Memphis Housing Security Study

Last registered on March 16, 2021

Pre-Trial

Trial Information

General Information

Title
Memphis Housing Security Study
RCT ID
AEARCTR-0007292
Initial registration date
March 09, 2021

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
March 10, 2021, 9:36 AM EST

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

Last updated
March 16, 2021, 9:29 AM EDT

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

Locations

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Primary Investigator

Affiliation
MIT

Other Primary Investigator(s)

PI Affiliation
MIT

Additional Trial Information

Status
In development
Start date
2021-03-01
End date
2024-12-31
Secondary IDs
Abstract
We study the effects of providing rental assistance to households in Memphis, Tennessee. We randomize offers of back- and future-rent to landlords. We also randomize the fraction of future rents owed by landlords and tenants. We plan to study the effects on take-up and evictions, and, if data are available, we hope to study effects on financial outcomes, labor market outcomes, social services take-up, and health, among other outcomes.
External Link(s)

Registration Citation

Citation
Rafkin, Charlie and Evan Soltas. 2021. "Memphis Housing Security Study ." AEA RCT Registry. March 16. https://doi.org/10.1257/rct.7292
Experimental Details

Interventions

Intervention(s)
We are assisting the City of Memphis and Shelby County, Tennessee in administering approximately $60 million in emergency rental assistance provided under the Consolidated Appropriations Act of 2021. The City and County are randomizing whether applicant households receive assistance through a weighted lottery and, in one arm of our analysis, randomizing the assistance packages offered to households and landlords. At this time, the first wave of applications has concluded but treatments have not been assigned.
Intervention Start Date
2021-03-17
Intervention End Date
2021-12-31

Primary Outcomes

Primary Outcomes (end points)
- Landlord take-up of assistance offer: The time period for measuring this outcome is determined by the window allowed to landlords to decide whether or not to accept. Landlord take-up will occur within roughly one month of the offer date.
- Eviction filings and formal eviction judgments: We will examine weekly filings and formal eviction judgments.


We will study the number of evictions within one to twelve months after, as well as two to five years after, the date of the initial wave in which the tenant applies.
Primary Outcomes (explanation)
We will directly measure tenant/landlord take-up, and we will collect eviction outcomes from local housing court filings.

We have tentatively designated these as our primary outcomes not because they are necessarily the most important but because we know that we will be able to measure them. It is not necessarily the case that our treatments must affect evictions for the treatment to affect our secondary outcomes. That is, our intention in specifying these as primary outcomes is not to indicate that eviction prevention is the key mediator for causal effects on the secondary outcomes.

Secondary Outcomes

Secondary Outcomes (end points)
We also intend to study the impacts of rental assistance on several other economic outcomes. Each of these groups of outcomes could become primary outcomes if we can link them. We have identified the outcomes of interest but have not yet been able to determine whether we will be able to access and merge administrative records held by the state government, local government, or private companies to be able to study them. Once we have secured the appropriate data access, we may consider some of these to be primary tenant outcomes.

- Housing security: number of moves, characteristics of any new residential neighborhoods moved to
- Labor market outcomes: employment, wage income, hourly wages, weekly hours (as measured in Tennessee UI records).
- Transfer benefit receipt (as measured in Tennessee administrative data on, e.g., SNAP)
- Financial outcomes (as measured from credit scores from credit reporting agencies). Health and hospitalization records
Homeless records: from the Homelessness Management Information System

We will study these outcomes within one to twelve months after, as well as two to five years after, the date of the initial wave in which the tenant applies. If surveys are administered, we anticipate that they will be during the year of treatment (i.e., 2021), but we leave open the possibility of longer-run survey outcomes.

We may also study the following landlord outcomes:
- Financial outcomes (as measured from credit reporting agencies)

Pending funding, IRB approval, and collaboration with our government and nonprofit partners, we may also administer tenant and landlord surveys to measure outcomes such as subjective probability of eviction, mental health, neighborhood and housing satisfaction, etc. These outcomes are more speculative, and we will amend the pre-registration before analyzing such data.
Secondary Outcomes (explanation)
We will provide a more precise definition of these outcome variables in an amendment once data availability has been determined.

Experimental Design

Experimental Design
Tenants apply via an online portal. After several weeks of applications, they are assigned to a treatment group. Those who are not treated are automatically re-entered in the lottery for back rent. At this time, we expect that tenants will only be randomized once for future rent. We will run multiple waves of the lotteries. Using information collected at the application, we can link tenants’ outcomes to public data on evictions records and administrative datasets from governments or private companies, and follow these outcomes over time.
Experimental Design Details
Not available
Randomization Method
Lottery conducted in Stata or similar statistical software by the investigators. The lottery will be conducted every few weeks (as determined by the City/County).
Randomization Unit
Household.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
There is a budget of approximately $60 million, which the City/County intends to spend in full. The number of treated and control households will depend on the number of applicants for rental assistance. Because the City/County plan to conduct a weighted lottery (where weights depend on household characteristics), the composition of applicants will also affect the number of treated and control households. Therefore, we cannot pre-register the number of clusters (or, equivalently, households). Approximately 2,000 households are entered into the back-rent lottery in the first wave. Approximately 1,250 will receive back rent in the first back rent lottery. Approximately 2,000 will receive back rent without entering the first lottery. Therefore, approximately 3,250 households will enter the first future rent lottery. We anticipate roughly 200–400 will be treated in the first future rent lottery but these numbers are subject to change.

Some households will receive assistance via a non-randomized “bulk settlement” process if their landlord has multiple tenants in need of rental assistance and requests funds for back rent via the bulk-settlement process. These households will be excluded from our study of back rents (though they may still be assigned to receive future rents). Other households with a pending eviction filing or utility shut-off notice will also be excluded from the back-rent lottery.
Sample size: planned number of observations
See above. The number of treated and control observations depends on the number and composition of applicants, which we cannot know at this stage.
Sample size (or number of clusters) by treatment arms
See above.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We can only conduct tentative power calculations because we are unsure of the sample size (see above). Projecting forward from the number of applications in the first wave, assuming applications will drop by 50% in the subsequent waves, and assuming we will conduct 10 waves total, we obtain the following results: - Evictions. We focus on the binary event of an eviction within one year of treatment. We assume the baseline probability is 15%. We are powered to detect, with 95% confidence, treatment effects of reductions in the eviction probability of 3.0 pp for back rent offers and 2.5 pp for future rent offers with at least 80% power. - Landlord acceptance of future rent offer. We focus on the binary event that a landlord accepts the future rent offer. We assume the acceptance probability = 1-(landlord contribution rate)*beta. We are powered to detect, with 95% confidence, a beta = 0.1 with at least 80% power. The above calculations are not the MDEs but rather an effect size of economic significance that we are powered to observe. As noted, it is possible that given the number of applications and budget, all people will be treated for back rent. In that case, we will use a design that uses the random timing of back rent offers. We do not present power calculations for such a design. We include fixed effects for waves and for the strata in this specification. However, we do not simulate the relationship between strata and potential outcomes, which would also affect power calculations.
IRB

Institutional Review Boards (IRBs)

IRB Name
Massachusetts Institute of Technology Committee on the Use of Humans as Experimental Subjects
IRB Approval Date
2021-03-01
IRB Approval Number
2102000316