ABSTRACT
Misuse of prescription opioids is a leading cause of premature death in the United States. We use state government administrative data and machine learning methods to examine whether the risk of future opioid dependence, abuse, or poisoning can be predicted in advance of an initial opioid prescription. Our models accurately predict these outcomes and identify particular prior nonopioid prescriptions, medical history, incarceration, and demographics as strong predictors. Using our estimates, we simulate a hypothetical policy which restricts new opioid prescriptions to only those with low predicted risk. The policy's potential benefits likely outweigh costs across demographic subgroups, even for lenient definitions of "high risk." Our findings suggest new avenues for prevention using state administrative data, which could aid providers in making better, data-informed decisions when weighing the medical benefits of opioid therapy against the risks.
Subject(s)
Algorithms , Analgesics, Opioid/therapeutic use , Drug Prescriptions/standards , Opioid-Related Disorders/drug therapy , Practice Patterns, Physicians'/standards , Prescription Drug Misuse/prevention & control , Risk Assessment/methods , Aged , Female , Humans , Machine Learning , Male , Middle Aged , Opioid-Related Disorders/epidemiology , Predictive Value of Tests , Rhode Island/epidemiologyABSTRACT
We study the impact of a public school choice lottery in Charlotte-Mecklenburg schools on college enrollment and degree completion. We find a significant overall increase in college attainment among lottery winners who attend their first choice school. Using rich administrative data on peers, teachers, course offerings and other inputs, we show that the impacts of choice are strongly predicted by gains on several measures of school quality. Gains in attainment are concentrated among girls. Girls respond to attending a better school with higher grades and increases in college-preparatory course-taking, while boys do not.
ABSTRACT
In this article we review the literature on financial literacy, financial education, and consumer financial outcomes. We consider how financial literacy is measured in the current literature, and examine how well the existing literature addresses whether financial education improves financial literacy or personal financial outcomes. We discuss the extent to which a competitive market provides incentives for firms to educate consumers or offer products that facilitate informed choice. We review the literature on alternative policies to improve financial outcomes, and compare the evidence to evidence on the efficacy and cost of financial education. Finally, we discuss directions for future research.
ABSTRACT
We formulate a test of the fungibility of money based on parallel shifts in the prices of different quality grades of a commodity. We embed the test in a discrete-choice model of product quality choice and estimate the model using panel microdata on gasoline purchases. We find that when gasoline prices rise consumers substitute to lower octane gasoline, to an extent that cannot be explained by income effects. Across a wide range of specifications, we consistently reject the null hypothesis that households treat "gas money" as fungible with other income. We compare the empirical fit of three psychological models of decision-making. A simple model of category budgeting fits the data well, with models of loss aversion and salience both capturing important features of the time series.
