MODELING THE IMPACT OF POLITICAL RISK COMPONENTS ON MAJOR MACROECONOMIC VARIABLES

Sahajdeep Kaur¹ --- Shah Krushali² --- C. G. Accamma³ --- Namitha K Cheriyan⁴⁺

^1,2,3Postgraduate Student, Department of Commerce, CHRIST (Deemed to be University), Bengaluru, India.
⁴Assistant Professor, Department of Commerce, CHRIST (Deemed to be University), Bengaluru, India.

Keywords: Political risk, Governance indicators, Macroeconomic variables, Panel data, Emerging markets, Frontier markets.

JEL Classification: C58; O11; O47.

ARTICLE HISTORY: Received:5 June 2019 Revised:17 July 2019 Accepted:23 August 2019 Published:20 September 2019.

Contribution/ Originality: This study contributes to the existing literature by empirically analysing the impact of political risk on the economic growth of eleven emerging and frontier Asian economies. It quantitatively investigated the impact of specific components of political risk on major macroeconomic indicators.

1. INTRODUCTION

Political risk is a vital element affecting the development of any country. Countries, whether emerging or developed, are not immune to getting influenced by the political conditions prevailing in them (Diamonte et al., 1996). It has become imperative to study political risk and its components to understand their influence on the development of a country. The worldwide governance indicators provided by World Bank attempt to capture all the possible factors linked with the governance of a country which could contribute to political risk. It includes voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, the rule of law, and control of corruption as the components of political risk.

Attempts have been made in the literature to study the impact of political risk (Lehkonen and Heimonen, 2015) and its components on stock markets across the globe. For instance, Dimic et al. (2015) examined the components of political risk given by ICRG (International Country Risk Guide) in relation to the stock market returns to find out the impact of composite political risk and its components on emerging, frontier and developed stock markets. The study found that the composite risk influences all the three types of the market whereas the impact of individual components differs from market to market.

Given the importance of macroeconomic variables such as GDP, consumer prices, foreign inflows, interest rates, etc. as the indicators of growth in an economy, it is important to analyze how the political risk and its components are affecting them over long-term. There are several studies analyzing the impact of political instability on the economic growth of a country. They highlight the importance of the degree of political uncertainty in determining the economic growth of countries. For instance, Tabassam et al. (2016) while examining the various factors that contribute to political instability such as terrorism, election, regime, and strikes in Pakistan, found  a positive relationship between political stability and economic growth. They identified terrorism as averting local as well as foreign investors from investing their money in the country which ultimately leads to low productivity. Similarly, Yu and Wang (2013) found that investors consider it risky to invest in China taking into account the country’s preferred election policies. Smales (2014) also identified increased levels of uncertainty around the election as leading to higher levels of uncertainty in the market.

Literature also states that there is increased interconnectedness among the economies and countries in the globalized scenario through trade and investment (Baek and Qian, 2011) Such trade and investment are often exposed to the political risk prevailing in different countries. Le and Zak (2006) identified that risks associated with political instability were the most influential risks among various others, such as economic risk and policy changes. Jensen (2008) revealed that with globalization, there are higher possibilities of political risks due to past violence. The study identified the level of democracy as a key indicator for assessing political risk. Democracy was found to be having a positive effect, while violence had a negative effect on political risk. Lehkonen and Heimonen (2015) also found that the emerging markets are influenced by both democracy and political risk where lower political risk can increase market returns.

The literature also analyzes the impact of individual components of political risk with the real exchange rate of different countries. Gobinda and Khan (2005) established that various components of political risk have impacts on the appreciation and/or depreciation of currencies. The earlier studies also identified corruption as a major component of political risk affecting economic growth as well as the stock market. For instance, Kaplan and Akçoraoğlu (2017) identified corruption as negatively related to economic growth in OECD countries. Another study conducted among democratic and non-democratic countries confirmed the negative correlation between corruption and economic growth, but the polity seemed to be an influencing factor to this correlation. It showed that corruption affects the economic growth in non-democratic countries, whereas the impact is mitigated in the democratic countries (Drury et al., 2006). On the other hand, Abu et al. (2014) emphasized that the increase in political stability and economic development could lead to a reduction in corruption over the long term and vice-versa.

From the foreign investment point of view, Dutta and Roy (2011) empirically proved the interrelationship between foreign direct investments, financial developments, and political instability, and recorded that the existence of political instability in a country can make the investors skeptical about their investment decisions in the country. They pointed out that steady progress in financial development could be achieved with political stability, which can increase the FDI.

Literature revealed that considering the political conditions prevailing in a country is imperative in determining the economic growth of that country. It emphasized the need to dissect the concept of political risk and study the factors contributing to such political risk to model the economic growth in any country. However, it was found that there were only limited studies considering the components of political risk as factors affecting the economic growth of a country. Most studies considered only GDP as a proxy for economic growth. It was found that many of the major macroeconomic variables have not been studied to find out the impact of the political risk components on these variables. This paper adds to the existing literature in this regard and analyzed the impact of political risk on economic growth by considering the various components of political risk as well as the major macroeconomic variables. It aimed at expanding on the current literature by quantitatively investigating the impact of specific components of political risk on some major macroeconomic indicators.

2. DATA AND METHODOLOGY

The study uses the annual data of eleven Asian economies which were constituents of the emerging frontier market (EFM) Asia index of MSCI from 2004 to 2016. The Asian counterparts in the MSCI Emerging Markets Index are China, India, Indonesia, Korea, Malaysia, Pakistan, Philippines, Taiwan, and Thailand and in the frontier market index are Bangladesh, Sri Lanka, and Vietnam. Taiwan was not included in the analysis due to the lack of data. The study analysed the impact of political risk components on the major macroeconomic variables of these economies. The macroeconomic variables considered in this study included gross domestic product (GDP), foreign direct investment (net inflows) (FDI), inflation (consumer prices) (INF), real interest rate (RIR) and official exchange rate (ER). The data about all these variables were sourced from the list of world development indicators given by the World Bank Group.

To capture the components of political risk, the study relied upon the worldwide governance indicators given by the World Bank (Kaufmann et al., 2010) which collects data from several renowned sources and rescales such data to fit into the governance indicators. By doing so, it leaves no scope for omitting any factors that may have some contribution to the political risk. Thus, this data was considered as the most suitable to represent all the aspects of political risk.

These indicators were classified into six categories namely voice and accountability (VA), political stability and absence of violence (PSAV), government effectiveness (GE), regulatory quality (RQ), rule of law (RL) and control of corruption (CC)  (Kaufmann et al., 2010). Voice and Accountability measured the freedom of citizens to select their government and their interaction with the government depending on the regime type. Political stability and absence of violence assessed the likelihood of violence in the country due to internal and/or external political conflicts and measures the government’s capability to stay in the system. Government effectiveness checked the quality of governance in providing civil and public services. It also measured the government’s capability to execute its pronounced policies and their implementation without any political pressure. Regulatory quality captured the effectiveness of the government in making functional policies that facilitate the development of the private sector and the flow of investments into the country. The rule of law observed the quality of contract enforcement and property rights. It assessed the power and fairness of the legal system, i.e. police, courts, chances of crime, and compliance with the law. Lastly, control of corruption uncovered the extent to which private gain is obtained through political power.

The countries were ranked from 0-100 in each category based on the effectiveness of governance. A lower rank implied weak governance indicating high political risk, and a higher rank implied good governance with low political risk.

As the data included a combination of cross-section and time series, this paper used panel data analysis to determine the impact of political risk components on the macroeconomic variables. Panel regression estimates were used to model the impact of each of the six political risk components on each of the macroeconomic variables. Under the panel data framework, the pooled OLS regression was estimated as given in Equation 1:

This model pooled all the observations together irrespective of the heterogeneity that may exist among the countries. Given this limitation of pooled OLS model that it does not distinguish between the countries, to incorporate the country-specific characteristics in the model, the study estimated the fixed-effect model as given in Equation 2:

The fixed effect model assumed an unique intercept for each country, which remains fixed over time. Therefore, taking into consideration the country-specific factors, this model estimated the impact of the country-specific factors on the dependent variables, i.e. the macroeconomic variables. However, the fixed-effect model suffers from the drawback of high chances of the lack of precise estimation arising out of the inclusion of a good number of dummies in the model. To overcome such drawbacks, the random-effect model was estimated, which introduced an error term to account for the country-specific factors as follows:

3. EMPIRICAL RESULTS AND DISCUSSIONS

3.1. Data Exploration

Descriptive statistics were used to understand the basic characteristics of the data used in the study. The descriptive statistics of the political risk components are presented in Table 1. The mean value of each political risk component ranged from 0 to 100, where 100 indicated no political risk, and 0 indicated the highest political risk on account of the corresponding component.

Table-1. Descriptive statistics.

Political risk components	Mean	Std. dev.	Min.	Max.
Voice and accountability (VA)	36.452	19.129	4.694	72.115
Political stability and absence of violence (PSAV)	25.837	19.627	0.473	65.217
Government effectiveness (GE)	53.915	17.835	19.117	86.255
Regulatory quality (RQ)	46.112	17.385	12.315	84.134
Rule of law (RL)	45.606	17.543	15.789	86.057
Control of corruption (CC)	39.003	17.011	1.463	72.815

The Table 1 shows that political stability and absence of violence which includes government instability, violence within the country and actions of foreign countries affecting the investment posed the highest risk for the countries as it had the lowest mean value.

On the other hand, government effectiveness posed the least amount of threat to the countries.  It implied that the countries considered in the study performed comparatively better in ensuring the quality of governance by providing effective civil and public services. There were no countries which were immune from political risk. Each of the political risk components had some impact on the countries considered as none of the parameters exhibited the maximum value of 100.

Table-2. Bivariate correlation analysis.

Political risk components	VA	PSAV	GE	RQ	RL	CC
VA	1	0.024	0.313	0.433	0.514	0.382
PSAV	0.024	1	0.658	0.554	0.646	0.683
GE	0.313	0.658	1	0.92	0.865	0.895
RQ	0.433	0.554	0.92	1	0.862	0.879
RL	0.514	0.646	0.865	0.862	1	0.941
CC	0.382	0.683	0.895	0.879	0.941	1

Correlation analysis was performed to find out the strength of the relationship between the components of political risk. Table 2 exhibits the results of the correlation analysis. It can be inferred from the table that all the components of political risk were strongly correlated at the 1% significance level except VA and PSAV, which exhibited a weak correlation among them. For all the other component pairs, the correlation coefficients were positive and ranged from 0.3 to 0.9, implying a moderate to strong correlation among them. Accounting for the high correlation coefficients among the components of political risk, the study estimated the regression models separately for each of the macroeconomic variables as well as political risk components except VA and PSAV.

3.2. Results of Panel Regression

The study used panel data regression models to study the impact of political risk components on major macroeconomic variables. Each panel in Table 3 through Table 7 presents the regression estimates for each of the political risk components individually except in panel A, which provides the coefficients of VA as well as PSAV. 

Model 1, model 2, and model 3 in the tables represent the pooled OLS model, fixed-effects model, and random effects model, respectively. The specification test provides the results of the specification tests employed where m demotes the statistics for Hausman test and F indicates the statistics for Wald test with ‘Sig.’ showing the significance level for the corresponding test, which helps to identify the model to be selected.

Table-3. Regression results with GDP as the dependent variable.

Panels	Model 1	Model 2	Model 3	Spec. Test
Panel A
Constant	7.138***	4.212***	5.992***
	-14.49	-2.915	-5.641
VA	-0.032***	0.035	-0.01	m=2.677
	(-3.174)	-0.865	(-0.457)	Sig.=0.262
PSAV	-0.005	0.011	0.007
	(-0.561)	-0.449	-0.387
Panel B
Constant	6.700***	2.559	5.251***
	-10.407	-1.196	-3.693	m=2.566
GE	-0.016	0.06	0.01	Sig. = 0.109
	(-1.474)	-1.517	-0.415
Panel C
Constant	7.394***	3.942**	6.221***
	-13.218	-2.194	-5.158	m=2.586
RQ	-0.034***	0.04	-0.009	Sig. = 0.107
	(-3.047)	-1.036	(-0.386)
Panel D
Constant	6.976***	5.667***	6.53***
	-12.516	-3.076	-5.273	m=0.352
RL	-0.025**	0.002	-0.016	Sig.=0.552
	(-2.263)	-0.071	(-0.653)
Panel E
Constant	6.619***	5.737***	6.166***
	-13.136	-4.945	-6.081	m=0.315
CC	-0.021*	0.001	-0.009	Sig.=0.574
	(-1.775)	-0.052	(-0.427)

Table 3 presents the impact of each of the political risk components on the GDP of the countries considered for the study. The results showed that the random effect model as represented by model 3 was appropriate for all the five panels from Panel A through Panel E. From model 3, it could be identified that none of the political risk components were significantly affecting the GDP of the countries considered for the study which went against the common perception.

Table 4 provides the results of regression models estimated to examine the impact of political risk components on the foreign direct investments (FDI) of the countries considered. The study identified the random effect model as the most appropriate, considering the result of the Hausman test in all the panels of Table 4.

The estimates of the random effect model in Panel B and Panel E provided the statistically significant estimates. It revealed that the government effectiveness and control of corruption were positively contributing to the FDI of the countries considered in the study implying that they could attract more foreign direct investments by improving their ranking on government effectiveness and control of corruption. In other words, when the quality of governance is improved in a country, it will create better infrastructure for foreign firms to operate in that country. Also, when the government can provide better civil and public services with prompt execution of its pronounced policies free from any political pressure, the foreign investors find it more attractive to invest in such countries.

Table-4. Regression results with FDI as the dependent variable.

Panels	Model 1	Model 2	Model 3	Spec. Test
Panel A
Constant	6.69E+10***	3.33E+10*	4.17E+10*
	-6.038	-1.85	-1.798
VA	-1.25E+09***	-1.47E+08	-4.31E+08	m=1.297
	(-5.405)	(-0.291)	(-0.984)	Sig.=0.522
PSAV	1.51E+08	-1.12E+08	-34830128
	-0.668	(-0.337)	(-0.112)
Panel B
Constant	1.41E+09	-2.31E+10	-1.82E+10
	-0.091	-0.872	(-0.616)	m=0.179
GE	4.39E+08	8.93E+08*	8.03E+08*	Sig.=0.671
	-1.625	-1.823	-1.821
Panel C
Constant	2.30E+10*	3.97E+10*	3.66E+10
	-1.673	-1.773	-1.36	m=0.108
RQ	43977314	-3.17E+08	-2.50E+08	Sig.=0.742
	-0.157	(-0.655)	(-0.57)
Panel D
Constant	3.50E+10**	-1.05E+10	-1.62E+09
	-2.593	(-0.463)	(-0.06)	m=0.833
RL	-2.18E+08	7.80E+08	5.85E+08	Sig.=0.361
	(-0.788)	-1.575	-1.31
Panel E
Constant	1.69E+10	-9.29E+09	-6.51E+09
	-1.394	(-0.659)	(-0.293)	m=0.412
CC	2.09E+08	8.81E+08**	8.10E+08**	Sig.=0.520
	-0.733	-2.46	-2.378

Similarly, when the ranking was improved in terms of the control of corruption, it indicated a low political risk in such countries on account of the former. This makes it feasible and beneficial for the foreign companies to invest in such countries as the firms don’t need to pay bribes to public officials for conducting their business which in turn reduces the cost of operation. It enables the business to establish and manage their operations systematically free from the pressure of public power.

Table 5 establishes the regression results with the real interest rate (RIR) as the dependent variable. The results of the specification test suggested that the random effect model was the most appropriate model for all the cases except for the one in Panel E where control of corruption was the independent variable. The null hypothesis of the Hausman test for the variables in Panel E was rejected at the 1% significance level and thus demanded the Wald test. The result of the Wald test as presented by F and the corresponding significance value suggested a fixed-effect model as the appropriate one. Panel A of Table 5 shows that the random effect estimates of voice and accountability were exhibiting a statistically significant positive impact on the real interest rate.

When voice and accountability are ensured, the policies of governments essentially focus on improving the standard of living of their people. They ensure substantive steps to curb the inflationary situation prevailing in the country, which successively increase the real interest rate.

Similarly, the fixed-effect model in Panel E exhibited a highly statistically significant positive coefficient for the control of corruption, indicating that controlling corruption also increased the real interest rate. When a country ranked high in the indicator of control of corruption, it signified better governance with lower political risk. Business finds it attractive to invest more money when corruption is controlled in the country as it reduces the cost of doing business. They borrow more money for investment controlling the money supply and thus curbing any inflationary situation prevailing in the economy, adding to an increase in the real interest rate.

Table-5. Regression results with RIR as the dependent variable

Panels	Model 1	Model 2	Model 3	Spec. Test
Panel A
Constant	1.901***	2.177	1.86**
	-2.771	-0.858	-2.426
VA	0.045***	-0.028	0.045***	m=4.431
	-3.169	(-0.396)	-2.795	Sig.=0.109
PSAV	-0.019	0.073	-0.017
	(-1.396)	-1.573	(-1.106)
Panel B
Constant	4.041***	-0.524	3.739***
	-4.474	(-0.138)	-2.824	m=1.427
GE	-0.018	0.066	-0.012	Sig.=0.232
	(-1.148)	-0.946	(-0.545)
Panel C
Constant	3.669***	0.019	3.389***
	-4.553	-0.006	-2.834	m=1.3
RQ	-0.013	0.065	-0.007	Sig.=0.254
	(-0.813)	-0.961	(-0.299)
Panel D
Constant	3.136***	-3.109	2.651**
	-3.951	(-0.971)	-2.213	m=3.711
RL	-0.001	0.135*	0.008	Sig.=0.054
	(-0.108)	-1.932	-0.364
Panel E
Constant	3.252***	-2.679	2.424**	m=8.664
	-4.565	(-1.352)	-2.349	Sig.=0.003
				F=3.455
CC	-0.005	0.147***	0.016	Sig.= 0.0005
	(-0.301)	-2.919	-0.683

Table 6 presents the regression results for the impact of political risk components on inflation in the countries considered. The specification test indicated a random effect model as appropriate for the regression models in all the panels. It was observed that all the governance indicators, except voice and accountability, were imposing a statistically significant negative impact on inflation. It can be inferred from the table that any increase in the political risk components viz. political stability and absence of violence (PSAV), government effectiveness (GE), regulatory quality (RQ), the rule of law (RL), and control of corruption (CC) could worsen the inflationary situations prevailing in the economy. In other words, the elimination of politically motivated violence including terrorism and existence of a politically stable environment, implementation of qualitative and sound policies, firmness of rules and laws and their compliance by the citizens and stringent control of corruption can curb the inflationary conditions in any economy.

Table-6. Regression results with inflation as the dependent variable.

Panels	Model 1	Model 2	Model 3	Spec. Test
Panel A
Constant	8.212***	7.500***	8.677***
	-9.843	-3.015	-4.942
VA	-0.023	0.08	-0.001	m=4.900
	(-1.360)	-1.159	(-0.032)	Sig.=0.086
PSAV	-0.064***	-0.184***	-0.114***
	(-3.788)	(-4.011)	(-3.560)
Panel B
Constant	12.442***	13.127***	12.517***
	-13.148	-3.395	-7.869	m=0.029
GE	-0.125***	-0.138*	-0.126***	Sig.=0.863
	(-7.525)	(-1.931)	(-4.553)
Panel C
Constant	11.710***	13.676***	11.951***
	-13.994	-4.271	-8.489	m=0.351
RQ	-0.130***	-0.173**	-0.136***	Sig.=0.553
	(-7.699)	(-2.506)	(-4.800)
Panel D
Constant	9.870***	12.097***	10.372***
	-10.917	-3.667	-5.648	m=0.373
RL	-0.091***	-0.140*	-0.102***	Sig.=0.541
	(-4.964)	(-1.952)	(-2.787)
Panel E
Constant	9.822***	9.984***	9.876***
	-12.379	-4.816	-6.745	m=0.004
CC	-0.106***	-0.110**	-0.107***	Sig.=0.946
	(-5.692)	(-2.095)	(-3.247)

Table-7. Regression results with ER as the dependent variable.

Panels	Model 1	Model 2	Model 3	Spec. Test
Panel A
Constant	3.523***	4.567***	4.565***
	-6.654	-46.35	-5.154	m=0.487
VA	0.014	0.006**	0.006**	Sig.=0.783
	-1.306	-2.199	-2.207
PSAV	0.032***	0.004**	0.004***
	-3.053	-2.463	-2.487
Panel B
Constant	6.505***	5.234***	5.239***
	-9.503	-34.394	-6.09	m=0.255
GE	-0.029**	-0.006**	-0.006**	Sig.=0.613
	(-2.462)	(-2.174)	(-2.207)
Panel C
Constant	6.422***	4.54***	4.546***
	-10.577	-36.181	-5.337	m=0.74
RQ	-0.032***	0.007***	0.007***	Sig.=0.389
	(-2.669)	-2.914	-2.871
Panel D
Constant	5.532***	4.635***	4.638***
	-9.085	-35.641	-5.326	m=0.166
RL	-0.013	0.005**	0.005**	Sig.=0.683
	(-1.103)	-2.076	-2.056
Panel E
Constant	5.717***	4.679***	4.681***
	-10.51	-57.87	-5.445	m=0.305
CC	-0.02	0.005***	0.005***	Sig.=0.58
	(-1.629)	-2.802	-2.783

Finally, Table 7 provides the estimates of regression models assessing how responsive the exchange rate of the selected countries were to the components of political risk. The specification test ensured the random-effect model as most appropriate for all the panels. The exchange rate considered in the study was the value of the local currency against the US dollar.

The results showed that it was only when the government provided commendable services to its citizens and formulated sound policies that the local currency appreciated significantly. Government policy implementation was found to have a direct impact on the currency value. As the foreign inflows (FDI) and exchange rate are closely related, combining the results from Table 4, the study identified that when the government attracted more foreign funds with its sound policies, it increased the demand for the local currency which resulted in the appreciation of the same. The improved political conditions in the country, not only attract foreign investors to invest in the country but also enable the local investors to invest in other countries as a result of an increase in their wealth. Such conditions escalate the demand for the dollar, which in turn decreased the value of the local currency.

Similarly, improved living standards arising out of better voice and accountability (VA), political stability and absence of violence (PSAV), regulatory quality (RQ), rule of law (RL), and control of corruption (CC) augment the import of foreign goods and services to the country which requires payments to be made in US dollars. An increased supply of local currency to the system to match the demand for the US dollar may then depreciate the value of the former.

4. CONCLUSION

This study examined the impact of political risk components on the macroeconomic variables of emerging and frontier economies of Asia. Given that these variables are the indicators of development of any country, the impact of political risk components on such variables was of significant importance. By modeling each macroeconomic variable individually, the study found that each of them is affected by different political risk components. The results showed that the exchange rate was the only macroeconomic variable which was affected by all the political risk components. FDI, inflation, and the real interest rate were also found to be affected by one or more political risk components. Most importantly, the study found that the GDP of the countries considered for the study was not affected by any of the political risk components, the reason for which could provide scope for further research. Similarly, it revealed that irrespective of the inter-linkages among the various macroeconomic variables established in the literature, they are not always influenced by the same political risk components, the reasoning for which is for future research to determine.

Funding: This study received no specific financial support.

Competing Interests: The authors declare that they have no competing interests.

Acknowledgement: All authors contributed equally to the conception and design of the study.

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