The application of quasiexperimental impact evaluation to remotely sensed measures of deforestation has yielded important evidence detailing the effectiveness of conservation policies. However, researchers have paid insufficient attention to the binary and nonrepeatable structure of most deforestation datasets. Using analytical proofs and simulations, we demonstrate that many commonly employed econometric approaches are biased when applied to binary and nonrepeatable outcomes. The significance, magnitude and even direction of estimated effects from many studies are likely incorrect, threatening to undermine the evidence base that underpins conservation policy adoption and design. To address these concerns, we provide guidance and new strategies for the design of panel econometric models that yield more reliable estimates of the impacts of forest conservation policies.

中文翻译：

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具有不可重复结果的影响评估：森林保护案例

将准实验影响评估应用于森林砍伐遥感测量已经产生了详细说明保护政策有效性的重要证据。然而，研究人员对大多数森林砍伐数据集的二元和不可重复结构关注不够。通过分析证明和模拟，我们证明许多常用的计量经济学方法在应用于二元和不可重复的结果时存在偏差。许多研究估计影响的重要性、程度甚至方向可能是不正确的，有可能破坏支撑保护政策采用和设计的证据基础。为了解决这些问题，我们为面板计量经济模型的设计提供了指导和新策略，这些模型可以对森林保护政策的影响进行更可靠的估计。