The Impact of Armed Conflict on Inward Foreign Direct Investment (FDI): An Analysis of Conflict Over Government, Conflict Over Territory, and FDI

ABSTRACT

We examine the relationship between armed conflict and foreign direct investment (FDI) in host countries. With data from 113 nations from 1984 to 2014, system GMM and macro-panel CCEMG estimators show that intrastate armed conflict decreases FDI in conflict-ridden hosts. Further, we distinguish two types of armed conflict – conflict over government and conflict over territory. Conflict over government has significant short-term and long-term negative impacts on FDI, while the effect of territorial conflicts appears insignificant. Our results also suggest that armed conflict's harmful impact on FDI mainly occurs in middle- and low-income host countries.

KEYWORDS:

• FDI
• political stability
• armed conflict
• conflict over government
• conflict over territory

JEL CODES:

• F21
• D74

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Notes

1 One example is the exacerbated risks of expropriation associated with instability, making FDI in a host country less attractive.

2 Interested readers are referred to Blair et al. (2022), who have thoroughly examined 15,583 books and articles and identified 75 peer reivewed studies (published in 1990 or later) that explicitly include a measure of conflict in the study of FDI determinants. Some of the 75 studies are not focusing on the relationship between conflict and FDI, but control for conflict in their empirical analysis.

3 Blair et al. (2022) show that out of the 75 studies they have identified, eight have adopted an estimation technique for potential endogeneity issues.

4 This monitoring cost c is paid to an auditor, who would check and confirm that the investor indeed has made a profit less than the contractual debt repayment.

5 $\int (1+\mathrm{\gamma \eta }{)}^{-1}{u}^{\mathrm{\prime }}(R_D^L)f(\eta )\mathrm{d\eta }=\int u^{\mathrm{\prime }}(R_D^L)f(\eta )\mathrm{d\eta }-\int {\displaystyle \frac{\mathrm{\gamma \eta }}{1+\mathrm{\gamma \eta }}{u}^{\mathrm{\prime }}(R_D^L)f(\eta )\mathrm{d\eta }=\int u^{\mathrm{\prime }}(R_D^L)f(\eta )\mathrm{d\eta }-\mathrm{cov}\left({\displaystyle \frac{\mathrm{\gamma \eta }}{1+\mathrm{\gamma \eta }},u^{\mathrm{\prime }}}\right),}$

where $E\left({\displaystyle \frac{\mathrm{\gamma \eta }}{1+\mathrm{\gamma \eta }}}\right)=0.$ From the above equation, we show that: ${\displaystyle \frac{\mathrm{\partial }\left({\displaystyle \frac{\mathrm{\gamma \eta }}{1+\mathrm{\gamma \eta }}}\right)}{\mathrm{\partial }\eta }>0\mathrm{and}{\displaystyle \frac{\mathrm{\partial }{u}^{\mathrm{\prime }}}{\mathrm{\partial }\eta }<0.}}$Therefore, $\mathrm{cov}(\cdot )<0$. We have $\mathrm{\Phi }<1$.

6 The first component on the right-hand side of equation (A7) is: $\mathrm{cov}\left(u^{\mathrm{\prime \prime }},\left[1-{\displaystyle \frac{1}{X_f(1+\mathrm{\gamma \eta })}(D+c)}\right]{\displaystyle \frac{\mathrm{\partial }{R}_D^L}{\mathrm{\partial }\gamma }}\right)=0,$as $\mathrm{\partial }{u}^{\mathrm{\prime \prime }}/\mathrm{\partial }\eta =u^{\mathrm{\prime \prime \prime }}\cdot \left(\mathrm{\partial }{R}_D^L/\mathrm{\partial \eta }\right)=0.$ The second component on the right-hand side of Equation (7) is equal to: $\left({\displaystyle \frac{D+c}{X_f}}\right)\mathrm{cov}\left(u^{\mathrm{\prime }},{\displaystyle \frac{\eta }{{(1+\mathrm{\gamma \eta })}^2}}\right)<0,$as $\mathrm{\partial }{u}^{\mathrm{\prime }}/\mathrm{\partial }\eta <0$, and ${\displaystyle \frac{\mathrm{\partial }\left[{\displaystyle \frac{\eta }{{(1+\mathrm{\gamma \eta })}^2}}\right]}{\mathrm{\partial }\eta }>0}$ for $|\eta |<1/\gamma$.

7 As discussed later in the paper, we use the system GMM estimation method. The first differencing of GMM removes country specific fixed effects.

8 The 12 components of the composite political risk index include government stability, socioeconomic conditions, investment profile, internal conflict, external conflict, corruption, military in politics, religious tensions, law and order, ethnic tensions, democratic accountability and bureaucracy quality. ICRG index is a sum of all 12 individual score for each country.

9 The conflict dummy takes the value of one if there is armed conflict during the three-year period.

10 The lagged variable, conflict_lag, equals one if there was an armed conflict in the past three years.

11 In high-income countries, armed conflicts are significantly less frequent compared to middle- and low-income countries. This might explain the insignificant results observed in the high-income subsample. According to the summary statistics presented in Table 2, the average number of conflicts, government-related conflicts, and territory-related conflicts in middle- and low-income countries is notably higher than in high-income countries. Furthermore, the t-test results support this observation – we conducted t-tests on mean equality; t-tests reject the null hypothesis of mean equality, suggest significant differences in the mean values of these conflict variables between middle- and low-income countries compared to high-income countries.

12 Total natural resources rents come from the World Bank, which includes the sum of oil rents, natural gas rents, coal rents, mineral rents and forest rents.

13 The capital stock and GDP data come from the Penn World Table version 10.

14 The variable is obtained from the World Bank. Broad money is the sum of currency outside banks; demand deposits other than those of the central government; the time, savings, and foreign currency deposits of resident sectors other than the central government; bank and traveler’s checks; and other securities such as certificates of deposit and commercial paper.

Additional information

Funding

This work was supported by Marquette University; Indiana university South Bend.