Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-22T02:14:24.407Z Has data issue: false hasContentIssue false
  • English
  • Français

Sponsoring Early Day Motions in the British House of Commons as a Response to Electoral Vulnerability*

Published online by Cambridge University Press:  08 November 2013

Michael Kellermann
Get access Rights & Permissions [Opens in a new window]

Abstract

While the importance of individual candidates in British elections has long been minimized, this article argues that early day motions (EDMs)—formal, non-binding expressions of opinion—allow backbench MPs to cultivate reputations with constituents. First, this article demonstrates that greater sponsorship of EDMs is associated with better electoral outcomes, which suggests that EDMs could help vulnerable MPs improve their electoral prospects. Secondly, a Bayesian hierarchical negative binomial hurdle model, which accounts for specific features of EDM sponsorship and is novel in political science, shows that members from electorally competitive constituencies are more likely to introduce EDMs, and introduce them more often, than members from less competitive constituencies. Moreover, this relationship has increased over the past 20 years.

Type
Original Articles
Information
Political Science Research and Methods , Volume 1 , Issue 2 , December 2013 , pp. 263 - 280
Copyright
Copyright © The European Political Science Association. This is a work of the U.S. Government and is not subject to copyright protection in the United States, 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

Michael Kellermann is Assistant Professor, Political Science Department, United States Naval Academy, 589 McNair Road, Annapolis, Maryland 21402-5030 ([email protected]). An earlier version of this article was presented at the 2011 Annual Meeting of the Midwest Political Science Association, Chicago, Illinois, 31 March–3 April 2011. Thanks to Steve Lem, Martin Hansen, Rebecca Nelson, Eleanor Powell, Kevin Quinn, G. Bingham Powell, Tiffany Davenport, James Alt, Nick Biziouras, the editors and three anonymous referees for helpful comments. This research was conducted with the support of a Naval Academy Research Council summer grant. Any views expressed are the author's and do not reflect the official policy or position of the United States Naval Academy, Department of Defense or the U.S. government. To view supplementary material for this article, please visit http://dx.doi.org/10.1017/psrm.2013.19

References

Bailey, DanielNason, Guy P.. 2008. ‘Cohesion of Major Political Parties’. British Politics 3(3):390–417.Google Scholar
Berrington, HughHague, Rod. 1998. ‘Europe, Thatcherism and Traditionalism: Opinion, Rebellion and the Maastricht Treaty in the Backbench Conservative Party, 1992–1994’. Western European Politics 21(1):44–71.Google Scholar
Berrington, Hugh B. 1973. Backbench Opinion in the House of Commons, 1945–55. Oxford: Pergamon Press.Google Scholar
Blais, AndréLago, Ignacio. 2009. ‘A General Measure of District Competitiveness’. Electoral Studies 28(1):94–100.Google Scholar
Bowler, Shaun. 2010. ‘Private Members’ Bills in the UK Parliament: Is There an ‘Electoral Connection’?’ Journal of Legislative Studies 16(4):476–494.Google Scholar
Bräuninger, Thomas, Brunner, MartinDäubler, Thomas. 2011. ‘Personal Vote-seeking in Flexible List Systems: How Electoral Incentives Shape Belgian MPs’ Bill Initiation Behaviour’. European Journal of Political Research 51(5):607–645.Google Scholar
Brunner, Martin. 2012. Parliaments and Legislative Activity: Motivations for Bill Introduction. Studien Zur Neuen Politischen Ökonomie Series. Wiesbaden: Springer Verlag.Google Scholar
Cain, Bruce, Ferejohn, JohnFiorina, Morris. 1987. The Personal Vote: Constituency Service and Electoral Independence. Cambridge, MA: Harvard University Press.Google Scholar
Cameron, A. ColinTrivedi, Pravin K.. 1986. ‘Econometric Models based on Count Data: Comparisons and Applications of some Estimators and Tests’. Journal of Applied Econometrics 1(1):29–53.Google Scholar
Carey, John M.Shugart, Matthew Soberg. 1995. ‘Incentives to Cultivate a Personal Vote: A Rank Ordering of Electoral Formulas’. Electoral Studies 14(4):417–439.Google Scholar
Childs, SarahWithey, Julie. 2004. ‘Women Representatives Acting for Women: Sex and the Signing of Early Day Motions in the 1997 British Parliament’. Political Studies 52(3):552–564.Google Scholar
Clarke, Kevin A. 2005. ‘The Phantom Menace: Omitted Variable Bias in Econometric Research’. Conflict Management and Peace Science 22(4):341–352.Google Scholar
Cox, Gary W. 1987. The Efficient Secret: The Cabinet and the Development of Political Parties in Victorian England. Cambridge: Cambridge University Press.Google Scholar
Finer, Samuel E., Berrington, Hugh B.Bartholomew, D.J.. 1961. Backbench Opinion in the House of Commons, 1955–59. Oxford: Pergamon Press.Google Scholar
Franklin, Mark N.Tappin, Michael. 1977. ‘Early Day Motions as Unobtrusive Measures of Backbench Opinion in Britain’. British Journal of Political Science 7(1):49–69.Google Scholar
Gaines, Brian J. 1998. ‘The Impersonal Vote? Constituency Service and Incumbency Advantage in British Elections, 1950–92’. Legislative Studies Quarterly 23(2):167–195.Google Scholar
Gay, Oonagh. 2005. ‘MPs Go Back to Their Constituencies’. Political Quarterly 71(1):57–66.Google Scholar
Gay, Oonagh, Cracknell, Richard, Hardacre, Jeremy, Fessey, Jean. 2010. Members 1979–2010. House of Commons Research Paper 10/33, House of Commons Library.Google Scholar
Greene, William. 2008. ‘Functional Forms for the Negative Binomial Model for Count Data’. Economics Letters 99(3):585–590.Google Scholar
House of Commons Information Office. 2010a. Early Day Motion Database, available at http://edmi.parliament.uk, accessed 6 June 2010.Google Scholar
House of Commons Information Office. 2010b. Factsheet P3: Early Day Motions. Series P No. 3.Google Scholar
House of Commons Procedure Committee. 2007. Public Petitions and Early Day Motions: First Report of Session 2006–07, Volume HC 513. London: The Stationery Office.Google Scholar
Kam, Christopher J. 2009. Party Discipline and Parliamentary Politics. Cambridge: Cambridge University Press.Google Scholar
Kellermann, Michael. 2012. ‘Estimating Ideal Points in the British House of Commons using Early Day Motions’. American Journal of Political Science 56(3):757–771.Google Scholar
Kellermann, MichaelShepsle, Kenneth A.. 2009. ‘Congressional Careers, Committee Assignments, and Seniority Randomization in the US House of Representatives’. Quarterly Journal of Political Science 4(2):87–101.Google Scholar
Leece, JohnBerrington, Hugh. 1977. ‘Measurements of Backbench Attitudes by Guttman Scaling of Early Day Motions: A Pilot Study, Labour, 1968–69’. British Journal of Political Science 7(4):529–541.Google Scholar
Loewen, Peter J., Koop, Royce, Settle, Jaime E.Fowler, James H.. 2013. ‘A Natural Experiment in Proposal Power and Electoral Success’. American Journal of Political Science forthcoming.Google Scholar
Min, YongyiAgresti, Alan. 2005. ‘Random Effects Models for Repeated Measures of Zero-inflated Count Data’. Statistical Modelling 5(1):1–19.Google Scholar
Neelon, Brian H., O'Malley, A. JamesNormand, Sharon-Lise T.. 2010. ‘A Bayesian Model for Repeated Measures Zero-inflated Count Data with Application to Outpatient Psychiatric Service Use’. Statistical Modelling 10(4):421–439.Google Scholar
Norris, Pippa. 1997. ‘The Puzzle of Constituency Service’. Journal of Legislative Studies 3(2):29–49.Google Scholar
Proksch, Sven-OliverSlapin, Jonathan B. 2012. ‘Institutional Foundations of Legislative Speech’. American Journal of Political Science 56(3):520–537.Google Scholar
Smith, Tim. 2013. ‘Are You Sitting Comfortably? Estimating Incumbency Advantage in the UK: 1983–2010 – A Research Note’. Electoral Studies 32(1):167–173.Google Scholar
Spiegelhalter, David J., Best, Nicola G., Carlin, Bradley P.Van Der Linde, Angelika. 2002. ‘Bayesian Measures of Model Complexity and Fit’. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(4):583–639.Google Scholar
Spiegelhalter, David J., Thomas, Andrew, Best, NicolaLunn, David. 2003. WinBugs Version 1.4: User Manual. Cambridge: Medical Research Council Biostatistics Unit.Google Scholar
Supplementary material: PDF

Kellermann Supplementary Material

Appendix

Download Kellermann Supplementary Material(PDF)
PDF 45.7 KB