Shift-Share设计：理论与推论
Shift-Share Designs: Theory and Inference
摘要：
我们研究的推断转移份额回归设计，如当一个区域的结果回归部门冲击的加权平均值，使用区域部门的份额作为权重。我们进行了一个安慰剂练习，在这个练习中，我们估计了由随机生成的部门冲击构建的转移-份额回归量对美国通勤区实际劳动力市场结果的影响。基于常用标准误差和5%标称显著性水平的检验拒绝了高达55%的安慰剂样品中的无效效应。我们使用一个程式化的经济模型来表明，这种过度拒绝问题的出现是因为回归残差在不同地区之间具有相似的部门份额，独立于其地理位置。我们推导出新的推理方法，是有效的任意跨区域相关的回归残差。我们使用流行的应用程序的转移-份额设计，我们的方法可能会导致更广泛的置信区间在实践中。
英文摘要：
We study inference in shift-share regression designs, such as when a regional outcome is regressed on a weighted average of sectoral shocks, using regional sector shares as weights. We conduct a placebo exercise in which we estimate the effect of a shift-share regressor constructed with randomly generated sectoral shocks on actual labor market outcomes across U.S. Commuting Zones. Tests based on commonly used standard errors with 5\% nominal significance level reject the null of no effect in up to 55\% of the placebo samples. We use a stylized economic model to show that this overrejection problem arises because regression residuals are correlated across regions with similar sectoral shares, independently of their geographic location. We derive novel inference methods that are valid under arbitrary cross-regional correlation in the regression residuals. We show using popular applications of shift-share designs that our methods may lead to substantially wider confidence intervals in practice.
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