I situate this dissertation and its contributions at the edge of the literature on interest group political behavior and congressional responsiveness. In particular, I use new strategies and tools to study interest group influence. In the first essay, I find that a machine-learning text-analysis detects latent patterns in the frequency of lobbying by the telecommunications industry in 2015. Meanwhile, members of Congress primarily focus on healthcare and taxes when they discuss policy issues on social media. In the second essay, I measure the change in political behavior of interest groups by ideology after the surprise result of the 2016 presidential election. The evidence suggests there was an increase in political spending by ideologically polarized interest groups shortly after the election. Finally, the cornerstone of this dissertation evaluates the results of two field experiments measuring congressional responsiveness to issue advocacy with a non-profit, non-partisan political advocacy organization. Counter to expectations in the interest group literature, I find that members of Congress are responsive on social media to interest group requests on a low-salience, non-partisan issue. These findings have important implications for representation and responsiveness in the U.S. Congress by highlighting areas of research that need further study and deeper evaluation.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D82J7TS2 |
| Date | January 2018 |
| Creators | Young, Carolina Ferrerosa |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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