Impact of FDI inflows on total factor productivity of India

The previous article showed a positive and significant impact of Foreign Direct Investment (FDI) on exports from India. This article attempts to empirically examine the relationship between FDI inflows and Total Factor Productivity (TFP). There are different types of factors of production; single or partial factor profitability. Labour and capital efficiency are examples of this. It is contended that in the short run, labour productivity is the best measure of factor productivity. But if one has to measure in the long-run, total factor productivity is better than labour productivity (Sargent and Rodriguez, 2000). The main aim of this article is to estimate the impact of FDI on TFP in India and whether FDI had an influence on TFP for the period 1980-2014. The reason for choosing the study period till 2014 is due to unavailability of more recent data.

Trends in FDI inflows and total factor productivity of India

The figure below shows the trend of FDI inflows and total factor productivity in India since 1980. In the recent years, total factor productivity has increased along with the boom in FDI inflows.

Empirical analysis

Data and study period

The annual time series data for FDI and TFP is taken from World Bank and FRED economic data respectively for the period 1980 to 2014. For the empirical analysis, we use the data for the following variables:

1. Foreign Direct Investment as a percentage of GDP.
2. Total factor productivity at constant national prices for India, Index 2011=1.

Tests applied

The following tests were performed on the data using SPSS and EViews software.

Table 1: Tests applied for the Empirical Analysis

Model

In the regression analysis, TFP is the dependent variable and FDI is the independent variable. The basic model to find out the impact of FDI on TFP is as follows:

Total Factor Productivity = f(FDI)

 We test the null hypothesis that FDI has no impact on Total Factor Productivity.

Unit root test

Before estimating the above equation it is important to check the stochastic property of the variables using ADF (Augmented Dickey-Fuller test) unit root test. After checking the stationarity, long run relationship is estimated with the help of Johansen co-integration test. The results of the unit root test have been given in table 2.

Table 2: Augmented Dickey-Fuller Unit Root Test Statistics for TFP and FDI

Note: A variable is stationary when the ADF t-statistics is greater than the critical values and non-stationary when t-statistics is less than the critical value.

Results of unit root test confirm that both the variables became stationary after first differencing. So we can investigate long-run relationship between the variables.

Co-integration test

Johansen test is applied for determining co-integration between the variables in the empirical model with quadratic deterministic trend.

Table 3: Johansen Co-integration Test (Trace and Max Value stat) Results for total factor productivity and FDI

Johansen test of cointegration is based on two statistics:

1. Eigen value statistic and
2. max statistic.

When the rank is zero, it means there is no co-integration relationship and if the rank is one it means there is one co-integration equation and so on. The above results of Johansen co-integration based on quadratic deterministic trend test imply that there is a co-integration between the two variables. The results of both trace and max statistic suggest that there exists a long run relationship between total factor productivity and FDI, meaning both the variables are moving in the same direction in long-run.

Granger causality test

The causality from FDI to TFP and vice versa is checked using Granger causality test.

Table 4: Granger Causality between FDI and total factor productivity

The above table presents the results of Granger causality test. Based on the p-values, first null hypothesis is not rejected and the second null hypothesis is rejected at 5% level of significance. The results show FDI does not granger cause total factor productivity, but total factor productivity granger causes FDI.  It implies unidirectional causality.

Regression analysis

The linear regression model helps to measure the impact of FDI on TFP. 

Table 5: Regression Coefficient of FDI

Note: Superscripts “*” denote 1% and 5% significance

The above table shows the regression results. It reveals that an increase in FDI will have a statistically significant impact on TFP. The coefficient value shows that a one percent increase in FDI will increase .1037235 percent increase in TFP. The p-value is statistically significant at 1% level of significance, rejecting the null hypothesis of no impact of FDI on TFP.

Positive relationship between FDI inflows and total factor productivity

The results of cointegration analysis found that there exists a long-run relationship between the two variables. Granger causality tests reveal unidirectional causality from total factor productivity to FDI. Regression results imply that FDI has a positive and statistically significant impact on total factor productivity, albeit with a very small coefficient value. The results are consistent with endogenous growth hypothesis that accentuates innovation dissemination through acclimatizing and adjusting foreign innovation as an imperative source of technological change in developing nations. As a concluding remark,  in this dynamic global world there are number of other variables like foreign trade, inflation, external deficit and exchange rate that may present a more accurate picture about the relationship between FDI and total factor productivity.

Reference

• Sargent, T. C. and Rodriguez, E. R. (2000) ‘Labour or Total Factor Productivity: Do We Need to Choose?’, International Productivity Monitor, 1(August), pp. 41–44.