Econometric model
Based on theoretical background, we use the following empirical model to analyze the impact of institutions on FDI:
$$ {Y}_{it}=\alpha +{\beta}_i{X}_{it}+{\gamma}_i{Z}_{it}+{U}_{it} $$
(1)
where Yit is the log of FDI inflows, Xit are vectors of the variables of interest of country i = (1, 2, 3, …, N) during period ‘t = (1, 2, 3, …, T). The variables of interest consist of indicators of political institutional quality, Zit are the vectors of control variables such as inflation, trade openness, GDP per capita, value added share of agriculture as a percentage of GDP, and infrastructure measured as mobile phone subscription per 100 people, and Uit is the error term.
FDI is the dependent variable. Prior empirical studies use different proxies of FDI, and in line with other studies, we use FDI inflows, which is the log of net inflow of FDI in current U.S. dollars. We use inflation as a proxy of macroeconomic instability and economic tension, as there is a negative relationship between inflation and FDI.
It is widely believed that the host country’s level of development is an important determinant of inward FDI. As the level of development increases, the population’s ability to purchase goods and services increases, which motivates foreign investors to invest. GDP per capita captures the level of development. Moreover, trade openness is a vital factor in promoting inward FDI because foreign investors prefer free trade over restricted trade. As the latter increases the cost of doing business, increases which discourages foreign investment. Theoretically, there is a positive relationship between trade openness and FDI (Kravis and Lipsey 1982; Culem 1988; Shah and Khan 2016). We use trade openness as a proxy of market-seeking FDI. Good infrastructure can attract greater FDI because it reduces operational costs (Khadaroo and Seetanah 2010). We use infrastructure as a proxy of efficiency-seeking FDI. We also include the value-added share of agriculture in GDP as an explanatory variable because FDI is an important source of investment in agriculture and can enhance agricultural productivity by introducing new technology (Tondl and Fornero 2010). In line with other studies, we use mobile phone subscriptions per 100 people as a proxy of infrastructure. Theoretically, there is positive relationship between infrastructure and inward FDI. Efficiency-seeking investment targets with relatively low costs of transport and communication (Dunning 2006).
We consider six indicators of institutional quality: control of corruption, political stability, rule of law, regulatory quality, voice and accountability, and government effectiveness (Kaufmann et al. 2007) to examine their impact on FDI inflows in developing and developed countries. Corruption refers to the use of public power for personal gain and covers a broad range of human actions. We use control of corruption as a proxy of institutional quality. Theoretically, there is a positive relationship between control of corruption and inward FDI. Political stability is an important factor that ensures the inflow of FDI. FDI is a long-term investment activity, and every type of threat discourages FDI inflows. Multinational corporations (MNCs) avoid FDI in cases of political instability due to high risk, and switch to risk-free countries (Meier 2006). By contrast, the rule of law encourages inward FDI. Rules and laws are sets of agreements by which countries implement FDI policies and that protect future returns (Hoff and Stiglitz 2005). The rule of law discourages market-unfriendly policies and minimizes risk. Regulatory quality boosts inward FDI by introducing market-friendly policies such as price controls, government intervention, and free movement of capital (Fazio and Talamo 2008). Regulatory quality captures the government’s ability to formulate and implement sound policies and regulations that promote economic development. Voice and Accountability have positive relations with FDI: through voice and accountability, a country’s citizens can enjoy many freedoms such as freedom of expression, freedom of association, and free media. Furthermore, when citizens select their government, they are in a position to reject corrupt governments. Voice and accountability are together responsible for providing a risk-free climate for domestic and foreign investors (Inter-American Development Bank 2001; Saadatmand and Choquette 2012). Government effectiveness captures the quality of public and civil service and the degree of its independence from political pressure (Buchanan et al. 2012).
We examine the impact of each indicator of institutional quality on FDI controlled with other macroeconomic variables. All these measures of institutional quality are highly correlated, so it is inappropriate to use all of them in a single equation (Globerman and Shapiro 2002). Appendix 1 reports the correlation matrix of the six governance indicators, which indicates a high correlation among variables.
Therefore, we use PCA to construct a composite index of institutions. We extract the first principal component of the six proxies of institutional quality using factor analysis (Globerman and Shapiro 2002; Buchanan et al. 2012), which we refer to as the institutional quality index. This index ranges from − 0.659 to + 2.48 for high-income countries, − 2.894 to 2.277 for upper-middle-income countries − 2.351 to 1.918 for lower-middle-income countries and − 0.436 to 0.754 for low-income countries. This index implies that institutions are more developed in developed countries than in developing countries.
To examine the impact of institutions on inward FDI, this paper used panel data of 20 low-income, 39 lower-middle-income, 44 upper-middle-income countries and 45 high-income countries for 1996 to 2016. We include low-income and lower-middle-income countries within the category of developing countries, and upper-middle income and higher-income countries within the set of developed countries. We select the countries and sample period according to the dataFootnote 1 availability.
The main sources of data are the World Development Indicators (WDI) and the World Governance Indicators (WGI). Data on inflation, trade openness, mobile phone subscriptions per 100 people, GDP per capita, and value-added share of agriculture as a percentage of GDP were obtained from the WDI.
We obtained data on the institutional variables control of corruption, voice and accountability, government effectiveness, political stability, rule and law, and regulatory quality from WGI.Footnote 2
Estimation methods
To investigate the impact of institutions on FDI, we estimate the following regression:
$$ {Y}_{it}={\alpha}_{{}^{\circ}}+{\alpha}_i{Y}_{it-1}+{\beta}_i{X}_{it}+{\gamma}_i{Z}_{it}+{U}_{it} $$
(2)
where α β and γ are the parameters we estimate. We cannot estimate this fixed effect regression using the least square dummy variable (LSDV) method if linear regression assumptions are not satisfied; for example, the means of the random term (U) should be zero and the covariance between U and X should be zero cov (Xit, Uit) = 0.
However, the literature on institutions and FDI indicates an issue with endogeneity in the institutional variable (Ali et al. 2010; Peres et al. 2018). Using LSDV method will result in biased and inconsistent estimators. We therefore address this problem by using system GMM, or instrumental variable method (Arellano and Bond 1991; Blundell and Bond 1998). The dynamic equation is
$$ \Delta {y}_{it}={\alpha}_0+\omega \Delta {lny}_{i,t-1}+\beta ln{X}_{it}+\gamma ln{Z}_{it}+{\eta}_i+ vi $$
(3)
System GMM is a combination of level and difference dynamic equations that improves on difference-GMM because it both supplements the equation in the first differences with the equation in levels and allows for the correction of measurement errors in the other regressors (Blundell and Bond 1998). The prerequisite for system GMM are that the autocorrelation at the first order autoregressive AR(1) process should be significant and autocorrelation at the second order autoregressive AR(2) should be insignificant.
Legal origin is the significant determinant of institutional quality and size of the financial market (Buchanan et al. 2012). For example, legal enforcement can secure property rights and quality enforcement of legal rights attracts FDI. Moreover, these rules vary across countries by legal origin, depending on whether the origin is English, French, Scandinavian, or German. However, the primary legal systems consist mainly of French civil law and English common law (David and Brierley 1985). Some argue that countries with institutions based on French civil law tend to offer protection to the fragile investor and have smaller capital markets, whereas common law countries offer strong protection and have larger capital markets (La Porta et al. 1997). We use legal origin as an instrument for institutional variables, lagged values of explanatory variables as instruments for the difference equation, and explanatory variables with lagged difference as instruments for the level equation. We use a maximum of five lags of the explanatory variables as instruments until the results pass the Sargan test.