Generating Evidence on the Impact of Cash Transfers for Rapid Rehousing Clients

Last registered on January 12, 2023


Trial Information

General Information

Generating Evidence on the Impact of Cash Transfers for Rapid Rehousing Clients
Initial registration date
November 07, 2022

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
November 08, 2022, 3:52 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
January 12, 2023, 9:33 AM EST

Last updated is the most recent time when changes to the trial's registration were published.


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Primary Investigator

University of Notre Dame

Other Primary Investigator(s)

PI Affiliation
University of Notre Dame
PI Affiliation
University of Notre Dame
PI Affiliation
Stanford University

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Rapid Re-Housing (RRH) is a program designed to help people transition from homelessness to stable housing. RRH offers a combination of temporary benefits that typically last between 6 and 24 months, such as housing identification assistance, rental subsidies, and case management. Yet, RRH is a short-term solution. Of those who do not explicitly re-experience homelessness, individuals may still face housing instability, financial insecurity, and poor health. Qualitative evidence generated by our partner Abode Services, a provider of homeless housing and services in CA’s Bay Area, suggests that a primary reason many return to homelessness is they still do not have a sufficiently stable income stream when the rental subsidies expire. We explore one possible way to bridge the gap: offering cash transfers to participants the year after exiting RRH. We quantify the impact of cash transfers through a randomized controlled trial (RCT), randomly offering monthly payments totaling approximately $13,000-$16,000 over 12 months. Our target sample is 990 individuals exiting RRH across five counties in the San Francisco Bay area. Using administrative data, we will measure the impact of cash transfers on homelessness, housing stability, financial security and other outcomes one and two years after study enrollment.
External Link(s)

Registration Citation

Batistich, Mary Kate et al. 2023. "Generating Evidence on the Impact of Cash Transfers for Rapid Rehousing Clients." AEA RCT Registry. January 12.
Experimental Details


Beginning in early 2023, clients who exit Abode Services’ RRH programs will have the option to participate in a cash transfer program. During the last case manager meeting prior to exit from RRH, the case managers will ask the client if they are interested in participating in a study that provides cash payments of $50 or more for one year.

Those who are randomized into the control group will receive $50 per month for 12 months. The treatment group will receive much larger monthly payments ranging from $800 to $2,000 depending on family type and month. These payments are roughly comparable to the average payment made by Abode’s RRH programs ($1,400) and will on average cover one-half to two-thirds of monthly rent ($2,100). Motivated by feedback from prior Abode RRH clients, the payments will be larger in the initial months than in later months to help with bigger expenses such as car repairs.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Homelessness program use
Housing moves
Having a formal address
Neighborhood characteristics

Credit score
Credit delinquencies
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Wage Rate
Job Exits
Job Starts

Child education

Health care utilization
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Following the construction of this study as a randomized control trial, those assigned to treatment and control groups should look equivalent to each other on average. Thus, any difference in outcomes between the groups could be attributed to their treatment status. After applicants have been randomized into these groups and the study begins, LEO will monitor outcomes into the future and compare them. For our analysis, we will estimate differences in outcomes between treatment and control group applicants using an intent-to-treat (ITT) framework controlling for baseline covariates. Baseline covariates will come directly from Abode’s administrative data or from data collected during the study intake process.

Specifically, we will estimate the following equation:
yi=β0 + Tiβ1 + xiβ2 + εi

where yi represents key outcome variables, such as returns to homelessness in the time period of interest, and xi represents a vector of observed characteristics for person i. The variables in x will include baseline characteristics such as age, age squared, categorical race variables, and indicators for ethnicity and gender. The key covariate in the analysis will be the dummy variable Ti which equals 1 if the respondent is assigned to a particular treatment arm and zero if in the respective control group. The term εi is an error term. We will measure the outcomes every year after randomization, determining short- and long-term effects of the intervention. Treatment-on-the-treated (TOT) effects will be estimated via an instrumental variable (IV) model, using assignment to treatment as an instrument for participation in services.

Experimental Design Details
Not available
Randomization Method
Computer-based randomization
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
990 families
Sample size: planned number of observations
990 families
Sample size (or number of clusters) by treatment arms
495 control families, 495 treatment families
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
For our analysis, we will estimate differences in outcomes between treatment and control group participants using an intent-to-treat (ITT) framework controlling for baseline covariates. The large sample size (N = 990) is an essential and unique feature of our study. We will be powered to detect a 6.5 percentage point (23 percent) decrease in the probability of becoming homeless 12 months after exit from RRH, assuming an untreated mean of 28 percent. Given the nature of the intervention, we expect take-up to be very high. Since about 4 percent of Abode’s RRH clients receive SSI or SSDI benefits, we conservatively assume that all SSI/SSDI recipients in the treatment group will decline to take up the cash transfers due to the impact on their existing benefits , resulting in a 96 percent take-up rate. With the current flow of clients exiting Abode Services’ RRH programs, we expect to reach a sufficient sample size after approximately two years of enrollment.. Better supporting individuals facing housing instability directly relates to intergenerational mobility and equity. We will explore subgroup heterogeneity across groups defined by baseline characteristics, such as gender, family type (adult-only households versus families with dependents), immigration status, and race and ethnicity.

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Analysis Plan Documents

Abode Pre-Analysis Plan 12.13.22.pdf

MD5: 875ca628a96d92f07bf774ee0006ab57

SHA1: 5d68522142c3fe69c012893a048f36c7ae60bed9

Uploaded At: January 12, 2023