U.S. presidential election forecasts are of widespread interest to political commentators, campaign strategists, research scientists, and the public. We argue that most fundamentals-based political science forecasts overstate what historical political and economic factors can tell us about the probable outcome of a forthcoming presidential election. Existing approaches generally overlook the uncertainty in coefficient estimates, decisions about model specifications, and the translation from popular vote shares to Electoral College outcomes. We introduce a Bayesian forecasting model for state-level presidential elections that accounts for each of these sources of error, and allows for the inclusion of structural predictors at both the national and state levels. Applying the model to presidential election data from 1952 to 2012, we demonstrate that, for covariates with typical levels of predictive power, the 95% prediction intervals for presidential vote shares should span approximately +/- 10% at the state level and +/- 7% at the national level. (C) 2015 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

Empirical analyses in social science frequently confront quantitative data that are clustered or grouped. To account for group-level variation and improve model fit, researchers will commonly specify either a fixed- or random-effects model. But current advice on which approach should be preferred, and under what conditions, remains vague and sometimes contradictory. This study performs a series of Monte Carlo simulations to evaluate the total error due to bias and variance in the inferences of each model, for typical sizes and types of datasets encountered in applied research. The results offer a typology of dataset characteristics to help researchers choose a preferred model.

We know that candidates and campaigns matter in democratic elections, but that knowledge may not be readily observed in most structural models of national election forecasting. For one, these models virtually never include direct, explicit candidate-related campaign variables as predictors. At most, these candidate/campaign variables are picked up indirectly, usually in polling measures, such as vote intention. For another, the models often manage accurate, ex ante forecasts of US presidential election results, even without the obvious pr​esence of such variables. In this effort, we aim to overcome this paradox by including more direct candidate and campaign measures in a long-standing structural equation model of presidential election forecasting, namely the Political Economy model. We find that inclusions of these candidate and campaign variables do improve the theoretical specification and the statistical performance of the model, and do yield generally more accurate forecasts. However, at least for the test case of the 2016 contest, that increased precision failed to substantively alter the Clinton popular vote forecast.

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The battle for public opinion in the Islamic world is an ongoing priority for U.S. diplomacy. The current debate over why many Muslims hold anti-American views revolves around whether they dislike fundamental aspects of American culture and government, or what Americans do in international affairs. We argue, instead, that Muslim anti-Americanism is predominantly a domestic, elite-led phenomenon that intensifies when there is greater competition between Islamist and secular-nationalist political factions within a country. Although more observant Muslims tend to be more anti-American, paradoxically the most anti-American countries are those in which Muslim populations are less religious overall, and thus more divided on the religious–secular issue dimension. We provide case study evidence consistent with this explanation, as well as a multilevel statistical analysis of public opinion data from nearly 13,000 Muslim respondents in 21 countries.

The relationship between a party's popular vote share and legislative seat share-its seats-votes swing ratio-is a key characteristic of democratic representation. This article introduces a general approach to estimating party-specific swing ratios in multiparty legislative elections, given results from only a single election. I estimate the joint density of party vote shares across districts using a finite mixture model for compositional data and then computationally evaluate this distribution to produce parties' expected change in legislative seats for plausible changes in their vote share. The method easily extends to systems with any number of parties, employing both majoritarian and proportional electoral rules. Applications to legislative elections in the United States, United Kingdom, Canada, and Botswana demonstrate how parties' swing ratios vary both within countries and over time, indicating that parties under majoritarian electoral rules are subject to unique and possibly divergent geographic-political constraints.

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, and 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 effects, which enables daily tracking of voter preferences toward 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 preelection polls from the 2008 presidential campaign and demonstrate that the victory of Barack Obama was never realistically in doubt.

Contingency tables are among the most basic and useful techniques available for analyzing categorical data, but they produce highly imprecise estimates in small samples or for population subgroups that arise following repeated stratification. I demonstrate that preprocessing an observed set of categorical variables using a latent class model can greatly improve the quality of table-based inferences. As a density estimator, the latent class model closely approximates the underlying joint distribution of the variables of interest, which enables reliable estimation of conditional probabilities and marginal effects, even among subgroups containing fewer than 40 observations. Though here focused on applications to public opinion, the procedure has a wide range of potential uses. I illustrate the benefits of the latent class model—based approach for greatly improved accuracy in estimating and forecasting vote preferences within small demographic subgroups using survey data from the 2004 and 2008 U.S. presidential election campaigns.

poLCA is a software package for the estimation of latent class and latent class regression models for polytomous outcome variables, implemented in the R statistical computing environment. Both models can be called using a single simple command line. The basic latent class model is a finite mixture model in which the component distributions are assumed to be multi-way cross-classification tables with all variables mutually independent. The latent class regression model further enables the researcher to estimate the effects of covariates on predicting latent class membership. poLCA uses expectation-maximization and Newton-Raphson algorithms to find maximum likelihood estimates of the model parameters.

The global food crisis of 2008 led to renewed interest in global food insecurity and how macro-level food prices impact household and individual level wellbeing. There is debate over the extent to which food price increases in 2008 eroded food security, the extent to which this effect was distributed across rural and urban locales, and the extent to which rural farmers might have benefited. Ethiopia's food prices increased particularly dramatically between 2005 and 2008 and here we ask whether there was a concomitant increase in household food insecurity, whether this decline was distributed equally across rural, urban, and semi-urban locales, and to what extent pre-crisis household capacities and vulnerabilities impacted 2008 household food insecurity levels. Data are drawn from a random sample of 2610 households in Southwest Ethiopia surveyed 2005/6 and again in mid to late 2008. Results show broad deterioration of household food insecurity relative to baseline but declines were most pronounced in the rural areas. Wealthier households and those that were relatively more food secure in 2005/6 tended to be more food secure in 2008, net of other factors, and these effects were most pronounced in urban areas. External shocks, such as a job loss or loss of crops, experienced by households were also associated with worse food insecurity in 2008 but few other household variables were associated with 2008 food insecurity. Our results also showed that rural farmers tended to produce small amounts for sale on markets, and thus were not able to enjoy the potential benefits that come from greater crop prices. We conclude that poverty, and not urban/rural difference, is the important variable for understanding the risk of food insecurity during a food crisis and that many rural farmers are too poor to take advantage of rapid rises in food prices.

This book investigates the effects of electoral systems on the relative legislative and, hence, regulatory influence of competing interests in society. Building on Ronald Rogowski and Mark Andreas Kayser's extension of the classic Stigler–Peltzman model of regulation, the authors demonstrate that majoritarian electoral arrangements should empower consumers relative to producers. Employing real price levels as a proxy for consumer power, the book rigorously establishes this proposition over time, within the OECD, and across a large sample of developing countries. Majoritarian electoral arrangements depress real prices by approximately ten percent, all else equal. The authors carefully construct and test their argument and broaden it to consider the overall welfare effects of electoral system design and the incentives of actors in the choice of electoral institutions.

We begin with some fundamental and still highly influential work on regulation and its effects by two leading economists of the mid-twentieth century, George Stigler and Sam Peltzman. We then move to develop a specific Stigler-Peltzman political support function and analyze the role of electoral responsiveness in it. We next consider possible welfare and distributional effects of regulated, high-price economic systems. We then consider how the analysis might differ in a small, open, export-dependent economy. Then, having analyzed the effect of various kinds of democratic constitutions, we consider some of the implications for nondemocracies, weakly institutionalized democracies, and less developed economies. Finally, we consider whether electoral systems can be regarded as exogenous and whether this affects our overall analysis. The Stigler-Peltzman Framework The essential insight of the Stigler-Peltzman (S-P) analysis of regulation can be conveyed by a single and widely familiar diagram shown in Figure 2.1 (cf. Peltzman 1976, p. 224). Suppose that the price of a given industry's product is represented on the horizontal axis and its profits on the vertical one. At the perfectly competitive price ( p c ), profits will be zero. To the extent that regulation in any of its familiar forms – licensure schemes that artificially restrict supply, regulatory boards that set minimum prices, impediments to efficient retailing, tariffs, quotas, and so on – raises the product's price above this competitive level, total industry profits begin to rise until price reaches the level that a monopoly would impose; this is denoted as p m . If regulation becomes so restrictive of supply as to push price even beyond this monopolistic level, industry profits again decline, returning to zero (or even becoming negative) as the price becomes prohibitive.

The battle for public opinion in the Islamic world is an ongoing priority for U.S. diplomacy. The current debate over why many Muslims hold anti-American views centers around whether individuals dislike “who Americans are” with respect to fundamental aspects of culture and government, or “what Americans do” policy-wise in international affairs. We propose, instead, that Muslim anti-Americanism is predominantly a domestic, elite-led phenomenon that intensifies when there is greater competition between Islamist and secular-nationalist political factions within a country. While more observant Muslims tend to be more anti-American, paradoxically the most anti-American countries are those with Muslim populations that are less religious overall, and thus more divided on the religious-secular issue dimension. We provide case study evidence consistent with this explanation, as well as an in-depth multilevel statistical analysis of public opinion data from over 12,000 Muslim respondents in 21 countries. Acknowledgements: The authors wish to thank Christopher Anderson, Ceren Belge, Giacomo Chiozza, Tom Clark, Jorge Dominguez, James Fearon, Nahomi Ichino, David Laitin, Monika Nalepa, Chris Reenock, Jeffrey Staton, Jonathan Wand, Carrie Wickham, and the audiences of the Notre Dame Kellogg Institute, the MIT Works-in-Progress Seminar Series and the Stanford Comparative Politics Workshop. Jana Marie Hutchinson, Ugur Pece, Jeremy Voss and Meredith Wheeler provided exemplary research assistance. The Pew Global Attitudes Project bears no responsibility for the interpretations presented or conclusions reached based on our analysis of the data.