实时不平等

Journal of Public Economics · 2022 · [{"name": "Thomas Blanchet", "affiliation": ["National Bureau of Economic Research", "University of California, Berkeley"]}, {"name": "Emmanuel Saez", "affiliation": ["National Bureau of Economic Research", "University of California, Berkeley"]}, {"name": "Gabriel Zucman", "affiliation": ["National Bureau of Economic Research", "University of California, Berkeley"]}]

中文摘要

本文构建了美国国民收入的月度分布。我们开发了一种方法，通过结合高频公共数据源，将年度分布分解并及时预测月度变化。这使我们能够在季度宏观经济增长数据发布后立即估计各社会群体的增长，并实时追踪经济衰退期间及其后政府政策的分配效应。我们通过将该方法追溯实施至1976年，对其进行了检验与验证。相关估计结果可在 https://realtimeinequality.org 获取。

Abstract

This paper constructs monthly distributions of national income for the United States. We develop a methodology to disaggregate annual distributions and project monthly changes in a timely manner by combining high-frequency public data sources. This allows us to estimate growth by social groups as soon as quarterly macroeconomic growth numbers are released, and to track the distributional impacts of government policies during and in the aftermath of recessions in real time. We test and validate our methodology by implementing it retrospectively back to 1976. Estimates are available at https://realtimeinequality.org.