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AbstractImpact evaluations assess the degree to which changes in outcomes can be attributed to an intervention rather than to other factors. Such attribution requires knowing what outcomes would have looked like in the absence of the intervention. This counterfactual world can be inferred only indirectly through evaluation designs that control for confounding factors. Some have argued that environmental policy is different from other social policy fields, and thus attempting to establish causality through identification of counterfactual outcomes is quixotic. This chapter argues that elucidating causal relationships through counterfactual thinking and experimental or quasi‐experimental designs is absolutely critical in environmental policy, and that many opportunities for doing so exist. Without more widespread application of such approaches, little progress will be made on building the evidence base in environmental policy. © Wiley Periodicals, Inc.

Failure to consider costs as well as benefits is common in many policy initiatives & analyses, particularly in the environmental arena. Economists & other policy scientists have demonstrated that integrating both cost & benefit information explicitly into the policy process can be vital to ensuring that scarce funds go as far as they can toward achieving policy objectives. The costs of acquiring & analyzing such information, however, can be substantial. The objective of this paper is to help policy analysts & practitioners identify the conditions under which integrating cost & benefit information is likely to be vital to effective decisionmaking, & the conditions under which failing to use both cost & benefit data would result in little, if any, loss in efficiency. These points are illustrated through a conceptual discussion & an empirical analysis of a conservation initiative in the United States. 2 Tables, 2 Figures, 39 References. [Copyright 2003 John Wiley and Sons, Ltd.]

AbstractFailure to consider costs as well as benefits is common in many policy initiatives and analyses, particularly
in the environmental arena. Economists and other policy scientists have demonstrated that integrating both cost
and benefit information explicitly into the policy process can be vital to ensuring that scarce funds go as far as
they can toward achieving policy objectives. The costs of acquiring and analyzing such information, however, can
be substantial. The objective of this paper is to help policy analysts and practitioners identify the conditions
under which integrating cost and benefit information is likely to be vital to effective decisionmaking, and the
conditions under which failing to use both cost and benefit data would result in little, if any, loss in
efficiency. These points are illustrated through a conceptual discussion and an empirical analysis of a
conservation initiative in the United States. © 2003 by the Association for Public Policy Analysis and
Management.

AbstractWe provide empirical evidence of research practices that can undermine the credibility of empirical research in agricultural economics. We find that, in four popular journals, most studies are underpowered, a characteristic that, when coupled with publication bias, yields unreliable and exaggerated effect sizes. This problem is exacerbated by selective reporting of statistically significant results and multiple hypothesis testing without any adjustments to statistical inferences. Survey respondents self‐report engaging in practices that are consistent with these empirical findings. Addressing these problems in agricultural economics will require that editors, reviewers, and donors change the norms and incentives for authors.

For decades, government agencies and nongovernmental organizations have invested in programs aimed at alleviating poverty and those aimed at protecting the environment. Whether these investments mutually reinforce each other or act in opposition has been widely debated by scholars. Studies that have tried to resolve this debate suffer from a variety of shortcomings, including the challenge of inferring causal relationships from non-experimental data. To help address some of these shortcomings, randomized controlled trials (RCTs) can play an important role. When done well, RCTs permit credible causal inferences and can be designed to directly test competing assumptions about how the world works. Yet few RCTs of poverty programs examine their effects on the environment. Worse, we know of no RCTs reporting the poverty effects of environmental interventions, which may be unsurprising given that environmental scholars rarely use RCTs. The lack of RCTs that can shed light on the relationships between actions to alleviate poverty and actions to reverse global environmental change is an obstacle to advancing the science and practice of sustainability. If scholars of poverty include environmental outcomes in their RCTs, and if environmental scholars use RCTs to study the poverty effects of environmental programs, the longrunning debates about the dual challenges of alleviating poverty and protecting the environment could be resolved.

Abstract
Economists use public goods experiments to develop and test theories of individual preferences and institutional design. Previous work demonstrates many participants in public goods experiments contribute to the public good out of confusion. We design experiments to provide insights on the consequences and causes of confusion. We establish that confusion amounts to more than statistical noise and does not dissipate with repetition (i.e. learning). Confused subjects use experimental parameters and the behavior of other players as cues, which confounds treatment effects and traditional strategies to identify other-regarding preferences through exogenous parameter changes and the modeling of reactions to other subjects' decisions. We argue that confusion stems from an inaccurate understanding of game incentives ("failure of game form recognition"), which is a consequence of the framing and inadequate payoff information in standard instructions. Modified instructions can substantially reduce confusion, and, in turn, change the distribution of contributions to the public good.