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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2503.11745 |
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| _version_ | 1866910012399419392 |
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| author | Bacik, Karol A. Ondras, Jan Rudkin, Aaron Dunkel, Jörn Kim, In Song |
| author_facet | Bacik, Karol A. Ondras, Jan Rudkin, Aaron Dunkel, Jörn Kim, In Song |
| contents | Lobbying networks constitute complex political systems that mobilize vast human and financial resources to influence governmental decision-making, often with profound national and global consequences. A comprehensive understanding of lobbying strategies and dynamics requires time-resolved, system-wide data, which are largely unavailable for most political systems. In the United States (U.S.), the Lobbying Disclosure Act (LDA) of 1995 mandates public reporting of all federal lobbying activities in detailed quarterly filings. However, extracting structured, quantitative information from these filings has remained technically challenging and labor-intensive. Here we present and analyze LobbyView, a relational database that integrates and disambiguates data from more than 1.6 million LDA reports. LobbyView provides access to detailed lobbying disclosures, reconciled corporate entities, and tools for linking LDA data to external legislative and corporate databases. We demonstrate the utility of LobbyView by examining both macro-level and highly granular lobbying dynamics. Specifically, we reconstruct the connectivity patterns of the U.S. lobbying network, and we show how they evolve over time, we identify organizational principles such as the accumulation of professional contacts within a small set of firms, and reveal how lobbying activity is synchronized with electoral cycles. Moreover, we introduce a probabilistic framework for analyzing lobbying behavior at the scale of individual bills, issues, or firms. We envision LobbyView as a resource not only for political scientists, but also for quantitative interdisciplinary research, enabling the application of methods from statistical physics, systems biology, and machine learning to the study of lobbying systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11745 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Measuring the dynamical evolution of the United States lobbying network Bacik, Karol A. Ondras, Jan Rudkin, Aaron Dunkel, Jörn Kim, In Song Physics and Society Lobbying networks constitute complex political systems that mobilize vast human and financial resources to influence governmental decision-making, often with profound national and global consequences. A comprehensive understanding of lobbying strategies and dynamics requires time-resolved, system-wide data, which are largely unavailable for most political systems. In the United States (U.S.), the Lobbying Disclosure Act (LDA) of 1995 mandates public reporting of all federal lobbying activities in detailed quarterly filings. However, extracting structured, quantitative information from these filings has remained technically challenging and labor-intensive. Here we present and analyze LobbyView, a relational database that integrates and disambiguates data from more than 1.6 million LDA reports. LobbyView provides access to detailed lobbying disclosures, reconciled corporate entities, and tools for linking LDA data to external legislative and corporate databases. We demonstrate the utility of LobbyView by examining both macro-level and highly granular lobbying dynamics. Specifically, we reconstruct the connectivity patterns of the U.S. lobbying network, and we show how they evolve over time, we identify organizational principles such as the accumulation of professional contacts within a small set of firms, and reveal how lobbying activity is synchronized with electoral cycles. Moreover, we introduce a probabilistic framework for analyzing lobbying behavior at the scale of individual bills, issues, or firms. We envision LobbyView as a resource not only for political scientists, but also for quantitative interdisciplinary research, enabling the application of methods from statistical physics, systems biology, and machine learning to the study of lobbying systems. |
| title | Measuring the dynamical evolution of the United States lobbying network |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2503.11745 |