M. R. Singariya^1,, Narain Sinha²

The objective of the present paper is to examine causal relationship among per capita GDP, agriculture and manufacturing sector output in India using time series data collected from CSO for the period 1970 to 2013. The study conducts an econometric investigation by applying methodologies, viz., Stationary tests, and Johansen’s Cointegration test, Johansen’s Vector Error Correction Model (VECM) in VAR and Impulse Response Function and Variance Decomposition Analysis. We have carried out the ADF and KPSS unit root tests. According to AIC and HQC criteria, the appropriate lag length is 2. Both the variables in log terms being I (1), Johansen’s co-integration test confirms one long run relationships among the variables at 5% significance level. It reveals that there exists bidirectional causality between agriculture sector and per GDP, while the unidirectional causality between the manufacturing sector and per capita GDP and between the agriculture and manufacturing sector. As the VECM involve selection of appropriate lag length. According to AIC criterion and Hannan-Quinn criterion; two lag lengths is considered to the present analysis. The signs of both the coefficients of the cointegrated equations are as expected and their magnitudes are responsible. However, results based on vector error correction model indicate a weak association between the sectors in the short run. Dynamic causality results show that the agriculture sector affects Per Capita GDP and manufacturing sector, while Per Capita GDP affects manufacturing sector strongly in the long run, thus causality seems to run from agriculture to PCGDP and manufacturing and from PCGDP to Manufacturing sector. It is also evident from impulse response function that any innovation in agriculture sector increases its own growth as well as growth of manufacturing sector in India. In consistence with the above finding, the present study argues that shocks originated from agriculture sector spill over to Per Capita GDP and Manufacturing sector in long run in India.

agriculture, manufacturing sector, Cointegration and Vector Error Correction Model, Impulse Response Function and Variance decomposition analysis