Does political risk matter for infrastructure investments? Empirical evidence

ABSTRACT Infrastructure assets are vital for economic development and integration, but they also encompass political risks. In Africa, infrastructure assets have remained a paradox where there is great potential for opportunities but very few projects get to the final phases. Adequate infrastructure can propagate the attainment of the Sustainable Development Goals whilst supporting recovery from the Covid-19 pandemic. Drawing from a longitudinal data set from 2000 to 2021 for 35 African countries, the paper empirically examined the nexus between infrastructure and political risk. Several techniques were employed to determine the dynamic effect, cointegration and causality between infrastructure and political risk. Controlling for the potential endogeneity in infrastructure the system Generalized Method of Moments, the relationship between political risk and infrastructure was ascertained. Furthermore, the ARDL-PMG was employed to determine the cointegration and causal relationship between infrastructure and political risk. The results suggest a cointegration between infrastructure assets and political risk. Infrastructure adjusts to changes in political risk to its long-run equilibrium at a speed of adjustment of 16.9 per cent. Bridging infrastructure gaps in Africa requires an extensive set of actions. Thus, the policy derivatives of our findings, suggest controlling the proliferation of political risk to support infrastructure investment.


Introduction
An essential for sustainable and inclusive growth globally is a modern and efficient infrastructure.In most emerging markets and developing economies infrastructure development is declining below the estimated required optimal level of 5 per cent of the gross domestic product (GDP) per annum until 2030 (WEF 2014).The 2030 agenda recognizes the importance of infrastructure investment, as goal 9 aims to 'build resilient infrastructure, promote sustainable industrialization and foster innovation (United Nations Department of Economic and Social Affairs (UNDESA), n.d.; United Nations Conference on Trade and Development (UNCTAD) 2021).Infrastructure development is a catalyst for economic development as it aids employment, trade and investment among others (Roland-Holst 2009; United Nations (UNCTAD) 2021).The quantity and quality of infrastructure is an epitome of sustainable development, Africa ranks consistently at the bottom in terms of quantity, access, and quality infrastructure investments.Henisz and Zelner (1999) postulated that projects with high sunk costs have very high political risks.Africa lacks the coverage of investments in essential infrastructure which include energy, road and rail transportation, and water infrastructure which are required for sustainable economic development and improved wellbeing (World Bank 2018;UNCTAD 2021).
The Infrastructure Consortium for Africa (ICA (2019) suggested that Africa's poor infrastructure in road, rail and harbors contributes 30-40% to the costs of tradeable goods among African countries.The industrialization of African economies is retarded by the lack of productive infrastructure that assists firms to have comparative advantage and thrive (Africa Development Bank (AfDB) 2018).Policy debates argue that infrastructure investment in Africa barely passes the planning stage as 80 per cent of infrastructure projects fade at the feasibility and business-planning stage (Brookings Institute, 2021).According to the World Bank (2015), investors identify political risks among the risks that determine the location of their investments.Infrastructure development is important for economic and productivity growth (Romp and De Haan 2007;Boston Consulting Group (BCG) 2015) yet political risk is a key determinant of infrastructure disinvestment in emerging and developing markets.AON (2021) argued that political risk flourishes in regions where political violence, institutional and regulatory risk and economic conditions allow it to build.Sixty per cent of global economies were exposed to increasing political risk for the year 2021 (AON, 2021).As investment in infrastructure is a long-term project different phases of the project might face different challenges of which political risk is important at all stages of infrastructure investment (World Bank, 2015;Müllner and Dorobantu 2022).
Monetary policy, fiscal policy and the prudential regulation of the banking sector all play a role in infrastructure investment.In Africa, infrastructure investment is largely financed by the government, and it is constrained by budgetary restrictions (World Bank 2018).Improved access, quantity and quality infrastructure are key for both productivities of human and physical capital and growth (Africa Renewal n.d.).The African Development Bank (AfDB, 2018) opined that Africa has an infrastructure investment gap in the range of $68-$108 billion.The World Bank (2018) opined that financing infrastructure investments is not the major hindering factor in Africa, but rather the lack of infrastructure governance which requires a political will.The empirical literature is very scant on the role of political risk on the quantity and quality of infrastructure in Africa, yet Africa's infrastructure is still lagging both in access, quantity and quality.
There is a consensus that political risk determines the level of infrastructure investment in an economy whilst infrastructure investment can also generate political risks (Müllner and Dorobantu 2022).It is also evident that most countries with better quantity and quality infrastructure have lower political risks than their peers.Lagging infrastructure investment in Africa is among the top challenges to policymakers to ensure cost-effective and efficient trade of goods and services (AfDB 2018;McKinsey 2020).Political risk carries several implications and higher political risks are harmful to infrastructure investment.Explicitly, political risks are among the inefficiencies and common impediments to infrastructure investment in developing countries (McKinsey 2020;Müllner and Dorobantu 2022).Therefore reducing political risk suggests an increase in infrastructure investments.Failure to address political risk factors implies that many developing countries remain exposed to infrastructure gaps and challenges.The main research question for this study is what is the impact of political risk on infrastructure in developing countries with a focus on African countries?
In this paper, the role of political risk on the quantity and quality of infrastructure in Africa is examined.There is considerable literature on the determinants of investment and risk, however most focused on foreign direct investment than particular focus on infrastructure (Asongu, Akpan, and Isihak 2018;Nunnenkamp, 2002).Investment in infrastructure assets naturally requires a longer period than the political cycles (Calderon and Servén 2004;Müllner and Dorobantu 2022).Inadequate infrastructure and higher political risks in developing countries particularly in Africa motivate this study.Infrastructure investment is instrumental to the improvement of trade integration and the attainment of sustainable development goals in Africa.The volatile and increasing global political risks and their effects on infrastructure investment can delay or deter investments in locations where these risks are too high.Additionally, the revenues for investors and cost base are contingent on political stability (Bizimana et al. 2021).There is a gap in the literature on the political risk-infrastructure nexus.This study contributes to the debate in the literature on the nexus between political risk and infrastructure investment in developing countries with a particular focus on Africa.
It is argued that only 38 per cent of Africa's population has access to electricity whilst the internet penetration rate in Africa is only 10 per cent (Africa Renewal n.d).The World Bank (2018) suggested that human and economic productivity growth in Africa is slow due to the poor state of infrastructure resulting in reduced economic percentage growth of 2 per cent annually with a 40 per cent reduction in business productivity (World Bank 2018).Most developing countries have inadequate quantity and quality of infrastructure.Irrespective of rich endowments in mineral and other natural resources Africa's productivity rate is the lowest in the world and lack of access, quantity and quality infrastructure is argued to be a contributing factor (World Bank 2018;Marsh, 2016).The gap in infrastructure investment in developing countries, Africa included can be reduced by addressing the risks that investors consider as key in infrastructure investments (Schwartz, Ruiz-Nuñez, and Chelsky 2014).
Empirical evidence on infrastructure investments is relatively thin and fragmented thereby making the synthesis of existing literature challenging.Therefore this paper contributes to the literature by extending the analysis of the nexus between infrastructure investment and political risk.Furthermore, the strength of this paper is a particular focus on African countries which tend to have higher political risk and a dearth of access to the quantity and quality infrastructure.The impact of Covid-19 has added challenges to financing the infrastructure gap in Africa through the disruption of several projects (see International Finance Corporation (IFC), 2021).However, the analysis of the role of Covid-19 on infrastructure investment is outside the scope of this paper.Therefore, the objective of this study is to examine the relationship between political risk and infrastructure investment in Africa.The study provides policymakers and companies alike to take a holistic view of the potential levers in infrastructure investment to mitigate political risk in infrastructure development.Furthermore, the findings of the paper stimulate further research on infrastructure investment and risk in emerging and developing economies.
This paper is organized as follows: Section 2 reviews the theoretical and empirical literature.Section 3 describes the data and discusses the methodology used for the study.Section 4 analyzes, reports and discusses the empirical results of the study.Concluding remarks for the study are summarized in Section 5.

Literature review
Theoretically, all forms of risk are negatively associated with different types of investment.Infrastructure features such as extended maturity period, sunk costs, the public and the governments as clients and large capital outlay make infrastructure investment more sensitive to political risk (Jiang et al. 2021;Müllner and Dorobantu 2022).Calderon, Cantu, and Chuhan-Pole (2018) argued that the strength of institutions governing public investment management systems and government procurement escalates the output multiplier of investment spending in infrastructure.Thus infrastructure quantity and quality are supposedly negatively affected by political risk.Araya, Schwartz, and Andres (2013), Pehlivanoğlu, Akdağ, and Alola (2021) found that political risk is a predictor of investment levels in developing countries as it is a major determinant of the geographical location of investment decisions.This was supported by Gupta and Sharma (2022) who found that political risks are among the major barriers to scaling up infrastructure investments globally.Conflict-affected countries are less attractive for long-term infrastructure investments, opined Aguirre (2016).The study further argued that conflict-affected countries exhibit a lag of 6-7 years to attract significant infrastructure development from the day the conflict ended which supported the previous assertions of Araya, Schwartz, and Andres (2013).The host countries' political risk is essential for long-term infrastructure investments (Müllner 2016;Araya, Schwartz, and Andres (2013); Gupta and Sharma 2022).Calderon and Servén (2004) suggest that political risk is endemic to most developing and emerging markets and investors appear to have a strong aversion to assets with high political risk.This was supported by Müllner and Dorobantu (2022) who examined the degree of political risk in developing countries and conclude that it reflects the probability of opportunistic behavior that can negatively affect investment in infrastructure.
There is no universal definition of political risk as it is defined as the generic concept addressing the risks to investments and contracts from political change or instability (Wells, 1998;Marsh, 2016).Walter (2016) opined that infrastructure development decisions take time and that developing sustainable infrastructure is crucial to economic growth.Furthermore, decisions relating to infrastructure are extremely sensitive to political and economic cycles, with political risk having a direct impact on infrastructure development (Percoco 2014;OECD, 2015).Moszoro et al. (2015) and Chang et al. (2018) found that investment in infrastructure is more sensitive to political risk compared to foreign direct investment.Developing countries are inclined to higher levels of political risk, yet they need infrastructure investment which is crucial in generating economic growth (Percoco 2014, World Bank, 2022).
Furthermore, the lack of large and deep financial markets in developing and emerging markets restricts investment in infrastructure (Ba, Gasmi, and Noumba 2010).Heavy bureaucracy and controls directly affect infrastructure investment and greenfield investments (investment in new infrastructure) are more prone to political risks (Stewart and Yermo 2012).Müllner and Dorobantu (2022) found that infrastructure investments are prone to political risks as at ex-ante, governments offer strong incentives to attract investment which they tend to strongly renege on these commitments ex-post of infrastructure investment.Political risk has a direct potential impact on the profitability and viability of infrastructure investment.Jiang et al. (2021) found that developing countries are more prone to political risk due to frequent policy changes, unstable governments and internal conflicts among others.A decrease in corruption levels and improvements in the rule of law is associated with an increase in infrastructure investment (Hammami et al. 2006;Fernando et al., 2017).However, this finding contradicts Banerjee, Oetzel, and Ranganathan (2006) whose findings suggest that highly corrupt countries with inefficient governments attract large private participation in infrastructure.Moszoro et. al. (2015) found that a weaker political and institutional environment increases the risk that infrastructure investors face.Thus the authors suggest that higher political risks lower the probability of investment in infrastructure as well as the amount invested.Industrialization and population growth are supported by sustainable infrastructure (Kahale 2011).Governance gaps in developing countries can impede the development of sustainable infrastructures such as roads, ports, and electrical power supplies (Wegrich, Kostka, and Hammerschmid 2017).
Furthermore, countries with a more stable macroeconomic environment receive more investments in infrastructure (Moszoro et al. 2015).In certain emerging and developing economies inflation is a principal concern-this includes not just high inflation but the overall volatility of inflation and the central bank's ability to control it, and to successfully telegraph policy to market participants (OECD, 2015).
Stable inflation, access to finance, freedom from corruption, rule of law, quality of regulations, and the number of disputes, are shown to be relevant factors for the determination of private participation in infrastructure (Moszoro et al. 2015).Hence, the relationship between political risk and infrastructure investments remains inconclusive.Section 3 discusses the data and methodology of the study.

Data and variables
The analysis comprises annual panel data for the period 2000-2021 for a sample of African countries.The countries included in the study were selected based on the availability of data for all the variables.The linkage between political risk institutions and infrastructure development in Africa is our particular concern.Included are 35 Africans of which all the data for the regression analysis variables are available.Most of the data were sourced from the World Development Indicators and the International Country Risk Group as shown in Table 1.The International Country Risk Guide (ICRG) provided by the Political Risk Services (PRS) Group was used to obtain the proxies for political risk.Twelve risk indicators which also provide the components of the political institution from the ICRG are used and they are defined as follows in Table 1.Consistency with Calderon andServén (2004, 2008) the study used physical infrastructure measures than monetary infrastructure measures.Table 1 summarized the definition of the variables and the source of the data.The choice of the explanatory variables is guided by literature on the determinants of infrastructure investments.
The nexus between infrastructure and political risk was examined hypothesizing that infrastructure investment is a function of political risk, economic growth, population, inflation and.

INFRAS = f (PR INDEX, GDPGR, POPN, INF)
Similar to other empirical studies that have studied the infrastructure-political risk nexus the variables of interest in Equation ( 1) are political risk (PR_INDEX), gross domestic product growth (GDPGR), population (POPN) and inflation (INF).Political risk is the main variable of interest as the determinant of infrastructure investment for the sample of countries in our study.The infrastructure investment and political risk nexus remain elusive.Empirical research on the nexus remains fragmented and inconclusive (Banerjee, Oetzel, and Ranganathan 2006;Pehlivanoğlu, Akdağ, and Alola 2021;Gupta and Sharma 2022).
The size of the population can increase the demand for greenfield infrastructure investments and this can put pressure on the brownfield such that replenishing infrastructure investment becomes a requirement (Heller 2010;Kahale 2011).Cerra et al. (2017) argued that infrastructure investment in developing countries is responsive to factors of social development such as population among others.Regan (2017) and Kumo (2012) suggested that the growth in the gross domestic product is a linchpin for infrastructure investment.This was contrasted by Stungwa and Daw (2021), and Banerjee, Duflo, and Qian (2020) who argued that the growth in the gross domestic product has no role in infrastructure investment.Hence it is inconclusive whether the growth in the gross domestic product is a determinant of infrastructure investment.In line with Cerra et al. (2017), the study also considered inflation as a determinant of infrastructure investment.

Methodology
The relationship between political risk and infrastructure was examined using the system generalized method of moments.The system (GMM) estimation developed by Arellano and Bond (1991) was employed to alleviate the endogeneity issues and bias stemming from the correlation between the lagged dependent variable and fixed effects in the error term.There are possibilities of endogeneity in the explanatory variables hence the study also tested for cross-section dependence.This approach permits relaxing the assumption of strong exogeneity of the explanatory variables by allowing them to be correlated with current and past realizations of the time-varying error term.In this context, we use a mixture of internal instruments in the spirit of Arellano and Bond (1991), which is suitable for the use of lags of the explanatory variables, along with external instruments for our variables of interest.Diagnostic estimations for time series and cross-sectional studies were performed before estimating the model using the system GMM.Sargan (1958) and Hansen (1982) tests were performed for the validity of the instruments.
The Blundell & Bond (2000) was applied to check for over-identifying restrictions, and the Arellano-Bond test (AR1) and (AR2) (see Roodman 2009) to check for correlation of the error terms that assure the effectiveness of the results (Arellano and Bond 1991;Windmeijer, 2005).Furthermore, the system GMM estimator was used since it does not require the use of external instruments other than the variables already included in the dataset.Therefore the system GMM applied to the econometric models is summarized in Equation (1).
where Δ is the differentiator; INFR is the infrastructure, PR_INDEX is a political risk index constructed using principal component analysis (PCA) with variables including government stability, socioeconomic pressures, investment profile, internal conflict, external conflict, level of corruption, the influence of the military in politics, religious tensions, law and order, ethnic groups tension, democratic accountability and the institutional strength and quality of the bureaucracy; X represent a vector of control variables which include economic growth, population, inflation and the m i captures the cross-country heterogeneity; 1 it represent the unobserved regression residual.Equation ( 1) is estimated consistently with Roodman (2009) where forward orthogonal deviations are used to restrict over-identification or limit instrument proliferation.The common factors are dealt with through the inclusion of period-specific dummies, and unobserved country effects are handled by differencing.
The GMM method has the advantage of controlling for the endogeneity between political risk and infrastructure investment and to control for this endogeneity, instrumental variables were relied upon.However, the system GMM methodology only gives the results for a deterministic relationship with limitations in using the methodology to examine the long run, short run and causal relationships between political risk and infrastructure investment.Infrastructure projects have longer cycles, thus the motivation to also examine this cointegrating and causal relationship.The panel-based autoregressive distributed lags (ARDL) was employed to determine the long-run and short-run effects (Pesaran et al., 1999;Apergis and Payne, 2010;Alola, Cop, and Alola 2019.).The panel ARDL has the advantage that it incorporates cointegration and also captures the short-run effects of the variables under study (see Engle and Granger 1987;Engle and Yoo, 1987;Hoffman and Rasche, 1996).The pooled mean group has the further advantage of allowing the intercepts, short-run coefficients, and the error variances to differ across groups whilst constraining the long-run coefficients to be the same across groups.Furthermore, the causal effects of the variables can be inferred using the same methodology.Thus the panel ARDL was run together with the error correction term for empirical analysis.Specifically, the models that were tested are specified in Equations ( 2)-( 5): ARDL Models.
Error Correction Models (ECM) where Δ is the first-difference operator; p, q is the lag length selected using the AIC, ECT is the error correction term, a is the constant, β & φ are short-run coefficients, φ is the coefficient of the error correction term and it shows the speed of adjustment to the long-run equilibrium, ω is the error term which is assumed to be normally distributed with zero mean and constant variance.The error correction term coefficient (φ) in the ECM equations explains the speed of adjustment of the system to the long-run equilibrium after a shock in the short run.The coefficient of the ECT (φ) is expected to be negative and statistically significant to show how the variables converge to the equilibrium level after short-run disequilibrium (Bildirici and Kayıkçı, 2013).

Results and discussion
The descriptive statistics of the study are presented in Table 2 with 768 total observations.The standard deviation of the population and the indicators for political risk were too high hence natural logarithm for the population was used in the analysis.
Correlation analysis is presented in the appendices.
Unit root/Stationarity Test.Table 3 presents the results of the unit root tests.The results in Table 3 indicate that the variables are stationary at level and at first difference.
After establishing the level of integration of the variable the deterministic relationship between infrastructure and political risk is tested using the System Generalized Method of Moment estimator.The empirical strategy is based on the estimation of Equations ( 2)-( 4) relating to infrastructure and political risk with a set of control variables in a panel data set.The results of the system GMM are reported in Table 4.
The study employed the principal component analysis (PCA) to build an aggregate index of political risk using 12 indicators from the ICRG as explained in Table 1.The results of the PCA are in Table A3 in the Appendix.
The study expected a negative relationship between infrastructure and political risk.However, there is a statistically significant and positive relationship between the synthetic index of political risk and infrastructure.For a unit increase in political risk infrastructure increased by 15.5 per cent for the panel of countries in our study.Banerjee, Oetzel, and Ranganathan (2006) argued that highly corrupt countries with inefficient governments attract large private participation in infrastructure as the investors are capable of 'purchasing' stability and safety for their infrastructure assets/ investments.This is also consistent with the findings of Siegel (2007), Faccio (2010), and Müllner and Dorobantu (2022) whose findings suggested that political risk can be managed by forming local political connections to secure the safety of infrastructure investments.Furthermore, the findings of this study are supported by prior studies such as Sobják (2018) who found that infrastructure projects in Africa are often hastily approved for personal interests than the socioeconomic rationale.Hence in this case the relationship between political risk and infrastructure investment can be positive.
However, there is a lack of sufficient empirical evidence on increased infrastructure development with an increase in political risk.Thus the positive relationship between infrastructure and political risk can be because of the use of a synthetic index for political risk (see the PCA in Table A3 in the Appendix).Individual indicators with a positive effect on infrastructure investments such as government stability, investment environment, law and order, democracy and bureaucracy which have a positive effect on infrastructure development have a total weighting of above 55 per cent.Hence this might have affected the net effects of the overall relationship between political risks to infrastructure.In addition, developing countries can have location-specific advantages that attract infrastructure investments from multinational corporations (MNCs) such that political risk is not sufficient enough to deter investment (see Dunning 1998;2014).Additionally, infrastructure investment in most developing countries is mostly provided by the public sector and private participation is typically channeled through Public-Private Partnerships (PPP).This is supported by the McKinsey (2020) report that the African government principally contributes 42 per cent of infrastructure investment projects.Public investment in infrastructure is less sensitive to political risk (McKinsey 2020).However, Gupta and Sharma (2022) contradict this assertion as the study argues that stateowned projects are prone to political risks and delays in the approval process.
Countries with higher economic growth are expected to attract more investments in infrastructure.In this study gross domestic product growth (GDPGR) has a significant and negative relationship with infrastructure nevertheless the relationship is weak.A unit increase in economic growth reduces infrastructure investment by 1 per cent.This result was not expected but given the context of the focus of this study growth in most countries in Africa has not filtered to improved infrastructure quantity and quality.The findings are consistent with Stungwa and Daw (2021), and Banerjee, Duflo, and Qian (2020) who found that infrastructure was not an instrument for gross domestic product growth.However, this was in contrast with Kumo (2012) who found a positive association between economic growth and infrastructure investment.There are other logistical challenges in Africa that affect infrastructure development such as poor prioritization of infrastructure projects, weaker feasibility studies on development projects and failure to adequately allocate risks despite progressive economic growth (McKinsey 2020).Notes: ***; **; * indicates that the null hypothesis of unit root tests is rejected at 1%, 5% and 10%, respectively.All the tests are at first difference (except where indicated otherwise.)Probabilities for all the tests assume asymptotic normality except for Fisher tests which are computed using the asymptotic Chi-square distribution.Source: Authors' calculations using Stata15.1 The study expected a positive relationship between population and infrastructure as the increase in population is expected to increase the demand for infrastructure.However, the study found a negative and statistically significant relationship between infrastructure and population.It is a possible result as higher and unexpected population density can put pressure on basic infrastructure (Asoka, Thuo, and Bunyasi 2013).If the size of the population is not determined and planned for it put surmountable pressure on basic infrastructure, this argument of a negative relationship between infrastructure and population is consistent with Tripathi (2017) for urban India.Furthermore, Baum-Snow's (2007) and Baum-Snow et al. (2017) studies in China supported a negative relationship between infrastructure and population as better infrastructure shifts the population away from major cities.

Cointegration and causality
The unit root tests in Table 2 had level and first difference stationary variables consequently a cointegration test was performed using 1 as the maximum lag (Table A1 in the Appendix) Cointegration and causality were therefore inferred using the pooled mean group (PMG).The pooled mean group was the best estimation method as determined by the (Hausman test) for testing the long-run relationship between infrastructure investment and political risk.Hence the results summarized in Table 5 are based on the PMG as it was a more efficient estimation method than MG and DFE as the Hausman test point to the rejection of the alternative model (MG) the results of which are in Table A3 in the Appendix.The results presented in Table 5 suggest a cointegration between the political risk index and infrastructure for the panel of countries in our study.Infrastructure development is sensitive and vulnerable to political risk in the panel of countries in our study.There is a negative relationship between infrastructure investment and political risk in developing countries.An increase in political risk reduces infrastructure by 32.2 per cent.Infrastructure assets are sensitive and vulnerable to political risk (OECD, 2015;McKinsey 2020).
A negative and significant coefficient of the error correction term indicates the presence of a long-run relationship and causal relationship between political risk and infrastructure investment for the countries in our sample.Furthermore, the error correction term signifies the speed of adjustment to the equilibrium path after short-run shocks.For this study, the error correction term is statistically significant at the 1% level with a negative coefficient.As expected the coefficient of the error correction term is negative and statistically significant.The speed of adjustment of the short-run disequilibrium to long-run equilibrium between the index of political risk and infrastructure is 16.9 per cent.That is any shift to a long-run equilibrium is corrected at speed of 16.9 per cent within a year.
Besides examining the cointegration between infrastructure and political risk the study determined the causal relationship between the variables and it is only, in the long run, where political risk causes infrastructure development.However, the synthetic index of political risk does not cause infrastructure in the short-run.There is a lack of empirical evidence on the causality between political risk and infrastructure investment.
Table 6 shows the relationship between the variables with political risk as the dependent variable (Equation ( 3)).
Estimating Equation (3) when political risk is the dependent variable the MG is the more efficient estimation method than PMG and DFE as the Hausman test point to the rejection of the alternative models (PMG & DFE).The results are presented in Table A4 in the appendix.
Table 6 shows that the political risk (political risk) and infrastructure are not cointegrated when political risk is used as the dependent variable and there are no causal effects of infrastructure on political risk in the panel of countries in the study.In summary, infrastructure does not cause political risk both in the short run and in the long run.

Conclusion and policy recommendation
The objective of this study was to determine the relationship between infrastructure investment and political risk in developing countries with a specific focus on African countries.The study was motivated by the increasing global political risks and inadequate infrastructure investments in developing countries, particularly in Africa.Infrastructure development is essential for the attainment of most sustainable development goals yet there is dearth of literature on the political risk-infrastructure nexus.Examining the nexus is essential as Africa has potential for infrastructure investment yet there is inadequate infrastructure to support the continental trade and economic integration.Using the system GMM the study determines the relationship between political risk and infrastructure investments.Contrary to the expectation of this study deterministic inference using the system GMM suggested a positive relationship between political risk and infrastructure assets.The results show that the infrastructure assets in Africa increase with an increase in political risk as the relationship is positive and statistically significant.
Furthermore, the study ascertained a cointegrating relationship between infrastructure and political risk using the pooled mean group.The findings of the studies suggest cointegration between political risk and infrastructure.In the long run the increase in political risk reduces the quantity of infrastructure by 32.2 per cent for the panel of countries in the study.The speed of adjustment to the long-run equilibrium between infrastructure and political risk is 16.9 per cent.Furthermore, the political risk has a causal effect on infrastructure.Although infrastructure development is found to be sensitive to political risk, the causal relationship that runs from infrastructure to political risk is not distinct.
Further research is recommended on the impact of Covid-19 on political risks in emerging and developing markets.During the pandemic, it appears there were politically motivated trade restrictions and empirical evidence is suggested on how this has affected future infrastructure investments.The limitation of the study was mainly on the availability of data, there is inadequate quantitative data available on the characteristics of infrastructure investments in developing/emerging markets.It is recommended to improve the availability of quality quantitative data to inform policy decisions.
Infrastructure investment in Africa appears to be more motivated by financial returns than its developmental impact therefore further research is needed on the developmental impact of infrastructure investments, especially the investments that foster regional integration.Policymakers need to create a conducive environment for investment in long-term projects such as infrastructure assets.The policymakers can ensure the establishment of strong institutions that limits the unilateral change of policies by the government to The influence of the military in politics could signal that the government is unable to function effectively and that, therefore, the country might have an unfavorable environment for business.

Table 1 .
Definition of variables.

Table 3 .
Unit root test results.

Table 5 .
The long-run relationship between infrastructure and political risk.

Table 6 .
The long-run relationship between infrastructure and political risk.reducepolitical risks emanating from policy uncertainty during political cycles.Furthermore, they should establish policy linkages that have positive outcomes on infrastructure investment.Finally, private players in infrastructure investment should focus on risk management of political risks in infrastructure investment in Africa than risk avoidance.

Table A1 .
Variable description, data sources and expected sign.quantifiessocioeconomicpressures at work in a society that might restrain government action or elevate social dissatisfaction and thus destabilize the political regime ICRG Negative (-) Investment Profile(INVEST)assesses the investment profile, that is, factors related to the risk of an investment that is not covered by other (financial and economic) risk components, such as contract viability (expropriation), profits repatriation or payment delays Stands for internal conflict, measuring political violence within the country and its actual or potential impact on governance by focusing on, for instance, civil war, terrorism, political violence or civil disorder., namely the risk to the incumbent government from foreign action, ranging from non-violent external pressure, such as diplomatic pressures, withholding aid or trade sanctions, to violent external pressures, ranging from cross-border conflicts to all-out war ICRG Negative (-)Religious Tensions(RELIG)Religious tensions stemming from the domination of society and/or governance by a single religious group seeking, for instance, to replace civil with religious law or to exclude other religions from the political and social process.relates to the democratic accountability of the government, that is, government responsiveness to its citizens including fundamental civil liberties and political rights ICRG Positive (+ve) Quality of bureaucracy (BUR)The institutional strength and quality of the bureaucracy might act as a shock absorber tending to reduce policy revisions if governments change.
quantifies socioeconomic pressures at work in a society that might restrain government action or elevate social dissatisfaction and thus destabilize the political regime ICRG Negative(-)