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We investigate the relationship between political networks, weak institutions, and election fraud during the 2010 parliamentary election in Afghanistan combining: (i) data on political connections between candidates and election officials; (ii) a nationwide controlled evaluation of a novel monitoring technology; and (iii) direct measurements of aggregation fraud. We find considerable evidence of aggregation fraud in favor of connected candidates and that the announcement of a new monitoring technology reduced theft of election materials by about 60 percent and vote counts for connected candidates by about 25 percent. The results have implications for electoral competition and are potentially actionable for policymakers. (JEL C93, D02, D72, K42, O17)

Citizens in emerging democracies vote at high rates, particularly given the high costs of voting. This title argues that community-level population dynamics and features of the electoral environment specific to recent democratizers increase the likelihood that individuals vote.

While many scholars argue that ethnicity drives voting behavior in Africa, recent quantitative work finds government performance also matters. But under what conditions do Africans use ethnicity or performance to inform their vote? We argue that the importance of ethnicity and performance is conditional on whether voters evaluate co-ethnics and incumbent candidates. We hypothesize co-ethnic voters will coordinate and form blocs, whereas non-co-ethnics are more likely to divide their support between candidates. We also hypothesize that while incumbent performance matters to all voters independent of ethnicity, citizens will forgive their co-ethnic incumbent's poor performance. Tests using data from a nationwide exit poll we conducted during Kenya's 2007 national election strongly support our hypotheses. Our results are robust to analyses concerning the potentially confounding relationship between ethnicity and performance.

How does media coverage of electoral campaigns distinguish parties and candidates in emerging democracies? To answer, we present a multi-step procedure that we apply in South Africa. First, we develop a theoretically informed classification of election coverage as either "narrow" or "broad" from within the entire corpus of news coverage during an electoral campaign. Second, to deploy our classification scheme, we use a supervised machine learning approach to classify news as "broad," "narrow," or "not election-related." Finally, we combine our supervised classification with a topic modeling algorithm (BERTTopic) that is based on Bidirectional Encoder Representations from Transformers (BERT), in addition to other statistical and machine learning methods. The combination of our classification scheme, BERTTopic, and associated methods allows us to identify the main election-related themes among broad and narrow election-related coverage, and how different candidates and parties are associated with these themes. We provide an in-depth discussion of our method for interested users in the social sciences. We then apply our proposed techniques on text from nearly 100,000 news articles during South Africa's 2014 campaign and test our empirical predictions about candidate and party coverage of corruption, the economy, health, public infrastructure, and security. The application of our method highlights a nuanced campaign environment in South Africa; candidates and parties frequently receive distinct and substantive coverage on key campaign themes.