These three input delivery schemes have two features in common: payment at harvest, and no payment in case of crop failure. Otherwise the details of input repayment vary a lot from one example to the next in the sharecropping example, costs are paid as a share of harvest; in the agricultural marketing board example, costs are deducted from the output price or paid jointly by villagers; in contract farming, costs are deducted from the value of the harvested crop. This much variation suggests that these contractual details are less important than the two principles listed above. Similar principles can be successfully applied to other technology delivery schemes, such as animal traction equipment.In my book on risk and rural development, I offer a simple extension of the Sandmo model which can account for these observations. Farmers are assumed to worry about out-of-pocket risk: they do not like to finish the year in the red. The addition of this simple assumption is sufficient to account for the success of the above-mentioned schemes even if farmers are otherwise risk neutral . This is important because we have argued earlier in this paper that the expected utility framework which assumes aversion to upside as well as downside risk may not be a very convincing. The question then is: why is assuming aversion to out-of-pocket risk any more reasonable than assuming risk aversion in an expected utility framework? Here behavioral economics comes to the rescue. Ever since Binswangers early work on risk aversion among ICRISAT farmers, researchers working on agricultural technology issues have been aware of experimental economics. But they may not have taken advantage of all its lessons. Results from laboratory experiments have long suggested that what humans fear is not risk but the prospect of loss . This is most easily demonstrated by experiments in which participants are asked to choose among lotteries with identical final payoffs, but a different sequence of events.

While participants often are willing to gamble for future gain, they are less willing to put earlier winnings at risk,maceta 30l even if final payoffs are identically distributed. This could explain why farmers are not willing to put assets at risk by buying agricultural inputs they are not guaranteed to recoup. By eliminating downside risk, the input delivery contracts discussed earlier do not remove upside risk but they deal with loss aversion. Laboratory experiments have also shown that humans have a poor intuitive understanding of low probability events. For instance, it is common for participants to experiments to be willing to pay the same for a risk reduction of one in a thousand or one in a million ñ even though the former should be worth one thousand times more than the latter. People are sensitive to whether they have recently been affected by similar events and can recall similar incidents. Indeed recent exposure to low probability events tend to dramatically raise peoples willingness to pay to protect themselves against the future recurrence of similar events. It follows that people respond to how the risk of future events is framed, and whether they can recognize past experiences in experimental situations. Finally people may be quite averse to small probability events that are beyond their control but not overly worried by high probability events they perceive to be under their control . Taken together, this evidence suggests that people are actually not very rational when it comes to small risks, but also that they are weary of downside risk beyond their control.People often overestimate their chances in risky ventures. As a result, they often want to over invest, provided they are sheltered from downside risk. This may explain why many entrepreneurs whose honesty is not in question seem keen to invest uncollateralized borrowed funds in risky projects. Such findings are in line with our earlier observation regarding the relative success of agricultural input delivery schemes that protect farmers from downside risk but expose them to considerable upside risk.

Taken together, these empirical regularities documented in laboratory experiments may help explain observed patterns of agricultural technology adoption. Recent field experiments add new insights to this body of knowledge. Of particular interest to our purpose is a recent paper by Duflo on fertilizer adoption in Kenya. At the end of the paper, the authors document a series of field experiment investigating the effect of fertilizer vouchers on input usage. They find much higher fertilizer usage among farmers who were offered a voucher for future fertilizer delivery at the time of selling their crop. This finding is broadly in line with experimental findings about quasi-hyperbolic preferences, forced savings contracts, and peoples desire to commit future expenditures . Duflo investigate possible explanations for their finding. Of interest is the observation that fertilizer usage drops significantly if the voucher is sold to farmers only a day or two after they sell their crop. Why this is the case is not entirely clear, however. One possibility is that the money has already found other uses, e.g., paying for debts and social obligations. Another possibility is reciprocity: when the voucher is sold by the buyer of the crop, the seller may feel some sense of obligation to reciprocate by purchasing a fertilizer voucher. More work is underway to disentangle these possible explanations. What they do suggest, however, is that input usage by small farmers in developing countries may be quite sensitive to the method of delivery and sale. Rational models of input purchases are not vindicated as there are strange behavioral responses to commitment devices offered to input purchasers. Peer effects may also matter. Ashaf et al. document an out grower scheme run by an NGO in Kenya. The authors evaluate a program in Kenya that encourages the production of export oriented crops by providing smallholder farmers with credit linked to agricultural extension and marketing services. They use an experimental design in which farmer self-help groups are randomly assigned to either a control group, a group receiving all DrumNet services, or a group receiving all services except credit. Among the services offered by DrumNet, credit is the most important, a finding that is consistent with the significant investment in capital and inputs required to produce the export crop. This result is also consistent with our earlier observation regarding downside risk. These results are to be compared to field experiments that offer crop insurance to small farmers. If Sandmos model is a fair representation of small farmers decision process,hydroponic grow system offering insurance corrects a market failure and is the preferred way to achieve first best. Two separate teams of researchers have experimented with crop insurance in two Indian states. Their results are summarized in a jointly authored paper . Both field experiments have in common the offer of a voluntary insurance contract that compensates farmers in case of deficient rainfall. Payment is based on objectively collected rainfall data.

Farmers purchase insurance in discrete units, with each unit equivalent to set payments conditional on rainfall. Farmers can obtain more insurance by buying more units. The modeling framework presented in Section 2 predicts that more risk averse farmers should purchase more insurance than risk neutral farmers. We also argued that the curvature of the value function V depends on the households capacity to self-insure through the accumulation of liquid assets. This implies that households with more assets need ñ and should purchase ñ less insurance. Since small Indian farmers are often poor, we would therefore expect widespread adoption, with many farmers purchasing enough insurance to protect themselves against much of rainfall risk. This is not what the authors find. Take-up is limited in the Gujarat experiment, only 20% of targeted farmers purchased the insurance but sensitive to price and additional marketing. Although results from the two experiments differ somewhat, risk averse households appear less, not more, likely to purchase insurance. Households do not purchase full coverage; on the contrary, they tend to purchase only one unit of insurance, no matter how large their risk exposure. Furthermore, insurance take-up is higher among wealthy households. None of these results are consistent with the standard Sandmo model. The authors also report that take-up is lower among households that are credit constrained. They argue that these results match predictions of an extended Sandmo model with borrowing constraints. Alternative explanations exist as well, such as lack of familiarity with the insurance product. Other patterns are more difficult to reconcile with the benchmark model. Participation in village networks and measures of familiarity with the insurance vendor are strongly correlated with insurance take-up decisions. While education does not seem to matter, endorsement from a trusted third party does. These results may reflect uncertainty about the product itself, given households limited experience with it. They are to be compared with those reported by Ashaf et al. on the role of farmer groups, and to those of Duflo regarding the possible reciprocity between farmers and crop buyers/input providers. Gine, Yang, Insurance and from Malawi report on another similar field experiment in Malawi. They implement a randomized field experiment to ask whether the provision of insurance against a major source of production risk induces farmers to take out loans to invest in a new crop variety. The study sample was composed of roughly 800 maize and groundnut farmers. The dominant source of production risk is the level of rainfall. The authors randomly select half of the farmers to be offered credit to purchase high-yielding hybrid maize and improved groundnut seeds. The other half are offered a similar credit package but required to purchase a weather insurance policy that partially or fully forgives the loan in the event of poor rainfall. If, as we have argued earlier, farmers are primarily concerned about the downside risk associated with credit, offering the insurance should boost take-up. Surprisingly, the authors find that take up is lower by 13 % among farmers offered insurance with the loan. At primafacie, this seems to reject downside risk concerns as the primary motive for low take-up of agricultural innovations. The authors however find suggestive evidence that the reduced take-up of the insured loan is due to the high cognitive cost of evaluating the insurance: the take-up of insured loans is positively correlated with farmer education levels, but not so for uninsured loan. This brings up another consideration, namely, that people have a complicated relationship with new products. Curiosity may tempt them into trying new products, but such impulse purchases may ultimately prove disappointing. People may therefore steel themselves against large impulse purchases, especially if they are poor. This would be consistent with richer Indian farmers purchasing rainfall insurance, but only one unit, while poorer farmers do not purchase any. Peoples ability to resist impulse purchases may be susceptible to manipulation by marketing efforts. This may explain why fertilizer vouchers in Kenya found more buyers when the purchase of the voucher was combined with the sale of the crop. Given this, adoption of new products may require reinforcement from peers: if others around them are adopting a new product, people may find it harder to resist buying it. This naturally generates threshold effects in adoption, an observation made a long time ago by Griliches . In his study of US farmers, Young and Burke similarly noted the importance of peer effects and conformity in the adoption of certain types of behavior. The emerging economic literature on social network effects has revived interest in diffusion and reinforcement effects. There is extensive circumstantial evidence that social networks matter for the adoption of agricultural technological and institutional innovations in developing countries . In a recent unpublished paper, Caria argues that Ghanaian farmers who are more risk averse are less likely to experiment with new technology. This may explain why risk averse farmers in Carias study look up to risk neutral neighbors for advice on new technology. Taken together, these field experiments suggest that input usage and the purchase of crop insurance are not well accounted for by the standard model presented in Section 2. While an ex tended model that includes credit constraints and downside risk considerations can explain some of the empirical regularities, other results indicate that subtle psychological manipulations affect take-up.