More than Mere Access: An Experiment on Moneyed Interests, Information Provision, and Legislative Action in Congress

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Abstract

Campaign donors and corporate interests have greater access to Congress, and the legislative agenda and policy outcomes reflect their preferences. How this privileged access converts into influence remains unclear because petitioner-legislator interactions are unobserved. In this article, we report the results of an original survey experiment of 436 congressional staffers. The vignette manipulates a petitioner’s identity, the substance of the request, and the supporting evidence being offered. We test how likely staff are to take a meeting, to use the information being offered, and to recommend taking a position consistent with the request, as well as whether they perceive the request to be congruent with constituent preferences. Donors and lobbyists are no more likely to be granted access than constituents, but staffers are more likely to use information and to make legislative action recommendations when the information source is an ideologically aligned think tank. Subgroup analysis suggests these effects are particularly strong among ideological extremists and strong partisans. And, information offered by aligned think tanks are thought to be representative of constituent opinion. Our results reveal the partisan and ideological predispositions that motivate legislative action that is more costly than merely granting access.

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Most cited references 81

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Methods of coping with social desirability bias: A review

Anton J. Nederhof (1985)

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Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments

Jens Hainmueller, Daniel Hopkins, Teppei Yamamoto (2014)

Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show howconjoint analysis, an experimental design yet to be widely applied in political science, enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. In conjoint analysis, respondents score a set of alternatives, where each has randomly varied attributes. Here, we undertake a formal identification analysis to integrate conjoint analysis with the potential outcomes framework for causal inference. We propose a new causal estimand and show that it can be nonparametrically identified and easily estimated from conjoint data using a fully randomized design. The analysis enables us to propose diagnostic checks for the identification assumptions. We then demonstrate the value of these techniques through empirical applications to voter decision making and attitudes toward immigrants.

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Validating vignette and conjoint survey experiments against real-world behavior.

Jens Hainmueller, Dominik Hangartner, Teppei Yamamoto (2015)

Survey experiments, like vignette and conjoint analyses, are widely used in the social sciences to elicit stated preferences and study how humans make multidimensional choices. However, there is a paucity of research on the external validity of these methods that examines whether the determinants that explain hypothetical choices made by survey respondents match the determinants that explain what subjects actually do when making similar choices in real-world situations. This study compares results from conjoint and vignette analyses on which immigrant attributes generate support for naturalization with closely corresponding behavioral data from a natural experiment in Switzerland, where some municipalities used referendums to decide on the citizenship applications of foreign residents. Using a representative sample from the same population and the official descriptions of applicant characteristics that voters received before each referendum as a behavioral benchmark, we find that the effects of the applicant attributes estimated from the survey experiments perform remarkably well in recovering the effects of the same attributes in the behavioral benchmark. We also find important differences in the relative performances of the different designs. Overall, the paired conjoint design, where respondents evaluate two immigrants side by side, comes closest to the behavioral benchmark; on average, its estimates are within 2% percentage points of the effects in the behavioral benchmark.