production process and economic growth of any country.

Trade openness had a significant positive impact on economic growth in BRICS under models 1 and 3 whilst trade openness was found to have had a non-significant positive influence on economic growth in BRICS under model 2. These results on trade openness led growth hypothesis resonate with authors such as Baltagi et al (2009) whose study argued that high levels of trade openness enables a country to export its goods and services, brings in foreign currency that would have long term positive impact on economic growth.

In line with researchers such as Mallik and Chowdhury (2001), this study found out that inflation had a significant negative influence on economic growth across all the three models in BRICS countries. Economic growth was also found to have been positively and significantly influenced by savings, results which resonate with authors such as McKinnon (1973) who argued that savings increases the quantity and value of investment that goes into the productive sectors of the economy.

7. Summary, Policy Implications and Suggested Future Research

The study investigated the impact of the complementarity between mining and financial development on economic growth in BRICS using the dynamic GMM approach with panel data ranging from 1995 to 2018. Extensive empirical research on the role of either (1) mining on economic growth or (2) financial development on economic growth have been done and it appears that their positive influence on economic growth is no longer debatable and is now a conclusive matter. What is still inconclusive is the non-linear influence (revealed by Arezki and Gylfason. 2011) of either mining or financial development on economic growth. Both mining and financial development were individually found to have had a significant positive impact on economic growth in BRICS. However, the study also observed that economic growth of BRICS was enhanced by the complementarity between mining and financial development, consistent with an argument put forward by Bakwena and Bodman (2010). BRICS countries are therefore urged to concurrently develop and implement policies targeted at improving mining sector operations and financial development in order to enhance economic growth. Future studies can investigate the various channels that enhances the mining sector’s influence on economic growth in BRICS.

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