Academic Achievement

DRIVER VALIDATION

Using Machine Learning to Uncover Which Skills Contribute to Students’ Future Economic Mobility
Jesse Bruhn
Brown University

This project team will use machine learning techniques to group-test questions in ways that capture specific underlying skills. Using math and reading tests from public schools in Texas, the researchers will link skill measures to outcomes later in life, including wages and whether a student attended college. This study could uncover skills important for economic mobility that are not currently articulated within standardized tests.

Understanding How Students’ Skills and the Structural Barriers They Face Affect Their Economic Mobility
Quentin Brummet and Paul Hanselman
NORC at the University of Chicago

This project team will examine how students’ skills interact with structural barriers to affect their economic mobility. The researchers will look at Oregon students’ test scores in math, English language arts, and science; exposure to school discipline; and attendance. The team will then work with the US Census Bureau to correlate these factors with postsecondary and labor market outcomes (e.g., employment and earnings), criminal legal system involvement, health insurance coverage, disability status, and program participation.

What Skills Help Students with Disabilities Thrive?
Jacob Hibel, Andrew Penner, Christopher Cleveland, and Andrew Saultz
University of California, Davis

The project team seeks to understand how the services and supports provided to students with disabilities during their K–12 education relate to their academic and life outcomes in early adulthood. The researchers will examine how these students’ test scores and exposure to school discipline are related to long-term outcomes, including postsecondary attainment, income, criminal legal contact, fertility, and mortality. Drawing on linked data from the State of Oregon and the US Census Bureau, the study will also consider how outcomes vary by what services a student receives and how long they received them.

How Does Academic Success in High School Translate into Economic Mobility?
John Papay
Brown University

This project team will examine how students’ grades, test scores, and other markers of academic success in high school are related to postsecondary attainment and earnings. Drawing on statewide data from Massachusetts, the study will also examine school course-taking patterns, grading standards, effectiveness in improving markers of academic success among disadvantaged students, and the relationships between these school-level measures and economic mobility.

Which Skills Are Linked with Future Academic Success and Economic Mobility?
Nolan Pope, George Zuo, and Cameron Conrad
University of Maryland, College Park

This project team seeks to fill gaps in the existing research by exploring which skills most strongly predict future academic success and economic mobility. The team will quantify whether specific competencies in math, reading, science, and social studies predict postsecondary attainment and economic outcomes, including earnings and industry. Using statewide data from Maryland, the study will measure skills using subscores on state exams in each of these subjects.

Which Test Questions Most Predict Economic Mobility?
Viviana Rodriguez and Jonathan Moreno-Medina
University of Texas at San Antonio

This project team will use test data from across Texas to identify which test questions and student answers are most predictive of long-term outcomes, such as high school graduation, college enrollment, and earnings. The researchers’ goal is to construct new test-based measures that predict mobility more strongly. The study will examine how achievement gaps on these new measures compare with gaps based on traditional test scores and which features of test questions are most predictive of long-term outcomes.

How Do Literacy Skills in Early Elementary School Predict High School and College Success?
Lindsay Weixler and Jon Valant
Tulane University

This project team will measure associations between literacy skills in grades K–3 and student outcomes through high school graduation and postsecondary enrollment. Using statewide data from Louisiana, the study will compare the predictive power of DIBELS scores with those of third-grade state reading tests and examine how results vary by students’ prekindergarten experience, socioeconomic status, and location (urban versus rural).

Career Preparedness

DRIVER VALIDATION

Do ACT WorkKeys Scores Predict Early College and Career Outcomes?
Sarah Fuller and Tom Swiderski
University of North Carolina at Chapel Hill

The project team will study whether the ACT WorkKeys career preparation assessment (which measures skills in graphic literacy, applied math, and workplace document comprehension) is correlated with postsecondary enrollment, employment, and earnings. The study will follow students who attended career and technical education programs in North Carolina for up to six years following their high school graduation. The researchers will also examine how WorkKeys scores and economic outcomes vary by race and ethnicity, gender, socioeconomic status, career and technical education concentration, postsecondary attendance, and local economic conditions.

Leveraging Professional Skills to Increase Economic Mobility and Racial Equity
Jason F. Jabbari, Shaun Dougherty, and Lauren Russell
Washington University in St. Louis

This project team will study how professional skills, competencies, and on-the-job performance in career-connected learning settings can increase economic mobility. The researchers will work with over 20 high schools from the Cristo Rey Network that predominantly serve low-income students of color. The schools employ a novel Corporate Work Study program that provides all students a tangible work-based learning experience one day per week at a local corporation. These experiences are accompanied by a school-based curriculum that focuses on professional skills and competencies, along with college preparation. The team will examine long-term outcomes, including college enrollment and persistence, employment, earnings, and credit report data.

Do Career and Technical Education Skills and Credentials Help Students in the Labor Market?
Daniel Kreisman and Thomas Goldring
Georgia State University

This project team will examine how skills and credentials obtained through career and technical education are rewarded in the labor market. To do so, the researchers will link NOCTI technical assessment scores from four Atlanta-area school districts to postsecondary and earnings data. The team will also analyze whether there is an additional earnings increase for obtaining industry-recognized credentials and how outcomes may vary across career and technical education pathways and student and school characteristics.

MEASURE DEVELOPMENT

Cocreating a Measure of Critical Career Readiness with Middle and High School Students
Craig Schwalbe and Charles Lea
Columbia University

This project team will work with a community advisory board of young people and partners from two schools to create a new measure of critical career readiness tailored to economically marginalized adolescents. The measure will assess how students’ awareness of employment discrimination, stigma, and access to employment-related resources can empower them to explore their career interests and plan their educational career trajectories.

Cognitive, Social and Emotional Skills

DRIVER VALIDATION

How Do Students’ Social and Emotional Skills Relate to Upward Mobility?
Yudan Chen
Augusta University

This project team will use nationally representative data from the Future of Families and Child Wellbeing Study to analyze how social and emotional skills (e.g., self-discipline, self-efficacy, and social skills) at ages 9 and 15 relate to a child’s education level, occupation, and earnings at age 22. The study will examine how these skills interact with individual-, school-, and district-level factors.

Evaluating the Relationship Between Social-Emotional Learning Competencies and Economic Mobility in Promise Neighborhoods
Karen Matthews, Wesley L. James, Jonathan Bennett, and Rachel Arthur
Delta Health Alliance

This project team will measure the relationship between social-emotional learning competencies in seventh through ninth grades and long-term outcomes (e.g., postsecondary enrollment and wages) among students facing multiple sources of disadvantage in rural Mississippi. The study will draw on data from the LifeSkills survey-based assessment of social-emotional learning competencies collected in Promise Neighborhoods.

MEASURE DEVELOPMENT WITH EARLY MOBILITY OUTCOMES

Do Critical Thinking Skills Predict Student Upward Mobility?
Beth Schueler and Jim Soland
University of Virginia

This project team will develop a way to measure critical thinking skills. To do so, they will analyze student performance on state English language arts test questions that capture critical thinking skills. Using statewide data, the researchers will then examine whether these critical thinking skills can predict students’ postsecondary attendance, persistence, graduation from institutions shown to increase economic mobility, and likelihood of voting.

MEASURE DEVELOPMENT

Can Assessment Metadata Capture Executive Functioning Skills?
Amy Taub
MDRC

This project team aims to develop reliable measures of early childhood executive functioning skills by analyzing metadata from two novel digital assessments of early academic skills. Building on the Measures for Early Success Initiative, the study will examine whether question response time or other metadata fields reflect executive function. The researchers will also evaluate the validity of these metadata measures across different individual, school, and community contexts to ensure they are equitable and effective.

Social Capital

MEASURE DEVELOPMENT

Is It Possible to Measure Youth Social Networks Using Administrative Data?
Huriya Jabbar and Sarah Winchell Lenhoff
University of Southern California

This project team will test whether it is possible to create a scalable measure of youth social capital using educational administrative data. The researchers will field student-level surveys focusing on cross-class and cross-race ties in four diverse high schools in metropolitan Detroit. They will then compare these results with administrative records on course enrollments and demographic characteristics.

Academic Achievement

DRIVER VALIDATION

Dual Enrollment Courses and Upward Mobility: Which Skills, and for Whom?
Barbara Biasi, Song Ma, and Zhengren Zhu
Yale University

This study aims to identify the skills earned through dual enrollment and how those skills drive adult labor market outcomes. The team will use large‑scale language models to infer likely skills embedded in community college syllabi and pair them with students’ grades. The researchers will organize these skills using the Burning Glass Institute’s skills taxonomy, which can be linked to local labor markets. Texas statewide data will be used to examine students’ Unemployment Insurance earnings, parental socioeconomic status, and county‑level disadvantage. At a time when high school redesign and dual enrollment opportunities are surging, the project will examine how specific dual enrollment experiences shape postsecondary enrollment, degree attainment, and early career outcomes, with particular attention to socioeconomic disparities. Using quasi‑experimental methods and novel AI‑based measures of course content, the study will also estimate the causal effects of dual enrollment participation and of the skills acquired through these courses, providing critical evidence for designing dual enrollment pathways that meaningfully enhance later economic mobility.

The Math Skills That Predict Upward Mobility and the Paths That Cultivate Them
Matthew Lenard, Darryl Hill, and Erica Litke
Florida State University

This study examines how specific math skills drive upward mobility. Using longitudinal administrative and assessment data, the project will track students’ skill development trajectories across multiple assessments—including subscores from SAT, ACT, AP, and IB math exams—and how they relate to postsecondary, labor market, and civic participation outcomes. Using other subscores (e.g., English language arts), the team will consider which mobility drivers are general cognitive drivers versus math-specific contributions. Additionally, the team will leverage a data-driven math placement policy in one of the nation’s largest school districts to examine how different pathways shape the development of the math skills that drive mobility. This project builds upon cohort 1 studies linking math sub scores from state assessments to economic mobility using widely used middle and high school assessments.

Pinpointing When Economically Valuable Skill Gaps Emerge and Where Intervention Can Make a Difference
Viviana Rodriguez and Jonathan Moreno-Medina
University of Texas at San Antonio

This project builds on an ongoing SUMI project that identifies which skill groupings from item-level standardized test data drive adult wages at age 25. Early findings have revealed a striking pattern: Specific item-anchored skills that drive economic mobility are largely similar between white and Hispanic students in third grade, but the gap widens sharply at middle school entry and is never recovered, even as high school narrows gaps in the skills needed to get a high school diploma. Yet critical questions remain regarding why this divergence in mobility-relevant skills occurs at the middle school transition, what skills are being emphasized in high school to reduce the diploma gap but not the wage gap, and whether the picture changes when tracking wages through age 30 rather than age 25. This project will directly address these remaining questions by leveraging existing Texas administrative data to identify precisely when and where disparities in these economically valuable competencies emerge, pinpointing the moments in the educational pipeline where targeted investment in mobility-driving skills is most likely to make a difference, and emphasizing the skills that could be further cultivated in high school.

Improving High School Transcript Records via Item Response Theory to Predict Postsecondary College and Labor Market Outcomes
Kenneth Shores and Sanford Student
University of Delaware

This project examines course grades and course‑taking patterns to develop a scalable measure of transcript strength that can identify which course choices most strongly correlate with postsecondary and labor market success, offering a more accurate way to account for course‑selection behavior and selection effects when comparing student trajectories. Building on prior work using Delaware data, the team will expand the transcript‑strength measure to Texas, refine it from an end‑of‑high‑school indicator to grade‑specific measures, and extend it into domain‑specific strength indexes, such as STEM (science, technology, engineering, and mathematics) and humanities, rather than a single unidimensional score. In so doing, the team will make transcripts a more useful tool for measuring skills. By linking domain‑specific measures to college major, occupational field, and outcome data (including college‑level earnings, employment, and early labor market trajectories), the study will generate actionable insights into how transcript‑embedded skills translate into later opportunities and how schools can better guide students toward courses that build the competencies most predictive of long‑term success. This project also complements SUMI’s niche of using existing, familiar data for new skill and mobility driver measurement and analysis.

Measuring What Matters in High School: How Grades and Tests Signal Skills and Shape Postsecondary Opportunities
Sarah Turner
University of Virginia

In an effort to build scalable measures of student skills, particularly in contexts where the meaning of grades is uncertain, this project uses statewide longitudinal Virginia data on course taking, grades, Standards of Learning scores, and linked higher education, workforce, and social safety net participation to determine when and for whom grades and test scores function as a reasonable proxy for skill development and as predictors of economic mobility. Drawing on the Virginia Longitudinal Data System, the study examines the alignment between grades and test scores, the influence of school‑ and district‑level structural factors on how skills are developed and assessed, and which high school indicators best predict postsecondary trajectories, labor market outcomes, and economic security. By integrating multiple academic and behavioral measures, the research aims to identify how both course grades and standardized test performance—as complementary but partially distinct measures of student competency—relate to underlying skill growth and to long-term outcomes, and to clarify the contexts in which each measure provides reliable signals of the competencies most consequential for upward mobility.

Career Preparation

DRIVER VALIDATION

For Whom Can Career and Technical Education Be an Engine of Upward Mobility?
Joshua Angrist, Parag Pathak, Bruce Sacerdote, Clemence Idoux, and Viola Corradini
Massachusetts Institute of Technology

This study is led through a long-standing partnership between Josh Angrist, Parag Pathak, and New York City Public Schools. They link the district’s high school admissions lottery and administrative data to novel Internal Revenue Service and Census Bureau data, providing causal evidence on the long‑run mobility impacts of skills developed in high school career and technical education (CTE) courses across comprehensive and specialized schools. The unique outcome data allow for more careful examination of economic mobility using parental education and income with the students’ later federal earnings records and social safety net participation to estimate the long‑term effects of New York City’s CTE pathways on adult labor market outcomes. The researchers also examine how different CTE programs cultivate academic and career‑oriented competencies that translate into mobility for different student groups. Leveraging randomized elements of New York City’s high school admissions system, the study generates causal evidence to illuminate how CTE works, for whom it is most effective, and how it can inform future policy and program design while complementing ongoing research supported by current grantees.

MEASURE DEVELOPMENT WITH EARLY MOBILITY OUTCOMES

Technical Education, Technical Skills, and Economic Mobility Through Careers: Evidence from Skill Credentials
Brian Jacob and Michael Ricks
University of Michigan

While industry increasingly looks to recognized credentials, we still know little about the pathways through which these credentials contribute to higher education success and early economic mobility. This project uses statewide Michigan data—guided by a principal investigator with 15 years of experience working with the state—to understand how skills translate to adult outcomes and to examine students’ postsecondary pathways, including how focused they are on career-aligned course sequences and how efficiently they move through required sequences into the labor market. The study evaluates whether high school skill‑based industry credentials are valid, fair, and predictive measures of students’ technical skills (beyond other academic skills) and whether earning the credentials improves career clarity and early in‑field postsecondary momentum. Using administrative data, the team will map credentials and academic records to O*NET skill categories through natural language processing; will test convergent, divergent, and predictive validity of the resulting skill measures; and will construct a course‑network‑based behavioral indicator of career clarity that captures both focus and momentum. The skills represented by credentials will be validated using postsecondary course‑taking patterns and indicators of career‑aligned progression.

Upwardly Mobile: Elevating and Celebrating AI Readiness Using Authentic Student Work
Caroline Sanchez Crozier and Melina Uncapher
Digital Leaders Now

This study builds on emerging high school capstone models with direct implications for high school redesign by creating an innovative, practical measure of AI‑era digital literacy skills. At a moment when AI has not yet been formally established as a mobility driver but is already reshaping higher education, the labor market, and K–12 teaching and learning, having a measure of students’ readiness to interact with it will be key in future years, regardless of their pathway. This project will design and validate a suite of authentic performance tasks that assess high school seniors’ digital reasoning, responsible decisionmaking, and collaboration in real work contexts, including a focused cohort of Latino students supported with linguistically responsive, near‑peer validation. These short, district‑deployable tasks (AI fact‑check and traceability; data‑to‑decision; collaboration‑in‑the‑open; prompt‑and‑revise) will generate scored artifacts, rubrics, calibration tools, practitioner dashboards, and open manuals for psychometric validation and fairness auditing, enabling schools to link verified skills to upward mobility indicators such as internships, Free Application for Federal Student Aid completion, and entry into career‑advancing programs. Developed through a collaboration between a neuroscientist and an educational organization with established school partnerships, the work aims to provide the field with practical tools for preparing students to graduate into an AI‑enabled economy.

Cognitive, Social, and Emotional Skills

DRIVER VALIDATION

Social Capital, Socioemotional Competencies, and the Transition to Young Adulthood
Steven Hemelt, Jane Cooley Fruehwirth, and Elc Estrera
University of North Carolina at Chapel Hill

This project investigates how two core socioemotional competencies—grit and cognitive engagement—shape students’ long-term mobility outcomes, including college enrollment, employment, and earnings. Using rich longitudinal data from the Wake County Public School System, the researchers will track the development of these competencies over the critical transition from middle to high school (eighth to ninth grade) and explore how malleable contextual factors, such as teacher-student relationships and peer support for learning, foster or constrain the growth of such competencies. The project will assess how relationships among these core competencies, contextual factors, and long-run mobility outcomes vary by gender, race and ethnicity, and economic background. By focusing on nonparent relationships and early indicators of engagement—issues central to current conversations about high school redesign and the supports needed for opportunity youth (i.e., individuals neither enrolled in college nor employed)—this work will offer policy‑relevant evidence on how schools can strengthen core and under investigated competencies related to long‑run economic mobility.

MEASURE DEVELOPMENT WITH EARLY MOBILITY OUTCOMES

Leveraging AI to Measure Mobility-Relevant Competencies from Student Volunteer Reflections
Ellen Altermatt, Bill Altermatt, and Andrea Rorrer
Utah Education Policy Center, University of Utah

This project uses a unique combination of data from a large-scale, lieutenant governor–backed high school service program and rich administrative datasets from Utah. These data will be used to develop measures of self-management, relationship skills, and self-awareness through large-scale, AI-informed text analysis of student reflections. By integrating administrative records with the Panorama Student Survey, the Youth Civic and Character Measures Toolkit, and reflective writing generated through the statewide service initiative, the team will assess whether AI-based measures capture the same socioemotional competencies identified by validated survey instruments and determine which of these skills are most strongly correlated with postsecondary outcomes and civic engagement. Linking AI-derived SEL indicators to mobility-boosting college enrollment patterns and civic behaviors such as voter registration, the study offers an innovative test of whether scalable AI methods can reliably measure the skills most predictive of students’ educational and civic trajectories.

Using Multimodal Classroom Data to Measure Student Competencies for Upward Mobility
Heather Hill and Jing Liu
Harvard University

This project uses AI-supported analysis of recorded classroom interactions to uncover the student skills embedded in real‑time instructional exchanges and assess how well these competencies—spanning both core mathematical reasoning tied to upward mobility and more durable skills such as agency, vocabulary use, and self‑management where disparities often emerge—serve as early precursors of long‑run mobility. Drawing on classroom audio recordings, surveys, and linked administrative data from the Midwest and South, the team applies natural language processing and machine learning methods to develop scalable measures that capture how students demonstrate these competencies during middle school math lessons and then links them to key high school outcomes such as attendance, course taking, and GPA. Led by a leading scholar of classroom interaction and mathematics education and supported by established partnerships with districts and education technology firms, the project is positioned to produce scalable tools that help teachers identify and strengthen mobility‑relevant skills in real time.

Do Social and Emotional Learning Trajectories Predict Upward Mobility: Evidence from California
Jonathan Schweig and Susha Roy
RAND

Responding to our call for deeper insight into how socioemotional skills develop, this project leverages longitudinal data from a California school district serving 27,000 students to generate novel estimates of how social and emotional learning (SEL) competencies—as defined by the CASEL framework—change across multiple years for individual students, moving beyond traditional point‑in‑time snapshots to produce growth measures of SEL development. Using student‑level trajectories from grades 7 to 9, the team will model how competencies such as self‑awareness, self‑management, and relationship skills evolve and examine how these developmental patterns predict early mobility‑relevant outcomes, including absenteeism, academic performance, advanced course taking, and on‑time high school graduation. By comparing growth‑based SEL indicators with static measures and assessing whether they more accurately capture student development across racial and socioeconomic groups, the study aims to improve the precision and usefulness of SEL measurement for districts seeking to understand and support skills tied to long‑run educational and economic opportunity.

MEASURE DEVELOPMENT

Ambient Information: Measuring Student Capacities from AI-Interaction Chat Data
Susanna Loeb
Stanford University

This project develops novel, highly scalable measures of cognitive, social, and emotional skills by analyzing behavioral data from students’ interactions with AI tools. The study is led by a distinguished scholar with deep research-practice partnerships with school systems nationwide and conducted in collaboration with leading AI companies. The project will draw on naturally occurring student–AI interaction data from several widely used AI platforms and traditional data from school district partnerships, including surveys, assessments, and administrative records. These data partnerships enable the project to develop new measures of higher-order thinking and self-management skills that can be captured frequently from passively generated AI interaction data and validated against traditional measures of student skills. The project lays the groundwork for building a scalable and comparable system for assessing and strengthening the durable skills students need for long‑run success.