Menu
Initiatives
We work in four main areas to remove barriers to opportunity. In all of our projects and initiatives, we focus on developing fact-based solutions to ensure that everyone, regardless of background, has the chance to pursue their full potential.
INITIATIVE TWO
Social Program Innovation
Improve policy impact to alleviate poverty and deliver economic opportunity.
FEATURED
Using Data and Science to Support Strong Families
Research by: Justine S. Hastings, Mark Howison, and Sarah E. Inman
RIPL developed state-of-the-art science to help policymakers make informed, data-driven decisions about where to direct support, understand long-term outcomes of current removal policy, and develop the resources needed to conduct their own robust, timely analysis to support strong families and protect vulnerable children in Rhode Island.
In 2017, about 674,000 children in the U.S. were determined by CPS to be victims of maltreatment or abuse, and 1,720 children died from abuse or neglect.1 Understanding the key drivers of maltreatment, where to send support services, and the impact of current policies on families is vital to developing fact-based strategies that work from evidence instead of instinct.
FEATURED
Making SNAP Work for Families
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.
FEATURED
Predicting High Risk Opioid Prescription Before They are Given
Research by: Justine S. Hastings, Mark Howison, and Sarah E. Inman
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….
The Effect of SNAP on the Composition of Purchased Foods
Research by: Justine Hastings, Ryan Kessler, and Jesse M. Shapiro
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.
The Causal Impact of Removing Children from Abusive and Neglectful Homes
Research by: Anthony Bald, Eric Chyn, Justine S. Hastings, and Margarita Machelett
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…
Employing Machine Learning to Lower Medicaid Costs
Research by: Justine Hastings, Mark Howison, and Sarah Inman
Rhode Island’s Medicaid expenses are the second highest in the US. How can we lower costs and improve care?
A key finding from Governor Gina Raimondo’s Working Group on Reinventing Medicaid is that Rhode Island could “potentially save $90 million annually by preventing non-emergency visits to emergency rooms,” including $18 million on non-emergency, emergency department (ED) claims. How can we uncover the drivers of preventable ED claims to propose policy innovations that lower costs and improve health?
Aligning Workforce Development with Labor Market Needs
Research by: Zakary Campbell, Eric Chyn, Justine Hastings, and Preston
White
Rhode Island spends more than $58 million annually on workforce development. Can we make these services more effective? Governor Gina Raimondo is committed to aligning workforce development efforts with labor demands in Rhode Island in order to get people back to work and grow the state’s economy.
Utilizing Big Data to Create a Low-Cost Method to Employ Rhode Islanders
Research by: Zakary Campbell, Justine Hastings, and Noah Kessler
Data Works: How Rhode Island uses data to train workers while saving millions of dollars. Federal dollars for job-training programs are often designated for certain types of individuals. The Rhode Island Department of Labor and Training (DLT) wants to increase the number of job-seekers who qualify as “dislocated workers.” This will allow DLT to use federal revenue streams to fund training programs, train more people and save valuable state funds.
Understanding the Value-Added of Labor Training Programs
Research by: Zakary Campbell, Eric Chyn, and Justine Hastings
Rhode Island annually funds labor training for more than 1,000 individuals. How can we ensure that these individuals get the most benefit from training? Effective training programs are those that efficiently and cost-effectively return people to jobs with good wages. However, program quality cannot be measured merely by comparing mean outcomes. Job training programs are available to a wide range of individuals who possess varied skills and experience; programs with good labor outcomes may merely be “cream-skimming” programs that appeal to individuals with better prospective employment opportunities.
Evaluating the Impact of SNAP on Household Spending
Research by: Justine Hastings and Jesse M. Shapiro
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.
Innovating With Technology to Measure Hunger and Happiness
Research by: Mintaka Angell, Rory Creedon, and Justine Hastings
How can we measure policy impact on important experiences like hunger and happiness? We must have an accurate way to measure food insecurity and hunger. Traditional survey methods ask questions about hunger retrospectively over long periods of time, from the past 30 days to the past year. This retrospective approach leads to recall bias – can you remember if you were hungry at any point two weeks ago? Respondents may provide inaccurate answers to questions, given the challenges associated with remembering, with specificity, subjective experiences like hunger.
Measuring Access to Healthy Foods for Low-Income Communities
Research by: Mintaka Angell, Rory Creedon, and Justine Hastings
How Can We Measure Access to Healthy Foods and the impact access has on low-income communitites? Traditional metrics of food access do not generate sufficiently reliable facts. Many measures use secondary data sources to infer food availability. Some look at store size, or group stores into broad categories (e.g., large grocery stores and small corner stores) which can be misleading with “small stores” becoming a proxy for having little fresh food.
INITIATIVE ONE
Closing The Achievement Gap
Provide all children from cradle to college with the opportunity to reach their full potential.
Closing the achievement gap using early investments in infant health
Research by: Eric Chyn, Samantha Gold, and Justine Hastings
Spending $4,000 on health investments at birth can save $7,500 in Medicaid costs before age 2, save $67,000 in social program expenditures by age 14, boost test scores in grades 3-8, and increase college enrollment by 17 percentage points for Very Low Birth Weight (VLBW) children in Rhode Island.
Introducing Rhode 2 College
Research by: Jonelle Ahiligwo, Mintaka Angell, Anthony Bald, Justine Hastings, and Victoria Kidd
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.
INITIATIVE tHREE
criminal Justice
Improve criminal justice programs to prevent crime and reduce recidivism.
Reducing Recidivism by Connecting Releasees with Social Services
Research by: Mintaka Angell, Justine Hastings, and Amamah Sardar
Thirty percent of Rhode Island inmates return to prison after one year. Can Supplemental Nutrition Assistance Program (SNAP) help reduce recidivism? RIPL analysis shows that individuals who enroll in SNAP post-release are 7.4 percentage points less likely to recidivate than their non-SNAP counterparts within the first six months. However, only 40% of individuals enroll in SNAP post-release even though nearly all qualify.
Analyzing Value Added to Improve In-Prison Training Programs
Research by: Jun Shepard and Justine Hastings
Rhode Island’s annual prison recidivism rate is 52%. Can we do better without spending more by improving in-prison programs? RIPL took a data-driven approach to identify low-cost solutions to lower recidivism rates. We started by measuring the effectiveness of in-prison training programs.
INITIATIVE FOUR
Regulation That Works
Ensure regulations meet valuable goals while minimizing negative repercussions.
FEATURED
The Hamilton Project Fact-Based Policy: How do States and Local Governments Accomplish It?
By: Justine Hastings
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.
FEATURED
Effects of Photo ID Laws on Registration and Turnout: Evidence From Rhode Island
By: Justine Hastings, Diego Focanti, and Francesco Maria Esposito
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.
