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Last Published
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Before
March 14, 2021 12:55 AM
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After
March 16, 2021 09:29 AM
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Intervention (Public)
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Before
We are assisting the City of Memphis/Shelby County 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, applications are open but treatments have not been assigned.
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After
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.
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Primary Outcomes (End Points)
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Before
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.
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After
- 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.
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Primary Outcomes (Explanation)
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Before
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.
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After
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.
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Experimental Design (Public)
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Before
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. We will run multiple waves of the lottery. 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.
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After
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.
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Randomization Method
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Before
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). Lottery weights will be determined by the City/County.
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After
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).
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Planned Number of Clusters
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Before
There is a budget of approximately $60 million, which the City/County intends to disperse 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).
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 lottery.
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After
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.
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Power calculation: Minimum Detectable Effect Size for Main Outcomes
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Before
We will file update in an amendment once we have more application data available.
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After
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.
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Additional Keyword(s)
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Before
Housing, eviction, public finance
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After
housing, eviction, public finance
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Intervention (Hidden)
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Before
We have several primary treatment arms:
- Pure control.
- Provision of back rent and utilities. Households will receive 100% repayment of back rent and utilities. Households are eligible for assistance on arrearages accumulated within twelve months of their application date.
- A second group of households will, in addition to back rent and utilities, also receive an offer made to their landlord on their behalf for future rent assistance. In particular, among the households who randomly receive back rent and utilities, we will randomly assign individuals to different rental assistance packages. Not all households who receive back rent will receive a future rent offer. The share of tenants who receive a future rent offer depends on the number of applications and a forthcoming City/County determination about the share of funds to be allocated to future rent. Tenants may receive up to three months of future rent. Packages are defined as shares to be contributed by the government, landlord, and tenant. For example, one package may specify that the contribution toward the next months of future rent will be: (government, landlord, tenant) = (20%, 80%, 0%), whereas another package may specify (government, landlord, tenant) = (30%, 20%, 50%).
Households that receive a future rent offer will receive this offer in addition to a back rent offer. No households receive future rent while having a (reported) rental debt. Therefore, the future rent treatment will identify the effect of receiving future rent, conditional on also receiving back rent.
Landlord contribution rate. We will test several levels of landlord write-offs from small (e.g., 10%) to large (e.g., 40%). For example, if a tenant owes $2,000 in rent for the next three months, a 10% write-off would require the landlord to forgive $200 in exchange for a government contribution. This arm will help us understand how much we can “stretch” the city’s funds further by imposing landlord write-offs.
Tenant contribution rate. We will hold the landlord’s write-off fixed but will test several levels of tenant contributions to future rent owed. The tenant contribution will range from very small (e.g., 0%) to requiring tenants to pay half of rent. This arm will help us understand how much we can “stretch” the city’s funds further by reducing, rather than fully relieving, tenants’ future rent burdens. We will randomize how much the tenant will be asked to contribute.
The precise rental assistance packages depend on the number and demographic composition of applicants, and are subject to final approval from the City/County. We therefore do not specify these here and will file an amendment at the appropriate time.
Randomization. We describe the randomization in more depth below. In waves of two to three weeks, we will randomly select a group of applicants for back and future rent. Applications for the first wave have opened. The first randomization will take place in approximately one week.
Description of lottery weights. The City/County has determined that it will conduct a “weighted” lottery, in which particularly at-risk groups receive a higher chance of treatment. The first randomization has not yet begun, and the City/County has determined that it will choose the final weights and weighting criteria once it sees the final number and composition of applicants. These weights may change in each wave; e.g., if one wave has an especially large number of weighted applicants, that may affect the weights in that wave.
Such groups may include: people with very low or low income (<30% or <50% AMI), people with disabilities, seniors, unemployed people, veterans, and households with 3 or more children. These weights may vary in each randomization. Our analysis will account for heterogeneity in treatment probabilities across households.
There may be an additional cross-randomization of provision of social services (e.g., housing counseling through a local non-profit). This cross-randomization has not yet been established. Such social services may be given to the control group.
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After
We have several primary treatment arms:
1. Pure control.
2. Provision of back rent and utilities. Households will receive 100% repayment of back rent and utilities. Households are eligible for assistance on arrearages accumulated within twelve months of their application date.
3. A second group of households will, in addition to back rent and utilities, also receive an offer made to their landlord on their behalf for future rent assistance. In particular, among the households who randomly receive back rent and utilities, we will randomly assign individuals to different rental assistance packages. Not all households who receive back rent will receive a future rent offer. The share of tenants who receive a future rent offer depends on the number of applications and a forthcoming City/County determination about the share of funds to be allocated to future rent. Tenants may receive up to three months of future rent. Packages are defined as shares to be contributed by the government, landlord, and tenant. For example, one package may specify that the contribution toward the next months of future rent will be: (government, landlord, tenant) = (20%, 80%, 0%), whereas another package may specify (government, landlord, tenant) = (30%, 20%, 50%).
Landlord shares represent percentage write-offs relative to the contract monthly rent. Landlords must accept the randomly-assigned haircut as a condition of receiving future rent assistance — that is, if the tenant accepts a haircut of x% and the government and tenants pay in total (1-x)% of rent, the landlord must recognize this as payment-in-full. When the assigned tenant contribution is positive, government assistance alone does not make tenants paid-in-full, and tenants thus have outstanding future rent obligations until their contributions are also paid. From the perspective of the tenant, in other words, a government share of x% and a landlord share of y% is equivalent to a reduction in their monthly rent of (x+y)% for the next three months.
Households that receive a future rent offer will receive this offer in addition to a back rent offer. No households receive future rent while having a (reported) rental debt. Therefore, the future rent treatment will identify the effect of receiving future rent, conditional on also receiving back rent.
Landlord contribution rate. We will test several levels of landlord write-offs from small (e.g., 10%) to large (e.g., 40%). For example, if a tenant owes $2,000 in rent for the next three months, a 10% write-off would require the landlord to forgive $200 in exchange for a government contribution. This arm will help us understand how much we can “stretch” the city’s funds further by imposing landlord write-offs.
Tenant contribution rate. We will hold the landlord’s write-off fixed but will test several levels of tenant contributions to future rent owed. The tenant contribution will range from very small (e.g., 0%) to requiring tenants to pay half of rent. This arm will help us understand how much we can “stretch” the city’s funds further by reducing, rather than fully relieving, tenants’ future rent burdens. We will randomize how much the tenant will be asked to contribute.
The precise rental assistance packages depend on the number and demographic composition of applicants, and are subject to final approval from the City/County. We therefore do not specify these here and will file an amendment at the appropriate time.
Randomization. We describe the randomization in more depth below. In waves of two to three weeks, we will randomly select a group of applicants for back and future rent. Applications for the first wave have opened. The first randomization for back rent is scheduled to occur in the next few days, and we expect the first randomization for future rent in the next two weeks.
Description of lottery weights. The City/County has determined that it will conduct a “weighted” lottery, in which particularly at-risk groups receive a higher chance of treatment. For the first wave, the City/County will weight according the following conditions:
- Low income (<50% AMI) households
- Households that include people with disabilities
- Households with a senior head of household (age ≥ 65 years)
- Households with veterans
- Households with 3 or more children
Households with none of these conditions will be assigned a weight of 1. Households with exactly one of these conditions will be assigned a weight of 1.15. Households with two or more of these conditions will be assigned a weight of 1.3. These weights determine a household’s probability of treatment as follows. In each wave, we will determine the overall share of households that can be treated, given a wave-specific budget level determined by the City/County. We will then randomly assign units to treatment so that the probability of being assigned to treatment is respectively 1.15x (1.3x) higher among households with exactly one condition (two or more conditions) than among households with no conditions. However, the partner may decide to change the weight values as well as weighting conditions may in future waves. If that occurs, we will file an amendment. Our analysis will account for heterogeneity in treatment probabilities across households.
Stratification. The City/County will implement a stratified random assignment. Randomization is stratified on both the weight as well as a binary indicator for whether, in total across rent and utility arrearages, the household is requesting in excess of $2,000, which is approximately the median requested amount in the first wave. The stratification variables may be revised in future waves. An amendment will be filed if this event occurs. Stratification serves both the implementation benefit of better controlling the outflow of funds in each wave and supports evaluation of heterogeneous treatment effects (see below regarding heterogeneity).
There may be an additional cross-randomization of provision of social services (e.g., housing counseling through a local non-profit). This cross-randomization has not yet been established. Such social services may be given to the control group.
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Secondary Outcomes (End Points)
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Before
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.
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After
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.
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