An important class of investment decisions is characterized by unrecoverable sunk costs, resolution of uncertainty through time, and the ability to invest in the future as an alternative to investing today. The options model provides guidance in such settings, including an investment decision rule called the "bad news principle": the downside investment state influences the investment decision whereas the upside investment state is ignored. This study takes a new approach to examining predictions of the options model by using the tools of experimental economics. Our evidence, which is drawn from student and professional trader subject pools, is broadly consonant with the options model.

Behavioral economics has become an important and integrated component of modern economics. Behavioral economists embrace the core principles of economics—optimization and equilibrium—and seek to develop and extend those ideas to make them more empirically accurate. Behavioral models assume that economic actors try to pick the best feasible option and those actors sometimes make mistakes. Behavioral ideas should be incorporated throughout the first-year undergraduate course. Instructors should also considering allocating a lecture (or more) to a focused discussion of behavioral concepts. We describe our approach to such a lecture, highlighting six modular principles and empirical examples that support them.

The gold standard in the sciences is uncovering causal relationships. A growing literature in economics utilizes field experiments as a methodology to establish causality between variables. Taking lessons from the economics literature, this study provides an “A‐to‐Z” description of how to conduct field experiments in accounting and finance. We begin by providing a user's guide into what a field experiment is, what behavioral parameters field experiments identify, and how to efficiently generate and analyze experimental data. We then provide a discussion of extant field experiments that touch on important issues in accounting and finance, and we also review areas that have ample opportunities for future field experimental explorations. We conclude that the time is ripe for field experimentation to deepen our understanding of important issues in accounting and finance.

Demand Reduction in Multiunit Auctions: Evidence from a Sportscard Field Experiment by John A. List and David Lucking-Reiley. Published in volume 90, issue 4, pages 961-972 of American Economic Review, September 2000

Experimental economists are leaving the reservation. They are recruiting subjects in the field rather than in the classroom, using field goods rather than induced valuations, and using field context rather than abstract terminology in instructions. We argue that there is something methodologically fundamental behind this trend. Field experiments differ from laboratory experiments in many ways. Although it is tempting to view field experiments as simply less controlled variants of laboratory experiments, we argue that to do so would be to seriously mischaracterize them. What passes for “control” in laboratory experiments might in fact be precisely the opposite if it is artificial to the subject or context of the task. We propose six factors that can be used to determine the field context of an experiment: the nature of the subject pool, the nature of the information that the subjects bring to the task, the nature of the commodity, the nature of the task or trading rules applied, the nature of the stakes, and the environment that subjects operate in.

Recent discoveries in behavioral economics have led scholars to question the underpinnings of neoclassical economics. We use insights gained from one of the most influential lines of behavioral research—gift exchange—in an attempt to maximize worker effort in two quite distinct tasks: data entry for a university library and door-to-door fundraising for a research center. In support of the received literature, our field evidence suggests that worker effort in the first few hours on the job is considerably higher in the “gift” treatment than in the “nongift” treatment. After the initial few hours, however, no difference in outcomes is observed, and overall the gift treatment yielded inferior aggregate outcomes for the employer: with the same budget we would have logged more data for our library and raised more money for our research center by using the market-clearing wage rather than by trying to induce greater effort with a gift of higher wages.

Previous empirical studies of information cascades use either naturally occurring data or laboratory experiments. We combine attractive elements from each of these lines of research by observing market professionals from the Chicago Board of Trade (CBOT) in a controlled environment. Analysis of over 1,500 individual decisions suggests that CBOT professionals behave differently from our student control group. For instance, professionals are better able to discern the quality of public signals and their decisions are not affected by the domain of earnings. These results have implications for market efficiency and are important in both a positive and normative sense.

Why do people vote? We design a field experiment to estimate a model of voting “because others will ask”. The expectation of being asked motivates turnout if individuals derive pride from telling others that they voted, or feel shame from admitting that they did not vote, provided that lying is costly. In a door-to-door survey about election turnout, we experimentally vary (1) the informational content and use of a flyer pre-announcing the survey, (2) the duration and payment for the survey, and (3) the incentives to lie about past voting. The experimental results indicate significant social image concerns. For the 2010 Congressional election, we estimate a value of voting “to tell others” of about $$$15, contributing 2 percentage points to turnout. Finally, we evaluate a get-out-the-vote intervention in which we tell potential voters that we will ask if they voted.

Policymakers often consider interventions at the scale of the population, or some other large scale. One of the sources of information about the potential effects of such interventions is experimental studies conducted at a significantly smaller scale. A common occurrence is for the treatment effects detected in these small-scale studies to diminish substantially in size when applied at the larger scale that is of interest to policymakers. This paper provides an overview of the main reasons for a breakdown in scalability. Understanding the principal mechanisms represents a first step toward formulating countermeasures that promote scalability.

Does individual behavior in a laboratory setting provide a reliable indicator of behavior in a naturally occurring setting? We consider this general methodological question in the context of eliciting risk attitudes. The controls that are typically employed in laboratory settings, such as the use of abstract lotteries, could lead subjects to employ behavioral rules that differ from the ones they employ in the field. Because it is field behavior that we are interested in understanding, those controls might be a confound in themselves if they result in differences in behavior. We find that the use of artificial monetary prizes provides a reliable measure of risk attitudes when the natural counterpart outcome has minimal uncertainty, but that it can provide an unreliable measure when the natural counterpart outcome has background risk. Behavior tended to be moderately risk averse when artificial monetary prizes were used or when there was minimal uncertainty in the natural nonmonetary outcome, but subjects drawn from the same population were much more risk averse when their attitudes were elicited using the natural nonmonetary outcome that had some background risk. These results are consistent with conventional expected utility theory for the effects of background risk on attitudes to risk.