Political Analysis

Genetic Optimization Using Derivatives We describe a new computer program that combines evolutionary algorithm methods with a derivative-based, quasi-Newton method to solve difficult unconstrained optimization problems. The program, called GENOUD (GENetic Optimization Using Derivatives), effectively solves problems that are nonlinear or perhaps even discontinuous in the parameters of the function to be optimized. When a statistical model's estimating function (for example, a log-likelihood) is nonlinear in the model's parameters, the function to be optimized will usually not be globally concave and may contain irregularities such as saddlepoints or discontinuous jumps. Optimization methods that rely on derivatives of the objective function may be unable to find any optimum at all. Or multiple local optima may exist, so that there is no guarantee that a derivative-based method will converge to the global optimum. We discuss the theoretical basis for expecting GENOUD to have a high probability of finding global optima. We conduct Monte Carlo experiments using scalar Normal mixture densities to illustrate this capability. We also use a system of four simultaneous nonlinear equations that has many parameters and multiple local optima to compare the performance of GENOUD to that of the Gauss-Newton algorithm in SAS's PROC MODEL.

Survey questions about quantities offer a number of advantages over more common qualitative questions. However, concerns about survey respondents' abilities to accurately report numbers have limited the use of quantitative questions. This paper shows quantitative questions are feasible and useful for the study of economic voting. First, survey respondents are capable of accurately assessing familiar economic quantities, such as the price of gas. Second, careful question design-in particular providing respondents with benchmark quantities-can reduce measurement error due to respondents not understanding the scale on which more complex quantities, such as the unemployment rate, are measured. Third, combining quantitative and qualitative questions sheds light on where partisan bias enters economic assessments: in perceiving, judging, or reporting economic quantities. The evidence indicates bias enters the reporting of assessments, and that this bias is smaller in quantitative questions than qualitative questions. * We thank

Social scientists generally enjoy substantial latitude in selecting measures and models for hypothesis testing. Coupled with publication and related biases, this latitude raises the concern that researchers may intentionally or unintentionally select models that yield positive findings, leading to an unreliable body of published research. To combat this "fishing" problem in medical studies, leading journals now require preregistration of designs that emphasize the prior identification of dependent and independent variables. However, we demonstrate here that even with this level of advanced specification, the scope for fishing is considerable when there is latitude over selection of covariates, subgroups, and other elements of an analysis plan. These concerns could be addressed through the use of a form of comprehensive registration. We experiment with such an approach in the context of an ongoing field experiment for which we drafted a complete "mock report" of findings using fake data on treatment assignment. We describe the advantages and disadvantages of this form of registration and propose that a comprehensive but nonbinding approach be adopted as a first step to combat fishing by social scientists. Likely effects of comprehensive but nonbinding registration are discussed, the principal advantage being communication rather than commitment, in particular that it generates a clear distinction between exploratory analyses and genuine tests.

Researchers have been aware for some time that models including a lagged value of the dependent variable may suffer from biased and inconsistent estimates when used in conjunction with OLS with residual autocorrelation. Specifically, in the presence of residual autocorrelation, the coefficient for the lagged dependent variable may be upwardly biased and estimates of the effect of explanatory factors on the dependent variable are biased downward. We use a Monte Carlo analysis to empirically assess how much bias is present when a lagged dependent variable is used under a wide variety of circumstances. We demonstrate that OLS when used with a lagged dependent variable under some circumstances is asymptotically unbiased, and that the bias present in many models with a lagged dependent variable is trivial with realistic levels of residual autocorrelation. From this analysis we develop several practical suggestions on when and how to appropriately use lagged dependent variables on the right hand side of a model.

The analysis of time series cross-sectional (TSCS) data has become increasingly popular in political science. Meanwhile, political scientists are also becoming more interested in the use of multilevel models. However, little work exists to understand the benefits of multilevel modeling when applied to TSCS data. We employ Monte Carlo simulations to benchmark the performance of a Bayesian multilevel model (BML) for TSCS data. We find that the BML performs as well or better than other common estimators for such data. Perhaps most importantly, the BML is more general and flexible, which offers researchers additional advantages.

In a relatively short period of time, Romanian political science has made considerable progress, moving from virtual obscurity to unchallenged local prominence. This article examines the efforts to date to institutionalize political science as a separate teaching and research discipline by presenting recently established political science university-level programs, the major groups of authors carrying out research on political phenomena and the recurrent themes emerging from relevant literature. Though the present article is concerned mainly with current developments, occasional references to the political science's position during the communist period are also made.

The Political Mind
The Role of the Unconscious in Political and Social Life

12 May - 13 July 2015

A series of 10 seminars on the Political Mind in the Summer term at the British Psychoanalytical Society (BPAS), Institute of Psychoanalysis, Byron House, 112a Shirland Road, London W9.

Seminar Leader and Organiser: Dr David Morgan, Psychoanalyst.  Institute of Psychoanalysis, Centre for the Advancement of Psychoanalytic Studies.


Programme

1)  May 12: David Morgan "Whistleblowers and Dissent: Moral Good or Self Interest? What are the psychological dimensions of defying a perverse or corrupt authority?"

2)  May 19: John Alderdice "Devoted Actors the psychology of Fundamentalism & Terrorism"

3)  May 26: Sally Weintrobe "Climate change and the culture of uncare"

4)  June 2: Phil Stokoe “The Impact of Power on the Mind of the Politician"

5)  June 9: Josh Cohen "Psychoanalysis and the Politics of Idleness."

6)  June 16: Bob Hinshelwood "Reflection or action: Never the twain shall meet"

7)  June 23: David Bell "All thats solid melts into air: Psychoanalytic reflections on the socio-economic crisis"

8)  June 30: Ruth McCall "The Psychoanalysis of Exclusion."

9)  July 6: Edgard Sanchez "Power and the manipulation of the masses. Third world perspectives."

10)  July 13: Michael Rustin "The Psychology of Neoliberalism"

For more information contact Dr. David Morgan at [email protected] and Marjory Goodall, Conference & Events Officer, Tel: 020 7563 5016  Email: [email protected]

Bookings via Beyond the Couch http://www.beyondthecouch.org.uk/?unique_name=events&item=196

Existing theoretical research on electoral politics and financial markets predict that when investors expect left parties -Democrats (US), Labor (UK)-to win elections market volatility increases. In addition, current econometric research on stock market volatility suggests that Markov-Switching models provide more accurate volatility forecasts and fit stock price volatility data better than linear or non-linear GARCH (Generalized Autoregressive Conditional Heteroscedasticity) models. We take issue with both of these claims. We construct a formal model which predicts that if traders anticipate that the Democratic candidate will win the Presidential election stock market volatility decreases. Using two data sets from the 2000 Presidential election we test our claim by estimating several GARCH, Exponential-GARCH (EGARCH), Fractionally Integrated Exponential-GARCH (FIEGARCH) and Markov-Switching models. We also conduct extensive out-of-sample forecasting tests to evaluate these competing statistical models. Results from the out-ofsample forecasts show-in contrast to prevailing claims-that GARCH and EGARCH models provide substantially more accurate forecasts than the Markov-Switching models. Estimates from all the competing statistical models support the predictions from our formal model. s leads to more variability in the traded stock's price. This property is vital for the theoretical analysis in this paper. In Appendix B, we provide formal definitions from Cambanis, and to characterize some technical properties of the multivariate ECC distribution, which are used to prove propositions from our model.

Multiplicative interaction models are common in the quantitative political science literature. This is so for good reason. Institutional arguments frequently imply that the relationship between political inputs and outcomes varies depending on the institutional context. Models of strategic interaction typically produce conditional hypotheses as well. Although conditional hypotheses are ubiquitous in political science and multiplicative interaction models have been found to capture their intuition quite well, a survey of the top three political science journals from 1998 to 2002 suggests that the execution of these models is often flawed and inferential errors are common. We believe that considerable progress in our understanding of the political world can occur if scholars follow the simple checklist of dos and don'ts for using multiplicative interaction models presented in this article. Only 10% of the articles in our survey followed the checklist. two anonymous reviewers for their extremely useful comments on this paper. We also thank the research assistants at Political Analysis-Jeronimo Cortina, Tse-hsin Chen, and Seung Jin Jang-for kindly double-checking the results from our literature survey. Finally, we are grateful to those authors who have provided us with their data. To accompany this paper, we have constructed a Web page at http:// homepages.nyu.edu/;mrg217/interaction.html that is devoted to multiplicative interaction models. On this page, you will find (i) the data and computer code necessary to replicate the analyses conducted here, (ii) information relating to marginal effects and standard errors in interaction models, (iii) STATA code for producing figures illustrating marginal effects and confidence intervals for a variety of continuous and limited dependent variable models, and (iv) detailed results from our literature survey. STATA 8 was the statistical package used in this study.

Many areas of political science focus on causal questions. Evidence from statistical analyses are often used to make the case for causal relationships. While statistical analyses can help establish causal relationships, it can also provide strong evidence of causality where none exists. In this essay, I provide an overview of the statistics of causal inference. Instead of focusing on specific statistical methods, such as matching, I focus more on the assumptions needed to give statistical estimates a causal interpretation. Such assumptions are often referred to as identification assumptions, and these assumptions are critical to any statistical analysis about causal effects. I outline a wide range of identification assumptions and highlight the design-based approach to causal inference. I conclude with an overview of statistical methods that are frequently used for causal inference. . For comments I thank the editors and the four anonymous reviewers. I also thank Rocío Titiunik, Jasjeet Sekhon, Paul Rosenbaum, and Dylan Small for many insightful conversations about these topics over the years. In the online Supplementary Materials, I provide further information about software tools to implement many of the methodologies discussed in this essay. sion Discontinuity Estimator." Review of Economic Studies 79 :933-959. Kang, Joseph DY, Joseph L Schafer et al. 2007. "Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data." Statistical science 22(4):523-539.

"Practitioners and academics are today convinced that ‘thinking politically’ is important to successful development
interventions. Since the early 2000s, attempts to mainstream political thinking in most donor agencies have used a political
economy analysis (PEA) approach, and yet this has been largely ineffective. This paper attempts to explain this failure
through re-focusing current debate on PEA.
The paper argues that the process of PEA is not fundamentally flawed and indeed agrees, as PEA advocates have consistently
argued throughout the 2000s, that success in future development programmes requires a wholesale re-thinking of the
relationship between politics and international development.
However, we argue that PEA has today become a tool or product ‘sold’ to donors and ‘done’ externally, and it is no longer
fit for purpose. We critique this type of ‘PEA™’,1
tracing its evolution from a transformative approach to policy-making
to a discrete instrument that is applied to specific ‘problems’, usually by external consultants. We draw attention to the
consistently faulty and introspective methodology that has informed the undertaking and application of PEA™.
Our analysis leads us to suggest that throwing away this model and doing something completely different is the only way
donors can hope to move forward with the ‘thinking politically’ agenda."

Цель. Представить основные принципы подготовки прикладного аналитического текста. Тема обладает исключительной прикладной значимостью с точки зрения аналитического обеспечения управленческой деятельности и принятия политических решений. Статья адресована как студентам, начинающим заниматься прикладным политическим анализом, так и профессиональным аналитикам, работающим в научно-образовательной среде, исследовательских фондах и государственном управлении. Методы. На основании существующих методических разработок в рамках теоретических дисциплин и собственного практического опыта автор систематизирует информацию и формализует ключевые положения, необходимые для формирования навыков и умений подготовки прикладной политической аналитики Результаты. В статье показаны особенность аналитического стиля, его отличия от научного и публицистического текста. Введено и проанализировано понятие «формата» аналитического исследования. Показаны различия в подготовке открытой и закрытой аналитики. Показана роль заказчика аналитической работы. Показаны различия анализа информационного поля и реальных политических процессов. Особое внимание уделяется структурным особенностям и внутренней архитектонике аналитического текста, общей логике построения прикладного анализа в удобном для заказчика (лица, принимающего решения) виде. Акцентировано значение управленческих рекомендаций как главного итога аналитической работы. Научная новизна. Статья обладает практической и дидактической значимостью и направлена на формирование у читателей умений и навыков подготовки прикладного аналитического текста. Систематизирована (по сути, введена в научный оборот) проблематика подготовки аналитических текстов, обеспечивающих принятие и сопровождение политических и управленческих решений. Предложены конкретные инструменты прикладного политического анализа, значимые для управленческой практики.
Ключевые слова: аналитический текст, методология аналитической работы, политический анализ, прикладной анализ.

This article examines the development of methods of political analysis concerned with ideas, beliefs and meanings and argues that these need to be supplemented by an approach attuned to the specific nature of political action. It argues that since politics involves the contest of ideas, beliefs and meanings, analysis should focus on arguments. Considering methods for the study of political arguments the article argues for a re-examination of the rhetorical tradition and the development of a Rhetorical Political Analysis (RPA). It then outlines the sorts of things this would examine, the questions it would ask and the ways in which it might go about answering them.

In Russian political reality, public experts and the media often refer to various «ratings». In conditions of low confidence in the election institution (according to the Levada Center and VCIOM), various kinds of «ranking tables» serve as a kind of substitute for electoral ratings. In part, the abundance of ratings of political elites (governors, deputies, ministers), based not on opinion polls, but on expert assessments and data analysis, is an indirect evidence of a decrease in the role of public opinion in influencing political processes. The ratings make up for the lack of information about political processes, as well as help implement the imitation or, as it is commonly called in a number of Russian media, «hybrid» model of democracy. The article analyzes the role of political ratings at present. The author comes to the conclusion that the ratings are currently not a reliable source of information, but rather a PR tool.

This article presents a novel automatic method of text analysis aimed at discovering patterns of lexical cohesion in political speech. The unit of analysis are groups of words with related meanings; the software is based on the results of a multiperson annotation experiment that captures reliably identified connections between words in a text. We illustrate the advantages of such a representation by juxtaposing results of a detailed hand-made analysis of Margaret Thatcher's rhetoric with analysis based on the automatically detected groups of words. We both corroborate previous findings regarding Thatcher's rhetorical tools and illuminate additional elements thereof. We suggest that lexical cohesion analysis is a promising technique to bridge the gap between quantitative and qualitative analyses of text as political material, by establishing units that are both robust enough to enable comprehensive coverage and coherent enough to support direct interpretation.

The Cox proportional hazards model is widely used to model durations in the social sciences. Although this model allows analysts to forgo choices about the form of the hazard, it demands careful attention to the proportional hazards assumption. To this end, a standard diagnostic method has been developed to test this assumption. I argue that the standard test for nonproportional hazards has been misunderstood in current practice. This test detects a variety of specification errors, and these specification errors must be corrected before one can correctly diagnose nonproportionality. In particular, unmodeled nonlinearity can appear as a violation of the proportional hazard assumption for the Cox model. Using both simulation and empirical examples, I demonstrate how an analyst might be led astray by incorrectly applying the nonproportionality test.

Semester Two, 2013 curriculum for a subject taught and co-ordinated at the University of Melbourne. The loose idea was to explore the experience of neoliberalism via readings from different disciplines, including sociology, psychoanalysis, the Frankfurt School and cultural studies.

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 article, 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 ...

This article presents a conceptual clarification of asymmetric hypotheses and a discussion of methodologies available to test them. Despite the existence of a litany of theories that posit asymmetric hypotheses, most empirical studies fail to capture their core insight: boundaries separating zones of data from areas that lack data are substantively interesting. We discuss existing set-theoretic and large-N approaches to the study of asymmetric hypotheses, introduce new ones from the literatures on stochastic frontier and data envelopment analysis, evaluate their relative merits, and give three examples of how asymmetric hypotheses can be studied with this suite of tools.

Equivalent separate-subsample (two-step) and pooled-sample (one-step) strategies exist for any multilevel-modeling task, but their relative practicality and efficacy depend on dataset dimensions and properties and researchers' goals. Separate-subsample strategies have difficulties incorporating cross-subsample information, often crucial in time-series crosssection or panel contexts (subsamples small and/or cross-subsample information great) but less relevant in pools of independently random surveys (subsamples large; cross-sample information small). Separate-subsample estimation also complicates retrieval of macrolevel-effect estimates, although they remain obtainable and may not be substantively central. Pooled-sample estimation, conversely, struggles with stochastic specifications that differ across levels (e.g., stochastic linear interactions in binary dependent-variable models). Moreover, pooled-sample estimation that models coefficient variation in a theoretically reduced manner rather than allowing each subsample coefficient vector to differ arbitrarily can suffer misspecification ills insofar as this reduced specification is lacking. Often, though, these ills are limited to inefficiencies and standard-error inaccuracies that familiar efficient (e.g., feasible generalized least squares) or consistent-standard-error estimation strategies can satisfactorily redress.

In this article, we respond to Shultziner's critique that argues that identical twins are more alike not because of genetic similarity, but because they select into more similar environments and respond to stimuli in comparable ways, and that these effects bias twin model estimates to such an extent that they are invalid. The essay further argues that the theory and methods that undergird twin models, as well as the empirical studies which rely upon them, are unaware of these potential biases. We correct this and other misunderstandings in the essay and find that gene-environment (GE) interplay is a well-articulated concept in behavior genetics and political science, operationalized as gene-environment correlation and gene-environment interaction. Both are incorporated into interpretations of the classical twin design (CTD) and estimated in numerous empirical studies through extensions of the CTD. We then conduct simulations to quantify the influence of GE interplay on estimates from the CTD. Due to the criticism's mischaracterization of the CTD and GE interplay, combined with the absence of any empirical evidence to counter what is presented in the extant literature and this article, we conclude that the critique does not enhance our understanding of the processes that drive political traits, genetic or otherwise.

Researchers who generate data often optimize efficiency and robustness by choosing stratified over simple random sampling designs. Yet, all theories of inference proposed to justify matching methods are based on simple random sampling. This is all the more troubling because, although these theories require exact matching, most matching applications resort to some form of ex post stratification (on a propensity score, distance metric, or the covariates) to find approximate matches, thus nullifying the statistical properties these theories are designed to ensure. Fortunately, the type of sampling used in a theory of inference is an axiom, rather than an assumption vulnerable to being proven wrong, and so we can replace simple with stratified sampling, so long as we can show, as we do here, that the implications of the theory are coherent and remain true. Properties of estimators based on this theory are much easier to understand and can be satisfied without the unattractive properties...

Would holding elections by mail increase voter turnout? Many electoral reform advocates predict that mail ballot elections will boost participation, basing their prediction on the higher turnout rates among absentee voters and on the rise in voter turnout after Oregon switched to voting by mail. However, selection problems inherent to studies of absentee voters and Oregon give us important reasons to doubt whether their results would extend to more general applications of voting by mail. In this paper, we isolate the effects of voting in mail ballot elections by taking advantage of a natural experiment in which voters are assigned in a nearly random process to vote by mail. We use matching methods to ensure that, in our analysis, the demographic characteristics of these voters mirror those of polling place voters who take part in the same elections. Drawing on data from a large sample of California counties in two general elections, we find that voting by mail does not deliver on the promise of greater participation in general elections. In fact, voters who are assigned to vote by mail turn out at lower rates than those who are sent to a polling place. Our analysis of a sample of local special elections, by contrast, indicates that voting by mail can increase turnout in these otherwise lowparticipation contests.

Interdisciplinary researches on political mentalities, gender, elites and political analysis.

Registered in:
Worldcat: https://www.worldcat.org/title/decizia-politica-o-formula-interdisciplinara-mentalitati-si-modelare/oclc/892097438&referer=brief_results
CLIO: http://clio.columbia.edu/catalog/9125825?counter=1
HOLIS: : http://id.lib.harvard.edu/aleph/013191489/catalog

Automated text analysis methods have made possible the classification of large corpora of text by measures such as topic and tone. Here, we provide a guide to help researchers navigate the consequential decisions they need to make before any measure can be produced from the text. We consider, both theoretically and empirically, the effects of such choices using as a running example efforts to measure the tone of New York Times coverage of the economy. We show that two reasonable approaches to corpus selection yield radically different corpora and we advocate for the use of keyword searches rather than predefined subject categories provided by news archives. We demonstrate the benefits of coding using article segments instead of sentences as units of analysis. We show that, given a fixed number of codings, it is better to increase the number of unique documents coded rather than the number of coders for each document. Finally, we find that supervised machine learning algorithms outpe...

This paper shows how syntactic information can be used to automatically extract clauses from text, consisting of a subject, predicate, and optional source. Since the output of this analysis can be seen as an enriched token list or bag of words, normal frequency based or corpus linguistic analyses can be used on this output. Taking the 2008–2009 Gaza war as an example, we show how corpus comparison, topic modelling, and semantic network analysis can be used to explore the differences between US and Chinese coverage of this war.

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 compar...

Theories that posit complex causation, or multiple causal paths, pervade the study of politics but have yet to find accurate statistical expression. To remedy this situation I derive new econometric procedures, Boolean probit and logit, based on the logic of complexity. The solution provides an answer to a puzzle in the rational deterrence literature: the divergence between theory and case-study findings, on the one hand, and the findings of quantitative studies, on the other, on the issue of the role of capabilities and willingness in the initiation of disputes. It also makes the case that different methodological traditions, rather than settling into "separate but equal" status, can instead inform and enrich one another.

This paper outlines a nonstationary, heterogeneous Markov model designed to estimate entry and exit transition probabilities at the micro-level from a time series of independent cross-sectional samples with a binary outcome variable. The model has its origins in the work of Moffitt (1993) and shares features with standard statistical methods for ecological inference. We show how ML estimates of the parameters can be obtained by the method-of-scoring, how to estimate time-varying covariate effects, and how to include non-backcastable variables in the model. The latter extension of the basic model is an important one as it strongly increases its potential application in a wide array of research contexts. The example illustration uses survey data on American presidential vote intentions from a five-wave panel study conducted by in 1976. We treat the panel data as independent cross sections and compare the estimates of the Markov model with the observations in the panel. Directions for future work are discussed. , , , )

Edited by Jasjeet Sekhon This article outlines a new method for surveys to study elections and voter attitudes. Pre-election surveys often suffer from an inability to identify and survey the likely electorate for the upcoming election. We propose a new and inexpensive method to conduct representative surveys of the electorate. We demonstrate the performance of our method in producing a representative sample of the future electorate that can be used to study campaign dynamics and many other issues. We compare pre-election outcome forecasts to election outcomes in seven primary and general election surveys conducted prior to the 2008 and 2010 primary and general elections in three states. The results indicate that the methodology produces representative samples, including in low-turnout elections such as primaries where traditional methods have difficulty consistently sampling the electorate. This new methodology combines Probability Proportional to Size (PPS) sampling, mailed invitation letters, and online administration of the questionnaire. The PPS sample is drawn based on a model employing variables from the publicly available voter file to produce a probability of voting score for each individual voter. The proposed method provides researchers a valuable tool to study the attitudes of the voting public.

The Internet offers a number of advantages as a survey mode: low marginal cost per completed response, capabilities for providing respondents with large quantities of information, speed, and elimination of interviewer bias. Those seeking these advantages confront the problem of representativeness both in terms of coverage of the population and capabilities for drawing random samples. Two major strategies have been pursued commercially to develop the Internet as a survey mode. One strategy, used by Harris ...

The concept of electoral competition is relevant to a variety of research agendas in political science, yet the question of how to measure electoral competition has received little direct attention. We revisit the distinction proposed by Giovanni Sartori between competition as a structure or rule of the game and competitiveness as an outcome of that game and argue that to understand which elections can be lost (and therefore when parties and leaders are potentially threatened by electoral accountability), scholars may be better off considering the full range of elections where competition is allowed. We provide a data set of all national elections between 1945 and 2006 and a measure of whether each election event is structured such that the competition is possible. We outline the pitfalls of other measures used by scholars to define the potential for electoral competition and show that such methods can lead to biased or incomplete findings. The new global data on elections and the m...

Previous work showed that, when partially autonomous central banks (CBs) and politically responsive governments share monetary-policymaking control, inflation occurs as a convex combination of the rates that would have occurred if the government fully controlled monetary policy and if, instead, the CB had full control. Thus, the anti-inflation effects of CB autonomy and conservatism (CBA) depend on all political-economic variables to which CBs and governments would respond differently and, vice versa, the degree of CBA mutes the inflation effects of all such variables. This paper extends that logic of shared policy-control to open political economies under differing exchange-rate regimes and degrees of international financial exposure. When domestic monetary authorities fully commit to fixed exchange-rates, they effectively delegate domestic inflation-control to foreign authorities. Similarly, in small, financially open economies, domestic policymakers must match domestic to foreign inflation to avoid massive exchange-rate pressures under any exchange regime. The new implications extend intuitively from the closedeconomy case. Broadly, the domestic-inflation effects of pegs and financial exposure depend on each other and on many other institutional and structural characteristics of the domestic and foreign political economies, and, vice versa, the domestic-inflation effects of domestic and foreign politicaleconomic conditions depend on exchange regimes and degrees of financial openness at home and abroad. The paper then shows how to model such complexly interactive hypotheses empirically compactly and substantively meaningfully and demonstrates that the postwar inflation records of 21 developed democracies decidedly favor such specifications over less-theoretically-informed linear or linear-interactive alternatives. The conclusion illustrates several specific examples of this general result and discusses potential applications of this approach to other instances of shared policycontrol.

The authors present the results of parallel telephone and Internet surveys to investigate their comparability. The telephone survey was administered to a national probability sample based on random digit dialing. The contemporaneous Internet survey was administered to a random sample of the data base of willing respondents assembled by Harris Interactive. The survey was replicated by Harris Interactive six months later, and by Knowledge Networks, which employs a randomly recruited panel, nine months later. The data facilitate comparisons in terms of demographic characteristics, environmental knowledge, and political opinions across survey modes. Knowledge and opinion questions generally show statistically significant but substantively modest difference across modes. With inclusion of standard demographic controls, typical relational models of interest to political scientists produce similar estimates of parameters across modes. The use of commercial Internet samples may thus already be reasonable for many types of social science research.

The directional and proximity models offer dramatically different theories for how voters make decisions and fundamentally divergent views of the supposed microfoundations on which vast bodies of literature in theoretical rational choice and empirical political behavior have been built. We demonstrate here that the empirical tests in the large and growing body of literature on this subject amount to theoretical debates about which statistical assumption is right. The key statistical assumptions have not been empirically ...

We advocate for an experimental approach to the study of personality and politics. In particular, we propose an "interactionist" model of political behavior in which the cognitive and behavioral effects of dispositional variables are qualified by experimentally-induced contexts. Our operating assumption is that the political effects of personality do not occur in a contextual vacuum, but instead are magnified by the presence of key precipitating or "activating" features of the political environment. We illustrate the approach with four experimental studies of authoritarianism. Results indicate that the effects of authoritarianism depend critically on the presence of situationally-induced threat. More generally, we argue that interactions between personality variables and experimental treatments can lead to valuable insights about when and why personality will make a meaningful contribution to public opinion and political behavior. Finally, we close with a critique of the traditional skepticism toward experimentation in political science, and suggest that external validity is an overrated virtue when the research goal is the development of theory rather than the description of "real world" phenomena.

Political parties are active when citizens choose among candidates in elections and when winning candidates choose among policy alternatives in government. But the inextricably linked institutions, incentives, and behavior that determine these multistage choices are substantively complex and analytically unwieldy, particularly if modeled explicitly and considered in total, from citizen preferences through government outcomes. To strike a balance between complexity and tractability, we modify standard spatial models of electoral competition and governmental policy-making to study how components of partisanship-such as candidate platform separation in elections, party ID-based voting, national partisan tides, and party-disciplined behavior in the legislature-are related to policy outcomes. We define partisan bias as the distance between the following two points in a conventional choice space: the ideal point of the median voter in the median legislative district and the policy outcome selected by the elected legislature. The study reveals that none of the party-in-electorate conditions is capable of producing partisan bias independently. Specified combinations of conditions, however, can significantly increase the bias and/or the variance of policy outcomes, sometimes in subtle ways.

Words matter in politics. The rhetoric that political elites employ structures civic discourse. The emergence of social media platforms as a medium of politics has enabled ordinary citizens to express their ideological inclinations by adopting the lexicon of political elites. This avails to researchers a rich new source of data in the study of political ideology. However, existing ideological text-scaling methods fail to produce meaningful inferences when applied to the short, informal style of textual content that is characteristic of social media platforms such as Twitter. This paper introduces the first viable approach to the estimation of individual-level ideological positions derived from social media content. This method allows us to position social media users—be they political elites, parties, or citizens—along a shared ideological dimension. We validate the proposed method by demonstrating correlation with existing measures of ideology across various political contexts and ...

Political scientists overwhelmingly seek evidence of causation by measuring changes in the mean of the distribution of the dependent variable. This article points out that some causal relationships produce changes in the variance, not the mean, of that distribution. It makes the case for variance-altering causation by demonstrating its empirical relevance to political scientists. The article also lays out an array of causal mechanisms-under the general headings of aggregation, contagion, and constraint-in order to demonstrate the logical coherence of variance-altering causation and the many ways in which it can arise. The discussion highlights the often-stringent empirical and logical requirements that must be met if the researcher hopes to make concrete predictions about changes in variance.

We advocate for an experimental approach to the study of personality and politics. In particular, we propose an "interactionist" model of political behavior in which the cognitive and behavioral effects of dispositional variables are qualified by experimentally-induced contexts. Our operating assumption is that the political effects of personality do not occur in a contextual vacuum, but instead are magnified by the presence of key precipitating or "activating" features of the political environment. We illustrate the approach with four experimental studies of authoritarianism. Results indicate that the effects of authoritarianism depend critically on the presence of situationally-induced threat. More generally, we argue that interactions between personality variables and experimental treatments can lead to valuable insights about when and why personality will make a meaningful contribution to public opinion and political behavior. Finally, we close with a critique of the traditional skepticism toward experimentation in political science, and suggest that external validity is an overrated virtue when the research goal is the development of theory rather than the description of "real world" phenomena. Experimentation and Personality, 3