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Can teacher pay-for-performance programs be successful? The case of the Texas Teacher Incentive Allotment

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DOI:

https://doi.org/10.14507/epaa.34.9573

Keywords:

performance pay, Teacher Incentive Allotment, program evaluation, validity, value-added models, Texas

Abstract

Pay-for-performance (PFP) programs in public education are controversial, and evaluations tend to focus on outcomes even though success also depends on design and implementation. We examine Texas’ Teacher Incentive Allotment (TIA), a large state-funded PFP program, using a three-part framework—design validity, implementation fidelity, and outcome utility. Drawing on administrative data from 79,198 teachers across 338 districts, we ask whether designations align with effectiveness, whether districts apply metrics consistently, and whether designation is associated with performance. Combining growth and observation measures, only 3% of teachers were designated despite below-threshold scores, and 8% not designated despite above-threshold scores. Also, designation aligned with performance (AUC = .95), indicating that districts largely applied state guidance appropriately. Designation was also associated with improved or sustained performance, though our observational design cannot establish causation. TIA illustrates how a large-scale PFP program can combine state rigor with local flexibility, with implications for policymakers weighing merit-pay reforms.

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Author Biographies

Jaehoon Lee, Texas Tech University

Jaehoon Lee, PhD, is an associate professor of educational psychology at Texas Tech University. His scholarly expertise spans advanced research methodologies, statistical modeling techniques, and measurement instruments applied across education, psychology, public health, and related disciplines. His recent work focuses on the evaluation and application of mixed-effects models, mixture models, Bayesian approaches, and propensity score methods for analyzing complex data sets.

Michael Strong, Texas Tech University

Michael Strong, PhD, is a research scientist in the College of Education at Texas Tech University. He is the former Director of Research at the New Teacher Center at the University of California, Santa Cruz, and at the Center on Deafness at the University of California, San Francisco. His current interests center on observing teaching behavior, applying AI to teaching evaluation, and measuring the effectiveness of teacher pay-for-performance programs.

Kristin Mansell, Texas Tech University

Kristin E. Mansell, PhD, is an assistant professor in the College of Education at Texas Tech University. Her research examines how school systems design roles, routines, and instructional structures to strengthen teaching and learning, with a particular focus on strategic staffing, blended and personalized learning, and instructional improvement in rural and high-need contexts. She leads statewide evaluation projects with the Texas Education Agency, including work on the Strategic Operations staffing model, the School Action Fund, and the Teacher Incentive Allotment, and explores teacher workforce dynamics such as retention, mobility, certification pathways, and the impact of professional learning.

Heather Greenhalgh-Spencer, Texas Tech University

Heather Greenhalgh-Spencer, PhD, is a professor in the Department of Curriculum and Instruction at Texas Tech University and Associate Dean of the Graduate School. Her research emerges at the intersection of educational technology, techno-philosophy, pedagogical innovation, strategic staffing, and school-workforce connections. Greenhalgh-Spencer also researches blended/personalized learning (BL/PL) and the ways that BL/PL can create multiple learning pathways and increased opportunities for all students. Her work intersects with issues in STEM education and the STEM (Engineering) pipeline. Dr. Greenhalgh-Spencer has published in multiple international journals of education. She teaches courses on e-learning, BL/PL pedagogies, pedagogical innovation, data literacy, and educational policy and philosophy.

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Published

2026-09-15

How to Cite

Lee, J., Strong, M., Mansell, K., & Greenhalgh-Spencer, H. (2026). Can teacher pay-for-performance programs be successful? The case of the Texas Teacher Incentive Allotment. Education Policy Analysis Archives, 34. https://doi.org/10.14507/epaa.34.9573

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