Saurabh Bhargava

Choosing a Health Insurance Plan: Complexity and Consequences

Individuals who dread the annual ritual of choosing a health insurance plan might take solace in learning that they are not alone in feeling overwhelmed by the complexity of plan choice. For many, selecting a health plan is a source of considerable confusion and distress. The complexity of plan choice arises in part from wide variation among plans across the 4 features that determine how health costs are shared between the insurer and enrollee: the deductible, co-payment, co-insurance, and out-of-pocket spending limits. Consumers aspiring to make an informed choice across plans must evaluate the trade-off between each of these cost-sharing features and premiums, after carefully considering their projected health expenses, because paying for greater cost-sharing makes most sense if a person anticipates significant medical costs. Recent research, however, suggests that most consumers do not understand even the basics of health insurance. A 2013 survey of 202 insured US adults found that only 14% could answer 4 simple multiplechoice questions regarding the definition of costsharing features. Additionally, when presented with a simplified plan, most respondents were unable to accurately estimate the cost of their medical services.1 Complicating decisions further, plans typically differ on additional dimensions, such as which physicians

Bored in the USA: Experience sampling and boredom in everyday life.

We report new evidence on the emotional, demographic, and situational correlates of boredom from a rich experience sample capturing 1.1 million emotional and time-use reports from 3,867 U.S. adults. Subjects report boredom in 2.8% of the 30-min sampling periods, and 63% of participants report experiencing boredom at least once across the 10-day sampling period. We find that boredom is more likely to co-occur with negative, rather than positive, emotions, and is particularly predictive of loneliness, anger, sadness, and worry. Boredom is more prevalent among men, youths, the unmarried, and those of lower income. We find that differences in how such demographic groups spend their time account for up to one third of the observed differences in overall boredom. The importance of situations in predicting boredom is additionally underscored by the high prevalence of boredom in specific situations involving monotonous or difficult tasks (e.g., working, studying) or contexts where one’s autonomy might be constrained (e.g., time with coworkers, afternoons, at school). Overall, our findings are consistent with cognitive accounts that cast boredom as emerging from situations in which engagement is difficult, and are less consistent with accounts that exclusively associate boredom with low arousal or with situations lacking in meaning.

A Reassessment of the Defense of Parenthood

Whereas parents typically believe that their children increase their happiness (e.g., Eibach & Mock 2011), decades of research have found little support for this contention (Dolan et al. 2008). In a recent paper, Nelson et al. (2013) address the issue with analyses of the World Values Survey (WVS; 2006) and an experience-sampling dataset (Carstensten et al. 2010). Though the authors carefully avoid making causal claims, they nevertheless conclude that the results provide (page 8) “...strong evidence challenging the widely held perception that children are a source of reduced well-being.”

Driving Under the (Cellular) Influence: The Link between Cell Phone Use and Vehicle Crashes

The link between cell phone use while driving and crash risk has in recent years become an area of active research. The most notable of the over 125 studies has concluded that cell phones produce a four-fold increase in relative crash risk--comparable to that produced by illicit levels of alcohol. In response, policy makers in fourteen states have either partially or fully restricted driver cell phone use. We investigate the causal link between cellular usage and crash rates by exploiting a natural experiment induced by a popular feature of cell phone plans in recent years--the discontinuity in marginal pricing at 9 pm on weekdays when plans transition from peak to off-peak pricing. We first document a jump in call volume of about 20-30% at peak to off-peak switching times for two large samples of callers from 2000-2001 and 2005. Using a double difference estimator which uses the era prior to price switching as a control (as well as weekends as a second control), we find no evidence for a rise in crashes after 9 pm on weekdays from 2002-2005. The 95% CI of the estimates rules out any increase in all crashes larger than .9% and any increase larger than 2.4% for fatal crashes. These estimates are at odds with the crash risks implied by the existing research. We confirm our results with three additional empirical approaches--we compare trends in cell phone ownership and crashes across areas of contiguous economic activity over time, investigate whether differences in urban versus rural crash rates mirror identified gaps in urban-rural cellular ownership, and finally estimate the impact of legislation banning driver cell phone use on crash rates. None of the additional analyses produces evidence for a positive link between cellular use and vehicle crashes.

The costs of poor health (plan choices) & prescriptions for reform

Evidence suggests that when confronted with a large menu of health plan choices, consumers may not select the most efficient (that is, the most cost-effective) option. In anticipation of such problems, the exchanges set up by the Affordable Care Act (ACA) were designed to help consumers navigate the complexity of plan choices. Yet little is known about the actual efficiency with which ACA enrollees select plans. We present an analysis of projected health spending and a series of hypothetical plan choice experiments to explore the financial consequences of inefficient choices among potential ACA enrollees, the likelihood of such inefficient choices, and the potential for improving efficiency with a more behaviorally informed choice architecture. Our findings indicate that choosing a plan incommensurate with ones expected health care needs would lead to significant overspending relative to the most cost-effective plan and that, despite attempts to design the exchanges so that they facilitate decision-making, a significant share of ACA enrollees may have made inefficient decisions. More promisingly, we find that although the metal labels used in the exchanges to organize plans (for example, Bronze and Silver) encourage choices that are no more efficient than those associated with generic plan labels (such as Plan A and Plan B), labels that more sensibly reflect the factors consumers ought to consider—for instance, labels that emphasize gradations in the need for health care—do lead to significant improvements in the efficiency of plan choices.

A behavioral blueprint for improving health care policy

Behavioral policy to improve health and health care often relies on interventions, such as nudges, which target individual behaviors. But the most promising applications of behavioral insights in this area involve more far-reaching and systemic interventions. In this article, we propose a series of policies inspired by behavioral research that we believe offer the greatest potential for success. These include interventions to improve health-related behaviors, health insurance access, decisions about insurance plans, end-of-life care, and rates of medical (for example, organ and blood) donation. We conclude with a discussion of new technologies, such as electronic medical records and web- or mobile-based decision apps, which can enhance doctor and patient adherence to best medical practices. These technologies, however, also pose new challenges that can undermine the effectiveness of medical care delivery.