WORKING PAPERS
MECHANISM DESIGN FOR PERSONALIZED POLICY: A FIELD EXPERIMENT INCENTIVIZING EXERCISE
With Rebecca Dizon-Ross; Revise and resubmit, Econometrica
Personalizing policies can theoretically increase their effectiveness. However, personalization is difficult when individual types are unobservable and the preferences of policymakers and individuals are not aligned, which could cause individuals to misreport their type. Mechanism design offers a strategy to overcome this issue: offer a menu of policy choices, and make it incentive-compatible for participants to choose the "right" variant. Using a field experiment that personalized incentives for exercise among 6,800 adults with diabetes and hypertension in urban India, we show that personalizing with an incentive-compatible choice menu substantially improves program performance, increasing the treatment effect of incentives on exercise by 75% without increasing program costs relative to a one-size-fits-all benchmark. Personalizing with mechanism design also performs favorably relative to another potential strategy for personalization: assigning policy variants based on observables.
Promoting lifestyle changes such as regular exercise is critical for the global fight against diabetes. One barrier to lifestyle change is impatience (i.e., heavy discounting of the future), which makes short-run financial incentives for lifestyle change a promising approach and also makes it important to ensure the incentives work well in the face of impatience. We evaluate whether providing incentives for exercise to diabetics can help address the problem of diabetes in India. We also test a novel prediction, namely that "time-bundled" contracts, where the payment for future effort is increasing in current effort, are more effective when agents are impatient. We find positive results on both fronts. First, incentives increase daily steps by roughly 20 percent (13 minutes of brisk walking) and improve blood sugar. Second, consistent with our prediction, time-bundled contracts work better for more impatient people.
MEASURING DEMAND FOR GROUNDWATER IRRIGATION: EXPERIMENTAL EVIDENCE FROM CONSERVATION PAYMENTS
With Nick Hagerty
We measure the price response of demand for groundwater and electricity in irrigated agriculture in Gujarat, India, where both resources are scarce and largely unregulated. To do so, we install meters and introduce a new program of payments for voluntary conservation through a randomized controlled trial. First, we use the price variation introduced by this program to estimate the price elasticity of groundwater demand, a key parameter required for efficient regulation by any means. Then, we evaluate conservation payments as a policy tool in itself. We measure treatment effects on water and energy consumption, as well as spillovers, mechanisms, and economic impacts. We also assess the program’s cost-effectiveness, testing whether there is opportunity for mutual gain between irrigators and electric utilities. This project will provide the first experimental evidence on groundwater pricing and among the first on conservation payments. Pilot evidence confirms that conservation payments are feasible and suggests large effects on water use. Baseline data collection is complete; the intervention is now paused due to the COVID-19 crisis but is set to launch upon resumption of India field operations.
IN THE WORKS
Encouraging Drug Abstinence with Dynamic Incentives
With Rebecca Dizon-Ross and Mindy Waite
Combatting the rise of the opioid epidemic is a central challenge of U.S. health care policy. A promising approach for improving welfare and decreasing medical costs of people with substance abuse disorders is offering incentive payments for healthy behaviors. This approach, broadly known as "contingency management" in the medical literature, has repeatedly shown to be effective in treating substance abuse. However, the use of incentives by treatment facilities remains extremely low. Furthermore, it is not well understood how to design optimal incentives over time to treat opioid abuse. We will conduct the first evaluation of a scalable incentive program delivered through a mobile application. Our experiment will also directly address a key open question in the literature on incentives for drug-users: how to dynamically adjust incentives for abstinence behaviors according to how well individuals are complying with incentivized behaviors. Behavior-dependent dynamic incentive schedules can take two overarching forms: escalating or de-escalating. Escalating schedules feature incentive payments for compliance that increase as individuals comply with the behavior, and decrease with failures to comply. Escalating incentive schedules increase the stakes of good behavior now by hinging the size of future earnings opportunities on current behavior, and are frequently tested in substance-use settings. However, a pitfall of escalating schedules is that they are poorly targeted: they pay the largest incentives to individuals who are complying with behaviors, and offer the smallest incentives to individuals who are struggling to achieve compliance. De-escalating schedules can address the poor targeting of escalating schedules. De-escalating schedules feature incentive payments that increase when individuals fail to comply with the behavior. By delivering larger incentives where they are needed most, these schedules could particularly help individuals when they are struggling, an especially desirable feature in the addition space where the costs of substance abuse may be convex. However, the stakes of good behavior are lower than in an escalating schedule, because failure to comply now increases the size of future earnings. Our experiment will empirically assess the tradeoff between these two approaches. Effects will be measured on abstinence outcomes, including longest duration of abstinence and the percentage of negative drug tests. In combination with survey data, variation from the experiment will shed light on the barriers to abstinence more broadly and inform our understanding of optimal incentive design. A randomized pilot at the Aurora Health Adult Behavioral Program in Milwauke, Wisconsin is currently under way (AEA Registry Record No. AEARCTR-0005000.)
DE-BIASING OVER-OPTIMISM ABOUT PERSONAL COVID-19 HEALTH RISK
With Seema Jayachandran and Rebecca Dizon-Ross
Providing people with information about their health risk is an important part of the policy response to a public health crisis. However, the most effective way to present such information is unknown, particularly in light of behavioral biases people have. One such bias is over-optimism about one's health risk (i.e., a tendency to believe that one's risk is lower than it is), which has been documented in many settings and shown to lead to riskier behaviors. This study aims to test whether interventions that offset people’s over-optimism can improve the effectiveness of information provision. We do so in the context of the COVID-19 pandemic, among a population that is particularly vulnerable to severe complications from COVID-19, namely diabetics, pre-diabetics and hypertensives, who represent a large and growing segment of the population in India.
PAYING FOR PREVENTION: THE ROLE OF INCENTIVES IN ELIMINATING CARE GAPS
An important aspect of the Affordable Care Act was an increased focus on quality-of-care. The act created new quality measures that emphasize closing gaps in care and decreasing the use of costly acute care through preventive services. While insurance providers now have substantial stake in encouraging their members to close preventive care gaps, there is limited evidence on the most effective means to do so. We conduct a randomized controlled trial among members of a large health insurance provider in a midwestern state who had one of seven critical care gaps in 2018. Members either receive a letter with an incentive to close their (or their child’s) care gap, a letter with information regarding the gap, or no letter. We find that while incentives are effective for encouraging closure of children and teens’ care gaps, the do not improve care gap closures for adults – and may even discourage gap closure among this population. Information regarding existing care gaps has no detectable effect on closures.