论文标题
量化收入和财富不平等的估计中的不确定性
Quantifying Uncertainties in Estimates of Income and Wealth Inequality
论文作者
论文摘要
我衡量影响美国经济不平等估计的不确定性，并研究正确估计的标准错误如何影响经验和结构性宏观经济研究的结果。在我的分析中，我依靠两个数据集：消费者财务调查（SCF），这是对家庭财务状况的三年期调查，以及个人税收模型公共使用文件（PUF），是个人所得税申报表的年度样本。尽管关注1988年至2018年期间前10名的六个收入和财富份额，但我的结果表明，忽略估计的财富和收入份额的不确定性可能会导致有关当前经济状况的错误结论，因此会导致不准确的预测和无效的政策建议。我的分析表明，对于正在考虑的六项前额份额收入股份，PUF估计值比使用SCF构建的股票要好得多。对于前十名的财富份额达到最高0.5％，SCF估计似乎比PUF估计值可靠。最后，对于两种最粒状的财富份额，最高的0.1％和0.01％，两个数据集都构成了无法轻易解决的非平凡挑战。
I measure the uncertainty affecting estimates of economic inequality in the US and investigate how accounting for properly estimated standard errors can affect the results of empirical and structural macroeconomic studies. In my analysis, I rely upon two data sets: the Survey of Consumer Finances (SCF), which is a triennial survey of household financial condition, and the Individual Tax Model Public Use File (PUF), an annual sample of individual income tax returns. While focusing on the six income and wealth shares of the top 10 to the top 0.01 percent between 1988 and 2018, my results suggest that ignoring uncertainties in estimated wealth and income shares can lead to erroneous conclusions about the current state of the economy and, therefore, lead to inaccurate predictions and ineffective policy recommendations. My analysis suggests that for the six top-decile income shares under consideration, the PUF estimates are considerably better than those constructed using the SCF; for wealth shares of the top 10 to the top 0.5 percent, the SCF estimates appear to be more reliable than the PUF estimates; finally, for the two most granular wealth shares, the top 0.1 and 0.01 percent, both data sets present non-trivial challenges that cannot be readily addressed.
