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academic Research Using NIMSP Data

academic Research Using NIMSP Data

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academic Research Using NIMSP Data

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  1. academic Research Using NIMSP Data Keith E. Hamm Rice University Presented at National Institute on Money in State Politics Conference entitled “Transparency 2013: Bright Minds, Dark Money.” May 30-June 2, 2013

  2. Promising Research Projects I. Network Analysis and Campaign Contributions II. Campaign Contributions and Individual Legislative Behavior III. Monte Carlo Simulations and Committee Outliers

  3. Campaign Finance and Network AnalysisJacIKettlerRice University

  4. Description of Network Analysis • Networks Sets of relations that connect (or leave disconnected) actors, institutions, or objects • Social network analysis emphasizes relationships Allows researchers to develop a better understanding of the social context around relationships and interactions

  5. Party and Campaign Networks • Jaci uses social network analysis to study party and campaign networks. Campaign donations as connections

  6. 2010 Texas Democratic and Republican Contribution Networks (Includes local parties and interest groups)

  7. 2010 Texas Republican Party

  8. 2010 Texas Democratic Party

  9. What are the possible uses of network analysis? • Identify influential actors in the party network • Determine how party organization impacts electoral activities • Explore how candidates fit into the broader party network and how their location impacts their success in the legislature

  10. Campaign contributions and Individual Legislative BehaviorGreg VonnahmeUniversity of Missouri – Kansas City

  11. Study 1 • Question: Why do some candidates attract contributions from many donors whereas other candidates have much smaller donor pools? • This prompted a closer examination of how contributions are made over time and led to a model of the emergence of inequalities relying on the Institute’s data for 2008.

  12. Study 1 continued • Traces contributions across time using date of contribution. • Major Finding: As a candidate builds a lead in the number of contributions, he or she becomes more likely to get additional contributions in the future, which further magnifies his or her lead and thus makes the candidate even more likely to get subsequent contributions.

  13. Study 2 • He uses NIMSP data to examine whether contributions are linked to “legislative entrepreneurship” (sponsoring and passing new laws) in state legislatures. • Finding: Study shows a positive link between contributions and legislation.

  14. Relationship between Total Contributions & Number of Bills Sponsored

  15. Relationship Between Total Contributions and Probability of Bill Becoming Law

  16. Campaign Contributions & Committee OutliersKeith Hamm Rice University Ronald Hedlund Northeastern UniversityNancy MartoranoMIller University of DaytonJaciKettler Rice University

  17. What do special interests expect in exchange for their contributions to electoral campaigns? • Not much empirical evidence linking contributions to legislators’ votes. • We argue that special interests expect (1)access and (2)effort/productivity in exchange for campaign contributions and other forms of electoral support. (Hall and Wayman 1990; Powell 2012)

  18. Return on Investment and Committee Members • Committee consideration is often the most vital stage of the legislative process. A key point is that committees act as gatekeepers. • It is only natural that special interests would want representation on the relevant committees. • Appointment to a relevant standing committee is the first step that a legislator must take if he or she is going to ensure that special interests receive a return on their investment.

  19. Goal of Study and Background • We are looking for outlier committees using contribution data. • Scholarly research on committee outliers is extensive. • General finding is that outlier committees are not the norm. Range 4% to 20%

  20. Design of Pilot Study • 2008 election and 2009/2010 legislative sessions • States Michigan, Pennsylvania, Oregon, Wisconsin, and West Virginia • Policy Areas Agriculture, Banking, Insurance, Education, Judiciary, and Labor and Commerce

  21. How to Measure Financial Outlier Committees? • % committee members receiving campaign contributions from relevant sectors (RS) • Mean campaign contribution from RS • Median campaign contribution from RS • Mean reliance on campaign contributions from RS • Median reliance on campaign contributions from RS

  22. Employed Monte Carlo Simulation • What is a Monte Carlo simulation?

  23. Enter Monte Carlo Simulation • Produce 10,000 random samples. • Plot the results.

  24. Determine Significance

  25. Pennsylvania House Labor Relations Committee –Mean Reliance Score

  26. Finding #1: Per Cent Significant Outliers for 5 Measures of Sector Contributions

  27. Finding #2: Per Cent Significant Outliers Reliance Measures of Sector Contributions by Issue Area

  28. Why are Some Members More Reliant on Sector Contributions? Finding # 3 • Most Significant Previous Committee Experience • Frequently Significant Related Occupation

  29. Future Research • Analysis needs to be expanded to more states, chambers and committee jurisdictions • Begin to develop the next stage of inquiry – assessing the impact of special interests contributions to a legislator on the actual activities of those legislators

  30. Possible research topics • Individual Level (1) Bill introductions (2) Attendance at committee meetings (3) Level of participation (4) Amendment activity (5) Floor behavior

  31. Possible research topics continued • Committee Level (1) agenda setting (2) who testifies at committee hearings (3) bill mark-up activity (4) floor acceptance of committee decisions

  32. Conclusion 1. Significant new research is being undertaken using the NIMSP data. 2. Scholars are trying to link money to behavior and policy outcomes using sophisticated social science research methods. 3. We have only scratched the surface. Need coordinated activity as outlined by Michael Malbin.

  33. The END