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En economía, la investigación se divide en dos grandes metodologías: los modelos teórico-matemáticos y los estudios empíricos. Estudiando modelos teóricos y métodos empíricos (ejemplificados en Randomized Controlled Trial (i.e. RCT)) se da cuenta de las limitaciones de ambos métodos. Se concluye que ninguno de estos puede generar explicaciones de cómo en realidad suceden las cosas, sino que solo de cómo posiblemente suceden. La razón es que ambos necesitan un enlace interpretativo que permita extrapolar desde su propio sistema (i.e. el del modelo y el del estudio empírico, respectivamente) hacia un sistema objetivo. Los modelos tienen dominio general y pueden dar cuenta de mecanismos. Los RCTs, al contrario, son válidos internamente (si cumplen ciertas condiciones) y están conectados al mundo real, pero su dominio es muy específico. Aunque ninguno logre responder preguntas amplias de un fenómeno de interés, pueden complementarse para generar extrapolaciones más confiables sobre un sistema objetivo. Sin embargo, esto solo podrá hacerse si se conocen bien los mecanismos y el contexto en que ocurre una evidencia.

The COVID-19 pandemic has caused great disruption to the service sector, and it has, in turn, adapted by implementing measures that reduce physical contact among employees and users; examples include home-office work and the setting of occupancy restrictions at indoor locations. The design of services in the context of a pandemic requires balancing between two objectives: (i) special measures must be implemented to maintain physical separation among people to reduce the risk of infection, and (ii) these sanitary measures also reduce process capacity, thereby increasing the waiting times of users. We study this problem in the context of election processes, in which balancing waiting time with public safety is of first order relevance to ensuring voter turnout, using as a real-world application the Chilean 2020 national referendum. Analyzing this problem requires a multidisciplinary approach that consists of integrating randomized experiments to measure how voters weigh infection risk relative to waiting time and stochastic modeling/discrete event simulation to prescribe recommendations for the service design—specifically setting capacity limits to trade off between overcrowding and process efficiency. Overall, our results shows that infection risk is an important factor affecting voter turnout during a pandemic and that capacity limits can be a useful design tool to balance these risks with other service quality measures. Some of these findings were considered in the guidelines that Servel provided to manage capacity and voter arrival patterns at voting centers.

The World Health Organization recommends using health-risk warnings on alcoholic beverages. This study examines the impact of separate or combined warning labels for at-risk groups and the general population on alcohol purchase decisions. In 2022, 7758 adults who consumed alcohol or were pregnant/lactating women (54.0% female, mean age=40.6years) were presented with an online store beverage section and randomly assigned to one of six warning labels in a between-subjects experimental design: no-warning, pregnant/lactating, drinking-driving, general cancer risk, combined warnings, and assorted warnings across bottles. General cancer risk warnings are more effective at reducing alcohol purchase decisions compared to warning labels for specific groups or labels using multiple warnings. In addition to warning labels, other policies should be considered for addressing well-known alcohol-related risks (e.g., drinking and driving).