We estimate economic impacts from decaying water infrastructure in the United States. Using water main breaks in Washington, DC, and a yearlong panel of hourly traffic speeds, we estimate causal effects of water main failures on traffic congestion. We use k-means clustering to create clusters of streets that are similar to each other: treated observations are compared to other units in the cluster. We identify heterogeneous treatment effects algorithmically while retaining straightforward standard error calculations. We find strong evidence of heterogeneous treatment effects across clusters but small welfare impacts of water main breaks on traffic patterns overall.

中文翻译：

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集中控制：供水基础设施故障的异质因果影响

我们估计了美国水基础设施衰败对经济的影响。使用华盛顿特区的供水总管中断和一年的每小时交通速度面板，我们估计供水总管故障对交通拥堵的因果影响。我们使用k均值聚类来创建彼此相似的街道集群：将处理后的观察结果与集群中的其他单元进行比较。我们通过算法识别异质治疗效果，同时保留直接的标准误差计算。我们发现了跨集群异质处理效果的有力证据，但总水管中断对整体交通模式的福利影响很小。