The story of the moment: risk averse cypriot farmers respond to drought management

Abstract

This article illustrates the importance of estimating risk preferences when evaluating water policy. Using agricultural production data from the Kiti region of Cyprus we estimate farmers’ risk preferences à la Antle (Journal of Business and Economic Statistics, 1, 192–201, 1983, American Journal of Agricultural Economics, 69, 509–22, 1987) and show sensitivity to higher order moments of profit, such as skewness. We show that farmers in the Kiti region are risk averse with risk premiums in the region of 20% of expected profit. We use these estimates to analyse the impact of a water quota from the perspective of three policy-makers who differ only in their understanding of farmers’ risk preferences. We show in the case of Kiti that policy-makers who model risk preferences incorrectly, that is, either; (a) assume risk neutrality or; (b) ignore down-side risk, wrongly predict the magnitude and direction of input responses and therefore the magnitude of welfare changes. This highlights the importance of accommodating preferences for higher order moments of profit in the evaluation of water policy.

Notes

¹ Ozanne (1998) provides evidence of the importance of risk preferences for policy analysis in agriculture.

² Such an approach goes as far back as Stiglitz (1974).

³ See for example, Pope (1982).

⁴ This assumption is not critical as long as farmers are price-takers. Extending the model by allowing for price risk in addition to production risk, although feasible, would not bring about significant changes in the analysis.

⁵ is either in absolute or relative terms. In the latter case, we would have for example , with the reference water consumption and δ the desired rate of reduction in water use.

⁶ With the multiplicative risk specification that we have here, ϵ could also represent output price risk, p. However, we wish to focus on climate as the particular source of uncertainty.

⁷ The inclusion of a constant term in the empirical model measures the extent to which the population exhibits profit maximizing behaviour. Theory suggests that for profit maximizing farmers this should be zero and hence in the results section we check this hypothesis for each model.

⁸ Down-side risk is concerned with asymmetric (skewed) statistical distributions of profit and in particular aversion to disastrous events (see e.g., Menezes et al ., 1980 for a discussion).

⁹ A comparison to national statistics (see Agricultural Research Institute, 1998) reveals that the Kiti region provides a reasonable representation of the composition of crop production in Cyprus as a whole.

¹⁰ Major crops in the region are coriander and broad bean (respectively, 13.5 and 16.5% of total area), barley and wheat (respectively, 18.9 and 14.6% of total area).

¹¹ Citrus is also grown in the Kiti area however the data for this production were too sparse for the estimation of risk parameters.

¹² Water requirements range from 2200 m3/ha/year for broad bean, to 6800 m3/ha/year for okra. Concerning cereals, these requirements are respectively 5500 m3/ha/year for bran, barley and wheat and 6500 m3/ha/year for corn.

¹³ Irrigation water costs depend on an area-specific pumping lift, ranging between 6.5 and 18.5 m. Marginal groundwater costs are estimated between 0.13 CYP/m³ and 0.37 CYP/m³, depending on the area (Koundouri, 2000).

¹⁴ All variables are rescaled by their SD.

¹⁵ The 4th moment, kurtosis, was also considered however, estimated coefficients were not significant. We concentrate on variance and skewness in order to illustrate the importance of higher order moments to farmers’ decisions.

¹⁶ However, this could be some remnant of the composite nature of the fertilizer input.

¹⁷ Given our assumption that land (and other factors), output and input prices are assumed fixed, it is unrealistic to draw inferences from a simulation scenario that results in input values very far from reference values. Presumably, if the quota policy requires large variations in water use from the reference case, farmers are likely to react, not only by adjusting the levels of their other inputs, but also by curtailing production on certain areas of land, possibly also modifying prices.