The impact of housing discrimination on the pollution exposure gap in the United States

2018-10-01

2019-07-19

(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)

Response time -- Duration of the lapse between inquiry and response
Length of response (message)

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).

The full set of rental property 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). Figure 1 in the supporting documents illustrates the strategy. Markers illustrate property listings, with red markers illustrating the sample of listings within one mile of a toxic plant and green markers denoting listings outside 1 mile. Table 1 and Figure 2 report the sample of zip codes with reported emissions that fall above the 80th percentile of the TRI, which constitute potential zip codes in the study. The sample may change based on the availability of properties on the date of the experiment and the balance of these properties at high and low levels of concentrations within a given zip code.

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)

Yes

16-18 zip codes out of 110 zip codes within one mile of a toxic plant and in the 80% percentile of total toxic releases

2400-2700 property listings. Expected sample: 2600 listings (property managers)

2400-2700 property listings. Expected sample: 2600 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 a matched design, inquiries will be sent out in equal numbers from racial groups (e.g. 2600 African American, 2600 LatinX/Hispanic, 2600 White)

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 within Zipcode quartiles of toxic concentrations: 25% for properties in the first quartile of toxic concentration, 21% in the second, 21% in the third and 33\% in the quartile with the highest toxic concentration. With respect to proximity to TRI facilities, 45% of the rental properties are located within 1 mile of a toxic plant. To compute the sample sizes and the minimum detectable effects of the interaction of race and proximity to a toxic plant, we assume a test power of 90% and a .05 significance level. In the sample of data from the Houston pre-trial, we estimate odds ratios of 0.65 (0.48), 0.76 (0.30), 0.70 (0.32) and 0.79 (0.37) for each of the quartiles and 1.27 (0.42) for the interaction with plant proximity. Standard errors are clustered at the Houston zip code level. We then simulate the effect of increasing the sample size in a conditional logit model with matched inquiries. Simulation results indicate that effect sizes of 0.41, 0.35, 0.65 and 1.12 can be detected with a sample size of 2,400 properties. Simulations for plant proximity suggest an effect size of 1.54 that can be detected with 3017 properties. Figures 5-7 in our supporting materials plot simulation results (odds ratios and p-values) at different sample sizes. Using an alternate approach from Demidenko (2007, 2008), our simulations indicate that we need approximately 2,680 properties to obtain for detectable odds ratios. 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 odds ratios of 0.52, 1.41, 1.70, 0.98 at 2,337 properties and an odds ratio for the interaction with proximity to toxic plants of 1.43 at 3676 properties. Figures 8-10 plot 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. References 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).