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Partisanship, Political Knowledge, and Changing Economic Conditions Lawrence, Christopher Uploaded 05-18-2012 Keywords political knowledge party identification hierarchical modeling economic voting public opinion political sophistication ANES 2008-09 Panel Abstract Existing research is replete with evidence that individuals’ perceptions of the state of the economy are seemingly only loosely connected to more objective evaluations of its state and are contaminated by partisan influences. This paper provides further evidence of why these partisan influences come about, by advancing the hypothesis that citizen political knowledge moderates the effect of partisanship on economic evaluations, grounded in Zaller’s Receive-Accept-Sample model of opinion formation and articulation. The paper also advances the hypothesis that more knowledgeable partisans will respond to changes in elite messaging regarding the economy fairly rapidly after a change in control of the government. I examine these propositions using data from the ANES panel study of public opinion between January 2008 and June 2010, and find evidence affirming the essential interactive role of knowledge and partisanship in the formation and articulation of evaluations of the national economy.

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Estimating Average Causal Effects Under General Interference Aronow, Peter Samii, Cyrus Uploaded 07-16-2012 Keywords interference SUTVA randomized experiments causal inference Abstract This paper presents randomization-based methods for estimating average causal effects under arbitrary interference of known form. We present conservative estimators of the randomization variance of the average treatment effects estimators and a justification for confidence intervals based on a normal approximation. Examples relevant to research in environmental protection, networks experiments, "viral marketing," two-stage disease prophylaxis trials, and stepped-wedge designs are presented.

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Computerized Adaptive Testing for Public Opinion Surveys Montgomery, Jacob Cutler, Josh Uploaded 06-19-2012 Keywords surveys item response CAT dynamic surveys CAT Abstract Survey researchers avoid using large multi-item scales to measure latent traits due to both the financial costs and the risk of driving up non-response rates. Typically, investigators select a subset of available scale items rather than asking the full battery. Reduced batteries, however, can sharply reduce measurement precision and introduce bias. In this paper, we present computerized adaptive testing (CAT) as a method for minimizing the number of questions each respondent must answer while preserving measurement accuracy and precision. CAT algorithms respond to individuals' previous answers to select subsequent questions that most efficiently reveal respondents' position on a latent dimension. We introduce the basic stages of a CAT algorithm and present the details for one approach to item-selection appropriate for public opinion research. We then demonstrate the advantages of CAT via simulation and by empirically comparing dynamic and static measures of political knowledge.

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Using Regression Discontinuity to Uncover the Personal Incumbency Advantage Erikson, Robert S. Titiunik, Rocio Uploaded 07-17-2012 Keywords regression discontinuity incumbency advantage Abstract We study the conditions under which estimating the incumbency advantage using a regression discontinuity (RD) design recovers the personal incumbency advantage in a two-party system. Lee (2008) introduced RD as a method for estimating the party incumbency advantage. We develop a simple model that expands the interpretation of the RD design and leads to unbiased estimates of the personal incumbency advantage. Our model yields the surprising result that the RD design double counts the personal incumbency advantage. We estimate the incumbency advantage using our model with data from U.S. House elections between 1968 and 2008. We also explore the estimation of the incumbency advantage beyond the limited RD conditions where knife-edge electoral shifts create the leverage for causal inference.

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Computerized Adaptive Testing for Public Opinion Surveys Montgomery, Jacob Cutler, Josh Uploaded 06-19-2012 Keywords surveys item response CAT dynamic surveys Abstract Survey researchers avoid using large multi-item scales to measure latent traits due to both the financial costs and the risk of driving up non-response rates. Typically, investigators select a subset of available scale items rather than asking the full battery. Reduced batteries, however, can sharply reduce measurement precision and introduce bias. In this paper, we present computerized adaptive testing (CAT) as a method for minimizing the number of questions each respondent must answer while preserving measurement accuracy and precision. CAT algorithms respond to individuals' previous answers to select subsequent questions that most efficiently reveal respondents' position on a latent dimension. We introduce the basic stages of a CAT algorithm and present the details for one approach to item-selection appropriate for public opinion research. We then demonstrate the advantages of CAT via simulation and by empirically comparing dynamic and static measures of political knowledge.

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An Alternative Solution to the Heckman Selection Problem: Selection Bias as Functional Form Misspecification Kenkel, Brenton Signorino, Curtis Uploaded 07-18-2012 Keywords selection models functional form misspecification nonparametric models polynomial regression Abstract The "selection problem" is typically seen as a form of omitted variable bias. We recast the problem as one of functional form misspecification and examine two situations in which flexible or nonparametric estimation techniques may be used as a complement or alternative to traditional selection models. First, we show that such techniques can allow a researcher to recover the conditional relationship between covariates and the expected outcome, even if data on the probability of selection into the subsample is unavailable. We demonstrate the validity of this approach analytically and using Monte Carlo simulations. Second, we show that flexible methods can be used to validate or improve a linear selection model specification when a researcher does possess the prior-stage data. We illustrate this process with an application to data from Mroz (1987) on women's wages.

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Explaining Fixed Effects: Random Effects modelling of Time-Series Cross-Sectional and Panel Data Bell, Andrew Jones, Kelvyn Uploaded 07-13-2012 Keywords Random Effects models Fixed Effects models Random coefficient models Mundlak formulation Fixed effects vector decomposition Hausman test Endogeneity Panel Data Time-Series Cross-Sectional Data Abstract This article challenges Fixed Effects (FE) modelling’s status as the ‘default option’ when using time-series-cross-sectional and panel data. We argue that understanding the difference between within- and between-effects of predictor variables is important when considering what modelling strategy to use. The downside of Random Effects (RE) compared to FE modelling – correlation between lower-level covariates and higher-level residuals - is a case of omitted variable bias, readily solvable using a variant of Mundlak’s (1978a) formulation. Consequently, RE modelling provides everything that FE modelling promises, and more. It allows time-invariant variables to be modelled, more parsimoniously than Plümper and Troeger’s (2007) suggested method. It is also readily extendable to Random Coefficients Models, allowing reliable, differential effects of variables without heavy parameterisation, the use of cross-level interactions between time-variant and invariant variables, and the modelling of complex variance functions. We are arguing not simply for technical solutions to endogeneity, but for the substantive importance of modelling context, and RE models’ ability to do so. Two empirical examples show that failing to do this can lead to misleading results. This paper is distinctive in stressing the substantive interpretations of within- and between-effects. This has implications beyond political science, to all datasets with multilevel structures.

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On the Use of Linear Fixed Eects Regression Models for Causal Inference Imai, Kosuke Kim, In Song Uploaded 07-23-2012 Keywords difference-in-differences first difference matching observational data panel data propensity score randomized experiments stratification Abstract Linear fixed effects regression models are a primary workhorse for causal inference among applied researchers. And yet, it has been shown that even when the treatment is exogenous within each unit, the linear regression models with unit-specific fixed effects may not consistently estimate the average treatment effect. In this paper, we offer a simple solution. Specifically, we show that weighted linear fixed effects regression models can accomodate a number of identification strategies including matching, stratification, first difference, propensity score weighting, and difference-in-differences. We prove the results by establishing finite sample equivalence relationships between weighted fixed effects and these estimators. Our analysis identifies the information implicitly used by standard fixed effects models to estimate counterfactual outcomes necessary for causal inference, highlighting the potential sources of their bias and inefficiency. In addition, we develop efficient computation strategies, model-based standard errors, and a specification test for weighted fixed effects estimators. Finally, we illustrate the proposed methodology by revisiting the controversy concerning the effects of the General Agreement on Tariffs and Trade (GATT) membership on international trade. Open-source software is available for fitting the proposed weighted linear fixed effects estimators.

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Validation: What Big Data Reveal About Survey Misreporting and the Real Electorate Hersh, Eitan Ansolabehere, Stephen Uploaded 07-13-2012 Keywords validation misreporting Catalist election administration turnout registration Abstract Social scientists rely on surveys to explain political behavior. From consistent over- reporting of voter turnout, it is evident that responses on survey items may be unreliable and lead scholars to incorrectly estimate the correlates of participation. Leveraging developments in technology and improvements in public records, we conduct the first ever fifty-state vote validation. We parse over-reporting due to response bias from over- reporting due to inaccurate respondents. We find that non-voters who are politically engaged and equipped with politically relevant resources consistently misreport that they voted. This finding cannot be explained by faulty registration records, which we measure with new indicators of election administration quality. Respondents are found to misreport only on survey items associated with socially desirable outcomes, which we find by validating items beyond voting, like race and party. We show that studies of representation and participation based on survey reports dramatically mis-estimate the differences between voters and non-voters.

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Conservative Vote Probabilities: An Easier Method for the Analysis of Roll Call Data Fowler, Anthony Hall, Andrew B. Uploaded 08-08-2012 Keywords Roll Call Ideology Congress Supreme Court State Legislatures Non-parametric Abstract We propose a new roll-call scaling method based on OLS which is easier to implement and understand than previous methods and also produces directly interpretable estimates. This measure, Conservative Vote Probability (CVP), indicates the probability that an individual legislator votes "conservatively" relative to the median legislator. CVP is a flexible non-parametric statistical technique that requires no complicated assumptions but still produces legislator scalings that correlate with previous roll call methods at extremely high levels. In this paper we introduce the methodology behind CVP and offer several substantive examples to demonstrate its e efficacy as an easier, more accessible alternative to previous roll call methods.

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Enhancing a Geographic Regression Discontinuity Design Through Matching to Estimate the Effect of Ballot Initiatives on Voter Turnout Keele, Luke Titiunik, Rocio Zubizarreta, Jose Uploaded 07-13-2012 Keywords matching causal inference geopgraphy regression discontinuity Abstract Of late there has been a renewed interest in natural experiments as a method for drawing causal inferences from observational data. One form of natural experiment exploits variation in geography where units in one geographic area receive a treatment while units in another area do not. In this kind of geographic natural experiment, the hope is that assignment to treatment via geographic location creates as-if random variation in treatment assignment. When this happens, adjustment for baseline covariates is unnecessary. In many applications, however, some adjustment for baseline covariates may be necessary due to strategic sorting around the border between treatment and control areas. As such, analysts may wish to combine identification strategies--using both spatial proximity and covariates--for more plausible inferences. Here we explore how to utilize spatial proximity as well as covariates in the analysis of geographic natural experiments. We contend that standard statistical tools are ill-equipped to exploit covariates as well as variation in treatment assignment that is a function of spatial proximity. We use a mixed integer programming matching algorithm to flexibly incorporate information about both the discontinuity and observed covariates which allows us to minimize spatial distance while preserving balance on observed covariates. We argue the combining both information about covariates and the discontinuity creates a method of estimation that can be informally thought of as doubly robust. We demonstrate the method with data on ballot initiatives and turnout in Milwaukee, WI.

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The Effects of Federal and State Audits on Municipal Accountability Systems: A Randomized Controlled Trial De La O, Ana Martel Garcia, Fernando Uploaded 08-09-2012 Keywords protocol randomized controlled trial audit accountability public goods governance Abstract Improving accountability in public service provision is one of the most pressing challenges that young democracies face. This research project contributes to an emerging body of literature that examines the role of accountability agencies. Specifically, we provide new evidence about the importance of superior audit institutions, a type of accountability agency. Our evidence is based on a field experiment we conducted in Mexico from March 2011 to July 2012. As part of the national audits program, we randomly assigned municipalities to be audited by federal auditors, by state auditors, and a control group. We estimate the effects of federal and state audits on a range of outcomes including municipal authorities' priorities for public spending, knowledge acquisition, perceptions of their own capacity, and compliance with program rules; we also probe directly perceptions about audit probabilities; and, we estimate the effects of audits on quarterly expenditure data. Finally, we explore how audits interact with institutional and political factors such as the incentives created by one-term limits, the careers of public officials (elected versus non-elected posts) and political clientelism.

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How Robust Standard Errors Expose Methodological Problems They Do Not Fix King, Gary Roberts, Margaret Uploaded 07-13-2012 Keywords robust standard errors clustered standard errors heteroskedasticity-consistent standard errors Abstract "Robust standard errors'' are used in a vast array of scholarship across all fields of empirical political science and most other social science disciplines. The popularity of this procedure stems from the fact that estimators of certain quantities in some models can be consistently estimated even under particular types of misspecification; and although classical standard errors are inconsistent in these situations, robust standard errors can sometimes be consistent. However, in applications where misspecification is bad enough to make classical and robust standard errors diverge, assuming that misspecification is nevertheless not so bad as to bias everything else requires considerable optimism. And even if the optimism is warranted, we show that settling for a misspecified model (even with robust standard errors) can be a big mistake, in that all but a few quantities of interest will be impossible to estimate (or simulate) from the model without bias. We suggest a different practice: Recognize that differences between robust and classical standard errors are like canaries in the coal mine, providing clear indications that your model is misspecified and your inferences are likely biased. At that point, it is often straightforward to use some of the numerous and venerable model checking diagnostics to locate the source of the problem, and then modern approaches to choosing a better model. With a variety of real examples, we demonstrate that following these procedures can drastically reduce biases, improve statistical inferences, and change substantive conclusions.

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Using Qualitative Information to Improve Causal Inference Glynn, Adam Ichino, Nahomi Uploaded 09-23-2012 Keywords case study causal inference qualitative mixed methods sensitivity analysis process-tracing observational study Abstract We demonstrate four techniques that utilize case studies to improve causal inference within the Rosenbaum [2002, 2009] approach to observational studies. This approach accommodates small to medium sample sizes in a nonparametric framework and does not require the elicitation of Bayesian priors. First, we show that this approach allows case studies to ameliorate the effects of poorly measured outcomes, sometimes reducing p-values. Second, we show that qualitative information can be incorporated in an analysis and presented as qualitative confidence intervals. Third, we demonstrate that a standard technique of comparative case studies can improve sensitivity analysis within this framework, sometimes reducing the sensitivity of p-values to unmeasured confounders. Finally, we demonstrate that qualitative information on the heterogeneity of treatments can be used to check the robustness of p-values. We illustrate these methods by examining the effect of not having a runoff provision on opposition harassment in transitional presidential elections in 1990s sub-Saharan Africa.

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Covariate Balancing Propensity Score Imai, Kosuke Ratkovic, Marc Uploaded 07-13-2012 Keywords causal inference instrumental variables inverse propensity score weighting marginal structural models observational studies propensity score matching randomized experiments Abstract The propensity score plays a central role in a variety of settings for causal inference. In particular, matching and weighting methods based on the estimated propensity score have become increasingly common in observational studies. Despite their popularity and theoretical appeal, the main practical difficulty of these methods is that the propensity score must be estimated. Researchers have found that slight misspecification of the propensity score model can result in substantial bias of estimated treatment effects. In this paper, we introduce covariate balancing propensity score (CBPS) estimation, which simultaneously optimizes the covariate balance and the prediction of treatment assignment. We exploit the dual characteristics of the propensity score as a covariate balancing score and the conditional probability of treatment assignment and estimate the CBPS within the generalized method of moments or empirical likelihood framework. We find that the CBPS dramatically improves the poor empirical performance of propensity score matching and weighting methods reported in the literature. We also show that the CBPS can be extended to a number of other important settings, including the estimation of generalized propensity score for non-binary treatments, causal inference in longitudinal settings, and the generalization of experimental and instrumental variable estimates to a target population.

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The 2011 Debt Ceiling Crisis and the 2012 House Elections: A Research Design Monogan, Jamie Uploaded 11-06-2012 Keywords registration causal inference coarsened exact matching congressional elections Abstract On August 1, 2011, the House of Representatives voted to raise the federal debt ceiling as well as to make cuts in discretionary spending. Although this vote allowed the federal government to avoid default, raising the debt ceiling was unpopular with the public and the vote cut across party lines. This paper proposes a research design for evaluating the effect of a House member's vote on the debt ceiling on two outcomes: the member's ability to retain his or her seat through the 2012 general election, and the incumbent's share of the two-party vote for members who face a general election competitor.

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A Mixed-Membership Approach to the Assessment of Political Ideology from Survey Responses Gross, Justin Manrique-Vallier, Daniel Uploaded 07-13-2012 Keywords latent structure model grade-of-membership mixed-membership latent variables measurement ideology beliefs core values attitudes discrete factor analysis survey response Abstract We employ mixed-membership (or grade-of-membership) techniques--of growing popularity in medical diagnostics, psychology, genetics, and machine learning--in order to identify prototypical profiles of survey respondents based on their answers to questions aimed at uncovering their basic orientations or ideological predispositions. In contrast with factor analytic techniques and IRT approaches, we treat both manifest and latent variables as categorical. A mixed membership model may be thought of as a generalization of latent class modeling, in which individuals act as members of more than one class. This notion is well-aligned with earlier theoretical work of Zaller, Feldman, Stimson, and others, who at times envision respondents to be internally complex, answering survey questions probabilistically according to what Zaller calls varying ``considerations.'' Reanalyzing data in this way, we develop new insights into the sorts of constraints that may structure mass belief systems.

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Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments Hainmueller, Jens Hopkins, Daniel Yamamoto, Teppei Uploaded 12-12-2012 Keywords potential outcomes average marginal component effects fractional factorial design orthogonal design randomized design survey experiments public opinion vote choice immigration Abstract For decades, market researchers have used conjoint analysis to understand how consumers make decisions when faced with multi-dimensional choices. In such analyses, respondents are asked to score or rank a set of alternatives, where each alternative is defined by multiple attributes which are varied randomly or intentionally. Political scientists are frequently interested in parallel questions about decision-making, yet to date conjoint analysis has seen little use within the field. In this manuscript, we demonstrate the potential value of conjoint analysis in political science, using examples about vote choice and immigrant admission to the United States. In doing so, we develop a set of statistical tools for drawing causal conclusions from stated preference data based on the potential outcomes framework of causal inference. We discuss the causal estimands of interest and provide a formal analysis of the assumptions required for identifying those quantities. Prior conjoint analyses have typically used designs which limit the number of unique conjoint profiles. We employ a survey experiment to compare this approach to a fully randomized approach. Both our formal analysis of the causal estimands and our empirical results highlight the potential biases of common approaches to conjoint analysis which restrict the number of profiles.

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Testing Interaction Hypotheses: Determining and Controlling the False Positive Rate Esarey, Justin Lawrence, Jane Uploaded 07-13-2012 Keywords interaction hypothesis testing significance EITM Abstract When a researcher suspects that the marginal effect of x on y varies with z, the usual approach is to plot dy/dx at different values of z (along with a confidence interval) in order to assess its magnitude and statistical significance. In this paper, we demonstrate that this approach results in inconsistent false positive (Type I error) rates that can be many times larger or smaller than advertised. Condtioning inference on the statistical significance of the interaction term does not solve this problem. However, we demonstrate that the problem can be avoided by exercising qualitative caution in the interpretation of marginal effects and via simple adjustments to exisiting test procedures.

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Reasoning about Interference Between Units} Bowers, Jake Fredrickson, Mark Panagopoulos, Costas Uploaded 07-13-2012 Keywords interference randomization inference SUTVA randomized experiments Fisher's sharp null hypothesis causal inference Abstract If an experimental treatment is experienced by both treated and control group units, tests of hypotheses about causal effects may be difficult to conceptualize let alone execute. In this paper, we show how counterfactual causal models may be written and tested when theories suggest spillover or other network-based interference among experimental units. We show that the ``no interference'' assumption need not constrain scholars who have interesting questions about interference. We offer researchers the ability to model theories about how treatment given to some units may come to influence outcomes for other units. We further show how to test hypotheses about these causal effects, and we provide tools to enable researchers to assess the operating characteristics of their tests given their own models, designs, test statistics, and data. The conceptual and methodological framework we develop here is particularly applicable to social networks, but may be usefully deployed whenever a researcher wonders about interference between units. Interference between units need not be an untestable assumption; instead, interference is an opportunity to ask meaningful questions about theoretically interesting phenomena.

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The Hidden American Immigration Consensus: A Conjoint Analysis of Attitudes Toward Immigrants Hainmueller, Jens Hopkins, Daniel Uploaded 07-14-2012 Keywords immigration public opinion conjoint analysis Abstract With immigration a salient issue, it is critical to understand Americans' attitudes toward immigrants. Past research points to several immigrant characteristics, both cultural and economic, that might influence attitudes. Yet it has not tested the competing hypotheses comprehensively. This paper uses a statistical tool from marketing---choice-based conjoint analysis---to test the relative influence of nine randomized immigrant attributes in generating support for admission. Drawing on a two-wave Knowledge Networks survey, it demonstrates that Americans view educated immigrants in high-status jobs favorably, while they view those who lack plans to work, have previously entered without authorization, or do not speak English unfavorably. Consistent with norms-based and sociotropic explanations, the immigrants most likely to be admitted are those expected to contribute economically and to comply with norms about work and assimilation. Remarkably, these preferences vary little with respondents' education, partisanship, or other attributes. Beneath partisan divisions over immigration lies a consensus about which immigrants to admit.

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Multiparty Government, Fiscal Institutions, and Public Spending Martin, Lanny Vanberg, Georg Uploaded 03-12-2012 Keywords public spending coalitions Europe fiscal crisis institutions Abstract In the wake of the 2008 global financial crisis, the size of the public sector has been a central, and often controversial, item on the political agenda, as governments from Europe to the United States have embarked on new campaigns to reduce public spending. Previous research on the political factors underlying public spending has naturally focused on the characteristics of the governments that make budgetary decisions. Most recently, scholars have argued, and shown empirically, that spending tends to be larger when cabinets are composed of multiple political parties, and larger still when those coalitions include more members. The key theoretical insight is that spending constitutes a ``common pool resource" problem, which is more difficult to solve for multiparty governments than for single-party administrations because doing so requires the cooperation of actors who are electorally accountable to separate constituencies. In this study, drawing on recent research on the impact of institutions on coalition policymaking, we challenge the prevailing wisdom in this area. Specifically, we argue that rules that reduce the influence of individual government parties in budget formulation, and increase their incentives to oppose the spending demands of their partners, significantly mitigate the common pool resource problem and thus reduce the expansionary effect of coalition governance on spending. Our empirical analysis of public spending in fifteen European democracies over a thirty-five year period supports our argument. Our findings demonstrate that in certain institutional environments, multiparty governments will spend no more than their single-party counterparts. Our conclusions also offer hope that appropriate institutional reforms may be part of a political solution to the financial woes currently confronting multiparty governments across Europe.

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Who's a Directional Voter and Who's a Proximity Voter? An Application of Finite Mixture Modeling to Issue Voting in the 2008 American Presidential Election Kropko, Jonathan Uploaded 07-15-2012 Keywords issue voting directional proximity ANES finite mixture modeling multiple imputation Abstract This project aims to use new methodology to help settle a longstanding debate in American politics: whether proximity or directional distance is more appropriate for voting models in Presidential elections; whether the two distances are better fits for different subsets of the electorate; and if so, what are the characteristics of the voters for whom each distance fits best? Unlike previous attempts to judge between the directional and proximity models, which have used summary statistics generated at the level of the whole sample to make inferences, this study compares the fit of the models for each individual observation. A finite mixture model, as recently described by Imai and Tingley (2012), estimates the probability that each observation could have been generated by each competing model. These probabilities can then be modeled using other covariates. Using the 2008 American National Election Study, I estimate the probability that each voter is using each kind of issue distance, and I test the hypothesis that voters with higher levels of political sophistication are more likely to evaluate candidates using a proximity model, and voters with lower levels of sophistication are more likely to evaluate candidates using a directional model. While strong evidence suggests that some voters are directional and some are proximity, no evidence is found that suggests sophistication influences the probability that each voter is directional or proximity. In addition, like previous studies, the relative strength of the directional and proximity models is found to depend crucially on modeling decisions, especially the use of each candidate's average placement in the sample versus each respondent's idiosyncratic placement of the candidates.

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Should I Use Fixed or Random Effects? Clark, Tom Linzer, Drew Uploaded 03-26-2012 Keywords Fixed effects Random effects Panel data TSCS multilevel simulation Abstract Empirical analyses in political science very commonly confront data that are grouped---multiple votes by individual legislators, multiple years in individual states, multiple conflicts during individual years, and so forth. Modeling these data presents a series of potential challenges, of which accounting for differences across the groups is perhaps the most well-known. Two widely-used methods are the use of either "fixed" or "random" effects models. However, how best to choose between these approaches remains unclear in the applied literature. We employ a series of simulation experiments to evaluate the relative performance of fixed and random effects estimators for varying types of datasets. We further investigate the commonly-used Hausman test, and demonstrate that it is neither a necessary nor sufficient statistic for deciding between fixed and random effects. We summarize the results into a typology of datasets to offer practical guidance to the applied researcher.

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Using Campaign Contributions to Estimate the Political Ideology of Individual Public Bureaucrats Across Time Chen, Jowei Uploaded 07-15-2012 Abstract Over the past decade, political scientists have devised various methods to measure the political ideologies of administrative agencies and high-ranking public bureaucrats. This paper uses political campaign contributions to estimate public bureaucrats’ political ideologies. Bureaucrat ideal points estimated via our method vary across time, compare meaningfully with ideological estimates in other branches of government, cover employees across a wide range of agencies, yield insight into intra-agency ideological variation, and can be updated with minimal labor. To demonstrate our method, we estimate the political ideologies of politically appointed administrators in the U.S. federal government. We then use those estimates to test hypotheses about how U.S. presidents strategically manage the process of appointing individuals to federal bureaucratic posts requiring Senate confirmation.

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Kernel Regularized Least Squares: Moving Beyond Linearity and Additivity Without Sacrificing Interpretability Hainmueller, Jens Hazlett, Chad Uploaded 04-25-2012 Keywords regression classification prediction Abstract We propose the use of Kernel Regularized Least Squares (KRLS) for social science modeling and inference problems. KRLS borrows from machine learning methods designed to solve regression and classification problems without relying on linearity or additivity assumptions. The method constructs a flexible hypothesis space that uses kernels as radial basis functions and finds the best fitting surface in this space by minimizing a complexity-penalized least squares problem. We provide an accessible explanation of the method and argue that it is well suited for social science inquiry because it avoids strong parametric assumptions and still allows for simple interpretation in ways analogous to OLS or other members of the GLM family. We also extend the method in several directions to make it more effective for social inquiry. In particular, we (1) derive new estimators for the pointwise marginal effects and their variances, (2) establish unbiasedness, consistency, and asymptotic normality of the KRLS estimator under fairly general conditions, (3) develop an automated approach to chose smoothing parameters, and (4) provide companion software. We illustrate the use of the methods through several simulations and a real-data example.

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Dynamic Bayesian Forecasting of Presidential Elections in the States Linzer, Drew Uploaded 07-16-2012 Keywords President Forecasting Public Opinion Elections Abstract I present a dynamic Bayesian forecasting model that enables early and accurate prediction of U.S. presidential election outcomes at the state level. The method systematically combines information from historical forecasting models in real time with results from the large number of state-level opinion surveys that are released publicly during the campaign. The result is a set of forecasts that are initially as good as the historical model, then gradually increase in accuracy as Election Day nears. I employ a hierarchical specification to overcome the limitation that not every state is polled on every day, allowing the model to borrow strength both across states and, through the use of random-walk priors, across time. The model also filters away day-to-day variation in the polls due to sampling error and national campaign eects, which enables daily tracking of voter preferences towards the presidential candidates at the state and national levels. Simulation techniques are used to estimate the candidates' probability of winning each state and, consequently, a majority of votes in the Electoral College. I apply the model to pre-election polls from the 2008 presidential campaign and demonstrate that the victory of Barack Obama was never realistically in doubt. The model is currently ready to be deployed for forecasting the outcome of the 2012 presidential election. Project website: votamatic.org

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We Have to Be Discrete About This: A Non-Parametric Imputation Technique for Missing Categorical Data Cranmer, Skyler Gill, Jeff Uploaded 04-30-2012 Keywords missing data categorical hot-decking MCAR multiple imputation MAR GLM regression missingness Abstract Missing values are a frequent problem in empirical political science research. Surprisingly, there has been little attention to the match between the measurement of the missing values and the correcting algorithms used. While multiple imputation is a vast improvement over the deletion of cases with missing values, it is often ill suited for imputing highly non-granular discrete data. We develop a simple technique for imputing missing values in such situations, which is a variant of hot deck imputation, drawing from the conditional distribution of the variable with missing values to preserve the discrete measure of the variable. This method is tested against existing techniques using Monte Carlo analysis and then applied to real data on democratisation and modernisation theory. We provide software for our imputation technique in a free and easy-to-use package for the \R\ statistical environment.

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Sweeping fewer things under the rug: tis often (usually?) better to model than be robust Beck, Nathaniel Uploaded 07-16-2012 Keywords Cluster Robust Standard Errors Moulton Problem Time Series Cross Section Data Difference in Difference Random Effects Abstract The use of ``robust'' standard errors is now commonplace in political science. This paper considers one such type of errors, those that are robust to clustering of the data. While these give accurate estimates of parameter variability, we often can do better by direct modeling of the clustering process; such modeling can give insight into important sources of cluster effects. Applications are to grouped data with group level variables, difference in difference designs and time-series--cross-section data. Analysts should always ask whether clustering can be no more than an estimation nuisance before simply resorting to cluster robust standard errors.