We study how partisan alignment across levels of government affects coalition formation and government stability using a regression discontinuity design and a large dataset of Spanish municipal elections. We document a positive effect of alignment on both government formation and stability. Alignment increases the probability that the most-voted party appoints the mayor and decreases the probability that the government is unseated during the term. Aligned parties also obtain sizeable electoral gains in the next elections. We show that these findings are not the consequence of favoritism in the allocation of transfers towards aligned governments.

The possibility to run fair electoral campaigns is necessary for the legitimization of modern democracies through elections. Yet, during a pandemic, the risk that electoral campaigns would enhance the spread of the disease is substantive. In this paper, we estimate the causal impact of electoral campaigns on the spread of COVID-19. Exploiting plausibly exogenous variation in the schedule of local elections across Italy, we show that the electoral campaign preceding this latter led to a significant worsening of the epidemiological situation related to the disease. Our results strongly highlight the importance of undertaking stringent measures along the entire electoral process to minimize its epidemiological consequences.

This paper compares policy outcomes under the runoff voting system and the plurality rule by exploiting the assignment of municipalities in Italy to different voting systems based on a population threshold. Mayors, who are the head of the local political power, are elected using a plurality rule in municipalities having fewer than 15,000 residents, while they are elected using a runoff system in municipalities above the threshold. Regression-discontinuity estimates show that municipalities under the runoff system spend 12 percent more than those under the plurality rule and that additional expenditures are not financed via higher taxation.

This paper proposes a novel methodology to identify the geographic market of local newspapers when information on their diffusion is not available or is not sufficiently granular. We illustrate the methodology using historical data from 154 newly digitized newspapers published in Italy between 1919 and 1922. Combining machine learning-augmented optical character recognition techniques, multi-way fixed-effect regressions, and GIS tools, our approach allows us to estimate markets based on news content. Text-based location of newspaper markets considerably improves over assuming that market boundaries coincide with administrative aggregations. We discuss how our technique strengthens the usage of newspapers as a granular and time-varying source of historical information and offers new avenues for identification strategies.