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Political Interaction in the Senate: Estimating a Political “Spatial” Weights Matrix and an Application to Lobbying Behavior


  • B. Andrew Chupp

    () (Department of Economics, Illinois State University)


Many motivations exist that cause legislators to behave strategically when voting. However, it is difficult to uncover the magnitude of the interaction among politicians. This paper takes a “spatial” approach to the problem, using a spatial autoregressive model where the elements of the spatial weights matrix are estimated as parameters. The political spatial weights matrix is calculated for 96 senators in the U.S. 110th Congress. Furthermore, I calculate the overall effect on voting from “flipping” a senator’s vote. I apply these measures to study political fundraising, mildly suggesting that political interest groups direct donations to senators with the most influence.

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  • B. Andrew Chupp, 2011. "Political Interaction in the Senate: Estimating a Political “Spatial” Weights Matrix and an Application to Lobbying Behavior," Working Paper Series 20111006, Illinois State University, Department of Economics.
  • Handle: RePEc:ils:wpaper:20111006

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    References listed on IDEAS

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    Cited by:

    1. Joshua C. Hall & Christopher Shultz & E. Frank Stephenson, 2015. "The Political Economy of Local Fracking Bans," Working Papers 15-37, Department of Economics, West Virginia University.

    More about this item


    Senate; Strategic Interaction; Yardstick Competition; Logrolling; Spatial Econometrics;

    JEL classification:

    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • P16 - Economic Systems - - Capitalist Systems - - - Political Economy of Capitalism
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models


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