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Terms of Trade and Supply Response of Indian Agriculture: Analysis in Cointegration Framework

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Surajit Deb (Ram Lal Anand College (Evening))
Abstract

In this paper, we examine the presence of stochastic trend (unit root) and structural break in various agriculture-industry terms of trade series in India. The results suggest that underlying data generating process of terms of trade are most likely non-stationary. We subsequently re-examine the aggregate supply response of Indian agriculture in this light. We investigate the presence of long-run functional relationship(s) underlying the supply response model through cointegration analysis and error correction framework. The multivariate results indicate presence of a cointegrating relationship in the supply response model. The vector error correction estimates suggest that short-run output adjustments are not related to changes in agricultural terms of trade in a temporal causal relationship. However, the short-run deviations in terms of trade from its long-term level create error-correction in the long-term output adjustments through changes in technology (irrigation). This may imply that agricultural growth can respond better if price incentives are combined with investments in irrigation.

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Paper provided by Centre for Development Economics, Delhi School of Economics in its series Working papers with number 115.

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Length: 43 pages
Date of creation: Mar 2003
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Handle: RePEc:cde:cdewps:115

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Related research
Keywords: domestic terms of trade; agricultural supply response; unit root; cointegration;

Find related papers by JEL classification:
Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions

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