Using Technology and Iterative Research to Strengthen the Social Safety Net

• Published date: 01 March 2023
• DOI: http://doi.org/10.1177/00027162231205391
• Author: Aimee Chabot,Maximilian Hell
• Date: 01 March 2023

256 ANNALS, AAPSS, 706, March 2023

DOI: 10.1177/00027162231205391

Using

Technology and

Iterative

Research to

Strengthen the

Social Safety

Net

By

AIMEE CHABOT

and

MAXIMILIAN HELL

1205391ANN THE ANNALS OF THE AMERICAN ACADEMYUSING TECHNOLOGY AND ITERATIVE RESEARCH

research-article2023

People who seek public benefits for their families often

struggle to navigate the complicated labyrinth of U.S.

safety net programs. We show that states can meaning-

fully improve access for families in need—even in the

absence of significant policy change—by adopting

widely used technology and common research prac-

tices. Technology and research can be applied to each

stage of benefits delivery: outreach, application, and

renewal. The suggestions we offer vary in their ease of

implementation: some are simple, like sending repeated

renewal reminders through as many channels of com-

munication as possible; others are more involved, like

bundling together separate program applications. We

argue that if states are to succeed at simplifying the

enrollment process—and thereby increase the share of

eligible people receiving benefits—they can help their

own cause by building and maintaining a modern tech-

nological infrastructure for data collection and analysis,

and then act on what those data reveal.

Keywords: safety net; public assistance; technology;

modernization; state capacity

Unlocking the Door to Safety

Net Benefits

To receive support from the U.S. social safety

net, people need to be proactive and engaged.

When looking for benefits, families in the U.S.

face a highly fragmented system, comprising a

Correspondence: aimeechabot@gmail.com

Aimee Chabot is an independent researcher who was

the first research knowledge manager at Code for

America, a civic technology nonprofit, where she syn-

thesized research findings across safety net projects.

Trained as a mixed-method behavioral scientist, she

completed her PhD at Duke University.

Maximilian Hell is a data scientist at Code for America,

where he focuses on tax benefits for low-income house-

holds. Previously, he worked at the Stanford Center on

Poverty and Inequality and completed a PhD in sociol-

ogy on trends in intergenerational income mobility in

the U.S.

USING TECHNOLOGY AND ITERATIVE RESEARCH 257

variety of programs with application and eligibility requirements that vary.

Frequently, the technological systems that support these programs and enable

families to apply for and receive benefits are also separated by program and

staffed and maintained distinctly. Once families have determined that they need

support—a decision that can itself be a challenging one—they then need to learn

which support programs exist, which ones might be helpful to them, whether

they are eligible, and how to apply for those benefits. This can turn into a

months-long process that involves scheduling and attending multiple interviews

and repeatedly providing documents to verify circumstances. This process entails

in-person visits; lengthy and difficult-to-understand forms; and interaction with

outdated, broken, or poorly designed technology platforms (see Herd and

Moynihan [2019] for a review of the obstacles applicants and recipients face).

And benefit seekers are those often least likely to be in a position to navigate this

obstacle course, given their high rates of unstable housing (Docker et al. 2019);

unpredictable work schedules (Guyot and Reeves 2020); limited access to child-

care, internet, and/or transportation (Ahmed et al. 2002; Baldiga et al. 2018;

Molla 2021); chronic illness (Davis 2012); and other stressors that only add to the

challenge of successfully enrolling in and maintaining benefits. Program partici-

pation rates are often accordingly low, and much support remains unused (Currie

2006).1 Moreover, depending on the kinds of setbacks a household is facing, the

available support is often asymmetric: that is, for a family temporarily short of

food but with stable housing, the programs typically work as intended; but for

those facing imminent homelessness, the safety net is far less effective despite

often being more consequential. Fewer than half of all applicants have been

provided payment for rent relief in California since that state’s benefit program

began in March 2021, and those who have received payment waited a median of

more than three months to be approved and another three weeks to receive pay-

ment (Treuhaft et al. 2022).

A growing body of research shows that, beyond the immediate relief that they

provide—and despite the challenges of accessing benefits—safety net programs

have long-term beneficial effects on multiple dimensions of well-being. For

example, receiving the Earned Income Tax Credit (EITC) leads to reductions in

low birth weights (Hoynes, Miller, and Simon 2015); improvements in maternal

health (Evans and Garthwaite 2014); and increases in children’s educational per-

formance, attainment, and college attendance (Bastian and Michelmore 2018;

Chetty, Friedman, and Rockoff 2014; Dahl and Lochner 2012; Manoli and

Turner 2018; Maxfield 2015). Receiving Supplemental Nutrition Assistance

Program (SNAP) benefits leads to improved health and economic outcomes,

especially for those who receive SNAP as children (Almond, Hoynes, and

Schanzenbach 2011; Hoynes, Schanzenbach, and Almond 2016). And both

Medicaid and the Special Supplemental Nutrition Program for Women, Infants,

and Children (WIC) have been shown to improve infant health (Currie and

Gruber 1996; Hoynes, Page, and Stevens 2011) and long-term health outcomes

(Carlson and Neuberger 2021; Soni 2020). Collyer et al. (this volume) document

how the Child Tax Credit (CTC), paid for by the American Rescue Plan, signifi-

cantly reduced poverty levels and food insufficiency for millions of children.