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首页> 外文期刊>Journal of the Association of Environmental and Resource Economists >Panel Data Designs and Estimators as Substitutes for Randomized Controlled Trials in the Evaluation of Public Programs
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Panel Data Designs and Estimators as Substitutes for Randomized Controlled Trials in the Evaluation of Public Programs

机译：面板数据设计和估计量替代公共项目评估中的随机对照试验

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In the evaluation of public programs, experimental designs are rare. Researchers instead rely on observational designs. Observational designs that use panel data are widely portrayed as superior to time-series or cross-sectional designs because they provide opportunities to control for observable and unobservable variables correlated with outcomes and exposure to a program. The most popular panel data evaluation designs use linear, fixed-effects estimators with additive individual and time effects. To assess the ability of observational designs to replicate results from experimental designs, scholars use design replications. No such replications have assessed popular, fixed-effects panel data models that exploit repeated observations before and after treatment assignment. We implement such a study using, as a benchmark, results from a randomized environmental program that included effective and ineffective treatments. The popular linear, fixed-effects estimator fails to generate impact estimates or statistical inferences similar to the experimental estimator. Applying common flexible model specifications or trimming procedures also fail to yield accurate estimates or inferences. However, following best practices for selecting a nonexperimen-tal comparison group and combining matching methods with panel data estimators, we replicate the experimental benchmarks. We demonstrate how the combination of panel and matching methods mitigates common concerns about specifying the correct functional form, the nature of treatment effect heterogeneity, and the way in which time enters the model. Our results are consistent with recent claims that design trumps methods in estimating treatment effects and that combining designs is more likely to approximate a randomized controlled trial than applying a single design.
机译：在评估公共程序时，很少有实验设计。相反，研究人员依靠观察设计。使用面板数据的观察设计被广泛描绘为优于时间序列或横截面设计，因为它们提供了控制与结果和程序暴露相关的可观察和不可观察变量的机会。最受欢迎的面板数据评估设计使用线性，固定效应估计器，并具有相加的个体效应和时间效应。为了评估观察性设计复制实验设计结果的能力，学者使用了设计复制。尚无此类复制品评估流行的，固定效应的面板数据模型，该模型利用了分配治疗前后的重复观察结果。我们以包括有效和无效治疗在内的随机环境计划的结果为基准，进行此类研究。流行的线性固定效应估计器无法生成类似于实验估计器的影响估计或统计推断。应用通用的灵活模型规范或修剪程序也无法得出准确的估计或推断。但是，遵循选择非实验比较组并将匹配方法与面板数据估算器结合的最佳实践，我们复制了实验基准。我们演示了面板和匹配方法的组合如何减轻有关指定正确功能形式，治疗效果异质性的性质以及时间进入模型的方式的共同担忧。我们的结果与最近的主张一致，即设计在评估治疗效果方面胜过方法，并且与应用单一设计相比，组合设计更有可能近似随机对照试验。

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