Impact of FDI inflows on the rate of inflation in India

The previous article tested the relationship and investigated the impact of Foreign Direct Investment (FDI) inflows on Gross Domestic Product (GDP) in the context of the Indian economy. The purpose of this article is to empirically examine the impact of FDI inflows on the rate of inflation in India. Therefore, this article considers the relation between FDI and another important macroeconomic variable namely rate of inflation.

Inflation has been a source of debate among economists with different viewpoints of structuralists and monetarists. The structuralists regard inflation to positively affect economic growth whereas the monetarists believe that inflation is harmful to economic growth (Mallik & Chowdhury, 2001). This growing concept in the literature of economics has led researchers to test the relationship between inflation and economic growth. Since the previous article found a positive and significant effect of FDI on the GDP, it is worth examining how FDI affects the rate of inflation in India and which viewpoint holds true for the Indian economy.

Empirical analysis to find the link between inflation and FDI

This article approaches to find out the link between inflation and FDI inflows in India by using time series data over the years 1980 to 2016. Therefore, below are the variables whose data is obtained from the World Bank database:

  1. FDI as a percentage of GDP.
  2. Consumer Price Index (CPI) as a measure of the inflation rate.

The empirical research so far has not yielded any concrete evidence regarding the causal relationship between FDI inflows and inflation. This is because there is a disagreement on the causality relationship between the two variables.

Name of the Test
Objective
Variables
Unit root Test To check stationarity in the data. FDI and Inflation rate
Johansen Cointegration Test To check the long run relationship. FDI and Inflation rate
Granger Causality Test To determine the direction of causality. FDI and Inflation rate
Time Series Regression To determine the impact of FDI on the inflation rate FDI and Inflation rate

Table 1: Tests for the empirical analysis

Model for empirical analysis

The basic model to find out the impact of FDI on the inflation rate is

rate of inflation = f (FDI)

Where inflation rate is the dependent variable and FDI inflow is the independent variable. This null hypothesis is tested; FDI inflows has no impact on the rate of inflation.

Figure 1: Inflation and FDI inflows in India (Source: World Bank)

Figure 1: Inflation and FDI inflows in India (World Bank, 2018) Note: Inflation and FDI net inflows (% of GDP)

Unit root test for examining the impact

The absence of the stationary test would make the regression results spurious. Before estimating the equation it is important to check the stochastic property of the variables. For this Augmented Dickey-Fuller unit root test (Dickey & Fuller, 1981) has been used. The table below shows the results of the ADF unit root test.

Series
 t statistic (ADF)
ADF at 1% Level
ADF at 5% Level
FDI -1.4197 -3.6267 -2.9458
ΔFDI -6.8668* -3.6329* -2.9484*
INF -2.0800 -3.6394 -2.9511
ΔINF -4.8226* -3.6463* -2.9540*

Table 2: Augmented Dickey-Fuller test statistics for FDI and inflation

The results of the unit root test suggest that all the variables are integrated of order I(1), therefore both the variables become stationary after first differencing.

Co-integration test for examining the impact

When the variables are non-stationary, they should be differenced and then used in the regression model to avoid spurious regression. There are numerous tests for co-integration analysis such as the co-integrating regression Durbin-Watson test, Engle-Granger Co-integration test and Johansen Co-integration test. Johansen test has been applied here to check for co-integration between the variables in the empirical model. This is because it has an advantage over others as it takes into consideration the possibility of multiple co-integrating vectors. 

Maximum Ranks
Trace Statistic
5% Critical Value
P-Value
Max Statistic
5% Critical Value
P-Value
0 16.1409 15.4947 0.0399* 13.1511 14.2646 0.0005*
1 2.9898 3.8416 0.0838* 2.9898 3.84146 0.0838*

Table 3: Johansen co-integration test (Trace and Max Value stat)

The results of Johansen co-integration test reveals there is a co-integration between the variables. The results of both trace and max statistic suggest that there is a long run association between the variables. They may be different from the other studies because of different econometric approach and sample size. Johansen test is based on the maximum likelihood method and is based on two statistics:

  1. Eigenvalue statistic and
  2. Max statistic.

When the rank is zero, it means there is no co-integration relationship and if the rank is one there is one co-integration equation and so on. Based on Johansen co-integration results, both the variables have a long-run association, which means an increase in FDI may increase inflation and vice versa. Hence this means both the variables are moving together in the long-run.

Granger causality test for examining the impact

This section gives an estimation of the causality from inflation to FDI and vice versa by applying Granger causality test to detect the nature of the causal relationship among the variables in consideration. The table below shows the results.

Equation
F-Stat
Prob
INF to FDI 1.26618 0.2965
FDI to INF 0.11040 0.8958

Table 4: Granger Causality between FDI and inflation

The results of Granger causality test reveals that there is no causality running from FDI to inflation or from inflation to FDI in India. The causality was not apparent because the Indian economy is one of the most integrated economies with higher capital outflows, export promotion due to market liquidity and flexible governmental policies.

Regression analysis to examine the impact

The above model is tested through a linear regression of time series to measure the impact of FDI on inflation.

Inflation
Coef
t-value
P-value
FDI -.5420494 -0.96 0.343
Cons 8.500787 11.98 0.000

Table 5: Regression Coefficient of FDI

The table above gives the regression results between inflation and FDI. The results reveal if there is an increase in FDI, inflation will decrease. The coefficients show that a 1% increase in FDI will cause a decline of -.542% in inflation. Furthermore, the p-value is not statistically significant at 5% level of significance. Thus, the null hypothesis cannot be rejected.

Reduction in inflation due to growth in FDI inflows

This study analyzes the impact of FDI on inflation in India over the period of 1980 – 2016 using time series data. The results of the Johansen co-integration test reveal that there is a statistically significant long-run relationship between FDI and inflation in India. However, the results of Granger Causality do not find causality between the variables. The results of the regression analysis also find a negative coefficient of FDI on inflation, albeit insignificant. Although the test results seem to contradict each other, FDI and inflation are only related in the long run, but not in the short run.

However, in the long run, FDI causes the supply of output to grow enough due to the technological advancement it brings. The increased supply of output causes the rate of inflation to fall from the interaction of demand and supply. Therefore a decline in the rate of inflation causes the economy to grow in the long run as per the structuralists view. Hence this view holds true for the Indian economy where an increase in FDI positively contributes to the growth of GDP by reducing the rate of inflation. This finding is therefore supportive of the findings of the previous article.

References

  • Dickey, D. A., & Fuller, W. A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica, 49(4), 1057–1072. https://doi.org/10.2307/1912517.
  • Mallik, G., & Chowdhury, A. (2001). Inflation and Economic Growth: Evidence from Four South Asian Countries. Asia-Pacific Development Journal, 8(1), 123–135.
  • World Bank. (2018). Inflation, consumer prices (annual %). Retrieved September 29, 2018, from https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG?locations=IN.

Umer Jeelanie Banday

Research Analyst at Project Guru
Umer Jeelanie Banday is Pursuing PhD, Economics and has an M.Phil degree in economics. He is currently working as a Research Associate with Ministry of Commerce, Govt of India. His work is centered on Macroeconomics with modern econometric approach. Broadly, his methodological research focuses on Panel data and Times series data analysis for causal inference and prediction.
Mr. Banday has been a recipient of “Young Scholar Grant’ to attend the 40th National Bureau of Economic Research (NBER) summer Institute in Massachusetts USA, selected by Indian Council for Research on International Economic Relations (ICRIER). He has to his credit various research papers in national and international journals. He also serves as a reviewer to various Journals of Taylor & Francis Group, Emerald, Sage and Member of editorial board Macrothink Institute, United States.
Umer Jeelanie Banday

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