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Below results based on the criteria 'bias'
Total number of records returned: 35

1
Paper
The-Stage Estimation of Stochastic Truncation Models with Limited Dependent Variables
Boehmke, Frederick

Uploaded 04-13-2000
Keywords selection bias
stochastic truncation
maximum likelihood
simulation
monte carlo
initiative
interest groups
Abstract Recent work has made progress in estimating models involving selection bias of a par­ ticularly strong nature: all nonrespondents are unit nonresponders, meaning that no data is available for them. These models are reasonable successful in circumstances where the dependent variable of interest is continuous, but they are less practical empirically when it is latent and only discrete outcomes or choices are observed. I develop a method in this paper to estimate these models that is much more practical in terms of estimation. The model uses a small amount of auxiliary information to estimate the selection equation and these parameters are then used to estimate the equation of interest in a maximum likelihood setting. After presenting monte carlo analysis to support the model, I apply the technique to a substantive problem: which interest groups are likely to turn to the initiative process to achieve their policy goals.

2
Paper
Does Private Money Buy Public Policy? Campaign Contributions and Regulatory Outcomes in Telecommunications
de Figueiredo, Rui

Uploaded 10-06-2006
Keywords campaign contributions
regulation
selection bias
omitted variable bias
Abstract To what extent can market participants affect the outcomes of regulatory policy? In this paper, we study the effects of one potential source of influence – campaign contributions – from competing interests in the local telecommunications industry, on regulatory policy decisions of state public utility commissions. Using a unique new data set, we find, in contrast to much of the literature on campaign contributions, that there is a significant effect of private money on regulatory outcomes. This result is robust to numerous alternative model specifications. We also assess the extent of omitted variable bias that would have to exist to obviate the estimated result. We find that for our result to be spurious, omitted variables would have to explain more than five times the variation in the mix of private money as is explained by the variables included in our analysis. We consider this to be very unlikely.

3
Paper
Respondent Uncertainty of Candidate Issue Positions and Its Effects on Estimates of Issue Salience
Glasgow, Garrett

Uploaded 03-23-1999
Keywords issue salience
uncertainty
coefficient bias
spatial models
Abstract (not transcribed)

4
Paper
Negative Results in Social Science
Lehrer, David
Leschke, Janine
Lhachimi, Stefan
Vasiliu, Ana
Weiffen, Brigitte

Uploaded 11-11-2006
Keywords methodology
negative results
philosophy of science
publication bias
Abstract Do academic publication standards reflect or determine research results? The article proposes minimal criteria for distinguishing useful ‘unpublishable’ results from low-quality research, and argues that the virtues of negative results have been overlooked. We consider the fate these results have suffered thus far, review arguments for and against their publication, and introduce a new initiative—a journal to disseminate negative results and advance debate on their recognition and use.

5
Paper
Logistic Regression in Rare Events Data (revised)
King, Gary
Zeng, Langche

Uploaded 07-09-1999
Keywords rare events
logit
logistic regression
binary dependent variables
bias correction
case-control
choice-based
endogenous selection
selection bias
Abstract This paper is for the \r\nmethods conference; it \r\nis a revised version of \r\na paper that was \r\npreviously sent to the \r\npaper server.

6
Paper
A method for measuring and decomposing electoral bias for the three-party case
Borisyuk, Galina
Johnston, Ron
Rallings, Colin
Thrasher, Michael

Uploaded 03-26-2008
Keywords electoral bias
decomposition of bias
British parliamentary elections
Abstract The paper provides a method for measuring and decomposing electoral bias for the three-party case. It builds on the two-party method first developed by Ralph Brookes in the late 1950s. Modifications to the original Brookes method developed in the early 1990s were designed to capture the third party effect in the overall distribution of bias but that bias continued to be expressed in terms of the two major parties. Recent general election results in Britain continue to show strong voter support for the third party. This new method specifically considers the three party situation and calculates both overall bias and also its decomposition at the 2005 general election. The results from this new method are then compared with those found by the Brookes method for each election held since 1983.

7
Paper
Logistic Regression in Rare Events Data
King, Gary
Zeng, Langche

Uploaded 05-20-1999
Keywords rare events
logit
logistic regression
binary dependent variables
bias correction
case-control
choice-based
endogenous selection
selection bias
Abstract Rare events are binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros (``nonevents''). In many literatures, rare events have proven difficult to explain and predict, a problem that seems to have at least two sources. First, popular statistical procedures, such as logistic regression, can sharply underestimate the probability of rare events. We recommend corrections that outperform existing methods and change the estimates of absolute and relative risks by as much as some estimated effects reported in the literature. Second, commonly used data collection strategies are grossly inefficient for rare events data. The fear of collecting data with too few events has led to data collections with huge numbers of observations but relatively few, and poorly measured, explanatory variables, such as in international conflict data with more than a quarter million dyads, only a few of which are at war. As it turns out, easy procedures exist for making valid inferences when sampling all available events (e.g., wars) and a tiny fraction of non-events (peace). This enables scholars to save as much as 99% of their (non-fixed) data collection costs, or to collect much more meaningful explanatory variables. We provide methods that link these two results, enabling both types of corrections to work simultaneously, and software that implements the methods developed.

8
Paper
The playing field shifts: predicting the seats-votes curve in the 2008 U.S. House election
Kastellec, Jonathan
Gelman, Andrew
Chandler, Jamie

Uploaded 06-01-2008
Keywords Congress
incumbency
partisan bias
seats-votes curve
Abstract This paper predicts the seats-votes curve for the 2008 U.S House elections. We document how the electoral playing field has shifted from a Republican advantage between 1996 and 2004 to a Democratic tilt today. Due to the shift in incumbency advantage from the Republicans to the Democrats, compounded by a greater number of retirements among Republican members, we show that the Democrats now enjoy a partisan bias, and can expect to win more seats than votes for the first time since 1992. While this bias is not as large as the advantage the Republicans held in 2006, it is likely to help the Democrats win more seats than votes and thus expand their majority.

9
Paper
Inference from Response-Based Samples with Limited Auxiliary Information
King, Gary
Zeng, Langche

Uploaded 07-09-1999
Keywords rare events
logit
logistic regression
binary dependent variables
bias correction
case-control
choice-based
endogenous selection
selection bias
epidemiology
Abstract This paper is for the methods conference; it is related to "Logistic Regression in Rare Events Data," also by us; the conference presentation will be based on both papers. We address a disagreement between epidemiologists and econometricians about inference in response-based (a.k.a. case-control, choice-based, retrospective, etc.) samples. Epidemiologists typically make the rare event assumption (that the probability of disease is arbitrarily small), which makes the relative risk easy to estimate via the odds ratio. Econometricians do not like this assumption since it is false and implies that attributable risk (a.k.a. a first difference) is zero, and they have developed methods that require no auxiliary information. These methods produce bounds on the quantities of interest that, unfortunately, are often fairly wide and always encompass a conclusion of no treatment effect (relative risks of 1 or attributable risks of 0) no matter how strong the true effect is. We simplify the existing bounds for attributable risk, making it much easier to estimate, and then suggest one possible resolution of the disagreement by providing a method that allows researchers to include easily available information (such as that the fraction of the population with the disease falls within at most [.001,.05]); this method considerably narrows the bounds on the quantities of interest. We also offer software to implement the methods suggested. We would very much appreciate any comments you might have!

10
Paper
Measurement Error as a Threat to Causal Inference: Acquiescence Bias and Deliberative Polling
Weiksner, G. Michael

Uploaded 06-29-2008
Keywords Causal inference
experiments
acquiescence bias
deliberative polling
measurement error
questionnaire design
Abstract Experiments, unlike observational studies, are rarely criticized for yielding invalid causal inferences. However, I identify measurement error as a threat to causal inference of an experiment. In particular, acquiescence bias, a common and substantial source of measurement error within surveys, may be correlated with experimental manipulations. Using data from a survey experiment embedded in a Deliberative Poll, I find that acquiescence bias causes significant measurement error and that the bias differs before and after deliberation. I conclude that even experimental researchers should heed the recommendation by questionnaire design researchers to refrain from asking agree/disagree questions completely and instead ask only construct-specific questions to avoid this threat to validity.

11
Paper
Selection Bias in a Model of Candidate Entry Decisions
Kanthak, Kristen
Morton, Becky
Gerber, Elisabeth R.

Uploaded 07-13-1999
Keywords selection bias
Poisson estimation
population uncertainty
Abstract In recent years, several states have changed or considered changing their laws regulating how political parties nominate candidates for office. We focus on one potentially important consequence of these changes: How do primary election laws affect candidate entry decisions? We have constructed and solved a formal model of individual candidate behavior in which potential candidates can choose to: 1) enter the electoral competition as major party candidates; 2) enter as minor party candidates; 3) enter as independents; or 4) not enter. Based on our analysis of the model, we hypothesize that the expected utility of each choice is a function, in part, of a state's primary election laws. We test our hypotheses with data on candidate choice from recent US Congressional elections. Estimation of our model is complicated, however, by the fact that we do not observe the choices of potential candidates who choose not to enter (i.e., the sample is truncated) and the observed dependent variable (i.e., candidate choices to run as major party, minor party, or independent candidates) is measured as a discrete, unordered polychotomous choice. We employ a two-stage Heckman (1979)-type estimation procedure that utilizes a Poisson framework for estimating candidate entry rates. We find that our estimates of the effects of electoral institutions on the partisan affiliation decisions of independent candidates are unaffected by sample selection. Our estimates of the partisan affiliation decisions of minor party candidates, however, change when we account for non-random sample selection.

12
Paper
Modeling Sample Selection for Durations with Time-Varying Covariates
Boehmke, Frederick

Uploaded 07-02-2008
Keywords selection
selection bias
duration
time-vary covariates
event history
exchange rates
Abstract We extend previous estimators for duration data that suffer from non-random sample selection to allow for time-varying covariates. Rather that a continuous-time duration model, we propose a discrete-time alternative that models the (constant) effects of sample selection at the time of selection across all years of the resulting spell. Properties of the estimator are compared to those of a naive discrete duration model through Monte Carlo analysis and indicate that our estimator outperforms the naive model when selection is non-trivial. We then apply this estimator to the question of the duration of monetary regimes.

13
Paper
The Influence of the Initiative Process on Interest Groups and Lobbying Techniques
Boehmke, Frederick

Uploaded 09-22-1999
Keywords Initiative
direct democracy
survey analysis
interest groups
lobbying
selection
bias
Abstract I use survey data on interest groups and their activities drawn from four state populations to test hypotheses about the implications of direct democracy for the characteristics and strategic choices of interest groups. I use this data to test predictions about direct democracy's effect for group populations, confirming previous work (Boehmke 1999b) and extending it by exploring more detailed characteristics such as membership and resources. I then link these characteristics to lobbying techniques to test if the initiative process has an impact at the group level. As expected, groups involved in initiative campaigns tend to accentuate outside lobbying strategies, but even groups not currently involved in initiatives are influenced by the possibility of its use. This is because the initiative process alters the characteristics that can be effectively used when attempting to influence policy. The analysis makes use of a technique to correct for heterogeneous response rates across group types. By gathering information about a high percentage of an additional, smaller sample, I am able to correct for this response rate differential through a weighting procedure. The correction is found to have a substantial effect on the results: its absence would leave the researcher to conclude that the initiative plays little role in state interest group activities. This data will also be used to test and correct for possible sample selection bias.

14
Paper
The Split Population Logit (SPopLogit): Modeling Measurement Bias in Binary Data
Beger, Andreas
DeMeritt, Jacqueline
Moore, Will
Hwang, Wonjae

Uploaded 02-03-2009
Keywords binary
limited dependent variables
measurement bias
unobservability
Abstract This study describes a split population logit model that can be useful to researchers who are modeling a binary dependent variable that is measured with a biased instrument. To motivate the study we identify two common, yet widely unrecognized, circumstances in which political scientists are likely to study dichotomous variables that have been measured with bias. In one such setting (e.g., surveys) the strategic interests of actors will lead them to misrepresent an attitude or behavior. In another such setting (e.g., content analysis of events) researchers' instruments are unable to distinguish between the absence of a characteristic or event and missing data. We briefly argue that "unobservability," "zero-inflated," and other models form a single class of models that allow researchers to model the bias in operational instruments, and thus not only correct bias in statistical inference but, more importantly, produce theoretical accounts of the bias and then test the hypotheses that those accounts imply. We derive the likelihood function for the split population logit model, describe the properties of its MLEs, present the results from a Monte Carlo study, and briefly describe code that researchers can use to implement the model in the Stata statistical package.

15
Paper
Voting, Abstention, and Individual Expectations in the 1992 Presidential Election
Herron, Michael C.

Uploaded 04-07-1998
Keywords voting
abstention
selection bias
1992 election
Abstract This paper develops and applies to the 1992 presidential election a statistical model of voting and abstention in three--candidate elections. The model allows us to estimate key preference--related covariates in 1992, the extent to which abstention rates were correlated with political preferences, and the impact on abstention rates of expectations regarding the election winner. Throughout this paper, we contrast our results with those in Alvarez and Nagler (1995), a study of the 1992 election that does not incorporate abstention, and in so doing we illustrate the selection bias risked by presidential election voting research that ignores abstention. Our results highlight the importance of retrospective voting in 1992, and we identify numerous policy issues, for example, the death penalty, environmental spending, and social security, that individuals used to distinguish the three candidates in the 1992 election. Abortion, we find, played only a minor role in candidate choice. We find support for the angry voting hypothesis, namely, that angry individuals often supported the independent candidate, Ross Perot. Concerning abstention, we find that supporters of the Democratic challenger Bill Clinton abstained at higher rates than supporters of Perot and the incumbent president George Bush. And, we find that expectations concerning the likelihood that Clinton was going to be victorious in 1992 influenced abstention rates. Namely, Clinton supporters who believed that Clinton was likely to win voted at higher rates than individuals who believed otherwise. The opposite relation holds for Bush supporters: such individuals, when they predicted a Clinton victory, frequently abstained from voting. The results in this paper suggests that empirical voting studies should explicitly model the impact of expectations on voting and abstention and, more generally, should model abstention as a viable, individual--level

16
Paper
Misspecification and the Propensity Score: When to Leave Out Relevant Pre-Treatment Variables
Clarke, Kevin A.
Kenkel, Brenton
Rueda, Miguel

Uploaded 07-14-2010
Keywords matching
propensity scores
conditioning
omitted variable bias
Abstract The popularity of propensity score matching has given rise to a robust, albeit informal, debate concerning the number of pre-treatment variables that should be included in the propensity score. The standard practice is to include all available pre-treatment variables in the propensity score. We demonstrate that this approach is not always optimal for the goal of reducing bias in the estimation of a treatment effect. We characterize conditions under which including an additional relevant variable in a propensity score increases the bias on the effect of interest across a variety of different implementations of the propensity score methodology. We find that matching within propensity score calipers is slightly more robust against such bias than other common methods.

17
Paper
Time-Series--Cross-Section Issues: Dynamics, 2004
Beck, Nathaniel
Katz, Jonathan

Uploaded 07-24-2004
Keywords Time-series--cross-section data
lagged dependent variables
Nickell bias
specification
integration
Abstract This paper deals with a variety of dynamic issues in the analysis of time-series--cross-section (TSCS) data raised by recent papers; it also more briefly treats some cross-sectional issues. Monte Carlo analysis shows that for typical TSCS data that fixed effects with a lagged dependent variable performs about as well as the much more complicated Kiviet estimator, and better than the Anderson-Hsiao estimator (both designed for panels). It is also shown that there is nothing pernicious in using a lagged dependent variable, and all dynamic models either implicitly or explicitly have such a variable; the differences between the models relate to assumptions about the speeds of adjustment of measured and unmeasured variables. When adjustment is quick it is hard to differentiate between the models, and analysts may choose on grounds of convenience (assuming that the model passes standard econometric tests). When adjustment is slow it may be the case that the data are integrated, which means that no method developed for the stationary case is appropriate. At the cross-sectional level, it is argued that the critical issue is assessing heterogeneity; a variety of strategies for this assessment are discussed.

18
Paper
The Two Faces of Public Opinion
Berinsky, Adam

Uploaded 04-13-1998
Keywords public opinion
selection bias
item non-response
social desirability
bivariate probit
Abstract In this paper I trace out the aggregate effects of the social forces in the survey interview that might influence the opinions which individuals express. First, I advance the "Mediated Communication" theory of the survey response, which builds on existing models of public opinion in the political science literature by accounting for effects related to the social context of the survey setting. I then discuss how the aggregation process could compound these individual-level effects to create an opinion signal which is a poor representation of the collective public's policy preferences. As an illustration of these effects and the resulting difficulties involved in measuring aggregate opinion on socially sensitive issues, I use National Elections Study (NES) data from 1990-1994 to show that public opinion polls overstate support for school integration. Specifically, individuals who harbor anti-integration sentiments are likely to hide their socially unacceptable opinions behind a mask of indifference. Finally, in order to confirm the validity of these findings, I show that the same methods which predict that opinion polls understate true opposition to government involvement in school integration also predict the results of the 1989 New York City mayoral election -- an election where the charged racial atmosphere made accurate polling difficult, if not impossible -- more accurately than the marginals of the pre-election polls taken in the weeks leading to the election. All told, these results suggests that survey questions on school integration -- and more generally questions on racial attitudes -- may provide an inaccurate picture of true public sentiment on such sensitive issues.

19
Paper
The Split Population Logit (SPopLogit): Modeling Measurement Bias in Binary Data
Beger, Andreas
DeMeritt, Jacqueline
Hwang, Wonjae
Moore, Will

Uploaded 02-28-2011
Keywords split populations
binary data
measurement error
bias
zero inflation
substantive inference
Abstract Researchers frequently face applied situations where their measurement of a binary outcome suffers from bias. Social desirability bias in survey work is the most widely appreciated circumstance, but the strategic incentives of human beings similarly induce bias in many measures outside of survey research (e.g., whether the absence of an armed attack indicates a country's satisfaction with the status quo or a calculation that the likely costs of war outweigh the likely benefits). In these circumstances the data we are able to observe do not reflect the distribution we wish to observe. This study introduces a statistical model that permits researchers to model the process that produces the bias, the split population logit (SPopLogit) model. It further presents a Monte Carlo simulation that demonstrates the effectiveness of the SPopLogit model, and then re-analyzes a study of sexual infidelity to illustrate the richness of the quantities of (empirical and theoretical) interest that can be estimated with the model. {\tt Stata ado} files that can be used to invoke the SPopLogit, as well as batch files that illustrate how to simulate commonly reported quantities of interest, are available for download from the WWW. The authors close by briefly identifying just a few of the many types of research projects that will benefit from abandoning logit and probit models in favor of the SPopLogit. NOTE: Files to implement SPopLogit and generate quantities of interest, as well as replication files for our Monte Carlo simulations and substantive application, are available at http://andybeger.wordpress.com/2011/02/03/split-population-logit/

20
Paper
Empirical Modeling Strategies for Spatial Interdependence: Omitted-Variable vs. Simultaneity Biases
Hays, Jude
Franzese, Robert

Uploaded 07-24-2004
Keywords Spatial Lag Models
Diffusion
Omitted Variable Bias
Simultaneity Bias
Abstract Scholars recognize that time-series-cross-section data typically correlate across time and space, yet they tend to model temporal dependence directly while addressing spatial interdependence solely as nuisance to be “corrected” (FGLS) or to which to be “robust” (PCSE). We demonstrate that directly modeling spatial interdependence is methodologically superior, offering efficiency gains and generally helping avoid biased estimates even of “non-spatial” effects. We first specify empirical models representing two modern approaches to comparative and international political economy: (context-conditional) open-economy comparative political-economy (i.e., common stimuli, varying responses) and international political-economy, which implies interdependence (plus closed-economy and orthogonal-open-economy predecessors). Then we evaluate four estimators—non-spatial OLS, spatial OLS, spatial 2SLS-IV, and spatial ML—for analyzing such models in spatially interdependent data. Non-spatial OLS suffers from potentially severe omitted-variable bias, tending to inflate estimates of common-stimuli effects especially. Spatial OLS, which specifies interdependence directly via spatial lags, dramatically improves estimates but suffers a simultaneity bias, which can be appreciable under strong interdependence. Spatial 2SLS-IV, which instruments for spatial lags of dependent variables with spatial lags of independent variables, yields unbiased and reasonably efficient estimates of both common-stimuli and diffusion effects, when its conditions hold: large samples and fully exogenous instruments. A tradeoff thus arises in practice between biased-but-efficient spatial OLS and consistent- (or, at least, less-biased-) but-inefficient spatial 2SLS-IV. Spatial ML produces good estimates of non-spatial effects under all conditions but is computationally demanding and tends to underestimate the strength of interdependence, appreciably so in small-N samples and when the true diffusion-strength is modest. We also explore the standard-error estimates from these four procedures, finding sizable inaccuracies by each estimator under differing conditions, and PCSE’s do not necessarily reduce these inaccuracies. By an accuracy-of-reported-standard-errors criterion, 2SLS-IV seems to dominate. Finally, we explore the spatial 2SLS-IV estimator under varying patterns of interdependence and endogeneity, finding that its estimates of diffusion strength suffer only when a condition we call cross-spatial endogeneity, wherein dependent variables (y’s) in some units cause explanatory variables (x’s) in others, prevails.

21
Paper
Bias and Responsiveness in Multiparty and Multigroup Representation
Monroe, Burt L.

Uploaded 07-21-1998
Keywords partisan bias
responsiveness
seats and votes
electoral systems
compositional data
JudgeIt
Abstract There is an extensive and expanding literature that examines methods for estimating the responsiveness and partisan bias of two-party electoral systems. Attempts to extend these methods into the multiparty domain appropriate for the vast majority of electoral systems, or to the analysis of the representation of other types of groups (e.g., regions, ethnic groups), have been limited. I describe index, multiyear, uniform swing, and variable swing methods -- along with novel graphical displays -- for analyzing seats-votes curves, bias, and responsiveness in multiparty systems. The variable swing method is a multiparty generalization of Gelman and King's "JudgeIt" model. Examples discussed include elections in the UK, Mauritius, and Costa Rica, and geographic representation worldwide. In comparing the various methods it is argued that variable swing is ideal for most applications, that uniform swing and index methods provide useful answers to a limited set of questions despite faulty assumptions, and that multiyear methods are generally not useful.

22
Paper
Selection Bias and Continuous-Time Duration Models: Consequences and a Proposed Solution
Boehmke, Frederick
Morey, Daniel
Shannon, Megan

Uploaded 07-15-2003
Keywords duration
selection bias
exponential
monte carlo
Abstract In this paper we explore the consequences of non-random sample selection for continuous time duration analysis. While the consequences of selectivity are reasonably well-understood in linear regression and common discrete choice models, we have little or no understanding of how it affects duration models. In this paper we study this issue by conducting a series of Monte Carlo analyses that estimate common duration models on data that suffer from selectivity. Our findings indicate that the consequences are severe: both coefficients and standard errors may be biased in an unknown direction. In addition, we find that selection bias may create the appearance of (non-existent) duration dependence. Given these difficulties, we develop a solution for self-selectivity bias in duration models and present evidence that demonstrates its superiority to models that ignore the problem.

23
Paper
The "Miracle" Revisited: An Examination of The Micro-Foundations of Aggregate Public Opinion
Berinsky, Adam

Uploaded 08-18-1997
Keywords public opinion
heteroskedastic probit
ordered probit
selection bias
item non-response
Abstract One of the best-known findings in the public opinion literature is that individual responses to survey questions, by and large, both exhibit little constraint and are highly unstable over time. One response to this bleak finding has been to search for coherence and stability at the aggregate level. Scholars who adopt this approach -- most notably Page and Shapiro (1992) -- argue that though most individuals are poorly informed about politics and may have unstable attitudes, the "miracle TRUNCATED.

24
Paper
A Copula Approach to the Problem of Selection Bias in Models of Government Survival
Chiba, Daina
Martin, Lanny
Stevenson, Randy

Uploaded 01-02-2014
Keywords selection bias
copula theory
duration models
government survival
government formation
Abstract Recent theories of coalition politics in parliamentary democracies suggest that government formation and survival are jointly determined outcomes. An important empirical implication of these theories is that the sample of observed governments analyzed in studies of government survival may be nonrandomly selected from the population of potential governments. This can lead to serious inferential problems. Unfortunately, current empirical models of government survival are unable to account for the possible biases arising from nonrandom selection. In this study, we use a copula-based framework to assess, and correct for, the dependence between the processes of government formation and survival. Our results suggest that existing studies of government survival, by ignoring the selection problem, significantly overstate the substantive importance of several covariates commonly included in empirical models.

25
Paper
Individual Choice and Ecological Analysis
McCue, Kenneth F.

Uploaded 12-02-2001
Keywords ecological regression
voter transitions
multivariate multinomial
split-ticket voting
aggregation bias
liner probability model
Abstract The use of the linear probability model in aggregate voting analysis has now received widespread attention in political science. This article shows that when the linear probability model is assumed to be consistent for the choice of the individual, it is actually a member of a general class of models for estimating individual responses from aggregate data. This class has the useful property that it defines the aggregate analysis problem as a function of the individual choice decisions, and allows the placement of most aggregate voting models into a common probabilistic framework. This framework allows the solution of such problems as inference of individual responses from aggregate data, estimation of the transition model, and the joint estimation and inference from individual and aggregate data. Examples with actual data are provided for these techniques with excellent results.

26
Paper
Legislative Entrepreneurship and Campaign Finance
Wawro, Gregory

Uploaded 07-21-1997
Keywords campaign finance
fixed effects
panel data
selection bias
Abstract Drawing on models of service--induced and investor PAC campaign contributions, I analyze the role that legislative entrepreneurship plays in PACs' contribution decisions. I explore the possibility that PACs use campaign contributions to invest in members of Congress with the expectation that members will reciprocate by engaging in entrepreneurial behavior to the benefit of PACs. To determine whether a relationship exists between legislative entrepreneurship and PAC contributions I compute measures of entrepreneurial behavior for individual members of the U.S. House using detailed data on bill sponsorship and congressional hearings from the 97th through the 101st Congress. In order to cleanly estimate the effects of legislative entrepreneurship, we need to account for unobservable member--specific factors that enter into the PAC contribution calculus. To account for such factors I employ panel data methods which require very few assumptions about the data and provide a way to test whether the manipulations of the data that are required for a panel analysis introduce bias.

27
Paper
Distortion magnified: New Labour and the British
Johnston, Ron
Rossiter, David
Charles, Pattie
Dorling, Danny

Uploaded 07-26-2001
Keywords electoral bias
Britain
gerrymander
Abstract UK election results show not only the characteristic disproportionality associated with plurality systems but also considerable bias in the allocation of seats relative to votes for the two main political parties (Conservative and Labour). Over the period 1950-1997 (the 1950 election was the first using constituencies defined by independent Boundary Commissions) this bias both increased and shifted from favouring the Conservatives to favouring Labour. By 1997, Labour would have won 82 more seats than Conservative with equal vote shares - the largest bias recorded for the period: and then in 2001 the pro-Labour bias increase to 141. This paper explores the reasons for this shift, using a procedure developed by Brookes for measuring and decomposing bias. Labour benefited because of the geography of iots successful campaigns in 1997 and 2001.

28
Paper
Heterogeneity and Bias in Models of Vote Choice
Berinsky, Adam

Uploaded 04-21-1997
Keywords voting models
selection bias
heteroskedasticity
missing data
Abstract Voters in the United States do not behave in a homogenous manner. Voting models typically account for such heterogeneity by seeking to decompose the process of vote choice into a number of distinct components. By examining voting choice data in this way, researchers are able to ascertain reasonable estimates of the average effect of various socio-economic and political variables on the candidate selection process. Models of this sort, while plausible, may not properly reflect the true heterogeneity of the American voter. At their core, simple models assume that voters use a common and uniform decision rule when deciding between candidates. But it is possible, if not likely, that different groups and classes of citizens use differently tructured processes to determine their choice of candidates. Searchers have attempted to account for this heterogeneity in a variety of ways. Rivers(1988) and Jackson (1992), for example, have accounted for differences in the voting behavior of individuals by allowing the mean effect of theoretically important variables to vary across individuals. While these approaches are extremely promising, in this paper I will take a different approach and examine three more subtle forms of heterogeneity in the vote choice process: (1) heterogeneity induced by non-random selection from the full population of citizens into the vote choice model sample; (2) heterogeneity due to the interaction of selection bias and non-constant variance; and (3) heterogeneity in the patterns of missing data across groups of the respondents. While much of the discussion in the paper is focused on the first two forms of heterogeneity, it is the third form of heterogeneity - one not typically addressed in the political science literature - that is the most important determinant of the degree of bias in vote choice models. Thus, heterogeneity within the sample of respondents affects the vote choice model estimates, just not in the way I originally envisioned. It is not just heterogeneity in the variance term, or in the selection into the vote choice process that poses a threat to accurate estimates of the power of the predictors in our vote choice models. Rather, it is the failure to preserve or account for the heterogeneity of the paths by which people answer survey questions that is the real bogeyman of vote choice models.

29
Paper
An Estimator for Some Binary-Outcome Selection Models without Exclusion Restrictions
Sartori, Anne E.

Uploaded 07-09-2001
Keywords selection bias
discrete choice
small-sample properties
Abstract This paper provides a new estimator for selection models with dichotomous dependent variables when identical factors affect the selection equation and the equation of interest. Such situations arise naturally in game-theoretic models where selection is typically nonrandom and identical explanatory variables influence all decisions under investigation. When its own identifying assumption is reasonable, the estimator allows the researcher to avoid the painful choice among identifying from functional form alone (using a Heckman-type estimator), adding a theoretically unjustified variable to the selection equation in a mistaken attempt to "boost" identification, or giving upon estimation entirely. The paper compares the small-sample properties of the estimator with those of the Heckman- type estimator and ordinary probit using Monte Carlo methods. A brief analysis of the causes of enduring rivalries and war, following Lemke and Reed (2001),

30
Paper
Death by Survey: Estimating Adult Mortality without Selection Bias
King, Gary
Gakidou, Emmanuela

Uploaded 07-14-2005
Keywords surveys
selection bias
mortality data
extrapolation
international relations
Abstract The widely used methods for estimating adult mortality rates from sample survey responses about the survival of siblings, parents, spouses, and others depend crucially on an assumption that we demonstrate does not hold in real data. We show that when this assumption is violated -- so that the mortality rate varies with sibship size -- mortality estimates can be massively biased. By using insights from work on the statistical analysis of selection bias, survey weighting, and extrapolation problems, we propose a new and relatively simple method of recovering the mortality rate with both greatly reduced potential for bias and increased clarity about the source of necessary assumptions.

31
Paper
Using Auxiliary Data to Estimate Selection Bias Models
Boehmke, Frederick

Uploaded 07-06-2001
Keywords selection bias
two-stage estimation
survey design
initiative
interest groups
Abstract Recent work has made progress in estimating models involving selection bias of a particularly strong nature: all nonrespondents are unit nonresponders, meaning that no data is available for them. These models are reasonable successful in circumstances where the dependent variable of interest is continuous, but they are less practical empirically when it is latent and only discrete outcomes or choices are observed. I develop a method in this paper to estimate these models that is much more practical in terms of estimation. The model uses a small amount of auxiliary information to estimate the selection equation parameters which are then held fixed to estimate the equation of interest parameters in a maximum likelihood setting. After presenting monte carlo analysis to support the model, I apply the technique to a substantive problem: which interest groups are likely to to be involved in support of potential initiatives to achieve their policy goals.

32
Paper
Estimating Incumbency Advantage and Campaign Spending Effect without the Simultaneity Bias
Fukumoto, Kentaro

Uploaded 07-16-2006
Keywords Incumbency Advantage
Campaign Spending
Simultaneity Bias
Bayesian Nash equilibria
normal vote
Abstract In estimating incumbency advantage and campaign spending effect, simultaneity problem is composed of stochastic dependence and parametric dependence. Scholars have tried to solve the former, while the present paper intends to tackle the latter. Its core idea is to estimate parameters by maximizing likelihood of all endogenous variables (vote, both parties' candidate qualities and campaign spending) simultaneously. In order to do it, I take advantage of theories of electoral politics rigorously, model each endogenous variables by the others (or their expectation), derive Bayesian Nash equilibria, and plug them into my estimator. I show superiority of my model compared to the conventional estimators by Monte Carlo simulation. Empirical application of this model to the recent U.S. House election data demonstrates that incumbency advantage is smaller than previously shown and that entry of incumbent and strong challenger is motivated by electoral prospect.

33
Paper
A New Look at Cold War Presidents' Use of Force: Aggregation Bias, Truncation, and Temporal Dynamic Issues
Mitchell, Sara McLaughlin
Moore, Will H.

Uploaded 09-07-2000
Keywords aggregation bias
truncation bias
use of force
PAR
Abstract This study re-examines the findings reported in a seminal study of US presidents' use of force during the Cold War (Ostrom and Job 1986). We identify three methodological issues that affect inferences drawn in studies of presidential decisions to use force: aggregation, truncation, and dynamics. We suggest that a dichotomous measure of uses of force introduces aggregation bias, while the decision to examine only major uses of force introduces truncation bias. To address these issues, we compare two types of use of force measures (dichotomous and event count), in addition to comparing results for major, minor, and all uses of force. In addition, we argue that Ostrom and Job's focus on rivalry leads one to anticipate the presence of temporal dependence or dynamics in the use of force series. We estimate a Poisson Autoregressive (PAR) model proposed by Brandt and Williams (2000), which is able to account for temporal dynamics in an event count model. Our findings demonstrate the importance of these three methodological issues. Results of the PAR model show dynamics in the use of force series. We also find that international variables have a larger substantive effect on the president's decision to use force than do the domestic variables. Our study thus overturns the most dramatic finding reported in the Ostrom and Job study, a finding that we contend was driven by bias and model specification problems.

34
Paper
Can political science literatures be believed? A study of publication bias in the APSR and the AJPS
Gerber, Alan
Malhotra, Neil

Uploaded 09-07-2006
Keywords publication bias
Abstract Despite great attention to the quality of research methods in individual studies, if the publication decisions of journals are a function of the statistical significance of research findings, the published literature as a whole may not produce an accurate measure of true effects. This paper examines the two most prominent political science journals (the APSR and the AJPS) and two major literatures in the discipline (the effect of negative advertisements and economic voting) to see if there is evidence of publication bias. We examine the effect of the .05 significance level on the pattern of published findings using what we term a “caliper” test and can reject the hypothesis of no publication bias at the 1 in 100,000,000 level. Our findings therefore strongly suggest that the results reported in the leading political science journals and in two important literatures are misleading and inaccurate due to publication bias. We also discuss some of the reasons for publication bias and propose reforms to reduce its impact on research.

35
Poster
Boundary that Matters? Causal Inference of the School Quality Effect on Land Prices
Fukumoto, Kentaro

Uploaded 07-18-2013
Keywords spatial differences-in-differences
endogeneity bias
measurement error
omitted variable bias
hedonic model
school quality
land prices
capitalization
Abstract According to the hedonic model, the effect of areal policy such as school quality is reflected, or capitalized, in land prices. The conventional OLS, however, suffers from endogeneity bias, measurement error, and omitted variable bias. To solve these problems, this paper proposes spatial differences-in-differences (DID). We match literally the nearest two sample points in a small block to make a pair. If the two points belong to different school-attendance areas, the pair is a treatment pair. Otherwise, the pair is a control pair. If school quality matters for land price, the variance of pairwise land price gap of the treatment pairs should be larger than that of the control pairs. Another new method, spatial and temporal DID, exploits introduction of school choice program to improve robustness against omitted variable bias. When applying these methods to data of Tokyo, F-test fails to reject the null hypothesis. We show, however, that land use zoning and floor area ratio have effects on land price by using spatial DID.


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