Data for: From legal doctrine to social transformation? Comparing U.S. voting rights, equal employment opportunity, and fair housing legislation

  • Nicholas Pedriana (Creator)
  • Robin Stryker (Purdue University) (Contributor)

Dataset

Description

(NOTE: the following should be read as a shorthand summary of how we constructed our study and use of qualitative sources therein. The paper itself elaborates in greater detail each of the following bullet points)This article is a historical-comparative study of three major civil rights statutes (voting rights, equal employment, and fair housing) and is motivated by one fundamental question: why are some civil rights laws more successful than others? Consensus exists among civil rights scholars that voting rights was far and away the most successful of the three; that fair housing was a general failure; and that equal employment opportunity was <em>moderately successful</em>. Our study seeks to explain this specific hierarchy of civil rights outcomes across these three cases. As is the case with all historical-comparative research, qualitative data and qualitative analysis are essential because each case is constructed and compared holistically with respect to historical events and outcomes, the actors involved and the choices they made (and why they made them), and the consequences of those actions for civil rights policy success.The larger questions of <em>which</em> qualitative data sources were used; <em>why</em> they were chosen, and <em>how</em> they were interpreted/analyzed rests on one guiding principle: <em>how do potential qualitative sources aid in the development and/or testing of the authors’ theoretical framework and/or hypotheses</em>? Particularly when dealing with broad sweeps of history, the vast majority of available qualitative data surrounding a particular set of historical processes and outcomes will not likely be directly relevant to the authors’ objectives. In sum, we were guided by three basic considerations: How do our core <em>theoretical</em> claims/hypotheses guide the selection of qualitative data?How do our data sources contribute <em>methologically</em> to a historical-comparative design?What is the reasoning behind our <em>empirical analyses/interpretations</em> of the qualitative data? It is common in historical-comparative research to initially analyze qualitative sources in the study’s front-end theoretical discussion/literature review in order to build a comparative design and theoretical framework that will guide the remainder of the paper. Those sources may rely on secondary data, primary data, or a combination of the two. Either way, this typically involves first considering alternative theories/explanations for the historical outcomes under consideration. If none of these current explanations can adequately explain <em>comparative outcomes</em> across all cases, then we must seek other theories/explanations. In our study, we identified four such alternative explanations cited by other scholars: 1) formal enforcement authority; 2) bureaucratic infrastructure; 3) policy entrepreneurship; and 4) white resistance. Each of these factors is usually (but not always) constructed as a binary qualitative “variable” that is either “present” or “absent” in each case, and determined by the secondary literature and/or primary data. We considered all four alternative explanations for civil rights policy success, concluding that none of them could explain the specific hierarchy of civil rights outcomes across all three cases. Instead, we built our own theory of civil rights policy success (rooted largely in the sociology of law/law and society literature) and identified one central factor (or “variable” if you like) we claimed <em>could</em> explain observed outcomes: <em>the extent to which each policy incorporated a “Group-Centered Effects (GCE) enforcement structure</em>. As our guiding theoretical principle, GCE-based enforcement then guided our selection and interpretation of the qualitative data in our study’s formal empirical-analytic sections. Logic of Annotation (NOTE: Annotations 1-3 provide some context and examples for how qualitative data initially helps in constructing the comparative design in ways described above) The vast majority of the annotations (annotations 4-19) in this project appear in the paper’s formal empirical-analytic sections, in which we try to show how our central GCE variable can explain observed comparative outcomes across all three cases. Thus, <em>we explicitly chose qualitative data that invoked or was directly related to GCE factors</em>. Potential sources included: Secondary studies/analyses of civil rights lawsArchival government documents such as: Memos and correspondences among officials from the Department of Justice (voting rights); the EEOC (equal employment); and/or HUD (fair housing)Agency reports, published guidelines/regulations, etc.U.S. Congressional hearingsSupreme Court rulingsNewspaper articles (especially the <em>New York Times</em> and <em>Washington Post</em>)Drawing on a broad sampling of qualitative data from the above sources, we constructed and compared three analytic “narratives” for each respective case, <em>keeping our analyses and conclusions tightly connected</em> to our GCE-informed theoretical framework. Put simply, we chose and interpreted the data in ways that showed how voting rights built the most aggressive GCE approach to civil rights enforcement; fair housing built the weakest GCE approach; and equal employment built a moderate GCE approach.Each of the 19 annotations for this project highlight one or more of the above themes relating qualitative data selection/interpretation to our theoretical and empirical objectives.Some annotations include links to web pages so the reader can themselves view the cited primary documents; when that is not feasible, I have included a supplemental Word file with photocopies of the annotated documents.All annotations include a corresponding “analytic note” in which I provide elaboration for why and how the highlighted qualitative source was chosen/interpreted.
Date made availableJan 8 2020
PublisherQualitative Data Repository
Geographical coverageUnited States

Cite this