This study explores the causal impacts of income on a rich array of employment outcomes, leveraging an experiment in which 1,000 low-income individuals were randomized into receiving $1,000 per month unconditionally for three years, with a control group of 2,000 participants receiving $50/month.
Errors in administrative processes are costly and burdensome for clients but are understudied. Using U.S. Unemployment Insurance data, this study finds that while automation improves accuracy in simpler programs, it can increase errors in more complex ones.
This article analyses ‘digital distortions’ in Rules as Code, which refer to disconnects between regulation and code that arise from interpretive choices in the encoding process.
This study investigates how administrative burdens influence differential receipt of income transfers after a family member loses a job, looking at Unemployment Insurance, Temporary Assistance for Needy Families, and the Supplemental Nutrition Assistance Program.
This article analyzes the strategic use of public policy as a tool for reshaping public opinion. Though progressive revisionists in the 1990s argued that reforming welfare could produce a public more willing to invest in anti-poverty efforts, welfare reform in the 1990s did little to shift public opinion. This study investigates the general conditions under which mass feedback effects should be viewed as more or less likely.
Well-designed, user-focused tools that allow for simple application are key to ensuring that families most in need receive the Child Tax Credit. Reaching these households will require a robust effort from the IRS to create user-friendly tools in partnership with organizations with a direct connection to eligible recipients.
A national survey of low-wage workers showing that administrative burdens in SNAP and Medicaid are common and strongly linked to food hardship, healthcare hardship, and chronic illness.
This article examines the concept of "viral cash" and suggests that the future growth of basic income programs will depend on advocacy networks rather than traditional policy diffusion across jurisdictions.
This research explores how software engineers are able to work with generative machine learning models. The results explore the benefits of generative code models and the challenges software engineers face when working with their outputs. The authors also argue for the need for intelligent user interfaces that help software engineers effectively work with generative code models.
