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.     

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 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.     

Political will and government anti‐corruption efforts: What does the evidence say?

“Political will” is oft‐cited as the major obstacle to government's anti‐corruption efforts. Notwithstanding, there is remarkably little systematic analysis of the concept, with some scholars describing it as the “slipperiest concept in the policy lexicon,” whereas others are calling for its empirical relevance. This paper tries to unpack the “black box” of political will by making it an empirically relevant concept drawing on evidence from two Asian countries; Singapore and Bangladesh. Four key indicators based on the works of earlier scholars are used including origin of the initiative; comprehension and extent of analysis; credible sanctions; and resource dedication and sustenance are used. The paper also uses Transparency International's Corruption Perception Index, World Bank's World Governance Indicators (Control of Corruption and Government Effectiveness), and Political, Economic and Risk Consultancy's annual survey in Asia, as outcome measures. Based on the empirical evidence from the two countries, the paper shows that political will indeed has a positive influence on government's anti‐corruption efforts. Although political will may not be sufficient, it is a necessary condition to fight corruption, and that the difference between the positions of Singapore and Bangladesh on various global corruption league tables may be attributed to political will.     

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 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.     

Network Analysis and the Law: Measuring the Legal Importance of Precedents at the U.S. Supreme Court

Timothy R. Johnson Department of Political Science, University of Minnesota, Twin Cities, 1414 Social Sciences Building, 267 19th Ave. South, Minneapolis, MN 55455 e-mail: trj{at}umn.edu James F. Spriggs, II Department of Political Science, Washington University in St. Louis, Campus Box 1063, One Brookings Drive, St Louis, MO 63130 e-mail: jspriggs{at}artsci.wustl.edu Sangick Jeon Department of Political Science, Stanford University, 616 Serra Street, Encina Hall West, Room 100, Stanford, CA 94305-6044 e-mail: sjeon{at}stanford.edu Paul J. Wahlbeck Department of Political Science, George Washington University, 1922 F Street, N.W. Suite 401, Washington, DC 20052 e-mail: wahlbeck{at}gwu.edu e-mail: jhfowler{at}ucsd.edu (corresponding author) We construct the complete network of 26,681 majority opinionswritten by the U.S. Supreme Court and the cases that cite themfrom 1791 to 2005. We describe a method for using the patternsin citations within and across cases to create importance scoresthat identify the most legally relevant precedents in the networkof Supreme Court law at any given point in time. Our measuresare superior to existing network-based alternatives and, forexample, offer information regarding case importance not evidentin simple citation counts. We also demonstrate the validityof our measures by showing that they are strongly correlatedwith the future citation behavior of state courts, the U.S.Courts of Appeals, and the U.S. Supreme Court. In so doing,we show that network analysis is a viable way of measuring howcentral a case is to law at the Court and suggest that it canbe used to measure other legal concepts. Authors' note: We appreciate the suggestions of Randy Calvert,Frank Cross, Pauline Kim, Andrew Martin, Richard Pacelle, JimRogers, Margo Schlanger, Amy Steigerwalt, and participants inthe Workshop on Empirical Research in the Law at WashingtonUniversity in St Louis School of Law. We presented former versionsof this article at the 2006 meeting of the Midwest PoliticalScience Association, Chicago, April 20–23; the 2006 meetingof the Southern Political Science Association, Atlanta, GA,January 5–7; and the 2006 Empirical Legal Studies Conference,Austin, TX, October 27–28.     

Reinventing the Hong Kong state or rediscovering it? From low interventionism to eclectic corporatism

Abstract

The low-intervention state of Hong Kong in colonial times did not originate from strong neo-liberal ideological convictions. It was an artefact of the colonial political configuration, a pragmatic governing strategy adapting to the political and economic needs at the time. Political changes, economic restructuring, divestiture and marketization of state institutions since the 1980s had brought a new state form after 1997. A new business and professional elite class, embedded in an eclectic corporatist structure, evolved and brought multilateral, ad hoc and particularistic bargaining, leading to more sectoral intervention after 1997. Fragmented state institutions nonetheless weakened state capacity, making it difficult for the post-1997 state to be highly penetrative, transformative or developmental.     

Time Series Intervals and Statistical Inference: The Effects of Temporal Aggregation on Event Data Analysis

While many areas of research in political science draw inferencesfrom temporally aggregated data, rarely have researchers exploredhow temporal aggregation biases parameter estimates. With somenotable exceptions (Freeman 1989, Political Analysis 1:61–98;Alt et al. 2001, Political Analysis 9:21–44; Thomas 2002,"Event Data Analysis and Threats from Temporal Aggregation")political science studies largely ignore how temporal aggregationaffects our inferences. This article expands upon others' workon this issue by assessing the effect of temporal aggregationdecisions on vector autoregressive (VAR) parameter estimates,significance levels, Granger causality tests, and impulse responsefunctions. While the study is relevant to all fields in politicalscience, the results directly apply to event data studies ofconflict and cooperation. The findings imply that politicalscientists should be wary of the impact that temporal aggregationhas on statistical inference.     

A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data

Joseph Bafumi Department of Government, Dartmouth College,6108 Silsby HallHanover, NH 03755 e-mail: joseph.bafumi{at}dartmouth.edu Luke Keele Department of Political Science, Ohio State University,2137 Derby Hall, 154 N Oval Mall, Columbus, OH 43210 e-mail: keele.4{at}polisci.osu.edu David Park Department of Political Science, George Washington University,1922 F Street, N.W. 414C, Washington, DC 20052 e-mail: dkp{at}gwu.edu e-mail: bshor{at}uchicago.edu (corresponding author) The analysis of time-series cross-sectional (TSCS) data hasbecome increasingly popular in political science. Meanwhile,political scientists are also becoming more interested in theuse of multilevel models (MLM). However, little work existsto understand the benefits of multilevel modeling when appliedto TSCS data. We employ Monte Carlo simulations to benchmarkthe performance of a Bayesian multilevel model for TSCS data.We find that the MLM performs as well or better than other commonestimators for such data. Most importantly, the MLM is moregeneral and offers researchers additional advantages. Authors' note: A previous version of this article was presentedat the 2005 Midwest Political Science Meeting. We would liketo thank the following for comments and advice in writing thispaper: Andrew Gelman, Nathaniel Beck, Greg Wawro, Sam Cooke,John Londregan, David Brandt. Any errors are our own.     

Inside the Presidential Communications Machine: An Interview with Martha Joynt Kumar

Martha Joynt Kumar is a professor of political science at Towson University. Her book, Managing the President’s Message: The White House Communications Operation (Johns Hopkins University Press, 2007), examines the media and communications operations of the administrations of Presidents Bill Clinton and George W. Bush. In March 2008, Professor Kumar was interviewed in the White House Press Briefing Room by Kevin R. Kosar on behalf of Public Administration Review. As this interview went to press, the American Political Science Association’s Presidency Research Group announced that it had awarded Managing the President’s Message the Richard E. Neustadt Award for best book on the U.S. presidency published in 2007.     

The Role of Marketing in Politics

ABSTRACT

Political marketing is an exciting new area. Research produced over the last decade has been pioneering in showing the applicability of marketing to politics. However, this article argues that the field now needs to move in a different direction if we are to reach political marketing's full potential. Political marketing needs a comprehensive approach: it can be applied not just to party-electoral behaviour but also legislatures, local government, the media, and public services, with both concepts and techniques from marketing, and an understanding from political science literature as well as management studies. The article, therefore, maps out the route to be taken to reach the end of the rainbow and the pot of gold that the political marketing field potentially offers.     

Much Ado About Very Little: Reply to Herron

Mitchell S. Sanders Department of Political Science, University of Notre Dame, Notre Dame, IN 46556 e-mail: msander1{at}nd.edu While Herron (2004, Political Analysis 12:182–190) iscorrect that sensitivity to changes in underlying scale andhow they affect estimates and inferences is generally important,our assumption in Rothenberg and Sanders (2000, American Journalof Political Science 44:310–319) that W-NOMINATE scalescan be directly compared from one Congress to another to studylegislative shirking is quite defensible because scale variabilityis not a substantial problem. Not only are the assumptions inour original analysis regarding variability very reasonable,because any variability is quite small, but effects on consistencyare marginal and, to the degree that they are relevant, indicatethat our test of the shirking hypothesis is conservative. Furthermore,even generous estimates of variability in W-NOMINATE betweenone immediate Congress and another have little impact on results.In addition, Herron's analysis includes an unaddressed censoringproblem that again, while unlikely to have much substantiverelevance, indicates that Rothenberg and Sanders have workedagainst themselves in trying to find shirking. In conclusion,the issues that Herron highlights are of marginal consequencefor the original analysis and, to the extent they matter, onlybuttress the findings generated and the inferences drawn.     

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 Linear Poisson Autoregressive Model: The Poisson AR(p) Model

Time series of event counts are common in political scienceand other social science applications. Presently, there arefew satisfactory methods for identifying the dynamics in suchdata and accounting for the dynamic processes in event countsregression. We address this issue by building on earlier workfor persistent event counts in the Poisson exponentially weightedmoving-average model (PEWMA) of Brandt et al. (American Journalof Political Science 44(4):823–843, 2000). We developan alternative model for stationary mean reverting data, thePoisson autoregressive model of order p, or PAR(p) model. Issuesof identification and model selection are also considered. Wethen evaluate the properties of this model and present bothMonte Carlo evidence and applications to illustrate.     

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.     

Ethics by Design: The Impact of Form of Government on Municipal Corruption

While trust in government at all levels is at an all‐time low, actual corruption at the municipal level has been declining. One factor often credited with this decline is the introduction of the council‐manager form of government. One of the key reasons the council‐manager form was created in the early 1900s was to act as an antidote to the corruption prevalent in the big‐city machine politics of the era. Despite this, no one has tested whether the council‐manager form has in fact influenced the decline in corruption rates. This article uses a rare events logit model to analyze corruption convictions in municipalities between 1990 and 2010 to determine which factors, including form of government, affect the probability that a corrupt act will occur. The findings indicate that municipalities with the council‐manager form are 57 percent less likely to have corruption convictions than municipalities with the mayor‐council form.     

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.     

Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls

Andrew Gelman Departments of Statistics and Political Science, Columbia University, New York, NY 10027 e-mail: gelman{at}stat.columbia.edu Joseph Bafumi Department of Political Science, Columbia University, New York, NY 10027 We fit a multilevel logistic regression model for the mean ofa binary response variable conditional on poststratificationcells. This approach combines the modeling approach often usedin small-area estimation with the population information usedin poststratification (see Gelman and Little 1997, Survey Methodology23:127–135). To validate the method, we apply it to U.S.preelection polls for 1988 and 1992, poststratified by state,region, and the usual demographic variables. We evaluate themodel by comparing it to state-level election outcomes. Themultilevel model outperforms more commonly used models in politicalscience. We envision the most important usage of this methodto be not forecasting elections but estimating public opinionon a variety of issues at the state level.     

The Persistence of Corruption and Regulatory Compliance Failures: Theory and Evidence

This paper examines the reasons whycorruption and policy distortions tend toexhibit a high degree of persistence incertain regimes. We identify circumstancesunder which a firm seeks to evaderegulations through (i) bribery of localinspectors, and (ii) by lobbying high-levelgovernment politicians to resist legalreforms designed to improve judicialefficiency (rule of law) and eliminatecorruption. We show that in some casespolitical instability reinforces thesetendencies. The analysis predicts that inpolitically unstable regimes, theinstitutions necessary to monitor andenforce compliance are weak. In suchcountries, corruption therefore is morepervasive, and the compliance withregulations is low. We test thesepredictions using cross-country data. Theempirical results support the predictionsof the model. Political instability reducesjudicial efficiency, which in turnstimulates corruption. Thus, the effect ofpolitical instability on corruption is notdirect, but occurs indirectly via itseffect on the degree of judicialefficiency. Finally, corruption lowers thelevel of regulatory compliance. Thus,political instability indirectly affects compliance, via judicial efficiency andcorruption.