Every year, over one million EU students choose a secondary school track based on teacher recommendations, yet little evidence shows this yields optimal assignments. Using Dutch data, we examine whether access to standardized test scores improves recommendation quality. We develop a Principal-Stratification metric in a quasi-randomized setting, conduct a welfare analysis that flexibly weights short- and long-term losses, and assess principal fairness by examining whether test-score access affects equity across protected attributes. Results are robust to replacing the Exclusion Restriction assumption underlying our main identification strategy with alternative assumptions. Allowing recommendation upgrades when test scores exceed expectations increases successful placement in more demanding tracks by at least 6%, while misplacing 7% of weaker students. Only unrealistically high weights on short-term losses would justify banning such upgrades. Test-score access also yields fairer recommendations for immigrant and low-SES students. Our methodology and findings contribute to the literature on algorithm-assisted human decisions.

翻译：每年，超过一百万欧盟学生依据教师推荐选择中学教育轨道，但鲜有证据表明这种做法能产生最优分配。利用荷兰数据，我们研究了获取标准化测试成绩是否会提高推荐质量。我们在准随机化环境中构建了主分层度量指标，开展了灵活权衡短期与长期损失的福利分析，并通过检验测试成绩获取是否影响受保护属性间的公平性来评估校长公平性。即使将主要识别策略所依赖的排他性约束假设替换为替代性假设，结果依然稳健。当测试成绩超出预期时允许推荐升级，可使更具挑战性轨道中的成功安置率至少提升6%，同时导致7%的弱势学生错位安置。仅当对短期损失赋予不切实际的高权重时，才应禁止此类升级。测试成绩的获取还为移民和低社会经济地位学生带来了更公平的推荐。我们的方法论与研究发现为算法辅助人类决策的相关文献作出了贡献。