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The evolution of South Africa's strategic policy directions was analyzed through the application of natural language processing (NLP) techniques to the texts of the Reconstruction and Development Plan, Growth, Employment And Redistribution, the Accelerated and Shared Growth Initiative South Africa, the New Growth Path, and the National Development Plan. In statistical, probabilistic topic modeling analysis, nine topics are identified across all development plans. In the decreasing order of proportion of development plans' documents, the topics relate to: climate change and resources; green economy; corruption and security; health; skills and training; economic growth; fiscal policy and macro-economy; reconstruction and democracy; and education. The study is a background note for the South Africa Systematic Country Diagnostic.

Examining the careers of democratic heads of state and government from 1960–2010, we find that one in every seven turns to the private sector after office. Distinguishing between the factors that attract leaders to business and those that render leaders attractive, we find that the global CEO compensation rates, cultural norms, having served in office in Anglo-Saxon countries as well as their personal background matter. We also find that certain economic outcomes and policies in office such as economic growth and reduction in state spending are often associated with post-tenure business careers. We do not find evidence, however, that leaders are able to implement policies with future careers in mind, which would in turn raise concerns over accountability.

In the absence of public information on the inner workings of the Russian political regime, especially during Medvedev's presidency, outside observers often have to rely on politicians' unguarded comments or subjective analysis. Instead, we turn to quantitative text analysis of political rhetoric. Treating governors as a quasi-expert panel, we argue that policy positions revealed in regional legislative addresses explain how elites perceived the distribution of power between Putin and Medvedev. We find that governors moved from a neutral position in 2009 to a clearly pro-Putin position in 2011, and that policy initiatives advocated by Medvedev all but evaporated from the rhetoric of governors in 2012.

Recent literature models leadership as a process of communication in which leaders' rhetorical signals facilitate followers' co-ordination. While some studies have explored the effects of leadership in experimental settings, there remains a lack of empirical research on the effectiveness of informational tools in real political environments. Using quantitative text analysis of federal and sub-national legislative addresses in Russia, this article empirically demonstrates that followers react to informational signals from leaders. It further theorizes that leaders use a combination of informational and non-informational tools to solve the co-ordination problem. The findings show that a mixture of informational and non-informational tools shapes followers' strategic calculi. Ignoring non-informational tools—and particularly the interrelationship between informational and non-informational tools—can threaten the internal validity of causal inference in the analysis of leadership effects on co-ordination.

AbstractIn the context of recent economic and financial crisis in Europe, questions about the power of the core executive to control fiscal outcomes are more important than ever. Why are some governments more effective in controlling spending while others fall prey to excessive overspending by individual cabinet ministers? We approach this question by lifting the veil of collective cabinet responsibility and focusing on intra-cabinet decision-making around budgetary allocation. Using the contributions of individual cabinet members during budget debates in Ireland, we estimate their positions on a latent dimension that represents their relative levels of support or opposition to the cabinet leadership. We find some evidence that ministers who are close to the finance minister receive a larger budget share, but under worsening macro-economic conditions closeness to the prime minister is a better predictor for budget allocations. Our results highlight potential fragility of the fiscal authority delegation mechanism in adverse economic environment.

The Comparative Manifesto Project (CMP) provides the only time series of estimated party policy positions in political science and has been extensively used in a wide variety of applications. Recent work (e.g., Benoit, Laver, and Mikhaylov 2009; Klingemann et al. 2006) focuses on nonsystematic sources of error in these estimates that arise from the text generation process. Our concern here, by contrast, is with error that arises during the text coding process since nearly all manifestos are coded only once by a single coder. First, we discuss reliability and misclassification in the context of hand-coded content analysis methods. Second, we report results of a coding experiment that used trained human coders to code sample manifestos provided by the CMP, allowing us to estimate the reliability of both coders and coding categories. Third, we compare our test codings to the published CMP "gold standard" codings of the test documents to assess accuracy and produce empirical estimates of a misclassification matrix for each coding category. Finally, we demonstrate the effect of coding misclassification on the CMP's most widely used index, its left-right scale. Our findings indicate that misclassification is a serious and systemic problem with the current CMP data set and coding process, suggesting the CMP scheme should be significantly simplified to address reliability issues.

Political text offers extraordinary potential as a source of information about the policy positions of political actors. Despite recent advances in computational text analysis, human interpretative coding of text remains an important source of text‐based data, ultimately required to validate more automatic techniques. The profession's main source of cross‐national, time‐series data on party policy positions comes from the human interpretative coding of party manifestos by the Comparative Manifesto Project (CMP). Despite widespread use of these data, the uncertainty associated with each point estimate has never been available, undermining the value of the dataset as a scientific resource. We propose a remedy. First, we characterize processes by which CMP data are generated. These include inherently stochastic processes of text authorship, as well as of the parsing and coding of observed text by humans. Second, we simulate these error‐generating processes by bootstrapping analyses of coded quasi‐sentences. This allows us to estimate precise levels of nonsystematic error for every category and scale reported by the CMP for its entire set of 3,000‐plus manifestos. Using our estimates of these errors, we show how to correct biased inferences, in recent prominently published work, derived from statistical analyses of error‐contaminated CMP data.