Replication data for: Measuring Immigration Policy

The dissertation consists of three chapters relating to the measurement of immigration policies, which developed out of my work as an initial co-author of the International Migration Policy and Law Analysis (IMPALA) Database Project. The first chapter entitled, “Brain Gain? Measuring skill bias in U.S. migrant admissions policy,” develops a conceptual and operational definition of skill bias. I apply the measure to new data revealing the level of skill bias in U.S. migrant admissions policy between 1965 and 2008. Skill bias in U.S. migrant admissions policy is both a critical determinant of the skill composition of the migrant population and a response to economic and public demand for highly skilled migrants. However, despite its central role, this is the first direct, comprehensive, annual measure of skill bias in U.S. migrant admissions policy. The second chapter entitled, “Stalled in the Senate: Explaining change in US migrant admissions policy since 1965,” presents new data characterizing change in U.S. migrant admissions policy as both expansive and infrequent over recent decades. I present a new theory of policy change in U.S. migrant admissions policy that incorporates the role of supermajoritarian decision making procedures and organized anti-immigration groups to better account for both the expansive nature and t he infrequency of policy change. The theory highlights the importance of a coalition of immigrant advocacy groups, employers and unions in achieving policy change and identifies the conditions under which this coalition is most likely to form and least likely to be blocked by an anti-immigration group opposition. The third chapter entitled, “Post-coding aggregation: A methodological principle for independent data collection,” presents a new technique developed to enable independent collection of flexible, high quality data: post-coding aggregation. Post-coding aggregation is a methodological principle that minimizes data loss, increases transparency, and grants data analysts the ability to decide how best to aggregate information to produce measures. I demonstrate how it increases the fl exibility of data use by expanding the utility of data collections for a wider range of research objectives and improves the reliability and the content validity of measures in data analysis.本论文包含三篇围绕移民政策测度展开的研究章节，其研究缘起于我作为《国际移民政策与法律分析（International Migration Policy and Law Analysis，IMPALA）数据库项目》的初始共同作者所参与的工作。 第一篇章节题为《人才红利？美国移民录取政策中的技能偏向性（skill bias）测度》，文中提出了技能偏向性的概念性与操作性定义。我将该测度方法应用于全新数据集，揭示了1965年至2008年间美国移民录取政策的技能偏向性水平。美国移民录取政策中的技能偏向性，既是决定移民人口技能结构的核心影响因素，亦是对高技能移民的经济与公共需求做出的响应。尽管该维度居于研究核心地位，但本研究仍是首次针对1965至2008年间美国移民录取政策的技能偏向性开展的直接、全面的年度测度。 第二篇章节题为《参议院中的停滞：1965年以来美国移民录取政策的变迁动因解析》，文中呈现了全新数据集，刻画了近几十年来美国移民录取政策既具扩张性又相对低频的变迁特征。本文提出了一套全新的美国移民录取政策变迁理论，该理论纳入了超多数决决策程序（supermajoritarian decision making procedures）与有组织反移民团体的作用，以更好地解释政策变迁的扩张性与低频性特征。该理论强调了移民倡导团体、雇主与工会组成的联盟在推动政策变迁中的核心作用，并识别出该联盟最易形成、且最不易遭到反移民团体阻挠的条件。 第三篇章节题为《后编码聚合（post-coding aggregation）：独立数据采集的方法论原则》，文中提出了一项旨在实现灵活、高质量数据独立采集的新技术：后编码聚合。作为一项方法论原则，后编码聚合可最大限度减少数据损失、提升研究透明度，并赋予数据分析师自主选择最优信息聚合方式以生成测度指标的能力。本文论证了该方法如何通过拓展数据集在更多研究目标下的应用价值，提升数据使用的灵活性，并改善数据分析中测度指标的可靠性与内容效度（content validity）。