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.     

Assessing the Trivialness, Relevance, and Relative Importance of Necessary or Sufficient Conditions in Social Science

Political scientists of all stripes have proposed numerous necessary or sufficient condition hypotheses. For methodologists a question is how can we assess the importance of these necessary conditions. This article addresses three central questions about the importance of necessary of sufficient conditions. The first concerns de “trivialness” of necessary or sufficient conditions. The second is how much a necessary or sufficient condition is “relevant?” The third important question deals with the relative importance of necessary or sufficient conditions: for example, ifX ₁ andX ₂ are necessary or sufficient conditions, is one more important than the other? The article develops measures to assess the importance of necessary or sufficient conditions in three related contexts: (1) Venn diagram, (2) 2×2 tables, and (3) fuzzy sets. Two empirical examples are discussed at length: (1) Skocpol’sStates and Social Revolutions: A Comparative Analysis of France, Russia, and China and (2) Ragin’s (2000) analysis of the cause of IMF riots.     

A "politically robust" experimental design for public policy evaluation, with application to the Mexican universal health insurance program.

We develop an approach to conducting large-scale randomized public policy experiments intended to be more robust to the political interventions that have ruined some or all parts of many similar previous efforts. Our proposed design is insulated from selection bias in some circumstances even if we lose observations; our inferences can still be unbiased even if politics disrupts any two of the three steps in our analytical procedures; and other empirical checks are available to validate the overall design. We illustrate with a design and empirical validation of an evaluation of the Mexican Seguro Popular de Salud (Universal Health Insurance)program we are conducting. Seguro Popular, which is intended to grow to provide medical care, drugs, preventative services, and financial health protection to the 50 million Mexicans without health insurance, is one of the largest health reforms of any country in the last two decades. The evaluation is also large scale, constituting one of the largest policy experiments to date and what may be the largest randomized health policy experiment ever.     

Tests of institutional versus non-institutional models of local public expenditure determination

Are institutional or non-institutional factors more important determinants of local public expenditure decisions? Employing data that allows us to directly identify the entire distribution of voter expenditure demands, we estimate models of deviations of actual expenditures from the expenditure demand of defensible benchmarks — namely, the median voter's expenditure demand and the mean of voters' expenditure demands. Three competing paradigms are employed to specify these models; namely, a non-institutional model, the reform government movement's explanation of how institutions affect local government decisions and a transactions-cost model of this same phenomenon. Predictions from these three competing models differ in many respects. Comparisons of results from estimating models of expenditure deviations are found to be generally more consistent with the non-institutional model than with either of the two institutional models. These results suggest that a competitive local public service market helps limit the ability of governments to tax and spend in excess of the demands of the median or mean voter.