Researchers are using administrative data to develop and test practical solutions to social policy problems. In this guest post Mary-Alice Doyle provides an example from her work at J-PAL North America. This blog post builds on a previous post highlighting 8 international and homegrown examples of where data analysis is focused on addressing vulnerability.
Pennsylvania: Administrative data used to identify eligibility for SNAP and encourage take-up
In the US over 36 million people receive the Supplemental Nutrition Assistance Program (SNAP), more commonly known as food stamps. SNAP is a means-tested benefit available to low-income households, worth about $240 per household per month, on average. In 2019 a two-person household would be eligible for SNAP if they had a combined income below roughly $36,000 (or around £26,000).
Recent reports suggest this benefit could be lost for up to 700,000 recipients, because of new rules limiting SNAP assistance for a maximum of three months ‘Hundreds of thousands of food stamp recipients have a new reason to panic’. Incredibly, some 36m people in the US rely on SNAP, and it provides $60bn dollars in support.
But many households with incomes below the cut-off for eligibility forego this important source of purchasing power. In this article, Mary-Alice describes a US based project she was involved in in that asked if there was a way we can identify these households, let them know they are eligible, and help them to apply?
Policy in Practice works with councils in the UK to run similar data-led take-up campaigns. The examples from Royal Borough of Greenwich and Haringey Council were recently presented to the London School of Economics, view slides here. Policy in Practice is actively seeking partner organisations to build the global evidence base on the power of administrative data to transform social policy. Contact firstname.lastname@example.org for details.
While working at J-PAL North America, a lab based at MIT, I helped manage a research project aimed at answering these questions. Like many other projects supported by J-PAL North America, we used administrative data and engaged community-facing partners to pursue policy-relevant research questions.
The project was a collaboration between a team of academic economists (Amy Finkelstein and Matthew Notwidigdo) and Benefits Data Trust (BDT), a non-profit based in Philadelphia which helps people navigate the benefits system.
Both BDT and the academic research team were interested in learning whether a low-touch, low-cost intervention could help more people claim their benefits. To test this, we first needed to find individuals that were eligible for SNAP but not claiming it.
We identified these individuals using data on enrolment in other income support programs. The idea is that if someone is eligible for another means-tested program, they are likely eligible for SNAP too. We worked with the state Department of Human Services to identify people who fit these criteria, so that BDT could contact them. They identified just over 31,000 individuals, focusing on people who were aged over 65 and living in Pennsylvania.
BDT sent two-thirds of these individuals a letter to tell them they were likely eligible for SNAP, with information about how to apply. The remaining one-third did not receive a letter. Individuals were randomly assigned to one of these groups – this allowed us to be confident that any differences in outcomes between the two groups would be because of the letters, and not because of other pre-existing differences between individuals.
To better understand the barriers to SNAP enrolment, the letter-receiving group was split in two: half received ‘information only’ – just the letter, and instructions to contact the Department of Human Services to apply. The other half received ‘information and assistance’ – the letter, and instructions to contact BDT’s dedicated call centre for help with the application.
Nine months after sending the letters, we worked with BDT and the state Department of Human Services to find out whether the letters ‘worked’ in getting more people enrolled in SNAP.
The Department of Human Services provided data to BDT on who of the original 31,000 individuals had signed up for SNAP and who had not. BDT then provided the research team with a version of the dataset. To ensure we were maintaining individuals’ privacy, BDT only shared the information that we needed to do the analysis, and not individuals’ personal identifiers like their name, address, or other details. Using this data, the research team was then able to estimate the impact of the letters on SNAP enrolment.
The letters worked. Of the group who did not receive the letters, 6% signed up for SNAP. Of those who received the ‘information only’ letters, 11% signed up. And of those who received the ‘information and assistance’ letters, 18% signed up. (More detail on these results is available in the academic paper.) Admittedly, even with the letters, most people didn’t enrol. But over 1,000 people started receiving additional income in the form of SNAP benefits as a result of this low-cost intervention.
Without access to administrative data, this research might not have been possible. We would have needed to survey households first to find out whether they were eligible for benefits, spending a lot of time in the process surveying households who turned out to be outside of our focus. Then after sending the letters to eligible households, we would have had to conduct another survey to find out whether the letters worked. The cost of this research would have been prohibitively high, and as a result, we probably wouldn’t have the answers.
As it was, the research wasn’t easy. I joined the project partway through and built off the hard work my colleagues had already done in getting the project off the ground. We needed to negotiate and navigate the process for sharing data between three organisations, investigate the source and meaning of each of the fields in the data, and establish robust data security procedures to ensure we protected individuals’ privacy every step of the way. But the real-world impact of this type of research, that we now know how to help thousands more low-income households get the support they are eligible for, is well worth the effort.
UK: Data analysis helps the Royal Borough of Greenwich improve the financial resilience of residents
This type of work is, of course, not unique to the US. We know that a lack of information is a barrier to claiming entitlements here in the UK as well. And we know that making good use of existing administrative records can mitigate this problem. The Royal Borough of Greenwich, for example, has worked with Policy in Practice to achieve similar successes; using administrative records, borough staff have been able to identify residents who may be missing out on their entitlements and help them through the application process. This short animation shows how, using the benefits take up screen in a LIFT Dashboard, a cohort is selected, refined and downloaded to allow targeted intervention activity to take place.
UK: Haringey Council turned data insights into action to boost carer’s financial resilience
Haringey Council wants to make better use of the data it holds on residents, to deliver more effective services while delivering savings. The corporate board identified that people, culture and processes are a barrier to the wider use of data.
Policy in Practice was commissioned by Haringey Council to turn data insights from advanced analytics into action. Together with the Business Insights team Policy in Practice used the LIFT Dashboard (Low-Income Family Tracker) to identify a time-limited campaign to support carers, drawing a clear line of sight between the activity and the Borough plan which identified a need to boost the financial resilience of carers. Financially resilient carers are likely to be able to continue caring for longer, reducing future demand for care packages.
Using a test and learn approach data insights were created to identify residents who could financially benefit, and then explored ways to best target support to them. A targeted intervention campaign encouraging take-up of backdated Pension Credit claims was conceived. Executed in just 3 weeks, the campaign was driven by data insights that identified 236 households with mixed-age couples who weren’t claiming support worth £9,800 per household. Policy in Practice calculated that the potential gain of the campaign was over £710,000 to residents and £220,000 to the council.
In addition to boosting the financial resilience of carers Policy in Practice worked with Haringey Council to help overcome cultural barriers that were holding back insight from being put into action. This short video of the Haringey team outlines the approach taken and learnings made.
- View our recent presentation to the London School of Economics on the potential for administrative data to transform social policy here
- Find out more about our work with Haringey Council here
- Find out more about our work with the Royal Borough of Greenwich here
- Join our next webinar, Designing effective data-led local authorities, which will showcase how public sector administrative data is being used for good. On Wednesday 15 January 2020 hear guest speakers, Fiona Clay-Poole, Neath Port Talbot Council and Mark Fowler, London Borough of Barking and Dagenham, discuss how they’re using data to transform their organisations intelligently. See details and register here