Alleviating administrative burdens in rental assistance promotes access and program efficiency

Applicants to government programs often face "administrative burdens" that can prevent people from accessing benefits for which they are qualified. However, it is difficult to evaluate the causal impact of relieving administrative burdens, as program requirements are rarely changed in ways that are amenable to quantitative causal inference methods. We evaluate the causal effect of reducing administrative burdens for applicants to emergency rental assistance (ERA), a program intended to support housing stability during the COVID-19 pandemic that distributed more than $46 billion to eligible renters. We analyze application data from two state housing authorities that quasi-randomly changed documentation requirements for applicants, in line with guidance provided by the U.S. Department of the Treasury. Using quasi-experimental methods that leverage strict eligibility cutoffs, we find that replacing the requirement to upload separate income verification documents with an automated eligibility check increased the probability that a rental assistance application was approved by approximately 15 percentage points. Further, and in line with staff reports that the program change streamlined time-consuming document review procedures, we show that eligible applications were processed nearly two weeks faster, a critical improvement for renters facing rental debts and eviction. Finally, we use data on ERA audits to show that increases in application approval and efficiency were met with no corresponding detectable increase in instances of suspected fraud.

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Publication Details

Journal
Proceedings of the National Academy of Sciences
Published
2026-05-12
DOI
https://doi.org/10.1073/pnas.2512756123
Primary Topic
Housing Market and Economics
Type
article
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article

Alleviating administrative burdens in rental assistance promotes access and program efficiency

Tyler Simko, Rebecca A. Johnson
Proceedings of the National Academy of Sciences
Housing Market and Economics
article

Alleviating administrative burdens in rental assistance promotes access and program efficiency

Tyler Simko, Rebecca A. Johnson
article en

Abstract

Applicants to government programs often face "administrative burdens" that can prevent people from accessing benefits for which they are qualified. However, it is difficult to evaluate the causal impact of relieving administrative burdens, as program requirements are rarely changed in ways that are amenable to quantitative causal inference methods. We evaluate the causal effect of reducing administrative burdens for applicants to emergency rental assistance (ERA), a program intended to support housing stability during the COVID-19 pandemic that distributed more than $46 billion to eligible renters. We analyze application data from two state housing authorities that quasi-randomly changed documentation requirements for applicants, in line with guidance provided by the U.S. Department of the Treasury. Using quasi-experimental methods that leverage strict eligibility cutoffs, we find that replacing the requirement to upload separate income verification documents with an automated eligibility check increased the probability that a rental assistance application was approved by approximately 15 percentage points. Further, and in line with staff reports that the program change streamlined time-consuming document review procedures, we show that eligible applications were processed nearly two weeks faster, a critical improvement for renters facing rental debts and eviction. Finally, we use data on ERA audits to show that increases in application approval and efficiency were met with no corresponding detectable increase in instances of suspected fraud.

Proceedings of the National Academy of SciencesVol. 123(20)
University of Michigan (US)
Openalex Percentile: Top 2%
Housing Market and Economics
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Alleviating administrative burdens in rental assistance promotes access and program efficiency — Tyler Simko, Rebecca A. Johnson · Proceedings of the National Academy of Sciences (2026) | TGRS Research Map | TGRS