The Supplementary Nutrition Assistance Program (SNAP) is an important federal program for millions of families in the United States, and cost $68 billion in fiscal year 2017. RIPL partnered with Rhode Island to develop a low-cost, data-driven pilot to leverage a bigger impact for families for each dollar spent on SNAP.
The once-monthly distribution of SNAP causes recipients to spend their benefits at the beginning of the month, often all at once. This rush to spend may contribute to a monthly cycle of food insecurity where caloric intake and fresh food consumption fall at the end of the month, and families face additional hardships including increased crime, school infractions, and hospital admissions.
Misuse of prescription opioids is a leading cause of premature death in the United States. We use new 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 non-opioid prescriptions, medical history, incarceration, and demographics as strong predictors.
Using our model estimates, we simulate a hypothetical policy which restricts new opioid prescriptions to only those with low predicted 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….
A new study finds that: SNAP participation has only a small effect on the nutritional quality of purchased grocery foods. The program’s effect is small compared to the variation in nutritional quality across households. Closing the gap in food-at-home spending between households of high and low socioeconomic status would not close the corresponding gap in the nutritional quality of purchased foods.
This paper uses administrative data to measure causal impacts of removing children from families investigated for abuse or neglect. We use the removal tendency of quasi-experimentally assigned child protective service investigators as an instrument for whether authorities removed and placed children into foster care. Our main analysis estimates impacts on educational outcomes by gender and age at the time of an investigation. We find that removal significantly increases standardized test scores for young girls. There are no detectable impacts on the test scores of girls removed at older ages or boys of any age. For older children, we also find few significant impacts…
A new study by Justine Hastings and Jesse Shapiro of Brown University and the Rhode Island Innovative Policy Lab (RIPL) finds that (1) every $100 in SNAP benefits leads to between $50 and $60 extra dollars of food spending each month; (2) an equivalent amount of cash benefits would lead to much smaller increases in food spending; (3) receipt of SNAP benefits makes households less likely to buy store brands or redeem discount coupons on SNAP-eligible food products; and (4) SNAP has a larger effect on food spending than tradi-tional economic models would predict.
Every child deserves an equal opportunity to succeed, but access to higher education varies greatly by socioeconomic status. College attendance is associated with improved long-term outcomes such as
social mobility, economic wellbeing, health, happiness, and lifespan. Despite these benefits, the national immediate college enrollment rate gap between low- and high-income students was 20 percentage
points in 2015. Existing research on need-based scholarships shows that even for large scholarship amounts, results are moderate at best with substantial costs attached.
Whether they are attempting to alleviate poverty, increase economic opportunity, or improve education and health care, state and local policymakers work to tackle some of the toughest problems facing society. To
make measurable progress in solving these problems, public policy needs to be effective, efficient, and evidence based.
Through the author’s collaboration with the state of Rhode Island, she has identified several challenges that policymakers face in successfully implementing fact-based policies: developing effective and secure data resources for insights, collecting the necessary technological resources and
expertise, and reliably defining and measuring program success.
We study the effect of photo ID laws on voting using a difference-in-differences estimation approach around Rhode Island’s implementation of a photo ID law. We employ anonymized administrative data to measure the law’s impact by comparing voting behavior among those with drivers’ licenses versus those without, before versus after the law. Turnout, registration, and voting conditional on registration fell for those without licenses after the law passed. We do not find evidence that people proactively obtained licenses in anticipation of the law, nor do we find that they substituted towards mail ballots which do not require a photo ID.