Automatic Enrollment for the People: Experimental Evidence on Default Enrollment in a Safety Net Program

• In Event: Automation, AI and Nudges: Improving Digital Interfaces to Reduce Administrative Burdens in the Safety Net

Abstract

A promising strategy for overcoming the learning and compliance costs that contribute to incomplete take-up of many services and programs is for administering agencies to default eligible individuals into enrollment. Legal and administrative barriers hinder the use of auto-enrollment processes for safety net programs, however, so evidence about their effects in this context is limited. In collaboration with a public housing authority and a community organization, we administer a randomized controlled trial to evaluate the effects of an opt-out enrollment design on take-up of a unique savings program available to recipients of federal housing assistance. Our study sample includes approximately 1,000 eligible non-enrollees, one half of whom were automatically enrolled and offered the option to opt-out, while the other half were only offered application assistance. We complement the RCT with qualitative interviews to evaluate participants’ experience with the different processes. Finally, we match study participants to extensive administrative data, including state earnings records, credit scores, and enrollment in other safety net programs, which we use to measure the intervention’s effects on targeting. 

Author

• Matthew Unrath, University of Southern California
  Presenting Author