Abstract
The authors revisit the question of alcohol consumption and public health over business cycles by decomposing overall alcohol consumption into drinking frequency and intensity in relation to consumer heterogeneity. To study this question, they use consumer-level panel data on the reported consumption (not purchases) of beer, which is the most heavily consumed alcoholic beverage and accounts for the majority of binge drinking in the United States. Leveraging the panel nature of the data, the authors find a negative (positive) relationship between unemployment and drinking frequency (intensity). Total consumption, which is the product of drinking frequency and intensity, is procyclical. To uncover differences in behavior across consumers and to provide policy recommendations at a segment level, the authors present a structural model where consumers simultaneously choose the frequency and intensity of their alcohol consumption. They find differences across consumers in their behaviors, notably with respect to income and age. They conduct policy simulations to compare the effectiveness of alcohol-related policies to counter the adverse effects of recessions on the health of vulnerable groups such as low-income and elderly populations.
Abstract
Our study aims to deepen the understanding of personalized digital nudges by evaluating their effects on energy?saving behavior. We conducted a field experiment with a leading smart metering company in South Korea to investigate whether customers save more energy when a personalized goal and feedback are provided, and how the impacts of nudges vary according to the types of misperception. Specifically, we focused on the behavior of customers who underestimate or overestimate their past electricity usage compared to their actual consumption. We merged daily energy consumption with a pre?experiment survey for the customers. We found that goal?setting and feedback mechanisms have a markedly different impact on each type of misperception. Underestimating customers reduced energy consumption only under the “goal setting with feedback treatment”. Conversely, overestimating customers reduced energy consumption even under the “goal setting without feedback” condition. The underlying mechanism is suggested as updating biased beliefs towards goal achievement. Overall, the results demonstrate that personalized nudges lead to heterogeneous behavioral responses and that service providers and policymakers can use these signals to enrich their planning of behavioral nudges.
Abstract
We propose a new methodology for forming arbitrage portfolios that utilizes the information contained in firm characteristics for both abnormal returns and factor loadings. The methodology gives maximal weight to risk-based interpretations of characteristics’ predictive power before any attribution is made to abnormal returns. We apply the methodology to simulated economies and to a large panel of U.S. stock returns. The methodology works well in our simulation and when applied to stocks. Empirically, we find the arbitrage portfolio has (statistically and economically) significant alphas relative to several popular asset pricing models and annualized Sharpe ratios ranging from 1.31 to 1.66.
Abstract
Sexual assault is one of the most repellant and costly crimes, which inflicts irrecoverable harms on victims and society. This study examines the effect of information technology (IT)-enabled ride-sharing platforms on sexual assaults. Drawing upon routine activity theory from the criminology literature, we posit that ride-sharing can reduce a passenger's risk of being a suitable target of sexual assault by providing a more reliable and timely transportation option for traveling to a safer place. By exploiting the nationwide quasi-experimental setting of Uber's city-by-city roilouts in the United States during 2005-2017, we demonstrate that Uber's entry into a city is negatively associated with the number of rape incidents. To zoom into the effects of ride-sharing at a more granular level, we employ precinct-hour-level data on Uber pickups and rape occurrences in New York City in 2015 and conduct spatiotemporal analyses. Our results from the spatiotemporal analyses corroborate those of the quasi-experiment and further reveal situational contingencies in the deterrent effect of ride-sharing. Specifically, ride-sharing contributes to a more significant reduction in the likelihood of rape occurrences in neighborhoods with limited transportation accessibility, and ride-sharing is more effective in deterring sexual crime in riskier circumstances, such as around alcohol-serving places on weekend nights or when the probability of crime occurrences increases. This study sheds new light on the potential of IT-enabled platforms to improve social well-being beyond their economic contributions and offers a new theoretical insight on the distinct role of digital platforms in public safety.
Abstract
We revisit one of the results in Cicala (2015) and show that the previously estimated large and significant effects of US electricity restructuring on fuel procurement are not robust to the presence of outliers. Using methodologies from the robust statistics literature, we estimate the effect to be less than one-half of the previous estimate and not statistically different from zero. The robust methodology also identifies as outliers the plants owned by a single company whose coal contracts were renegotiated before discussions about restructuring even started.