发表机构
UC San Diego（加州大学圣地亚哥分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究验证了基于LM行政记录估计美国次州级工会密度的可靠性，发现其在州级与CPS高度相关，但在国会选区、通勤区和县级相关性减半，且存在超过100%的不合理值，结论是该方法不适用于精细地理尺度的工会密度测量。
AI 中文摘要
工会在地方层面是重要的经济和政治参与者，但美国工会会员人数的主要数据来源——当前人口调查（CPS）——在州级以下并不可靠。因此，研究人员转向工会向联邦“LM”文件报告的会员人数，对工会进行地理定位并汇总其报告的会员人数，以估计县、通勤区和国会选区的工会密度。我们质疑这种做法是否合理。我们开发并论证了基于CPS的多层回归后分层（MrP）方法作为基准，然后从州级向下验证基于LM的估计。在州级，LM、CPS和MrP高度相关，但在国会选区、通勤区和县级，这种相关性大约减半。这种偏差部分源于报告中的“块状性”，它产生了不可能的值，包括超过100%的工会密度。公共部门工会会员的缺失仅能解释这种差异的一小部分。这些问题具有实质性后果：在标准的通勤区“中国冲击”回归中，更换密度度量会使进口暴露系数的大小、显著性和符号发生变化。我们得出结论，LM记录仍然是研究工会作为组织的丰富资源，但汇总LM报告的会员人数并不是在精细地理分辨率下衡量工会密度的可靠方法。
英文摘要
Unions are important economic and political actors at the local level, but the primary source on US union membership, the Current Population Survey (CPS), is unreliable below the state level. Researchers have therefore turned to union-reported membership in federal "LM" filings, geolocating unions and aggregating their reported membership to estimate union density for counties, commuting zones, and congressional districts. We ask whether this practice is sound. We develop and defend multilevel regression with poststratification (MrP) on the CPS as a benchmark, then validate LM-based estimates against it from the state level down. LM, CPS, and MrP are highly correlated at the state level, but this correlation is roughly halved at the congressional-district, commuting-zone, and county levels. The breakdown is driven in part by reporting "lumpiness," which produces improbable values, including union densities exceeding 100%. Missing public sector union members explains only a small part of this divergence. These issues are substantively consequential: in a standard commuting-zone "China shock" regression, swapping density measures changes the import-exposure coefficient enough to alter its magnitude, significance, and sign. We conclude that LM records remain a rich resource for studying unions as organizations, but aggregating LM-reported membership is not a reliable way to measure union density at fine geographic resolution.
Comments35 pages, 8 figures, 7 tables + Appendices
