Doctoral Dissertation Research: Union Mergers and the Labor Movement

加州大学欧文分校劳工运动如何扭转工会密度下降的趋势？一些劳工活动人士认为，合并即使不能扭转这种颓势，也可能有助于减缓这种颓势。他们认为，合并精简了工会，使他们能够更有效地组织和讨价还价。合并批评者不同意；如果执行不当，合并可能会减少会员和当地人的权利，最终削弱工会。本文利用档案数据来考察并购的决定因素和结果。三种类型的数据为本项目提供了信息：1)1886-2000年每个联盟的联盟特征数据；2)每一份合并协议和每一工会在每次合并前后的宪法条款；3)两个工会在合并过程中的案例分析。该项目有几个理论目标。首先，它通过将通常适用于企业的理论扩展到民主组织，将劳动研究和组织理论的文献联系起来。其次，它评估了合并与工会衰落/增长之间的关系。最后，它评估了合并如何影响女性和少数族裔的领导角色。这个项目的更广泛的影响是提供对合并的后果的分析，在规模方面，级别和文件的参与，以及领导者之间的多样性。通过公开这项研究，劳工活动家将有经验的、历史的证据来指导他们的决定。此外，该数据集的深度、广度和纵向质量将为学者提供新的数据集，从而有助于未来的研究。

SES-0928236Judith Stepan-NorrisJasmine KerrisseyUniversity of California, IrvineHow can the labor movement reverse the decline in union density? Some labor activists suggest that mergers may help to slow, if not reverse, the decline. Mergers, they argue, streamline unions, enabling them to organize and bargain more effectively. Merger critics disagree; if implemented incorrectly, mergers could decrease the rights of members and locals and ultimately weaken unions. This dissertation uses archival data to examine the determinants and outcomes of mergers. Three types of data inform this project: 1) data on union characteristics for each union from 1886-2000; 2) each merger agreement and constitutional clauses for each union before and after each merger; and 3) case study analysis of two unions as they merge over time. The project has several theoretical objectives. First, it links literature in labor studies and organization theory by extending theory that is typically applied to firms to democratic organizations. Second, it evaluates the relationship between mergers and the decline/growth of unions. Finally, it assesses how mergers have impacted the leadership roles of women and minorities. The broader impacts of this project are to provide an analysis of the consequences of mergers, in terms of the size, rank and file participation, and diversity among leaders. By making this research publicly available, labor activists will have empirical, historical evidence on which to inform their decisions. Moreover, the depth, breadth, and longitudinal quality of this dataset will provide scholars with a new dataset, thus contributing to future studies.