Background: Many place-based randomized trials and quasi-experiments use a pair of cross-section surveys, rather than panel surveys, to estimate the average treatment effect of an intervention. In these studies, a random sample of individuals in each geographic cluster is selected for a baseline (preintervention) survey, and an independent random sample is selected for an endline (postintervention) survey. Objective: This design raises the question, given a fixed budget, how should a researcher allocate resources between the baseline and endline surveys to maximize the precision of the estimated average treatment effect? Results: We formalize this allocation problem and show that although the optimal share of interviews allocated to the baseline survey is always less than one-half, it is an increasing function of the total number of interviews per cluster, the cluster-level correlation between the baseline measure and the endline outcome, and the intracluster correlation coefficient. An example using multicountry survey data from Africa illustrates how the optimal allocation formulas can be combined with data to inform decisions at the planning stage. Another example uses data from a digital political advertising experiment in Texas to explore how precision would have varied with alternative allocations.

中文翻译：

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基于地点的随机试验和准实验中访谈对基线和最终调查的优化分配。

背景：许多基于地点的随机试验和准实验使用一对横断面调查而不是小组调查来估计干预措施的平均治疗效果。在这些研究中，每个地理集群中的个体随机样本被选择用于基线（干预前）调查，并选择独立随机样本用于最终（干预后）调查。目标：这种设计提出了一个问题，在给定固定预算的情况下，研究人员应如何在基线调查和最终调查之间分配资源，以最大限度地提高估计平均治疗效果的精度？结果：我们形式化了这个分配问题，并表明，尽管分配给基线调查的最佳采访比例始终小于二分之一，但它是每个集群采访总数的递增函数，各个集群之间的集群级相关性基线测量和最终结果，以及簇内相关系数。使用非洲多国调查数据的示例说明了如何将最佳分配公式与数据相结合，为规划阶段的决策提供信息。另一个例子使用德克萨斯州数字政治广告实验的数据来探索精确度如何随着替代分配而变化。