Institutions and FDI: evidence from developed and developing countries

Samina Sabir; Anum Rafique; Kamran Abbas

doi:10.1186/s40854-019-0123-7

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Open Access
Published: 16 February 2019

Institutions and FDI: evidence from developed and developing countries

Financial Innovationvolume 5, Article number: 8 (2019) | Download Citation

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Abstract

This study investigates the impact of institutional quality on Foreign Direct Investment (FDI) inflows using panel data for low, lower-middle, upper-middle and high-income countries for the sample period of 1996–2016 using the system Generalized Method of Moments (GMM). The empirical results confirm that institutional quality has a positive impact on FDI in all group of countries. The magnitude of the coefficients of control of corruption, government effectiveness, political stability, regulatory quality, rule of law, and voice and accountability for FDI inflows are greater in developed countries than in developing countries. We conclude that institutional quality is a more important determinant of FDI in developed countries than in developed countries. However, GDP per capita, agriculture value-added as a percentage of GDP, and inflation influence FDI inflows negatively in developed countries, while GDP per capita, trade openness, agriculture value-added as a percentage of GDP, and infrastructure have positive and statistically significant impacts on FDI inflows in developing countries. Trade openness as a percentage of GDP and infrastructure positively affect FDI in developed countries. From our analysis, we infer that institutional quality is a more important determinant of FDI in developed countries than in developing countries.

Introduction

The traditional neoclassical growth model claims that differences in countries’ per capita incomes are due to differences in their capital accumulation, which are in turn due to their differing saving rates. Therefore, differences in capital accumulation are due to differences in countries’ saving rates (Solow 1956; Koopmans 1965). Furthermore, developing countries are characterized by low per capita income, poverty, unemployment, high population growth and low savings rates. Certainly, low levels of savings and investments create savings-investment gaps that have negative impacts on economic growth and development.

Foreign direct investment (FDI) helps to fill the gap between savings and required level of investment (Sabir and Khan 2018). Globalization increased the importance of FDI worldwide and endogenous growth theories emphasize that FDI is a key determinant of economic growth because it is a source of technological transfer from developed countries to developing countries (Chenaf-Nicet and Rougier 2016). FDI can directly and indirectly reduce unemployment (Lipsey 2001) and increases productivity by improving the skills and knowledge of workers in the host country.

Since 1990, competition enhanced among developed countries and developing countries to attract FDI inflows in term of reducing taxes and providing subsidies. Many developing countries adopted policies to facilitate FDI inflows and monitor FDI operations (World Bank 2013). Such as financial sector adjustment program, structural adjustment programs, economic recovery programmes and economic partnership agreements (Asamoah et al. 2016). Since 1990, FDI increased in developing countries, including among members of the South Asian Association of Regional Cooperation (SAARC), the Association of Southeast Asian Nations (ASEAN), Sub-Saharan African countries and Central Asian countries. Perhaps these developing countries are benefitting from FDI inflows, such as improved technology, better management skills and increased capital accumulation, exports, employment, higher productivity, and economic growth.

Dunning (1988) proposed the eclectic paradigm theory that decision of a foreign investor to invest in a host country depends on the size of the firm, administration and management systems, labor and transportation costs, government policies, as well as institutions and political stability. Foreign investors may be more concerned about risk and return when they enter the foreign markets (Fedderke and Romm 2006).

The relationship between institutions and economic performance has attracted attention from researchers and policy makers for the last few decades. North and Thomas (1973) emphasize that economic factors such as capital accumulation, per capita income, and innovation are not the only factors that create differences in a country’s economic growth and development; systematic differences in institutional quality are also responsible for those differences. For example, North and South Korea have the same economic attributes but differ in their economic outcomes (for example, South Korea has a higher per capita GDP than North Korea does) due to the differences in their institutions (Acemoglu et al. 2006). Indeed, economic institutions are important because they shape the behavior of economic actors and stakeholders (Sabir and Zahid 2012). In countries with secure well-protected property rights, people are incentivized to make domestic and foreign investments that boost economic growth. Otherwise, investment and growth lag behind.

Poor institutions impede FDI and can act like a tax, thus increasing the cost of FDI (Buchanan et al. 2012). Investors are unwilling to invest in countries where institutions encourage corruption, nepotism and red tape because these factors increase the cost of doing business (Mengistu and Adhikary 2011).

Harms and Ursprung (2002) find that foreign investors invest in countries with strong democratic structures, while autocratic societies often experience policy reversal and attract less FDI. Some studies find that institutional quality positively and significantly impacts FDI inflow in Asia and Latin America (Gani 2007), SAARC, Central Asian countries and the ASEAN region (Ullah and Khan 2017). Lucke and Eichler (2016) estimate the impact of institutional and cultural determinants of FDI in developing countries and find a positive association between institutions and FDI in developing countries and that foreign investors prefer to invest in countries that are politically unstable and have less diverse societies. Peres et al. (2018) analyze the impact of institutional quality on FDI inflows in developed and developing countries using corruption and the rule of law as measures of institutional quality, and find that institutions have an insignificant impact on FDI in developing countries due to the weak structure of institutions. Moreover, institutional quality has a positive and significant impact on FDI in developed countries. Other studies analyze the impact of institutions on FDI inflows at various stages of development. Therefore, good quality institutions in the host country are a precondition for attracting FDI inflows into that country. This study examines the impact of the indicators of institutional quality (such as political stability, control of corruption, rule of law, voice and accountability, regulatory quality and government effectiveness) on inward FDI in developing and developed countries. These are indicators of three important dimensions of institutional quality: 1) political stability, 2) administrative quality, and 3) democratic accountability. We examine the separate effect of each indicator on FDI inflows in low, lower-middle, high, and upper-middle-income countries.

Institutional quality indicators are highly correlated (Globerman and Shapiro 2002; Daude and Stein 2007; Buchanan et al. 2012), and it is not possible to include all indicators in a single equation (Ullah and Khan 2017). Therefore, we construct an institutional quality index using principal component analysis (PCA). The basic purpose of using this technique is to combine the six indicators of institutional quality into a single variable that duplicates the original data with minimal loss of information. The empirical analysis uses panel data of 59 and 89 developing and developed countries, respectively for 1996–2016. To avoid heteroscedasticity, autocorrelation, omitted variable bias and endogeneity problems, we use the system Generalized Method of Moments (GMM).

This paper offers three contributions to the literature. First, the results indicate that the effect of institutions on FDI inflows varies across countries. To analyze the impact of institutions on FDI, we separate developing countries into low and lower-middle-income countries, and developed countries into upper-middle and high-income countries. Second, we examine the impact of each indicator of institutional quality on FDI inflows separately for both developing and developed countries. Third, we construct an institutional quality index to study its impact on FDI.

The remainder of the study proceeds as follows: Section “Theoretical framework” provides the theoretical background on institutions and foreign direct investment. Section “Methodology and data” presents the methodology and data. Section “Results and discussion” discusses the empirical results, and section “Conclusion” concludes.

Theoretical framework

North (1990) defines institutions as human invented constraints consisting of structural, economic, political and social issues. Indeed, institutions represent the formal and informal rules of the game in which different players and economic actors interact and perform actions to maximize their profits and returns.

According to North (1990), good institutions affect economic activities through different channels such as by reducing the transaction, manufacturing and production costs. Moreover, good quality institutions help reduce the cost of doing business, which increases profitability. However markets with poor institutions take up more time and resources for monitoring. When property rights are poorly protected and contract enforcement is difficult, the risk premium is high and economic activity is slower. International investors hesitate to invest in such a risky and unconducive environment. By contrast, a risk-free environment is a good location for the source country and good institutions also lead to better FDI utilization. Lucas (1993) suggests that in emerging economies, institutional factors, as compared to purely economic factors, play an important role in attracting inward FDI.

In summary, the host country’s institutional quality affects profitability, and institutionally strong countries can attract foreign investors by offering high returns. Dunning (1998) expands the concept of locational advantage by adding institutional factors along with economic factors. He argues that foreign investors prefer locations that offer the best economic and institutional facilities. Hence, foreign investors’ decisions depend on the rate of return based on sound institutions and other macroeconomic indicators.

Based Dunning’s eclectic paradigm theory and North’s institutional theory, inward FDI depends on market size, natural and human resources, efficiency seeking and the institutional quality of the host country. We can algebraically write this relationship as

$$ FDI=f\left( Market\ size, macroeconomic\ stability, Institutions\right) $$

where FDI is foreign direct investment, market size is an important determinant of FDI in the host country and is proxied with GDP per capita, macroeconomic stability indicates the economic situation of the host country that affects FDI measured by inflation. Inflation indicates the internal economic tension and ability of the central bank and government to control the money supply and balance the budget (Schneider and Frey 1985; Buchanan et al. 2012). Some argue that a higher rate of inflation leads to lower FDI (Bruno 1993; Buchanan et al. 2012). According to the theoretical literature, institutional quality positively impacts economic growth (North 1990; King and Levine 1993; Knack and Keefer 1995; Mauro 1995; Acemoglu and Verdier 1998; Ali et al. 2010). However, some studies examine the effects of institutional quality on FDI with a focus on the rule of law, property rights enforcement, control of corruption, voice and accountability, and government effectiveness. These studies find that weak institutions are negatively associated with FDI (Bénassy-Quéré et al. 2007; Buchanan et al. 2012).

Methodology and data

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 Y_it is the log of FDI inflows, X_it 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, Z_it 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 U_it 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 data^{Footnote 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 (X_it, U_it) = 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.

Results and discussion

Estimation results for developing countries

Table 1 reports the empirical results for low and lower-middle income countries. First, we estimate our fixed effect model by employing the LSDV method and use the Wu Hausman test to check for endogeneity in the institutional variable, trade openness, lnGDP per capita, and all other explanatory variables.

Table 1 Estimation Results for Developing Countries

Institutions and FDI: evidence from developed and developing countries

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Abstract

Introduction

Theoretical framework

Methodology and data

Econometric model

Estimation methods

Results and discussion

Estimation results for developing countries

Estimation results of developed countries

Impact of the institutional quality indicators on FDI in developing and developed countries

Conclusion

Notes

Abbreviations

References

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Kashmir Institute of Economics, University of Azad Jammu & Kashmir, Muzaffarabad, Azad Kashmir, Pakistan

Department of Statistics, University of Azad Jammu & Kashmir, Muzaffarabad, Pakistan

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Appendices

Appendix 1

Appendix 2

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