Role of Farmers’ Risk and Ambiguity Preferences on Fertilization Decisions: An Experiment

In the context of climate change, the agricultural sector offers a large number of mitigation possibilities through diverse practices, such as the reduction of pollutant inputs. However, most farmers do not adopt the mitigation practices recommended, including the reduction of nitrogen fertilization. At the same time, various uncertainties characterize agricultural production, so that the farmer’s risk and ambiguity preferences may be potential determinants to the adoption of mitigation practices. In this context, the objective of the article is to determine if the farmer’s risk and ambiguity preferences explain (or not) the fertilization decision. A questionnaire was submitted to French farmers to elicit risk and ambiguity preferences through lottery choices, and ask questions about fertilization. Two regressions were realized, the first to explain the total fertilization and the second to identify the determinants of the first fertilization application. The results reveal that respondents were mostly risk-averse and ambiguity-neutral. In addition, risk and ambiguity aversion impact fertilization practices through diverse drivers in opposite directions. Indeed, being risk-averse is associated with a lower level of total fertilization, whereas ambiguity aversion has a positive and significant impact on the level of fertilization at the first application. This last result highlights the need to reduce the uncertainty farmers face.

Fractional Degree Stochastic Dominance

We develop a continuum of stochastic dominance rules for expected utility maximizers. The new rules encompass the traditional integer-degree stochastic dominance; between adjacent integer degrees, they formulate the consensus of individuals whose absolute risk aversion at the corresponding integer degree has a negative lower bound. By extending the concept of “uniform risk aversion” previously proposed in the literature to high-order risk preferences, we interpret the fractionalized degree parameter as a benchmark individual relative to whom all considered individuals are uniformly no less risk averse in the lottery choices. The equivalent distribution conditions for the new rules are provided, and the fractional degree “increase in risk” is defined. We generalize the previously defined notion of “risk apportionment” and demonstrate its usefulness in characterizing comparative statics of risk changes in fractional degrees. This paper was accepted by David Simchi-Levi, decision analysis.

To pay or not to pay: Measuring risk preferences in lab and field

Measuring risk preferences in the field is critical for policy, however, it can be costly. For instance, the commonly used measure of Holt and Laury (2002) relies on a dozen of lottery choices and payments which makes it time-consuming and costly. We propose a short version of the Holt and Laury (2002) which produces in the lab (Spain) the same results as the long HL. Using the short HL in the field (Honduras and Nigeria), we observe that paying or not for the measurement of risk preferences produces the same findings using a faster and cheaper measure.

Joining a group diverts regret and responsibility away from the individual

It has recently been proposed that a key motivation for joining groups is the protection from the negative consequences of undesirable outcomes. To test this claim, we investigated how experienced outcomes triggering loss and regret impacted people's tendency to decide alone or join a group, and how decisions differed when voluntarily made alone versus in group. Replicated across two experiments, participants ( n = 125 and n = 496) selected whether to play alone or contribute their vote to a group decision. Next, they chose between two lotteries with different probabilities of winning and losing. The higher the negative outcome, the more participants switched from deciding alone to with others. When joining a group to choose the lottery, choices were less driven by outcome and regret anticipation. Moreover, negative outcomes experienced alone, not part of a group vote, led to worse subsequent choices than positive outcomes. These results suggest that the protective shield of the collective reduces the influence of negative emotions that may help individuals re-evaluate past choices.

A Bayesian Method for Characterizing Population Heterogeneity

A stylized fact from laboratory experiments is that there is much heterogeneity in human behavior. We present and demonstrate a computationally practical non-parametric Bayesian method for characterizing this heterogeneity. In addition, we define the concept of behaviorally distinguishable parameter vectors, and use the Bayesian posterior to say what proportion of the population lies in meaningful regions. These methods are then demonstrated using laboratory data on lottery choices and the rank-dependent expected utility model. In contrast to other analyses, we find that 79% of the subject population is not behaviorally distinguishable from the ordinary expected utility model.

Belief Elicitation with a Synchronized Lottery Choice Menu That Is Invariant to Risk Attitudes

This paper uses a Bayesian information processing task to compare belief elicitation mechanisms including a quadratic scoring rule, a Becker-DeGroot-Marschak pricing procedure, and a two-stage menu of lottery choices that is structured to identify a precise point of probability indifference. The choice menu yields a higher incidence of correct Bayesian responses and lower belief error averages. Unlike the quadratic scoring rule, the binary payoffs for the lottery choice mechanism are synchronized to provide theoretical incentive-compatibility regardless of risk attitudes. In addition, the choice menu structure is more transparent and intuitive than the Becker-DeGroot-Marschak procedure. (JEL C91, D44, D81, D83)