People Judge Discrimination Against Women More Harshly Than Discrimination Against Men - Does Statistical Fairness Discrimination Explain Why?

Previous research has shown that people care less about men than about women who are left behind. We show that this finding extends to the domain of labor market discrimination: In identical scenarios, people judge discrimination against women more morally bad than discrimination against men. This result holds in a representative sample of the US population and in a larger but not representative sample of Amazon Mechanical Turk (Mturk) respondents. We test if this gender gap is driven by statistical fairness discrimination, a process in which people use the gender of the victim to draw inferences about other characteristics which matter for their fairness judgments. We test this explanation with a survey experiment in which we explicitly hold information about the victim of discrimination constant. Our results provide only mixed support for the statistical fairness discrimination explanation. In our representative sample, we see no meaningful or significant effect of the information treatments. By contrast, in our Mturk sample, we see that providing additional information partly reduces the effect of the victim's gender on judgment of the discriminator. While people may engage in statistical fairness discrimination, this process is unlikely to be an exhaustive explanation for why discrimination against women is judged as worse.

Do Capitation-based Reimbursement Systems Underfund Tertiary Healthcare Providers? Evidence from New Zealand.

One of the main concerns about capitation-based reimbursement systems is that tertiary institutions may be underfunded due to insufficient reimbursements of more complicated cases. We test this hypothesis with a data set from New Zealand that, in 2003, introduced a capitation system where public healthcare provider funding is primarily based on the characteristics of the regional population. Investigating the funding for all cases from 2003 to 2011, we find evidence that tertiary providers are at a disadvantage compared with secondary providers. The reasons are that tertiary providers not only attract the most complicated, but also the highest number of cases. Our findings suggest that accurate risk adjustment is crucial to the success of a capitation-based reimbursement system. Copyright © 2017 John Wiley & Sons, Ltd.

Malpractice liability, technology choice and negative defensive medicine.

We extend the theoretical literature on the impact of malpractice liability by allowing for two treatment technologies, a safe and a risky one. The safe technology bears no failure risk, but leads to patient-specific disutility since it cannot completely solve the health problems. By contrast, the risky technology (for instance a surgery) may entirely cure patients, but fail with some probability depending on the hospital's care level. Tight malpractice liability increases care levels if the risky technology is chosen at all, but also leads to excessively high incentives for avoiding the liability exposure by adopting the safe technology. We refer to this distortion toward the safe technology as negative defensive medicine. Taking the problem of negative defensive medicine seriously, the second best optimal liability needs to balance between the over-incentive for the safe technology in case of tough liability and the incentive to adopt little care for the risky technology in case of weak liability. In a model with errors in court, we find that gross negligence where hospitals are held liable only for very low care levels outperforms standard negligence, even though standard negligence would implement the first best efficient care level.