论文标题
衡量税收和公共服务对财产价值的影响：双重机器学习方法
Measuring the Impact of Taxes and Public Services on Property Values: A Double Machine Learning Approach
论文作者
论文摘要
房地产价格如何应对当地税和当地公共服务的变化？从OATES（1969）开始，试图衡量这一点，但由于缺乏本地公共服务控制。最近的工作试图通过使用准实验方法来克服此类数据限制。我们重新审视了这个基本问题，但采用了另一种经验策略，该策略将Chernozhukov等人的双重机器学习估算器配对。 （2018年）在2010 - 2016年期间，具有947个随时间变化的本地特征和公共服务控制的新型数据集。我们发现，适当控制当地公共服务和特征控制量使当地所得税对房价的估计影响增加了一倍。我们还利用数据集的独特功能来证明在更大的市政竞争的领域中，税收资本化更强，从而支持了TIEBOUT假设的核心意义。最后，我们衡量公共服务，教育和犯罪对房价的影响以及当地税对移民的影响。
How do property prices respond to changes in local taxes and local public services? Attempts to measure this, starting with Oates (1969), have suffered from a lack of local public service controls. Recent work attempts to overcome such data limitations through the use of quasi-experimental methods. We revisit this fundamental problem, but adopt a different empirical strategy that pairs the double machine learning estimator of Chernozhukov et al. (2018) with a novel dataset of 947 time-varying local characteristic and public service controls for all municipalities in Sweden over the 2010-2016 period. We find that properly controlling for local public service and characteristic controls more than doubles the estimated impact of local income taxes on house prices. We also exploit the unique features of our dataset to demonstrate that tax capitalization is stronger in areas with greater municipal competition, providing support for a core implication of the Tiebout hypothesis. Finally, we measure the impact of public services, education, and crime on house prices and the effect of local taxes on migration.
