Journal of Political Risk, Vol. 12, No. 8, August 2024

Simon Muwando¹
University of Lusaka

Victor Gumbo²
University of Botswana

Gelson Tembo³
University of Zambia

 

Abstract

The world has experienced a dramatic increase in the flow of transnational investments following increased internationalization and globalization of firms in the previous decade. Country risk exposure is a cause for concern for all the institutions that are engaged in multinational trade and finance. The main objective of this study was to model the Zambia’s country risk. A mixed method with concurrent research design was employed. An autoregressive distributed lag technique was employed on annual data from the 1994 to 2018 period. Country beta was used as a proxy for indicating country risk. The findings of the study revealed that the main determinants of country risk of Zambia in the short run are beta, current account balance, political risk, unemployment rate, and short-term interest rates. In the long run, country risk of Zambia is mainly influenced by current account balance, betas, political risk and unemployment rate. Effective policies need to be implemented by authorities to manage persistent current account deficits and political risk.

Key Terms: country risk; country risk analysis; internationalization; globalization; autoregressive distributed lag; Zambia; globalization

  1. Introduction

The computation and the trade-off between country risk and macroeconomic variables is a topic of concern within the area of multinational trade and finance. All institutions that are involved in managing international investment portfolios are vulnerable to country risk, as it is perceived as a systematic risk that cannot be easily managed by investors. According to Shapiro (1999), country risk is defined as “the general level of political and economic uncertainty in a country affecting the value of loans or investments in that country”. There are numerous instances of country risk and these include political crises in the 1960s and 1970s, the oil crisis and collapse of the fixed exchange rate system in the 1970s, financial debt crises in the 1980s, financial crises in the 1990s and subprime lending of 2007, and the sovereign and private debt crisis in 2010 (San-Martin-Albizuri and Rodriguez-Castellanos, 2018).

All of these crises renewed the interest of different stakeholders in the concept of country risk. They fuelled an ongoing debate among rating agencies, policymakers (including public debt managers, bank regulators, fiscal authorities and central bankers) and academics on how to measure or estimate country risk (Blommestein and Turner, 2012). Modern contributors to this debate advocate for indicators to capture country risk; these criteria range from macroeconomic to financial formulas to credit ratings (Blommestein et al., 2010). Despite the pros and cons of each of the recommended measures, no single one has emerged as wholly acceptable (Blommestein and Turner, 2012).

2. Review of Empirical Studies

Empirical studies have used different proxies as indicators of country risk. Verma and Verma (2014) analysed the responsiveness of country risk in Asian markets to a group of domestic and global macroeconomic factors. In their study, the difference between the return from the equities and risk-free rate of return was used as the proxy for measuring country risk. The findings of the study established that, among the global factors, the exchange rate (US dollar price) had a significant positive effect on country risk – except in the case of Malaysia. The findings on the impact of exchange rate volatility on country betas concurs with Oetzel et al. (2000), Gangemi et al. (2000), Jeon (2001), Verma and Soydemir (2006) and Basu et al. (2011) who found that currency risk is a major determinant of country risk. This is because we live in a global village where markets are highly integrated (Levitt, 1983). A shock in one country threatens to surpass its borders. Thus, global factors for instance, currency crisis influences country risk the most. However, this finding contrasts with Muwando and Gumbo (2013) who found that political risk is the significant factor influencing Zimbabwe’s country risk.

Tourani-Rad et al. (2006) estimated the country risk of New Zealand from 1985 to 2000. A set of macroeconomic variables that include New Zealand’s commodity index, net trade balance, USD/NZD exchange rate⁴, AUD/NZD exchange rate⁵, M₃ money supply⁶, 90-day bill yield, 10-year government bond yield, food price index, monetary conditions index, and trade-weighted index was selected. The difference between local market index return and world market returns was used as a proxy for measuring country risk. A multivariate regression analysis technique was used to estimate country volatility. The findings of the study revealed that the USD/NZD exchange rate and monetary conditions index are the major drivers of country betas. Using the ARIMA technique, Andrade and Teles (2004) studied the country risk of Brazil from 1991 to 2002. The explanatory variables considered include foreign reserves, oil price, nominal interest rate and public sector financial borrowing needs (primary deficit). Using the univariate ARIMA, the study found that reserves, public debt and nominal interest rate were negatively related to country risk while oil price was positively related. The study also found that manipulation of the nominal interest rate is essential to reducing county risk.  Andrade and Teles adopted the same proxy as Tourani-Rad et al. ibid. This method is prone to large errors, putting the results into question.

Goldberg and Veitch (2002) analysed the determinants of country risk for Argentina for the 1992 to 2008 period. The variables chosen for the study include consumer price index, exchange rate (Argentina), exchange rate (Brazil), exchange rate (Chile), exchange rate (Mexico), money supply, reserve money and international reserves. An ARIMA technique was employed to estimate the model for this study. The study established that only Brazilian and Mexican exchange rate crises, not the country’s macroeconomic fundamentals, were the major factors influencing Argentina’s country risk. This implies that the contagion effect was the main driver of changes in country risk. This finding is in line with Goldberg and Veitch (2010) who established that foreign exchange rates and gold prices were major drivers of South Africa’s risk prior to financial integration. However, using the same approach, this finding contradicts with Gangemi et al. (2000) who modelled Australia’s country risk for the 1974 to 1994 period. In this study, the trade-weighted index was found to be the only variable with a substantial positive impact on country risk and asset returns. The study also found that a gain in the value of the local currency positively impacted Australian country risk and that external shocks are essential to the performance of the economy. Therefore, country risk is mainly influenced by economic, financial, and political variables (Erb et al. (1996); Groenewold and Fraser, 1997; Gangemi et al. (2000); Oetze et al. (2011); Muwando, Gumbo and Tembo (2022)).

The paper is subdivided into five additional sections: Section 3 provides a brief overview of the Zambian economy. Section 4 outlines the determinants of country risk chosen for the conceptual model. Section 5 presents the study’s conceptual framework. Section 6 outlines the econometric methodology that was adopted. Section 7 analyses, interprets and discusses the study’s findings. Finally, Section 8 presents the conclusions of the study.

3. Background of the possible financial, economic and political variables driving the country risk for Zambia

Despite its abundant natural resources, Zambia is perceived as one of the most impoverished countries in Africa with the majority (58%) earning less than the International Poverty Datum Line of USD2.15 daily (as of 2022). Its per capita GDP rose from USD 611.87 in 1994 to USD 1352.16 in 2018 (World Bank, 2019). Zambia’s major investors include South Africa, India, Japan, Netherlands, Sweden, Canada, Australia, United Kingdom, China and the United States (United Nations conference on Trade and Development (UNCTAD), 2022). Copper production is one of the key factors that drive its economic growth and development (Page and Velde, 2004). Its over-dependence on copper has exposed it to volatile commodity prices. The Zambian economy is perceived as a risk destination for FDI inflows due to increasing cases of political violence and corruption. Furthermore, this is manifested by low aggregate values of the control of corruption index from 1980 to 2022 with a rating of 33⁷ out of 100 as of 2022 (Mbao, 2011; GAN Business Anti-corruption report, 2017; World Bank, 2013, Transparent.org, 2022). Electoral malpractices were rampant in Zambia during the 1980-2012 period as the legal framework was ineffective in extensively protecting the post-electoral system (Yezi, 2013, p.17, Chungu, 2015). Figure 1 below shows the international ranking of Zambia in terms of corruption. 

Figure 1: International ranking of Zambia in terms of corruption

Source: Transparency International (2019)

NB: rank, lowest=”Very Clean” while highest = “very corrupt”

Figure 1 indicates that Zambia is perceived as one of the most corrupt countries in the world with a higher ranking during the 2001 to 2018 period.

Zambia’s FDI flows fell from USD – 173 million in 2020 to USD – 457 million in 2021 due to the Covid-19 pandemic, even though, the total stock of FDI was estimated at USD 18.9 billion (UNCTAD, 2022). According to Maravi (2007) and Banda (2013), Zambia still needs to revise its investment policies to attract more FDI. Despite granting several tax incentives to foreign investors, authorities need to reduce taxes on mining companies, uncertainties concerning the tax framework and lower the level of interest rates. The regulatory environment needs to lower the bureaucracy and costs when obtaining commercial licences as this limits entrepreneurial activity. Furthermore, the protection of property rights and the enforcement of contracts needs to be improved as it falls short of international standards (World Investment Report, 2022).  Based on the World Bank indicator for FDI net outflows (% of GDP), most of the emerging economies are well ahead of the Zambian economy in terms of FDI performance during the 1986 to 2004 period (United Nations Conference on Trade and Development, World Investment Report, 2005). After introducing new reforms in the 1990s, Zambia’s FDI inflows grew positively (Development Policy Research Unit, 2000; United Nations Conference on Trade and Development, 2006). The FDI and portfolio investment inflows decreased in 2018 (World, 2019).

The Zambian authorities reduced the inflation rate to a single digit figure in 2006 (Central Statistical Office Zambia, 2012; World Bank, 2013). Inflation in Zambia has hovered around its medium term range of 6-8% (World Bank, 2019). The average inflation rate as of 2022 was 11% (CSO, 2022). This implies that interest rates and inflation levels are still high in this country due to the continuous surge in cost of living; that is, high commodity prices for fuel and food. The Zambian exchange rate depreciates from 0.669 kwacha per USD in 1994 to an average of 10.45 kwacha per USD in 2018 (Bank of Zambia, 2019; World Bank, 2019; International Monetary Fund, 2019). This implies that the Zambian local currency fluctuates much comparatively to other regional currencies.

Unemployment rates are extremely high in Zambia. The majority are impoverished and remain unemployed (Yezi, 2013:18). The 2017 unemployment rate stood at 41.2% from 13% in 2010, vis-a-viz, the country’s constant economic growth rate of a minimum 3% annually (Central Statistical Office Zambia, 2018). This may imply that the country’s public resources are concentrated among very few individuals (Africa Development Bank, 2015; Organisation for Economic Co-operation Development, 2015 and United Nations Development Programme, 2015).

The Southern African Development Community (SADC) region faces an unusual heavy debt burden in comparison with other low-income countries. Zambia’s debt stood at 206.1% of GDP in 1991 and fell to 78.1% in 2018 (International Monetary Fund, 2012 and 2019). All these ratios are way above the recommended 3% of GDP indicating that this country may suffer from the debt trap in the long run.

Persistent current account deficits continue to haunt the country (World Bank, 2019). The Zambian government failed to meet the SADC target of a current account deficit of less than 9% in 2011 due to its over-dependence on imports (United Nations Development Programme, 2014 and 2015; SADC, 2014). Despite the risk associated with Zambia, the African Development Bank dedicated more than USD 1 billion to supporting the public sector infrastructure projects. Moreover, it also profited from debt relief under the Heavily Indebted Poor Country and Multilateral debt initiatives (African Economic Outlook, 2019).

According to the International Monetary Fund (IMF) report on Zambia (2019), the country’s medium-outlook is unclear as it is facing many economic challenges including severe debt exposures; drought and a subdued mining sector that are both stunting the growth of the economy; widening current account deficits and inflationary pressures leading to exchange rate depreciation, increased debt servicing costs and subsequently the crowding out of private social investment.

4. Determinants of country risk chosen for the conceptual model

The determinants of country risk used in this study were derived from previous empirical research and from the suggestion of theoretical researchers on sovereign and international borrowings. Moreover, choice of the variables was subject to data availability. The drivers of country risk are categorized into economic, financial variables and political variables (Hoti, 2005). Political variables are composed of legal factors, political instability; economic factors include per capita GDP, GDP deflator, current account balance, and unemployment rate; financial variables are composed of external debt balance, short term interest rates. The chosen variables are explained in detail below.

4.1 Per capita GDP

According to Vij (2005), this variable indicates the level of economic development of a country. GDP per capita is also used by multi-lateral institutions to rank nations for analytical purposes and to establish their creditworthiness. This variable is important because it indicates the overall economic conditions in a country and economically measures the productivity of a country. According to Feder and Just (1977), richer countries can more easily reduce consumption expenditure than poorer countries. Countries with low GDP per capita are generally less creditworthy. Thus, per capita GDP negatively influences to country risk.

4.2 Gross Domestic Product (GDP) Deflator

According to Mohr (2008), the GDP deflator is a factor adjustment for the effect of changes in prices on changes in nominal GDP. It is generally considered as the best inclusive indicator of inflation because it includes a wide range of products and services in its calculation. Alternatively, GDP Deflator is the proportion of nominal GDP to real GDP. In effect, the basket of goods that constitutes this deflator encompasses all the final products/services produced within the geographic boundaries of the country. Thus, GDP Deflator equals Nominal GDP/ Real GDP (Muwando and Gumbo, 2013). The perceived link between interest rates and inflation, and the high-risk environment of the late 1980s is positive: high interest rates were the result of an ever-increasing inflation rate. Therefore, the signs of the interest rate variables should be positive meaning that an unanticipated increase in interest rates will result in an increase in country beta. Furthermore, countries with low GDP deflators are stable and less risky because the GDP Deflator is an indicator of inflation. This implies that the GDP deflator positively influences country risk.

4.3 External Debt Balance

The external debt balance indicates the accumulated fiscal performance of a country (Muwando and Gumbo, 2013 citing Vij, 2005). According to Black et al. (1999), external debt is “the sum of all outstanding external financial liabilities of public sector with legal caveats of principal repayment and debt servicing”. As the debt increases significantly, there is a higher probability of debt trap. Moreover, a large debt stock generally enhances the probability of the public sector’s failure of the public sector to honour the servicing of debt, hence it increases default risk (Guardia, 2004). Therefore, emerging countries with large external debt are riskier than those with low debt. This is because they are susceptible to foreign exchange crises, making the probability of defaulting higher (Muwando and Gumbo, 2013 citing Frank and Cline, 1971 and Cline, 1984). Alternatively, total external public debt can also be defined as “the debt owed to non-residents repayable in foreign currency goods or services” (Vij, 2005). In the minds of the investors, a country’s commitment to honour its debts is indicated by a lower debt to GDP percentage over time, country risk is reduced (Montes and Tiberto, 2012). Thus, external debt positively drives country risk.

4.4 Current Account Balance

According to Mohr (2008), current account surplus or deficit is “the difference between exports and imports of goods, services and income”. Country risk can be managed by raising current account position (surplus), which enhances the liquidity position of a country and thus reduces the country’s default risk (Muwando and Gumbo, 2013 citing Montes and Tiberto, 2012). It is among the most essential tools to foresee crisis in a country. The current account balance indicates the willingness and capability of a country to pay external obligations and the level of foreign exchange reserves. Current account surplus is inversely related to default risk whilst current account deficit mostly equates to the amount of new financing required by a country (Cline, 1984). Current account balance also shows the level of international competitiveness of a country. Countries with large current surplus have very low country betas and are more creditworthy. Hence, current account surplus negatively influences country risk whilst current account deficit positively affects country risk.

4.5 Short Term Interest Rates

Favero and Giavazzi (2004) argue that if interest rates rise, they increase the public debt default risk, which in turn, leads to a vicious circle of increases in interest and debt, making monetary control irrelevant and unsustainable, thereby resulting in failure to manage inflationary pressures in the economy. According to the Fischer Effect, currencies of countries with a higher interest rate differential should bear higher inflation rate differentials, making them riskier than those with lower rates. However, Blanchard (2004) argues that a rise in country risk and inflation can be determined by a rise in interest rates. Blanchard further points that when the debt rises to an unusual level, the increased risk of defaulting on the debt would have a negative impact upon the influx of capital, reducing the capital account balance, triggering exchange rate depreciation, and consequently, inflation. This makes monetary policy ineffective. These findings converge with Andrade and Teles (2007) who argue that using monetary policy (interest rates) during a crisis is ineffective in reducing country risk. Favero and Giavazzi, and Blanchard’s perspectives converge with Andrade and Teles (2005) – citing Garcia and Brandao (2001) – who found that country risk is one of the most significant factors that determined high interest rates during a period when exchange rate is managed, that is the real plan period. Furthermore, Montes and Tiberto (2012) point out that as the real interest rate decreases and becomes stable, investments increase followed by economic growth. Thus, in turn, country risk is reduced as the reputation and credibility of monetary authority improves. In line with the above perspective, Andrade and Teles (2005) argue that an increase in interest rates reduces country risk.

4.6 Unemployment Rate

Emerging countries generally have high unemployment rates, are politically unstable and are very volatile in comparison to developed countries. High unemployment rates lead to social unrest, mental distress (Dooley, Fielding and Levi, 1996) and marital dissolution (Wade and Pevali, 2004). For instances, the Arab Spring of Tunisia in January 2011 was – in part – because of high widespread unemployment, especially among the educated youth. Countries with high unemployment are rated as risky destinations for future investment (Stampini and Chouchane, 2011). This is because it helps to explain labour sufficiency, strength and performance of a country. As the youth unemployment rate increases, country risk increases due to enhanced probability of political instability (Avila, 2010; Azeng and Thierry, 2015).

4.7 Political Risk or Instability

The definition of political risk is continuously debated. Some researchers define it to be “the probability that political forces will negatively affect a firm’s profit or impede the attainment of other critical business objectives” (Rugman,Hodgetts and Collinson, 2006). This implies that the impact of political risk can either be direct through nationalization and expropriation or indirect through taxes and monetary policies. Other researchers understand it to be as “the risk of a strategic, financial, or personnel loss for a firm because of non-market factors such as macroeconomic and social policies or events related to political instability, that is, terrorism, riots, coups, civil war and insurrection” (Chopra, 2015; Muwando and Gumbo, 2013 citing Kennedy, 1998; Pongo, Bybee and Burchard, 2012 citing Kennedy,1998).

According to Citron and Nicklesburg (1987), disturbing political events normally precede debt rescheduling. Thus, nations with political stability are less likely to default. Furthermore, Brewer and Rivoli (1990) argue that political instability reduces the willingness and capacity of a country to service its debt. They further state that political instability may indirectly quicken debt service problems through a decline in long term capital flows and a consequent unwillingness of lenders to roll over matured loans. In the long run, political instability may lead to the following: sluggish economic growth, inflationary pressures, domestic bottlenecks, and production shortage because of disequilibrium between exports and imports balance (Burton and Inoue, 1985).  Hileman (2012) concurs with the latter argument, arguing that political instability is often accompanied by inflationary pressure even if controls are not there. Political risk index indicates how non-business political events such as wars, regime changes and terrorist attacks affect the profitability of businesses² (Muwando and Gumbo, 2013). Countries which are politically stable are more creditworthy. This implies that political risk positively drives country risk.

5. Conceptual framework for the current study

The conceptual framework was based on the work of Erb et al. (1996) who assessed the economic importance of political, economic and financial measures of country risk. They found that country risk measures are highly correlated with equity valuation measures, such as price-to-book ratios. The major difference between their study and the current study is country beta, which is a time varying parameter – computed through the covariance of the local index returns and world market index returns relative to the variance of the world markets index returns – proposed and used as an acceptable proxy for county risk. Country beta is a concept based on notion of local equity returns sensitivity as a result of changes in world market returns (Brigham, Gapenski and Ehrhardt, 1999, p.180); this implies that the more responsive the local market returns to the global market returns, the higher the country risk. This is a better option because country beta in this context is very objective, reflecting the actual risk inherent in that country’s equity market as opposed to using the above-mentioned proxies, whose methodology for computing them is very subjective. The final conceptual framework for the country risk as a function of economic, financial and political variables is in Figure 2 below.

Figure 2: A depiction of the current study’s conceptual framework

In figure 2, country risk as measured by beta is determined by economic, financial and political variables.

6. Methodology

A quantitative econometric technique was employed to improve the accuracy and objectiveness of results from collecting secondary data (Bryman, 2016). The study employed the Autoregressive Distributed Lagged (ARDL) Bounds test procedure on annual data collected from 1994 to 2018 to identify the determinants of Zambia’s country risk. According to Pesaran (2009) and Pesaran et al. (2001), the ARDL cointegration approach is an econometric technique for finding the short run and long run relationship between series with different orders of integration (regardless of being stationary at level and or at the first difference), and is most suitable for small sample sizes (Almahmoud, 2014). This approach was adopted because all the variables were stationary at level and at the first difference, as well as because this study has a small sample size. The technique also allows for the incorporation of multiple independent variables in the model to estimate the dependent variable. In other words, an ARDL model was used to assess the explanatory power of macroeconomic factors on Zambia’s country risk.

6.1 Model Specification

According to Vij (2005), the country risk model can be expressed as follows:

The notation X_ni indicates the values of the n^th independent variable for the case i. The beta terms are unknown parameters and the ε_i terms are independent random variables that are normally distributed with mean zero and constant variance, δ².

Muwando, Gumbo and Tembo (2022) citing Erb et al. (1996a) express country risk as:

is the economic-related risk for country i in the period t;

is the political-related risk for country i in the period t;

is the financial-related risk for country i in the period t.

Equation (2) above implies that country risk depends on economic-related risk, political-related risk and financial-related risk.

In this study, the country beta model further takes the following form:

Where:   

CR_i is the country risk at time t;

α is the intercept or constant;

β_i  to  β_n are unknown parameters;

X_1i  to X_ni are country risk drivers;

ε_t terms are independent random variables that are normally distributed with mean zero and constant variance, σ².

According to Choong et al. (2003) citing Pesaran et al. (2001), the ARDL technique is applied by modelling the long-run equation [4] as a general vector autoregressive [VAR] model of order p in z_t. This implies that:

Where:   

Pesaran, Shin & Smith (2001) further proposed the following vector error correction model [VECM] corresponding to [4]:

Where  ≡ 1- L is the difference operator,

In this study, Z_t = (CA_, CAPITA, DEFLATOR, ED, PSAV, UN, WSTIR). Γ is an n x n matrix (short run dynamics coefficients), = αβ′ where α is an n x 1 column vector (the matrix of loadings) denoting the speed of short run adjustment to disequilibrium and β′ is an 1 x n cointegrating row vector (the matrix of cointegrating vectors) representing the matrix of the coefficients of long run dynamics such that Y_t converge in their long run equilibrium. Finally, ε_t is an n x 1 vector of white noise error term (Choong et al., 2003; Oteng-Abayie and Frimpong, 2006). In other words, is the vector of variable and       respectively; Y_t is an I(1) dependent variable denoted by CR_t ;  (CA_, CAPITA, DEFLATOR, ED, PSAV, UN, WSTIR) a vector matrix of I(0) and I(1).

The conditional Vector Error Correction Model (VECM) becomes:

The determinants of country risk used in this study were derived from previous empirical studies of country risk that dealt exclusively with emerging market equity returns and from the suggestion of theoretical research on sovereign and international borrowings (Basu, Deepthi and Reddy, 2011; Tourani-Rad et al. 2006; Andrade and Teles, 2004; Gangemi et al. 2000; Vij, 2005; Wdowinski, 2004; Goldberg and Veitch, 2002). Moreover, choice of variables for modelling country risk of Zambia was subject to data availability. The set of macroeconomic factors chosen had a major domestic and international influence on the Zambian economy. These include political risk, GDP deflator, per capita GDP, external debt balance, current account balance, interest rate and unemployment rate.

The procedure for the selection of various independent variables used to estimate country risk was as follows:

Pesaran and Pesaran (2009) and Pesaran, Shin and Smith (2001) advocated an ARDL bound testing technique that was employed to test the impact on country risk – as measured by annual country betas – of economic, political and financial variables, and also to establish the behaviour of country risk drivers in the short and long run. The major advantage of an ARDL method over other techniques is that it is used in time – series data notwithstanding their order of integration of variables, that is whether I(0), I(1) and/or fractionally integrated (Almahmoud, 2014 citing Pesaran and Pesaran, 2009). Furthermore, the technique can also test for cointegration by the bounds testing approach and then estimate the short and long run dynamics (Almahmoud, 2014, p.89; Nkoro and Uko, 2016). This method also captures the dynamic effects of both the lagged dependent variables that represent the autoregressive portion and lagged independent variables that constitute the distributed part of the model. Omission of variables and autocorrelation in the error term can be eradicated when the appropriate number of lags of regressor and regress and variables are factored into the model (Gujarat, 2012). The technique is also robust and efficient with samples of different sizes, especially the smaller sizes, for instance, the present study. From equation [6] above, the conditional VECM is expressed in the following form:

6.2 ARDL Bounds Testing Procedure

According to Kumar (2010), the ARDL Bounds test procedure fundamentally encompasses three steps. First, equation [7] is estimated using the Ordinary Least Squares (OLS) method to determine the presence of long run dynamics among the selected factors by performing a joint hypothesis F-test for the lagged variables (Oteng-Abayie and Frimpong, 2006; Saungweme and Odhiambo, 2019).

This implies that the following hypothesis is to be tested as follows:

The test which normalizes CR_t is denoted by

According to Kumar (2010) and Pesaran, Shin and Smith (2001, p.290), two asymptotic critical values bounds provide a test for cointegration when the explanatory variables are integrated at level d, that is I(d)  where 0 ≤ d ≤1.  The lower value of d assumes that the explanatory variables are stationary at level, I(0) and the upper value of d assumes that they are purely stationary at the first difference, I(1). Suppose the F-calculated is larger than the upper F-critical value, we reject the H_o and conclude that there is a long run relationship among the series despite the orders of integration for the time series. On the other hand, if the F-calculated is less than the lower critical value, we fail to reject the null hypothesis and conclude that there is no long run relationship among the series. Finally, if the F-calculated lies between the lower and the upper critical values, the result cannot be concluded (Nieh and Wang, 2005, Ben Jebli, 2016). The critical values used in this study were extracted from Pesaran, Shin and Smith (2001) table.

Second, if cointegration exists, the conditional ARDL(p,q¹,q²,q³,q⁴,q⁵,q⁶,q⁷) long run model for CR_t is estimated as follows:

The orders of the ARDL (p,q¹,q²,q³,q⁴,q⁵,q⁶,q⁷) model in the seven variables is chosen using three criterions: Akaike Information Criterion (AIC), Schwarz Information Criterion (SIC) and Hannan-Quinn criterion (HQC) criterion (Pesaran and Smith, 1995).

Lastly, the Error Correction Model (ECM) is estimated to capture the short-run coefficients of the model. The ECM has the following specifications:

Betas_t is the outcome of the covariance between the local equity index return and World Market equity index return divided by the variance of the world market index return. The local equity index is the locally denominated stock indexes for Zambia (LSE). The proxy for the global market index is the MSCI emerging markets Index. MSCI emerging markets Index was chosen because it comprises stocks in emerging economies hence, it is the best benchmark for comparison with emerging economies of Zambia.

Stock Index Returns were computed using the formula given below:

Where:   

R_t represents Stock Index Returns at time t;

S_t represents the Stock index at time t;

S_t-1 represents the Stock index at time t lagged once.

The computed returns in Equation (10) are log-normalized to improve the normality of the Betas_t parameter, confirming the significance of normality in all statistical analysis.

The economic, financial and political variables mentioned above serve as the explanatory variables that were used to compute the predictive power of the dependent variable Betas_t (Muwando and Gumbo, 2013). External debt and current account balance portray the role of the fiscal authorities on the economy while interest rates reflect the monetary policy in Zambia. Political stability and absence of violence index was used as a proxy for political risk.

Rationality and consistency of the main assumptions made in the models were tested by performing the residual, stability and coefficient diagnostic tests.

6.3 Sources of Data

The annual data for the stock exchanges, the MSCI emerging market index and the chosen variables, was collected between 1994 and 2018. To get annual country beta (β) for Zambia, the local index returns, LSE, were used. The secondary data for the proxy of World market returns was obtained from the MSCI emerging market index. The secondary data for the proxy of political risk was obtained from World Bank governance indicators. Secondary data for the economic variables was collected from the Central Statistical Offices, Central Bank, Ministry of Finance, World Bank and IMF while that for the financial variables was collected from the Ministry of Finance and Central Bank, World Bank and IMF.

7. Interpretation, Analysis and Discussion of the results

7.1 The estimated annual country betas

The annual country betas (Betas_t) were computed by dividing the covariance of the local index returns and world market returns by the variance of world market returns. This gives the numerical value of country risk, which is objective and reflective of the risk inherent in a country. The results of the estimated annual betas are shown Figure 3 below:

Source: Researcher’s own compilation from E-views 10

NB: December 2019 and December 2020 betas are forecasts using the model

The results in Figure 3 indicate that Zambia is a moderately risky destination for investments because most of its estimated annual betas are slightly bigger. Generally, the annual country beta values are smaller and this converges with empirical literature that emerging markets have lower beta than developed markets (Wdowinski, 2004 citing Harvey, 1995 and Erb et al., 1996). The sharp increase in forecasted beta in Zambia may been attributed to anticipated deteriorating economic, financial and political factors.

7.2 Multicollinearity test

Two variables – per capita GDP and GDP deflator – were highly correlated. In designing the model, the GDP deflator was excluded because it had a high probability value. The outcomes of multicollinearity tests are shown below:

Table 3: Correlation matrix  

Source: Researcher’s own analysis using E-Views 10

From the table above, there is no multicollinearity problem because all the correlation coefficients are less than 0.8

7.3 Normality distribution tests

The results of the Jarque-Bera tests are presented in Table 4 below.

Table 4: Normality Test  

Since Jargue-Bera p-values in Table 4 are more than 0.05, we fail to reject H_o and conclude that all the residuals are normally distributed. Hence, the statistical data for Zambia follows a normal distribution.

7.4 Stationarity tests

The results of unity root tests for the model type ‘Intercept without trend’ are shown in Table 5 below.

In Table 5 above, only annual country betas are stationary at level [I(0)] while the other variables – such as current account balance, per capita GDP, political stability and absence of violence index, external debt and weighted short term interest rates – were differenced once[I(1)] for them to be stationary. 

7.5 Optimum lag length

To perform a cointegration test among the variables in the ARDL bound testing, it is a prerequisite to establish the optimal lag to avoid the hypothesis of serially correlated residuals in the cointegrated equation. The researcher limits the estimation to two lags since the possibility of serially uncorrelated residuals will occur when the number of lags is increased. However, it must be done parsimoniously to avoid an over-parameterization problem (Pesaran et al., 2001). The results of optimum lag selection are shown in Table 6 below.

Table 6: Optimum lag selection

Source: Researcher’s own compilation from E-Views 10

NB: ** denotes optimal lag chosen.

In Table 5 above, lag 2 was chosen as the optimum lag for an ARDL model of Zambia as it has the lowest value for all three criterions.

7.6 Co-integration Testing using ARDL Bound Test

The results of the ARDL Bound test for cointegration are shown in Table 7 below.

Table 7: ARDL Bound test for Cointegration

Unrestricted intercept and no trend

Source: Researcher’s own compilation from E-Views 10

From the table above, the F-Statistic (20.18) is greater than I(1) the critical values (3.61) and so we reject the null hypothesis at the 5% level and conclude that there is cointegration among the variables; there is a long run relationship between country risk and a set of selected economic, political and financial variables (current account balance, per capita GDP, external debt, political stability and absence of violence index, unemployment rate and weighted short term interest rates). Thus, a long run ARDL can be estimated with two lags for both countries.

7.7 Long run dynamics results

The results of the long run ARDL model coefficients are shown in the Table 8 below.

Table 8: Estimated long run ARDL model coefficients

Source: Research estimation results from E-Views 10

NB   * denotes significance at 0.05

From Table 8, it can be observed that Betas in one-year lag [Betas(-1)] has a significant long run relationship with country risk. One-year lagged beta is statistically significant at the 5% level of significance because its p-value is less than 5%. With a coefficient of -1.15884, country risk decreases by 1.16% when annual beta increases by 1%, ceteris paribus. The long run p-values suggest that current account balance in one-year lag [CA(-1)] and two-year lag [CA(-2)] have a significant long run relationship with country risk because their p-values are less than 5%. If current account lagged once increase by 1%, country risk increases by 10% (0.10487), ceteris paribus. Furthermore, country risk increases by 8% (0.0849) when current account lagged twice increases by 1%, ceteris paribus. These findings concur with Ferreira (2010), who found that current account significantly influences the country risk of Brazil. It can also be observed that political stability and absence of violence index in one-year lag [PSAV(-1)] and two-year lag [PSAV(-2)] have a significant negative long run relationship with country risk. Political stability and the absence of violence index in one-year lag and two-year lag are statistically significant at the 5% level of significance since their p-values are less than 0.05. In conclusion, country risk decreases by 1.97% (-1.96785) when political stability and absence of violence index in one-year lag increases by 1%, ceteris paribus. In addition, when one-year lagged political stability and absence of violence index increases by 1%, country risk decreases by 1.64% (-1.635), ceteris paribus. This is in line with Vij (2005), Basu, Deepthi and Reddy (2011) and Muwando and Gumbo (2013), who established that political risk and absence of violence is the main driver of country risk. The long run p-values indicate that unemployment rate lagged twice [UN(-2)]  has a significant  influence on country risk. Unemployment rate lagged twice is statistically significant at the 5% level of significance since its p-value is less than 5%. In conclusion, country risk decreases by 25% (-0.25581) when unemployment rate increases by 1%, ceteris paribus. This contradicts the apriori conditions that an increase in unemployment increases country risk. Therefore, the long run determinants of country risk of Zambia are current account balance, betas, political risk and unemployment rate. 

7.8 Error Correction Model (ECM)

The results of the error correction model are presented in the Tables 9 below.

Table 9: Estimated Error Correction Results

Source: Research estimation results from E-Views 10

NB   * denotes significance at 0.05 level

In Table 9, ECT(-1) = -1.4082 is statistically significant at the 5% significance level, implying that the speed of adjustment towards long run equilibrium is 140.82%. If there is shock in any of the short term variables, the whole system gets back to long run equilibrium at a speed of 140.82%. Since the model is correctly specified, a high coefficient of ECT(-1) (above 1 with negative sign and significant) may imply that the system is convergent, yet, has an oscillatory adjustment process; the error correction process fluctuates around the long run value in a dampening manner. However, once this process is complete, convergence to the equilibrium path is rapid.

Table 8 also indicates that differenced one-year lagged beta [D(Betas(-1))] has a significant short run relationship with country risk. Beta in one-year lag is statistically significant at the 5% level of significance since its p-value is less than 5%. In conclusion, country risk has a 1.08% negative change when beta increases by 1%, ceteris paribus. The short run p-values also suggest that differenced one-year lagged current account balance D(CA(-1))] and differenced two-year lagged current account balance [D(CA(-2)) have a significant relationship with country risk. Current account balance in one-year lag and in two-year lag are statistically significant at the 5% significance level because their p-value is lower than the 5%. In conclusion, country risk increases by 0.1% ¹ when one-year lagged current account balance increases by 1%, ceteris paribus. Furthermore, when two-year lagged current account balance increases by 1%, country risk increases by 0.08% ². This finding is in line with apriori conditions; Ferreira (2010) and Cline (1984) who argue that current account surplus is inversely related to the default risk whilst current account deficit is positively related to country risk and mostly equates to the amount of new financing required by a country.

It can also be observed that differenced one-year lagged political stability and absence of violence index [D(PSAV(-1))]and two-year lagged political stability and absence of violence index [D(PSAV(-2))] have a significant negative short run relationship with country risk. Political stability and absence of violence index in one-year lag and two-year lag are statistically significant at the 5% level of significance since their p-values are less than 0.05. In conclusion, country risk decreases by 2.15% (-2.15318) when political stability and absence of violence index in one-year lag increases by 1%, ceteris paribus. In addition, when political stability and absence of violence index in two-year lag increases by 1% country risk decreases by 1.82% (-1.82465), ceteris paribus. The short run p-values indicate that unemployment rate in two-year lag [D(UN(-2))]  has a significant  influence on country risk. Two-year lagged unemployment rate is statistically significant at the 5% level of significance since its p-value is less than 5%. In conclusion, country risk decreases by 0.3% (-0.30197) when unemployment rate increases, on average, by 1%, ceteris paribus. This finding contrasts the a priori conditions.  It can also be observed that weighted short term interest rates in one-year lag [D(WSTIR(-1))] have a significant short run relationship with country risk. Weighted short term interest rates in one-year lag are statistically significant since its p-value is less than 5%. In conclusion, country risk decreases by 18.84% (0.18847) when weighted short term interest rates rise by 100%, ceteris paribus.  This is in line with Andrade and Teles (2004) who argue that short term interest rates are inversely related to country risk. 

7.9 Residual diagnostic Tests of the Error Correction Model

The Error Correction Model (ECM) was tested for serial autocorrelation and heteroscedasticity by conducting the Breusch-Godfrey Serial correlation LM test and Breusch-Pagan-Godfrey test, respectively. The results are shown in Table 10 below.

 Table 10: Summary of Serial Correlation and Heteroscedasticity test

Source: Researcher’s own compilation from E-Views 10

Since p-value is greater than 0.05 for the serial autocorrelation tests, we fail to reject the null hypothesis and conclude that the model does not have serial correlation. For the heteroscedasticity test, p-value is greater than 0.05, we fail to reject the null hypothesis and conclude that the model is homoscedastic.

7.10 Stability diagnostic Tests

The results of CUSUM and CUSUM square test are shown in Figure 4 and Figure 5 below.

Source: Research estimation results from E-views 10

In Figures above, CUSUM and CUSUM squares lie within the 5% boundary, implying that the error correction model is stable and reliable to determine country risk for Zambia.

7.11 Model Specification Test

The Ramsey RESET test to check specification errors was done. A correctly specified model will generate an adequate picture of the relationship between country risk and its drivers. The Ramsey test results are shown in Table 11 below:

Source: Researcher’s own compilation from E-Views 10

Since the p-value of the Ramsey RESET test statistic is greater than 0.05 we do not reject the null hypothesis and conclude that the model is correctly specified.

8. Conclusion

The study concluded that the determinants of country risk of Zambia in the short run are beta, unemployment rate, political risk, weighted short term interest rates, and current account balance, even though current account is not one-to-one responsive to country risk. The study concluded that the long run determinants for the country risk of Zambia are annual betas, political stability and absence of violence, unemployment and current account balance. 

The study concluded that if there is a shock in the short term variables, the whole economy of Zambia adjusts with a speed of 140.83% to reach its equilibrium in the long run. Its error correction process fluctuates around the long run value in a dampening manner (oscillatory adjustment process). However, once this process is complete, convergence to the equilibrium path is rapid.

These results are critical to different stakeholders in managing country risk. The key to country risk management is to critically assess its drivers so that the government can implement the policies necessary to manage these determinants. Based on the conclusion above, the Zambian authorities need to implement policies necessary to reduce persistent current account deficits, political risk, unemployment rate, and external debt. Potential and existing investors may engage the services of both private and public political risk insurers like International Finance Corporation (IFC) and Multilateral Investment Guarantee Agency (MIGA), which provide them with cover against expropriation, currency blockage, breach of contract, sequestration, and confiscation. Creditors (especially exporters) should rely on export cover and insurance guarantees; for example, most Organisation for Economic Co-operation and Development (OECD) countries have established official export credit agencies (ECAs) to enhance exports and foreign investment while managing country risk.

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Footnotes

1.PhD graduate  UNILUS, Zambia. Email: simonmuwando@gmail.com

2.Professor at University of Botswana. Email:victor.gumbo@gmail.com

3.Professor at University of Zambia. Email:tembogel@gmail.com

4. Amount of New Zealand dollars required to purchase one United States Dollar.

5. Amount of New Zealand dollars required to purchase one Australian Dollar.

6. It is the broad money as it encompasses currency with public, current and savings deposits with the banking system, bank-issued certificates of deposit, term deposits, call/term borrowing from non-deposit corporations, and other deposit with the central bank.

7. https://www.transparency.org/en/cpi/2021/index/zmb accessed on 25 February 2023

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