Working Paper
Shrum T. Experiments in Ranching: Rain-Index Insurance and Investment in Production and Drought Risk Management. Working Paper.Abstract

Rainfall is one of the biggest predictors of profit in the ranching industry. Yet, rainfall varies widely from year to year. A major drought can put ranchers out of business and even a minor drought can significantly reduce their profit. In response to this risk, the USDA Risk Management Agency has developed a new policy called the Pasture, Rangeland and Forage (PRF) Insurance. This insurance is unique in the United States because it pays off based on rainfall, not measured losses. Rain-index policies such as the PRF program can reduce problems of moral hazard because insured ranchers cannot affect their own payout once they have purchased the policy.


However, the rain-index insurance may still affect rancher behavior. First, with the current levels of subsidies, the rain-index policy has a positive expected value: on average, it pays out more in indemnities than ranchers pay in premiums. Increasing the profitability of the ranching industry is likely to increase the intensity of ranching, both by increasing the number of firms in the industry and by increasing the number of cattle in an optimally profitable herd for a given rancher. When cattle production is more profitable, then the marginal revenues of production inputs increase leading to higher levels of investment in those inputs. The main inputs to cattle ranching are land, rainfall, mother cows, and supplemental feed. Rainfall is outside of the rancher's control and, in this study, ranch size is fixed. Therefore, the rancher can only modify their investment in herd size and supplemental feed. Second, the rain-index policy transfers drought risk from the rancher to the insurance system. Transferring drought risk may lead ranchers to reduce investments in other types of drought risk management investments. For example, the rancher may be less likely to purchase supplemental feed in a low rainfall year if they know that they are likely to receive a check from the insurance company that will offset their revenue losses from having a lower weight herd. Conversely, they could be more likely to purchase supplemental feed in a low rainfall year if they were previously cash constrained and know that they will likely have an insurance payout that will offset their expenditures.


In this paper, I introduce the Drought Ranching Insurance Response R Model (DRIR-R) and use a ranching simulation driven by the model to test these questions experimentally. The DRIR-R simulation is tested with a non-ranching study population recruited from MTurk. This paper describes the first test of an experimental paradigm that will soon be used with ranchers whose behavior is expected to best reflect the actual practices of the industry. The simulation is built from the DRIR-R model which simulates a cow-calf ranching operation in periodic drought. Participants choose their investment in supplemental feed to offset low forage growth in drought years. They also choose the number of cows and calves they sell each year which affects their current revenues and future herd size. Among the study population of non-ranchers in the simulation, I find that the rain-index insurance does not affect average herd size but does affect extreme herd management behavior. I also find that the rain-index insurance increases the investment in supplemental feed, especially for those who are risk averse. These experimental findings are a first step in using the DRIR-R model simulation to help better understand the impact of the rain-index insurance on two important aspects of the cattle ranching industry: grazing intensity and drought adaptation.

Shrum T. The Salience of Future Climate Impacts and the Willingness to Pay for Climate Change Mitigation. Working Paper.Abstract


Investing in climate change mitigation has substantial benefits, but those benefits unfold over many decades. Economic theory addresses the separation of costs and benefits across time by discounting future benefits according to an appropriate social discount rate. By comparing the upfront cost to a discounted stream of future benefits, we can determine the optimal level of investment. However, we see individuals using implicit discount rates far higher than expected when they make decisions with upfront costs and a flow of benefits over a long time horizon. The mismatch between what economic theory predicts and what we see in actual behavior leads to the question of whether the extended time horizon between the mitigation decision and the benefits of that decision may hinder optimal investment in climate change mitigation in ways that rational choice theory does not predict. The immediate costs of the decision loom large in the decision-maker's mind while the future benefits of the choice have a lower prominence. As a result, climate change mitigation decisions may be prone to a salience heuristic -- a cognitive shortcut that substitutes the salience of benefits with the value of benefits. In an online randomized control experiment, I test whether focusing attention on the future risks and challenges of climate change will increase the willingness to pay for climate change mitigation. I also measure whether these treatments shift the decision-maker's implicit discount rate. In an Essay treatment, participants write an essay on the risks and challenges of climate change. In a Letter treatment, participants write a message on the risks and challenges of climate change directed to a particular individual living in the year 2050. I find that compared to a control group, both writing tasks that focus attention on the future risks and challenges of climate change increase the willingness to donate to climate change mitigation efforts. I also find that the Letter treatment reduces the decision-maker's discount rate, but the finding is only marginally significant. These findings contribute to the understanding of how to bridge the psychological distance between choice and consequence for climate change mitigation. This study also has broader implications for how psychological distance and the salience heuristic may influence a wide range of decisions from personal health choices to retirement savings.


In Press
Shrum T, Travis W, Williams T, Lih E. Managing Climate Risks on the Ranch with Limited Drought Information. In Press. Publisher's VersionAbstract

Ranching involves complex decision-making and risk management in the face of uncertainty about climate conditions. The profitability and sustainability of ranching depend heavily on sufficient and timely rainfall for rangeland forage production. As a result, ranchers may either adopt conservative long-term stocking strategies as a hedge against drought or practice a more dynamic approach in which they vary stocking rates and supplemental feed in response to drought. Yet, some strategies require more information about climate risks than is often available to ranchers. We review the literature to draw out the drought management options as well as the tools and products for drought monitoring and early warning that are available to ranchers. We find that a large gap remains between the information needs of ranchers seeking to adapt dynamically to drought and the information that is available. Moreover, even when actionable information is available, it is unclear whether ranchers are optimally incorporating that information into their risk management decisions. Further research is needed to understand how to package existing information into risk management decision tools in a way that addresses cognitive and operational barriers to support timely decisions that will reduce the impact of drought on profits and the long-term sustainability of rangelands. Due to the multi-faceted nature of climate risk management in ranching, further study of ranching behavior and decisions has the potential to bring new insights into climate risk management and decision and risk theory far beyond the field of ranching and agriculture.

Shrum T. Distinguishing Development in Carbon Dioxide Modeling. Yale School of Forestry and Environmental Studies. 2009. t.shrum_mastersthesis.pdf