In this article, we analyze the structure and content of the political conversations that took place through the microblogging platform Twitter in the context of the 2011 Spanish legislative elections and the 2012 U.S. presidential elections. Using a unique database of nearly 70 million tweets collected during both election campaigns, we find that Twitter replicates most of the existing inequalities in public political exchanges. Twitter users who write about politics tend to be male, to live in urban areas, and to have extreme ideological preferences. Our results have important implications for future research on the relationship between social media and politics, since they highlight the need to correct for potential biases derived from these sources of inequality.
Corruption scandals have been found to have significant but mild electoral effects in the comparative literature (Golden 2006). However, most studies have assumed that voters punish all kinds of illegal practices. This article challenges this assumption by distinguishing between two types of corruption, according to the type of welfare consequences they have for the constituency. This hypothesis is tested using data from the 2011 Spanish local elections. We exploit the abundance of corruption allegations associated with the Spanish housing boom, which generated income gains for a wide segment of the electorate in the short term. We find that voters ignore corruption when there are side benefits to it, and that punishment is only administered in those cases in which they do not receive compensation.
Is it possible to predict malfeasance in public procurement? With the proliferation of e-procurement systems in the public sector, anti-corruption agencies and watchdog organizations have access to valuable sources of information with which to identify transactions that are likely to become troublesome and why. In this article, we discuss the promises and challenges of using machine learning models to predict inefficiency and corruption in public procurement. We illustrate this approach with a dataset with more than two million public procurement contracts in Colombia. We trained machine learning models to predict which of them will result in corruption investigations, a breach of contract, or implementation inefficiencies. We then discuss how our models can help practitioners better understand the drivers of corruption and inefficiency in public procurement. Our approach will be useful to governments interested in exploiting large administrative datasets to improve the provision of public goods, and it highlights some of the tradeoffs and challenges that they might face throughout this process.
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