The Wrong Politics for the Right Neighborhood? Political Cues, Rental Housing, and Spatial Sorting

Last registered on July 13, 2026
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Pre-Trial

Trial Information

General Information

Title
The Wrong Politics for the Right Neighborhood? Political Cues, Rental Housing, and Spatial Sorting
RCT ID
AEARCTR-0019115
Initial registration date
July 07, 2026

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
July 13, 2026, 7:37 AM EDT

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

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Primary Investigator

Name
Ignacio Sarmiento-Barbieri
Affiliation
University of Los Andes
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Other Primary Investigator(s)

PI Name
Leopoldo Fergusson
PI Affiliation
University of Los Andes
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PI Name
Gabriela Mejia
PI Affiliation
University of Los Andes
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Additional Trial Information

Status
On going
Start date
2026-05-04
End date
2026-08-21
Keywords
Behavior, Electoral, Welfare
Additional Keywords
Discrimination; Housing market; Elections;Segregation; Correspondence studies
JEL code(s)
C93; J71 ; D72; R31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In polarized democracies, supporters of opposing political camps increasingly live in different neighborhoods, and rental markets are one gate through which this segregation can operate. We ask whether displaying political affiliation reduces access to rental housing in Bogotá, a polarized and highly segregated city where the high-amenity neighborhoods tenants most seek lean right. We run a correspondence experiment during the 2026 Colombian presidential election, sending one standardized WhatsApp inquiry per rental listing before, between, and after its two rounds. The applicant’s WhatsApp profile picture signals support for the left-wing candidate, support for the right-wing candidate, or nothing (no-photo control); we independently randomize the applicant’s name, accent, neighborhood belonging, and gender, with name and accent signaling perceived class and social origin to separate political from class-based discrimination. Outcomes capture whether landlords respond with availability, and how quickly and how fully they engage. We first estimate the average penalty each political signal carries across the city; our primary estimand is the access a left-wing applicant loses in right-leaning, high-amenity neighborhoods; and we test, in either direction, whether this penalty differs in left-leaning neighborhoods.
External Link(s)

Registration Citation

Citation
Fergusson, Leopoldo, Gabriela Mejia and Ignacio Sarmiento-Barbieri. 2026. "The Wrong Politics for the Right Neighborhood? Political Cues, Rental Housing, and Spatial Sorting." AEA RCT Registry. July 13. https://doi.org/10.1257/rct.19115-1.0
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Experimental Details

Interventions

Intervention(s)
We conduct a between-listing correspondence experiment in Bogotá’s rental housing market during the 2026 Colombian presidential election. Each listing receives one WhatsApp inquiry from a fictitious applicant. The main treatment is political affiliation, randomized through the applicant’s WhatsApp profile picture, which signals support for the left-wing candidate, support for the right-wing candidate, or nothing (no photo, the control). We independently randomize the applicant’s name, accent (a WhatsApp voice note), a neighborhood-belonging cue, and gender; name and accent signal perceived class and social origin, separating political from class-based discrimination. The primary outcome is whether the landlord responds within seven days confirming that the unit is available. Secondary outcomes include the speed, quality, and length of the landlord’s reply, and the number of messages needed to secure a viewing.
Intervention Start Date
2026-05-04
Intervention End Date
2026-08-21

Primary Outcomes

Primary Outcomes (end points)
Effective response: a binary indicator equal to 1 if, within seven days, the landlord or agent directs the applicant toward the property by confirming availability, offering to schedule a visit, or providing contact information for follow-up.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
1. Any response received (binary; expected near-ceiling, analyzed as a descriptive check).
2. Quality of response: effort and completeness of the landlord’s engagement, human-coded on a structured rubric.
3. Length of response: word count across all landlord messages in the thread.
4. Response time: elapsed hours from the applicant’s first message to the landlord’s first reply, plus a categorical version with thresholds set blind to treatment.
5. Number of interactions required to obtain an effective response.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We implement a between-listing correspondence design. Each rental listing receives exactly one WhatsApp application from a fictitious applicant whose profile picture is randomized across the political affiliation conditions; the applicant’s name, accent, neighborhood belonging, and gender are independently randomized. The sample is rental listings from the two major online housing platforms in Bogotá, restricted to estratos 3 through 6. Data collection spans the 2026 Colombian presidential first round (May 31) and runoff (June 21), letting us study whether political discrimination varies with electoral salience.
Experimental Design Details
Not available
Randomization Method
Randomization is performed by computer at the time of daily application assignment. Daily batches are drawn half from lower-estrato properties (estratos 3–4) and half from higher-estrato properties (estratos 5–6), and assignment is stratified so that the political conditions are balanced within each group.
Political affiliation. In Wave 1 (pre-first-round), Candidate 3 (Left) is assigned with probability 1/3; Candidates 1 and 2 (Right) are each assigned with probability 1/6; the no-photo control is assigned with probability 1/3. This weighting equalizes the Left, Right, and Control cells in expectation, because two of the four conditions are right-wing. In Wave 2 (post-first-round), the two finalists and the no-photo control are each assigned with probability 1/3.
Other dimensions. Name class (high vs. low), accent class (high vs. low), neighborhood belonging (prior connection vs. interest only), and applicant gender (female vs. male) are each drawn with equal probability (50/50), independently of the political affiliation draw and of each other.
Randomization Unit
The rental listing. Each listing receives exactly one application, and all treatment dimensions are randomized at the listing level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Between 2,000 and 5,000 rental listings.
Sample size: planned number of observations
Between 2,000 and 5,000 applications (one per listing, since each listing is contacted exactly once).
Sample size (or number of clusters) by treatment arms
Wave 1 (between 700 and 1,000 listings): Left condition 1/3; each right-wing candidate 1/6; no-photo control 1/3.
Wave 2 (between 1,300 and 4,000 listings): each of the two finalist conditions 1/3; no-photo control 1/3.
Pooled across both waves: left cell approximately 1/3; right cell approximately 1/3; no-photo control approximately 1/3.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Power is assessed by Monte Carlo simulation calibrated to preliminary data: the baseline (no-photo control) effective-response probability is about 0.56, and 66.5 percent of geolocated preliminary listings fall in right-leaning neighborhoods, the share over which the primary estimand is identified. Because the sample is bounded by the rental market, we fix the sample size and report minimum detectable effects at 80 percent power and a 5 percent two-sided test, at the expected sample of about 3,000 listings and at 4,000 and 5,000 should the market allow a larger sample. At the expected 3,000 listings, the minimum detectable effect is 6.7 percentage points for the average left penalty and 7.9 percentage points for the primary quantity, the left-signal penalty in right-leaning neighborhoods. At 4,000 and 5,000 listings the primary minimum detectable effect falls to 7.0 and 5.9 percentage points (6.0 and 5.3 for the average penalty). The interaction—whether the left penalty in right-leaning neighborhoods differs from the one in left-leaning neighborhoods—has a minimum detectable effect of about 14 percentage points at 3,000 listings, falling to about 10 at 5,000.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Comité de Ética de Investigaciones, Universidad de los Andes
IRB Approval Date
2026-04-13
IRB Approval Number
Acta No. 2029 de 2026
Analysis Plan