*Article* **The Impact of Commodity Price Shocks on Banking System Stability in Developing Countries**

**Nicholas Ngepah, Margarida Liandra Andrade da Silva and Charles Shaaba Saba \***

Auckland Park Kingsway Campus, University of Johannesburg, Johannesburg 2006, South Africa; nngepah@uj.ac.za (N.N.); dasilvamargarida7@gmail.com (M.L.A.d.S.)

**\*** Correspondence: sabacharlesshaaba@yahoo.com

**Abstract:** This study examines the impact of commodity price shocks on the banking sector stability of 18 African commodity-exporting economies using an unbalanced panel dataset spanning a 16-year period from 2000–2015. The study on the impact of commodity price shocks on African commodityexporting economies' banking sectors 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. The results for the disaggregated shocks reveal that both positive and negative shocks weaken banking sector stability. In addition, commodity price shocks are discovered to decrease credit extension to the private sector, highlighting an additional channel through which the impact of commodity price shocks may be perpetuated to the real economy.

**Keywords:** commodity price shocks; banking sector stability; panel data; Africa

**Citation:** Ngepah, Nicholas, Margarida Liandra Andrade da Silva, and Charles Shaaba Saba. 2022. The Impact of Commodity Price Shocks on Banking System Stability in Developing Countries. *Economies* 10: 91. https://doi.org/10.3390/ economies10040091

Academic Editor: Ralf Fendel

Received: 2 March 2022 Accepted: 1 April 2022 Published: 12 April 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

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 the literature, with a plethora of research focusing on how it impacts economic growth, debt, conflict, and financial development (Hamilton 1983, 2009; Deaton and Miller 1995; Lescaroux and Mignon 2008; Kilian et al. 2009; Rafiq et al. 2016; Montfort and Ouedraogo 2017; Bangara and Dunne 2018). Limited research has examined the possible impact of commodity price shocks on financial sector stability, specifically on banking sector stability (Alodayni 2016; Kinda et al. 2016; Agarwal et al. 2017; Eberhardt and Presbitero 2018). Commodity price shocks affect the corporate, household, government, and 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 perpetuated to the real economy.

African economies are mainly dominated by large domestic and foreign banks (Chironga et al. 2018), and as such, banking stability (or instability) can play a significant role in lessening (or intensifying) the impact of commodity price shocks on the macroeconomy (Poghosyan and Hesse 2009; Miyajima 2016; Kooros and Semetesy 2016; Alodayni 2016; Kinda et al. 2016). For example, the 1980s and 1990s comprised extensive banking crises, with most of the instability 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 have further supported African banking sectors (Caggiano et al. 2013; Bangara and Dunne 2018). Despite this resilience, macroeconomic 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. In

2014–2015, several economies began experiencing financial distress, indicated by declining bank profitability and deteriorating asset quality (UNDP 2016; IMF 2017). Even though the country-specific problems faced by these countries may have contributed to the financial distress, the sharp and persistent decline of commodity prices has certainly perpetuated the issue for commodity-exporting economies (see Figure 1).

**Figure 1.** Commodity price indices (2005 = 100). **Source**: Author's own presentation using IMF data.

Given these developments and considerations, this study examines the vulnerability of the banking sectors of 18 African countries to commodity price shocks. The analysis covers the period spanning 2000 to 2015. The dependence of African economies on commodity exports has long been debated and analyzed. Even though most African countries benefit 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 has focused on how the banking sectors of African economies are 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. This study contributes to the literature in three key ways. First, the study emphasizes the role of commodity price shocks in triggering banking sector instability. In a related paper, Kinda et al. (2016) showed that commodity price shocks are associated with financial sector fragility in developing countries. Kinda et al. (2016) limited the focus of their study, focusing only on minerals, fuels, and metals. This study extends the research by Kinda et al. (2016) by focusing on most commodity groups. Second, while previous studies have focused on advanced, emerging, developing (not just African), and low-income countries, this study examines the experience of only African commodity-exporting 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, while Kinda et al. (2016) outlined how the financial sector responds to both negative and positive shocks, this study examines this relationship using overall positive and negative shocks. Further, the study contributes to the extant literature by emphasizing

the differences in commodity price asymmetries between various commodity groups. To the best of the author's knowledge, the only work that emphasized this relationship was Addison et al. (2016).

Employing a panel fixed effects (FE) model, the results of the study indicate that commodity price shocks weaken banking sector stability through increasing bank credit risk (NPLs). More specifically, a one-unit increase1 in the commodity price shock increases bank credit risk by 0.381%, which is in line with previous studies (Kinda et al. 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 positive shocks, surprisingly, have the greatest impact on banking sector stability. The lack of asymmetry is in line with Addison et al. (2016), who found, using a similar commodity price shock measure, that positive and negative agricultural commodity price shocks in sub-Saharan African countries did not necessarily respond differently. Finally, the estimation of the impact of commodity price shocks on bank lending<sup>2</sup> shows that commodity price shocks do indeed decrease bank lending, which is in line with Agarwal and colleagues (2017). As a matter of fact, negative mineral, fuel, metal, and chemical price shocks have substantive negative implications on bank lending in African countries. Given these findings, this study deduces that commodity price shocks do not only have an impact on banking sector stability (which can be perpetuated to the real economy) but also have a direct impact on bank lending as a means of economic growth and development (Greenwald and Stiglitz 1991, 2003).

The remainder of the study is divided as follows. Section 2 briefly discusses the channels through which commodity price shocks can impact the economy and the banking sector. Section 3 reviews the theoretical and empirical literature. Section 4 defines the data, model specification, and estimation techniques. Section 5 presents the results, and Section 6 provides the main conclusions.

#### **2. Commodity Price Shocks—Transmission Mechanism**

Economic relationships are hardly ever clear and direct. This is no different when trying to understand and examine the relationship between commodity price shocks and banking sector stability. In order to unpack this, the various transmission channels through which commodity price shocks may impact the economy (with a specific focus on how the banking sector is impacted) are briefly discussed. *To be specific, the macroeconomic, fiscal, exchange rate, and banking channels are discussed. Further, the scenario discussed below is based on the assumption of a decline in commodity prices. One would expect the opposite deductions in the case of an increase in commodity prices.*

Macroeconomic channel: Following a fall in commodity prices, economies usually experience a decline in exports, investment, and output. Declining exports, investment, and output weigh on the corporate and household sectors. Exports decline and, thus, economies fail to generate as much export revenue as is generated during periods of higher commodity prices. Investment in commodity extraction and supporting industries weakens, 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; Hammond 2011; Christensen 2016). African commodity exporters experience economic growth averaging 5% each year. A reversal of this growth was witnessed following the commodity price crash that began in late 2014 (Ighobor 2016). For example, Nigeria'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 firm production and, thus, revenue, unemployment may rise, leaving households at risk in an already vulnerable economic environment (Blanchard and Gal 2008). Vulnerable firms and individuals means a greater

risk of defaulting on payments, impacting bank balance sheets and, through contagion3, the greater banking system (Makri et al. 2014).

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; UNDP 2015; Christensen 2016; Ighobor 2016). This reliance means that negative commodity price shocks can certainly decrease fiscal performance (Spatafora and Samake 2012; Kinda et al. 2016). A decline in export revenue causes a decline in government revenue (and, thus, a decline in government expenditure) of commoditydependent economies. Kinda et al. (2016) reiterated this by saying that commodity price shocks reduce tax revenue, worsen terms of trade, increase fiscal deficits, and decrease the competitiveness4 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.

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 increases 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 increases the vulnerability of the banking sector and, thus, impacts its stability.

Banking 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 witnessed during the 2007–2008 global financial crisis (GFC), banks freely extend credit during periods of economic and financial boom. Similarly, during periods of commodity boom, domestic credit extension grows, with banks extending credit even to the less creditworthy. Credit extension is important for growth and development, but rapid and extensive credit growth can seriously impact the stability of the financial system. Second, previous research indicated that commodity exporters held 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 negative shocks that could lead to sizeable withdrawals" (Kinda et al. 2016; Christensen 2016). Challenges in one bank can spread to other banks; this can result in bank runs5 with the potential to completely destabilize 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.

#### **3. Literature Review**

A theoretical model underpinning the analysis on the determinants of credit risk is the financial accelerator theory. This theory posits that endogenous developments in the credit markets propagate shocks to the real macroeconomic environment (Bernanke et al. 1999). The theory posits that credit shock is amplified through information asymmetries between lenders and borrowers and through a balance sheet effect. Credit risk is one of the largest risks faced by banks. As such, several studies have focused on the implications of credit risk on the banking system (Mpofu and Nikolaidou 2018).

During periods of commodity price boom, banks generate a lot of liquidity, which makes them more lax in their lending (Ftiti et al. 2016). Thus, banks may increase lending during commodity price booms, but the opposite may hold during commodity price busts, resulting in both a reduction in credit extension and a deterioration in loan quality. This notion is supported by Ftiti et al. (2016), who analyzed the relationship between the commodity price cycle and credit cycle in three commodity-exporting African economies. Their findings indicated that the credit market is sensitive to persistent commodity price shocks. Kablan et al. (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 et al. (2017) also emphasized that a commodity boom reversal affects both the macroeconomic and financial sectors, decreasing commodity exporters' capacities to service their debts. Knock-on effects increase NPLs and weigh on banking sector stability, which, in African economies, eventually impacts the entire financial system. The findings of Kablan et al. (2017) are crucial given the volatility and uncertainty related to commodity prices. The views of both Ftiti et al. (2016) and Kablan et al. (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 NPLs.

Most of the literature related to this study has focused specifically on oil prices. For example, Miyajima (2016), with evidence from Saudi Arabia and using generalized method of moments (GMM) and panel vector autoregression (PVAR) methods, indicated that low oil prices and non-oil GDP led to a rise in NPLs. In turn, this transmitted to the balance sheets of banks through weak macroeconomic variables. This is in line with Alodayni (2016), who focused on the oil–macrofinancial linkages in the Gulf Cooperation Council countries (GCC) region. The study, also employing a panel GMM and PVAR model, on 24 GCC banks during the period 2000 to 2014 established that oil prices, along with other macroeconomic variables, have an impact on NPLs and that higher NPLs have adverse effects on GCC economies. Al-Khazali and Mirzaei (2017) also established related results when they analyzed the impact of oil price movements on the NPLs of 30 oil-exporting countries over the period 2000 to 2014 using panel GMM. Their results revealed three things: first, that a rise (or fall) in oil prices leads to a decrease (or increase) in the NPLs of oil-exporting economies; second, that oil price shocks have asymmetric effects on bad loans (NPLs), and finally, that the negative impact of adverse oil price shocks has 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 from 2000 to 2014. The study employed a system GMM technique and a PVAR model to assess the macroeconomic and bank-specific determinants of NPLs and the feedback loops between macroeconomic and bank balance sheet variables, respectively. In the first place, the study established that bank asset quality (NPLs) is impacted by oil prices and macroeconomic variables; second, the study also established feedback loops between oil price movements and bank balance sheets, emphasizing 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 et al. (2016) and Eberhardt and Presbitero (2018). Kinda et al. (2016) examined how commodity price shocks impact financial sector fragility by focusing on 71 commodity-exporting emerging and developing economies for the period of 1997 to 2013. The study employed a panel fixed effects model to estimate the effect of commodity price busts on financial soundness indicators6. The results revealed that commodity price shocks weaken the financial sector and that larger shocks have a greater impact on financial sector stability. The study then went on to analyze 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 of 1981 to 2015. The authors employed a random effects Mundlak logit model in their estimation. Their results are in line with the findings from Kinda et al. (2016), showing that commodity price movements are an economically substantial and robust driver of banking crises in LICs. These findings are in line with Kaminsky and Reinhart (1999), who provided evidence for how instability in the banking sector can trigger a financial crisis. The study found, using a sample of emerging market economies, that risk in the banking sector leads to a currency crisis. The authors indicated that, when and if a currency crisis deepens, it spreads to the entire economy. In the empirical literature, the studies of Rudolf et al. (2021), Doumenis et al. (2021), and Sami and Abdallah (2022), among others, have highlighted the importance of digital commodities (such as Bitcoin and cryptocurrency), but given that this study is not focused on the impact of digital commodities on banking systems in Africa, we paid less attention to the review of previous studies focusing on digital commodities and the effect they have on banking systems of African countries. While most of the empirical literature on the linkages between commodity price shocks and credit risk has focused 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), focused only on fuel, mineral, and metal commodities. This study is also motivated by Kinda et al. (2016) focusing 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 their banking sectors could encounter because of commodity price shocks. This study further expands on the previous literature by examining how the various commodity groups impact the banking sector and how they impact bank credit extension.

#### **4. Data and Methodology**

#### *4.1. Methodology*

Several equations were estimated to analyze the relationship between commodity price shocks and banking sector stability. This study adopted a model similar to that employed by Kinda et al. (2016). Panel data was characterized by observations of multiple phenomena which were obtained over multiple periods of time. The characteristics of the panel data were synonymous to the data sample used in this study, making panel analysis the most appropriate technique (Kinda et al. 2016). More specifically, the panel fixed effects7 econometric model was employed because each country included in the sample had its own unique set of economic, political, and institutional characteristics that could be correlated with the explanatory variables. The panel fixed effects technique controlled these country-specific effects and prevented biased estimates.

Related studies, such as Alodayni (2016), Kooros and Semetesy (2016), and Al-Khazali and Mirzaei (2017), have opted to employ a system generalized method of moments (SGMM) technique. It is a system estimator that combines the regressions in differences and levels, resulting in consistent estimates of the parameters of interest. The consistency of this model, however, depends on the validity of the moment conditions (Arellano and Bover 1995). The Sargan<sup>8</sup> test of over-identified instruments was employed to test the overall validity of the instruments and, thus, the consistency of the model. The null hypothesis was rejected, rendering the SGMM an inappropriate method for this study. As a result, the FE model was employed. The equations that were estimated are shown below.

The baseline model estimated the effect of the overall commodity price shocks on banking sector stability. The empirical specification takes the following general form:

$$NPL\_{it} = \beta\_0 + \beta\_1 CSP\_{it} + \sum \gamma \chi X\_{i,tK} + \sum \gamma \chi\_{m} Z\_{i,tm} + \varepsilon\_{it} \tag{1}$$

where *NPLit* represents the banking sector stability variable (non-performing loans). *CPSit* represents the commodity price shock variable. ∑ *γKXi*,*tK* and ∑ *γKmZi*,*tm* represent the vectors of the banking specific and macroeconomic control variables, respectively, and, finally, *εit* represents the error term, including country-specific fixed effects and an idiosyncratic term.

Equation (1) was re-estimated using a positive and a negative commodity price shock. These shocks were derived from the overall commodity price shock equation:

$$NPL\_{it} = \beta\_0 + \beta\_1 CSPps\_{it} + \sum \gamma\_K X\_{i,tK} + \sum \gamma\_{Km} Z\_{i,tm} + \varepsilon\_{it} \tag{2}$$

$$NPL\_{it} = \beta\_0 + \beta\_1 \text{CPSnerg}\_{it} + \sum \gamma\_K X\_{i,tK} + \sum \gamma\_{Km} Z\_{i,tm} + \varepsilon\_{it} \tag{3}$$

where all other variables remain as in (1), while *CPSposit* and *CPSnegit* represent positive and negative commodity price shocks, respectively.

The equations for the disaggregated commodity groups (agriculture, minerals, fuels, metals, and chemicals) were estimated using the same equations.

#### *4.2. Data*

An unbalanced panel dataset of 18 commodity-exporting African countries and the list of commodities can be found in Tables A1 and A2, respectively. The dataset comprised bankspecific financial stability indicator (FSI) (IMF 2006), macroeconomic, and commodity data for all the countries in question. The data period of 2000 to 2015 captured 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 were sourced from the Federal Reserve Economic Data (FRED) of St. Louis and from the World Bank (WB) Global Financial Development databases. The macroeconomic (control variables) data were compiled using data from the World Bank Global Financial Development database and the IMF. The United Nation (UN) Comtrade database served as the source for the disaggregated commodities data.

The variables were defined as follows: the main dependent variable was non-performing loans (NPLs). It was a ratio of NPLs to total loans and was employed as a measure of credit risk in the study. Domestic credit extension was also employed as a dependent variable when estimating the impact of commodity price shocks on bank lending.

A number of independent variables were included in the study. The bank specific variables of profitability, capital adequacy, and liquidity were some of the variables used as financial stability indicators (IMF 2006). This is in line with studies, such as Kinda et al. (2016) and Eberhardt and Presbitero (2018), that used these variables, alongside others, as determinants of banking sector fragility and banking crises, respectively. In essence, the banking sector variables acted as proxies for a country's financial sector position.

The study also considered variables that could act as proxies for macroeconomic policy sustainability and stabilization issues, as well proxies for monetary and fiscal policy. The macroeconomic variables included economic growth, inflation, and unemployment. The monetary policy proxy variables included change in the exchange rate, real interest rate, M2 over external reserves, and domestic savings. Government revenue was employed as the fiscal policy proxy. These variables, as well as their expected priori, are summarized in Table A3.

There are various approaches through which commodity price shocks have been quantified in the literature. This study adopted the real commodity price change measure as a proxy for commodity price shocks (Mork 1989; Poghosyan and Hesse 2009). The commodity price shock measure in this study was computed per country, per time period (annual), and per commodity. The real commodity price measure is indicated below:<sup>9</sup>

$$cps\_t = \frac{\sum\_{i=1}^{365} \min[0, \log(p\_t) - \min[\log(p\_{t-1})]] \* 100}{365} \tag{4}$$

where *cpsit* is the commodity price shock for country *i* at time t; *pit* is the commodity export revenue in the current period; and *pit*−<sup>1</sup> is the commodity export revenue in the preceding period. *Cpsit*, therefore, simply measures the annual commodity price shock for every country and each commodity included in the study for the period of 2000–2015.

Prior to computing a commodity price shock variable, unit root tests were conducted in order to ensure that all variables were stationary. This was performed in light of the concern raised by Kinda et al. (2016) that the commodity price measure above does not account for the potential trend related to price changes, making the commodity price measure nonstationary. A Phillips–Perron unit root test (Phillips 1987; Phillips and Perron 1988) with a time trend and lag of 5 was computed for all the relevant variables. The results reported in Table A4 show that all variables, including the commodity price shock (CPS) variable, contained no unit roots at level and were, therefore, stationary10. Two additional CPS variables were computed by splitting the original shock into positive and negative commodity shocks during estimation. This allowed the study to test for symmetry between positive and negative CPSs11.

#### **5. Empirical Results and Discussion**

#### *5.1. Descriptive and Correlation Analysis*

Tables A5 and A6 provide the summary statistics and correlation analysis of the variables employed in the study, respectively. The mean for the dependent variable, NPL (credit risk), was 9.187; this was much higher than those obtained in other developing countries. According to Dietrich and Wanzenried (2014), the mean NPLs for low, middle, and high income economies were 1.990, 1.970, and 0.730, respectively. This high NPL level emphasizes the risk and vulnerability faced by banks in commodity-exporting African economies. There are advanced economies with elevated levels of NPLs, although the average is still below the 9.187 established in this study. In Europe, for example, the average rate of NPLs in 2016 was 5.1% (Magnus et al. 2017), which is also much higher than the 0.73 average established by Dietrich and Wanzenried (2014).

The mean for the capital adequacy ratio (NPL provisioning) was substantially high at 61.558%, indicating that African banks are safe and highly likely to meet their financial obligations12. The mean value for profitability (return on assets) is 1.958; it is almost in line but slightly lower than what was established for Middle East and North Africa (MENA) and sub-Saharan African banks, where values have been reported at 2.250 and 2.35, respectively (Flamini et al. 2009; Poghosyan and Hesse 2009). The mean liquidity ratio was substantially high at 29.838 in comparison to the average growth in deposits for low, middle, and high income countries of 21.630, 14.290, and 7.621, respectively (Dietrich and Wanzenried 2014). This high liquidity ratio implied that African banks are in a position to sufficiently cover current debt obligations without needing to raise funds in the capital markets.

The correlation matrix (Table A6) indicates that the CPS variable was positively correlated to NPL (0.128). A positive relationship was also observed between the CPS variable and the other banking sector variables in the study (capital adequacy (0.009), profitability (0.081), and liquidity (0.093)), emphasising the possible impact of commodity price dynamics on the banking sector, as established by Kinda et al. (2016). Another important negative correlation was that of the CPS and domestic credit extension (−0.115); this correlation is in line with findings from Agarwal et al. (2017).

Unemployment had the strongest negative relationship with NPL; this was expected, since loss of revenue weighs on the ability to service debt and vice versa. NPL was also highly negatively related to government revenue (−0.246) and domestic credit (−0.196). The correlation between NPL and real economic growth was also negative and significant (−0.014), but not as strong.

#### *5.2. Results*

The baseline model presented in Table 1 shows that the CPS coefficient increased credit risk (non-performing loans). This finding was the same across all three models, but the pooled OLS and SGMM models had weaknesses. The pooled OLS model was biased because it failed to account for the unique differences between countries, which could impact the dependent variable. On the other hand, the SGMM model was inconsistent because it failed the Sargan test. This indicated that the pooled OLS and the SGMM models were not appropriate models for this study; going forward, only the FE results are presented and discussed.


**Table 1.** Baseline results: the impact of commodity price shocks on NPLs.

Standard errors in parentheses. \*\*\* *p* < 0.01, \*\* *p* < 0.05, \* *p* < 0.1.

Discussing the results in more detail shows that a one-unit change in the CPS yielded a 0.381% increase in credit risk (Column 4, Table 1). The CPS coefficient of 0.381% was positive and strongly significant at a 1% level of significance. These findings are in line with similar studies (Kinda et al. 2016). The study, therefore, concluded that CPS increases bank credit risk and, thus, poses a risk to the stability of African commodity-exporting economies' banking sectors. This finding adds to the current limited literature on the relationship between CPSs and banking sector stability. Most importantly, it emphasizes that CPSs can yield both macroeconomic and banking sector instability risks.

Briefly focussing on the other banking variables, the coefficient for profitability behaved as expected and was strongly significant at a 1% level of significance. The capital adequacy and the liquidity coefficients did not yield the expected signs, but they were also strongly significant at a 1% level of significance. Basel 3 requirements maintain that rising capital adequacy requirements should act as a safety net for the banking sector and uphold financial stability (Bank for International Settlements 2010), hence the prior statement that it would decrease credit risk. Oduor et al. (2017) established that higher capital adequacy ratios do not necessarily make African banks safer. Similarly, higher liquidity may also not necessarily mean safer banking systems in the case of African countries.

With the macroeconomic variables, real economic growth behaved as expected and was strongly significant at a 1% level of significance. The unemployment and inflation coefficients behaved as expected but were insignificant at all levels. All other macroeconomic variables yielded the expected signs and were significant.

While the CPS variable (Column 4, Table 1 in the previous page) provided valuable information about the impact of unexpected CPSs on banking sector stability, it did not provide any information on whether the impact of a positive CPS on banking sector stability differed from that of a negative CPS. The baseline model was augmented by positive and negative CPS variables in the next estimation.

The estimation results reported in Columns 5 and 6 in Table 1 suggested that positive CPSs in African commodity-exporting economies have a bigger impact on credit risk than negative CPSs. The results indicate that a unit increase in the CPS variable increased credit risk by 0.926% with a 1% level of significance. Even though the negative CPS also increased credit risk, the effect of the coefficient was not significant. These results are not in line with similar studies on the impact of CPSs on banking sector stability (Kinda et al. 2016). These interesting findings are partly explained by the results of Addison et al. (2016), who found, using a similar CPS measure, that positive and negative agricultural CPSs in sub-Saharan African countries did not necessarily respond differently from responses in economic growth13. These findings emphasize the importance of disaggregating shocks and isolating the African region because African banking sectors do not seem to respond to positive and negative CPSs in the same manner as those of other developing countries.

#### *5.3. Sensitivity Analysis—Commodity Sub-Categories*

This section examines how the banking sector was impacted by the different commodity group shocks, comparing its findings to those reported in the baseline models. The results for the disaggregated commodities are reported in Tables A7–A10.

The estimation results reported in Table A7 suggested that agricultural price shocks increased credit risk and, thus, pose a risk to banking sector stability. The agricultural price shock indicated that a one-unit increase in the agricultural price shock resulted in a 0.394% increase in credit risk; the coefficient was significant at a 1% level of significance. When disaggregated, the results yielded positive coefficients for both the positive and negative agricultural price shocks, but the effect of the coefficients were not significant. The result of no asymmetry between a positive and negative agricultural price shock in African economies was again reiterated in line with the baseline model and Addison et al. (2016).

Table A8 indicates that the mineral and fuel price shock had a positive but insignificant (0.404%) effect on bank credit risk. Further, the disaggregated result (Columns 2 and 3 of Table A8) yielded negative (−0.122%) and positive (1.029%) coefficients for the positive and negative price shocks, as expected, but with no significant<sup>14</sup> impact on credit risk. Even though insignificant, these results behaved as expected and were in line with previous studies that had focused specifically on mineral- and fuel- exporting countries (Poghosyan and Hesse 2009; Alodayni 2016; Miyajima 2016; Al-Khazali and Mirzaei 2017). Al-Khazali and Mirzaei (2017) also finds evidence of asymmetric mineral and fuel price shocks. These findings imply that mineral and fuel commodities are one of the main (or only) commodities where the findings for Africa are exactly in line with those of other developing countries.

The estimation results in Table A9 suggested that metal price shocks significantly increased bank credit risk, with a unit increase in the metal price shock resulting in a 0.324% increase in credit risk at a 10% level of significance. Results for the disaggregated shocks indicated that, with a one-unit increase for the positive, metal price shock increased credit risk by 1.109% at a 5% level of significance. While a negative metal price shock yielded the expected positive sign, the effect was insignificant at all levels. The results behaved as the agricultural results, showing no asymmetry between positive and negative metal price shocks. This implies that metal price fluctuations, in general, could pose a threat to banking sector stability in African countries.

Table A10 presents the results for the chemicals commodity group. The chemicals price shock had a positive but insignificant impact on credit risk (0.375%). The positive chemical price shock was also positive but highly significant with a coefficient of 2.332%. None of the African countries included in the study export chemicals15, so it was not surprising that a positive chemical price shock resulted in a large increase in bank credit risk. Conversely, a negative chemical price shock yielded a negative coefficient (−0.449%) with no significant effect on credit risk. These results imply that, even though African economies are not exporters of chemicals, their banking systems are still vulnerable to rising chemical price shocks, probably as a result of the exposure of the firms to whom they lend.

#### *5.4. Do Commodity Price Shocks Impact Domestic Lending?*

The empirical estimations so far have shown that CPSs increase bank credit risk and, as such, pose a threat to the stability of the banking sector. While instability in the banking sector has been shown to trickle down into the real economy (Agarwal et al. 2017), this section examines whether CPSs have a direct impact on domestic credit extension in commodity-exporting economies.

The results in Table 2 (below) show that the CPS yielded a negative coefficient of −0.053% (as expected) but that it had no significant effect on bank credit extension. When disaggregated, the results revealed that a positive shock increased domestic credit extension (as one would expect) by 0.667% and was significant at a 1% level of significance. Further, the negative shock indicated that commodity price busts substantially decreased domestic credit extension (−0.910%); this result was also established to be significant at a 1% level of significance. These results are in line with Greenwald et al. (1984) and Stiglitz (2016), who said that macroeconomic conditions that have implications for bank balance sheets or that increase risk perceptions usually lead to a contraction in the supply of funds by banks. The findings are also supported by the findings of Agarwal et al. (2017).

In addition to the aggregated findings, Tables A11–A14 show that the overall agricultural, mineral, fuel, and metal price shocks had no significant effect on credit extension in commodity-exporting African economies. However, the overall chemical price shock was found to statistically and significantly decrease credit extension by 0.377% in African countries. The exposure of banks in the sector could be direct or indirect (through firms that are exposed to the sector to which banks lend). When disaggregated, positive agricultural and chemical price shocks, again, had no significant effect on credit extension. However, the mineral, fuel, and metal positive shocks were found to statistically and significantly increase credit extension. Finally, negative price shocks in the mineral, fuel, chemical, and metal commodity groups seemed to have large negative impacts on bank lending in African countries. It is important to outline that, while all coefficients for the agricultural group were insignificant, the results did indeed show the true reality of the agricultural sector in African countries. The agricultural sector has constantly struggled and continues to struggle with accessing funding from the banking sector (Varangis 2018). Therefore, it is not entirely surprising that agricultural shocks had no significant effect on bank lending. The statistically significant findings are in line with Greenwald et al. (1984) and Stiglitz (2016), who said that macroeconomic conditions that have implications for bank balance sheets or that increase risk perceptions usually lead to a contraction in the supply of funds by banks. These findings are further supported by the findings of Agarwal et al. (2017). As

previously observed, the African countries included in this sample have extremely high capital adequacy ratios. High capital adequacy ratios have a negative impact on bank lending, since they limit the amount available for lending. This, combined with the fact that CPSs decrease lending, could, therefore, worsen lending conditions and stifle economic growth and development. Overall, the results revealed that certain CPSs not only weaken banking sector stability through credit risk but could also have a direct impact on bank credit extension.


**Table 2.** The impact of commodity price shocks on credit extension.

Note: Robust standard errors in parentheses; \*\*\* *p* < 0.01, \*\* *p* < 0.05, \* *p* < 0.1.

#### **6. Conclusions**

Considering the volatility of commodity prices and African economies' dependence on commodities, this study investigated the impact of CPSs on the banking system stability of African commodity-exporting economies. The study employed a FE model on a sample of 18 African commodity-exporting economies. The findings revealed that CPSs are associated with a rise in bank credit risk (NPL) and, thus, pose a risk to the banking sector stability of African commodity-exporting countries. An important finding from this study was that positive and negative CPSs do not necessarily vary in their impact on banking sector stability. This finding is not in line with previous studies (Kinda et al. 2016). These results are supported by Addison et al. (2016), who established that positive and negative agricultural price shocks in African countries do not necessarily yield asymmetric results16.

When disaggregated, the agricultural and metal price shocks behaved as in the baseline model, with the shocks increasing credit risk and, thus, posing a threat to banking sector stability. These two commodity groups indicated no asymmetry as both positive and negative shocks yielded positive signs. The positive metal price shock was the only statistically significant coefficient. The positive and negative mineral and fuels shock yielded the desired effects, but none was statistically significant. The mineral and fuel sector was the only commodity group that seemed to behave in line with other developing countries. In contrast, a positive chemical price shock significantly increased bank credit risk, which can possibly be explained by the fact that African economies import more chemicals than they export. The negative chemical price yielded the expected sign but was statistically significant. The results for the mineral, fuel, and chemical commodity groups indicated asymmetry when the CPS was disaggregated.

Following the estimation of the impact of CPSs on bank lending, the findings indicated that CPSs decrease bank lending. When disaggregated, the results revealed that positive CPSs insignificantly increased bank lending but that a negative CPS substantially and significantly decreased bank lending. These results suggested that, while a positive CPS boosts bank lending, the boost is not to the same magnitude that a negative CPS decreases bank lending. Further, negative price shocks in the mineral, fuel, chemical, and metal commodity groups seemed to have large negative impacts on bank lending in African countries. Therefore, the study deduced that, even though CPSs weaken banking sector stability through credit risk, they could also have a direct impact on bank lending as bank perceptions of macroeconomic risks rise. The results of this study cannot be generalized for all developing countries given that the banking system and financial sector of the African region differs from other regions of the world.

The main policy implication of this study is that it highlighted that commodity price shocks can impact the banking sector of African commodity-dependent countries. This finding implies that African countries need to adopt and implement policies that protect the banking sector from CPSs. The study makes the following recommendations: First, considering the finding that commodity price shocks can impact the banking sector, as well as credit extension to the private sector, African central banks need to strengthen the macroprudential regulation and oversight of the banking sector in order to ensure that it remains resilient to CPSs. Further, their policies should help mitigate systemic risk so that the vulnerabilities faced by one sector do not spill over to other sectors in the economy. Second, the study also found that both positive and negative shocks weigh on banking sector stability. This finding highlights the need for African economies to extensively diversify their exports and economic activities. A more diversified economy means that countries can rely on alternative sources of revenue. This is especially important for the agricultural and metal-dependent African countries. Third, in line with the finding that mineral and fuel, as well as chemical, price shocks resulted in a substantial increase in credit risk, African economies must establish and maintain a robust sovereign wealth fund<sup>17</sup> that can be used to protect the economies from excess export revenue volatility.

The managerial implications of this study are: (i) Bank managers and the financial sector should put mechanisms in place such as consistently maintaining enough fiscal reserves (e.g., through the establishment of a sovereign wealth fund) because this will help reduce the detrimental impact that is usually associated with commodity price fluctuations on the banking system. (ii) Bank managers and the financial sector should partner with the government by strongly supporting the development of counter-cyclical capital buffers that will help mitigate the impact of commodity price shocks on bank balance sheets. (iii) Bank managers and stakeholders in the banking sector should closely and regularly monitor and anticipate uncertainty that may likely occur in the return process of agricultural projects, since, by nature, agricultural projects supported by loans are sensitive to many risk factors (e.g., price of inputs, demand, weather conditions, and uncertainty of spot price of produce). (iv) Stakeholders in the banking sector should adopt macroprudential policies, since they act as an important factor for the stability of the financial sector and given that they are also gaining attention internationally as a useful tool to address system-wide risks in the banking sector. (v) Bank managers and stakeholders should revisit prudent guidelines to stem the credit risks associated with the systemic risks of oil price volatility and should also consider establishing early warning and response mechanisms for commodity price shocks in order to operate with better performance.

Provided that this study focused on 18 African commodity-exporting economies, it would be beneficial for future research to probe the CPS and banking sector stability relationship for a single commodity-exporting country. The study used aggregated banking data; it would be extremely interesting to analyze this relationship at a bank-specific level for African economies, as it would provide more granular information on the banks that pose the greatest risk to banking sector stability. In addition, with the popularity of crossborder bank expansions in Africa, research on whether banking sector instability in a host country (and resulting CPSs) exacerbates banking sector instability in the home country would also be of interest. Finally, the NPL data employed in the study were aggregated; it would be extremely useful to find granular data that separates credit risk by government, corporate, and household sectors.

The biggest difficulty with this study was data collection. Data for African economies are quite difficult to collect, and this made it impossible to include a larger sample of African countries. Additionally, the dataset was unbalanced, but the econometric method employed was suitable for an unbalanced dataset. Another limitation of this study was that it could not extend the period to cover the COVID-19 pandemic period due to data problems for the variables used. Therefore, future studies should take into account the pandemic period for the purpose of obtaining a better understanding in terms of the impact of commodity price shocks on banking system stability in developing countries. Future studies should also investigate by forecasting the commodity price shocks and the possible impact they will have on the banking system stability of developing countries.

**Author Contributions:** Conceptualization, N.N. and M.L.A.d.S.; methodology, N.N., M.L.A.d.S. and C.S.S.; software, N.N.; validation, N.N., M.L.A.d.S. and C.S.S.; formal analysis, N.N., M.L.A.d.S. and C.S.S.; investigation, N.N., M.L.A.d.S. and C.S.S.; resources, N.N., M.L.A.d.S. and C.S.S.; data curation, N.N., M.L.A.d.S. and C.S.S.; writing—original draft preparation, N.N., M.L.A.d.S. and C.S.S.; writing—review and editing, N.N., M.L.A.d.S. and C.S.S.; visualization, N.N., M.L.A.d.S. and C.S.S.; supervision, N.N. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data used for this study can be found at: (i) Federal Reserve Economic Data (FRED) of St. Louis (https://fred.stlouisfed.org/# (accessed on 1 July 2016)); (ii) The World Bank (WB) Global Financial Development databases (https://www.worldbank.org/ en/publication/gfdr/data/global-financial-development-database (accessed on 1 July 2016)) and the IMF (https://www.imf.org/en/Data (accessed on 1 July 2016)); and (iii) The United Nation (UN) Comtrade database (https://comtrade.un.org/ (accessed on 1 July 2016)).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**

**Table A1.** List of countries included in the sample.


**Table A2.** Commodity groups included in the sample.




#### **Table A3.** Data description and sources.


**Source**: Author collection.

**Table A4.** Phillip-Perron unit root test.


**Source:** Author computations. H0: All panels contain unit roots; \*\*\* *p* < 0.01, \*\* *p* < 0.05, \* *p* < 0.1.


**Table A5.** Descriptive statistics.

**Source:** Author computations.

**Table A6.** Correlation matrix.


**Source:** Author computations. \*\*\* *p* < 0.01, \*\* *p* < 0.05 and \* *p* < 0.10.

**Table A7.** Agricultural commodity price shock and the banking sector.



**Dependent Variable: Credit Risk**




Robust standard errors in parentheses; \*\*\* *p* < 0.01, \*\* *p* < 0.05, \* *p* < 0.1. **Source**: Author computations.


**Table A9.** Metal commodity price shock and the banking sector.

**Table A10.** Chemical commodity price shock and the banking sector.






Robust standard errors in parentheses; \*\*\* *p* < 0.01, \*\* *p* < 0.05, \* *p* < 0.1. **Source**: Author computations.

**Table A12.** The impact of mineral and fuel price shocks on credit extension.


Robust standard errors in parentheses; \*\*\* *p* < 0.01, \*\* *p* < 0.05, \* *p* < 0.1. **Source**: Author computations.



**Table A14.** The impact of chemical price shocks on credit extension.


Robust standard errors in parentheses; \*\*\* *p* < 0.01, \*\* p < 0.05, \* *p* < 0.1. **Source**: Author computations.

#### **Notes**


#### **References**


Sami, Mina, and Wael Abdallah. 2022. Does Cryptocurrency Hurt African Firms? *Risks* 10: 53. [CrossRef]


### *Article* **Money Supply and Inflation after COVID-19**

**Orkideh Gharehgozli \* and Sunhyung Lee**

Department of Economics, Feliciano School of Business, Montclair State University, 1 Normal Avenue, Montclair, NJ 07043, USA; lees@montclair.edu

**\*** Correspondence: gharehgozlio@montclair.edu

**Abstract:** The core personal consumption expenditure (PCE) price index, the Federal Reserve's preferred inflation gauge, rose to 5.2 percent on January 2022, which is the highest rate of increase since 40 years ago. Our estimates show that the annualized quarterly core PCE prices could reach 5.45% in the second quarter of 2022 and are as high as 8.57% in a longer time horizon unless corrected with restrictive monetary policies. Thus, the inflation shock since COVID-19 is not transitory, but it is persistent. As economists expect the Federal Reserve to tighten the money supply in March 2022, the insufficient policy responses may be attributed to a failure to incorporate a unique macroeconomic shock to unemployment during the pandemic. We propose a modified vector autoregression (VAR) model to examine structural shocks after COVID-19, and our proposed model performs well in forecasting future price levels in times of a pandemic.

**Keywords:** inflation; forecast; time series; vector autoregressiion; pandemic; COVID-19; unemployment rate

#### **1. Introduction**

"We tend to use [transitory] to mean that it won't leave a permanent mark in the form of higher inflation. I think it's probably a good time to retire that word and try to explain more clearly what we mean", Federal Reserve Chairman Jerome Powell said during a congressional hearing on Tuesday, 2 December 2021.

To combat the negative economic effects of COVID-19, the Federal Reserve has used an unprecedented combination of monetary and fiscal policies. Clarida et al. (2021) provides an excellent summary of how the Federal Reserve deployed its conventional tools to support the U.S. economy in 2020 and contribute to robust economic recovery in 2021. The tools included large-scale asset purchase programs (Vissing-Jorgensen 2021), near-zero interest rates, and subsidized loan programs. On top of the expansionary monetary policies, Congress authorized various types of expansionary fiscal policies, including the \$2.2 trillion Coronavirus Aid, Relief, and Economic Security (CARES) Act (Bhutta et al. 2020).

These expansionary monetary and fiscal policies led to a large increase in the supply of money. Figure 1 depicts M2 money supply (M2) in seasonally adjusted billions of dollars and its percent change (M2P) at a monthly level from 1959:01 to 2022:02. M2 since 1959 shows a slow and steady growth until 2000, growing to approximately \$5 trillion in the 40-year span. Between 2000 and 2020, M2 grew from \$5 trillion to \$15 trillion, an increase of \$10 trillion in 20 years. Due to the aforementioned expansionary policies in response to COVID-19, the level of M2 grew from approximately \$15 trillion in 2020:01 to \$22 trillion in 2022:02, an increase of \$7 trillion in 2 years. The magnitude of the increase in M2 is quite astonishing compared to the rather slow and steady historical growth. At any month since 1959 and before 2020, the monthly percent change in M2 was within 2 percent except for 2.8 percent in 1983:01, which occurred during the oil shock crisis. Even during the Global Financial Crisis of 2007–2009, the monthly growth rate was within the 2 percent range. In contrast, the COVID-19 money supply growth rate is unprecedented. In March, April, and May 2020, the money supply grew by 3.4, 6.3, and 4.9 percent, respectively.

**Citation:** Gharehgozli, Orkideh, and Sunhyung, Lee. 2022. Money Supply and Inflation after COVID-19. *Economies* 10: 101. https://doi.org/ 10.3390/economies10050101

Academic Editor: Robert Czudaj

Received: 22 March 2022 Accepted: 26 April 2022 Published: 28 April 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Figure 1.** M2 money supply (M2, left, billions of dollars) and M2 money supply percent change (M2P, right, %), monthly, seasonally adjusted, 1959:01–2022:02, Source: Board of Governors of the U.S. Federal Reserve System.

With the increase in the money supply, the debate about its impact on inflation has reemerged. The original idea behind the relationship between the money supply and inflation stems from the quantity theory of money (Humphrey 1974). The theory states that the quantity of money in circulation primarily affected the general level of prices. Brunner and Meltzer (1972); Brunner et al. (1980); Cagan (1989); Friedman (1989); Friedman and Schwartz (2008), and other monetarists show that a sudden increase in the money supply resulted in a proportional increase in inflation, and hence, the government should curtail the money supply to control the price level. In contrast, Ball et al. (1988); Cogley and Sbordone (2008); Del Negro et al. (2015); Galí (2015), and other Keynesian economists have challenged the quantity theory of money. The main argument is that an increase in the money supply has led to a decrease in the velocity of money and a rise in real income, which would stimulate aggregate demand and the economy would achieve full employment. For instance, Mishkin (2009) contends that the expansionary monetary policy was effective in reducing adverse effects from financial disruptions and managing an upward shift in inflation risks during the Global Financial Crisis.

However, the price level in the U.S. has substantially been increasing since 2021 and well into 2022. At the end of 2021, Federal Reserve Chairman Jerome Powell acknowledged that the upward trend in inflation is no longer transitory, reversing from the original stance.<sup>1</sup> The headline U.S. inflation rate rose to 7.5 percent in January 2022, which is the highest rate of increase since 1982.2 The core personal consumption expenditure (PCE) price index, the Federal Reserve's preferred inflation gauge, rose to 5.2 percent, also with the highest rate of increase since 1983.<sup>3</sup> Given that the core PCE prices have been well over their target rate of 2 percent, the Federal Reserve increased the interest rate on March 2022, which is a major shift in the U.S. monetary policy, and it will continue to raise the rate at least until the end of 2022, although there is a disagreement about the incremental of each raise.4

Forecasting inflation after COVID-19 has been a difficult task using a traditional econometric model given the unique macroeconomic variations during the pandemic. Vector autoregression (VAR) is one of the most popular models in macroeconomics to measure the responses of outcome variables to exogenous shocks and forecast future

outcomes (e.g., Giordano et al. 2007; Gharehgozli et al. 2020). However, the COVID-19 pandemic has created challenges to the VAR model, as the U.S. economy experienced economic disruptions at an unprecedented scale. Namely, the unemployment rate in April 2020 was 14.7 percent, an increase of 10 percentage points in a single month. Lenza and Primiceri (2020) point out that this type of unprecedented irregularity in the data will contaminate the pre-pandemic fit of the VAR model.

To tackle the challenge of using a VAR model in times of a pandemic, macro-econometricians are trying to incorporate this outlier, extreme observation, or contamination of data into the model. The literature provides two major solutions. A first strand of literature applies restrictions to the estimation. For instance, Lenza and Primiceri (2020) suggest an ad hoc strategy of removing outliers for parameter estimation. Economists can re-scale the April 2020 parameter, provided that this re-scaling is common to all shocks. The solution provides a flexibility in the model because the exact timing of the volatility change is known, which makes it much simpler than a typical time-varying volatility model. Unfortunately, the proposed solution is not suitable for forecasting because it significantly undermines uncertainty. Schorfheide and Song (2021) suggest that an existing mixed-frequency VAR model can still be used with some modification without a major ad hoc change. However, the modification still includes excluding a few months of outliers, which could jeopardize the model's forecasting performance. A second strand of literature gets help from additional information. For instance, Foroni et al. (2020) use information from the Global Financial Crisis to adjust post-pandemic forecasts. Ng (2021) treats COVID-19 as a persistent health crisis with large economic consequences and "de-COVID" the data so that economic shocks within the VAR model can be identified. COVID-19 indicators, such as hospitalization, positive cases, and deaths, are used to either eliminate or include additional information for the modeling.

In line with the literature proposing alternatives to the traditional VAR approach, we propose a new model to examine the macroeconomic behaviors in times of a pandemic. Our model stems from the point of view that macroeconomic outcomes that originate with labor market dislocations differ from those in which labor markets play a less active role. Namely, domestic lockdown policies across different U.S. states in March and April 2020 served as an exogenous shock to unemployment. The domestic lockdown policies are unprecedented even in past epidemic episodes, which make the COVID-19 recession unique compared to any other historical crises. Furthermore, the so-called "Great Resignation", during which workers have voluntarily decided not to return to work until work safety and an increase in real wages are guaranteed, has increased instability in unemployment. Thus, we assume that the labor market has been substantially distorted during the pandemic due to exogenous shocks, such as the lockdown policies and the Great Resignation. Our logic is in sync with an argument made in Aastveit et al. (2017), which show that the association between GDP and unemployment has been shifted since the Global Financial Crisis in 2008.

The rest of this paper is structured as follows. Section 2 presents our VAR model and describes the data. Section 3 discusses findings from the main methodology and sensitivity analysis. Section 4 concludes the paper.

#### **2. Model Specification**

In this section, we introduce our VAR model and the identification scheme for the structural shocks and then discuss our data.

A VAR model is, in principle, a simple multivariate model in which each variable is explained by its own past values and the past values of all the other variables. In other words, it describes the evolution of a set of k variables, called endogenous variables, over time and, therefore, enables us to study the responses of each variable to substantial changes in others through the impulse response analysis, forecast error variance decomposition, historical decomposition, and the analysis of forecast scenarios (e.g., Hashimzade and Thornton 2021).

In the econometrics literature, the main stimulus for much recent work on VAR models is the paper by Sims (1980), based on the idea of using an unrestricted vector of past values of variables for forecasting. Since then, the literature has been full of studies in which a VAR is employed to study the relationship between economic indicators, and many of these studies are focused on the dynamics of the macroeconomic variables and the effects of events and interventions on these dynamics (e.g., Adeniran et al. 2016; Berisha 2020; Okoro 2014; Ronit and Divya 2014; Zuhroh et al. 2018).

One advantage of the VAR model is that we can typically treat all variables as a priori endogenous. Thereby, they account for Sims (1980)'s critique that the exogeneity assumptions for some of the variables in simultaneous equations models are ad hoc and often not backed by fully developed theories (e.g., Hashimzade and Thornton 2021). A VAR model does not assume any direction for the relationships unless restricted. Restrictions, including the exogeneity of some of the variables, may be imposed on VAR models based on statistical procedures. Structural VAR analysis, then, attempts to investigate structural economic hypotheses with the help of VAR models. While in the structural VAR, variables can have contemporaneous effects on each other, in a reduced-form structural VAR, the contemporaneous effects are considered in the error term, and while no variable has a direct contemporaneous effect on other variables, the occurrence of one structural shock can potentially lead to the occurrence of shocks in all error terms, thus creating contemporaneous movement in all endogenous variables.

There are some caveats in working with the VAR models. The estimation of autoregressive models requires that the data be fully observed. With the existence of missing values, this is not possible, rendering it impossible to estimate the model (e.g., Bashir and Wei 2018), or large samples of observations involving time series variables that cover many years are needed to estimate the VAR model; these are seldom available for regional studies (e.g., LeSage and Krivelyova 1999). VAR models are criticized because they do not shed any light on the underlying structure of the economy, as they do not aim to estimate causal relationships. Though this criticism is not important when the purpose of VAR is forecasting, it is relevant when the objective is to find causal relations among the macroeconomic variables.

We find that the structural VAR explained below is an appropriate model to address the inquiry of this study, which is not necessary to estimate the causal relationships between the variables in the model, but to employ their dynamics to forecast the future of the main variable of interest. The structural VAR enables us to follow and include the observed structural pattern of the economy (after the pandemic) and restrict the order of the shocks in the system to observe the responses of the variables.

#### *2.1. Methodology*

Nakamura and Steinsson (2018) provide a perspective on different identification strategies and approaches used to study the effect of monetary policy on macroeconomic indicators and describe their caveats. They give a critical assessment of several of the main methods, such as "matching moments"; those focused on identifying causal effects such as instrumental variables, difference-indifference analysis, regression discontinuities, randomized controlled trials; as well as vector autoregression. One important point they explain is the importance of finding an exogenous or surprise component of a monetary policy to assess the effects (and any "direct causal inference"). Romer and Romer (2004) suggest that the dispersion between realized values and the expected values of the indicators are the exogenous or unexpected component. Nakamura and Steinsson (2018) also discuss a standard VAR model regarding monetary policies and argue that an assumption must be made about whether the contemporaneous correlation between the variables is taken to reflect a causal influence. For instance, it is common to assume that the federal funds rate does not affect output and inflation contemporaneously.

VAR models are flexible multivariate time series models, which provide a rich account of the complex forms of autocorrelation and cross-correlation that are typical of macroeconomic variables. Ba ´nbura et al. (2015); Del Negro et al. (2020); Giannone et al. (2015); Lenza and Primiceri (2020); Ng (2021); Romer and Romer (2004) all have different orderings of

variables within the VAR model. In a typical VAR model, we can treat all variables as a priori endogenous. A VAR model does not assume any direction for the relationships, but restrictions, including the exogeneity of some of the variables, may be imposed based on statistical procedures. Structural VAR analysis, then, attempts to impose and investigate whether structural economic hypotheses and variables can have contemporaneous effects on each other. In a reduced-form structural VAR, the contemporaneous effects are considered in the error term, and the occurrence of one structural shock can potentially lead to the occurrence of shocks in all error terms, thus creating contemporaneous movement in all endogenous variables.

Consider the set of *yt* = {*UNEMPt*, *GDPPCt*, *M*2*t*, *M*2*Vt*, *PCECOREt*}; in our reducedform VAR model, we perform:

$$y\_t = a + \beta\_t + \sum\_{k=1}^{5} \rho\_k y\_{t-k} + \nu\_{t\prime} \quad \quad \quad \quad t = 1, \dots, T,\tag{1}$$

*α* is the intercept, and *β<sup>t</sup>* is the time trend; *ρ<sup>k</sup>* represents a 5 matrix collecting the estimated coefficients, and *ν<sup>t</sup>* is the idiosyncratic error term. We discuss the choice of the variables further below, but the contribution of our model is the choice of the variables and the direction of the shocks, which the VAR model as described enables us to study. The pandemic and lockdowns caused an exogenous (dramatic) unemployment shock, followed by a severe shock in the economic activity (GDP). The supply of money was raised to a historical peak, and the velocity of money followed. This has caused contemporaneous and long-term effects on core inflation. Note that a VAR model does not assume any direction for the relationships. Therefore, the coefficients pick up the dynamics of the variables over the period under study without any arbitrary restriction put on any variables. Therefore, again, this model is first estimated without any restrictions.

Only in the case of the structural shocks, *ut* are identified from a Cholesky scheme restriction imposed on B such that *ν<sup>t</sup>* = *But* or:

$$\nu\_{t} \equiv \begin{pmatrix} \nu\_{t}^{\text{IINEMP}} \\ \nu\_{t}^{\text{GDPPC}} \\ \nu\_{t}^{\text{M2}} \\ \nu\_{t}^{\text{M2}V} \\ \nu\_{t}^{\text{PCECORE}} \end{pmatrix} = \begin{pmatrix} b\_{11} & 0 & 0 & 0 & 0 \\ b\_{21} & b\_{22} & 0 & 0 & 0 \\ b\_{31} & b\_{32} & b\_{33} & 0 & 0 \\ b\_{41} & b\_{42} & b\_{43} & b\_{44} & 0 \\ b\_{51} & b\_{52} & b\_{53} & b\_{54} & b\_{55} \end{pmatrix} \begin{pmatrix} \nu\_{t}^{\text{IINEM}} \\ \nu\_{t}^{\text{GDPPC}} \\ \nu\_{t}^{\text{M2}} \\ \nu\_{t}^{\text{M2}V} \\ \nu\_{t}^{\text{PECCORE}} \end{pmatrix}$$

The variables of interest in our model are: real GDP per capita (*GDPPC*), measured in chained 2012 USD; unemployment rate (*UNEMP*), measured as the number of unemployed as a percentage of the labor force; *M2* money supply (*M2*); velocity of money *M2* (*M2V*); and core inflation (*PCECORE*), measured as personal consumption expenditures excluding food and energy (chain-type price index), as a percentage change from a year ago. All of our variables are seasonally adjusted and observed at a quarterly level. For a detailed explanation of the data sources and descriptions, please see Appendix A.

Note that the VAR model will capture the co-movement of the variables over time. However, we can set a scheme for the structural shocks. The contribution of our study is the choice of the direction of the shocks, which the VAR model as described above enables us to study. By design, the first structural shock *uUNEMP <sup>t</sup>* stands for an exogenous (dramatic) unemployment shock caused by the pandemic and lockdowns, and *uGDPPC <sup>t</sup>* stands for an output shock. Note that the order of the restrictions in this analysis is specific to the current pandemic and the economic responses. By nature, monetary and fiscal policies are highdimensional, and over the time under study, other macroeconomics indicators were affected as well. We ordered the variables from the most to least exogenous based on our theory. The dramatic shock in the unemployment rate was indeed exogenous, caused by the severe lockdowns starting in March and April 2020. *uGDPPC <sup>t</sup>* can be assumed to contemporaneously correspond to the unemployment shock and, along with the unemployment shock, to have contemporaneous effects on monetary policies and the supply of money. *uM*<sup>2</sup> *<sup>t</sup>* and *uM*2*<sup>V</sup> <sup>t</sup>*

refer to the shocks to money supply and velocity of money, which contemporaneously affect core inflation. Finally, *uPCECORE <sup>t</sup>* refers to the shock to core inflation.

The main difference between the traditional VAR ordering and our VAR ordering is that we prioritized the exogenous shocks to the unemployment rate during the COVID-19 crisis. In previous recessions, such as during the Global Financial Crisis in 2008, a negative economic shock had a detrimental effect on GDP growth first. Then, the depressed economy caused an increase in the unemployment rate as the economy adjusted to the negative demand shock via employment. In contrast, we emphasize that macroeconomic variations after COVID-19 must be reorganized. U.S. states enforced unprecedented lockdown policies in March and April 2020, which had a direct impact on the labor market. Thus, this shock to the workforce was the most significant contributor to the inception and intensification of the COVID-19 recession. Our ordering of variables in the VAR model can best reflect the simultaneous effects of our variables of interest during the pandemic.

In our reduced-form structural VAR model, we estimate all the parameters from ordinary least squares (OLS) regressions. The Akaike information criterion (AIC) recommends the number of lags to consider in our model to be five. All series were seasonally adjusted, and we considered a constant and a trend in our series.

#### *2.2. Data*

We incorporated major macroeconomic indicators of the inflation suggested by the literature to understand the future direction of core prices, while considering the logical direction of the endogeneity of these indicators under the recent shocks caused by the pandemic. Then, we used a multivariate VAR model, which captures the historical dynamics of these major macroeconomic indicators of inflation and informs us about the future movements of these variables under current circumstances. We worked with the quarterly data of the unemployment rate, real GDP per capita, M2 money supply, the velocity of money, and core PCE prices. Our VAR model will provide the responses of these variables to the current shocks. The highly continuous co-variation of these series over a long period, incorporated in a VAR model that captures such variation of economic time series (without assuming any direction for causal relationship), enhances our ability to more precisely estimate and measure the magnitude of the shocks these series have encountered recently.

We used high-frequency data, observed at a quarterly level, over a long time series (1960:Q1 to 2021:Q4). The prediction of the dynamics of macroeconomic indicators at a higher frequency, especially for inflation, will help policymakers design appropriate monetary policies to circumvent the wide-ranging negative effects of the recession. The higher-frequency provides more degrees of freedom, which allows us to be more precise in understanding the relationship between inflation and the other indicators that directly affect core prices under the recent economic downturn.

As mentioned earlier, variables included in the analysis are real GDP per capita, the unemployment rate, M2 money supply, the velocity of money M2, and core inflation (for a detailed explanation of the data sources and descriptions, please see Appendix A).

Figure 2 shows the time series of GDP per capita, the unemployment rate, M2, the velocity of money, and core inflation during the sample period from 1960:Q1 to 2021:Q4. Overall, the indirect relationship between GDP and the unemployment rate, as well as money supply and the velocity of money, is evident. However, the core inflation does not follow any clear pattern. In the early 1990s, the inflation rate was at around 4%, followed by a decline to 2% until late 1999. With the beginning of the year 2000, the inflation rate in the U.S. rose again, and it reached a peak in late 2007, which is officially known as the year when the U.S. economy slowed down and entered the Great Recession. With the beginning of the crisis, inflation followed the decline and stayed below 2% until the end of the sample period. Exceptions are the years 2011 and 2012, where the inflation rate in the U.S. was at around 3%. The recent shocks in these monetary indicators had never been experienced in the last six decades in the U.S. We provide a sensitivity analysis for the period of the Great

Recession (2008:Q1 to 2009:Q2), but we should emphasize that the magnitude of the shocks are not comparable to that period.

**Figure 2.** Unemployment rate, real GDP per capita, and M2 on the left panel and velocity of money and core inflation on the right panel for 1960:Q1 to 2021:Q4; data series are quarterly data and are seasonally adjusted.

#### **3. Estimation Results**

#### *3.1. Main Results*

Figure 3 summarizes the impulse response functions (IRFs) of the main variables of interest, core inflation, to a one standard deviation positive shock in other indicators: unemployment rate, GDP per capita, M2, and velocity of money. By design, the most significant contemporaneous response is with respect to the velocity of money. A one standard deviation positive shock in the velocity of money significantly increases core inflation. Positive shocks in M2 have a lagged positive effect on core inflation. On the other hand, in the case of recessions when there is a negative shock in the unemployment rate, following the logical trend, core inflation shows a negative downward response. Note that we worked with real GDP per capita, while the velocity of money incorporates nominal GDP.

**Figure 3.** Impulse response functions, and core inflation with respect to a shock in other indicators.

One standard deviation of the velocity of money (3.97 percent) would cause a maximum of a 0.404 percent increase in core inflation. The second quarter of 2020 recorded the highest negative shock in the velocity of money, with 6.1-times the standard deviation. This means that a potential positive response would amount to 6.1 × 0.404 = 2.46 percent. One standard deviation of M2 (3.55 percent) would cause a maximum of a 0.475 percent increase in core inflation. The first quarter of 2021 recorded the highest positive shock in M2 with 7.27-times the standard deviation. This means that a potential positive response would amount to 7.27 × 0.475 = 3.45 percent. Our findings suggest that, although the directions of the velocity of money and M2 shocks are opposite (as in Figure 2), even with the continuous increase in the money supply and with a recovering GDP, we expect a significant and persistent rise in core inflation during the COVID-19 crisis.

Figure 4 shows the forecast error variance decomposition (FEVD) for core inflation. The FEVD graph depicts the contribution of each individual shock as a share of the total area in a given time period. In the first quarter, we see that M2V, M2 money supply (M2SL), GDPPC, and UNEMP explain over 40 percent of the variability in core inflation, with M2V being the most significant explanatory indicator. M2V continues to play a substantial role in explaining variations in core inflation up to the ninth quarter. Beginning in the ninth quarter, we see an increasing role of M2 money supply (M2SL) as a component of the core inflation indicator, and the rising trend continues for several quarters onward. The decomposition analysis suggests that the substantial share of variations in core inflation can be explained initially by the velocity of money, then by the money supply. The result of the Granger test (order three) confirms the significance of the money supply indicator in defining core inflation with an F-statistic of 5.32 and *p*-value of 0.001, while for the reverse relationship, the F-statistic is 1.66 with a *p*-value of 0.176, indicating an insignificant relationship.

**Figure 4.** Forecast error variance decomposition for core inflation.

Assuming that M2V and M2 will have discretionary trends, Figure 5 shows the VAR forecast results for GDP per capita (GDPPC), the unemployment rate (UNEMP), and core inflation (PCECORE). The dashed vertical line represents the end of 2021. Hence, we are forecasting for the first quarter of 2022 and onward. Our model suggests that the unemployment rate will increase over 6 percent with a 95% confidence interval (CI). Adjustments in the labor market will continue during the first few quarters of 2022, as more people will be willing to work and actively seek employment. A gradual easement in COVID-19 health mandates in major states, such as New York relaxing mask mandates, will help contribute to an increase in the labor force. However, as more people return to the labor force, not all the newly added labor force will be able to secure employment. This is because we could experience a labor market surplus as firms may be reluctant to hire more workers at higher wages since the pandemic. As the labor market adjusts and the unemployment rate increases, our model suggests that our quarterly economic growth rate may decrease by approximately 2.5 percent.

Our estimates also indicate that core inflation will increase in the near future. For the first quarter of 2022, core inflation will rise to an average value of 5.03% (with a 95% CI of [4.53, 5.52]). For the second quarter of 2022, our model suggests that core inflation could increase to 5.45% (with a 95% CI of [4.62, 6.28]). Our forecasting analysis shows that inflation could rise as high as 8.57% (with a 95% CI of [6.02, 11.12]) in the future

horizon. All of this evidence signals that the rise in inflation in the U.S. since 2021 is not "transitory", but it is relatively "persistent". Hence, the expansionary fiscal and monetary policies in 2020 will have a lingering effect on the U.S. economy unless corrected with contractionary policies.

**Figure 5.** VAR forecast trends for GDP per capita, unemployment rate, and core inflation.

#### *3.2. Sensitivity Analysis*

Our model assumes that the disruption to the labor market during the pandemic due to stringent lockdown policies resulted in an extreme unemployment rate of 14.7 percent in April 2020. No historical lockdown policies are comparable to that of the COVID-19 crisis, which makes the pandemic period VAR analysis unique. Aastveit et al. (2017) show that the evolution of the unemployment rate during the Global Financial Crisis is different relative to its past behavior. Even though the Global Financial Crisis and COVID-19 crisis are vastly different in terms of the underlying causes, economic consequences, and policy responses, these crises share a fundamental parameter instability in the unemployment rate. Thus, we examined whether our model can provide a robust forecasting estimate of inflation during the Global Financial Crisis in 2008.<sup>5</sup>

We restricted our sample period from 1690:Q1 to 2009:Q3 and introduced the Global Financial Crisis shocks accordingly. Then, we forecast core inflation from 2009:Q4 to 2010:Q4. Table 1 provides the values for actual inflation, predicted inflation using our VAR model, and the absolute difference between the two parameters, which we call dispersion. We see an absolute difference of 0.3 percentage points for the first quarter. In contrast, the subsequent dispersion values are very minimal, with a maximum difference of 0.1 percentage points. Thus, the sensitivity analysis result suggests that our VAR model specification is adequate for forecasting inflation, during which there is an idiosyncratic movement in unemployment.


**Table 1.** Actual inflation, predicted inflation, and absolute dispersion during the Financial Crisis in 2008.

#### **4. Discussion**

At the inception of our paper in mid-2021, the interest rate remained low and the Federal Reserve was cautious about raising the rates based on the "transitory" view of the rising inflation, as we discussed in Section 1. The unprecedented increase in the money supply as shown in Figure 1 and the uniqueness of the COVID-19 recession, especially with the domestic lockdowns in March and April 2020, as argued in Section 2.1, may have led to a more persistent upward shift in inflation. Our forecasts indicate that the core inflation rate will hover around a high 4% and the rate will continue to climb up in the near future. Hence, we have shown that a change in policy is necessary to correct for the upward pressure on the long-run inflation. In line with our prediction and given the persistent inflation, the Federal Reserve increased the interest rate on March 2022 by 0.25 percentage point6.

We compared our predictions in 2022 with other predictions and examined how our predictions fared against other forecasts. In a press conference on 16 March 20227, Federal Reserve Chairman Jerome Powell stated that the median inflation projection of FOMC participants is 4.3 percent in 2022, 2.7 percent in 2023, and 2.3 percent in 2024. Chairman Powell added that the recent trajectory is much higher than their own projection in December 2021 and noted that the FOMC participants continue to see risks as weighted to the upside. These estimates are similar to our predictions. Furthermore, a result from the monthly Bloomberg survey of 70 economists on April 2022 shows that the average core inflation for 2022 will be approximately 4.7%.<sup>8</sup> Their estimate falls within our confidence interval.

The lockdowns in March and April 2020 and the consequent expansionary fiscal and monetary policies led to an unprecedented increase in the level of money supply. These government policies are not unusual as the Federal Reserve used conventional monetary tools such as lowering the interest rates and increasing asset purchases during the Global Financial Crisis (Mishkin 2009). However, the lesson from COVID-19 seems to indicate that forecasting inflation in times of a pandemic is different from in times of a financial crisis. The main difference was the lockdowns, which directly affected the unemployment rate, and our proposed model reflected this macroeconomic behavior. Hence, a major policy implication of our study is that the traditional ordering of the VAR model may not be sufficient when modeling the money supply and inflation in the current or future pandemics.

#### **5. Conclusions**

January 2022 marks the highest U.S. inflation rate in 40 years. The Federal Reserve began tightening the monetary policy in March 2022 to combat the high inflation. We showed that the traditional model of inflation forecasts may not capture all of the macroeconomic behaviors during a pandemic. The direct impact on the unemployment rate because of the lockdowns in March and April 2020 is the main difference from previous recessions. Incorporating this main difference into the model could have allowed us to realize that the COVID-19's era inflation is not transitory.

Our proposed model predicts that the annualized quarterly core inflation rate could rise to 5.03% for the first quarter of 2022 and to 5.45% for the second quarter. In a longer time horizon, we forecast that the inflation rate could reach as high as 8.57% unless corrected with appropriate monetary policies. We also showed that the high inflation after COVID-19 is not transitory, but it is persistent. That is, the recent economic recovery and the excessive supply of M2 from fiscal and monetary policies have increased the core inflation rate beyond a transitory phase.

We contribute to the literature by proposing a changed VAR model specification to forecast inflation after COVID-19. The main modification is incorporating the exogenous shocks, namely domestic lockdown policies, to unemployment during the pandemic. Our proposed VAR model reflects the real macroeconomic behaviors during the pandemic, carefully contemplates the contemporaneous effects of these indicators, and performs well in forecasting future price levels. One of the main implications of our analysis is that the macroeconomic indicators during the recent pandemic-era recession may have different parameters than those from any other recessions. Failing to re-scale these differences may have contributed to the insufficient policy responses to the inflation shocks by the Federal Reserve.

We conclude with three caveats of our research. First, we designed our VAR strategy for forecasting inflation during a pandemic time only. Second, we did not incorporate inflation expectations. Third, our approach does not incorporate up-to-date methods, such as using high-frequency movement in interest rate futures around FOMC announcement dates or using external instrumental variables to identify monetary policy shocks. We believe these are important topics for future research.

**Author Contributions:** Conceptualization, O.G. and S.L.; Data curation, O.G. and S.L.; Formal analysis, O.G. and S.L.; Investigation, S.L.; Methodology, O.G. and S.L.; Writing—original draft, O.G.; Writing—review and editing, S.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**

The data sources employed for the analysis are summarized in Table A1.


**Table A1.** Descriptive statistics.

Table A2 provides the descriptive statistics.

**Table A2.** Descriptive statistics.


#### **Notes**


#### **References**


<sup>6</sup> See Note 4 above.

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