Determinants of FDI flows into India Essay Example

fiercely to entice potential overseas investments for industrial advancement and economic progress. However, simply opening up an economy to foreign investors does not guarantee their attraction.

The decision to invest is influenced by various factors such as a country's economic and industrial performance, production costs, taxation rates, exchange rate fluctuations, infrastructure development, local market conditions, and political stability. Despite some economies implementing full capital account convertibility, foreign investors are still hesitant to invest for multiple reasons. It is crucial for economies reliant on significant FDI flows for development purposes to identify the key factors that attract foreign direct investments. Developing countries have been the main recipients of global investment flows since the 1990s, with China and India being particularly appealing destinations for FDI (UNCTAD Report, 2009).

India's economy was tightly controlled both internally and externally during the first three decades of economic planning. However, by the end of the 1980s, a severe economic crisis arose due to a significant balance of payment deficit that had been accumulating over time. This deficit had reached a critical point where India was at risk of defaulting on its external obligations.

The crisis was not solely caused by factors such as the OPEC oil price increase and the Gulf crisis. The government also adopted a less conservative approach to balancing their books in the second half of the 1980s, leading to a drastic increase in financial deficits during this period (Haggard and Kaufman, 1992).

The India Development Report in 1997 revealed that the financial shortage of the centre was 6.3% of GDP in the first half of the 1980s, which increased to 8.2% in the second half and reached 8.4% in 1990-91. This

was higher than the average of 3.9% during the 1970s, causing inflation and impacting the balance of payment. To tackle this issue, the Government resorted to rapid borrowing, worsening the balance of payment and prompting changes in economic policy.

In order to address these challenges, economic policy reforms were introduced in India in 1991, opening up its market to foreign participants. In recent decades, there has been a significant relaxation on restrictions for Foreign Direct Investment (FDI) across almost all sectors except for limited areas like atomic energy, single brand retailing, chit finance, lottery, gambling and sectors not accessible to private participants. Despite allowing full FDI participation in numerous industries, China continues to attract more FDI inflow compared to India's economy.

The text stresses the importance of researching the factors that affect foreign direct investments (FDI) in India. While many studies have focused on identifying these factors in China, there have been limited studies conducted for India. The objective of this study is to identify the key factors that attract FDI into India and provide recommendations to policymakers on how to enhance them to attract more productive FDI.

Literature Review

Empirical research has extensively examined the main determinants of FDI in both developed and developing countries. This section offers an overview of the significant contributions made in this field.

The literature on Foreign Direct Investment (FDI) primarily concentrates on the correlation between macroeconomic indicators and FDI. In general, trade between countries is greatly influenced by the Gross Domestic Product (GDP) of the host country. Shapiro (1998) argues that the market size (GDP) in the host country directly impacts anticipated investment returns. Similarly, MacDermott (2007) discovers a positive relationship between FDI

and higher GDP in the host country. Certain researchers have also utilized GDP as an alternative gauge for market size.

The main idea is that a larger market size encourages more FDI in an economic system. Kravis and Lipesey (1982) and Blomstrom and Lipsey (1991) confirmed this hypothesis in their research, showing that market size positively affects FDI flow. In a study conducted by Lv Na and W.S. Lightfoot (2006) on FDI determinants in different parts of China, GDP was used as a proxy for market size and potential. The study revealed that market size has a significant impact on attracting FDI, and it also identified the quality of labor and degree of openness as important factors influencing FDI distribution. Additionally, high labor costs discourage the inflow of FDI.

In line with the aforementioned survey, Makki et.al (2004) analyzed the determinants of foreign direct investments by US food processing industry in developed and developing countries. They found that market size, per-capita income, and openness significantly affect US food processing houses' decisions to invest abroad, but their influence differs between developed and developing countries. James B. Ang (2007) analyzed annual time series data and found that real GDP has a significant positive impact on FDI inflows in Malaysia.

The survey also indicated that an increase in fiscal development, infrastructure development, and trade openness leads to a boost in foreign direct investment (FDI) and higher statutory corporate tax rates. However, a stronger grasp of the real exchange rate appears to discourage inflows of FDI. Interestingly, the findings suggest that higher macroeconomic uncertainty actually results in more FDI inflows. The literature highlights significant and interesting outcomes regarding how infrastructure development in

the host country affects FDI flows. According to Asiedu (2002), improved infrastructure in the host economy positively impacts non-sub Saharan African economies, while trade openness has a positive effect on Sub Saharan African countries. Cheng and Kwan (2000) also confirm that good infrastructure attracts more FDI in China.

The researchers found that the big regional market had a positive impact on FDI in China, while high labor costs had a negative impact. Markusen (1984) and Helpman (1984) discussed that access to markets (horizontal FDI) and low wages for production (vertical FDI) are important motivations for FDI. Luger and Shetty (1985) analyzed foreign direct investments into North America and found that state government spending policies significantly influenced foreign investors, while higher wages discouraged FDI. Coughlin et al. (1991) also found that higher wages deterred foreign direct investment, but higher unemployment rates attracted it in the US. Other factors that attracted FDI were higher per capita income, higher densities of manufacturing activity, extensive transportation infrastructure, and larger promotional expenditures. Additionally, higher taxes deterred foreign direct investment in the US.

Hines (1996) found that high state taxation rates in America have a significant negative impact on foreign direct investment (FDI). Hines (1999), in another study, compared the tax sensitivity of "non-credit-system" foreign investors to that of "credit-system" foreign investors and discovered that non-credit-system investors are more affected by higher tax rates, resulting in a larger decrease in FDI. Similarly, Hartman (1984) examined the behavior of foreign affiliates in the United States, specifically focusing on host state tax rates and returns. Through separate regressions for retained earnings FDI and new transfer FDI, Hartman concluded that host state tax rates significantly influence

retained earnings FDI while new transfer FDI does not show significant responsiveness to host state tax rates.

An apparent assumption is that understanding currency discourages foreign direct investment (FDI). Froot and Stein (1991) examined that there is an increase in inward FDI when the US currency depreciates. Klein and Rosengren (1994) presented evidence that exchange rate depreciation raises US FDI using various samples of US FDI. Blonigen (1997) confirms that there is an increase in inward US acquisition FDI by Japanese companies in response to real dollar depreciations relative to the yen, using industry-level data on Japanese mergers and acquisition FDI into the US. Several studies discussed the complex nature of FDI, such as export platform FDI. Hanson, Mataloni, and Slaughter (2001) discussed the significance of export platform FDI by utilizing data on the foreign operations of US companies.

According to research, multinationals have discovered that low host-country trade barriers encourage export platform FDI, while large host-country markets discourage it. Ekholm et al. (2003) and Bergstrand et al. later confirmed this finding.

In 2004, it was estimated that US outbound foreign direct investment (FDI) into certain host states serves as a production platform for exporting to neighboring host states. Baltagi et al. (2004) analyzed the map of affiliates of Multi-National Enterprises located in different host states. The study found that these affiliates transport intermediate goods for further processing and transport the finished products back to the parent state. The importance of the adjacent state effect has also been emphasized in recent literature.

Coughlin and Segev (2000) reported that foreign direct investment (FDI) into neighboring states has a positive effect on FDI into a Chinese state. This finding was further

supported by Baltagi et al. (2004), who developed a theoretical model of multinational enterprise (MNE) activity in a multi-country world. This model predicts that various factors of neighboring states, such as GDP, trade costs, distance, labor skills, investment risk, etc., should influence FDI into a focal state. These predictions were based on data about US outbound FDI into OECD states.

Methodology

The purpose of this survey is to measure the specific determiners of foreign direct investing influxs in to India over a period of 19 years from 1991-92 to 2009-10. By analyzing data on a macroeconomic level, this survey provides insight into the macro economic variables that favor the flow of FDI in to India. Previous research has identified certain macro economic variables that are important determiners of FDI flows in India. Therefore, this study focuses on analyzing these variables. The performance of an economy plays a crucial role in attracting foreign investors.

The Real GDP of an economy is one of the most important performance variables and is considered a significant factor in determining FDI. Infrastructure development is also recognized as a crucial determinant of FDI as it enhances capital productivity and expands resource availability in the host economy. Government spending on infrastructure serves as a proxy for infrastructure development.

(2002), Cheng and Kwan (2000), Luger and Shetty (1985), Coughlin et Al. (1991)). Generally, investors prefer economic systems with greater trade openness. The volume of imports and exports over a period of time has been considered as a measure of trade openness (Asiedu, E. (2002), Fedderke, J.)

W.,; A.; Romm, A. T. (2006)).

Several studies (Froot and Stein, 1991; Klein and Rosengren, 1994; Blonigen, 1997)

have considered the real effectual exchange rate as a factor in analyzing the premise that a decrease in the currency value of the host state leads to an increase in the flow of foreign direct investment (FDI). This study also takes into account the impact of reducing the taxation rate as an effective measure to boost FDI, with the corporate taxation rate for foreign companies being one of the determinants of FDI (Hines, 1999; Coughlin et al., 1991; Hartman, 1984). The Average Real Wage Index is used to measure the effect of labor costs on FDI flow (Coughlin et al.).

(1991), Luger and Shetty (1985), Markusen (1984), and Helpman (1984) have been considered in this survey. Additionally, Leonard K Cheng and Yum K Kwan (2000) have also been taken into account. In this survey, the determiners of FDI in India include involvement rate and Index of Industrial production. The impact of the slowdown of exogenic variables on the flow of FDI into India has been analyzed. Generally in economics, the dependence of endogenous variables on exogenic variables takes time to respond. States with a positive record of previous GDP, economic growth rate, trade openness, and government expenditure are expected to attract foreign investors in the future period (Biglaiserand and Derouen (2006)).

It is also argued that, there is a time lag relationship between the flow of Foreign Direct Investment (FDI) and its determinants such as interest rate and real effective exchange rate (Xing (2006)). Therefore, we have replaced all the exogenous variables with one period lag and specified an empirical model for the study. FDIt = I?0 + I?1 RGDPt-1 + I?2REERt-1 + I?3IIPt-1 + I?4 IRt-1

+ I?5TOt-1 + I?6TR t-1 + I?7RWt-1 + I?8INF t-1 + et (where, FDI - Foreign Direct Investment inflows, RGDP - Real Gross Domestic Product, REER - Real Effective Exchange Rate, IIP - Index of Industrial Production, IR - Interest Rate, TO - trade openness, TR - Corporate Tax rate, RW - Average Real Wagess, INF - Infrastructure Development, t - time, t-1 - first lag, e - error term.) To analyze the influence of the above mentioned determinants on the flow of FDI into India, Ordinary Least Squares technique (OLS) has been applied.

However, in order to use the OLS technique, the theoretical model must be free from issues such as Multicollinearity, Heteroscedasticity, Autocorrelation, and Endogeneity (Jose Miguel Giner and Graciela Giner, 2004). The residuals should follow a normal distribution and the time series data should also be stationary. This paper examines the issue of Multicollinearity by comparing the tolerance level and variance inflation factor with the R-squared value of the subsidiary regression. A tolerance value less than (1-R squared) and a VIF greater than 1/(1-R squared) indicate the presence of multicollinearity among the regressors. Another important assumption is the absence of heteroscedasticity, which is tested using White's General Test for heteroscedasticity.

If the value obtained from an subsidiary arrested development ( n*RA? ) chi-square with a critical value, ( uA? = I±1+ I±2x1+ I±3x2+ I±4 x1A?+ I±5 x2A?+ I±6 x1x2 +I? ), is less than the table value, then there is no heteroscedasticity. The normalcy of remainders has been tested using the histogram and Shapiro Wilk trial. Additionally, the presence of auto-correlation has been checked using the Durbin-Watson vitamin D trial. If the value between

du and 4 - du is 500, this indicates that there is no auto-correlation. Lastly, stationarity has been tested using the unit root trial ( a?†Yt = I?Yt- 1+ut ) .

If the coefficient (I?) is not equal to zero, the time series is considered to be stationary. Endogeneity issue was also examined by evaluating the correlation value between the regressors and residuals (Damodar N. Gujarati and Sangeetha, 2007).

Results and Implications

The results in Annexure I indicate that all conditions for the mentioned model were satisfied, except for the issue of multicollinearity.

The tolerance value in this model is less than (1-R square) in the case of GDP, Trade Volume, corporate taxes, and equal to (1-R square) in the case of the remaining independent variables. However, the inflation factor is greater than 1/(1-R square) for GDP, Trade volume, Corporate taxes, Average real wages, and slightly less than 1/(1-R square) for the remaining regressors. This suggests that the independent variables in the model are significantly related to each other. However, some variables may need to be removed from the model to avoid multicollinearity, which would result in specification bias. To address this issue, we transformed the data using factor analysis to reduce the variables into factors with zero correlations. Annexure II provides the results of KMO and Bartlett's Test, which assess the adequacy of the sample for factor analysis and test for an identity matrix correlation matrix respectively.

The Kaiser-Meyer-Olkin Measure of Sampling Adequacy (0.680) exceeds the threshold of 0.5, indicating satisfactory factor analysis for further investigation. The significant Bartlett's test of spherelessness (p = 0.000) reinforces that the correlation matrix is not an identity matrix. To enhance understanding of

the factors, Varimax orthogonal rotation was employed to clarify the factor pattern. As a result of the factor analysis, two factors were extracted from a set of eight variables.

The first and second factors have eigenvalues of 4.638 and 2.376 respectively. The rotated factor matrix displays the weights of eight variables on these two factors. The higher the absolute value of the weight, the more significant the variable's contribution to the factor. Variables including Real GDP, IIP, Trade openness, and Infrastructure Development have higher weights on factor 1 and lower weights on factor 2. On the other hand, variables like Real Effective Exchange Rate, Interest Rate, Corporate Tax rate, Average Real pay Index, and Infrastructure Development have higher weights on factor 2 and lower weights on factor 1. The measurement of an economy's growth can be done using macro-economic factors such as GDP, IIP, Trade Volume, and Infrastructure Development. Foreign investors often consider these factors to assess the strength and stability of the host economy.

Hence, factor 1 is named Growth factor, while another important premise is that investors generally seek inexpensive destinations. Several cost-related factors are involved in investment decisions, and when considering macro level cost factors, attributes such as labor cost, tax rate, interest rate, and exchange rate play a significant role. Thus, factor 2 is named Cost factor.
After solving the multicollinearity issue in the original model by reducing the eight regressors into two factors (growth and cost factors), we use these factors as regressors in the new model, depicted as: FDIt = I?0 + I?1GFt-1 + I?2CFt-1 + et.
Furthermore, the OLS technique's aforementioned conditions have been verified for this new model, which

is also unaffected by multicollinearity issue.

White's General Test for Heteroscedasticity was used to investigate the presence of heteroscedasticity in the model. The test yielded a chi-square value of 6.81 and a probability value of 0.24, indicating that there is currently no heteroscedasticity in this model. The residuals in this transformed model follow a normal distribution, as evidenced by the bell-shaped curve observed in the histogram of residuals and the Shapiro-Wilk test. The calculated test statistic is 0.927 with a probability value of 0.175, suggesting that the residuals are normally distributed.

The theoretical model is also free from the problem of auto-correlation, as evidenced by the Durbin-Watson D statistic of 1.592, which falls between du and 4 - du (1.535 and 4 - 1.535). The time series model is also stationary, as determined by the unit root test, and the coefficients for all variables were found to be significant at 10 percent. The residuals are not correlated with the exogenous variables, indicating no endogeneity problem. Thus, the aforementioned model fulfills all major conditions for utilizing the OLS technique (Table: 1).

Therefore, this theoretical model is considered the best model for identifying the main factors of FDI. In addition to meeting all the requirements for using the OLS technique, the overall explanatory power of the model is high. The F-statistic (70.719) indicates that all the Betas are not equal to zero, suggesting that at least one of the variables in the model is useful. The adjusted R-squared is 0.891, indicating that 89.1% of the variation in FDI is explained by the exogenous variables in the model.

The Ordinary Least Squares (OLS) analysis reveals that both the Constant and Growth Factor

have a significant impact on Foreign Direct Investment (FDI). Even if the Growth and Cost Factor remain unchanged, FDI will still change by Rs 38,094 chromium. If the Growth factor changes by 1 unit, FDI will change by Rs 46,391.3 chromium. The interpretation of the Cost factor is not applicable in this manner as the variable is not statistically significant (t stat).

The impact of Growth factor on FDI is supported, while the same cannot be said for the Cost factor in this model. Thus, it can be concluded that foreign direct investment inflows into India are more influenced by the growth factor rather than the cost factor. This study considers the growth factor as comprising Real GDP, Index of Industrial Production, Trade volume, and infrastructure development. These variables are compressed and treated as a single factor. The importance of all variables under the growth factor is equal, as evident from the factor loadings in the rotated component matrix (Annexure II). The variation in the values of factor loadings is very small, less than 0.022. Therefore, it can be inferred that all variables comprising the growth factor have an equal impact on the influx of foreign direct investment in India.

The analysis reveals that the main factor attracting FDI flows into India is the growth factor, rather than cost factors. Investment decisions are made based on the economic growth trajectory of the host economy, and investors prefer destinations with high growth rates and infrastructure development, even if production costs and other investment-related expenses are low. Therefore, it is evident that most global investments are directed towards developing economies with high growth rates and infrastructure development, rather than

low-cost under-developed economies.

Decision

Foreign Direct Investment is an inevitable phenomenon in the recent wave of globalization, and every country is competing to attract foreign direct investments to enhance development. This study has empirically tested the major determinants of FDI in India and has identified that a growth factor is the important determiner of foreign direct investment in our economy.

The survey implies that the economic performance of India attracts foreign investors. Improvements in the growth factor can result in an increase in both Greenfield investment and our bargaining power compared to other developing economies.