In this paper, we develop and test a general measure of policy expenditures in the American states. Our approach is to construct a spatial proximity model of yearly state program spending. The empirical analysis reveals that state spending patterns vary along a clear and readily-interpretable unidimensional continuum which differentiates policies that provide particularized benefits to needy constituencies from policies that provide broader collective goods. Based upon standard evaluative criteria, the variable created from our model possesses some highly desirable characteristics. And, it compares favorably to other measures of state policy activity. The net result is a yearly score for each state which summarizes that state's spending across all major program areas. More generally, we believe that our variable can be interpreted as valid and reliable representational measurement of state policy priorities. In this capacity, it could occupy an important position within models of state politics.

Analyzing macro-political processes is complicated by four interrelated problems: model scale, endogeneity, persistence, and specification uncertainty. These problems are endemic in the study of political economy, public opinion, international relations, and other kinds of macro-political research. We show how a Bayesian structural time series approach addresses them. Our illustration is a structurally identified, nine-equation model of the U.S. political-economic system. It combines key features of the model of Erikson, MacKuen, and Stimson (2002) of the American macropolity with those of a leading macroeconomic model of the United States (Sims and Zha, 1998; Leeper, Sims, and Zha, 1996). This Bayesian structural model, with a loosely informed prior, yields the best performance in terms of model fit and dynamics. This model 1) confirms existing results about the countercyclical nature of monetary policy (Williams 1990); 2) reveals informational sources of approval dynamics: innovations in information variables affect consumer sentiment and approval and the impacts on consumer sentiment feed-forward into subsequent approval changes; 3) finds that the real economy does not have any major impacts on key macropolity variables; and 4) concludes, contrary to Erikson, MacKuen, and Stimson (2002), that macropartisanship does not depend on the evolution of the real economy in the short or medium term and only very weakly on informational variables in the long term.

Correctly measuring district preferences is crucial for empirical research on legislative responsiveness and voting behavior. This article argues that the common practice of using presidential vote shares to measure congressional district ideology systematically produces incorrect estimates. I propose an alternative method that employs multiple election returns to estimate voters' ideological distributions within districts. I develop two estimation procedures—a least squared error model and a Bayesian model—and test each with simulations and empirical applications. The models are shown to outperform vote shares, and they are validated with direct measures of voter ideology and out-of-sample election predictions. Beyond estimating district ideology, these models provide valuable information on constituency heterogeneity—an important, but often immeasurable, quantity for research on representatives— strategic behavior.

A search of recent political science literature and conference presentations shows substantial fascination with the concept of the natural experiment. However, there seems to be a wide array of definitions and applications employed in research that purports to analyze natural experiments. In this introductory essay to the special issue, we attempt to define natural experiments and discuss related issues of research design. In addition, we briefly explore the basic methodological issues around the appropriate analysis of natural experiments and give an overview of different techniques. The overarching theme of this essay and of this issue is to encourage applied researchers to look for natural experiments in their own work and to think more systematically about research design.

Embedding experiments within surveys has reinvigorated survey research. Several survey experiments are generally embedded within a survey, and analysts treat each of these experiments as self-contained. We investigate whether experiments are self-contained or if earlier treatments affect later experiments, which we call “experimental spillover.” We consider two types of bias that might be introduced by spillover: mean and inference biases. Using a simple procedure, we test for experimental spillover in two data sets: the 1991 Race and Politics Survey and a survey containing several experiments pertaining to foreign policy attitudes. We find some evidence of spillover and suggest solutions to avoid bias.

Endogeneity of explanatory variables is now receiving the concern it deserves in the empirical political science literature. Instrumental variables (IVs) estimators, such as two-stage least squares (2SLS), are the primary means for tackling this problem. These estimators solve the endogeneity problem by “instrumenting” the endogenous regressors using exogenous variables (the instruments). In many applications, a problem that the IV approach must overcome is that of weak instruments (WIs), where the instruments only weakly identify the regression coefficients of interest. With WIs, the infinite-sample properties (e.g., consistency) used to justify the use of estimators like 2SLS are on thin ground because these estimators have poor small-sample properties. Specifically, they may suffer from excessive bias and/or Type I error. We highlight the WI problem in the context of empirical testing of “protection for sale” model that predicts the cross-sectional pattern of trade protection as a function of political organization, imports and output. These variables are endogenous. Importantly, the instruments used to solve the endogeneity problem are weak. A method better suited to exact inference with WIs is the limited information maximum likelihood (LIML) estimator. Censoring in the dependent variable in the application requires a nonlinear Tobit LIML estimator.

Although there are compelling theoretical reasons to believe that unequal political representation in a legislature leads to an unequal distribution of funds, testing such theories empirically is challenging because it is difficult to separate the effects of representation from the effects of either population levels or changes. We leverage the natural experiment generated by infrequent and discrete census apportionment cycles to estimate the distributional effects of malapportionment in the U.S. House of Representatives. We find that changes in representation cause changes in the distribution of federal outlays to the states. Our method is exportable to any democratic system in which reapportionments are regular, infrequent, and nonstrategic.

In this paper I investigate the trade-off a legislature faces in the choice of instruments to ensure accountability by bureaucrats with private information. The legislature can either design a state-contingent incentive scheme or “menu law” to elicit the bureau's information or it can simply limit the set of choices open to the bureaucrat and let it choose as it wishes (an action restriction). I show that the optimal action restriction is simply a connected interval of the policy space. However, this class of instruments is not optimal without some sort of limitation on the set of levers of control available to the legislature. I then analyze one such limitation salient in politics, the legislative principal's inability to commit to honor a schedule of (state contingent) policy choices and transfer payments for a menu law. In this case the optimal action restriction outperforms (in terms of the legislature's welfare) the best available menu law.

Standard estimation procedures assume that empirical observations are accurate reflections of the true values of the dependent variable, but this assumption is dubious when modeling self-reported data on sensitive topics. List experiments (a.k.a. item count techniques) can nullify incentives for respondents to misrepresent themselves to interviewers, but current data analysis techniques are limited to difference-in-means tests. I present a revised procedure and statistical estimator called LISTIT that enable multivariate modeling of list experiment data. Monte Carlo simulations and a field test in Lebanon explore the behavior of this estimator.

DW-NOMINATE scores for the U.S. Congress are widely used measures of legislators' ideological locations over time. These scores have been used in a large number of studies in political science and closely related fields. In this paper, we extend the work of Lewis and Poole (2004) on the parametric bootstrap to DW-NOMINATE and obtain standard errors for the legislator ideal points. These standard errors are in the range of 1%–4% of the range of DW-NOMINATE coordinates.

In this case study of the impact of West German television on public support for the East German communist regime, we evaluate the conventional wisdom in the democratization literature that foreign mass media undermine authoritarian rule. We exploit formerly classified survey data and a natural experiment to identify the effect of foreign media exposure using instrumental variable estimators. Contrary to conventional wisdom, East Germans exposed to West German television were more satisfied with life in East Germany and more supportive of the East German regime. To explain this surprising finding, we show that East Germans used West German television primarily as a source of entertainment. Behavioral data on regional patterns in exit visa applications and archival evidence on the reaction of the East German regime to the availability of West German television corroborate this result.

Although there has been considerable attention to the question of how much polarization there is in the mass electorate, there has been much less attention paid to the mechanism that causes polarization. I provide evidence demonstrating the occurrence of individual-level conversion—individual Democrats and Republicans becoming more liberal and conservative. Although over the short term most of the observed changes are quite small and cannot be distinguished from measurement error, over time and many respondents, these movements aggregate to generate polarization. Small individual-level preference shifts provide an important foundation for aggregate polarization.

Studies on national identity differentiate between nationalistic attitudes and constructive patriotism (CP) as two more specific expressions of national identity and as theoretically two distinct concepts. After a brief discussion of the theoretical literature, the following questions are examined: (1) Can nationalism and CP be empirically identified as two distinct concepts?; (2) Is their meaning fully or partially invariant across countries?; and (3) Is it possible to compare their means across countries? Data from the International Social Survey Program (ISSP) 2003 National Identity Module are utilized to answer these questions in a sample of 34 countries. Items to measure nationalism and CP are chosen based on the literature, and a series of confirmatory factor analyses to test for configural, measurement (metric), and scalar invariance are performed. Full or partial metric invariance is a necessary condition for equivalence of meaning across cultures and for a meaningful comparison of associations with other theoretical constructs. Scalar invariance is a necessary condition for comparison of means across countries. Findings reveal that nationalism and CP emerge as two distinct constructs. However, in some countries, some items that were intended to measure one construct also measure the other construct. Furthermore, configural and metric invariance are found across the full set of 34 countries. Consequently, researchers may now use the ISSP data to study relationships among nationalism, CP, and other theoretical constructs across these nations. However, the analysis did not support scalar invariance, making it problematic for comparing the means of nationalism and CP across countries.

Political scientists often analyze data in which the observational units are clustered into politically or socially meaningful groups with an interest in estimating the effects that group-level factors have on individual-level behavior. Even in the presence of low levels of intracluster correlation, it is well known among statisticians that ignoring the clustered nature of such data overstates the precision estimates for group-level effects. Although a number of methods that account for clustering are available, their precision estimates are poorly understood, making it difficult for researchers to choose among approaches. In this paper, we explicate and compare commonly used methods (clustered robust standard errors (SEs), random effects, hierarchical linear model, and aggregated ordinary least squares) of estimating the SEs for group-level effects. We demonstrate analytically and with the help of empirical examples that under ideal conditions there is no meaningful difference in the SEs generated by these methods. We conclude with advice on the ways in which analysts can increase the efficiency of clustered designs.

Regression discontinuity (RD) designs enable researchers to estimate causal effects using observational data. These causal effects are identified at the point of discontinuity that distinguishes those observations that do or do not receive the treatment. One challenge in applying RD in practice is that data may be sparse in the immediate vicinity of the discontinuity. Expanding the analysis to observations outside this immediate vicinity may improve the statistical precision with which treatment effects are estimated, but including more distant observations also increases the risk of bias. Model specification is another source of uncertainty; as the bandwidth around the cutoff point expands, linear approximations may break down, requiring more flexible functional forms. Using data from a large randomized experiment conducted by Gerber, Green, and Larimer (2008), this study attempts to recover an experimental benchmark using RD and assesses the uncertainty introduced by various aspects of model and bandwidth selection. More generally, we demonstrate how experimental benchmarks can be used to gauge and improve the reliability of RD analyses.

Ideal point estimation is a topic of central importance in political science. Published work relying on the ideal point estimates of Poole and Rosenthal for the U.S. Congress is too numerous to list. Recent work has applied ideal point estimation to the state legislatures, Latin American chambers, the Supreme Court, and many other chambers. Although most existing ideal point estimators perform well when the number of voters and the number of bills is large, some important applications involve small chambers. We develop an estimator that does not suffer from the incidental parameters problem and, hence, can be used to estimate ideal points in small chambers. Our Monte Carlo experiments show that our estimator offers an improvement over conventional estimators for small chambers. We apply our estimator to estimate the ideal points of Supreme Court justices in a multidimensional space.

Although the presidential coattail effect has been an object of frequent study, the question of whether popular congressional candidates boost vote shares in return for their parties' presidential candidates remains unexplored. This article investigates whether so-called “reverse coattails” exist using a regression discontinuity design with congressional district-level data from presidential elections between 1952 and 2004. Taking incumbency to be near-randomly distributed in cases where congressional candidates have just won or lost their previous elections, I find that the numerous substantial advantages of congressional incumbency have no effect on presidential returns for these incumbents' parties. This null finding underscores my claim that the existing coattail literature deserves greater scrutiny. My results also prompt a rethinking of the nature of the advantages that incumbents bring to their campaigns and may help deepen our understanding of partisanship in the United States.

In this paper, we apply information theoretic measures to voting in the U.S. Senate in 2003. We assess the associations between pairs of senators and groups of senators based on the votes they cast. For pairs, we use similarity-based methods, including hierarchical clustering and multidimensional scaling. To identify groups of senators, we use principal component analysis. We also apply a discrete multinomial latent variable model that we have developed. In doing so, we identify blocs of cohesive voters within the Senate and contrast it with continuous ideal point methods. We find more nuanced blocs than simply the two-party division. Under the bloc-voting model, the Senate can be interpreted as a weighted vote system, and we are able to estimate the empirical voting power of individual blocs through what-if analysis.

As political scientists begin to incorporate biological influences as explanatory factors in political behavior, the need to present a methodological road map for utilizing biometric genetic theory and twin data is apparent. The classical twin design (CTD) remains the most popular design for initial examinations of the source of variance among social and political behaviors, and a vast majority of advanced variance components models as well as some molecular analyses are extensions of the CTD. Thus, it is appropriate to begin a series of works with the CTD and its most common variants. The CTD has strong roots in biometrical genetic theory and provides estimates of the correlations between observed traits of monozygotic and dizygotic twins in terms of underlying genetic and environmental influences. The majority of these analyses utilize SEMs of observed covariances for both twin types to assess the relative importance of these “latent” factors.

This paper replicates and extends Groseclose, Levitt, and Snyder, “Comparing Interest Group Scores Across Time and Chambers: Adjusted ADA Scores for the U.S. Congress,” which appeared in the American Political Science Review (1999/93:33–50). We replicate the most recent unpublished extension by Dr. Groseclose and research assistants for years 1947–1999, and then we extend the analysis to include years 2000 through 2007. We make available inflation-adjusted ADA scores from 1947 through 2007, allowing scholars to incorporate the most recent interest group scores into their analyses.