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Public Choice

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01.09.2014

Political interaction in the senate: estimating a political “spatial” weights matrix and an application to lobbying behavior

verfasst von: B. Andrew Chupp

Erschienen in: Public Choice | Ausgabe 3-4/2014

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Abstract

Many motivations exist that cause legislators to behave strategically when voting. These include logrolling, towing the party line, or political favor trading. However, it has proven difficult in the existing literature to uncover the magnitude of the interaction among politicians. This paper takes a “spatial” approach to the problem, using a spatial autoregressive model to not only uncover the magnitude of interactions, but also the direction of the interactions. In contrast to most applications of spatial econometrics, I allow the elements of the spatial weights matrix to be estimated as parameters. The political spatial weights matrix is calculated for 96 senators in the 110th U.S. Congress. Furthermore, in a marginal effects simulation, I calculate the overall effect on voting from “flipping” a senator’s vote, allowing flipped votes to have a cascading effect. I apply these measures to study political fundraising, mildly suggesting that political interest groups direct donations to senators with the most influence.

Vorheriger Artikel Ministerial gatekeeping and parliamentary involvement in the implementation process of EU directives

Nächster Artikel Transaction costs can encourage Coasean bargaining

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In spatial econometrics, the choice of specification (between the spatial autoregressive model and the spatial error model) is primarily dictated by prior beliefs about the data generating process. One could argue that interaction among senators’ votes is due to correlated errors (implying SEM) or political pressures (implying SAR). A combined approach, with both weighted errors and weighted variables is often used, but is impractical due to the need to estimate two weighting matrices, essentially doubling the number of parameters in the model. Since political interaction is the focus of this paper, I choose to use the SAR model and let my exogenous variables capture as much of the spatial error as possible. While this approach is certainly open to debate, I find that my methodology is justified due to the infeasibility of the alternative.

The matrix has k(k−1) elements rather than k×k because the diagonal entries in a weighting matrix must always be zero.

For more examples of this cardinal approach to voting, see Russett (1967), Rai (1972), Thacker (1999), Luif (2003), and Dreher and Boockmann (2007).

Senators Feingold and Kohl always voted liberal.

Where a state did not have a Fortune 1000 company headquartered therein, I used the largest employer in the state.

This method, while one of the most common, is not the only method of estimating ideology. For other methodologies, see Carson and Oppenheimer (1984), Jackson and Kingdon (1992), Hill et al. (1997), and Reeher (2001).

This method of recovering ideology is particularly influential in the political science literature. See, for example, Rosenthal and Voeten (2004).

I maintain consistency by reversing the sign of the total effect when voting moves down a category (rather than up).

The marginal analysis simulation was conducted using R (a statistical programming language). Code is available upon request from the author.

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Titel: Political interaction in the senate: estimating a political “spatial” weights matrix and an application to lobbying behavior
verfasst von: B. Andrew Chupp
Publikationsdatum: 01.09.2014
Verlag: Springer US
Erschienen in: Public Choice / Ausgabe 3-4/2014
Print ISSN: 0048-5829
Elektronische ISSN: 1573-7101
DOI: https://doi.org/10.1007/s11127-013-0111-9

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