Empirical versus Theoretical Claims about Extreme Counterfactuals: A Response

Langche Zeng Department of Political Science, University of California, San Diego, La Jolla, CA 92093 e-mail: lazeng{at}ucsd.edu In response to the data-based measures of model dependence proposedin King and Zeng (2006), Sambanis and Michaelides (2008) proposealternative measures that rely upon assumptions untestable inobservational data. If these assumptions are correct, then theirmeasures are appropriate and ours, based solely on the empiricaldata, may be too conservative. If instead, and as is usuallythe case, the researcher is not certain of the precise functionalform of the data generating process, the distribution from whichthe data are drawn, and the applicability of these modelingassumptions to new counterfactuals, then the data-based measuresproposed in King and Zeng (2006) are much preferred. After all,the point of model dependence checks is to verify empirically,rather than to stipulate by assumption, the effects of modelingassumptions on counterfactual inferences. Author’s note: Easy-to-use software to implement the methodsdiscussed here, called "WhatIf: Software for Evaluating Counterfactuals,"is available at http://gking.harvard.edu/whatif. All informationnecessary to replicate the analyses herein can be found in King and Zeng (2008).Conflict of interest statement. None declared.     

Not again! Public perception,regulation, and nanotechnology

It is often argued that immediate government action regarding nanotechnology is needed to ensure that public opinion does not mistakenly view nanotechnology as dangerous, to restore public trust in government, and to increase the legitimacy of government action through increased public participation. This article questions whether governments can achieve these goals. As the world lurches toward regulation of nanotechnology, we should ask Why the rush? Can anticipatory action, perceived as the government doing something, fulfill the competing hopes to “restore trust,”“pave the way” for nanotechnology, “increase awareness,” and “satisfy democratic notions of accountability”? Or is government action more likely to increase existing divisions over nanotechnology's future?     

How Leadership and Public Service Motivation Enhance Innovative Behavior

Prior research has linked the innovative behavior of public sector employees to desirable outcomes such as improved efficiency and higher public service quality. However, questions regarding the drivers of innovative behavior among employees have received limited attention. This article employs psychological empowerment theory to examine the underlying processes by which entrepreneurial leadership and public service motivation (PSM) shape innovative behavior among civil servants. Based on three‐wave data from 281 Chinese civil servants and their 59 department heads, entrepreneurial leadership is found to positively influence subordinates' innovative behavior by enhancing two dimensions of psychological empowerment: meaning and impact. Additionally, PSM was found to influence subordinates' innovative behavior by enhancing the dimensions of meaning and competence. These findings suggest that to facilitate innovative behavior among employees, public organizations should consider introducing training that encourages leaders to serve as entrepreneurial role models and recruit employees with high levels of PSM.     

The Who,When, and Where of Executive Nominations: Integrating Agency Independence and Appointee Ideology

In recent years, scholars have expended considerable efforts to understand the executive appointment process and the forces influencing the choices made by the president and the Senate. However, some factors integral to theoretical models have not been well integrated empirically, and other relevant factors have not been incorporated much at all. Here, we focus on one determinant corresponding to the former critique—nominee ideology—and another corresponding to the latter—the independence of decision makers in the targeted agencies. We examine a series of theoretically driven hypotheses regarding the effects of both ideology and independence on who gets nominated and if and when nominees are eventually confirmed. Results show nominee ideology and decision maker independence matter a great deal and factor into presidential strategic choices and senatorial responses in ways according to expectations. Our findings have important ramifications for understanding appointments empirically and for future theoretical development.     

Computer‐Assisted Keyword and Document Set Discovery from Unstructured Text

The (unheralded) first step in many applications of automated text analysis involves selecting keywords to choose documents from a large text corpus for further study. Although all substantive results depend on this choice, researchers usually pick keywords in ad hoc ways that are far from optimal and usually biased. Most seem to think that keyword selection is easy, since they do Google searches every day, but we demonstrate that humans perform exceedingly poorly at this basic task. We offer a better approach, one that also can help with following conversations where participants rapidly innovate language to evade authorities, seek political advantage, or express creativity; generic web searching; eDiscovery; look‐alike modeling; industry and intelligence analysis; and sentiment and topic analysis. We develop a computer‐assisted (as opposed to fully automated or human‐only) statistical approach that suggests keywords from available text without needing structured data as inputs. This framing poses the statistical problem in a new way, which leads to a widely applicable algorithm. Our specific approach is based on training classifiers, extracting information from (rather than correcting) their mistakes, and summarizing results with easy‐to‐understand Boolean search strings. We illustrate how the technique works with analyses of English texts about the Boston Marathon bombings, Chinese social media posts designed to evade censorship, and others.     

Determinants of Foreign Aid: Rivalry and Domestic Instability

Foreign aid is usually seen as a form of international cooperation. Thus, the expectation is that states engaged in international rivalry with one another should be unlikely to provide each other aid. However, they do provide their enemies aid. We consider how situations of uncertainty influence aid transfers between states. We argue that states may provide each other aid to limit uncertainty from potential regime changes that could lead to war. Such uncertainty is particularly bad for rivals who are prone to militarized conflict. We find that rivals may provide one another foreign aid when one of the countries is experiencing regime-threatening levels of domestic instability. We compare these results to the behavior of nonrivals and find that: Rivals are likely to provide their enemies aid in times of uncertainty; rivals are no less likely to give aid to each other than are nonrivals; and rivals provide more aid during times of instability than do nonrivals.     

What to Do about Missing Values in Time‐Series Cross‐Section Data

Applications of modern methods for analyzing data with missing values, based primarily on multiple imputation, have in the last half‐decade become common in American politics and political behavior. Scholars in this subset of political science have thus increasingly avoided the biases and inefficiencies caused by ad hoc methods like listwise deletion and best guess imputation. However, researchers in much of comparative politics and international relations, and others with similar data, have been unable to do the same because the best available imputation methods work poorly with the time‐series cross‐section data structures common in these fields. We attempt to rectify this situation with three related developments. First, we build a multiple imputation model that allows smooth time trends, shifts across cross‐sectional units, and correlations over time and space, resulting in far more accurate imputations. Second, we enable analysts to incorporate knowledge from area studies experts via priors on individual missing cell values, rather than on difficult‐to‐interpret model parameters. Third, because these tasks could not be accomplished within existing imputation algorithms, in that they cannot handle as many variables as needed even in the simpler cross‐sectional data for which they were designed, we also develop a new algorithm that substantially expands the range of computationally feasible data types and sizes for which multiple imputation can be used. These developments also make it possible to implement the methods introduced here in freely available open source software that is considerably more reliable than existing algorithms.