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This chapter examines the external validity of lab experiments on corruption by evaluating the extent to which experimental results are robust to the degree of field context included in the experimental design. To do so, we follow Harrison and List (2004) and partition corruption experiments into four classes depending on their field context. A comparison of the results obtained within each class reveals that similar treatment effects tend to emerge. Although a definitive answer to the external validity question has yet to be provided, these preliminary results provide some support to the external validity of lab experiments on corruption.

There are several ways to define words. One is to ascertain the formal definition by looking it up in the dictionary. Another is to identify what it is that you want the word-label to differentiate.

Group incentive schemes have been shown to be positively associated with firm performance but it remains an open question whether this association can be explained by the motivating characteristics of the group-incentive scheme itself, or if this is due to factors that tend to accompany group-incentive schemes. We use a controlled experiment to directly test if group-incentive schemes can motivate sustained individual effort in the absence of rules, norms, and institutions that are known to mitigate free-riding behavior.

We use a controlled lab experiment that randomly assigns subjects to one of three compensation contracts used to incentivize an onerous effort task. Two of the compensation contracts are group-incentive schemes where subjects have an incentive to free-ride on the efforts of their coworkers, and the third (control) is a flat-wage contract.

We find that both group-incentive schemes resulted in sustained, higher performance relative to the flat-wage compensation contract. Further, we do not find evidence of free-riding behavior under the two group-incentive schemes.

Although we do find sustained cooperation/performance over the three work periods of our experiment under the group-incentive schemes, further testing would be required to evaluate whether group-incentive schemes can sustain cooperation over a longer time horizon without complementary norms, policies, or institutions that mitigate free-riding.

By unambiguously showing that group-incentive schemes can, by themselves, motivate workers to provide sustained levels of effort, this suggests that the “1/n problem” may be, in part, an artifact of the rational-actor modeling conventions.

If we are to examine the role of “controls” in different experimental settings, it is appropriate that the word be defined carefully. The Oxford English Dictionary (Second Edition) defines the verb “control” in the following manner: “To exercise restraint or direction upon the free action of; to hold sway over, exercise power or authority over; to dominate, command.” So the word means something more active and interventionist than is suggested by it’s colloquial clinical usage. Control can include such mundane things as ensuring sterile equipment in a chemistry lab, to restrain the free flow of germs and unwanted particles that might contaminate some test.

The use of behavioral insights and experimental methods has recently gained momentum among health policy-makers. There is a tendency, however, to reduce behavioral insights applications in health to “nudges,” and to reduce experiments in health to “randomized controlled trials” (RCTs). We argue that there is much more to behavioral insights and experimental methods in health economics than just nudges and RCTs. First, there is a broad and rich array of complementary experimental methods spanning the lab to the field, and all of them could prove useful in health economics. Second, there are a host of challenges in health economics, policy, and management where the application of behavioral insights and experimental methods is timely and highly promising. We illustrate this point by describing applications of experimental methods and behavioral insights to one specific topic of fundamental relevance for health research and policy: the experimental elicitation and econometric estimation of risk and time preferences. We start by reviewing the main methods of measuring risk and time preferences in health. We then focus on the “behavioral econometrics” approach to jointly elicit and estimate risk and time preferences, and we illustrate its state-of-the-art applications to health.

We review the existing laboratory experimental studies on corruption that have generated results with clear policy implications. We present and discuss experimental findings on the role that both monetary incentives and nonmonetary motivations may play in corruption decision-making, and, hence, in the fight against corruption.

Purpose – The chapter reports a laboratory emissions trading experiment with imperfect enforcement that introduces environmental framing as a treatment variable.

Methodology – The research uses the methodology of laboratory experimental economics. In the current design, subjects self-reported their “emissions” at the end of each trading period and were inspected probabilistically and fined when they underreported.

Findings – Transaction volume and compliance rates were significantly lower in the environmentally framed condition, compared to the more standard neutrally framed control.

Practical implications – The latter result suggests that environmental framing reduced subjects' incentives to honestly report “pollution” to the experimental “regulator.” As experimenters employ more “framed field experiments” outside the lab, it may be important to evaluate such pure framing effects in the lab if a main research goal is to compare lab and field experiment outcomes.

Originality/value – Experimental economics research rarely manipulates environmental framing as a treatment variable. The substantial impact of framing on subject behavior documented in this study highlights its importance, particularly in the presence of moral concerns such as honest reporting.

This chapter reviews research on the question–behavior effect, the phenomenon that asking questions influences respondents’ behavior. Two distinct research streams, the self-prophecy effect, concerned with socially normative behaviors, and the mere measurement effect, dealing with purchase behaviors without socially normative significance, are identified. Despite the recent attempt at integration, it is argued that there are fundamental differences between the two effects. Distinctions are also drawn between lab-based and field-based mere measurement effects, and between normatively consistent and implicit attitude-driven, normatively inconsistent self-prophecy effects. Key studies, theoretical explanations, and moderators of each effect are discussed, potential unanswered questions and research opportunities are identified, and significant managerial and policy implications are highlighted.

This chapter reviews the existing empirical evidence on how social insurance affects health. Social insurance encompasses programs primarily designed to insure against health risks, such as health insurance, sick leave insurance, accident insurance, long-term care insurance, and disability insurance as well as other programs, such as unemployment insurance, pension insurance, and country-specific social insurance programs. These insurance systems exist in almost all developed countries around the world. This chapter discusses the state-of-the art evidence on each of these social insurance systems, briefly reviews the empirical methods for identifying causal effects, and examines possible limitations to these methods. The findings reveal robust and rich evidence on first-stage behavioral responses (“moral hazard”) to changes in insurance coverage. Surprisingly, evidence on how changes in coverage impact beneficiaries’ health is scant and inconclusive. This lack of identified causal health effects is directly related to limitations on how human health is typically measured, limitations on the empirical approaches, and a paucity of administrative panel data spanning long-time horizons. Future research must be conducted to fill these gaps. Of particular importance is evidence on how these social insurance systems interact and affect human health over the life cycle.