@EdWorkingPaper{ai26-1467,
title = "The Anatomy of a High-Return Question: Text, Skills, and the Economics of Achievement Measurement",
author = "Jonathan Moreno-Medina, Eric Nielsen, Viviana Rodriguez",
institution = "Annenberg Institute at Brown University",
number = "1467",
year = "2026",
month = "April",
URL = "http://www.edworkingpapers.com/ai26-1467",
abstract = {Standardized test scores aggregate item (question) responses into a single scalar, collapsing distinct skills into an undifferentiated measure of proficiency. Which of these component skills matter most for long-run economic outcomes is a question that aggregate scores cannot answer. We develop a framework that looks both inside the score - re-weighting items by their predictive power for a chosen outcome ("item-level prices") - and inside the item - using the digitized text of each question to identify what skills drive the variation in these prices. We apply this framework to over 3,500 items linked to approximately 1 billion student-by-item-response records and adult earnings from Texas administrative data. Achievement scales that weight items by their estimated economic prices yield white-minority gaps roughly 45% larger than conventional scales and substantially reorder individual student rankings. To interpret these prices, we show that item text carries economically relevant information beyond standard psychometric characteristics, and we develop a novel text-based mapping of items to the over 600 skills comprising the Common Core State Standards. The mapping reveals that procedural, spatial, and automation-exposed mathematics skills have the highest estimated prices, while basic reading comprehension dominates more fine-grained reading skills. To our knowledge, this provides the first standards-based evidence on which K-12 curricular skills predict long-run labor-market outcomes.},
}