Hiding Identity

Last registered on January 13, 2025

Pre-Trial

Trial Information

General Information

Title
Hiding Identity
RCT ID
AEARCTR-0015151
Initial registration date
January 10, 2025

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
January 13, 2025, 1:12 PM EST

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

Last updated
January 13, 2025, 8:44 PM EST

Last updated is the most recent time when changes to the trial's registration were published.

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Primary Investigator

Affiliation
UBC

Other Primary Investigator(s)

PI Affiliation
Duke
PI Affiliation
UC Davis
PI Affiliation
UCSD

Additional Trial Information

Status
On going
Start date
2025-01-07
End date
2025-07-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Marginalized groups can avoid discrimination by hiding their group identity, and by “passing” for another identity. How successful are these strategies at masking identity, and which types of people are most successful? And does the hiding of identity change intergroup interactions? We answer these questions for the case of caste in north India. We are recruiting high and low-caste men to work together on data entry tasks for one day. Worker pairs will be randomized to either have their caste identity revealed (through common knowledge of full names), or hidden (through common knowledge only of first names), or to be told to hide their caste from their partner.
External Link(s)

Registration Citation

Citation
Chakraborty, Anujit et al. 2025. "Hiding Identity." AEA RCT Registry. January 13. https://doi.org/10.1257/rct.15151-1.1
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2025-01-11
Intervention End Date
2025-06-30

Primary Outcomes

Primary Outcomes (end points)
GUESS ACCURACY
Workers are asked to guess the caste category and jati of their partner, to report how confident they are in the caste category guess, and to rank order the likelihood of each caste category. To avoid gaming, the guesses are not incentivised (though for robustness, we plan to incentivise the guesses of a random subset of participants towards the end of the experiment). Workers are also asked whether they think their partner will be able to guess their caste category correctly, allowing us to characterise whether workers have sophisticated beliefs about their ability to hide their caste.


PRODUCTIVITY / TASK (MIS)ALLOCATION
Number of fields entered (quantity), fraction correctly entered (quality), number correctly entered (mix of quantity and quality), identity of the partner that does the high status task (at task-by-day level).
SOCIAL RELATIONS
Incentivised reservation wage to work with the partner again. Workers are also asked whether they expect to meet up in future. Overlapping circles measure of identity fusion. Physical closeness coded from a photograph of the worker pair.
TRUST
Trust game played with partner, covering (i) amount to share if selected as sender, (ii) guess of how much partner shares if they are selected as sender, (iii) amount to return if selected as receiver (using the strategy method).
WELL-BEING AT WORK
Self-reports of how happy/self-conscious/confident/relaxed/anxious at work (answers from 0 = not at all to 3 = very). We have baseline measures of each to use as controls.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
GUESS MECHANISMS
Guesses of first and last name, reports of how they made their guess (e.g. by knowing the name, from physical appearance, etc.), reports of how they learned the name (if they say they know it), what they noticed about physical appearance (if it helped inform their guess).
PRODUCTIVITY / ALLOCATION
Self-report of happiness with how tasks were allocated (3-point scale: happy with allocation / would prefer to do more tablet work / would prefer to do less tablet work), report of whether they could have done better if participant had their way with task allocation. 10-point self-reports of own performance and partner’s performance.
SOCIAL RELATIONS
Each worker is asked to report how friendly / rude / bossy their partner was to them during the workday and how much tension they had with their partner, on a scale of 1 to 10. Self-report of whether able to show true self, and reports of what information about self was shared with the partner. Guess accuracy about age, marital status, and wealth of the partner, and how likely it is that the partner’s father works in a high-paid occupation.
MISCELLANEOUS
Open-text question asking what the participant thinks the study is about. Task-pair-level self-reported happiness and task difficulty.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We are recruiting high- (General caste) and low-caste (Scheduled caste) Hindu men with caste-distinctive last names in Uttar Pradesh. Participants complete a baseline survey when recruited at their home. Eligible participants are called in for one day of data entry work, with pay depending on performance and role assignment. There are three treatment groups, assigned with equal probability:

(i) HL-FullName: high-caste is paired with randomly-chosen low-caste; caste is made common knowledge through full names.
(ii) HL-FirstName: high-caste is paired with randomly-chosen low-caste; common knowledge of first names only.
(iii) HL-Hide: high-caste is paired with randomly-chosen low-caste; common knowledge of first names only; both workers are asked not to share their last name or other caste-identifying information with their partner.

We will use this design to answer two main sets of questions. First, we are interested in how successful attempts to hide caste are, and whether participants have sophisticated beliefs about whether others know their caste. For this we use endline questions that ask workers to guess the caste of their partner, and we see how guess accuracy is affected by the different treatment groups. In addition, we will explore (1) how guess accuracy compares with what an ML model can achieve using our baseline data, photographs, and voice recordings of each worker, (2) which baseline attributes (e.g. education, skin colour) predict more successful “passing,” and (3) which baseline attributes predict more successful guessing of the caste of partners.

Second, we are interested in how caste hiding affects task allocation, productivity, and social relations with the partner, with social relations captured by questions at endline. The data entry tasks themselves will vary in difficulty, with a subset of tasks carried out in random order. This allows us to test whether productivity effects depend on the nature and difficulty of the task.

For all tasks but one, pairs will also be asked to decide which worker does the “high-status” task of controlling the tablet, and which does the “low-status” task of reading the information to be recorded (from a set of printed sheets). The high-status task comes with a monetary bonus. We can then test for the effects of the treatments on task allocation. One task will be randomly chosen to have the high vs. low status role assignment randomly assigned.

For our analysis, we will run OLS regressions with randomization strata fixed effects, age and education, and a baseline-measured dependent variable when available. We will cluster standard errors at the pair-level (for outcomes measured at the individual level).

[Amendment January 13, 2025, when N = 58 had been completed] Our primary interest is in comparing endline outcomes between the three treatment groups. In the case that the caste-guessing accuracy is similar in the HL-Hide and HL-FirstName treatment groups, we will also pool HL-Hide and HL-FirstName for a higher-powered test of the effects of hiding caste.
Experimental Design Details
Not available
Randomization Method
Randomization will be done day-by-day using R code integrated with a shiny app interface. The randomization will be stratified by day-by-caste (where caste = high or low).
Randomization Unit
Individual-level
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Approx. 1,000 to 2,000 individuals (500 to 1,000 pairs), depending on funding.
Sample size: planned number of observations
Approx. 1,000 to 2,000 individuals (500 to 1,000 pairs), depending on funding. Note that productivity data will be at the pair-level.
Sample size (or number of clusters) by treatment arms
Each treatment: ⅓ of pairs
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
UBC BREB
IRB Approval Date
2025-01-06
IRB Approval Number
H24-03626