Analyzing the effect of risk on risk taking by farmers in developing countries is harder to do. It is empirically difficult to formally test theories that relate decisions made by poor households with the relative riskiness of the options available to them. There two main reasons for this. First, it is very difficult to obtain measurable variation in risk across individuals. The reason is that, by definition, risk materializes over time. Consequently, a lot of information is required to construct reasonable measures of risk. Secondly, even when measures of riskiness can be constructed, sufficient exogenous variation in risk must be available to distinguish what can reasonably be attributed to risk as opposed to other features typically correlated with risk. For instance, different agro-climatic regions have different crop-specific risk levels. But they also differ in many other respects, not least the profitability of different crops or activities. Given this, it is difficult to ascribe a causal interpretation to empirical regularities, even if they can be shown to be present. This probably explains why there is very little research on the effect of risk on behavior among rural households in developing countries. Using survey data from Pakistani farmers involved in dairy production, Kurosaki and Fafchamps show that observed cropping patterns are consistent with farmers desire to cover their fodder production needs to reduce exposure to input price risk. In this paper,berry pots risk measures are constructed by combining longitudinal price data with cross-section yield variation.

The effect of risk on decisions is estimated using a structural model that allows for risk averse preferences. Using panel household data on rural Ethiopia, Rogg shows that the asset holdings and portfolio mix of rural households is correlated with relative riskiness in a way that is consistent with theory. Hill shows that more risk averse Ugandan farmers were less likely to replant coffee trees, given the risk represented by the coffee wilt disease. In a different vein, Portner uses historical data on hurricane incidence in Honduras to construct a measure of location specific hurricane risk. The author then uses this risk measure to estimate the effect of risk on education decisions. He shows that locations with a higher risk of hurricane invest more in education, even though hurricane events themselves have a negative effect on education. Portner interpret these results as suggesting that households invest in education so as to be better able to escape the worst consequences of future hurricanes. Though valiant, all these studies suffer from the need to make some assumptions to achieve identification. In particular, they have to make assumptions about the absence of omitted variable bias ñ e.g., the risk measure is not capturing something else ñ and about possible endogenous placement ñe.g., risk averse individuals may have left areas more affected by risk. Other authors have sought to simulate the anticipated gain from risk reduction. If risk aversion explains farmers reluctance to adopt new technologies, it should be that the prospect for risk reduction is large. Using detailed data on ICRISAT farmers in India, Walker and Ryan estimate the welfare gain that would be induced by a complete elimination of millet yield risk. They find that the equivalent variation of the complete elimination of such risk is only a small proportion of total income. One may argue that these findings come from the fact that millet is a drought-resistant crop with low variance, so perhaps they may not be representative of the risk reduction achieved by avoiding drought-vulnerable crops.

What the Walker and Ryan simulation illustrates, however, is that farmers grow different crops and in general have diversified sources of income, so that risk associated with a single crop need not make a large contribution to total income risk. Health shocks, in contrast, may be of more importance because they affect the households ability to produce and generate income. Fafchamps and Lund and De Weerdt and Fafchamps indeed find that transfers and informal loans respond to health shocks. While rigorous empirical evidence on the relationship between risk and risk taking is hard to find for rural households in developing countries, there is ample circumstantial evidence that the Sandmo model is not consistent with farmers behavior. First of all, farmers by definition engage in activities that carry a lot of risk. So they do not appear to shy away from risk. Existing theory suggests that farmers are more likely to engage in risky activities if they are well insured. Is this the case? Not really.Although many examples have been found of informal and semi-formal insurance mechanisms operating in poor rural communities, the evidence also shows that these mechanisms nearly never provide adequate protection against shocks . It is therefore very unlikely that the reason why small farmers engage in risk activities is because they are well insured. Could it be then that they have sufficient liquid assets to self-insure? There is indeed ample evidence that rural households across the developing world accumulate savings or liquid assets as a form of precautionary savings . But these assets are seldom sufficient to smooth consumption. Fafchamps, Udry and Czukas and Kazianga and Udry , for instance, show that Burkina Faso rural households affected by the 1984 drought refrained from selling cattle and opted to reduce consumption instead and may have incurred excess mortality as a result.

The reason offered for this result is that farmers fear losing productive assets. Distress sale of land or cattle appears to be seen with great reluctance by many rural households: it may solve an immediate scarcity problem, but it would lead to more severe poverty in the future, a point formalized for instance in Carter and Zimmerman . Lybbert, Barrett, Desta and Coppock revisit this issue in the context of East African pastoralists,maceteros fresas showing that herders who have too few animals to sustain themselves during transhumance cannot maintain a pastoralist lifestyle ñ and face a much higher probability of losing all their livestock. What these two examples suggest is that poor farmers deal with risk in ways that appear different from those suggested by Sandmos model. In Burkina Faso, farmers prefer to reduce consumption rather than sell cattle. In East Africa, pastoralists prefer to hold onto their animals to preserve their lifestyle. In both cases, households appear remarkably willing to toughen it up, that is, to face up to the consequences of risk. Of course their choices are severely limited, but the evidence does not seem to indicate that poor farmers shy away from risky activities. There is another reason why Sandmos model is a poor candidate to explain resistance to innovation. Much agricultural technology is divisible. This is particularly true for much Green Revolution type technology, such as improved seeds, chemical fertilizer, and pesticides. This dramatically reduces the risk associated with farmer experimentation since it is fairly easy to try out a new technology on a small scale before adopting it on the whole farm. Yet agricultural surveys provide little evidence of small scale experimentation by farmers in developing countries. Partial adoption of a new crop or technology would also make sense from a diversification point of view: even though a new crop or technology may be more risky than an existing one, combining both may nevertheless reduce risk relative to the old technology alone. For this reason, one would expect risk averse farmers to keenly adopt new divisible technologies, but only partially. Yet farmers often seem to switch entirely to a new technique of production, even though they may subsequently revert to the old technology if the outcome was unsatisfactory. This kind of behavior is difficult to reconcile with the idea that farmers seek to minimize risk. Sub-Saharan African is often mentioned as a place where farmers have been very reluctant to the introduction of new agricultural practices. This is often taken as a reason for the poor agricultural performance of the continent. Yet such claims fail to acknowledge that African agriculture has dramatically changed over the last century or so. Perhaps the most obvious and the most far reaching change has been the introduction of new crops maize, rice, sweet potatoes, cassava, tomato, potato, to name but a few. These crops have spread massively over the last two decades, with some government support.New cash crops have also emerged that are grown by small farmers, either for export or for local urban markets. This is true for Africa ñ e.g., pineapple, green beans, onion . It is even more true for India where an agriculture traditionally centered on staple foods is rapidly moving towards horticulture and the production of high risk/high return crops.

External intervention has often been instrumental in fostering these changes, primarily in terms of marketing and input distribution . But adoption has been locally widespread even though these crops often are quite risky, with volatile prices and variable yields. Based on these experiences, risk aversion does not appear to have been the impediment to agricultural innovation that it was once thought to be. There seems to be little value to the idea that risk aversion pulls poor agricultural households away from decisions that would, in time, make them more prosperous. Risk aversion appears a poor candidate to explain persistent rural poverty. There nevertheless remain a number of puzzles that continue to defy explanation. If farmers are not risk averse in the Sandmo sense, how can we explain that decentralized market forces seem to have a difficult time delivering agricultural inputs to poor farmers in developing countries. Successful input distribution schemes appear to combine two key features: they provide inputs on credit; and they eliminate out-of-pocket risk without eliminating upside risk, that is, they are designed in such a way that the farmer pays for inputs only if the crop is successful. The first and most enduring example of an input delivery scheme that shares these features is sharecropping. In a sharecropping contract, a farmer pays for land with a portion of the harvest produced by that land. While up-front payment can be requested for fixed rental contracts, this is not possible for sharecropping contracts since payment can only be assessed after harvest. This means that land is defac to given on credit. It is also common for the landlord to provide other inputs on credit . Sharecropping therefore provides farmers with agricultural inputs on credit. Furthermore, it eliminates bankruptcy risk: if the crop fails, nothing is paid.In spite of initial fears regarding landlords willingness to invest in new technology , the bulk of the evidence now indicates that sharecropping is an effective way of delivering input credit to producers . The second example is taken from the input delivery practices of agricultural marketing boards during and after the colonial period in Sub-Saharan Africa.It was common practice for agricultural marketing board to provide farmers with agricultural inputs at the beginning of the season and to recoup the cost of these inputs at harvest time. Since many of these marketing boards had a monopsony on the cash crop they were responsible for, producers could not abscond from the credit they had received by selling to someone else.This method of recouping input credit through monopsony means that farmers are responsible for input costs only up to the value of their cash crop output. The method by which this is accomplished varies . But the end result is the same: in case of crop failure, producers pay nothing. The third example comes from contract farming. In many ways, contract farming resembles what agricultural marketing boards do: they provide affiliated growers with seeds and inputs and promise to purchase all or part of their output, at which time inputs costs are deducted from the output price. The crop itself serves as collateral for the inputs and the contractor often has the right to harvest the crop to recoup the cost of the inputs.Although in theory contractors could seek to recover all input costs on growers assets in case of crop failure, they hesitate to do so not to antagonize their growers. So, defacto, growers pay nothing in case of crop failure.