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Name-based treatments have been used in observational studies and experiments to study the differential effect of identity—commonly race or ethnic minority status. These treatments are typically assumed to signal only a single characteristic. If names unintentionally signal other characteristics, then the treatment can violate information equivalence, and estimated treatment effects cannot be attributed to the desired characteristic alone. Using results from a name perception study paired with an original correspondence audit experiment of U.S. state legislators, we show that names manipulate perceptions of minority status, socioeconomic status (SES), and migrant status. Our audit study shows that low SES status is related to reply rates both across and within each racial category. These results provide evidence that discrimination cannot be easily attributed singularly to the intended treatment of minority status but rather reflect a more multifaceted form of discrimination. More generally, our results provide an example of how name-based treatments manipulate more than the intended characteristic, which means that estimated treatment effects cannot be interpreted as being manipulated solely by the desired characteristic. Future studies with name-based or other informational treatments should account for the potential violation of information equivalence in their research design and interpretation of results.
In many situations, incentives exist to acquire knowledge and make correct political decisions. We conduct an experiment that contributes to a small but growing literature on incentives and political knowledge, testing the effect of certain and uncertain incentives on knowledge. Our experiment builds on the basic theoretical point that acquiring and using information is costly, and incentives for accurate answers will lead respondents to expend greater effort on the task and be more likely to answer knowledge questions correctly. We test the effect of certain and uncertain incentives and find that both increase effort and accuracy relative to the control condition of no incentives for accuracy. Holding constant the expected benefit of knowledge, we do not observe behavioral differences associated with the probability of earning an incentive for knowledge accuracy. These results suggest that measures of subject performance in knowledge tasks are contingent on the incentives they face. Therefore, to ensure the validity of experimental tasks and the related behavioral measures, we need to ensure a correspondence between the context we are trying to learn about and our experimental design.
Communication is central to solving coordination problems in politics. In this paper, we show that both the communication network and what people know about the network structure affect coordination. Increases in the number of connections between people make coordination easier and so does increasing the amount of information people have about the structure. We also demonstrate that highly connected nodes in the network can facilitate coordination, but only if individuals have sufficient knowledge to identify the presence of these nodes. Our results suggest the importance of understanding network knowledge and its effects on behavior.
Recent political events have prompted an examination of the analytical tools and conceptual frameworks used in political science to understand voting and candidate choice. Scholars in the behavioral tradition have highlighted the empirical relationship between racial resentment and anti-black affect among white voters during and after President Obama’s successful run for re-election. The theoretical role of white identity within the context of the privileged status of this racial group has seen much less scholarly attention by political scientists, particularly with respect to racial group identification and its implications. To address this lacuna, we argue that racial identification among white voters can be conceived of as a utility-based trait relevant to candidate choice, combining a social-psychological approach of group membership together with a rational choice perspective. This conceptualization of the political utility of white racial identity provides wider conceptual latitude for empirical tests and explanations of voting in U.S. elections.
In the previous chapters, we used a regression-based approach for case selection. Like any approach, relying on regression has important trade-offs that researchers need to be aware of. Perhaps most importantly, this approach relies on the functional form assumptions of the underlying regression analysis, which researchers may not want to adopt in selecting cases for pathway analysis. The good news is that there is nothing about pathway analysis that requires the use of regression analysis. We have concentrated on regression analysis because it is well known by researchers, and we think likely to be used in practice. We recognize that there are a host of other options to leverage existing large-N data to select cases for comparative pathway analysis.
This chapter illustrates one alternative to regression-based case selection. It explores how a researcher might use a common matching approach to select cases, and we apply the technique to an example we also discuss in the next chapter: the relationship between natural resource exports and civil conflict. A matching approach is useful, because it is explicitly designed for making comparisons. This is valuable in our context, because it fits well with the underlying importance of comparative research strategies. It is worth noting that others have also combined matching approaches with case studies, but the goal in prior approaches has been to understand causal effects (Nielsen 2014; Rosenbaum and Silber 2001); in general, matching approaches aimed at improving causal inference focus on identifying comparable treatment and control cases based on pretreatment co-variates that are related to treatment (Ho et al. 2007; Rubin 2008). In pathway analysis, however, the goal differs. By definition, pathway analysis assumes that the causal effect has been identified in previous scholarship and matching is used as a means to identify cases that are appropriate for exploring the mechanisms underlying the previously identified relationship.
So far in this book we have focused on using various quantitative methods to guide case selection in order to explore the substantive relationship between a key explanatory variable (X1) and an outcome (Y). The discussion assumes that, in some settings, researchers do not have strong reasons for selecting particular cases, and so are using quantitative data to determine case selection. However, in other cases, researchers often have compelling reasons for examining certain cases, including theoretical, empirical, and practical reasons (such as access to data sources, cost effectiveness, and preexisting expertise). Under these circumstances, the issue is not which cases to choose initially, but how to gain perspective on the cases already chosen and perhaps how to choose cases for additional analysis.
This chapter illustrates how large-N data can help gain perspective on previously chosen cases by shedding light on how they fit within the broader universe of cases that could have been chosen, thus providing some leverage over how the extant case studies compare to unstudied cases. This can help a researcher understand how to generalize to unstudied cases and how to identify future cases that might offer promising comparisons.
The use of large-N data to gain perspective on previously selected cases is best explored by example. In this chapter we focus on the relationship between natural resources (X1) and civil conflict (Y), which was also the focus of Chapter 6.We first explain why this example is useful, then we review the existing pathway analyses conducted by Michael Ross (2004) and the large-N data analysis of Paul Collier and Anke Hoeffler (2004), and finally we demonstrate how to use the large-N data to gain some perspective on the cases selected by Ross and how to identify avenues for future inquiry.
Social scientists have identified a need to move beyond the analysis of correlation among variables to the study of causal mechanisms that link them. Nicholas Weller and Jeb Barnes propose that a solution lies in 'pathway analysis', the use of case studies to explore the causal links between related variables. This book focuses on how the small-N component of multi-method research can meaningfully contribute and add value to the study of causal mechanisms. The authors present both an extended rationale for the unique role that case studies can play in causal mechanism research, and a detailed view of the types of knowledge that case studies should try to generate and how to leverage existing large-N data to guide the case selection process. The authors explain how to use their approach both to select cases and to provide context on previously studied cases.
The primary purpose of pathway analysis is to build knowledge about the causal mechanisms that link X1 to Y across settings. This implies the need to perform comparative analyses that can generate knowledge and/or hypotheses about the broader population of cases that feature the X1/Y relationship. Before delving into the details of case selection, it is important to consider how researchers should prepare for pathway analysis. As an initial matter, researchers must clarify their specific goals. Once oriented to the basic task, they should: (1) determine whether the basic analytic requisites of pathway analysis are met; (2) identify what is already known about the X1/Y relationship; and (3) take stock of relevant measures.
The threshold task: clarifying the goals of pathway analysis
The purpose of using pathway analysis differs from other, more familiar types of research, which is why it is important for researchers to clarify these goals from the start. On this score, James Mahoney and Gary Goertz (2006) usefully distinguish between two ideal types of research in the social sciences: causes of effects and effects of causes. Causes-of-effects research seeks to provide “thick description” of the emergence of singular events or outcomes in particular settings (Mahoney and Goertz 2006; see Brady and Collier 2004; Geertz 1973). For US politics or history research, for example, possible questions might include: Why World War II? Why did the New Deal coalition collapse? What factors caused the most recent financial crisis? Why did the US Supreme Court decide Bush v. Gore as it did? For these types of questions, case selection is driven mostly by the substantive importance of the outcome to be explained, and the research primarily emphasizes case-specific internal validity (“Did the research get the story right?”), and not external validity (“Do the story's lessons apply in other contexts?”), although scholars often try to draw some broader lessons from their detailed descriptions.
In the last chapter, we applied our method to several simple textbook examples, including a hypothetical that posited a linear X1/Y relationship with equifinality (that is, multiple pathways between X1 and Y). In this chapter, we turn to a discussion of case selection in a more complex empirical setting: one that features a non-linear relationship between X1 and Y and the relationship is embedded in a longer causal chain that involves dynamic processes. Under these conditions, researchers must decide where in the causal process to investigate and how to deal with temporal dynamics in their large- and small-N studies. This chapter grapples with these issues by using an example drawn from the policy diffusion literature. As we make clear, in the context of pathway analysis, conceptualizing what is the key explanatory variable and what is the mechanism is not determined by the labels used in prior research or deep philosophical concerns, but rather by practical concerns regarding the relevant research question, how variables are used within the existing empirical research, and data availability. Once the relevant literature is properly understood, our method can be applied in a relatively straightforward manner.
International policy diffusion
The literature on international policy diffusion is both immense and contested. For purposes of this chapter, therefore, we distinguish between two very different types of studies. One type focuses on the threshold question of whether policy choices spread across political boundaries; that is, whether policy adoption is purely a function of domestic pressures (Berry and Berry 1990; Gray 1973, 1994; Simmons et al. 2008; cf. Volden et al. 2008). The second type of study essentially assumes that policies diffuse across borders, so its focus is on how and under what conditions policy decisions of one country spill over into others. In general, the literature that assumes the existence of diffusion and focuses on how policy diffusion works is more interesting for our purposes, as the search for mechanisms lies at its center (Simmons et al. 2008).
To this point, we have mainly focused on the early stages of mechanism-centered research, particularly selecting cases for pathway analysis that explore the links underlying an association between some explanatory variable (X1) and an outcome (Y). However, the results of pathway analysis not only can provide a map of mechanisms underlying an X1/Y relationship, but also can be used to “look forward” to provide insight into the feasibility and nature of future studies of mechanisms. From this vantage, pathway analysis is best understood as part of a larger research agenda that spans multiple years, multiple research approaches, and multiple researchers.
The forward-looking purposes of pathway analysis are largely ignored in the literature for a variety of possible reasons. Partly this omission may reflect the limited state of knowledge about mechanisms in many areas. Quantitative studies of mechanisms require that a researcher has already identified some basic information about the mechanisms linking X1 and Y and understand the basic structure of the X1/Y relationship (see Table 2.2 in Chapter 2). This omission also may reflect that the literature on how to study mechanisms quantitatively is relatively new and rapidly evolving. Nevertheless, there are some areas in which researchers have accumulated a considerable amount of insight into mechanisms, and there are compelling reasons for pursuing further mechanism-centered research. For researchers working in such an area, the question to ask is: How might insights from pathway analysis inform future studies on mechanisms?
This book has examined pathway analysis from a number of angles: how to prepare for it by reading the literature in light of its analytic requisites and different ideal types of X1/Y relationships; how to select cases for it based on the expected X1/Y relationships and variation in case characteristics; how to use these tools to gain perspective on cases already selected for process tracing; and how the results from pathway analysis might inform future studies of mechanisms. This chapter seeks to put these pieces together and review the role of pathway analysis in a continuing mixed-method research agenda on mechanisms. The argument is that pathway analysis serves as a critical bridge from what we know about an X1/Y relationship through large-N studies to detailed maps of the mechanisms connecting X1 and Y, which can be used to assess the feasibility of future quantitative studies of mechanisms and the appropriate goals of future qualitative work on mechanisms. From this perspective, good pathway analysis advances the search for mechanisms by systematically building on what we already know about the X1/Y relationship, generating insights into the links between variables, and clarifying avenues of future inquiry.
The role of pathway analysis in the mixed-methods search for mechanisms
A central theme of the book is that pathway analysis constitutes a distinct mode of inquiry. Whereas most research in the social sciences seeks to understand the causes of events or estimate average effects of some variable, X1, on an outcome, Y (Mahoney and Goertz 2006), pathway analysis seeks to (1) understand the mechanisms underlying the X1/Y relationship in particular cases and (2) generate insights from these cases about mechanisms in the unstudied population of cases featuring the X1/Y relationship. By its very nature, pathway analysis connects the literature on the X1/Y relationship to an ongoing, mixed-method research agenda that first seeks to map mechanisms so that we have a better understanding of the X1/Y relationship and then seeks to inform future, mechanism-centered work.
The approach we present in this book is not the only way to select cases for pathway analysis. The literature suggests two alternatives: the variable-based approach and the residual-based approach. This chapter reviews these methods, critiques them, and compares their application to our method using several textbook examples drawn from John Gerring's Case Study Research: Principles and Practices (2007). We use examples from Gerring for several reasons. They are simple and heuristically useful. These examples have been used to demonstrate the utility of the prior case selection approaches, and therefore they provide a benchmark against which to demonstrate the advantages of our approach. In subsequent chapters, we apply our approach to more complex examples, but many of the basic points – such as the need to select comparative cases, the usefulness of considering both the expected X1/Y relationship and variation in case characteristics, the utility of visualizing patterns within the data, and using case control strategies – can be seen even in this chapter's simple examples.
Overview of the variable-based approach
The variable-based approach has the advantage of simplicity; it urges researchers to seek cases with extreme values of X1 and Y (see Seawright and Gerring 2008). The assumption of this approach is that cases with large X1 and Y values are most likely to feature the X1/Y relationship and thus provide a promising case for exploring causal pathways. The values of X1 and Y are potentially useful pieces of information for those interested in pathway analysis, but focusing on the X1 and Y values alone is problematic for a number of reasons. First, the variable-based approach does not deal with the problem of potential confounds; it simply assumes a straightforward X1/Y relationship. Second, if we select cases with extreme values of X1 and Y, it is difficult to assess whether these cases are outliers in the absence of a strong theory about the nature of the relationship.
Researchers have long recognized that “the cases you choose affect the answers you get” (Geddes 1990). Accordingly, it is critical to select cases carefully and in a transparent manner. This chapter lays out our general approach for selecting cases for pathway analysis. It begins by briefly reviewing the analytic goals of pathway analysis and how they relate to the general criteria for case selection. It then outlines some of the key challenges in applying these criteria and ends with practical advice for implementing these general principles.
The goals of pathway analysis and case selection
As discussed in the last chapter, pathway analysis ultimately has two goals: (1) to gain insight into the mechanisms that connect some explanatory variable (X1) and some outcome (Y) in specific cases; and (2) to use the insights from these cases to generate hypotheses about mechanisms in the unstudied population of cases that feature the X1/Y relationship.
These two goals, in turn, imply several principles for case selection (see Figure 3.1). The first goal of pathway analysis suggests the expected relationship criteria, which means the degree to which individual cases are expected to feature the relationship of interest between X1 and Y given existing theory, empirical knowledge, and large-N studies. It is perhaps obvious, but studying mechanisms that underlie the X1/Y relationship requires identifying cases where the X1 variable is related to the Y, controlling for possible confounds (X2) (Gerring 2007). If the relationship between X1 and Y differs based on the values of X1, then a researcher needs to understand how the relationship depends on the value of X1.