Unemployment rates are extremely high in Zambia. The majority are languishing in poverty and remain unemployed (Yezi, 2013:18). In 2010, unemployment rates stood at 13%, respectively, vis-a-viz, the country’ constant economic growth rate of a minim 3% annually. 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 2017 unemployment rate stood at 41.2% (Central Statistical Office Zambia, 2018).

The Zambian economy has performed relatively well within the Southern African Development Community (SADC) region despite a decline in GDP per capita and a lower economic growth rate caused by reduced production in the mining sector (Rasmussen, 2015; United Nations Development Programme, 2012). The 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 this 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 on 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 oblique as it is facing many economic challenges including severe debt exposures; drought and a subdued mining sector that are stunting the growth of the economy; widening current account deficits and inflationary pressures leading to exchange rate depreciation, increase in debt servicing costs and subsequently crowding out private social investment.

3. Conceptual framework for the current study

From the previous review of theoretical literature, the conceptual framework for the country risk as a function of economic, financial and political variables is in Figure 2 below.

4. Methodology

A mixed method with concurrent research design was employed in order to improve the accuracy of judgments by collecting different kinds of data surrounding the same phenomenon, namely, country risk. In terms of the quantitative approach, the study employed the Autoregressive Distributed Lagged (ARDL) Bounds test procedure on annual data collected from 1994 to 2018, in order to identify the determinants of Zambia’s country risk. This approach was adopted because all the variables were stationary at level and at the first difference; moreover, it accommodates for a small sample size. It also allows for the integration of the 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. To complement the quantitative research design, an exploratory design through personal interviews was used. This was done to gain deeper comprehension of the main determinants of country risk, and to find out the respondents’ view on the impact of political, economic and financial risk on Zambia’s country risk.

4.1 Model Specification

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

(1)

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.

Erb, Harvey and Viskanta (1996a) express country risk as:

(2)

Where:     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.

This means that country risk in equation (2) depends on economic related risk, political related risk and financial related risk.

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

(3)

Where:            CRi is the country risk at time t;

                         α is the intercept or constant;

                        βi  to  βn are unknown parameters;

                       X1i  to Xni are country risk drivers;

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

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

(4)

Where  represents observation z at time t

             represents observation at time t-i

 represents [k + 1] – a vector of intercept [drift];

            α represents [k + 1] – a vector of trend coefficients;

            represents model coefficients.

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

 (5)

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).

(6)

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, Choi and Wilson, 2006; Andrade and Teles, 2004; Gangemi, Brooks and Faff, 2000; Vij, 2005; Wdowinski, 2004; Goldberg and Veitch, 2002). Moreover, choice of variables was subject to data availability.  The set of macroeconomic factors (independent variables) chosen and assessed 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.

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 run and long run. The major merit 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 run and long run dynamics (Almahmoud, 2014, p.89; Nkoro and Uko, 2016). It 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 regressand variables are factored into the model (Gujarat, 2012). The technique is also robust and efficient with samples of different sizes, especially those of smaller sizes for instance, the present study. From equation [6] above, the conditional VECM is expressed in the following form:

4.2 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 in order 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 CRt 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_tis 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 Stock index at time t;

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

The computed returns in Equation (10) are log-normalised in order to improve the normality of the Betas_tparameter and this confirms 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 portrays 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.

The population of study encompasses bank executives, monetary authorities, fiscal authorities, potential and existing investors, especially one that appreciates the concept of country risk and the impact of economic, financial and political fundamentals on country risk. Personal interviews were mainly used as the instruments for collecting primary data, while secondary data was collected from secondary sources (Central Bank, CSO, Ministry of Finance, IMF, World Bank, newspapers and journal articles) for the chosen financial, economic and political variables, and then recorded systematically. The researcher carefully selected interviewees through snowball sampling and scheduled appointments with the key informants on the relevant study. Snowball sampling assumes relevant respondents are connected so that those connections can be used to construct a sample from a small initial sample. In other words, it involves building a sample through referrals, as each respondent recommends others (Bacon-Shone, 2013). Snowball sampling was used to explore the interviewees’ perception of the main determinants of country risk, their impact on country risk and ways of managing country risk.

5 Interpretation, Analysis and Discussion of the results

5.1 Personal interviews response rate

The personal interviews response rate is shown below.

Table 1: Interview response rate

Scheduled Interviews	15
Actual Interviews conducted	8
Response rate	53%

Source: Researcher’s own analysis using Ms Excel

Out of fifteen interviews planned to be conducted in each country, eight interviews were conducted in Zambia. This implies a 53% response rate.

5.2 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 particular country. The results of the estimated annual betas are shown Figure 3 below:

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. This concurs with the interviewees’ response, as they perceived Zambia’s economy to be less stable. 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, Harvey and Viskanta, 1996). The sharp increase in forecasted beta in Zambia may be attributed to deteriorating economic, financial and political factors.

5.3 Multicollinearity test

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

Table 2: Correlation matrix  

	CA	CAPITA	PSAV	ED	UN	WSTIR
CA	1.000000	0.656600	0.626400	-0.665896	-0.205368	0.276803
CAPITA	0.656600	1.000000	0.725999	-0.909195	-0.752508	0.553528
PSAV	0.626400	0.725999	1.000000	-0.749934	-0.513872	0.153651
ED	-0.665896	-0.909195	-0.749934	1.000000	0.798350	-0.336502
UN	-0.205368	-0.752508	-0.513872	0.798350	1.000000	-0.354907
WSTIR	0.276803	0.553528	0.153651	-0.336502	-0.354907	1.000000

Source: Researcher’s own analysis using EViews 10

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

5.4 Normality distribution tests

The results of the Jarque-Bera tests are presented in Table 3 below.

Table 3: Normality Test  

	BETAS	CA	CAPITA	DEFLATOR	PSAV	ED	UN	WSTIR
Mean	0.165632	-3.429200	943.9525	76.63896	0.224800	0.953671	11.00000	2.353600
Median	0.120000	-3.300000	1030.282	66.51300	0.200000	0.534000	10.61000	1.650000
Maximum	1.712100	7.500000	1839.537	201.3040	0.660000	2.081900	18.50000	6.260000
Minimum	-0.735000	-16.50000	330.2830	6.346000	-0.280000	0.186500	7.750000	0.660000
Std. Dev.	0.571677	6.708557	547.4558	59.29485	0.238574	0.747204	3.049057	1.789408
Skewness	0.966783	-0.311951	0.193542	0.539574	-0.230161	0.320238	0.780679	0.998420
Kurtosis	3.936484	2.376537	1.425575	2.133782	2.611433	1.347535	2.970018	2.795727
Jarque-Bera	4.807998	0.810374	2.738176	1.994679	0.378001	3.271716	2.540352	4.196975
Probability	0.090356	0.666852	0.254339	0.368859	0.827786	0.194785	0.280782	0.122642
Sum	4.140800	-85.73000	23598.81	1915.974	5.620000	23.84177	275.0000	58.84000
Sum Sq. Dev.	7.843551	1080.114	7192989.	84381.11	1.366024	13.39954	223.1220	76.84758
Observations	25	25	25	25	25	25	25	25

Source: Researcher’s own analysis using EViews 10

Since Jargue-Bera p-values in Table 3 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.

 

5.5 Stationarity tests

The results of unity root tests for the model type ‘Intercept without trend’ are shown in Table 4 below.

Table 4: Augmented Dickey-Fuller (ADF) Unit roots test results for stationarity

Variable	Significance Level	Test statistic[I(0)]	Critical values[I(0)]	P-value I(0)]	Test statistic [I(I)]	Critical values [I(I)]	P-value [I(I)]
Annual Country Betas	1% level 5% level 10% level	-4.946	-3.738 -2.992 -2.636	0.0006
Current account as % of GDP	1% level 5% level 10% level	-1.889	-3.738 -2.993 -2.636	0.3315	-5.871	-3.753 -2.998 -2.639	0.000
Per Capita GDP	1% level 5% level 10% level	-0.735	-3.738 -2.992 -2.636	0.8193	-4.183	-3.753 -2.998 -2.639	0.004
Political Stability and Absence of Violence Index	1% level 5% level 10% level	-1.943	-3.738 -2.992 -2.636	0.3083	-5.370	-3.7536 -2.9984 -2.6392	0.000
External debt as % of GDP	1% level 5% level 10% level	-1.326	-3.753 -2.999 -2.639	0.5996	-4.643	-3.753 -2.999 -2.639	0.001
Unemployment Rate	1% level 5% level 10% level	-2.012	-3.770 -3.005 -2.642	0.2798	-4.910	-3.770 -3.005 -2.642	0.000
Weighted Short Term Interest rates	1% level 5% level 10% level	-0.462	-3.753 -2.998 -2.639	0.8819	-8.767	-3.753 -2.998 -2.639	0.000

Source: Researcher’s own compilation from EViews 10

In the Table 4 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 stationarity.

5.6 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 has to be done parsimoniously to avoid an over-parameterization problem (Pesaran, Shin and Smith, 2001). The results of optimum lag selection are shown in Table 5 below.

Table 5: Optimum lag selection

Lags	AIC	SIC	HQC
1	2.08004	2.47532	2.17970
2	0.94054**	1.68444**	1.11578**

Source: Researcher’s own compilation from EViews 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 the entire three criterions.

5.7 Co-integration Testing using ARDL Bound Test

The results of the ARDL Bound test for cointegration are shown in Table 6 below.

Table 6: ARDL Bound test for Cointegration

Unrestricted intercept and no trend

Dependent variable	F-statistic	Upper Bound	Lower Bound	Remark	What is next??
Betast	Fbetas = 20.18	2.45	3.61	Cointegration exist	Estimate ECM(Error Correction Model)

Source: Researcher’s own compilation from EViews 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). This is affirmed by the interviewees as they perceived current account, per capita GDP, external debt, political stability and absence of violence index, unemployment rate and weighted short term interest rates to be major drivers of Zambia’s country risk. Thus, a long run ARDL can be estimated with 2 lags for both countries.

 

5.8 Long-run dynamics results

The results of the long run ARDL model coefficients are shown in the Table 7 below.

Table 7: Estimated long run ARDL model coefficients

C	Coefficient	Std. Error	t-Statistic	P-value
C	4.58858	1.09541	4.18892	0.0030
Betas(-1)	-1.15884	0.21500	-5.38988	0.0007   *
Betas(-2)	-0.02909	0.15192	-0.19150	0.8529
CA(-1)	0.10487	0.01920	5.46355	0.0006   *
CA(-2)	0.08479	0.024482	3.41651	0.0091    *
Capita(-1)	0.000083	0.00068	0.12092	0.9067
Capita(-2)	-0.00111	0.00060	-1.86808	0.0987
ED(-1)	-0.37506	0.50720	-0.73948	0.4807
ED(-2)	0.56065	0.46501	1.20567	0.2624
PSAV(-1)	-1.96895	0.51397	-3.83086	0.0050   *
PSAV(-2)	-1.63500	0.56232	-2.90761	0.0197   *
WSTIR(-1)	-0.15719	0.08390	-1.87378	0.0978
WSTIR(-2)	0.10437	0.06442	1.62027	0.1438
UN(-1)	0.07300	0.06649	1.09794	0.3042
UN(-2)	-0.25581	0.08427	-3.03556	0.0162   *

Source: Research estimation results from EViews 10

NB   * denotes significance at 0.05

From Table 7, 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 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 increases 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 absence of violence index in one-year lag and two-year lag are statistically significant at 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 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.

5.9 Error Correction Model (ECM)

The results of the error correction model are presented in the Tables 8 below.

Table 8: Estimated Error Correction Results

Variable	Coefficient	Std. Error	t-Statistic	P-value
C	-0.01292	0.06718	-0.19236	0.8538
D(Betas(-1))	-1.08437	0.15893	-6.82301	0.0005 *
D(Betas(-2))	-0.06602	0.13338	-0.49501	0.6382
D(CA(-1))	0.10076	0.01691	5.95905	0.0010 *
D(CA(-2))	0.08088	0.01806	4.47877	0.0042 *
D(Deflator(-1))	0.00031	0.00051	0.61540	0.5609
D(Deflator(-2))	0.00085	0.00047	-1.80920	0.1204
D(ED(-1))	-0.17030	0.45398	-0.37512	0.7205
D(ED(-2))	0.48840	0.40122	1.21729	0.2692
D(PSAV(-1))	-2.15318	0.42085	-5.11630	0.0022 *
D(PSAV(-2))	-1.82465	0.40413	-4.51504	0.0040 *
D(UN(-1))	0.11060	0.05573	1.98460	0.0944
D(UN(-2))	-0.30197	0.06812	-4.43275	0.0044 *
D(WSTIR(-1))	-0.18847	0.06601	-2.85524	0.0290 *
D(WSTIR(-2))	0.07199	0.06846	1.05163	0.3335
ECT(-1)	-1.40825	0.48287	-2.91643	0.0268 *

Source: Research estimation results from EViews 10

NB   * denotes significance at 0.05 level

In Table 8, 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 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 10% when one-year lagged current account balance increases by 100%, ceteris paribus. Furthermore, when two-year lagged current account balance increases by 100%, country risk increases by 8%. This finding is in line with apriori conditions and Cline (1984) who argues 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 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 5% level of significance since its p-value is less than 5%. In conclusion, country risk decreases by 30% (-0.30197) when unemployment rate increases by 100%, ceteris paribus. This finding contrasts the apriori 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.

5.10 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 9 below.

Table 9: Summary of Serial Correlation and Heteroscedasticity test

Residual diagnostics	Type of test	F-statistic	P-value
Serial Autocorrelation	Breusch-Godfrey Serial correlation LM test	0.4778	0.6514
Heteroscedasticity	Breusch-Pagan-Godfrey test	0.2055	0.9940

Source: Researcher’s own compilation from EViews 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.

5.11 Stability diagnostic Tests

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

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.

5.12 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 10 below:

5.13 Interviewees’ views on determinants of country risk

The interviewees perceived that GDP per capita, GDP deflator, external debt balance, current account balance, weighted short term interest rates, unemployment rate and political risk influence country risk. All of the interviewees point that external debt followed by current account balance, and then political risk are the major divers of country risk in Zambia. They further point to the heavy dependence on external loans, signalling an increase in default risk and country risk increase. This is in line with Ofstad and Tjønneland (2019) who argue that Zambia has borrowed heavily from China since 2012 and is now at risk falling into the debt trap. The authors further state that Zambia once benefited from the Heavily Indebted Poor Country debt relief in 2005.They also point to the persistent current deficits caused by overreliance on copper production. Fluctuation in the copper price on the international market worsens the terms of trade by negatively affecting the export receipts. This leads to an increase in Zambia’s country risk. They also mentioned that unemployment rate is extremely high leading to an increase in cases of social unrest and political risk.

5.14 Interviewees’ views on expected relationship country risk and its drivers

The results of the views of interviewees on the expected relationship between country risk and independent economic, financial and political variables indicates that the interviewees’ responses were in line with a priori conditions. They perceived that all the mentioned explanatory factors [per capita GDP(-); GDP deflator(+); external debt(+); current account balance(+/-); weighted short term interest rates(+/-); political risk(+); unemployment(+)] had the expected sign.

5.15 Interviewees’ views on whether country risk is diversifiable or not?
All of the interviewees perceived that country risk form a systematic risk (non-diversifiable) and is beyond the control of private investors. They suggest that, even if they diversify their portfolios in different countries, the contagion effect associated with this risk may adversely affect the return on their investments. This coincides with empirical literature that country risk is systematic in nature and cannot be diversified in the country’s financial portfolio (Erb, Harvey and Viskanta, 1997; Naumoski, 2011; Gangemi, Brooks and Faff, 2000; Damodaran, 2003; Esch, Keiffer and Lopez, 2005). Interviewees also point out that fiscal and monetary policy makers should implement effective and efficient policies to manage financial, economic and political variables that drive country risk.

6. Conclusion

The study concluded that the short run drivers for the country risk of Zambia 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. These findings converge with Ferreira (2010) who found that current account as a percentage of GDP largely influences 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 finding that interest rates are not statistically significant concurs with Gangemi, Brooks and Faff (2000) and Verma and Soydemir (2006) who established that interest rates had a trivial influence on Australian and Latin American country risk.

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 and then the government must 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 have to 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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