Creating Moves to Opportunity in Seattle and King County

Families will receive access to housing locator services to assist in their housing search. The services provided will include marketability counseling to help the families prepare for their housing search, opportunity area education to introduce families to the opportunity neighborhoods, and assistance in finding available apartments. These services will be provided by a third-party provider. Participating families will have access to financial assistance to help cover costs that may be higher in opportunity neighborhoods, such as security deposits, moving expenses, and application fees, in the event families do not have the funds to cover those expenses themselves. Landlords in opportunity areas will also have incentives to lease to voucher-holders, including access to a risk mitigation fund to cover damages above and beyond a security deposit. There will be more intensive recruitment efforts to increase the number of landlords willing to lease to voucher-holders in opportunity areas during the program as well.

In Phase II of the study, enrolled families assigned to the treatment group families received one of three possible sets of CMTO services: Coaching Services and Resources, Cost-Optimized Services, and Financial Assistance. These three interventions are based either in whole or in part on the services provided in Phase I, and the breakdown is provided below.Due to the COVID-19 pandemic, the PHAs decided to suspend enrollment in the phase two of CMTO in mid-March of 2020. The PHAs and Navigators continued to provide services for families who had already enrolled in the program, though nearly all interactions were shifted to a remote capacity given the need to follow local social distancing guidelines. This decision was made due to the public health situation, though PHA staff did consult with staff at Opportunity Insights in making the decision.

Coaching Services and Resources: Families could receive Rental Application Coaching, Opportunity Area Education, Housing Search Assistance, Flexible Financial Assistance, and Access to Landlords from Landlord Outreach as described above. This intervention is nearly identical to the intervention from Phase I, with slight deviations outlined above.

Cost-Optimized Services: Families in this group received pared down versions of the interventions described above. For example, families had only one in-person meeting with the Family Navigator, and fewer conversations via text or phone compared to those in the Coaching Services and Resources group. Families in this group did not receive direct customized unit referrals (unless their vouchers were for 3+ bedroom units), but the navigators would help with the housing search process if the family requested such assistance. Additionally, security deposit assistance is capped based on the bedroom size of the family’s voucher using the following limits: 0-1BD: $500, 2BD: $750, 3BD: $1000, 4BD: $1250, 5BD: $1500. Families could access additional financial assistance to cover costs related to application fees, but only for three units at the most.

Financial Assistance: Families in this group only received the full Flexible Financial Assistance available to families in the Coaching Services and Resources group. However, they received none of the other supports aside from some simple opportunity area education so they knew where they were eligible to use the financial assistance.

2018-04-02

2021-05-07

Our core short-run outcomes are housing mobility and neighborhood environment characteristics. These include

• Initial lease up rate in an opportunity area (Defined at the family-level as 1 = lease up in opportunity neighborhood; 0 = does not lease up in opportunity neighborhood)

• Exposure to higher opportunity neighborhood quality. Our primary measure of this will be exposure-weighted mean neighborhood quality for the two years after random assignment using the Chetty and Hendren (2018 QJE) mean upward mobility rate of each neighborhood (Census tract). We will consider alternative ways to measure the exposure to neighborhood quality, including

i. Share of post-random assignment period spent residing in a high opportunity neighborhood (using a discrete measure of high opportunity)

ii. Point-in-time measures include measuring opportunity neighborhood residency status (binary or continuous measure using tract-level upward mobility rate) at a given amount of time after random assignment. For example, initial outcomes may be measured as the likelihood each family is living in an opportunity neighborhood one year after being randomly assigned to treatment or control groups.

• Census tract level mean household income rank for families at the 25th percentile of the parent income distribution in the neighborhood families chose using data from the Opportunity Atlas as laid out in Chetty, Friedman, Hendren, Jones and Porter (2018 NBER).

References:

Chetty, Raj and Nathaniel Hendren. 2018. “The Impacts of Neighborhoods on Intergenerational Mobility II: County-Level Estimates.” Quarterly Journal of Economics 133(3), 1163-1228.

Chetty, Raj and Nathaniel Hendren. 2018. “The Impacts of Neighborhoods on Intergenerational Mobility II: County-Level Estimates.” Quarterly Journal of Economics 133(3), 1163-1228.

Chetty, Raj, John N Friedman, Nathaniel Hendren, Maggie R Jones, and Sonya R Porter. 2018. The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility. Working Paper, Working Paper Series 25147. National Bureau of Economic Research, October.

The RCT’s goal is to test which interventions alleviate constraints that decrease the likelihood of low-income families moving to higher-opportunity neighborhoods for their children. Thus, the primary short-run outcome of interest indicating intervention effectiveness will be the likelihood of moving to an opportunity neighborhood, coupled with a measure of treatment persistence and intensity given by exposure-weighted neighborhood quality metrics. A key short-run mediating outcome is the overall housing voucher lease-up rate. CMTO interventions could increase overall housing voucher lease-up rates by providing assistance in housing search, but the CMTO could reduce overall lease-up rates by focusing housing search on higher-opportunity neighborhoods where it is more difficult to lease-up.

Secondary short-run outcomes include
• Teen birth rates and incarceration rates (both for families at the 25th percentile of the parent income distribution) in the neighborhood families chose based on data from the Opportunity Atlas as laid out in Chetty, Friedman, Hendren, Jones and Porter (2018 NBER).
• Apartment and neighborhood characteristics including the number of miles moved, size of the new unit in square feet, and total rent amount paid to the owner.
• A family's self-reported satisfaction with their new neighborhood, and their certainty about wanting to stay in their new neighborhood based on post-move survey data.
• Housing voucher lease up rate (1 = lease up with the housing voucher; 0 = does not lease up).

Interim secondary outcomes on families include

• Children’s standardized test scores (state percentile rank)
• Household adults’ employment and earnings

Longer-run child outcomes include

• Post-random assignment childhood exposure-weighted neighborhood quality from random assignment to age 18 (or early 20s) based on the Chetty and Hendren (2018 QJE) measure of neighborhood (Census tract) upward mobility. This is the primary measure of neighborhood environment that the intervention is designed to support.
• Core outcomes from IRS administrative tax data as defined in Chetty, Hendren, and Katz (2016 AER, Table 11) include
i. Individual earnings
ii. Household income
iii. College attendance from age 18-20 (%)
iv. College quality from age 18-20
v. Marital status
vi. Poverty share in zip code of residence (%) (or in Census tract of residence if possible with future versions of administrative tax data)

References:

Chetty, Raj and Nathaniel Hendren. 2018. “The Impacts of Neighborhoods on Intergenerational Mobility I: Childhood Exposure Effects.” Quarterly Journal of Economics 133(3), forthcoming.

Chetty, Raj and Nathaniel Hendren. 2018. “The Impacts of Neighborhoods on Intergenerational Mobility II: County-Level Estimates.” Quarterly Journal of Economics 133(3), forthcoming.

Chetty, Raj, Nathaniel Hendren, and Lawrence F. Katz. 2016. "The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment." American Economic Review, 106(4): 855-902.

Chetty, Raj, John N Friedman, Nathaniel Hendren, Maggie R Jones, and Sonya R Porter. 2018. The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility. Working Paper, Working Paper Series 25147. National Bureau of Economic Research, October.

We will also attempt to measure other short-run outcomes that we do not expect to change substantially in response to the CMTO interventions based on prior research including children’s educational outcomes and household income. In the longer-run, based on prior research on opportunity neighborhood exposure during childhood, we hypothesize that there will be an effect on longer-run child outcomes such as college attendance, individual and household income, marital status and out-of-wedlock childbearing, and adult neighborhood quality.

All families with at least one child under age 15, applying for federal housing choice vouchers (HCVs) through SHA or KCHA and eligible for that program (e.g. meeting income requirements) will be offered enrollment in CMTO. If they consent to participate in the study, they will be randomly assigned to a treatment group or a control group. In the first phase of the evaluation, families in the treatment group will have access to all the CMTO services in addition to the regular services provided by the housing authorities for new HCV participants. The control group will only have access to the normal housing authority services. A second phase of research is planned to further test the more promising components of CMTO services and understand their relative cost effectiveness. The services to be tested in this phase will be selected based upon qualitative research during the first phase around which services families are finding most helpful in making opportunity moves.

randomization done in office by computer

household

No

n/a

1284 households

642 treatment, 642 control across Phases 1 and 2 of the RCT

Assuming conventional 80% power and 95% confidence intervals, preliminary power calculations suggest that the minimum detectable effect size in the first year of the intervention is a 5 percentage-point change (46%) in the likelihood of moving to an opportunity neighborhood when comparing treatment and control groups. Using statistics provided by the Seattle and King County Housing Authorities, we based our calculations on assumptions on the projected number of new families with at least one child under age 15 issued a voucher each year (N = 1284), equally sized treatment and control group sizes, and the usual fraction of families issued vouchers who move to an opportunity neighborhood absent any intervention (11%, standard deviation = 0.31). Although we are interested in outcomes conditional on voucher take up, randomization will occur at the level of issued voucher, and we assume that historical 68% lease-up rates (weighted average across SHA and KCHA) will prevail for both treatment and control. We further assume that gamma = 5% of families assigned to the treatment group will opt-out of services. To calculate the minimum detectable effect size with an expected sample size of 1284 households, we followed equation (4.3) in Orr (1999) Minimum Detectable Effect = 2k*sigma/sqrt((1-gamma)*N) = 0.0502 where k = 2.8 is a constant determined by desired statistical size (5%) and power (80%); sigma = 0.31 is the standard deviation of the unconditional outcome of interest (the fraction of households issued a voucher that lease up in an opportunity neighborhood); gamma = 0.05 is the fraction of sample that decline program participation; and N = 1284 is the total number of vouchers to eligible families expected to be issued during the study across the two housing authorities. Although this is a large effect in relative terms, it is small in absolute terms (less than an extra 100 families a year moving to opportunity neighborhoods), and it is smaller than effects seen in the original Moving to Opportunity experiment evaluation and those estimated non-experimentally for the Baltimore Housing Mobility Program. Looking specifically at the power calculations for Phase I alone, and following the same assumptions as above, the Minimum Detectable Effect for the Phase I sample (642 issuances across treatment and control) rises to .0710 meaning that we anticipate having sufficient power to detect a 7.1 percentage point treatment effect between the treatment and control group. Phase Two consists of three distinct treatment arms with smaller sample sizes per arm and thus larger minimum detectable effects. Again the total sample target was 642 issuances, with 25% of families randomly assigned to one of the three treatment groups and the control group. Focusing specifically on the minimum detectable effects in the pairwise comparison context, our minimum detectable effect increases to 0.1003 meaning that we anticipate having sufficient power to detect a 10.0 percentage point treatment effect across any two treatment arms.

Phase 2 Amendment to CMTO Seattle/King County Pre-Analysis Plan

CMTO Seattle/King County Pre-Analysis Plan