The Constrained Instability of Majority Rule: Experiments on the Robustness of the Uncovered Set

Michael S. Lynch Department of Political Science, University of Kansas, 504 Blake Hall, Lawrence, KS 66044 e-mail: mlynch{at}ku.edu Gary J. Miller and Itai Sened Department of Political Science, Washington University in St. Louis, Campus Box 1063, One Brooking Drive, St. Louis, MO 63130 e-mail: gjmiller{at}wustl.edu e-mail: sened{at}wustl.edu (corresponding author) The uncovered set has frequently been proposed as a solutionconcept for majority rule settings. This paper tests this propositionusing a new technique for estimating uncovered sets and a seriesof experiments, including five-player computer-mediated experimentsand 35-player paper-format experiments. The results supportthe theoretic appeal of the uncovered set. Outcomes overwhelminglylie in or near the uncovered set. Furthermore, when preferencesshift, outcomes track the uncovered set. Although outcomes tendto occur within the uncovered set, they are not necessarilystable; majority dominance relationships still produce instability,albeit constrained by the uncovered set. Authors' note: We thank Matthew M. Schneider for research assistance.We thank James Holloway, Tse-Min Lin, Jim Granato, Randall L.Calvert, Rick K. Wilson, faculty and students of the Juan MarchInstitute, and reviewers of Political Analysis for their veryhelpful comments and suggestions.     

Testing the Predictions of the Multidimensional Spatial Voting Model with Roll Call Data

e-mail: gjeong{at}artsci.wustl.edu This paper develops a procedure for locating proposals and legislatorsin a multidimensional policy space by applying agenda-constrainedideal point estimation. Placing proposals and legislators onthe same scale allows an empirical test of the predictions ofthe spatial voting model. I illustrate this procedure by testingthe predictive power of the uncovered set—a solution conceptof the multidimensional spatial voting model—using rollcall data from the U.S. Senate. Since empirical tests of thepredictive power of the uncovered set have been limited to experimentaldata, this is the first empirical test of the concept's predictivepower using real-world data. Author's note: An earlier version of this paper was presentedat the 2006 Annual Meeting of Political Methodology Society.I am grateful to Andrew Martin, Gary Miller, Dan O'Neill, DavidPark, Robert Walker, and three anonymous reviewers for theirhelpful comments. I am especially indebted to Gary Miller forhis insights and advice. All remaining errors are my own.     

The Uncovered Set and the Limits of Legislative Action

Ivan Jeliazkov Department of Economics, University of California, Irvine,Irvine, CA Itai Sened Department of Political Science, Washington University in St. Louis, St. Louis, MO We present a simulation technique for sorting out the size,shape, and location of the uncovered set to estimate the setof enactable outcomes in "real-world" social choice situations,such as the contemporary Congress. The uncovered set is a well-knownbut underexploited solution concept in the literature on spatialvoting games and collective choice mechanisms. We explain thissolution concept in nontechnical terms, submit some theoreticalobservations to improve our theoretical grasp of it, and providea simulation technique that makes it possible to estimate thisset and thus enable a series of tests of its empirical relevance.     

Improving Data Quality: Actors, Incentives, and Capabilities

Devesh Kapur Centre for Advanced Study of India, University of Pennsylvania, 3600 Market Street, Suite 560, Philadelphia, PA 19104 e-mail: dkapur{at}sas.upenn.edu e-mail: herrera{at}fas.harvard.edu (corresponding author) This paper examines the construction and use of data sets inpolitical science. We focus on three interrelated questions:How might we assess data quality? What factors shape data quality?and How can these factors be addressed to improve data quality?We first outline some problems with existing data set quality,including issues of validity, coverage, and accuracy, and wediscuss some ways of identifying problems as well as some consequencesof data quality problems. The core of the paper addresses thesecond question by analyzing the incentives and capabilitiesfacing four key actors in a data supply chain: respondents,data collection agencies (including state bureaucracies andprivate organizations), international organizations, and finally,academic scholars. We conclude by making some suggestions forimproving the use and construction of data sets. Authors' note: For generous comments at many stages in the paper,the authors would like to thank Dawn Brancati, Bear Braumoeller,Kanchan Chandra, Jorge Dominguez, Errol D'Souza, Richard Grossman,Ana Grzymala-Busse, Andrew Kydd, David Laitin, Daniel Posner,Jasjeet Sekhon, Hillel Soifer, Jessica Wallack, and Steven Wilkinsonand the Comparative Politics Research Workshop at Harvard University,and the anonymous reviewers from Political Analysis. The authorstake full responsibility for any errors. An earlier versionof this paper was presented at the American Political ScienceAssociation Annual Meetings, Boston, MA, August 2002.     

Bayesian and Likelihood Inference for 2 x 2 Ecological Tables: An Incomplete-Data Approach

Ying Lu Department of Sociology, University of Colorado at Boulder, Boulder, CO 80309 e-mail: ying.lu{at}colorado.edu Aaron Strauss Department of Politics, Princeton University, Princeton, NJ 08544 e-mail: abstraus{at}princeton.edu e-mail: kimai{at}princeton.edu (corresponding author) Ecological inference is a statistical problem where aggregate-leveldata are used to make inferences about individual-level behavior.In this article, we conduct a theoretical and empirical studyof Bayesian and likelihood inference for 2 x 2 ecological tablesby applying the general statistical framework of incompletedata. We first show that the ecological inference problem canbe decomposed into three factors: distributional effects, whichaddress the possible misspecification of parametric modelingassumptions about the unknown distribution of missing data;contextual effects, which represent the possible correlationbetween missing data and observed variables; and aggregationeffects, which are directly related to the loss of informationcaused by data aggregation. We then examine how these threefactors affect inference and offer new statistical methods toaddress each of them. To deal with distributional effects, wepropose a nonparametric Bayesian model based on a Dirichletprocess prior, which relaxes common parametric assumptions.We also identify the statistical adjustments necessary to accountfor contextual effects. Finally, although little can be doneto cope with aggregation effects, we offer a method to quantifythe magnitude of such effects in order to formally assess itsseverity. We use simulated and real data sets to empiricallyinvestigate the consequences of these three factors and to evaluatethe performance of our proposed methods. C code, along withan easy-to-use R interface, is publicly available for implementingour proposed methods (Imai, Lu, and Strauss, forthcoming). Authors' note: This article is in the part based on two workingpapers by Imai and Lu, "Parametric and Nonparamateric BayesianModels for Ecological Inference in 2 x 2 Tables" and "QuantifyingMissing Information in Ecological Inference." Various versionsof these papers were presented at the 2004 Joint StatisticalMeetings, the Second Cape Cod Monte Carlo Workshop, the 2004Annual Political Methodology Summer Meeting, and the 2005 AnnualMeeting of the American Political Science Association. We thankanonymous referees, Larry Bartels, Wendy Tam Cho, Jianqing Fan,Gary King, Xiao-Li Meng, Kevin Quinn, Phil Shively, David vanDyk, Jon Wakefield, and seminar participants at New York University(the Northeast Political Methodology conference), at PrincetonUniversity (Economics Department and Office of Population Research),and at the University of Virginia (Statistics Department) forhelpful comments.     

A Comment on Diagnostic Tools for Counterfactual Inference

Alexander Michaelides London School of Economics, Department of Economics, Houghton Street, London WC2A 2AE, UK e-mail: a.michaelides{at}lse.ac.uk We evaluate two diagnostic tools used to determine if counterfactualanalysis requires extrapolation. Counterfactuals based on extrapolationare model dependent and might not support empirically validinferences. The diagnostics help researchers identify thosecounterfactual "what if" questions that are empirically plausible.We show, through simple Monte Carlo experiments, that thesediagnostics will often detect extrapolation, suggesting thatthere is a risk of biased counterfactual inference when thereis no such risk of extrapolation bias in the data. This is becausethe diagnostics are affected by what we call the n/k problem:as the number of data points relative to the number of explanatoryvariables decreases, the diagnostics are more likely to detectthe risk of extrapolation bias even when such risk does notexist. We conclude that the diagnostics provide too severe atest for many data sets used in political science. Author's note: We thank Komei Fukuda, Don Green, Alan Gerber,and Jasjeet Sekhon for their generous help, Mike Kane for assistancewith R programming, and five anonymous referees for constructivecomments.     

The Introduction of Voter Registration and Its Effect on Turnout

David M. Konisky Department of Political Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Room E53-386, Cambridge, MA 02139 e-mail: sda{at}mit.edu e-mail: konisky{at}mit.edu (corresponding author) Studies of voter turnout across states find that those withmore facilitative registration laws have higher turnout rates.Eliminating registration barriers altogether is estimated toraise voter participation rates by up to 10%. This article presentspanel estimates of the effects of introducing registration thatexploits changes in registration laws and turnout within states.New York and Ohio imposed registration requirements on all oftheir counties in 1965 and 1977, respectively. We find thatthe introduction of registration to counties that did not previouslyrequire registration decreased participation over the long termby three to five percentage points. Though significant, thisis lower than estimates of the effects of registration fromcross-sectional studies and suggests that expectations aboutthe effects of registration reforms on turnout may be overstated.     

The Search for Comparability: Response to Binder

Lawrence S. Rothenberg Department of Political Science, University of Rochester, Rochester, NY 14627 e-mail: lrot{at}mail.rochester.edu (corresponding author) Binder (n.d., Taking the measure of Congress: Reply to Chiouand Rothenberg. Political Analysis. Forthcoming) highlightsareas of agreement and disagreement with our discussion of preferencemeasurement and legislative gridlock. We now both agree thatW-NOMINATE scores—employed in Binder (1999, The dynamicsof legislative gridlock. American Political Science Review 9:519–33)to measure key independent variables, including bicameral differences—shouldnot be used when examining multichamber legislatures over time.We continue to disagree over whether Common Space scores orBinder's conference vote measure is superior. In this response,we show that, although several of the theoretical and statisticalobjections that Binder (n.d.) raises to our Common Space measuredo not apply, they are all relevant for her conference voteanalog. Additionally, we detail how, despite protests to thecontrary, the conference vote measure is plagued by insufficientdata. Finally, we demonstrate how new efforts to show that Binder's (1999)results continue to hold are not robust.     

A Robust Transformation Procedure for Interpreting Political Text

Georg Vanberg Department of Political Science, University of North Carolina, Chapel Hill, NC 27599-3265 e-mail: gvanberg{at}unc.edu (corresponding author) In a recent article in the American Political Science Review,Laver, Benoit, and Garry (2003, "Extracting policy positionsfrom political texts using words as data," 97:311–331)propose a new method for conducting content analysis. TheirWordscores approach, by automating text-coding procedures, representsan advance in content analysis that will potentially have alarge long-term impact on research across the discipline. Toallow substantive interpretation, the scores produced by theWordscores procedure require transformation. In this note, weaddress several shortcomings in the transformation procedureintroduced in the original program. We demonstrate that theoriginal transformation distorts the metric on which contentscores are placed—hindering the ability of scholars tomake meaningful comparisons across texts—and that it isvery sensitive to the texts that are scored—opening upthe possibility that researchers may generate, inadvertentlyor not, results that depend on the texts they choose to includein their analyses. We propose a transformation procedure thatsolves these problems. Authors' note: We would like to thank Ken Benoit, Michael Laver,three anonymous referees, and the editor for comments on earlierversions of this article.     

Comparing Legislators and Legislatures: The Dynamics of Legislative Gridlock Reconsidered

Lawrence S. Rothenberg Department of Political Science, University of Rochester, Rochester, NY 14627 e-mail: lrot{at}mail.rochester.edu (corresponding author) Although political methodologists are well aware of measurementissues and the problems that can be created, such concerns arenot always front and center when we are doing substantive research.Here, we show how choices in measuring legislative preferenceshave influenced our understanding of what determines legislativeoutputs. Specifically, we replicate and extend Binder's highlyinfluential analysis (Binder, Sarah A. 1999. The dynamics oflegislative gridlock, 1947–96. American Political ScienceReview 93:519–33; see also Binder, Sarah A. 2003. Stalemate:Causes and consequences of legislative gridlock. Washington,DC: Brookings Institution) of legislative gridlock, which emphasizeshow partisan, electoral, and institutional characteristics generatemajor legislative initiatives. Binder purports to show thatexamining the proportion, rather than the absolute number, ofkey policy proposals passed leads to the inference that thesefeatures, rather than divided government, are crucial for explaininggridlock. However, we demonstrate that this finding is underminedby flaws in preference measurement. Binder's results are a functionof using W-NOMINATE scores never designed for comparing Senateto House members or for analyzing multiple Congresses jointly.When preferences are more appropriately measured with commonspace scores (Poole, Keith T. 1998. Recovering a basic spacefrom a set of issue scales. American Journal of Political Science42:964–93), there is no evidence that the factors thatshe highlights matter. Authors' note: Thanks to Sarah Binder and Keith Poole for furnishingdata used in our analysis and to Chris Achen and Kevin Clarkefor advice. All errors remain our own. Online appendix is availableon the Political Analysis Web site.     

A Simple Distribution-Free Test for Nonnested Model Selection

e-mail: kevin.clarke{at}rochester.edu This paper considers a simple distribution-free test for nonnestedmodel selection. The new test is shown to be asymptoticallymore efficient than the well-known Vuong test when the distributionof individual log-likelihood ratios is highly peaked. MonteCarlo results demonstrate that for many applied research situations,this distribution is indeed highly peaked. The simulation furtherdemonstrates that the proposed test has greater power than theVuong test under these conditions. The substantive applicationaddresses the effect of domestic political institutions on foreignpolicy decision making. Do domestic institutions have effectsbecause they hold political leaders accountable, or do theysimply promote political norms that shape elite bargaining behavior?The results indicate that the latter model has greater explanatorypower. Authors' note: This work was supported by National Science FoundationGrant SES-0213771. I thank Paul K. Huth and Todd L. Allee forgraciously sharing their data and code. I also thank Bear Braumoeller,Curtis Signorino, Tasos Kalandrakis, participants in the NorthEastMethodology Program, New York University, 2003, and the reviewersfor their comments. Errors remain my own. Supplementary materialsare available on the Political Analysis Web site.     

Bayesian Approaches for Limited Dependent Variable Change Point Problems

e-mail: spln{at}mail.rochester.edu Limited dependent variable (LDV) data are common in politicalscience, and political methodologists have given much good adviceon dealing with them. We review some methods for LDV "changepoint problems" and demonstrate the use of Bayesian approachesfor count, binary, and duration-type data. Our applicationsare drawn from American politics, Comparative politics, andInternational Political Economy. We discuss the tradeoffs bothphilosophically and computationally. We conclude with possibilitiesfor multiple change point work. Author's note: This paper is a revised version of my "second-yearpaper" presented to the department in September 2005, and Ithank attendees for feedback. For comments on an earlier draft,I am grateful to Kevin Clarke, David Firth, Jeff Gill, KosukeImai, Tasos Kalandrakis, Andrew Martin, Kevin Quinn, Curt Signorino,Randy Stone, and two anonymous referees. Any remaining errorsand omissions remain mine and mine alone.     

Ignoramus, Ignorabimus? On Uncertainty in Ecological Inference

Thomas Gschwend Center for Doctoral Studies in Social and Behavioral Sciences, University of Mannheim, D7, 27, 68131 Mannheim, Germany e-mail: gschwend{at}uni-mannheim.de Ron J. Johnston School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UK e-mail: r.johnston{at}bristol.ac.uk e-mail: elff{at}sowi.uni-mannheim.de (corresponding author) Models of ecological inference (EI) have to rely on crucialassumptions about the individual-level data-generating process,which cannot be tested because of the unavailability of thesedata. However, these assumptions may be violated by the unknowndata and this may lead to serious bias of estimates and predictions.The amount of bias, however, cannot be assessed without informationthat is unavailable in typical applications of EI. We thereforeconstruct a model that at least approximately accounts for theadditional, nonsampling error that may result from possiblebias incurred by an EI procedure, a model that builds on thePrinciple of Maximum Entropy. By means of a systematic simulationexperiment, we examine the performance of prediction intervalsbased on this second-stage Maximum Entropy model. The resultsof this simulation study suggest that these prediction intervalsare at least approximately correct if all possible configurationsof the unknown data are taken into account. Finally, we applyour method to a real-world example, where we actually know thetrue values and are able to assess the performance of our method:the prediction of district-level percentages of split-ticketvoting in the 1996 General Election of New Zealand. It turnsout that in 95.5% of the New Zealand voting districts, the actualpercentage of split-ticket votes lies inside the 95% predictionintervals constructed by our method. Authors' note: We thank three anonymous reviewers for helpfulcomments and suggestions on earlier versions of this paper.An appendix giving some technical background information concerningour proposed method, as well as data, R code, and C code toreplicate analyses presented in this paper are available fromthe Political Analysis Web site. Later versions of the codewill be packaged into an R library and made publicly availableon CRAN (http://cran.r-project.org) and on the correspondingauthor's Web site.     

Parties in Elections, Parties in Government, and Partisan Bias

Adam Meirowitz Department of Politics, Princeton University, Princeton, NJ 08544 e-mail: ameirowi{at}princeton.edu Thomas Romer Department of Politics and Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ 08544 e-mail: romer{at}princeton.edu Political parties are active when citizens choose among candidatesin elections and when winning candidates choose among policyalternatives in government. But the inextricably linked institutions,incentives, and behavior that determine these multistage choicesare substantively complex and analytically unwieldy, particularlyif modeled explicitly and considered in total, from citizenpreferences through government outcomes. To strike a balancebetween complexity and tractability, we modify standard spatialmodels of electoral competition and governmental policy-makingto study how components of partisanship—such as candidateplatform separation in elections, party ID-based voting, nationalpartisan tides, and party-disciplined behavior in the legislature—arerelated to policy outcomes. We define partisan bias as the distancebetween the following two points in a conventional choice space:the ideal point of the median voter in the median legislativedistrict and the policy outcome selected by the elected legislature.The study reveals that none of the party-in-electorate conditionsis capable of producing partisan bias independently. Specifiedcombinations of conditions, however, can significantly increasethe bias and/or the variance of policy outcomes, sometimes insubtle ways.     

Event Dependence and Heterogeneity in Duration Models: The Conditional Frailty Model

Suzanna De Boef and Kyle A. Joyce Department of Political Science, 219 Pond Laboratory, The Pennsylvania State University, University Park, PA 16802 e-mail: sdeboef{at}psu.edu e-mail: kjoyce{at}psu.edu e-mail: jboxstef+{at}osu.edu (corresponding author) We introduce the conditional frailty model, an event historymodel that separates and accounts for both event dependenceand heterogeneity in repeated events processes. Event dependenceand heterogeneity create within-subject correlation in eventtimes thereby violating the assumptions of standard event historymodels. Simulations show the advantage of the conditional frailtymodel. Specifically they demonstrate the model's ability todisentangle the sources of within-subject correlation as wellas the gains in both efficiency and bias of the model when comparedto the widely used alternatives, which often produce conflictingconclusions. Two substantive political science problems illustratethe usefulness and interpretation of the model: state policyadoption and terrorist attacks. Authors' note: Three anonymous reviewers gave valuable advice.Replication materials and an online appendix are available onthe Political Analysis Web site. Any errors are our own responsibility.     

In Defense of Comparative Statics: Specifying Empirical Tests of Models of Strategic Interaction

Christopher Zorn Department of Political Science, University of South Carolina, Columbia, SC 29208 e-mail: zorn{at}sc.edu e-mail: ccarrub{at}emory.edu (corresponding author) Beginning in 1999, Curtis Signorino challenged the use of traditionallogits and probits analysis for testing discrete-choice, strategicmodels. Signorino argues that the complex parametric relationshipsgenerated by even the simplest strategic models can lead towildly inaccurate inferences if one applies these traditionalapproaches. In their stead, Signorino proposes generating stochasticformal models, from which one can directly derive a maximumlikelihood estimator. We propose a simpler, alternative methodologyfor theoretically and empirically accounting for strategic behavior.In particular, we propose carefully and correctly deriving one'scomparative statics from one's formal model, whether it is stochasticor deterministic does not particularly matter, and using standardlogit or probit estimation techniques to test the predictions.We demonstrate that this approach performs almost identicallyto Signorino's more complex suggestion. Authors' note: We would like to thank Randy Calvert, Mark Hallerberg,Andrew Martin, Eric Reinhardt, Chris Stanton, and Craig Voldenfor their valuable feedback on this project. All remaining errorsare our own. Replication materials are available at the PoliticalAnalysis Web site.     

Shirking in the Contemporary Congress: A Reappraisal

Michael H. Crespin Department of Political Science, Michigan State University, 303 S. Kedzie Hall, East Lansing, MI 48824 e-mail: e-mail: crespinm{at}msu.edu Jeffery A. Jenkins Department of Political Science, Northwestern University, 601 University Place, Evanston, IL 60208 e-mail: e-mail: j-jenkins3{at}northwestern.edu Ryan J. Vander Wielen Department of Political Science, Washington University in St. Louis, Campus Box 1027, One Brookings Drive, St. Louis, MO 63130 e-mail: e-mail: rjvander{at}artsci.wustl.edu This paper replicates the findings that appeared in the article"Severing the Electoral Connection: Shirking in the ContemporaryCongress," published in the American Journal of Political Science(44:316–325), in which Lawrence Rothenberg and MitchellSanders incorporated a new research design and, contrary toall previous studies, found evidence of ideological shirkingin the U.S. House of Representatives. We investigate the robustnessof their results by reestimating their model with Congress-specificfixed effects and find that their results no longer hold.     

Spatial Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data

Jude C. Hays Department of Political Science, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL 61801 e-mail: jchays{at}uiuc.edu e-mail: franzese{at}umich.edu (corresponding author) In this paper, we demonstrate the econometric consequences ofdifferent specification and estimation choices in the analysisof spatially interdependent data and show how to calculate andpresent spatial effect estimates substantively. We considerfour common estimators—nonspatial OLS, spatial OLS, spatial2SLS, and spatial ML. We examine analytically the respectiveomitted-variable and simultaneity biases of nonspatial OLS andspatial OLS in the simplest case and then evaluate the performanceof all four estimators in bias, efficiency, and SE accuracyterms under more realistic conditions using Monte Carlo experiments.We provide empirical illustration, showing how to calculateand present spatial effect estimates effectively, using dataon European governments' active labor market expenditures. Ourmain conclusions are that spatial OLS, despite its simultaneity,performs acceptably under low-to-moderate interdependence strengthand reasonable sample dimensions. Spatial 2SLS or spatial MLmay be advised for other conditions, but, unless interdependenceis truly absent or minuscule, any of the spatial estimatorsunambiguously, and often dramatically, dominates on all threecriteria the nonspatial OLS commonly used currently in empiricalwork in political science. Authors' note: This research was supported in part by NationalScience Foundation grant no. 0318045. We thank Chris Achen,Jim Alt, Kenichi Ariga, Neal Beck, Jake Bowers, Kerwin Charles,Bryce Corrigan, Tom Cusack, David Darmofal, Jakob de Haan, JohnDinardo, Zach Elkins, John Freeman, Fabrizio Gilardi, KristianGleditsch, Mark Hallerberg, John Jackson, Aya Kachi, JonathanKatz, Mark Kayser, Achim Kemmerling, Gary King, Hasan Kirmanoglu,James Kuklinski, Tse-Min Lin, Xiaobo Lu, Walter Mebane, CovadongaMeseguer, Michael Peress, Thomas Pluemper, Dennis Quinn, MeganReif, Frances Rosenbluth, Ken Scheve, Phil Schrodt, Beth Simmons,Duane Swank, Wendy Tam Cho, Craig Volden, Michael Ward, andGregory J. Wawro for useful comments on this and/or other workin our broader project on spatial econometric models in politicalscience. Bryce Corrigan, Aya Kachi, and Xiaobo Lu each providedexcellent research assistance and Kristian Gleditsch, Mark Hallerberg,and Duane Swank also generously shared data. We alone are responsiblefor any errors.     

UK OC OK? Interpreting Optimal Classification Scores for the U.K. House of Commons

Iain McLean Nuffield College, Oxford University, Oxford OX1 1NF, United Kingdom. e-mail: iain.mclean{at}nuffield.ox.ac.uk e-mail: spln{at}mail.rochester.edu (corresponding author) Poole's (2000, Non-parametric unfolding of binary choice data.Political Analysis 8:211–37) nonparametric Optimal Classificationprocedure for binary data produces misleading rank orderingswhen applied to the modern House of Commons. With simulationsand qualitative evidence, we show that the problem arises fromthe government-versus-opposition nature of British (Westminster)parliamentary politics and the strategic voting that is entailedtherein. We suggest that political scientists think seriouslyabout strategic voting in legislatures when interpreting resultsfrom such techniques.     

Statistical Backwards Induction: A Simple Method for Estimating Recursive Strategic Models

Curtis S. Signorino 303 Harkness Hall, Department of Political Science, University of Rochester, Rochester, NY 14627 e-mail: curt.signorino{at}rochester.edu Robert W. Walker Department of Political Science, Center for Applied Statistics, Washington University in Saint Louis, Campus Box 1063, One Brookings Drive, St. Louis, MO 63130 e-mail: rww{at}wustl.edu e-mail: mbas{at}gov.harvard.edu (corresponding author) We present a simple method for estimating regressions basedon recursive extensive-form games. Our procedure, which canbe implemented in most standard statistical packages, involvessequentially estimating standard logits (or probits) in a manneranalogous to backwards induction. We demonstrate that the techniqueproduces consistent parameter estimates and show how to calculateconsistent standard errors. To illustrate the method, we replicateLeblang's (2003) study of speculative attacks by financial marketsand government responses to these attacks. Authors' note: Our thanks to Kevin Clarke, John Londregan, JeffRitter, Ahmer Tarar, and Kuzey Yilmaz for helpful discussionsconcerning this paper. A previous version was presented at the2002 Political Methodology Summer Meeting.