Adding value to spatially managed inputs by understanding site-specific yield response

Many mechanized crop producers and agribusinesses are fascinated with precision agriculture technology, but adoption has lagged behind the expectations. Among the reasons for slow adoption of precision agriculture technology is that initial users focused excessively on in‐field benefits from variable‐rate fertilizer application using regional average fertilizer recommendations. This article illustrates how greater use of site‐specific crop response information can improve variable rate input application recommendations. Precision agriculture is spatial information technology applied to agriculture. The technologies include global position systems (GPS), geographic information systems (GIS), yield monitoring sensors, and computer controlled within‐field variable rate application (VRA) equipment. Experimentation with these technologies is occurring everywhere there is large scale mechanized agriculture. Commercial use has been greatest in the US, where 43% of farm retailers offered VRA services in 2001. Except for certain high‐value crops like sugar beet, farmer adoption of VRA has been modest. The farm level profitability of VRA continues to be questionable for bulk commodity crops. The theoretical model and illustration presented here suggest that VRA fertilization has not yet reached its profitability potential. Most VKA field trials to date have relied upon existing state‐wide or regional input rate recommendations. Unobserved soil characteristics can potentially interact with an input to make its effect on yield vary site‐specifically within fields. Failure to use site‐specific response functions for VRA applications may lead to a misallocation of inputs just as great as that which results from using uniform applications instead of VRA. Agricultural economists have a long history of estimating output response to input applications. Several have started to develop tools to estimate site‐specific responses from yield monitor and other precision agriculture data. Likewise, agricultural economists have developed an important body of research results on information value based on managing variability—typically in temporal settings. With these tools, a major potential exists to develop further benefits from precision agriculture technologies that permit truly spatially tailored input applications.     

Tests of ex ante versus ex post theories of collateral using private and public information

Collateral is a widely used, but not well understood, debt contracting feature. Two broad strands of theoretical literature explain collateral as arising from the existence of either ex ante private information or ex post incentive problems between borrowers and lenders. However, the extant empirical literature has been unable to isolate each of these effects. This paper attempts to do so using a credit registry that is unique in that it allows the researcher to have access to some private information about borrower risk that is unobserved by the lender. The data also include public information about borrower risk, loan contract terms, and ex post performance for both secured and unsecured loans. The results suggest that the ex post theories of collateral are empirically dominant, although the ex ante theories are also valid for customers with short borrower–lender relations that are relatively unknown to the lender.     

The impact of government ownership on dividend policy in China

We investigate the impact of State ownership on Chinese corporate dividend policy. We find that Chinese firms' dividend payout rates respond fairly quickly to earnings changes, and the average actual payout ratio for Chinese firms falls between the payout ratios for emerging-market and developed firms. These results are consistent with the dividend policies of developing economies in general. We also find that dividend payouts among dividend-paying firms, and the likelihood that a firm will pay a dividend, are increasing in State ownership. Our findings are consistent with the State's need for cash flow as a partial motivation for continued State ownership of a significant portion of the corporate economy, and support the agency and tax clientele explanations for dividend policy.     

Role thinking: Standing in other people’s shoes to forecast decisions in conflicts

When forecasting decisions in conflict situations, experts are often advised to figuratively stand in the other person’s shoes. We refer to this as “role thinking”, because, in practice, the advice is to think about how other protagonists will view the situation in order to predict their decisions. We tested the effect of role thinking on forecast accuracy. We obtained 101 role-thinking forecasts of the decisions that would be made in nine diverse conflicts from 27 Naval postgraduate students (experts) and 107 role-thinking forecasts from 103 second-year organizational behavior students (novices). The accuracy of the novices’ forecasts was 33% and that of the experts’ was 31%; both were little different from chance (guessing), which was 28%. The small improvement in accuracy from role-thinking strengthens the finding from earlier research that it is not sufficient to think hard about a situation in order to predict the decisions which groups of people will make when they are in conflict. Instead, it is useful to ask groups of role players to simulate the situation. When groups of novice participants adopted the roles of protagonists in the aforementioned nine conflicts and interacted with each other, their group decisions predicted the actual decisions with an accuracy of 60%.     

Comparing face-to-face meetings, nominal groups, Delphi and prediction markets on an estimation task

We conducted laboratory experiments for analyzing the accuracy of three structured approaches (nominal groups, Delphi, and prediction markets) relative to traditional face-to-face meetings (FTF). We recruited 227 participants (11 groups per method) who were required to solve a quantitative judgment task that did not involve distributed knowledge. This task consisted of ten factual questions, which required percentage estimates. While we did not find statistically significant differences in accuracy between the four methods overall, the results differed somewhat at the individual question level. Delphi was as accurate as FTF for eight questions and outperformed FTF for two questions. By comparison, prediction markets did not outperform FTF for any of the questions and were inferior for three questions. The relative performances of nominal groups and FTF were mixed and the differences were small. We also compared the results from the three structured approaches to prior individual estimates and staticized groups. The three structured approaches were more accurate than participants’ prior individual estimates. Delphi was also more accurate than staticized groups. Nominal groups and prediction markets provided little additional value relative to a simple average of the forecasts. In addition, we examined participants’ perceptions of the group and the group process. The participants rated personal communications more favorably than computer-mediated interactions. The group interactions in FTF and nominal groups were perceived as being highly cooperative and effective. Prediction markets were rated least favourably: prediction market participants were least satisfied with the group process and perceived their method as the most difficult.     

Bear beta

We test whether bear market risk, time variation in the probability of future bear market states, is priced. We construct an Arrow–Debreu security that pays off in bear market states (AD Bear) from traded Standard & Poor’s (S&P) 500 index options and use its returns to measure bear market risk. We find that bear beta (exposure to bear market risk) has a strong relation with expected stock returns that is robust, persistent, and remains strong among liquid and large stocks. Historical bear beta also predicts future bear market risk exposure. We conclude that bear market risk is priced in the cross section of stock returns.