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Exploring Higher Order Risk Preferences of Farmers in a Water-Scarce Region: Evidence from a Field Experiment in West Bengal, India

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Abstract

We estimate higher order risk preferences of farmers from a water scarce region in West Bengal, India. We then examine correlations across risk aversion, prudence and temperance attitudes of farmers, and explore associations of these preferences with household characteristics. Our experimental findings indicate risk seeking behaviour of farmers. We find that farmers took more prudent and temperate decisions. Risk aversion and prudence have a significant negative correlation, whereas prudence and temperance have a significant positive correlation. Individual characteristics such as age, education, and entrepreneurship, income and value of assets are also correlated with various risk preferences. Increased stated drought resilience of farmers is positively correlated with prudence. Farmers affected by water scarcity in the Kharif make imprudent choices. Farmers with higher cropping intensities exhibit high levels of prudence and temperance. These findings have important theoretical and policy implications which we discuss in the paper.

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Notes

  1. RA is a tendency to avoid risks of any kind, and preference for sure gain over a gamble, even when gamble has higher or identical expected utility value (Kahneman and Tversky 1984). It is represented as a negative sign of the second derivative of the utility function (Eeckhoudt et al. 1995).

  2. LA is a tendency to overweigh losses compared to gains of the same amount.

  3. AA is an attitude of preferring outcomes with a known probability distribution than those with an unknown probability distribution.

  4. PR is an individual’s preference for adding unavoidable risks and losses to their risk profile at a higher income level rather than at a lower income level. PR is linked with precautionary savings, that is, individuals save more whenever background risks increase (Noussair et al. 2014). It is a third order risk preference represented by positive third derivative of the utility function.

  5. TE is defined as an individual’s inclination towards exercising self-restraint while accepting independent risks, and is a negative fourth order derivative of the utility function. Eeckhoudt and Schlesinger (2006) defined temperate individuals as those who preferred lottery options that allocated two risks of negative outcomes by placing them in two different choice sets rather than aggregating both outcomes in a single set. Alternatively explained, a temperate individual (as opposed to an intemperate one) will be less willing to take on another risky choice involving a significant negative outcome when faced with a background risk that could also yield a negative outcome.

  6. $$\text{Water scarcity index for each crop}=\frac{\left(\text{optimal irrigation-Applied irrigation}\right)}{\text{Optimal irrigation}}$$

    Water scarcity index (WSI) is measured as a ratio of the difference between optimal and applied irrigation to optimal irrigation. The WSI is zero if irrigation applied is equal to or larger than stated optimal irrigation, whereas WSI takes a value greater than zero at applied irrigation of less than optimal.

  7. Experimental procedures and instructions are available as supplementary material.

  8. Participants were rewarded with a fixed monetary payoff as compensation for participating in these experiments. They were also awarded with a payoff that varied depending on the winnings from the decision choices made. Participants were told that all the choices numbered 1 to 35 would be rolled inside the bingo cage and the numbers that rolled out from the bingo cage would determine their payoff. To avoid a high negative payoff, the numbers corresponding to the payoff from the game ‘RA with small probability of large losses’ were not kept inside the bingo cage. Also, the numbers corresponding to the AA choices were not put inside the bingo cage.

  9. The wage rate fixed by the Government of West Bengal was INR 225 per day exclusive of food, and INR 209 per day with food for the agricultural labourers (Government of West Bengal, 2016). 1 INR is equivalent to 0.014 US dollar .

  10. The amount of monetary payoff affects the decision effort of the individuals in experimental games, which is also termed as incentive effect (Smith and Walker, 1993). In the risk aversion series of our game, the maximum incentive was INR 60 and minimum incentive was INR 10, which is relatively lesser than the half day wages of the farmers and also lesser as compared to the incentives in the rest of the series. Relatively low incentive, and lack of very high difference between higher and lower payoff in the task could have made farmers risk seekers.

  11. In three sessions covering 50 participants (N=191), prudent choice was kept as option A and imprudent as option B whereas, in rest of the sessions, we kept imprudent choice as option A and prudent as option B. In two sessions for TE series (23 participants; N = 191), we kept temperate choices as option A. In all other sessions, we kept temperate choices as option B and intemperate choices as option A. In OLS regression, ordering did not affect results.

  12. $$\text{Water scarcity index for each crop}=\frac{\left(\text{optimal irrigation-Applied irrigation}\right)}{\text{Optimal irrigation}}$$

    Water scarcity index (WSI) is measured as a ratio of the difference between optimal and applied irrigation to optimal irrigation. The WSI is zero if irrigation applied is equal to or larger than stated optimal irrigation, whereas WSI takes a value greater than zero at applied irrigation of less than optimal.

  13. Cropping intensity is measured as the ratio of net sown area to gross cropped area multiplied by 100.

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Acknowledgements

Kanchan Joshi would like to thank the Australian Government for providing her with the “Australian Government Research Training Program Scholarship”. Authors are grateful for the generous financial support from the Department of Environmental Sciences, Macquarie University and partial funding from the Australian Centre for International Agricultural Research. We thank Bolpur Manab Jamin team, West Bengal and Institute of Economic Growth, India for their support. We heartily thank all the farmers who voluntarily consented to take part in the experiments and surveys. Without their generous time and involvement, this research would not have been possible. We thank Professor Arjan Verschoor, Professor John Rolfe, Dr Simanti Banerjee, Professor Stephen Knowles, and anonymous reviewers for their helpful comments/suggestions that aided in improving this manuscript.

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  1. Thiagu Ranganathan

    Present address: Centre for Development Studies, Thiruvananthapuram, Kerala, India

  2. Ram Ranjan

    Present address: Department of Economics, School of Humanities and Social Sciences, Shiv Nadar University, Greater Noida, Uttar Pradesh, India

Authors and Affiliations

  1. Department of Earth and Environmental Sciences, Macquarie University, Sydney, Australia

    Kanchan Joshi & Ram Ranjan

  2. Institute of Economic Growth, University of Delhi (North Campus), Delhi, India

    Thiagu Ranganathan

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  1. Kanchan Joshi
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  2. Thiagu Ranganathan
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  3. Ram Ranjan
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Contributions

KJ performed conceptualization, designed methodology, created model, conducted data analysis and wrote the original draft. TR and RR provided guidance in conceptualization, methodology design and implementation; reviewed statistical analysis; and reviewed and edited the manuscript.

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Correspondence to Kanchan Joshi.

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Supplementary file 1 (PDF 325 KB)

Appendix

Appendix

See Tables 6, 7.

Table 6 List of explanatory variables
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Table 7 Variance inflation factor (VIF) of explanatory variables
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Joshi, K., Ranganathan, T. & Ranjan, R. Exploring Higher Order Risk Preferences of Farmers in a Water-Scarce Region: Evidence from a Field Experiment in West Bengal, India. J. Quant. Econ. 19, 317–344 (2021). https://doi.org/10.1007/s40953-021-00232-4

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