The impact of commodity price shocks on banking system stability in developing countries

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(1)COPYRIGHT AND CITATION CONSIDERATIONS FOR THIS THESIS/ DISSERTATION. o Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. o NonCommercial — You may not use the material for commercial purposes.. o ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.. How to cite this thesis Surname, Initial(s). (2012). Title of the thesis or dissertation (Doctoral Thesis / Master’s Dissertation). Johannesburg: University of Johannesburg. Available from: http://hdl.handle.net/102000/0002 (Accessed: 22 August 2017)..

(2) The impact of commodity price shocks on banking system stability in developing countries by. Margarida Liandra Andrade da Silva. A dissertation submitted in fulfilment for the Degree of Master’s in Commerce in Development Economics. at the College of Business and Economics UNIVERSITY OF JOHANNESBURG Supervisor: Prof N Ngepah. 2018.

(3) DECLARATIO N I certify that the minor dissertation/ dissertation/ thesis submitted by me for the degree Master’s of Commerce (Development Economics) at the University of Johannesburg is my independent work and has not been submitted by me for a degree at another university.. ----------------------------------------------------------------------MARGARIDA LIAN DRA AN DRADE DA SILVA ii.

(4) ACKN O WLEDGEMEN TS First, I would like to thank God for the constant guidance and support throughout my studies. It has certainly not been an easy road but my faith in you, even though at times faltered, helped me get to where I am today. Secondly, I would like to thank my employer, the South African Reserve Bank, for investing in my studies and allowing me the opportunity to finally conclude the realisation of this dream. Thirdly, I would like to thank my supervisor, Professor N icholas N gepah, for the support and guidance. Working with you helped me appreciate this qualification more. I learnt a lot from you Prof. Thank you. My appreciation to my editors, Liza Marx and Danielle Fontaine from Academic and Professional Editing Services (APES), for their assistance in proofreading, copy-editing and formatting my work. Finally, I would like to thank my family and friends; without you this would not were possible. Thank you to my mother, Bernarda Marina da Silva, for the continuous financial and emotional support that you provided throughout my studies. And last but not least, to Erio Muxito, you have been there from day one; you have listened to my complaints, frustrations, achievements and failures. Thank you for your advice and your support.. iii.

(5) ABSTRACT This study examines the impact of commodity price shocks on the banking sector stability of African commodity-exporting economies. The study uses an unbalanced panel dataset comprising 18 commodity-exporting African economies spanning a 16-year period from 2000-2015. The dataset covers the 2007/ 2008 Global Financial Crisis and the recent 2014/ 2015 commodity price bust. The impact of commodity price shocks on African commodity-exporting economies’ banking sector was estimated using a panel fixed effects model. The empirical findings indicate that commodity price shocks increase bank credit risk (non-performing loans) and thus, pose a risk to the banking sector stability of African commodity-exporting economies. When disaggregated by positive and negative commodity price shocks, the results reveal that both types of shocks weaken banking sector stability. Whilst this is a surprising finding, it implies possible symmetry between positive and negative shocks for African countries. The commodity group results indicate that the agricultural and metals groups significantly increase credit risk, whilst the minerals and fuels, and the chemical groups have a positive but insignificant impact on banking sector stability. In addition, commodity price shocks are discovered to decrease credit extension to the private sector, emphasising an additional channel through which the impact of commodity price shocks may be transmitted to the real economy. Keywords: Commodity price shocks; banking sector stability; African countries; credit extension to the private sector; panel data; fixed effects model. iv.

(6) TABLE O F CO N TEN TS DECLARATIO N .................................................................................................................................. ii ACKN O WLEDGEMEN TS ................................................................................................................ iii ABSTRACT .......................................................................................................................................... iv TABLE O F CO N TEN TS..................................................................................................................... v LIST O F FIGURES ............................................................................................................................. vii LIST O F TABLES ............................................................................................................................... vii CH APTER 1: IN TRO DUCTIO N ...................................................................................................... 1 1.1.. Introduction and background of the study ............................................................................... 1. 1.3.. Research questions ..................................................................................................................... 6. 1.4.. Research objective ...................................................................................................................... 7. 1.5. O rganisation of the study .......................................................................................................... 7 CH APTER 2: CO MMO DITY PRICE DYN AMICS: DEPEN DEN CE, BAN KIN G SECTO R AN D TRAN SMISSIO N MECH AN ISMS........................................................................................... 8 2.1.. Commodity-dependence in African countries .......................................................................... 8. 2.2.. The importance of banking sector stability .............................................................................. 9. 2.3.. Transmission channel of commodity price shocks................................................................... 9. CH APTER 3: LITERATURE REVIEW ........................................................................................... 13 3.1.. TH EO RETICAL REVIEW .................................................................................................... 13. 3.1.1. Macro-financial linkages and determinants of bank credit risk .............................................. 13 3.2.. EMPIRICAL REVIEW ........................................................................................................... 14. 3.2.1. Determinants of credit risk in the banking sector .................................................................. 14 3.2.2. Macro-financial linkages, commodity price shocks and banking sector stability ................. 15 CH APTER 4: METH O DO LO GY .................................................................................................... 22 4.1.. TH EO RETICAL MO DEL ..................................................................................................... 22. 4.1.1. The monetary theory of production and the credit theory ................................................... 22 4.2.. FUN CTIO N AL FO RM ......................................................................................................... 25. 4.3.. DATA AN D VARIABLES..................................................................................................... 28. 4.3.1. Definition of variables ............................................................................................................. 30 4.3.2. Control variables ...................................................................................................................... 31 4.4.. ESTIMATIO N TECH N IQ UE .............................................................................................. 33. 4.4.1. Testing of linear models: pooled ordinary least squares, random effects and fixed effects ... 34 4.4.2. System generalised method of moments versus the fixed effects model ................................ 35 5.1.. DESCRIPTIVE STATISTICS................................................................................................. 37. 5.1.1. Analysis of means..................................................................................................................... 37 5.1.2. Analysis of correlation matrix ................................................................................................. 38 5.2.. RESULTS ................................................................................................................................. 39 v.

(7) 5.2.1. Baseline results.......................................................................................................................... 39 5.2.2. Sensitivity analysis ................................................................................................................... 42 5.2.3. Do commodity price shocks impact domestic lending? ......................................................... 47 CH APTER 6: CO N CLUSIO N ......................................................................................................... 53 6.1.. PO LICY IMPLICATIO N S .................................................................................................... 54. 6.2.. RECO MMEN DATIO N S....................................................................................................... 54. 6.3.. AREAS O F FUTURE RESEARCH ...................................................................................... 54. BIBLIO GRAPH Y................................................................................................................................ 56 Appendices ........................................................................................................................................... 61. vi.

(8) LIST O F FIGURES Figure 1: Commodity price index (2005= 100) ................................................................................. 2. LIST O F TABLES Table 1: Data description and sources............................................................................................. 33 Table 2: Descriptive statistics.......................................................................................................... 38 Table 3: Correlation matrix ............................................................................................................ 39 Table 4: Baseline results and their impact of commodity price shocks on N PLs ............................ 40 Table 5: Agricultural commodity price shock and N PLs ............................................................... 43 Table 6: Mineral and fuel commodity price shock and N PLs......................................................... 44 Table 7: Metal commodity price shock and N PLs.......................................................................... 45 Table 8: Chemical commodity price shock and N PLs.................................................................... 46 Table 9: The impact of commodity price shocks on credit extension ............................................. 48 Table 10: The impact of agricultural price shocks on credit extension ........................................... 49 Table 11: The impact of mineral and fuel price shocks on credit extension ................................... 50 Table 12: The impact of metal price shocks on credit extension .................................................... 51 Table 13: The impact of chemical price shocks on credit extension ............................................... 52. vii.

(9) LIST O F ACRO N YMS AN D ABBREVIATIO N S ADB - Asian Development Bank AIC - Akaike’s Information Criterion CPS - Commodity Price Shock EDF - Expected Default Frequency FE - Fixed Effects FRED - Federal Reserve Economic Data FSI - Financial Stability Indicators GCC countries - Gulf Cooperation Council countries GDP - Gross Domestic Product 2007/ 2008 GFC - 2007/ 2008 Global Financial Crisis GMM - Generalised Method of Moments H Q IC - H annan and Q uinn Information Criterion IMF - International Monetary Fund LGD - Loss Given Default LICs - Low-Income Countries LLP - Loan Loss Provision MEN A countries - Middle Eastern and N orth African countries MTP - Monetary Theory of Production O LS - O rdinary Least Squares PVAR - Panel Vector Auto-Regression RE - Random Effects RO A - Return on Assets N PL - N on-Performing Loan SBIC - Schwarz’s Beyesian Information Criterion SGMM - System Generalised Method of Moments SWF - Sovereign Wealth Fund UN DP - United N ations Development Programme UN - United N ations VIX - CBO E Volatility Index WB - World Bank. viii.

(10) CH APTER 1: IN TRO DUCTIO N 1.1.. Introduction and background of the study. African countries are highly dependent on commodities; this exposes them to risks of economic, political and financial instability (Christensen, 2016). The economic and political implications of commodity-dependence are well-rooted in literature, with a plethora of research focussing on how it impacts economic growth, debt, conflict, and financial development (H amilton, 1983, 2007; Deaton and R. Miller, 1995; Lescaroux and Mignon, 2008; Kilian et al., 2009; Rafiq et al., 2016; Montfort and O uedraogo, 2017; Bangara and Dunne, 2018). Limited research examined the possible impact of commodity price shocks on financial sector stability, specifically banking sector stability (Alodayni, 2015; Kinda, Mlachila and O uedraogo, 2016; Agarwal, Duttagupta and Presbitero, 2017; Eberhardt and Presbitero, 2018). Commodity price shocks affect the corporate, household, government and the banking sectors of the economy (Christensen, 2016). The banking sector may, therefore, be an additional channel through which the impact of commodity price shocks is transmitted to the real economy. The 2007/ 2008 Global Financial Crisis (2007/ 2008 GFC) provided evidence of this possible phenomenon, where instability in the financial sector spilled over into the real sector. The 2007/ 2008 GFC highlighted the importance of a stable banking system, emphasising its significance in efficiently pooling and allocating resources for the economy. Stability is critical for banks to perform their functions effectively and efficiently. Any element that affects banking sector stability has the potential to affect the smooth economic cycle directly and/ or indirectly; this is particularly true for bank -based financial systems such as those established in African economies. The financial structure of African economies is also largely dominated by large domestic and foreign banks (Chironga et al., 2018). This implies that stability (instability) in the banking sectors of African commodity-exporting economies can lessen (intensify) the impact of commodity price shocks on the macro-economy (Poghosyan and H esse, 2009; Alodayni, 2015; Kinda, Mlachila and O uedraogo, 2016; Kooros and Semetesy, 2016; Miyajima, 2016). For example, the 1980s and 1990s comprised extensive banking crises, with most of the instability being concentrated in commodity-exporting economies (Eberhardt and Presbitero, 2018). Few African economies experienced banking crises during this period. According to Eberhardt and Presbitero (2018), factors such as long periods of economic growth, financial deepening, and high and stable commodity prices contributed to the resilience of African banking sectors. Structural reforms for sound macroeconomic policies and improved regulatory frameworks further supported African banking sectors (Caggiano et al., 2013; Bangara and Dunne, 2018). Despite this resilience, macroeconomic 1.

(11) and banking sector vulnerabilities are clearly still in place and are likely to emerge as financial deepening increases and as the financial system becomes more complex. Whilst the structure1 of African economies may have changed substantially since the banking crises of the 1980s and 1990s, this does not mean that these economies are not vulnerable to external shocks. In 2014/ 2015, several economies began experiencing financial distress, indicated by declining bank profitability and deteriorating asset quality (UN DP, 2016; IMF, 2017). Even though the country-specific problems faced by these economies may have contributed to the financial distress, the sharp and persistent decline of commodity prices certainly perpetuated the issue for commodity-exporting countries (Figure 1). They remain dependent on commodity-exporting revenues and as such, remain vulnerable to its boom and bust cycles. Exposure and vulnerability to commodities is still a prominent issue for African countries. The implications of this exposure is further emphasised by Fernández et al. (2017) who say that commodity markets recently became more financialised, increasing the exposure of the financial sector to commodities and thus posing a threat to its stability. Thus, the recent decline in commodity prices2 (Figure 1), which the IMF (2015) expects to remain persistently low for some time, could have dire and lasting implications for commodity-dependent developing economies’ banking sectors.. Figure 1: Commodity price index (2005= 100) 250. Index. 200. 150. 100. 50. 0 2016. 2015. 2014. 2013. 2012. Metal. 2011. 2010. Energy. 2009. 2008. 2007. 2006. 2005. 2004. 2003. 2002. 2001. 2000. All Commodities. Agriculture. Source: Author’s own representation using IMF (2018) data. 1. Improved financial deepening, regulatory framework, financial and economic integration, and economic diversification (Bluedorn et al., 2014; Eberhardt and Presbitero, 2018). 2. In 2015, commodity prices declined below levels witnessed during the 2007/ 2008 GFC. They have since improved slightly but remain substantially below pre-2015 levels.. 2.

(12) The impact of commodity price shocks seems clear and direct when examining their relationship with exporting firms and say, government revenue3. H owever, the relationship between commodity price shocks and the banking sector is unclear and indirect. Commodity-exporting-firms, whether private or state-owned, often receive loans from banks; this is not an uncommon occurrence for banks in Africa. According to the International Monetary Fund (IMF) (2013), the large exposure of N igerian banks to the commodity sector exacerbated the 2008/ 2009 N igerian banking crisis. Ghana, another African commodity-exporter, has a banking system that is dominated by state banks (N ikolaidou and Vogiazas, 2017). If these state-owned banks provide loan to commodityexporting firms, they expose themselves to the volatility and instability of commodity prices and this could have dire implications for the entire banking system. The impact could therefore occur through an exporting firm or even through government being unable to service their debt as a result of lower-than-expected commodity-export revenue. Consequently, this would weigh on a particular bank’s stability and, if ongoing, could impact the entire banking system. Commodity price shocks can also weigh on the bank’s balance sheets through a surge in bank withdrawals following a significant decrease in prices4 (Kinda, et al., 2016). According to Kinda et al. (2016), if shocks are large enough, they could also impact the banking sector through reducing inter national reserves and increasing currency mismatch risk. Whilst these scenarios cannot be completely avoided, the instability, volatility, and unpredictability of commodity prices increase the risk that they will occur in a disorderly and unexpected manner, which could possibly destabilise the financial sector. Most of the literature on the commodity price shock and financial system relationship focussed on capital markets in developing and advanced economies (Iwayemi and Fowowe, 2011; Fowowe, 2013; Fratzscher, Schneider and Robays, 2014). Few studies paid specific attention to the impact of commodity price shocks on the banking sector stability of commodity-exporting African economies with bank-based financial systems (Kinda, et al., 2016; Agarwal et al., 2017). Bank-based systems are highly beneficial in African countries as they account for a great percentage of the financial system; it is therefore appropriate to focus on the banking sector variables/ indicators rather than capital market variables/ indicators. Most research mentioned was specifically concerned with oil price shocks; this study focusses on all commodities. The commodities are disaggregated by commodity group to determine the impact of the various commodity groups5 on the. 3 4 5. In circumstances where the commodity firms are government owned and export revenues are allocated to government. Reduces banks’ liquidity and may potentially result in a liquidity mismatch. Agriculture; fuels and minerals; metals, and chemicals.. 3.

(13) banking sector. This is relevant because some commodities are more volatile than others and may thus pose a greater risk to the stability of the banking sector. Given these developments, this study examined the vulnerability of the banking sector to commodity price shocks, focussing on the experience of African commodity-exporting economies. To be specific, the non-performing loans (N PLs) ratio, a measure of credit risk (IMF, 2006), is used to examine the impact of commodity price shocks on banking system stability. In addition, the study sheds light on how commodity price shocks impact bank lending to the private sector and thus the real economy. The African countries included in this study are highly dependent on commodity exports, making a good case for this research. The entire dataset includes bank-specific, macroeconomic and commodities data covering the period 2000-2015. The commodity price shock variable is computed using an asymmetric commodity price measure (Mork, 1989; Farzanegan and Markwardt, 2009). Further, the commodity price shock variable is disaggregated into a positive and negative shock to infer on the existence of asymmetry and assess its impact on the banking sector. The preferred empirical results for the study are derived from a panel fixed effects model in line with Kinda and colleagues (2016) and Agarwal colleagues, (2017). The study includes 18 African commodity-exporting countries, each with its unique set of economic, political, and institutional characteristics that could be correlated with the explanatory variables. Panel fixed effects is the most appropriate technique to estimate the impact of commodity price shocks on banking sector stability as it controls for country-specific effects and prevents biased estimates. Whilst previous similar studies (Louzis et al., 2012; Alodayni, 2015; Kooros and Semetesy, 2016; Al-Khazali and Mirzaei, 2017)6 employed the System Generalised Method of Moments (SGMM), its estimates were established to be inconsistent in this study following the Sargan test 7. As a result, the SGMM was rendered an inappropriate model for this study. The results of the study indicate that commodity price shocks weaken banking sector stability through increasing bank credit risk (N PLs). More specifically, a one unit increase 8 in the commodity price shock increases bank credit risk by 0.381%, in line with previous studies (Kinda, Mlachila and O uedraogo, 2016). When disaggregated by positive and negative commodity price shocks, the results reveal that both positive and negative commodity price shocks weaken banking sector stability and that the positive shock, surprisingly, has the greatest impact on banking sector stability. The lack of asymmetry is line with Addison and colleagues (2016), who found, using a similar commodity price. 6. Study focused on oil prices and not commodity prices. Sargan Test of over-identified instruments tests the validity of the instruments used in the estimation process. 8 This increase represents the magnitude of the shock. This is an shock that does not distinguish between a negative or positive shock. 7. 4.

(14) shock measure, that positive and negative agricultural commodity price shocks for subSaharan African countries do not necessarily respond differently. The disaggregated commodity groups indicate that the agricultural and met al price shocks significantly weaken the banking sector, whilst the mineral and fuel, and the chemical shocks have the expected signs but are statistically insignificant. When disaggregated by positive and negative shocks, the agricultural and metal shocks reveal no asymmetry, implying that both (positive and negative) shocks weaken the banking sector. Asymmetry is observed with the mineral and fuel, and the chemical shocks. The study finds that a positive metal price and chemical price shock 9 have the largest negative impact on banking sector stability 10. Finally, the estimation of the impact of commodity price shocks on bank lending shows that commodity price shocks do indeed decrease bank lending in line with Agarwal and colleagues (2017). As a matter of fact, negative mineral and fuel, metal and chemical price shocks have substantive negative implications on bank lending in African countries. Given these findings, the study deduces that commodity price shocks do not only have an impact on banking sector stability (which can be transmitted to the real economy) but also has a direct impact on bank lending as a means of economic growth and development (Greenwald and Stiglitz, 1991, 2003).. 1.2.. Motivation for and significance of the study. The dependence of African economies on commodity exports has long been debated and analysed. Even though most African countries benefited from commodity price booms, commodity price busts remain a concern due to their magnitude and duration. Commodity price volatility may not be avoided, but countries can ensure that they are not largely impacted by diversifying and reducing their commodity-dependence. There is clear consensus on the impact of commodity price shocks on macroeconomic factors. Limited research focusses on how the banking sector of African economies is impacted. There is a need to examine whether the banking sector may be an additional channel through which commodity price shocks impact the real economy. The 2007/ 2008 GCF brought to light the pieces that were missing in maintaining financial sector stability. The close link between commodity markets and the banking sector (Kinda et al., 2016) therefore supports the need to understand how the financial sector is impacted by commodity price shocks. 9 Chemicals do not constitute a large (if any) portion of African economies’ commodity exports. They import this commodity and are thus negatively impacted by an increase in the price. 10 The results for the mineral and fuel, and chemical price shocks underscore the validity of the comm odity price shock measure employed.. 5.

(15) This study contributes to the literature in three key ways. First, the study emphasises the role of commodity price shocks in triggering banking sector instability. In a related paper, Kinda and colleagues (2016) show that commodity price shocks are associated with financial sector fragility in developing countries. Kinda and colleagues (2016) limited the focus of their study, focussing only on minerals, fuels and metals. This study extended the research by Kinda and colleagues (2016) by focussing on most commodity groups. Second, whilst previous studies focussed on advanced, emerging, developing (not just African), and low-income countries, this study examined the experience of only African commodityexporting countries. This is specifically relevant because of the financial sector vulnerabilities that were revealed in African countries following the 2015 commodity price decline. African economies’ exposure to and dependence on commodity prices increased financial sector vulnerabilities in these countries (Eberhardt and Presbitero, 2018). Third, whilst Kinda and colleagues (2016) outline how the financial sector responds to both negative and positive shocks, this study examined this relationship using overall, positive and negative shocks. Further, the study contributes to extant literature by emphasising the differences in commodity price asymmetries between various commodity groups. To the best of the author’s knowledge, the only work that emphasised this relationship is Addison et al. (2016).. 1.3.. Research questions. The following research questions are addressed in this study: . To what extent do commodity price shocks impact on the banking sector stability of African commodity-exporting countries?. . Is there a statistically significant relationship between commodity price shocks and N on-Performing Loans (N PLs) as a measure of credit risk in the banking sector?. . Do positive and negative commodity price shocks have an asymmetric (symmetric) relationship with N PLs?. . Does such an asymmetric (symmetric) relationship vary from one commodity group to another?. . To what extent do commodity price shocks impact credit extension 11 to the private sector?. 11. Credit extension and bank lending are used interchangeably. .. 6.

(16) 1.4.. Research objective. The main objective of this study is to determine the impact of commodity price shocks on banking sector stability. Specifically, this study has: . Empirically examined the impact of commodity price shocks on N PLs in commoditydependent African economies.. . Determined whether positive and negative commodity shocks have varying impacts on N PLs.. . Assessed whether the impact of commodity price shocks on N PLs varies for various commodity groups.. . Examined the impact of commodity price shocks on bank lending to the private sector.. 1.5.. O rganisation of the study. The remainder of the study is divided as follows. Chapter 2 comprises a brief discussion on African commodity price dynamics. The literature review of the study, consisting of both a theoretical and empirical component is discussed in Chapter Three. Chapter Four comprises the methodology and data. Chapter Five outlines the results of the study. Finally, Chapter Six includes the conclusion, policy recommendations, areas for future research, and limitations of the study.. 7.

(17) CH APTER 2: CO MMO DITY PRICE DYN AMICS: DEPEN DEN CE, BAN KIN G SECTO R AN D TRAN SMISSIO N MECH AN ISMS The implications of commodity price shocks on the real economy were a long and extensively debated discussion, especially in African economies where commodity revenues account for a substantial portion of gross domestic product (GDP) and total export revenue. As previously mentioned, the implications of commodity price shocks for the economy were extensively researched, but little research focussed on whether the volatility of commodities has negative implications for the financial sector, specifically the banking sector. A stable banking sector plays an important intermediary role amongst economic agents. It is, therefore, important to understand and examine risks that could destabilise the sector and weigh on the real economy. As established in several economic relationships, the impact of commodity price shocks on the banking sector may not be straightforward; it can be both direct and indirect. This section briefly discusses the implications of commodity-dependence, the importance of the banking sector, and the various transmission channels through which commodity price shocks may impact the economy (with a specific focus on how the ban king sector is impacted).. 2.1.. Commodity-dependence in African countries. African economies have yet to overcome their dependence on commodities. Available data shows that commodity exports account for approximately 60% of export revenue in 28 of the 55 African countries (UN DP, 2016). African countries’ exports are not as diversified as they should be. As a result, they remain vulnerable to commodity price boom and bust cycles and are exposed to adverse external shocks which have the potential to destabilise their economies. In 2014/ 2015, commodity prices declined by about 50%, with all major commodity groups12 affected (UN DP, 2016). The decline in commodity prices resulted in significant losses in export and foreign exchange earnings for commodity-exporting economies (Kinda, et al., 2016; UN DP, 2016). The economic and financial hardships faced by several African economies only re-ignited the long-discussed and debated concerns about the impact of commodity-dependence on African economic growth and development. Commodity prices are volatile and unstable in nature, and they weigh on economies’ ability to sustainably and consistently generate revenue, and achieve economic growth. There have been several viewpoints claiming that the recent drop in commodity prices marked the end of the long and ongoing period of high commodity 12. With the exception of cocoa beans (UN DP, 2016).. 8.

(18) prices that began in the early 2000s (Baffes et al., 2015). This viewpoint is also supported by Canuto (2014) and the IMF (2015), who expect prices to remain persistently low for a relatively long period of time. Whilst there is nothing wrong with benefiting from commodity-export revenues, it is an unsustainable source of revenue. There is a clear need for African economies to diversify their export base and thus, enhance t heir revenue stream. Diversification will not only ensure sustainable and consistent revenue but will also act as a buffer during periods of lower commodity prices.. 2.2.. The importance of banking sector stability. The banking sector, as a subset of the financial sector, fulfils an important intermediary role in lending surplus funds to firms, government, and households. These deficit units therefore rely on the banking system for credit (Aluko and Ajayi, 2018). A robust banking sector supports economic growth and development; it is a necessary prerequisite for economic development. It promotes growth through mobilising savings for production, providing existing and potential investment information, and facilitating trading, diversification and risk management (ADB, 2015). The banking sector in African economies is an important participant of the undeveloped financial system. In fact,it can be considered the heart of the financial sector 13. The dominance of the banking sector in African economies also makes it the main source of risk for their entire financial system (Dwumfour, 2017). The 2007/ 2008 GFC showed how financial institutions’ balance sheets can negatively impact the entire financial sector, resulting in financial instability which often, spreads to other sectors of the economy. According to the Asian Development Bank (ADB) (ADB, 2015), it is important to assess macroeconomic performance alongside the healthiness of financial institutions. Financial institutions’ balance sheets should sufficiently and continuously be monitored so that vulnerabilities (such as commodity price shocks) that may spread to other sectors of the economy can be identified. Efficient financial intermediation therefore requires a stable financial system that is resilient to external shocks.. 2.3.. Transmission channel of commodity price shocks. In line with the view that commodity price shocks can impact the banking sector through various channels, this section outlines the macroeconomic, fiscal, exchange rate, and. 13. Banking sectors in Africa often account for a substantial (if not the entire) amount of total financial sector assets (Allen, O tchere and Senbet, 2011).. 9.

(19) banking channels. This section reveals the links of the various sectors to the banking sectors. The discussion that ensues only focuses on the transmission that occurs in the event of a decline in commodity prices. Logically, one would expect the opposite deductions in the case of an increase in commodity prices. a. Macroeconomic channel Following a fall in commodity prices, economies would experience a decline in exports, investment and output. Declining exports, investment and output would weigh on the corporate and household sectors. Exports decline and thus economies fail to generate as much export revenue as that generated during periods of higher commodity prices. Investment in commodity extraction and supporting industries would weaken, impacting not only actual output but also potential output (Christensen, 2016). Several authors have established a negative relationship between commodity price shocks and economic growth (Deaton and Miller, 1995; Dehn, 2000; Karl, 2004; Bruckner and Ciccone, 2010; H ammond, 2011; Christensen, 2016). African commodity-exporters, experienced economic growth averaging 5% a year. A reversal of this growth was witnessed, following the commodity price crash that began in late 2014 (Ighobor, 2016). For example, N igeria’s oil revenue accounts for approximately 90% of its export revenue, as a result of the decline in commodity prices, its revenue declined substantially and the country’s economic growth moderated from 5.4% in 2014 to 2.9% in 2016 (Ighobor, 2016). Low growth can impact firms’, governments’ and consumers’ ability to service their bank debts which, in turn, exposes the banking sector to credit risk. In line with a fall in commodity-exporting firms’ production, and thus revenue, unemployment may rise, leaving households worse off in an already vulnerable economic environment (Blanchard and Gal, 2008). Vulnerable firms and individuals means greater risk of defaulting on payments, impacting bank balance sheets and through contagion 14, the greater banking system (Makri et al., 2014). b. Fiscal channel African commodity-exporting countries rely heavily on commodity-export revenue to boost and support economic growth and development. The commodity-export proceeds of some countries in Africa account for more than 70% of the national budget (Alesina et al., 2008; UN DP, 2015; Christensen, 2016; Ighobor, 2016). This reliance means that negative commodity price shocks can certainly decrease fiscal performance (Spatafora and Samake, 14. A shock in one institution and/ or economy which spreads and impacts other institutions and/ or economies.. 10.

(20) 2012; Kinda et al., 2016). A decline in export revenue would cause a decline in government revenue (and thus a decline in government expenditure) of commodity-dependent economies. Kinda and colleagues (2016) reiterated this by saying that commodity price shocks reduce tax revenue, worsen terms of trade, increase fiscal deficits, and also decrease the competitiveness15 of government-dependent institutions. Governments also borrow from the banking sector, so a reduction in government revenue will also impact their ability to service their bank (and other) debts. Commodity price shocks can therefore also pose a banking stability risk through the weakening of fiscal performance. c. Exchange rate channel It is also important to note that as commodity-exporters, African economies encounter two possible scenarios: first, increasing foreign exchange reserves as a result of higher prices or second, decreasing foreign exchange reserves due to lower commodity prices. A substantial decline in commodity prices can increase fiscal deficits and impact exchange rate reserves. This may influence the government and domestic banks to borrow internationally to withstand domestic economic conditions brought on by commodity price shocks. In turn, this will increase the foreign denominated debt of both agents (Kinda et al. , 2016). Any sudden and substantial depreciation of the domestic currency or increase in international interest rates will increase the vulnerability of the banking sector and thus impact its stability. d. Bank channel African countries’ dependence on commodities may also have a direct impact on the banking system. First, commodity dependence structures the bank lending channel in ways which can create ‘system risk’ not just for the banking system but also for the greater financial system (Christensen, 2016). As was witnessed during the 2007/ 2008 GFC, banks freely extend credit during periods of economic and financial boom. Similarly, during periods of commodity booms, domestic credit extension grows, with banks extending credit even to the less creditworthy (Christensen, 2016). Credit extension is important for growth and development, but rapid and extensive credit growth can seriously impact the stability of the financial system. Second, research indicated that commodity-exporters could hold savings as a precautionary measure to address the volatile nature of commodity prices (Bems and Filho, 2011). “If the windfalls are saved in domestic banks, this could threaten the banking sector in case of 15. Competitiveness of companies that depend on government contracts is compromised.. 11.

(21) negative shocks that could lead to sizeable withdrawals” (Christensen, 2016; Kinda, Mlachila and O uedraogo, 2016). Challenges in one bank can have a domino effect on other banks; this could result in bank runs16 with the potential to completely destabilise the financial system. There were several bank-runs during the 2007/ 2008 GFC and the linkages between banks and financial institutions resulted in contagion, impacting the stability of the entire international financial system.. 16. Run on a bank occurs when a large number of depositors, fearing that their bank will be unable to repay their deposits in full and on time, simultaneously try to withdraw their funds immediately.. 12.

(22) CH APTER 3: LITERATURE REVIEW Research on the relationship between commodity price shocks and banking sector stability is necessarily a popular one. Fernández et al. (2017) indicate that commodity price shocks have become more financialised and thus pose a threat to the stability of the financial sector. The commodity price shock and banking sector stability nexus is important to examine, not only because of their exposure to each other, but also because of the potential macro-financial linkage risks that could be perpetuated to the prevailing economic conditions. Claessens and Kose (2018) maintain that shocks stemming from the real economy can be worsened through the financial sector, and the financial sector can also be a source of shock for the real economy, thereby amplifying macroeconomic fluctuations. African economies must examine and monitor the possible risks that their financial sectors, specifically the banking sectors, may encounter because of commodity export dependence. This chapter is divided into two separate sections. The first part, theoretical review, briefly discusses macro-financial linkages and credit risk. The second part discusses the empirical views that underpin this study. Literature specifically focussing on the determinants of credit risk, macro-financial linkages, and banking sector stability is reviewed. Due to the limited research focussing on all commodities, oil price shocks and commodity price shocks17 literature is reviewed simultaneously.. 3.1.. TH EO RETICAL REVIEW. 3.1.1. Macro-financial linkages and determinants of bank credit risk Theoretical model underpinning the analysis on the determinants of credit risk is the financial accelerator theory. This theory posits that endogenous developments in t he credit markets propagate shocks to the real macroeconomic environment (Bernanke et al., 1999). Theory posits that the credit shock is amplified through information asymmetries between lender and borrowers, and through a balance sheet effect. Credit risk is one of the largest risks faced by banks. As such, several studies focussed on the implications of credit risk on the banking system (Mpofu and N ikolaidou, 2018). Literature generally focusses on one of four measures of credit risk (Beck, Jakubik and Piloiu, 2015): (a) expected default frequency (EDF): a forward-looking measure of the probability that a firm, household or government will default, (b) loan loss provisions (LLP): an expense set aside as an allowance for the possibility of default, (c) loss given default (LGD): the amount of money 17. Due to limited research focusing on commodity price shocks and banking sector stability.. 13.

(23) lost when a borrower defaults on a loan and, (d) N PL: A loan for which a borrower has not made a payment for a period of 90 days or more. The N PL ratio is one of two variables18 used by the IMF to determine the loan quality of a financial institution (IMF, 2006). It is thus employed as a proxy for bank credit risk in this study. Prior to the 2007/ 2008 2007/ 2008 GFC, N PLs were at stable levels; following the 2007/ 2008 GFC, N PLs increased sharply. This deterioration in bank loan portfolios caused distress in the banking systems of advanced and emerging market economies alike (Radivojevic and Jovovic, 2017). According to Radivojevic and Jovovic (2017), the link between increasing N PLs and banking sector credibility is one of the main factors t hat contribute to the failure of credit policy. Banking sector stability and its likelihood to fail is highly dependent on the portion of N PLs and as such, N PLs serve as an indicator of defaults in the financial sector (Radivojevic and Jovovic, 2017). Concerning factors determining credit risk, authors choose to group them into two categories: Macroeconomic determinants (also referred to as systemic determinants) and individual (bank, firm, household) specific determinants (also known as un-systemic determinants) (Louzis et al., 2012; Castro, 2013; Mpofu and N ikolaidou, 2018). Systemic determinants include variables such as economic growth, employment, lending rate, taxes, and political policies. It includes factors that specifically impact an individual’s ability to service their debt (Mpofu and N ikolaidou, 2018)19. Un-systemic determinants are factors with the ability to influence the likelihood of a borrower re-paying their debt. Factors such as a debtors’ personality, financial solvency, capital, bank management, bank efficiency, and so forth would be classified as un-systemic determinants20. 3.2.. EMPIRICAL REVIEW. 3.2.1. Determinants of credit risk in the banking sector As previously mentioned, the 2007/ 2008 GFC prompted an interest in the two-way linkages that exist between the financial and macroeconomic sectors. As already noted, several authors discuss the macroeconomic and the bank-specific determinants that influence bank balance sheets and the debt-service capacity of borrowers. The bankspecific determinants of N PLs commonly include lending rates, credit growth, etc. (Keeton and Morris, 1987; Berger and DeYoung, 1997; Jiménez and Saurina, 2006; Espinoza and Prasad, 2010; Alodayni, 2015; Al-Khazali and Mirzaei, 2017). In addition, 18 19 20. Sectoral distribution of loans to total loans is another measure. Most authors focus on the macroeconomic factors that impact N PLs. Most authors focus on the bank-specific determinants.. 14.

(24) literature on the banking sector identifies bad management, bank inefficiency, poor monitoring and controls as factors that increase N PLs. The second strand focusses on the macroeconomic determinants of N PLs. There are numerous studies that linked macroeconomic variables to N PLs. The variables commonly mentioned are economic growth, interest rates, unemployment, and inflation rates (Demirguc-Kunt and H uizinga, 1999; Q uagliariello, 2007; Louzis et al., 2012; Castro, 2013; Makri, Tsagkanos and Bellas, 2014; Ghosh, 2015). This study considered both strands since commodity price shocks are a macroeconomic phenomenon, but the decision to extend credit (bank lending) to commodity-exporting firms may be both macroeconomic and financial 21. Most of the literature focussing specifically on African economies established that N PLs are mainly driven by macroeconomic factors. Fofack (2005), using a cross-country fixed effects model with 16 sub-Saharan African economies over the period 1993 to 2002, indicates that N PLs have a negative relationship with GDP per capita and a positive relationship with real exchange rate appreciation, real interest rates, and broad money. Inflation, economic growth and bank lending are all established to be insignificant determinants of N PLs. A more recent study by N ikolaidou and Vogiazas (2017) examines the determinants of N PLs in five sub-Saharan African countries using the Autoregressive Distributed Lag (ARDL) model. The findings indicate that increasing money supply conditions decreases N PLs in all countries included in the sample, but that bank -specific variables are significant in South Africa and Uganda only, whilst the country-specific (macroeconomic) variables drive N PLs in Kenya, South Africa, and Zambia. Mpofu and N ikolaidou (2018) analysed only the macroeconomic determinants of credit risk in 22 subSaharan African countries for the period 2000 to 2016. The authors show, using a dynamic panel model, that an increase in GDP decreases credit risk, whilst inflation, bank lending, trade openness, the CBO E Volatility Index (VIX) and the 2007/ 2008 GFC have a positive impact on credit risk (increase credit risk). The findings for the relationship of N PLs with economic growth, inflation and bank lending in Mpofu and N ikolaidou (2018) differs from the results established in Fofack (2005).. 3.2.2. Macro-financial linkages, commodity price shocks and banking sector stability During periods of commodity price booms, banks generate a lot of liquidity which makes them more lax in their lending (Ftiti et al., 2016). Thus, banks could be increasing lending during commodity price booms, but the opposite may hold during commodity price busts, 21. Given that macroeconomic conditions can influence bank lending behaviour (Greenwald and Stiglitz 1991 & 2003; Agarwal et al., 2017).. 15.

(25) resulting in both a reduction in credit extension and deterioration in loan quality. This notion is supported by Ftiti and colleagues (2016) who analysed the relationship between the commodity price cycle and credit cycle in three commodity-exporting African economies. Their findings indicate that the credit market is sensitive to persistent commodity price shocks. Kablan and colleagues (2017) who used a sample of African commodity-exporting countries, established similar results showing a positive relationship between commodity price booms and credit growth. Kablan and colleagues (2017) also emphasise that a commodity boom reversal affects both the macroeconomic and financial sectors, decreasing commodity-exporters’ capacity to service their debts. Knock-on effects increase N PLs; weigh on banking sector stability which, in African economies, would eventually impact the entire financial system. The findings of Kablan and colleagues, (2017) are crucial given the volatility and uncertainty related to commodity prices. The views of both Ftiti and colleagues (2016) and Kablan and colleagues (2017) are in line with Cashin and McDermott (2002) who established that African economies’ commodity-dependence makes them sensitive to lending booms and thus rising N PLs. More closely related to this study’s empirical question, Miyajima (2016) with evidence from Saudi Arabia and using Generalised Method of Moments (GMM) and Panel Vector Auto-Regression (PVAR) methods, indicated that low oil prices and non-oil GDP lead to a rise in N PLs. In turn, this transmits to the balance sheets of banks through weak macroeconomic variables. This is in line with Alodayni (2015) who focussed on the oilmacro-financial linkages in the Gulf Cooperation Council countries (GCC) region. The study, also employing a panel GMM and PVAR model, on 24 GCC banks for the period 2000 to 2014, established that oil prices along with other macroeconomic variables have an impact on N PLs, and that higher N PLs have adverse effects on GCC economies. AlKhazali and Mirzaei (2017) also established related results when they analysed the impact of oil price movements on the N PLs of 30 oil-exporting countries over the period 2000 to 2014 using panel GMM. Their results revealed three things: first, that a rise (fall) in oil prices leads to a decrease (increase) in N PLs of oil-exporting economies; second, that, oil price shocks have asymmetric effects on bad loans (N PLs), and finally, that the negative impact of adverse oil price shocks have greater implications for the loans of large banks. These findings are significant considering that the banking sectors in developing countries (specifically African countries), dominate the financial sector (Allen et al., 2011). Any vulnerability in the banking sector, therefore, places the whole system at risk. Kooros and Semetesy (2016) assessed the relationship between international oil prices and the financial system in GCC countries. Their analysis incorporated data for 42 GCC banks spanning 16.

(26) from 2000 to 2014. The study employed a system GMM technique and a PVAR model to assess the macroeconomic and bank-specific determinants of N PLs and the feedback loops between macroeconomic and bank balance sheet variables, respectively. In the first place, the study established that bank asset quality (N PLs) is impacted by oil prices and macroeconomic variables; second, the study also established feedback loops between oil price movements and bank balance sheets, emphasising the notion that instability in the banking sector results in unwanted economic consequences for the real sector. The closest literature to this empirical study comes from Kinda and colleagues (2016) and Eberhardt and Presbitero, (2018). Kinda and colleagues (2016) examined how commodity price shocks impact financial sector fragility by focussing on 71 commodity-exporting emerging and developing economies for the period 1997 to 2013. The study employs a panel fixed effects model to estimate the effect of commodity price busts on financial soundness indicators22. The results reveal that commodity price shocks weaken the financial sector and that larger shocks have a greater impact on financial sector stability. The study then goes on to analyse a banking crisis using a conditional fixed effects logit model; the results of this estimation indicated that commodity price shocks are associated with banking crises. Eberhardt and Presbitero (2018) developed an empirical model to predict the relationship between commodity price movements and banking crises on a sample of 60 low-income countries (LICs) over the period 1981 to 2015. The authors employ a random effects Mundlak logit model in their estimation. Their results are in line with the findings from Kinda and colleagues (2016), showing that commodity price movements are an economically substantial and robust driver of banking crisis in LICs. These findings are in line with Kaminsky and Reinhart (1999) who provided evidence of how instability in the banking sector can trigger a financial crisis. The study finds, using a sample of emerging market economies, that risk in the banking sector leads to a currency crisis. The authors indicate that when and if the currency crisis deepens, it spreads to the entire economy. Whilst most empirical literature on the linkages between commodity price shocks and credit risk focussed specifically on oil price shocks, this study adds to the current limited research by considering all commodities. Including all commodities broadens the scope of the research and thus allows for a more comprehensive analysis. The paper closest to this study, Kinda, et al. (2016), focussed only on fuels, minerals and metal commodities. This study is also motivated by Kinda et al., (2016) focussing on emerging and developing countries, without isolating African economies. African economies are isolated in this study because of their dependence on commodity exports and the potential vulnerability 22. Capital adequacy, asset quality, earnings and profitability, liquidity and sensitivity to market risk (IMF, 2006).. 17.

(27) their banking sectors could encounter because of commodity price shocks. This study further extends on previous literature by examining how the various commodity groups impact the banking sector and how they impact bank credit extension.. 18.

(28) CH APTER 4: METH O DO LO GY 4.1.. TH EO RETICAL MO DEL. As indicated in the literature, the most common channel through which commodity price shocks may impact the banking sector is through credit risk. Firms, governments or households in commodity-exporting African economies are more likely to default on payments during periods of commodity price busts, whilst during periods of commodity price booms, the opposite would be expected. During periods of negative commodity price shocks, the banking sector may behave in a manner that will either maximise net income or minimise default risk. Positive commodity price shocks may imply that the banking sector is more open to lending, whilst negative commodity price shocks may imply that the banking sector is a bit more reluctant to lend (due to concerns that some borrowers will be unable to repay their debts). Refusal to extend credit or extending credit at high interest rates may result in a slowdown in economic growth and a rise in N PLs as agents fail to service their debt. Declining banking activity in the wake of slowing economic activity exacerbates this problem, making the banking sector even more reluctant to lend. A decline in banking activity will thus have implications on the growth and development of these commodity-exporting economies. The theoretical specification of the models is discussed briefly below.. 4.1.1. The monetary theory of production and the credit theory The standard monetary theory of production (MTP) economy comprises three economic agents: banks, firms, and workers (Graziani, 2003). “In a monetary production economy, money is intimately linked to the labour force and to the primary, intermediate and final commodities circulating in the real sector” (Bortis, 2010). The MTP theory, therefore, maintains that the banking system in the economy creates money in line with the idea that banks lend money, firms extend wages and produce commodities, and workers provide labour. “The circular process of the monetary economy starts with bargaining in the money market between banks and firms. Banks supply firms with initial finance; firms use this money to pay workers and to start production” (Graziani, 2003). The monetary circuit will only lapse with the repayment of the initial finance to banks (Graziani, 2003). The monetary circuit may fail to lapse because of unfavourable macroeconomic conditions that make it difficult for firms to make profit and repay their bank loans. The unfavourable macroeconomic environment and firms’ failure to service their debt increases banks’ risk perception and thus weighs on banks’ willingness to extend loans (Greenwald 22.

(29) and Stiglitz 1991 & 2003). Economic circumstances (large or small shocks) with implications for the net worth or risk of particular assets of banks have long-run negative effects on credit availability and typically leads to a significant contraction in the supply of funds by banks (Greenwald, Stiglitz and Weiss, 1984; Stiglitz, 2016). Therefore, commodity-exporting firms encounter the risk of being unable to access financing when they are facing harsh macroeconomic conditions as a result of commodity price shocks. This study aims to determine the impact that commodity price shocks have on the banking system and whether they negatively impact banks’ lending behaviour. This is done under the assumption that the relationship between banks and commodity price shocks exists through the financing that banks extend to firms, specifically commodity 23 firms, for the extraction and production of commodities. This study is concerned with banks and firms, but the international economy is also included because commodities are exported internationally. Further, the prices of commodities are internationally determined and not determined by the firms. The equation below draws from the MTP equation in Graziani (2003). It is extended in this study to include the banks’ loan portfolio as seen in Greenwald and Stiglitz (1991). The MTP equation assumes that banks lend to commodity specific firms at a certain rate and that these firms export commodities at an internationally determined price (demand and supply). After lending to firms, banks expect firms to repay these loans at an agreed upon time. Should this not happen, the monetary circuit remains open, possibly resulting in banks being unwilling to extend further funds. 𝑋′ = 𝑝𝑎𝑁. (1). 𝐴𝐷′ = 𝑐𝑤𝑁 + 𝑝𝑏𝑎𝑁. (2). 𝑤. 𝑃′ = ( ) [(1 − 𝑠)(1 − 𝑏)]. (3). 𝑃′ = 𝑝𝑎𝑁 − 𝑐𝑤𝑁 − 𝑖𝑤𝑁. (4). µ = µ(𝑀, 𝑖, 𝑒). (5). 𝜎 = 𝜎(𝑀, 𝑖, 𝑒). (6). Y = 𝑌(𝑀, 𝑖, 𝑒, 𝛺). (7). 𝑎. where (1) defines international. aggregate commodity supply, 𝑋 is international. commodity output, 𝑎 is labour and 𝑁 is employment; (2) defines international aggregate commodity demand, 𝐶 is international. 23. demand for commodity goods, 𝑐 is the. In this case, a commodity refers to agricultural, energy or metal products.. 23.

(30) international consumer’s propensity to consume, 𝑤 is the unitary money wage, 𝑏 is firms’ real investments and 𝑝 is the international market price; (3) defines the international equilibrium price level, 𝑠 is the international propensity to save; (4) defines the net profits of the firm, with 𝐼 equal to investment, 𝐹 is the firms’ initial finance and 𝑖 is the interest rate; (5) and (6) define the mean and standard deviation of the banks’ loan portfolio, respectively; (7) defines the banks’ gross return from its portfolio where M is the number of dollars lent, 𝑒 is the expenditure on screening and monitoring and 𝛺 is the state of the business cycle, representing the undiversifiable risk of the bank’s portfolio. Keynes (1971) and Graziani (2003) both suggest that equation (4), the net profits of the commodity firm, would be zero if there were no additional external sources of liquidity. Keynes (1971)24 maintains that the profits of the firm will be equal to zero when savings (S) is equal to investment (I). Similarly, Graziani (2003) says that the net profits of the firm would be zero because the money wage bill acts as both a source of revenue and a cost for the firms, assuming that no other input costs are incurred. “In this situation, the amount of money firms spend on paying workers equals the amount of money they receive when workers spend their income, provided that workers’ “propensity to consume is unitary” (Graziani, 2003). For this study, the net profits of the firm are dependent on the internationally determined commodity prices. These commodity prices are volatile and uncertain in nature and therefore cause firms’ profits to be volatile and uncertain. The net profits of a commodity firm would therefore be zero if the revenue achieved (𝑝𝑎𝑁) is equal to the costs incurred by the commodity firm (𝑖𝑤𝑁), if the consumers’ propensity to consume is unitary (8). In this way, firms would gain zero gross money profits if 𝑐 = 1. Since the interest rate, 𝑖 charged on loans by banks is generally higher than zero, the gross money profits will always be below zero as indicated in (8) below: 𝑝𝑎𝑁 = 𝑖𝑤𝑁 𝑎𝑛𝑑 𝑖 > 0 → 𝑃 = 𝑝𝑎𝑁 − 𝑤𝑁 − 𝑖𝑤𝑁 > 0. (8). From (5), (6) and (7), it is assumed that µ𝑀= 𝐸𝑌, (𝜎𝑀)2 = E(𝑌 − Ȳ)2 and 𝑌Ω > 0, banks will also be impacted by the macroeconomic circumstances, specifically the circumstances faced by the firms to whom they lend. Banks will encounter the loan portfolio indicated below (9), where 𝜌 represents interest cost and 𝜙 bankruptcy cost. Equation (9) summarises the proposition that an increase in macroeconomic risk results in the rise of 24. S= I. 24.

(31) default. H igher default risk increases the probability of the bank going into bankruptcy and thus leads to a decrease in bank lending activity. Banks will, therefore, continue lending only to the point where the expected return equals the costs of the funds, including the increased expected bankruptcy costs. 𝐸𝑌𝑁 − 𝜌 = 𝜙. (9). Equations (8) and (9) indicate how international commodity prices could impact the profitability or even break-even ability of commodity-exporting firms. The volatile nature of international commodity prices is bound to place firms under financial pressure. Because banks extend loans to these firms, any financial pressure or vulnerability experienced by firms will eventually impact banks by increasing credit risk and weighing on their willingness to lend to firms. This implies that commodity price shocks can impact exporters’ banking sector stability and credit availability.. 4.2.. FUN CTIO N AL FO RM. Several equations are estimated to analyse the relationship between commodity price shocks and banking sector stability. This study adopted a model similar to that employed in Kinda et al. (2016). Panel data is characterised by observations of multiple phenomena which are obtained over multiple periods of time. The characteristics of panel data are synonymous to the data sample used in this study, making panel analysis the most appropriate technique (Kinda, Mlachila and O uedraogo, 2016). The main specification examines the impact that commodity price shocks have on banking sector stability. The remaining specifications examine the impact of positive and negative shocks, the impact of the various commodity groups and finally, how commodity price shocks impact domestic bank lending. The basic form is as follows: 𝑁𝑃𝐿𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + ∑𝛾𝐾𝑚 𝑍𝑖,𝑡𝑚 + 𝜀𝑖𝑡. (10). Where 𝑁𝑃𝐿𝑖𝑡 represents the banking sector stability variable (N PL). 𝐶𝑃𝑆𝑖𝑡 represents the commodity price shock variable. ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 and ∑𝛾𝐾𝑚 𝑍𝑖,𝑡𝑚 represent the vectors of the bank-specific and macroeconomic control variables respectively and finally, 𝜀𝑖𝑡 represents the error term including country-specific fixed effects and an idiosyncratic term. Equation (10) is re-estimated using a positive and a negative commodity price shock. 25.

(32) 𝑁𝑃𝐿𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑝𝑜𝑠𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + ∑𝛾𝐾𝑚 𝑍𝑖,𝑡𝑚 + 𝜀𝑖𝑡 (11) 𝑁𝑃𝐿𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑛𝑒𝑔𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + ∑𝛾𝐾𝑚 𝑍𝑖,𝑡𝑚 + 𝜀𝑖𝑡. (12). Where all other variables remain as in (10), 𝐶𝑃𝑆𝑝𝑜𝑠𝑖𝑡 and 𝐶𝑃𝑆𝑛𝑒𝑔𝑖𝑡 represent positive and negative commodity price shocks, respectively. In addition, model (10) is estimated for the four various commodity groups. The disaggregated model is also estimated using all three25 commodity price shocks. Equation (10) re-estimated using the agriculture commodity price shock 𝑁𝑃𝐿𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + ∑𝛾𝐾𝑚 𝑍𝑖,𝑡𝑚 + 𝜀𝑖𝑡. (13). 𝑁𝑃𝐿𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑝𝑜𝑠𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + ∑𝛾𝐾𝑚 𝑍𝑖,𝑡𝑚 + 𝜀𝑖𝑡. (14). 𝑁𝑃𝐿𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑛𝑒𝑔𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + ∑𝛾𝐾𝑚 𝑍𝑖,𝑡𝑚 + 𝜀𝑖𝑡. (15). Equation (1) re-estimated using the minerals and fuels commodity price shock 𝑁𝑃𝐿𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + ∑𝛾𝐾𝑚 𝑍𝑖,𝑡𝑚 + 𝜀𝑖𝑡. (16). 𝑁𝑃𝐿𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑝𝑜𝑠𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + ∑𝛾𝐾𝑚 𝑍𝑖,𝑡𝑚 + 𝜀𝑖𝑡. (17). 𝑁𝑃𝐿𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑛𝑒𝑔𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + ∑𝛾𝐾𝑚 𝑍𝑖,𝑡𝑚 + 𝜀𝑖𝑡. (18). Equation (10) re-estimated using the metals commodity price shock 𝑁𝑃𝐿𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + ∑𝛾𝐾𝑚 𝑍𝑖,𝑡𝑚 + 𝜀𝑖𝑡. (19). 𝑁𝑃𝐿𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑝𝑜𝑠𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + ∑𝛾𝐾𝑚 𝑍𝑖,𝑡𝑚 + 𝜀𝑖𝑡. (20). 𝑁𝑃𝐿𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑛𝑒𝑔𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + ∑𝛾𝐾𝑚 𝑍𝑖,𝑡𝑚 + 𝜀𝑖𝑡. (21). Equation (10) re-estimated using the chemicals commodity price shock 𝑁𝑃𝐿𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + ∑𝛾𝐾𝑚 𝑍𝑖,𝑡𝑚 + 𝜀𝑖𝑡. (22). 𝑁𝑃𝐿𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑝𝑜𝑠𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + ∑𝛾𝐾𝑚 𝑍𝑖,𝑡𝑚 + 𝜀𝑖𝑡. (23). 𝑁𝑃𝐿𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑛𝑒𝑔𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + ∑𝛾𝐾𝑚 𝑍𝑖,𝑡𝑚 + 𝜀𝑖𝑡. (24). 25. O verall, positive and negative shocks.. 26.

(33) All. variables. included. in. equation. 11-24. remain. as. in. (10).. 𝐶𝑃𝑆𝑝𝑜𝑠𝑖𝑡 and. 𝐶𝑃𝑆𝑛𝑒𝑔𝑖𝑡 represent the positive and negative commodity price shocks, respectively. The final model examines how commodity price shocks impact bank lending. The empirical specification for this model takes the following form: 𝐷𝑜𝑚𝑐𝑟𝑒𝑑𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + 𝜀𝑖𝑡. (25). 𝐷𝑜𝑚𝑐𝑟𝑒𝑑𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑝𝑜𝑠𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + 𝜀𝑖𝑡. (26). 𝐷𝑜𝑚𝑐𝑟𝑒𝑑𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑛𝑒𝑔𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + 𝜀𝑖𝑡. (27). Where 𝐷𝑜𝑚𝑐𝑟𝑒𝑑𝑖𝑡. represents. domestic. credit. extension,. 𝐶𝑃𝑆𝑖𝑡 ,. 𝐶𝑃𝑆𝑝𝑜𝑠𝑖𝑡 and. 𝐶𝑃𝑆𝑛𝑒𝑔𝑖𝑡 represent the overall positive and negative commodity price shock variables. ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 represents the vectors of the macroeconomic control variables and finally, 𝜀𝑖𝑡 represents the error term including country-specific fixed effects and an idiosyncratic term. Similarly, model (25) is estimated for the four various commodity groups. The disaggregated model is also estimated using all three26 commodity price shocks. Equation (25) re-estimated using the agriculture commodity price shock 𝐷𝑜𝑚𝑐𝑟𝑒𝑑𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + 𝜀𝑖𝑡. (28). 𝐷𝑜𝑚𝑐𝑟𝑒𝑑𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑝𝑜𝑠𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + 𝜀𝑖𝑡. (29). 𝐷𝑜𝑚𝑐𝑟𝑒𝑑𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑛𝑒𝑔𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + 𝜀𝑖𝑡. (30). Equation (25) re-estimated using the minerals and fuels commodity price shock 𝐷𝑜𝑚𝑐𝑟𝑒𝑑𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + 𝜀𝑖𝑡. (31). 𝐷𝑜𝑚𝑐𝑟𝑒𝑑𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑝𝑜𝑠𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + 𝜀𝑖𝑡. (32). 𝐷𝑜𝑚𝑐𝑟𝑒𝑑𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑛𝑒𝑔𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + 𝜀𝑖𝑡. (33). Equation (25) re-estimated using the metals commodity price shock 𝐷𝑜𝑚𝑐𝑟𝑒𝑑𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + 𝜀𝑖𝑡. (34). 𝐷𝑜𝑚𝑐𝑟𝑒𝑑𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑝𝑜𝑠𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + 𝜀𝑖𝑡. (35). 𝐷𝑜𝑚𝑐𝑟𝑒𝑑𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑛𝑒𝑔𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + 𝜀𝑖𝑡. (36). 26. O verall, positive and negative shocks.. 27.

(34) Equation (25) re-estimated using the chemicals commodity price shock 𝐷𝑜𝑚𝑐𝑟𝑒𝑑𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + 𝜀𝑖𝑡. (37). 𝐷𝑜𝑚𝑐𝑟𝑒𝑑𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑝𝑜𝑠𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + 𝜀𝑖𝑡. (38). 𝐷𝑜𝑚𝑐𝑟𝑒𝑑𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑃𝑆𝑛𝑒𝑔𝑖𝑡 + ∑𝛾𝐾 𝑋𝑖,𝑡𝐾 + 𝜀𝑖𝑡. (39). All. variables. included. in. equation. 28-39. remain. as. in. (25).. 𝐶𝑃𝑆𝑝𝑜𝑠𝑖𝑡 and. 𝐶𝑃𝑆𝑛𝑒𝑔𝑖𝑡 represent the positive and negative commodity price shocks, respectively.. 4.3.. DATA AN D VARIABLES. An unbalanced panel dataset of 18 commodity-exporting African countries (Appendix 1) was constructed. The dataset comprises bank-specific financial stability indicators (FSIs) (IMF, 2006), macroeconomic, and commodities data for all the countries in question. The data period of 2000 to 2015 captures the commodity price bust (and the 2007/ 2008 GFC) that occurred in 2007/ 2008 and the recent 2014/ 2015 one. The bank-specific FSI data was sourced from the Federal Reserve Bank of St. Louis’ Economic Data (FRED) and from the World Bank’s (WB) Global Financial Development databases. The macroeconomic (control variables) data was compiled using data from the World Bank’s Global Financial Development database and the IMF. The United N ation’s (UN ) CO MTRADE database served as the source for the disaggregated commodities data. Following the extraction of the data from the various sources, the initial dataset was divided into three separate datasets: the banking dataset, the macroeconomic dataset, and the commodities dataset. The aggregated bank-specific FSIs dataset includes 4 of the 12 core FSIs: N PLs (credit risk proxy), Provisions to N PLs (Capital adequacy), return on assets (Profitability), Liquid assets to deposits ratios (Liquidity). The ideal bank-specific FSI dataset would have been disaggregated and sourced from the balance sheets of the individual banks in each country. This approach would have allowed the study to estimate the impact of commodity price shocks on the credit risk of individual banks, whilst also allowing the study to infer on the vulnerability of specific banks to commodity price shocks, and therefore understand the magnitude of a vulnerable bank’s impact on the banking system 27. The macroeconomic variables were compiled and included in the study for control purposes. The original commodities data was disaggregated by commodity, commodity 27. Bigger banks usually pose the greatest risk to the banking sector. Interconnectedness between banks in a particular country, however, also increases the risk of contagion should one bank fail.. 28.

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