A reverse sunk cost effect in risky decision making: Sometimes we have too much invested to gamble

The sunk cost effect refers to the empirical finding that people tend to let their decisions be influenced by costs made at an earlier time in such a way that they are more risk seeking than they would be had they not made these costs. This finding seems to be in conflict with economic theory which implies that only incremental costs and benefits should affect decisions. The effect is often explained in terms of prospect theory of (Kahneman, D., Tversky, A., 1979. Prospect theory: An analysis of decision under risk. Econometrica 47, 263–291), suggesting that sunk costs may induce a ‘loss frame,’ consequently causing risk seeking behavior. We argue that sunk costs may also result in risk aversion. In the present study we investigated the effect of time and effort investments (Behavioral Sunk Costs) on risky decision making in gain and loss situations. The results show that, in agreement with prospect theory, participants were more risk averse in gain situations than in loss situations. Moreover, incurring Behavioral Sunk Costs appeared to increase risk aversive choices, i.e., a reverse sunk cost effect. Furthermore, the results suggest that, in loss situations, Behavioral Sunk Costs mainly lead to risk aversive behavior if opting for the ‘safe’ alternative is not accompanied by an increased possibility to regret the decision.     

Use of incentives to motivate healthy behaviors among employees

We conducted an extensive literature search of both published and unpublished sources to assess the use and effectiveness of worksite interventions that offer financial and other incentives. Many companies are using financial incentives, either alone or in combination with other interventions, to motivate employees to adopt and maintain healthier lifestyles. These incentives include cash bonuses, paid vacation days, and health insurance rebates. Although more research is needed, the literature suggests that financial incentives, if properly implemented, may successfully promote behavioral change among employees. Eric A. Finkelstein Ph.D., M.H.A. currently conducts economic and health policy research at RTI International, teaches an undergraduate Health Economics course at Duke University, and is a member of RTI-UNC Center of Excellence in Health promotion Economics. He focuses on the economic causes and consequences of health behaviors, with a primary emphasis on behaviors related to obesity. He has published several peer-reviewed papers in this area. A recent publication entitled “National Medical Expenditures Attributable to Overweight and Obesity,” was published in Health Affairs and garnered national media attention, including a front-page story in USA Today, coverage in Time magazine and the Washington Post, and was reported in other radio, television, newspaper, and Internet outlets across the country. That paper is now routinely cited as a driving motivation to reduce obesity rates. Dr. Finkelstein currently leads several projects concerning the causes and consequences of health behaviors for the CDC and other public and private sector agencies, and frequently speaks at conferences concerning the economic impact of obesity and strategies for reducing this burden. Katherine M. Kosa, M.S. conducts economic and food and nutrition policy research at RTI International with a primary focus on consumer behavior research. She recently completed a study to examine the general public's attitude and support for obesity interventions recently introduced at the state and federal level. To inform labeling policy decisions for USDA and FDA, Ms. Kosa conducts focus groups and surveys to understand consumer attitudes, knowledge, and practices.     

HIV/AIDS and Cross-National Convergence in Life Expectancy

This article analyzes the effect of HIV/AIDS on the cross-national convergence in life expectancy as well as infant and child survival rates by comparing three scenarios. One is based on historical and future best-guess estimated values given the existence of the epidemic. The second scenario assumes that the effect of the epidemic is much worse than expected. The final scenario is based on hypothetical values derived from estimations where the mortality caused by the epidemic is removed. For life expectancy, convergence becomes stalled in the late 1980s (without weighting by country population size) or 1990s (with weighting). Convergence in infant and child survival rates does not become stalled, but slows down. These results are mainly attributable to the epidemic since all signs of stalled convergence or even divergence disappear in the “No AIDS scenario.” Given the existence of the epidemic, however, the reduced degree of inequality in life expectancy attained by 1985 is only expected to be achieved again by 2015 at the earliest. If the epidemic turns out much worse than expected, divergence could continue to 2050. No divergence is to be expected in infant and child survival rates in any of the scenarios.     

A comparison study of realtime fatality rates: severe acute respiratory syndrome in Hong Kong, Singapore, Taiwan, Toronto and Beijing, China

Summary.  In an outbreak of a completely new infectious disease like severe acute respiratory syndrome (SARS), estimation of the fatality rate over the course of the epidemic is of clinical and epidemiological importance. In contrast with the constant case fatality rate, a new measure, termed the 'realtime' fatality rate, is proposed for monitoring the new emerging epidemic at a population level. A competing risk model implemented via a counting process is used to estimate the realtime fatality rate in an epidemic of SARS. It can capture and reflect the time-varying nature of the fatality rate over the course of the outbreak in a timely and accurate manner. More importantly, it can provide information on the efficacy of a certain treatment and management policy for the disease. The method has been applied to the SARS data from the regions affected, namely Hong Kong, Singapore, Toronto, Taiwan and Beijing. The magnitudes and patterns of the estimated fatalities are virtually the same except in Beijing, which has a lower rate. It is speculated that the effect is linked to the different treatment protocols that were used. The standard estimate of the case fatality rate that was used by the World Health Organization has been shown to be unable to provide useful information to monitor the time-varying fatalities that are caused by the epidemic.     

The Greater Ability of Graphical Versus Numerical Displays to Increase Risk Avoidance Involves a Common Mechanism

By displaying a risk reduction of 50% graphically rather than numerically, Stone, Yates, and Parker significantly increased professed risk-avoidant behavior. The current experiments replicated this effect at various risk ratios. Specifically, participants were willing to spend more money to reduce a risk when the risk information was displayed by asterisks rather than by numbers for risk-reduction ratios ranging from 3% to 97%. Transforming the amount participants were willing to spend to logarithms significantly improved a linear fit to the data, suggesting that participants convert this variable within the decision-making process. Moreover, a log-linear model affords an exceptional fit to both the graphical and numerical data, suggesting that a graphical presentation elicits the same decision-making mechanism as does the numerical display. In addition, the data also suggest that each person removed from harm is weighted more by some additional factor in the graphical compared to the numerical presentations.     

Sample Size and Power Calculation in Comparing Diagnostic Accuracy of Biomarkers with Pooled Assessments

When testing of a biomarker is costly, pooling of samples becomes a useful and efficient alternative (Faraggi et al., 2003). In this paper, we develop procedures for sample size and power calculations for planning a study comparing the accuracy of biomarkers in diagnosis of diseases with pooled samples. Explicit formulas are derived for several important pooling strategies. The effects of pooling samples on sample size and power of the test are also discussed.