Campaign contributions are a staple of congressional life. Yet, the search for tangible effects of congressional donations often focuses on the association between contributions and votes on congressional bills. We present an alternative approach by considering the relationship between money and legislators’ speech. Floor speeches are an important component of congressional behavior, and reflect a legislator’s policy priorities and positions in a way that voting cannot. Our research provides the first comprehensive analysis of the association between a legislator’s campaign donors and the policy issues they prioritize with congressional speech. Ultimately, we find a robust relationship between donors and speech, indicating a more pervasive role of money in politics than previously assumed. We use a machine learning framework on a new dataset that brings together legislator metadata for all representatives in the US House between 1995 and 2018, including committee assignments, legislative speech, donation records, and information about Political Action Committees. We compare information about donations against other potential explanatory variables, such as party affiliation, home state, and committee assignments, and find that donors consistently have the strongest association with legislators’ issue-attention. We further contribute a procedure for identifying speech and donation events that occur in close proximity to one another and share meaningful connections, identifying the proverbial needles in the haystack of speech and donation activity in Congress which may be cases of interest for investigative journalism. Taken together, our framework, data, and findings can help increase the transparency of the role of money in politics.
- Award ID(s):
- 2047488
- NSF-PAR ID:
- 10316726
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 118
- Issue:
- 50
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
Farjam, Mike (Ed.)
-
null (Ed.)Abstract Sentiment, judgments and expressed positions are crucial concepts across international relations and the social sciences more generally. Yet, contemporary quantitative research has conventionally avoided the most direct and nuanced source of this information: political and social texts. In contrast, qualitative research has long relied on the patterns in texts to understand detailed trends in public opinion, social issues, the terms of international alliances, and the positions of politicians. Yet, qualitative human reading does not scale to the accelerating mass of digital information available currently. Researchers are in need of automated tools that can extract meaningful opinions and judgments from texts. Thus, there is an emerging opportunity to marry the model-based, inferential focus of quantitative methodology, as exemplified by ideal point models, with high resolution, qualitative interpretations of language and positions. We suggest that using alternatives to simple bag of words (BOW) representations and re-focusing on aspect-sentiment representations of text will aid researchers in systematically extracting people’s judgments and what is being judged at scale. The experimental results below show that our approach which automates the extraction of aspect and sentiment MWE pairs, outperforms BOW in classification tasks, while providing more interpretable parameters. By connecting expressed sentiment and the aspects being judged, PULSAR (Parsing Unstructured Language into Sentiment-Aspect Representations) also has deep implications for understanding the underlying dimensionality of issue positions and ideal points estimated with text. Our approach to parsing text into aspects-sentiment expressions recovers both expressive phrases (akin to categorical votes), as well as the aspects that are being judged (akin to bills). Thus, PULSAR or future systems like it, open up new avenues for the systematic analysis of high-dimensional opinions and judgments at scale within existing ideal point models.more » « less
-
Abstract The field of sustainability science has grown significantly over the past two decades in terms of both conceptual development and empirical research. Systems-focused analysis is critical to building generalizable knowledge in the field, yet much relevant research does not take a systems view. Systems-oriented analytical frameworks can help researchers conceptualize and analyze sustainability-relevant systems, but existing frameworks may lack access or utility outside a particular research tradition. In this article, we outline the human–technical–environmental (HTE) framework, which provides analysts from different disciplinary backgrounds and fields of study a common way to advance systems-focused research on sustainability issues. We detail a step-by-step guide for the application of the HTE framework through a matrix-based approach for identifying system components, studying interactions among system components, and examining interventions targeting components and/or their interactions for the purpose of advancing sustainability. We demonstrate the applicability of the HTE framework and the matrix-based approach through an analysis of an empirical case of coal-fired power plants and mercury pollution, which is relevant to large-scale sustainability transitions. Based on this analysis, we identify specific insights related to the applicability of upstream and downstream leverage points, connections between energy markets and the use of pollution control technologies, and the importance of institutions fitting both biophysical dynamics and socioeconomic and political dynamics. Further application of the HTE framework and the identification of insights can help develop systems-oriented analysis, and inform societal efforts to advance sustainability, as well as contribute to the formulation of empirically grounded middle-range theories related to sustainability systems and sustainability transitions. We conclude with a discussion of areas for further development and application of the HTE framework.
-
Ever increasing numbers of wind turbines, communication towers, power lines, and aerial vehicles are clear evidence of our growing reliance on infrastructure in the lower aerosphere. As this infrastructure expands, it is important to understand public perceptions of an increasingly crowded sky. To gauge tolerance for aerial crowding, 251 participants from across the US completed a survey where they rated tolerance for a series of aerial infrastructure images (i.e., towers, turbines, and airborne vehicles) in four landscapes with varying degrees of pre-existing ground-level infrastructure that approximated rural, suburban, and urban settings. We predicted lower tolerance for aerial infrastructure 1) in more natural scenes and 2) among rural residents. In general, participants preferred an open aesthetic with relatively little aerial infrastructure across all landscape types. No clear association was found between infrastructure tolerance and natural scenes nor rural residency, with participants slightly less tolerant of infrastructure in the suburban scene. Tolerance scores were generally similar across age, income levels, and political affiliations. Women indicated less crowding tolerance than men, with this effect driven by a disproportionate number of women with zero tolerance for aerial infrastructure. African Americans and Asians had higher tolerance scores than other racial/ethnic groups, but these trends may have been affected by low sample sizes of non-white participants. Our survey revealed fewer differences in crowding tolerance across demographic groups than might be expected given widely reported political and geographic polarization in the U.S. Attitudes toward aerial infrastructure were varied with few associations with demographic parameters suggesting that public opinion has not yet solidified with regard to this issue, making possible opportunities for consensus building with regard to responsible development of aerial infrastructure.
-
Analyzing ideology and polarization is of critical importance in advancing our grasp of modern politics. Recent research has made great strides towards understanding the ideological bias (i.e., stance) of news media along the left-right spectrum. In this work, we instead take a novel and more nuanced approach for the study of ideology based on its left or right positions on the issue being discussed. Aligned with the theoretical accounts in political science, we treat ideology as a multi-dimensional construct, and introduce the first diachronic dataset of news articles whose ideological positions are annotated by trained political scientists and linguists at the paragraph level. We showcase that, by controlling for the author{'}s stance, our method allows for the quantitative and temporal measurement and analysis of polarization as a multidimensional ideological distance. We further present baseline models for ideology prediction, outlining a challenging task distinct from stance detection.more » « less