NEW UPDATE: Completed trials may now upload and register supplementary documents (e.g. null results reports, populated pre-analysis plans, or post-trial results reports) in the Post Trial section under Reports, Papers, & Other Materials.
Improving Social Mobility by Helping Rural Students Make Informed College Choices
Country Proposed Start Date
Initial registration date
October 27, 2019
October 28, 2019 1:04 PM EDT
This section is unavailable to the public. Use the button below
to request access to this information.
Other Primary Investigator(s)
The University of Chicago
Additional Trial Information
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. Registration 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.
Intervention Start Date
Intervention End Date
Primary Outcomes (end points)
Whether students make more informed choice in college application
Primary Outcomes (explanation)
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
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
Randomize by a computer
Was the treatment clustered?
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)
INSTITUTIONAL REVIEW BOARDS (IRBs)
The Committee for Protection of Human Subjects at UC Berkeley
IRB Approval Date