Foreign Direct Investment and Economic Growth in India: An Econometric Approach

Transcription

1 Foreign Direct Investment and Economic Growth in India: An Econometric Approach Dr. Karnika Gupta Dept. of Commerce, Kurukshetra University, Kurukshetra Ishu Garg Dept. of Economics, Kurukshetra University, Kurukshetra Abstract: Foreign direct investment (FDI) has been established as a most helpful international capital to the host country compared to portfolio investment that has short term characteristics. Many world economies including India have obtained financial benefits from FDI inflows for their economic growth. However, the fact that FDI does not bring immediate returns to any economy cannot be denied. Similar to other investments, it needs certain time for its effusive contribution. Therefore, average time required for FDI to make its contribution to economic growth is an important aspect which needs to be studied. Keeping this backdrop, the present study is undertaken to examine the time lag required for FDI to make its utmost impact on economic growth in India. For this purpose, data on FDI and GDP (taken as an indicator of economic growth in the study) for the period to are analyzed with the help of lag regression models. The findings confirm that FDI requires a time period of three years to make its contribution to the economic growth in a significant and utmost favorable manner. Thus, there is need for the regular rise in FDI to bring continuous increase in economic growth. To attract sufficient FDI, Government of India needs to improve the investment climate for foreign capital through the maintenance of political as well as economic stability along with curbing corruption. Key words: FDI, Economic Growth, GDP, Lag Regression Model, Indian Economy I. INTRODUCTION Foreign direct investment (FDI), being a non-debt financial capital is a most preferred way of capital inflow in any economy. FDI plays a complementary role in overall capital formation by filling the gap between domestic investments and savings. It raises the level of investment in the host economy, which by multiplier effect leads to increase in employment, income and savings; and thus contributes to the economic growth of that economy [1]; [2]. Besides, FDI is an important vehicle for the transfer of technology and knowledge. It also generates increasing returns in production via positive externalities and productive spillovers which if utilized efficiently also leads to sustainable economic growth [3]; [4]. In developing countries like India, FDI helps to tackle socio-economic problems such as unemployment, deficit balance of payment, lack of capacity, scarcity of foreign exchange and poor technological ability [5]. FDI to India means the investment by non-resident person/entity of India, in the capital of an Indian entity/company and thus, 6

2 becomes helpful in the progress of our economy [6]. Indeed, it is also true that the benefits which FDI brings are not attained immediately rather as every investment requires some time period to fetch returns, FDI also has a payback period [7]; [8]. For this reason, the average time required for FDI to make its contribution in economic growth is an important aspect and needs due consideration in research works. From this viewpoint, the present study enquires about the time that is required for FDI in India to make its utmost impact on economic growth. The paper is organized as follows: Section two reviews the concerned literature and Section three explains the research methodology adopted to attain the purpose of the present study. Section four is dedicated to the analysis and results; whereas sections five and six conclude the study with policy implications. II. LITERATURE REVIEW AND OBJECTIVES The research studies on the aspect of FDI have been directed to and formulated in different manners. FDI and its contribution to economic growth have always attracted the interest of researchers, economists and policymakers all over the world; therefore, there is no dearth of literature on the relationship of these two concepts. In the earlier stage, few studies [9]; [10]; [11] had shown that FDI has a negative impact on the growth of the developing countries. But in the early 1960s researchers like Rodan [12], Chenery and Strout [13] argued that as far as developing countries are concerned, foreign capital inflows had a favorable effect on the economic efficiency and growth. Conversely, Kasibhatla and Sawhney [14] in the U.S. supported a unidirectional causality from GDP to FDI and not the reverse causation. However, parallel to the present purpose, here the literature concerning the impact of FDI on economic growth is reviewed and presented. Bashir [15] examined the relationship between FDI and growth empirically in some MENA (Middle East and North African) countries, using panel data. The study found that FDI leads to economic growth; the effect however varies across regions and over time. Alam [16] in his comparative study of FDI and economic growth for the economy of India and Bangladesh found that the impact of FDI on growth is more in case of Indian economy as compared to Bangladesh yet it is not satisfactory. Kundan and Qingliang [7] emphasized on the time lag and found that FDI flows to the host country begin to have a positive effect after four years. It means FDI investment brings returns to the economy after four years. Adhikary [17] explored the impact of FDI on the host country s exports, the rate of inflation, domestic demand and the country s trade openness (export and import ratio). The author used the distributed delayed model and the causality test. It was obtained that FDI changed the export volume during the first year but the other indicators like inflation rate, domestic demand and the trade openness increased at three and four years after the investments are attracted. 7

3 Agarwal and Khan [18] while studying the two countries India and China attained that 1 per cent increase in FDI would result in 0.07 per cent increase in GDP of China and 0.02 per cent increase in GDP of India. Besides, China s growth is more affected by FDI, than India s growth. Mustafa and Santhirasegaram [5] examined the impact of FDI in promoting economic growth by using the time series annual data from in Sri Lanka. Multiple regression models were used to estimate the impact of FDI on economic growth. The results revealed that the actual impact of FDI can be felt after time lag of two years. Kuliaviene and Solnyskiniene [8] determined the particular impact of Foreign Direct Investment (FDI) on the Lithuanian Gross Domestic Product (GDP) using lag analysis. On the basis of results, they concluded that an optimum lag was two years when investments from foreign countries start to affect the country s economy. Motivating from the above literature, the paper works on the following two objectives. 1) To investigate the relationship between FDI and economic growth. 2) To find the time lag required for FDI to make its utmost contribution in economic growth. III. RESEARCH METHODOLOGY The present study is exclusively based on secondary data which is collected from Handbook of Statistics on Indian economy [19] published annually by Reserve Bank of India. Data on FDI and GDP (Gross Domestic Product) was collected for the period of to GDP is termed as an indicator of economic growth. As per the purpose, GDP is to be taken as dependent variable (the cause) and FDI as independent variable (the effect). The causal relationship between FDI and economic growth is judged with the help of regression models with varying time lags. It is considered that FDI for the period t brings increase in GDP through multiplier effect in the next period t+1. Consequently, regression model is tested with varying time lags and is shown by the way of equation 1. GDP t = b 0+ b 1 FDI t-k + u.(1) In equation 1, t signifies the time period without any time lag and t-k implies the time period after considering the time lag as k indicates the values 0, 1, 2 so on to run regression model with varying time lags. Thus, GDP t point towards the Gross Domestic Product of time t period and FDI t-k is a sign of Foreign direct investment of the past years. b0 is regression intercept, b1 denotes the regression coefficient (slope) and u is randam disturbance term. Hence, a linear regression model is implicit in equation 1 which depicts that GDP is regressed on FDI and GDP of period t depends on FDI of period t-k. If k=0, GDPt is regressed on FDIt that is for the same year. When, k=1, then impact of t-1 (for example: ) year s FDI on GDP of year t (for example: ) is studied through the model. Similarly, if k is taken to be 2, then the model (1) shows the influence of FDI of t-2 year (for example: 8

4 ) on GDP of year t (for example: ). The similar is applicable to each time lag. Here, Ordinary Least Square (OLS) estimation method is employed for estimating the unknown parameters (b0 and b1). IV. ANALYSES AND RESULTS The examination corresponding to the present objectives is completed by first analyzing the trend of relationship between FDI and GDP. For the same, data on GDP and FDI are given in table 1. TABLE1 FDI AND GDP IN INDIA DURING TO Sr. No. Years GDP FDI In Rupees Billion % Increase In Rupees Billion % Change % ( ) % ( ) % ( ) % ( ) % ( ) % ( ) % ( ) % ( ) % ( ) % ( ) % ( ) % ( ) Source: Handbook of Statistics on Indian Economy ( ) [19] and Researchers Calculations Notes: indicates increase over previous year and highlights decrease over previous year % ( ) % ( ) % ( ) 35.89% ( ) 46.42% ( ) % ( ) 35.81% ( ) 36.78% ( ) % ( ) % ( ) 16.93% ( ) -5.32% ( ) Table 1 reveals that, GDP has continuously increased over the years; however the rate of increase varies across years. On the other hand, FDI shows the tendency of ups and downs. At first, FDI rose speedily in (% = 58.90) but then starts falling for the coming two years. The spectacular rise in FDI is seen in year (% = ) which was more than two and half times over the previous year. In , GDP is also increased by more amount than earlier (% = 16.28). It may be due to appreciable rise in FDI. Once again the FDI flourished in (% = 36.78) after that it declined till but got recovery in (% = 16.93). Indeed, the increments in GDP remained continue; probably, due to the influence of FDI of current and past years. However, in comparison of year , both GDP and FDI have increased in but their path of increment differs which can be shown in the following figure 1. 9

5 Source: Prepared from data given in table 1 Fig1. Trends in GDP and FDI during to In figure 1, increasing trend of GDP and mixed (increasing and decreasing) trend of FDI is highlighted. It becomes clear that the GDP attains its peak value in while FDI reaches to highest level in and growth path of GDP is looking smooth in comparison to FDI. Despite of getting some appreciable increments, FDI have faced declining trend for some years. FDI experienced major rise and fall in the years and respectively. In spite of a fall in FDI in the year , a considerable rise is felt in GDP; either it may be the lagged impact of FDI on GDP or the influence of other economic factors or both. However, the present paper undertakes FDI as an influencing factor of GDP and makes an attempt to assess its true impact through identifying the time lag required for FDI to contribute to GDP under partial equilibrium framework. TABLE 2 RESULTS OF LAGGED REGRESSION MODELS Time Lag (k) b0 b1 SEb1 t b1 R R 2 Adj. R 2 F-value DW (d) * * 0.50 PA * * 0.68 PA * * 0.85 IN * * 1.22 # * * 0.50 PA * * 0.64 IN ** ** 0.78 IN ns ns 0.86 IN ns ns 0.45 Source: Researchers calculations from data given in table 1 Notes: *and** indicates less than 1 per cent and 5 per cent level of significance respectively ns implies not significant values PA shows Positive IN symbolizes values where Durbin-Watson test is Inconclusive # implies Non (1 Per cent Level of Significance) 10

6 In this direction, table 2 explores the results of different lagged regression models. The estimators of the parameters of lag models have been fitted through OLS method. The estimated values of the regression coefficient (b1) with its standard error (SEb1), coefficients of correlation (R), coefficients of determination (R 2 and adjusted R 2 ), statistics of t, F and Durbin-Watson (DW or d) are presented. Regression coefficient (b1) is a measure to judge the strength of independent variable (FDI) in predicting the dependent variable (GDP). It is clear from table 2 that the value of regression coefficient (b1) increases from to as k rises from 0 to 7 but declines to at k=8. The value of t-statistic increases to highest level (t=7.57) at k=3 and then it starts declining to lowest (0.37) when k is 8. The estimated regression coefficient from models having time lag 0 to 5 years is statistically significant at less than 1 per cent level of significance where as the regression coefficient in case of k=6 is statistically significant at 5 per cent level of significance. Therefore, FDI is important variable affecting the GDP, in each time lag except k=7 and k=8. But, the greatest influence of FDI on GDP arises only for k=3. However, from the point of view of Standard error of regression coefficient (which gives an indication of how much the estimated value of b1 is likely to vary from the corresponding population parameter), the model having the minimum standard error is regarded as the best. In table 2, SEb1 is decreasing till k=3 and after that it starts rising. In case of k=3, standard error is found to be least (4.61), therefore, the model having lagged 3 is best. Similarly, to measure the strength of linear relationship between FDI and GDP, coefficient (R) is employed. As there is one dependent variable (GDP) and one independent variable (FDI), the correlation is the simple bi-variate correlation between the two. In table 2, correlation coefficient (R) increases when k goes from 0 to 3 and attains highest value (R=0.94) at k=3. As the time lag exceeds from 3, value of R falls continuously and reaches to lowest (R=0.21) when k=8. Hence, the strength of relationship between GDP and the FDI is highest when the time lag is of 3 years. Besides, in order to find out the significant time lag on the basis of goodness of fit of the models, R 2 and adjusted R 2 are used which highlight that how many variations in GDP is explained by FDI. Till k=3, both R 2 and adjusted R 2 rise and achieve their highest value of 0.88 and 0.86 respectively when k=3. This implies FDI is capable of explaining 86 per cent of variations in GDP. Therefore it can be said that among all, the model with time lag of 3 years is the best fit and like so the highest impact of FDI on GDP comes after 3 years. Along with this, overall significance of the models is judged through F-statistic. In the beginning, the value of F-statistic increases and reaches to highest level (F=57.33) at k=3. But after k=3, it starts declining and becomes lowest (F=0.14) when k=8. This analysis exhibits that regression models are significant during time lags from 0 to 6 years and after that they are insignificant. Whatsoever be the values of F-statistics are, the significance is highest at k=3. Lastly, the Durbin-Watson statistic is computed to test whether the error terms are auto correlated or not, as the best prediction from regression coefficients will be possible if the model is free from autocorrelation. Durbin and Watson had mentioned the conditions to verify autocorrelation and non-autocorrelation in any regression model which are shown in figure 2. The figure denotes that the DW (d) statistic always ranges from 0 to 4. Further, upper (du) and lower (dl) bounds are also established for critical DW values. When the model is auto correlated, it may take two 11

7 positions namely positive autocorrelation and negative autocorrelation. If d statistics lies between 0 and lower bound dl (0 < d < dl) the error terms are positively auto correlated, if its value is between 4-dL and 4 (4-dL < d < 4) error terms are negatively auto correlated. In case the calculated value of d lies between dl and du or 4- du and 4- dl, the test becomes inconclusive. Further, if d U < d < 4-d L, it implies that the error terms are not auto correlated or in other words, the model is free from autocorrelation. Positive Test is Inconclusive (No Decision) Non Test is Inconclusive (No Decision) Negative dl du 4- du 4- dl Source: Adapted from Madnani (2009) [20] Fig.2. Conditions for Testing /Non Aligning with the above conditions, the DW values as are obtained by the present analysis are tested and the results can be studied from figure 3. Positive Test is Inconclusive (No Decision) Non Test is Inconclusive (No Decision) Negative dk=0 = 0.50 {d L= 0.738, d U = 1.038} dk=1 = 0.68 {dl= 0.697, du = 1.023} d K=4 = 0.50 {dl= 0.554, du = 0.998} dk=2 = 0.85 {dl= 0.653, du = 1.010} dk=5 = 0.64 {d L= 0.497, d U = 1.003} dk=6 = 0.78 {dl= 0.435, du = 1.036} d K=7 = 0.86 {dl= 0.390, du = 1.142} d K=3 = 1.22 {dl= 0.604, du = 1.001} du 2 4- du 4- dl 4 dl Source: Compiled by Researchers on the basis of calculated DW statistics Fig.3. Categorization of Computed DW statistics In the present case, the calculated value of DW (d) statistic (for one explanatory variable at 1 per cent level of significance) is indicating the presence of positive autocorrelation for k = 0,1 and 4. But for k = 2, 5, 6 and 7, the calculated values of d are found to be lie between dl and du, therefore in these cases Durbin Watson test becomes inconclusive (i.e. no decision can be made). As far as time lag of 3 (k=3) is concerned, the calculated value of d is 1.22 which is greater than the upper bound namely du =1.001 and is less than 4-dU ( ) that is here, du < d < 4-d U (1.001 < 1.22 < 2.999); so, the error terms are not auto correlated. Therefore, the lag model with k=3 is free 12

8 from autocorrelation. In this case, OLS estimators become unbiased and the best parameters; thereby, can be used for prediction. V. CONCLUSION AND DISCUSSIONS Overall, from the results of lagged regression models, it becomes clear that FDI is positively and significantly related to GDP, when the time lag ranges between 1 to 6 years. But the relationship between FDI and GDP is found to be highly significant when time lag is of three years as all statistical values are in its favor. At this point, an increase in FDI by rupees one billion brings a rise of rupees 34.9 billion in GDP. Consequently, it can be concluded that FDI leads to the economic growth of Indian economy. However, it requires a time period of three years to make its contribution to the economic growth in a significant and utmost favorable manner. In this way, the findings are in collaboration with the researchers like Rodan [12], Chenery and Strout [13], and Bashir [15] for exclaiming a significant positive effect of FDI on economic growth. Side by side, some academics: Kundan and Qingliang [7], Mustafa and Santhirasegaram [5] and Kuliaviene and Solnyskiniene [8] also provide support by stating that FDI requires certain time to contribute to economic growth. However, their findings are in contradiction with the present findings of three years lag period as they obtained lag periods of four years and two years in their studies. But the reason of dissimilar results may be because of difference in the economies from Indian economy which was the basis of study in these researches. VI. POLICY IMPLICATIONS AND RESEARCH DIRECTIONS The present study reveals that FDI contributes in economic growth significantly after a period of three years. Thus, there is need to encourage foreign direct investment every year to enhance economic growth in Indian economy. For that, Government of India should improve the investment climate for foreign capital through the maintenance of political as well as economic stability along with curbing corruption. Moreover, by providing adequate market size, easy accessibility to export market, developed infrastructure, cost-effectiveness and by other means more FDI can be attracted. Along with this, there is a rationale for the adoption of innovative policies and good governance practices on par with international standards to make India as a most preferred destination for foreign capital. Besides, there are some research directions originate from the study. FDI is not the sole determinant of GDP, other variables also affect it; future researchers can explore these determinants. The impact of FDI on GDP should be studied together with other macro economic variables to make the analysis as general equilibrium approach. Further, from the existing literature and the results of present study, it is evident that the time required by FDI to produce its influence on GDP varies from economy to economy. So, it creates interest to examine time lags in the contribution of FDI in GDP in various economies. 13

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