Do autocrats favor their supporters during economic shocks? I introduce a model of redistribution and regime stability that shows how in-group favors can be a strategic response to economic downturns. The model predicts that, as economic shocks worsen, autocrats may favor their supporters and confront opposition protests to save on appeasement costs. I test the model's main results in two empirical settings. First, I focus on the Venezuelan blackouts of 2019. Consistent with the model, the Maduro regime was more likely to exempt regime-supporting regions affected by the blackout from later power rationing. Moreover, blackout-induced protests were limited to opposition-leaning regions. I then focus on negative rainfall shocks in Sub-Saharan Africa. Droughts magnify differences in development, protests and state-coercion outcomes in favor of leaders' home regions.
This paper documents how violence resulting from the Mexican Drug War hindered local export growth. Focusing on exports allows us to abstract from demand factors and estimate effects on local capacity to supply foreign markets. We compare exports of the same product to the same country, but facing differential exposure to violence after a close electoral outcome. Firms exogenously exposed to the Drug War experienced lower export growth. Violence eroded the local capacity to attract capital investment, disproportionately hampering large exporters and capital-intensive activities.
Do gains from globalization erode support for economic nationalism? We implement a shift-share strategy to study how NAFTA-enhanced local access to US-markets affected Mexican demands for protectionist platforms. The left, led by Andrés Manuel López Obrador (AMLO), under-performed in cities benefiting from export access gains during the 2006 presidential elections. This effect is observed strictly in 2006, the only post-NAFTA election in which debates over trade integration played a salient role. Our findings are robust to controls for import competing pressures from NAFTA and the China Shock. AMLO's 2006 protectionist platform likely cost him that year's election, and campaign media strategies in 2012 map to this earlier backlash.
Given how candidates’ scarce time is often devoted to visits aiming to stimulate local support, the limited causal evidence on these effects is surprising. Comparing outcomes between visited and non-visited places is likely to yield biased estimates. This paper studies the local electoral effects of Henrique Capriles’ visits in the 2012 Venezuelan presidential election. Leveraging the panel structure of electoral data and unique detailed data on the determinants of other local campaign efforts, I estimate that Capriles’ visits eroded Chavismo’s vote shares by 0.6 percentage points. Turnout levels seem unaffected, suggesting a persuasive effect of visits. Effects are concentrated in low priority States and States with a Chavista governor, suggesting that visits matter most in regions receiving less campaign resources and stronger rival presence. Effects are also driven by Capriles’ later campaign visits, highlighting the relevance of the timing of candidate appearance. These results suggest that visits affected electoral outcomes by enhancing local information about the candidate.
As the COVID-19 pandemic pushed firms to comply with social distancing guidelines, the relative demand for work that could be performed from home was expected to increase. However, while employment in “remotable" occupations was relatively resilient during the pandemic, online job postings, which measure demand for new hires, for these occupations dropped disproportionately. This apparent contradiction is not explained by prior job “churning" in “non-remote” jobs, nor by the recomposition of the labor market across economic sectors. The underperformance of postings for “remotable” jobs during the pandemic is concentrated in essential occupations and occupations with high returns to experience.
Managers make predictions all the time: How fast will my markets grow? How much inventory do I need? How intensively should I monitor my suppliers? Which potential customers will be most responsive to a particular marketing campaign? Which job candidates should I employ? Machine learning, data science, big data, and predictive analytics all use statistical techniques to predict an outcome. This case enables students to begin using data to make predictions and teaches the core metrics to evaluate how accurate predictions are. It helps students understand how to choose among alternative model specifications and introduces the concepts of overfitting and in-sample versus out-of-sample prediction. The case discussion also promotes an understanding of factors beyond prediction accuracy—such as transparency and perceived fairness—that managers need to consider when deciding which predictive algorithm to deploy. The class discussion also helps students appreciate the differences between prediction, correlation, and causation. The case protagonist recently joined a new data science team at the U.S. Occupational Safety and Health Administration (OSHA), a government agency, and needs to evaluate and recommend one of several alternative approaches that OSHA should use to improve how it targets its government inspections of workplaces to better assure safe working conditions. The case includes a dataset and exercise.