Governments produce vast and growing quantities of freely available text: laws, rules, budgets, press releases, and so forth. This information flood is facilitating important, growing research programs in policy and public administration. However, tightening research budgets and the information's vast scale forces political science and public policy to aspire to do more with less. Meeting this challenge means applied researchers must innovate. This article makes two contributions for practical text coding—the process of sorting government text into researcher‐defined coding schemes. First, we propose a method of combining human coding with automated computer classification for large data sets. Second, we present a well‐known algorithm for automated text classification, the Naïve Bayes classifier, and provide software for working with it. We argue and provide evidence that this method can help applied researchers using human coders to get more from their research budgets, and we demonstrate the method using classical examples from the study of policy agendas.

Although foreign policies often fail to successfully promote democracy, over a decade of empirical research indicates that foreign aid specifically for democracy promotion is remarkably successful at improving the survival and institutional strength of fragile democracies. However, these measures cannot tell us how well democracy aid supports the central promise of democracy: accountable government. Since institutions can be subverted in various ways that undermine accountability, it is vital to know whether democracy aid supports accountability to assess its overall success. We provide evidence for this by analyzing incumbent turnover in elections, following poor economic performance—the economic vote—as a measure of voting to achieve performance accountability. In our analysis of over 1,100 elections in 114 developing countries between 1975 and 2010, we find distinct evidence that increasing receipt of democracy aid is associated with more economic voting. Results are robust to numerous alternative empirical specifications.

This article reports the first empirical evidence that politicians delegate to trusted bureaucrats to diminish political responsibility for policy. Political science has been perennially concerned with why political leaders delegate authority to bureaucrats, but this work's focus on advanced democracies has overlooked how corruption and political influence over bureaucrats can turn delegation into a means of obfuscating responsibility. Using a measure that differentiates political corruption from corruption at lower levels of government and a new data set of policy making on more than 600 European Commission directives in the 10 former communist European Union (EU) member states, I show that political-level corruption is associated with increased delegation to bureaucrats. This relationship between political corruption and bureaucratic discretion is conditional upon the political independence of the bureaucracy, such that politicians engaged in corruption delegate more to reduce clarity of responsibility only when they possess informal means to influence bureaucrats.

Why do cities spend scarce resources lobbying the federal government? The hierarchy of U.S. government provides various pathways for local representation. Nevertheless, cities regularly invest in paid representation. This presents a puzzle for American democracy. Why do cities lobby, and do they lobby strategically? We quantify for the first time the extent of this phenomenon and examine its determinants using new data on 498 cities across forty-five states from 1998 to 2008. We find that economic distress pushes cities to lobby, but does not impact expenditures. Cities in competitive congressional districts, and therefore crucial to national politics, spend more on lobbying.

Theories of public policy change, despite their differences, converge on one point of strong agreement: the relationship between policy and its causes can and does change over time. This consensus yields numerous empirical implications, but our standard analytical tools are inadequate for testing them. As a result, the dynamic and transformative relationships predicted by policy theories have been left largely unexplored in time series analysis of public policy. This article introduces dynamic linear modelling (DLM) as a useful statistical tool for exploring time-varying relationships in public policy. The article offers a detailed exposition of the DLM approach and illustrates its usefulness with a time series analysis of United States defense policy from 1957 to 2010. The results point the way for a new attention to dynamics in the policy process, and the article concludes with a discussion of how this research programme can profit from applying DLMs.