Relationships among Export, FDI and Growth in India: An Application of Auto Regressive Distributed Lag (ARDL) Bounds Testing Approach

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India is an interesting and increasingly important case for the study of relationship among growth, Trade and Foreign Direct Investment. India is the second largest country in the world, with a population of over 1.123 billion (in 2007) which is more than one-sixth of all the people in the world. The output of India accounts for almost 8% of global GDP when measured appropriately. In particular, when National Income is measured using PPP (purchasing power parity) reflecting the actual purchasing power of a country's currency, India is fourth after the US, China, and Japan (with PPP GNI $3078.7 billion compared to only 2782.7 billion for Germany in the fifth position and $4,420.6 for Japan in the third position in 2007: see World Development Report 2009). Moreover, India alone has accounted for roughly one fifth of global GDP growth in the last five years. During the period between 2000 and 2007, the growth rate of Indian Economy was an impressive 7.8% per year compared to only 3.2% for the world, only 2.4% for High Income, only 4.3% for upper middle Income, and only 5.6% for low Income countries (World Development Report, 2009). India is fast catching up in overall growth with the champions, the East Asia and Pacific region countries, which have so far been ahead and above (8.9% during this period).

Relatively low wages and vast reservoir of trained manpower make India a natural destination for foreign direct investment (FDI). Until recently, however, India has attracted only a small share of global FDI, primarily due to government restrictions on foreign involvement in the economy. But beginning in 1991 and accelerating rapidly since 2000, India has liberalized its investment regulations and actively encouraged new foreign investment: a sharp reversal from decades of discouraging economic integration with the global economy. India's recent liberalizations and foreign investment de-regulations have generated strong interest by foreign investors, turning India into one of the fastest growing destinations for global foreign direct investment (FDI) inflows. Foreign firms are setting up joint ventures and wholly owned enterprises in various services and manufacturing sectors. Net foreign direct investment (FDI) flows into India reached $22.8 billion in 2007, more than five times the $4.0 billion recorded during 2001. In 2008 it is recorded to jump to $34.4 billion. India has emerged as the second most attractive destination for FDI after China and ahead of the US, Russia and Brazil. According to the News report published in October 2009 by the Trade Council of Denmark, India achieved a stunning 85.1% increase in foreign direct investment flows in 2008, the highest increase across all countries, even as global flows declined by 14.5%, says the findings (quoting a recent UNCTAD study--Assessing the impact of the current financial and economic crisis on global FDI flows). Similarly, export volume has increased from a mere $16.6 billion in 1990 to $ 163.1 billion in 2008 (an increase of over 1000 % in 18 years!). Policy makers and Research scholars have been touting this impressive export and FDI growth in recent decades as the vehicles for India's accelerated growth in the recent years and possibly in decades to come.

In the last two decades there have been several studies on such relationships investigating Export led or Foreign Investment led growth in India, but all suffer from methodological issues. Most studies ignore the time series nonstationarity properties of these macro variables which can lead to spurious Regressions and Correlations. Some do investigate the nonstationarity properties but then perform the Granger Causality Tests using simple VAR or VECM or Johansen-Juselius cointegration procedures with the exception of Shirazi and Abdul-Manap (2005), which does not include FDI. But Toda and Phillips (1993) have provided evidence that the Granger causality tests in error correction Models (ECMs) still contain the possibility of incorrect inference and suffer from nuisance parameter dependency asymptotically (Lutkephol 2004, p. …


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