# Digital School Profiles Project Sketch

## Extended abstract (draft)

We study how the design of school information on a centralized admissions platform shapes demand and equity, exploiting a large-scale randomized intervention in Chile's national school-choice system in which roughly 600 schools were assigned to receive digital profiles composed of standardized text, photos, and videos embedded directly in the official explorer. Using platform analytics as well as application and enrollment data, we show that these profiles raise user engagement sharply: during the final SAE week the explorer reached about 114,000 users (roughly 15,000 per day), with families exploring an average of seven schools and three detailed profiles per session, and profiled schools receiving roughly five times more clicks than comparable control schools, especially when the profile contained video. These demand shifts translate into enrollment gains of roughly three additional students per profiled school, which—combined with existing estimates of value-added and lifetime earnings—implies a net present value of about USD 20,000 relative to an implementation cost near USD 100 per profile. The effects are disproportionately larger for disadvantaged families, closing about 30 percent of the pre-existing application gap between poor and non-poor households. Low-cost improvements in how school attributes are presented on centralized platforms can therefore reallocate demand toward under-chosen schools, improve enrollments, and enhance equity.

## Working title ideas

- Digital School Profiles and Equitable School Choice: Evidence from a Nationwide Experiment
- Rich Media, Demand, and Inequality in Centralized School Choice Platforms

## Core research questions

1. Does providing rich, standardized digital profiles for schools increase families' search and engagement on the platform?
2. Do these profiles shift application and enrollment patterns toward profiled schools, and by how much?
3. Do rich-media profiles narrow socio-economic gaps in applications and enrollments?
4. Are such profiles cost-effective relative to their impact on enrollment and equity?

## Experimental design and data

- School-level RCT with approximately 600 schools randomly offered a digital profile that appears inside the national explorer (text + photo + optional video).
- Platform analytics capture clicks on school cards, pins on the map, profile views, and time spent per session.
- Administrative data provide application submissions, assignments, enrollments, and SES classifications (poor vs non-poor).
- Timing focuses on the final SAE week where platform traffic peaks (114k users, ~15k per day).

## Key outcomes and benchmarks

- Engagement: users explore an average of 7 schools and 3 profiles per session; profiled schools receive about 5x more clicks, with larger effects when a video is present.
- Applications: share of applications going to profiled schools rises enough to close roughly 30% of the initial SES gap.
- Enrollment: treated schools enroll about +3 students relative to control, implying meaningful seat reallocation.

## Cost-effectiveness

- Implementation cost: approximately USD 100 per school to produce and upload a standardized profile.
- Benefits: +3 enrolled students per school translate into roughly USD 20k in NPV using existing value-added / lifetime-earnings calibrations from SAE/SME work.
- Result: very high benefit-to-cost ratio, even under conservative earnings assumptions.

## Suggested paper structure

1. **Introduction**: situate centralized choice platforms and information frictions; preview engagement, enrollment, equity, and cost-effectiveness results.
2. **Institutional context and intervention**: describe SAE, MIME, explorer UI, and standardized profile components.
3. **Experimental design and data**: document the randomization, take-up, and data sources (clickstream + admin).
4. **Engagement and demand effects**: clicks, profile views, pins, and session-level activity.
5. **Applications, enrollment, and equity**: shifts in rank-order lists, enrollments, SES heterogeneity, and gap closures.
6. **Welfare and policy implications**: benefit-cost analysis, scalability, connection to prior information interventions.

## Immediate next steps

- Clean and document the clickstream variables (sessions, unique users, profile view events).
- Finalize SES classification and link it to application and enrollment records.
- Flesh out heterogeneity analyses (video vs no video, baseline demand level, school type).
- Draft figures for engagement, application shifts, equity gap closure, and cost-effectiveness waterfall.