Introduction
Data
The researcher used World Bank Worldwide Indicators to analyze the significance of political and institutional factors on foreign direct investment levels in developing countries (Singh, 2019). Bellinger and Son (2019) highlight the six indicators used to determine institutional qualities of a country have been used formerly for empirical research by other institutions. The indicators include controlling corruption, , ensuring stability in the political sphere, effective work of the government, strong law, and regulatory quality (Bellinger & Son, 2019). This study aims at establishing which of the factors has the most significant impact on FDI flows in developing countries.
Control of corruption is the level at which the government in a particular country can contain the prevalence of illegal payments, bribes, illegal activities by bureaucrats and has protection over foreign from criminal charges by public officers and other forms of corruption.
Bellinger and Son (2019) believe that government effectiveness is measured as the commitment of the government to implement public policies, the competitiveness and qualification of public workers, and the quality of government services. Government interferences can impact FDI levels by imposing strict policies on foreign investors. In addition, political stability is an indicator of a prolonged stay of a ruling party to be in power. It reduces the risk of a government being opposed or overthrown illegally. A politically stable country has a good business environment, hence, attract more FDI flows.
Consequently, regulatory quality indicates the friendliness of a country’s public policies to foreign investors. Such policies include taxation, the procedure for investing, accessibility to financial support, and price controls (Ahlers & Nellis, 2016). Regulatory quality influences FDI flows by reducing the amount of power and public policies that govern the market and unofficial investments. Moreover, the rule of law constitutional aspects of the country such as the level of predictability of the judicial system, how crime is perceived, and the methods used in enforcing contracts (Singh, 2019). Bailey (2018) argues that by making the rule of law clear and predictable and untightening business activities, a government can positively influence FDI flows.
Voice and accountability relate to the democratic nature of government such as civil rights and the power given to the citizen of a country to control public processes (Li et al., 2018). A government could increase FDI flows by increasing public participation in political systems and activities while promoting democracy. In this research, the stated indicators shall have a mean zero and a unit standard deviation between -2.5 to +2.5. The size of the indicator is directly proportional to the institutional quality.
Additionally, the researcher used pull and push factors in the analysis of data to represent commons FDI determinants. The pull and push factors are selected from previous literature. To analyze the relevance of global shocks on FDI, the researcher used international risk measures and global liquidity as the two factors that have strongly been emphasized by recent literature. According to Yang (2018), capital flows are significantly affected by fluctuations in global liquidity resulting from global leverage.
The researcher measured global liquidity using the weighted average of money growth from the G7 countries retrieved from the World Development Indicators (WDI) database. Another external factor used in the research is global risk retrieved from the Bank of International Settlements (BIS) database (Bellinger and Son, 2019). Global risk is measured by the given volatility of S&P index options.
As an indicator for market potential, the size of a market, GDP per capita, macroeconomics stability, and inflation were used as pull factors. The researcher used developing countries’ GDP lagged values. A change in nominal exchange rates was also perceived to be a determinant of a country’s FDI inflows to address the uncertainty that results from exchange rate fluctuations. Financial development is also considered as a pull factor in the analysis. Wu et al. (2020) note that trade openness can be used to represent the trade-oriented policies of a country. Trade openness is used as a ratio of the summation of overall trade to GDP.
Lastly, the researcher used dummy variables to differentiate the relation of financial crisis years from 1970 to 2019. These points were employed to determine the results of the financial crisis and the foreign direct investment flows, political and other institutional factors (Wu et al., 2020). The researcher retrieved all the pull factors data from the World Development Indicators (WDI) database.
Methodology
In the literature review, previous studies of the impact of political and institutional factors on FDI inflow, FDI evidence remains fixed. The evidence included has been affected by the measurement and methodological limitations.
To estimate the determinants of F.D.I in this study the researcher used three models. Below is the first model:
FDIGDP = ß0 + ß1n + INSTn +ßM POLM +ßM CONTROLM + Ė
Where;
- INST represents institutional variables,
- POL represents political variables
- CONTROL represents the control variables.
In the second model, the researcher did not include institutional factors, thus omitting the vector INST.
However, one cannot include more than one institution in the equation as they are correlated between each other (Shah, 2016). Thus, the researcher introduced the 11 institutional variables to create a specific OLS estimation (Shah, 2016). To eliminate the shortcoming “of pooled OLS, time and region dummies” are included into the equation (Shah, 2016, p. 99). The next equation is presented in the following way:
The second data set includes a large number of data than the first since it includes 264 countries. Therefore the generalized method of moments (GMM) is applicable to account for endogeneity effectively (Ullah et al., 2020). Employing this approach, the researcher processes the first, second, and third lags of variables (Ullah et al., 2020). The researcher can also handle endogeneity using the method proposed above (Shah, 2016). Since in the second example T is shorter and N is longer, the Arellano–Bond GMM estimator is viewed as the better option (Ullah et al., 2020). The researcher used the following estimation variable:
Table 1: Expected signs for various variables.
Results
Table 2: FDI Data.
Table 3: Results for the OLS Estimation.
Table 4: Results for the Arellano-Bond GMM estimation.
Interpretation of the Data
Notes: The researcher adjusted standard errors for auto-correction and heteroscedasticity through cluster-robust VCE estimators provided by STATA. The parenthesis is t/z values. The sources and definition of data are recorded in Table 1. The dummies for different regions and times are not recorded.
- *10% significance
- **5% significance
- ***1% significance
All regression results are presented in Tables 3 and 4 above. Table 3 reports the pooled OLS results with FDI as the dependent variable for the first data set. WGI_fa and infrastructure promote the foreign direct investment in various areas experiencing fast growth. Moreover, numerical data proves that GDP and readiness to trade influence FDI, leading to the increase in its amount (Choi and Baek, 2017). The OLS results may be biased according to the literature on endogeneity. However, none of chosen variables linked to the political factor indicates significance which outlines an issue with multicollinearity and insufficient country effects as a result of limitations of the OLS method (Shah, 2016).
The result from the second data set is recorded in the following Table 4. They reveal the results of the Arellano-Bond GMM estimation. The infrastructure co-efficient and lagged FDI are substantial and meet expectations. Conversely, the coefficient for trade openness is no longer has a determinant impact on FDI. Government effectiveness is the only factor among other political ones that have the expected number and significant coefficient on FDI (Sabir et al., 2019).
Political factors have high coefficients and are linked to each other, which means that the change in one can affect the remaining ones (Sabir et al., 2019). However, the absence of problems in the political sphere and low corruption are not powerful determinants.
Additionally, the two data set reveal that macroeconomics, the size of a market, and labour force are cannot critically influence the foreign investment (Sabir et al., 2019). The impact of these factors may be offset and should be considered separately (Nguyen et al., 2018). Also, the measurement method used by the researcher to determine the size of a market is not the best, although most data is available.
From the above analysis, the coefficient of institutional factors does not have an impact on the contribution of FDI in developing countries. On the other hand, similar to previous literature, political factors mentioned above have an impact on the percentage of FDI in a developing country (Eisenman & Yang, 2018). Certainly, these results explain the progressive increase of FDI levels in developing countries and the decreased levels of FDI in the developed countries.
Discussion of the results
In the analysis section all the political and institutional factors outlined in the first section have been analyzed against FDI as the independent variable. The individual analysis of each variable was to eliminate multicollinearity issues.
In relevance to the research results, the significance of the politics and institutions depend on the institutional factor. The analysis reveals that control of corruption is significant and has a positive effect on FDI flows in developing countries (Choi & Baek, 2017). Choi and Baek (2017) believe that an increase in corruption cases in a country has an inverse impact on FDI flows level. For governments to attract more FDI flows, they must tighten corruption controls in their countries. In addition, the degree of government effectiveness is also displayed to have a positive correlation to foreign direct investments (OECD, 2020). The results suggest that a less dictatorial and supportive government are critical drivers of FDI inflows in a country.
Moreover, voice and accountability were also analyzed against the level of foreign direct investments. From the results, one could draw that voice and accountability is a significant motivator of FDI levels in a country. According to Li et al. (2018), it is a comprehensive indicator of healthy functioning political systems, upholding fundamental rights and liberties, and a striking image of a country. However, from the above results, the other three factors (rule of law, regulatory policies, and political stability) do not affect the flow of foreign direct investments (Ahlers & Nellis, 2016). Therefore, developing countries with ineffective government and corruption control of corruption may have a shortage of foreign direct investments.
In the study, the researcher also considered the impacts of push and pull factors on the level of foreign direct investments in developing countries. In the analysis, the lagged values with positive coefficients show that the factors influenced FDI levels in previous years. The results show that the measure of global liquidity is insignificant regarding FDI flows in a country (Quer et al., 2018). The finding can be related to the stability of FDIs as compared to the portfolio investment whereby fluctuations in global liquidity might be less influential to FDI flows (Eisenman & Yang, 2018).
On the other hand, the global risk is displayed to have a negative correlation and therefore significant on FDI flows. Hu et al. (2021) highlight that, an increase in global risk in the financial markets translates to a decrease in foreign direct investments. This is a result of investors paying more attention to cash balances amid global financial fluctuations. Most investors prefer to wait for stability in financial markets to mitigate financial risks.
Certainly, on the pull factors, trade openness is the only variable that is revealed to have a positive correlation, thus significant to FDI flows. Trade openness determines the size of consumer demand in international markets. Also, Quer et al. (2018) highlight its significance in determining the size of facilities that are based on exports in a host country. The researcher added a financial crisis dummy on the results, it reveals that financial crisis has a negatively significant effect on FDI flows in a country. According to Bailey (2018), during a financial crisis, investors hold their capital for future investments. However, fluctuations in exchange rates and a country’s financial development have little to no significance to FDI inflows in developing countries.
Reflection on the Results
I perceived foreign investment to be very crucial, especially in developing countries. Foreign direct investments (FDI) aid in pulling together resources, generating income for the host country, creating job opportunities among other benefits. These benefits help to grow the financial state of a nation, especially developing countries (Yang, 2018). I am well convinced that investors have the primary aim of making profits for the money given. Therefore, before making an investment decision, an entrepreneur has to weigh various factors that could expose them to any financial risk (Bailey, 2018). On these bases, I researched to weigh the significance of political and institutional environmental factors on foreign direct investment flows in developing countries.
I drew several thoughts from the analysis of the research data which I shall discuss in this section. One of my conclusions about the results is that developing countries focus on political factors such as control of corruption, voice and accountability, and government effectiveness as a strategy for attracting foreign investors. Most governments of developing countries have less control over the corrupt practices in their countries (Sabir et al., 2019). Developing countries have a higher corruption rate as compared to their developed countries counterparts. Corrupt economies tend to reward the lesser qualified candidate surpassing the nature of healthy competition which shuns away foreign investors.
Additionally, I perceive voice and accountability as equally important to control corruption when determining the level of FDI inflows. When a country has a high level of voice and accountability, the citizen is less likely to be manipulated by the people in authority (Nguyen et al., 2018). Hence, due to fear of interference by the citizen, political officials have a high likelihood of avoiding manipulating practices that may ruin business processes (Hu et al., 2021). Thus, in my view, that is the reason for investors choosing to invest in highly democratic countries.
Also, I am sure that government should boost effectiveness in public policy, providing more authority and independence to civil servants to improve their competency. OECD (2020) believes that an effective government makes the processes of conducting various business activities easy, hence friendly and attractive to foreign investors. However, I am amused by the fact in statistically, market size, macroeconomics, Level of GDP per capital are insignificant to the percentage of FDI flows in a country (Hu et al., 2021). Before conducting the study, I perceived that all institutional factors are significant determinants of FDI a fact that has been proved wrong by the research. Institutional and financial factors such as rule of law, regulatory policies, and political stability do not influence levels of FDI in the country.
Contribution of the Study
This research contributes significantly to the development of literature by answering the research question; To what extent do political risks and the institutional environment have an impact on the inward FDI flow on the host country in developing or emerging economies.
This study is deeply rooted in business and economics. It also plays a role in expanding knowledge on international relations. Primarily, the results of the study are useful to governments in developing countries and foreign investors. The government of developing countries can source information necessary to increase the level of foreign direct investments from the results and analysis of the study (Yang, 2018). In addition, foreign investors could use the information in the study to review the different factors that previous investors considered when investing in developing countries. Since the research uses data from 1970 to 2019, it provides an updated analysis of various political and institutional factors that impact the inward flow of FDI in host countries.
Limitations of the study
Although the research has successfully established political and institutional factors affecting FDI inwards flows in developing countries, it is not without limitations. One limitation is linked to the analysis of the research results where the value of market size, macroeconomics instability, and labor force may be offset since the lagged value of FDI is used in the calculation (Quer et al., 2018). In addition, there is a limitation in allocating various values for different political and institutional factors without determining their weight or relevance to different investors. The study assumes that all the political and institutional factors involved in the research have the same weight and level of importance to all investors.
Avenues for Future Research
To clearly understand the level of contribution of various political and institutional factors to foreign direct investments, there needs to be a study to determine the level of significance each contributing factor has on various foreign investors. For instance, control of corruption may have greater relevance to investors in the oil industry than in the banking industry. Due to a lack of specific weight values, previous literature generalizes all political and institutional factors to be of the same value to all investors.
Conclusion
This research studies the impact institutional and political factors have on foreign direct investments in developing nations at the moment. The institutional variables considered in the study are market size, macroeconomics, and labor force while the political factors include government effectiveness, control of corruption, and political stability. From the analysis of the results, institutional factors have been revealed to have an insignificant impact on the level of FDI in developing countries. In addition, political factors have shown to be significantly relevant to the levels of FDI.
The researcher used OLS and Arrellano-Bond GMM methods to analyze the two sets of data using estimated coefficients. Unlike the researcher’s expectations, the analysis has revealed institutional factors to have no significance on the level of FDI. Also, political factors have been recorded to have a significant impact on FDI, similar to previous literature. Other factors that greatly impact the level of FDI in developing countries include trade openness, infrastructure, and past levels of FDI.
Institution factors are critical and have progressive importance in the implementation of FDI in developing countries during recent years. However, the results of this study have revealed that institutional factors are not essential determinants of the level of FDI in a country. Political variables proved to be vital for FDI levels in developing countries, hence, countries severe control of corruption and powerful governments attract a higher percentage of FDI. Ultimately, investors are more likely to invest in countries with more trade openness and good infrastructure.
References
Ahlers, T., & Nellis, J. (2016). Building effective institutions—The biggest challenge. Global Journal of Emerging Market Economies, 8(2), 247–270. Web.
Bailey, N. (2018). Exploring the relationship between institutional factors and FDI attractiveness: A meta-analytic review. International Business Review, 27(1), 139–148. Web.
Bellinger, N. M., & Son, B. (2019). Political parties and foreign direct investment inflows among developing countries. Political Studies, 67(3), 712–731. Web.
Choi, Y., & Baek, J. (2017). Does FDI matter to economic growth in India? Economies, 5(2), 20. Web.
Eisenman, J., & Yang, F. (2018). Organizational structure, policy learning, and economic performance: Evidence from the Chinese commune. Socius. Web.
Hu, D., You, K., & Esiyok, B. (2021). Foreign direct investment among developing markets and its technological impact on host: Evidence from spatial analysis of Chinese investment in Africa. Technological Forecasting and Social Change, 166, 120593. Web.
Li, H., Tang, S.-Y., & Lo, C. W.-H. (2018). The institutional antecedents of managerial metworking in Chinese environmental NGOs. Nonprofit and Voluntary Sector Quarterly, 47(2), 325–346. Web.
Nguyen, C. P., Schinckus, C., Su, T. D., & Chong, F. (2018). Institutions, inward foreign direct investment, trade openness, and credit level in emerging market economies. Review of Development Finance, 8(2), 75–88. Web.
OECD. (2020). Business insights on emerging markets. OECD Development Centre. Web.
Quer, D., Claver, E., & Rienda, L. (2018). The influence of political risk, inertia, and imitative behavior on the location choice of Chinese multinational enterprises: does state ownership matter? International Journal of Emerging Markets, 13(3), 518–535. Web.
Sabir, S., Rafique, A., & Abbas, K. (2019). Institutions and FDI : Evidence from developed and developing countries. Financial Innovation, 5(1), 8. Web.
Shah, M. H. (2016). Financial development and foreign direct investment: The case of the Middle East and North African (MENA) developing nations. University of Haripur Journal of Management, 1(2), 93-109.
Singh, B. P. (2019). Does governance matter? Evidence from BRICS. Global Business Review. Web.
Ullah, A., Zhao, X., Kamal, M., & Zheng, J. (2020). Environmental regulations and inward FDI in China: Fresh evidence from the asymmetric autoregressive distributed lag approach. International Journal of Finance & Economics, 1-17. Web.
Wu, Y., Wang, J., Ji, S., & Song, Z. (2020). Renewable energy investment risk assessment for nations along with China’s Belt & Road Initiative: An ANP-cloud model method. Energy, 190(1), 116381. Web.
Yang, J. (2018). Subnational institutions and location choice of emerging market firms. Journal of International Management, 24(4), 317–332. Web.