David Darmofal

Elite Cues and Citizen Disagreement with Expert Opinion

Though scholars have long been concerned about the quality of citizens’ political decision making, we still know little about why citizens disagree with the best-informed opinion in society, that of public policy experts. In this article, I examine the factors that lead citizens to disagree with expert opinion on questions of public policy. I find that both elite cues and individual-level attributes of citizens lead individuals to disagree with experts. In contrast to the expectations of many recent studies of cue taking, I find that citizens are more likely to disagree with expert opinion when political elites they favor challenge this opinion. Citizens also disagree with experts as a consequence of low levels of knowledge, existing policy preferences, and life experiences. The study’s results challenge the optimistic conclusions of many recent studies of cue taking and argue that there is significant value in the conventional wisdom that preceded these studies. Elite cues are not a consistent means to effective policy judgments. Instead, when it comes to reaching effective policy decisions, there is no substitute for knowledge.

The Aggregate Dynamics of Campaigns

Daily interactions between partisan elites, the media, and citizens are the driving dynamic of election campaigns and the central determinant of their outcomes. Accordingly, we develop a theory of campaign dynamics that departs from previous top-down models of campaign effects in its emphasis on the reciprocal campaign interactions between these actors. We examine these interactions with daily data on campaign expenditures, media coverage, and voter support in the 2000 presidential campaign. We find that partisan elites, the media, and citizens each played critical and interdependent roles in creating the dynamics of the campaign and producing the closest election in decades. We also find that the Gore campaign was hindered by its delayed responsiveness to the Bush campaign and its unwillingness to reinforce positive media coverage of Gore with increased campaign expenditures.

The Political Geography of the New Deal Realignment

Political geography has long played a prominent role in conceptions of political realignments. In this article, I apply a spatial analysis to examine the political geography of voting during one of the principal political realignments in American electoral history, the 1928—1936 Democratic realignment. The spatial analysis challenges some of our common conceptions of this realignment. For example, increased support for the Democrats and Al Smith in 1928 was not limited to urban areas, as Smith enjoyed widespread increases in Democratic support in largely rural Western locations. In the 1932 election, unemployment actually impeded shifts toward Franklin Roosevelt and the Democrats in most locales. Changes in voter support during this period were highly localized and subnational. Geographically Weighted Regressions demonstrate that this localized political geography was shaped by extensive geographic variation in how political and demographic factors influenced voting behavior across the United States.

Bringing Together Spatial Demography and Political Science: Reexamining the Big Sort

In this chapter we examine the arguments in Bishop’s The Big Sort: Why the Clustering of Like-Minded America is Tearing us Apart from the perspective of a stronger synthesis between demographers and political scientists. We argue that such a synthesis can provide considerable insights into the question of the geographic sorting of partisans. After examining critiques leveled by political scientists against the analyses in The Big Sort, we examine the quite limited consideration of migration studies in Bishop’s book. Here, we identify four central limitations in the book that are produced by this inattention to migration studies. We conclude by examining the opportunity that The Big Sort and its arguments provide for the movement away from research silos and toward greater interdisciplinary research on migration-induced political polarization. Ironically, if Bishop is correct that “the clustering of like-minded Americans is tearing us apart,” the clustering of like-minded scholars – demographers and political scientists engaging in a closer, more fruitful dialogue – may provide us with insights that can help remedy any negative effects of geographic polarization.

The Social Sciences and Spatial Analysis

“[F]ull information should be given as to the degree in which the customs of the tribes and races which are compared together are independent. It might be, that some of the tribes had derived them from a common source, so that they were duplicate copies of the same original. …It would give a useful idea of the distribution of the several customs and of their relative prevalence in the world, if a map were so marked by shadings and colour as to present a picture of their geographical ranges.” Sir Francis Galton at The Royal Anthropological Institute, 1888 The Journal of the Anthropological Institute of Great Britain and Ireland 18: 270. INTRODUCTION Concepts of space and geography play prominent roles in many social science theories. In fields as diverse as anthropology, criminology, demography, political science, sociology, and public health, our theories predict that spatially proximate units are more likely to behave similarly than spatially distant units. These theories, in short, predict positive spatial autocorrelation or spatial dependence, the spatial clustering of similar behaviors, processes, and events among neighboring observations. This common interest in geography across the social sciences is not surprising. The social sciences are defined by their focus on phenomena that are inherently social and interdependent. Shared concerns combine with spatial proximity to promote familiarity. This familiarity in turn breeds both contempt and conflict and interaction and interdependence. Until recently our ability to incorporate the spatial dimension of our theories in our models was quite limited, relying primarily on dummy variables to capture differences in behavior across geographically disparate units. Such an approach is suboptimal, as it is unable to address some of the central issues posed by spatially dependent data. Consider, for example, Sir Francis Galtons comment in the epigraph to this chapter. Sir Galtons comment in response to Edward Tylors presentation at the Royal Anthropological Institute in November 1888 clearly ranks among the most influential comments expressed at an academic presentation, remembered as it is more than a century later. Sir Galtons critique, which has since come to be known as Galtons problem, focuses on the critical substantive distinction between two alternative explanations for spatially dependent behavior.

Diagnosing Spatial Dependence in the Presence of Covariates

When employing the spatial diagnostics examined in Chapter 4, social scientists will often find evidence of spatial autocorrelation (see, e.g., Eff 2004). As discussed in Chapter 1, this predisposition of social science data toward spatial autocorrelation often results from interdependence between the units studied by social scientists. In other cases, social science data exhibit spatial dependence not as a result of behavioral interdependence but as a consequence of spatial clustering in the sources of behaviors of interest to social scientists. The spatial dependence, in short, may be consistent with either a spatial lag model or a spatial error model. Substantive theory will often lead scholars to believe that a spatial lag specification or a spatial error specification is more appropriate for their particular substantive application. Scholars may, for example, expect that a spatial diffusion process is at work and thus believe that a spatial lag model is warranted. Although such a specification may seem appropriate, such a theoretical expectation should not go untested. It would be inappropriate to estimate a diffusion model with a spatially lagged dependent variable if the spatial dependence diagnosed via, for example, the univariate Morans I , is instead produced by spatial clustering in the sources of otherwise independent behaviors. This model misspecification will lead the researcher to inappropriate substantive inferences about the nature of the spatial dependence in her data. Inappropriate spatial model specification is all the more problematic because of the close mathematical relationship between a spatial lag model and a spatial error model with spatial autoregressive error dependence. As this chapter will discuss, a spatial autoregressive error model can be rewritten as a spatial Durbin model with both spatially lagged dependent and independent variables if a set of nonlinear common factor constraints are valid. Because of this close relationship between spatial autoregressive dependence in a spatial lag model and spatial autoregressive dependence in a spatial error model, a significant spatial parameter in a spatial lag model may reflect spatial clustering in omitted sources of the behavior of interest rather than true spatial lag dependence consistent with a diffusion process.

Defining Neighbors via a Spatial Weights Matrix

THE IMPORTANCE OF SPACE IN THE SOCIAL SCIENCES All social science data are spatial data. The behaviors, processes, and events we seek to explain occur at specific geographic locations. As discussed in Chapter 1, these geographic locations are often central to our understanding of these phenomena. Consider, for example, research on behavioral interactions between units in shared networks (see, e.g., Huckfeldt and Sprague 1987, 1988). Research has shown that spatial proximity affects the nature of interactions between actors in these networks (Baybeck and Huckfeldt 2002). This mirrors a long line of research in international relations that has found that spatial proximity between countries promotes interactions between countries (Most and Starr 1980; Starr 2002). This spatial proximity in turn affects a variety of behaviors and processes of interest to scholars and observers alike, including democratization (Gleditsch andWard 2006), civil wars (Salehyan and Gleditsch 2006; Gleditsch 2007), and war (Gleditsch and Ward 2000). Similarly, consider the interest of both observers and scholars in the causes and consequences of poverty (see, e.g., Wilson 1987). Here, both researchers and pundits have recognized that geographic locations marked by deep poverty are increasingly segregated from economic opportunity and the opportunity for the residents in these locations to participate fully in American society. Inherent here again is the recognition that geography matters and that understanding the factors that produce poverty at the local level is a critical first step in producing policy options that can alleviate this poverty and produce positive outcomes for both the residents of these locations and for society as a whole. These are but two of many prominent examples that reflect a growing interest in spatial concerns within the social sciences. Its easy to think of a myriad of additional examples that highlight how we are increasingly becoming attuned to the importance of geography in our lives.

Getting Data Ready for a Spatial Analysis

Spatial data require some minimal work at the early stages of analysis to get these data ready for a spatial analysis. This work is not onerous and can typically be completed in just a few minutes. However, the steps in this work are unique to spatial data and many researchers in the social sciences are not familiar with these steps. As a consequence, it is useful to briefly discuss them. Researchers interested in exploring the details of these steps will find additional, detailed information on them in manuals for geographic information systems (GIS) and other software. The main issue in setting up data for a spatial analysis is that datasets in the social sciences do not include geometric information on the units the researcher is seeking to examine. To be sure, these data do typically include identifiers and use of these geographic identifiers, such as Federal Information Processing Standards (FIPS) codes, is very helpful in linking social science data up to files that contain features of the polygons examined, such as their size, boundaries, and locations. But typically, social science data do not include these latter features themselves, and thus researchers must link, or join, their datasets to a file that contains this information. The standard such geographic file in spatial analysis is the shapefile developed by Environmental Systems Research Institute (ESRI) for use with its Arcview GIS software in the early 1990s. Today, ESRIs principal GIS product is ArcGIS, which includes a suite of component applications, including ArcMap, ArcCatalog, ArcScene, and ArcGlobe. One of the more efficient ways to join ones data is to do so in ArcMap, utilizing an identifier variable that is included in both the shapefile and in the researchers dataset. Often this variable will be the units’ FIPS codes. Shapefiles are readily available for a variety of polygons of interest via the Internet. Many colleges and universities have GIS units on campus that can aid researchers in finding shapefiles if a search on theWeb proves unsuccessful.

Web Resources for Spatial Analysis

Several helpful websites and listservs for spatial analysis have been developed as the use of spatial techniques has increased over the past two decades. This appendix considers some of the most helpful Web resources on spatial analysis in these two categories. WEBSITES AI-GEOSTATS (http://www.ai-geostats.org/): This website provides a variety of resources for geostatistics and spatial analysis. Among these are links to software, papers, books, conferences, and job openings. The site also provides links to the AI-GEOSTATS mailing list. Center for Spatially Integrated Social Science (CSISS) (http://www.csiss.org/): Funded in 1999 by the National Science Foundation, The Center for Spatially Integrated Social Science (CSISS) (housed at the University of California, Santa Barbara) is designed to advance the dissemination of spatial techniques and perspectives in the social sciences. The CSISS website provides a variety of resources for social scientists wishing to apply spatial analysis in their research. Among these are a searchable database of more than 17,000 references featuring spatial applications in the social sciences from 1990 to 2004. The website also includes video clips of workshops, descriptions of “classic” geographically oriented studies in the social sciences, and links to software tools. GeoDa Center (http://geodacenter.asu.edu/): Founded by Luc Anselin, the GeoDa Center for Geospatial Analysis and Computation at Arizona State University has become a leading repository for spatial studies, the dissemination of spatial software tools, and the provision of training and support. The website provides a link for downloading GeoDa and also provides tutorials for GeoDa and for spatial packages in R. The site also provides an extensive set of e-talks, lectures on spatial analysis, tools, and techniques. The website also provides links to recent working papers by scholars affiliated with the GeoDa Center as well as to spatial data that can be downloaded for analysis. GISpopsci.org (http://gispopsci.org) : This project is a collaboration between the Population Research Institute (The Pennsylvania State University) and the Center for Spatially Integrated Social Science (University of California, Santa Barbara). The site, whose development was funded in part by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), provides a variety of resources for research and instruction on advanced spatial analysis in the population sciences and spatial demography. The website maintains an extensive bibliography of citations applicable to research in spatial demography and the population sciences. The site also contains links to course syllabi on spatial topics.

Spatial Lag and Spatial Error Models

If the ordinary least squares (OLS) diagnostics discussed in the previous chapter indicate the existence of spatial lag or spatial error dependence, the researcher will wish to model the type of dependence indicated by these diagnostics. If the OLS diagnostics indicate the presence of a diffusion process, the researcher will wish to estimate a spatial lag model via maximum likelihood (ML) estimation or an instrumental variables specification incorporating instruments for the spatially lagged dependent variable. Alternatively, if the OLS diagnostics indicate the existence of spatial error dependence, the researcher may choose to estimate a more fully specified OLS model to model the spatial dependence or may choose to employ a ML or generalized method of moments (GMM) approach incorporating the spatial dependence in the errors. The spatial dependence diagnosed via the diagnostics discussed in Chapter 5 may alternatively be produced by spatial heterogeneity in the effects of covariates. If this is the only source of spatial dependence, modeling this heterogeneity will be sufficient to capture the spatial dependence. As a consequence, any specification search should also consider the possibility of spatial heterogeneity, which is the focus of Chapter 7. This chapter will first, however, examine alternative approaches for modeling spatial dependence if spatial heterogeneity is not present. This chapter begins by examining ML estimation of spatial lag models that derives from Ord (1975). Next, I explore alternative instrumental variables and GMM estimators for spatial lag dependence. Next, I turn to approaches for estimating spatial error models. I conclude by considering areas of concern in the estimation of spatial models. These include estimators for large sample sizes and diagnostics for continued spatial dependence. MAXIMUM LIKELIHOOD SPATIAL LAG ESTIMATION The mixed regressive, spatial autoregressive model, or spatial lag model, extends the pure spatial autoregressive model considered in Section 3.2 to include also the set of covariates and associated parameters: y = ρ W y +Xβ+e where X is again an N by K matrix of observations on the covariates, β is a K by 1 vector of parameters, and the remaining notation is as discussed in Section 3.2.