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Improving Social Mobility by Helping Rural Students Make Informed College Choices Country Proposed Start Date
Last registered on October 28, 2019

Pre-Trial

Trial Information
General Information
Title
Improving Social Mobility by Helping Rural Students Make Informed College Choices Country Proposed Start Date
RCT ID
AEARCTR-0004952
Initial registration date
October 27, 2019
Last updated
October 28, 2019 1:04 PM EDT
Location(s)

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Primary Investigator
Affiliation
UC Berkeley
Other Primary Investigator(s)
PI Affiliation
Princeton University
PI Affiliation
The University of Chicago
Additional Trial Information
Status
On going
Start date
2019-06-01
End date
2020-09-01
Secondary IDs
Abstract
Centralized admission mechanisms, especially the “Deferred Acceptance (DA)” algorithm, have been
increasingly adopted in college admission among developing countries. For fairness, exam score is often the only
determinant for admission in these systems. Using administrative data from China, which uses a constrained DA
mechanism for college admission, we find that conditional on exam score, poor and disadvantaged students frequently
use dominated strategies (put unambiguously more selective colleges in lower-ranked positions) in college application,
resulting in substantially worse admission outcomes. In collaboration with an influential research center at Peking
University, we propose a randomized controlled trial to understand why disadvantaged students make dominated
choices in college application, and how these suboptimal high-stakes decisions could be improved. The interventions
consist in tutoring on the DA mechanism, customized presentation of college list, and providing admission probability
with the aid of machine learning techniques. The general equilibrium implications of such interventions will be
evaluated.
External Link(s)
Registration Citation
Citation
Wang, Shaoda, Ao Wang and Xiaoyang Ye. 2019. "Improving Social Mobility by Helping Rural Students Make Informed College Choices Country Proposed Start Date ." AEA RCT Registry. October 28. https://doi.org/10.1257/rct.4952-1.1.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2019-06-23
Intervention End Date
2020-09-01
Primary Outcomes
Primary Outcomes (end points)
Whether students make more informed choice in college application
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Based on students' basic information, declared preferences and their score, our algorithm will generate a customized list of colleges for each student who participate in the survey experiment. We will randomly pick half of the colleges on the customized list and present them to students to facilitate their application. This variation is cross-randomization across all arms. In addition to the customized list, we will provide students with useful information such as telling them the admission probability of relevant colleges, or teach them to correctly order the colleges in their rank-order lists.
Experimental Design Details
Not available
Randomization Method
Randomize by a computer
Randomization Unit
Inidividual
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
Roughly 50 schools
Sample size: planned number of observations
364 in the first round (this summer), expect to get more (up to roughly 2,000) in the next round (next summer)
Sample size (or number of clusters) by treatment arms
Arm 1 (control): 84 completed this summer, expect to get more in the next round.
Arm 2 (information about probability only): 97 completed this summer, expect to get more in the next round.
Arm 3 (information about probability and tutorial): 183 completed this summer, expect to get more in the next round.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
The Committee for Protection of Human Subjects at UC Berkeley
IRB Approval Date
2019-05-31
IRB Approval Number
N/A