Working Papers | ConsiliumBots

Making Algorithmic Transparency Meaningful: Evidence from Chile's National School Admissions Platform

Issue 05 | August 10, 2026 | Chile

Authors: Christopher A. Neilson, Exequiel Medina, Leonardo Ortiz Mesías

Governments worldwide increasingly deploy algorithmic systems to allocate scarce public goods. The dominant transparency paradigm focuses on expert audit rather than citizen comprehension. We propose a substantive standard, Meaningful Algorithmic Transparency (MAT): information delivered to affected users must be proactive (placed into awareness, not merely published), tailored (referring to the user's decision context), and actionable (delivered before the decision closes). We develop the argument through Chile's Sistema de Admisión Escolar (SAE), a national school admissions platform serving roughly 470,000 families per year. Two measurements document the residual comprehension gap. Linking the SAE Satisfaction Survey to administrative truth for four cycles (2020–2023, N≈140,000), we show that families systematically overstate their placement probability: among applicants the platform can pre-identify as high-risk, the median family believes the risk is around 20% when it is in fact around 80%. The bias is uneven: at the same objective risk, lower-SES applicants understate by more, with a conditional gradient that is statistically stable across all four cycles. In the SAE Parent Surveys (2023–25), self-reported familiarity with a key procedural rule nearly doubled across cycles while verifiable comprehension barely moved (2.3% to 4.9%). The comprehension gap is therefore persistent and unevenly distributed, falling more heavily on the families whose outcomes most depend on effective information delivery, even when the underlying mechanism gives those families priority. We propose six operational components and show how Chile's existing regulatory architecture (access-to-information law, algorithmic-transparency recommendations, data-protection safeguards, and sector-specific regulation) can be articulated to make the standard enforceable.

Keywords: algorithmic transparency, automated decision-making, meaningful transparency, citizen comprehension, algorithmic allocation, public sector algorithms

Versions:

Version 1 · August 2026 (current)
First working-paper version. Assembled outside the repository; there is no LaTeX source to recompile.

The Global Gender Gap in STEM Applications: Pipeline vs. Choice

Issue 03 | September 05, 2026 | Australia, Brazil, Chile, China, Finland, Greece, Spain, Sweden, Taiwan, Uganda

Authors: Isaac Ahimbisibwe, Adam Altmejd, Georgy Artemov, Andrés Barrios-Fernández, Aspasia Bizopoulou, Martti Kaila, Jin-Tan Liu, Rigissa Megalokonomou, José Montalbán, Christopher A. Neilson, Sebastián Otero, Jintao Sun, Xiaoyang Ye

Women make up only 35% of STEM graduates worldwide, a share unchanged for a decade. We separate two sources of this gap: gender differences in academic preparation (the pipeline) and in application decisions (the choice gap). We use data from ten centralized university admissions systems where eligibility is set by academic performance and places are assigned through variants of deferred acceptance, so students have little incentive to misreport. We focus on high-achieving students, for whom access constraints are least likely to bind, which isolates application behavior from the most obvious barriers. The pipeline gap varies widely, from a female disadvantage in Uganda to a female advantage in Sweden, and broadly tracks labor-market gender parity. The choice gap does not. It is large and negative everywhere: even among top scorers, women are about 24 percentage points less likely than men to rank a STEM program first, and the gap exceeds 20 points in eight of the ten settings. The pattern holds when STEM is counted anywhere on the list, not only first. Closing the pipeline gap entirely would still leave high-achieving STEM applicants majority male, between 57% and 75%, in every setting we study.

Keywords: gender inequality, STEM gender gap, centralized application platforms

JEL: I23, I24, J16, D47

Versions:

Version 2 · September 2026 (current)
Revised version. Extends the analysis to every threshold from the top 5% to the top 30% and to the any-choice measure, replaces the GDP comparison with an ILO-based wage parity index, adds the institutional appendix and the cross-field comparison, and corrects a co-author's name and the author order.
Version 1 · November 2025
First working-paper version, as circulated in November 2025.

Designing Smart School Choice Recommendations: Heuristics for Sure Alternatives

Issue 02 | June 17, 2025 | Chile

Authors: Ignacio Lepe

The proposed research introduces a school choice policy that recommends guaranteed school alternatives for students left unassigned under stable matching. Leveraging revealed preferences, it designs a personalized recommendation mechanism that offers voluntary allocations to unlisted but potentially desirable nearby schools. Results indicate that in the main round, the mechanism can (i) reduce the proportion of unmatched applicants by up to 50%, (ii) increase expected aggregate utility by 2–6%, and (iii) concentrate utility gains among applicants who apply to oversubscribed programs. Overall, the policy offers a cost-effective and scalable solution to improve match outcomes by redistributing excess demand within centralized assignment systems.

Keywords: school choice, market design, sure alternative

JEL: C78, D47, I21, I28, D83

Versions:

Version 1 · June 2025 (current)
First working-paper version.

Approximating the Equilibrium Effects of Informed School Choice

Issue 01 | July 29, 2019 | Chile

Authors: Claudia Allende, Francisco Gallego, Christopher Neilson

This paper studies the potential small and large scale effects of a policy designed to produce more informed consumers in the market for primary education. We develop and test a personalized information provision intervention that targets families of public Pre-K students entering elementary schools in Chile. Using a randomized control trial, we find that the intervention shifts parents' choices toward schools with higher average test scores, higher value added, higher prices, and schools that tend to be further from their homes. Tracking students with administrative data, we find that student academic achievement on test scores was approximately 0.2 standard deviations higher among treated families five years after the intervention. To quantitatively gauge how average treatment effects might vary in a scaled up version of this policy, we embed the randomized control trial within a structural model of school choice and competition where price and quality are chosen endogenously and schools face capacity constraints.

Keywords: information, incentives, school choice, randomized control trial

JEL: L1, L38, I2, O1

Versions:

Version 1 · July 2019 (current)
First working-paper version.

How to Cite

How to Cite

How to Cite

How to Cite