An agency model of contracts used in California's processing-tomato industry is estimated in three stages. We first estimate growers' stochastic production possibilities, and then, for a given vector of preference parameters, compute an optimal compensation schedule. Finally, we compare computed compensations with actual compensations and choose preference parameters to minimise distance between the two. Assuming perfect competition and risk neutrality for processors, we obtain an estimate of 0.08 for growers' measure of constant absolute risk aversion, and find that growers who face higher-powered incentives produce higher levels of soluble solids, at a cost that is 1.8 per cent greater than otherwise. Efficiency losses from information constraints are 1 per cent of mean compensation, whereas existing quality measurement improves efficiency by 1.08 per cent.
