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Consumption Smoothing in Rural Punjab in the Green Revolution Years

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  • Pushkar Maitra

    (University of Sydney)

Abstract

This paper examines how well consumption is insured against income shocks using a panel of households from rural Punjab during the green revolution years. I estimate the effects of income changes on consumption, after controlling for aggregate shocks through changes in village level consumption. By classifying households on the basis of landholding, I examine whether certain section of the population are better able to insure consumption against idiosyncratic income shocks. The null hypothesis of full insurance is universally rejected irrespective of the land holding, though there is evidence of partial insurance for all land classes. The rejection of full insurance is the strongest for landless households with around 40% of an idiosyncratic income shock being passed onto current consumption. In contrast the estimated marginal propensity to consume out of changes in idiosyncratic income is only 12% for the wealthiest households. I also find that for all households food is better protected from income shocks than total consumption and landed households are able to insure education from idiosyncratic income shocks.

Suggested Citation

  • Pushkar Maitra, 1998. "Consumption Smoothing in Rural Punjab in the Green Revolution Years," Indian Economic Review, Department of Economics, Delhi School of Economics, vol. 33(2), pages 185-196, July.
    • Handle: RePEc:dse:indecr:v:33:y:1998:i:2:p:185-196
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    JEL classification:

    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
    • O16 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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