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The impact of housing discrimination on the pollution exposure gap in the United States
Initial registration date
October 07, 2018
May 16, 2019 11:18 AM EDT
University of Illinois at Urbana Champaign
Other Primary Investigator(s)
University of Illinois, Urbana-Champaign
Additional Trial Information
The choice of residential location is a critical economic decision for households in the United States. Recent research has shown that neighborhood pollution exposures can have significant effects on health outcomes, disproportionately affecting minority households. In this project, we seek to determine the extent to which discrimination in the rental market for housing affects local pollution exposures and contributes to inequity in health outcomes. This study will generate experimental evidence on the impact of discriminatory behavior on housing choices of renters using a major online rental housing platform within a tight radius of plants that emit toxic pollutants in the United States. Our project will use a correspondence study design to elicit differential response rates to housing inquiries on a major online rental housing platform. In addition to estimating the reduced-form effects of discriminatory behavior on housing choice within our locations, we will simulate a housing search process for thousands of renters that vary in income and preferences for housing and neighborhood attributes. This welfare-theoretic approach allows us to disentangle the effect of discriminatory behavior on household sorting and estimate its impact on welfare. Utility weights allow us to assign a dollar amount to the welfare effects of these constraints.
Christensen, Peter, Ignacio Sarmiento-Barbieri and Christopher Timmins. 2019. "The impact of housing discrimination on the pollution exposure gap in the United States." AEA RCT Registry. May 16.
Intervention Start Date
Intervention End Date
Primary Outcomes (end points)
(1) Response rate by race and by proximity to toxic release inventory facilities
(2) Heterogeneity in response rate by location attributes (pollution): particulate matter exposures, chemical toxics (RSEI), NOx, SOx.
(3) Heterogeneity in response rate by applicant characteristics: gender, mother's education level (high, middle, low)
Primary Outcomes (explanation)
Secondary Outcomes (end points)
Response time -- Duration of the lapse between inquiry and response
Length of response (message)
Secondary Outcomes (explanation)
Our experiment uses a paired (tripled) experimental design. Responses from property managers will be captured in email (gmail address associated with each name), phone messages (individual phone numbers associated with each name), and text messages. Responses that indicate housing availability will be coded, as well as the time stamp, message length, and sentiment of responses. The full set of rental listings will be compiled for the neighborhood (zip codes within 1 mile) of a toxic plant that reports toxic emissions to the Environmental Protection Agency's Toxic Release Inventory (TRI).
Experimental Design Details
The paired experimental design used in this study requires that inquiries for each listing are made from each of the three racial groups that we study. Immediately following compilation of the relevant listings in a given market, an inquiry will be sent for each using a randomly assigned name drawn from each of the 3 racial groups. Each rental apartment will therefore receive 3 separate inquiries in the course of an experimental trial. Listings are divided into 3 blocks to ensure that inquiries for the same listing will never be sent from two race groups on the same day. Inquiries will be sent at an interval of 2-10 minutes over the course of the 3 day period. The names and within each racial group are randomly assigned to a listing with equal probability and the sequence of inquiries is also randomly assigned.
Individual real estate listing (property manager)
Was the treatment clustered?
Sample size: planned number of clusters
110 zip codes within one mile of a toxic plant
Sample size: planned number of observations
3500-4000 property listings. Expected sample: 3700 listings (property managers)
Sample size (or number of clusters) by treatment arms
3500-4000 property listings. Expected sample: 3700 listings (property managers). Sample of listings within 1 mile varies by zip code, but at least 30% of listings fall within 1 mile of a facility. Based on matched design, inquiries will be sent out in equal numbers from racial groups (e.g. 3700 African American, 3700 LatinX/Hispanic, 3700 White)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We use existing apartment listing data from the same online platform in a pre-trial in Houston, TX to identify the sample size requirements for statistical power. The Houston pre-trial data contains 1563 listings. The pre-trial yielded a 17.9% response rate to white names and 16.7% to names associated with African American or LatinX/Hispanic names (non white names). It also yielded a relatively balanced sample with respect to proximity to TRI facilities: 45% of the rental properties where in the neighborhood of a toxic plant (within 1 mile).
To compute the sample sizes and the minimum detectable effects of the interaction of race and proximity to toxic plant we assume 90% test power and .05 significance level. In the small of data from the Houston pre-trial, we estimate an odds ratio of 1.27 (0.42) for the interaction. Standard errors are clustered at the Houston zip code level. We then simulate the effect of increasing sample size in a conditional logit model with paired inquiries. Simulation results suggest an effect size 1.54 that can be detected with 3017 properties. Figures 3 and 4 in our supporting materials shows simulation results for different sample sizes, for odds ration and p-values. Alternatively, if we use Demidenko (2007, 2008) approach to calculate the number of listings it yields that we need about 2,433 properties to obtain for that detectable odds ratio.
Phillips (2016) provides evidence of within-trial impacts when multiple inquiries sent in matched correspondence designs in competitive labor markets. In a sample restricted to responses to the first inquiry and based on a simple logit model, our simulations show that we should be able to detect an effect with an odds ratio of 1.43 at 3676 properties. Figures 5 and 6 shows the results of these simulations.
These power calculations are limited by available data from the Houston pre-trial and the incidence of discriminatory behavior that may be particular to the Houston housing market.
Demidenko E. (2007). "Sample size determination for logistic regression revisited." Statistics in Medicine 26:3385-3397
Demidenko E. (2008) "Sample size and optimal design for logistic regression with binary interaction." Statistics in Medicine, 27:36-46
Phillips, David C. "Do comparisons of fictional applicants measure discrimination when search externalities are present? evidence from existing experiments." The Economic Journal (2016).
INSTITUTIONAL REVIEW BOARDS (IRBs)
University of Illinois, Urbana-Champaign Review Board
IRB Approval Date
IRB Approval Number