Custodes Futurifor the advancement of humanity

Educational factor, barrier 4 of 6

Teacher quality

Chetty, Friedman, and Rockoff (2014) linked U.S. school records to adult tax records.

Evidence

  • Long-run effects. Teachers who raise test scores more ("high value-added" teachers) also raise students' chances of attending college and later earnings, and reduce teen pregnancy.
  • Replacing a weak teacher. Replacing a teacher in the bottom 5% with an average teacher would raise students' combined lifetime income by about US$250,000 per classroom. A published comment (Rothstein, 2017) found these long-run estimates sensitive to modeling choices, so this figure should be treated as uncertain.
  • What helps. Identifying and supporting effective teachers. Long-run effects are contested (Rothstein, 2017).

Why it matters: Unequal access to effective teachers is itself a major barrier, independent of whether a school exists.

Proposed and experimental methods

Methods that are proposed, under trial, approved in some places, or tried and then failed. Each shows a stage label and an evidence rating. A stage label shows how far a method has progressed, not whether it works. The stage labels are explained on the educational factor page.

  • In-classroom teacher coaching (Large trial, B). A meta-analysis of 60 causal studies found that coaching improved teaching practice by 0.49 standard deviations and student achievement by 0.18, but effects from larger programs were only a fraction of those from small programs (Kraft et al., 2018). In South African public primary schools, with the same lesson plans in both arms, reading rose by 0.24 standard deviations with in-class coaching versus 0.12 with centralized training (Cilliers et al., 2020).
  • High-stakes teacher evaluation with dismissal threats and bonuses (Approved but not scaled, contested, B). Under the IMPACT system in Washington, DC, teachers just below the dismissal threshold left voluntarily about 11 percentage points more often, and those who stayed improved by about 0.27 standard deviations, while teachers near a large bonus threshold also improved (Dee and Wyckoff, 2015). The design compares teachers just on either side of rating cutoffs, so it shows effects near those cutoffs only.
  • AI assistants that coach tutors in real time (Early trial, C). Builds on the AI tutor entry in "Fixed beliefs about ability" in the psychological factors, but here the AI guides the human tutor instead of the student. In a randomized trial with 900 tutors and 1,800 K-12 students, students whose tutors had the tool were 4 percentage points more likely to master topics, and 9 points more with lower-rated tutors, at about $20 per tutor a year (Wang et al., 2025). The study is not peer reviewed, was run by the tool's developers, and tutors reported some suggestions not suited to grade level.
  • Failed: school-wide teacher bonuses in New York City (Failed or reversed, B). Builds on the loss-framed incentive entry in "Loss aversion and status quo bias" in the psychological factors. A randomized trial in more than 200 New York City schools found no effect on test scores, attendance, or graduation, and possibly lower achievement in larger schools (Fryer, 2013). A separate analysis found small math gains only where incentives to free ride on colleagues were weakest (Goodman and Turner, 2013).
  • Failed: doubling teacher pay without conditions (Failed or reversed, B). In a randomized trial in Indonesia, doubling the base pay of certified teachers improved their satisfaction with income and reduced outside jobs, but after two and three years it had no impact on teacher effort or student learning (de Ree et al., 2018).

Sources cited on this page

  1. Chetty, R., Friedman, J. N., & Rockoff, J. E. (2014). Measuring the impacts of teachers II: teacher value-added and student outcomes in adulthood. American Economic Review, 104(9), 2633-2679. AEA C Limited: B for test-score effects
  2. Rothstein, J. (2017). Measuring the impacts of teachers: Comment. American Economic Review, 107(6), 1656-1684. AEA B Moderate
  3. Cilliers, J., Fleisch, B., Prinsloo, C., & Taylor, S. (2020). How to improve teaching practice? An experimental comparison of centralized training and in-classroom coaching. Journal of Human Resources, 55(3), 926-962. link B Moderate
  4. de Ree, J., Muralidharan, K., Pradhan, M., & Rogers, H. (2018). Double for nothing? Experimental evidence on an unconditional teacher salary increase in Indonesia. The Quarterly Journal of Economics, 133(2), 993-1039. link B Moderate
  5. Dee, T. S., & Wyckoff, J. (2015). Incentives, selection, and teacher performance: Evidence from IMPACT. Journal of Policy Analysis and Management, 34(2), 267-297. link B Moderate
  6. Fryer, R. G. (2013). Teacher incentives and student achievement: Evidence from New York City public schools. Journal of Labor Economics, 31(2), 373-407. link B Moderate
  7. Goodman, S. F., & Turner, L. J. (2013). The design of teacher incentive pay and educational outcomes: Evidence from the New York City bonus program. Journal of Labor Economics, 31(2), 409-420. link B Moderate
  8. Kraft, M. A., Blazar, D., & Hogan, D. (2018). The effect of teacher coaching on instruction and achievement: A meta-analysis of the causal evidence. Review of Educational Research, 88(4), 547-588. link B Moderate
  9. Wang, R. E., Ribeiro, A. T., Robinson, C. D., Loeb, S., & Demszky, D. (2025). Tutor CoPilot: A human-AI approach for scaling real-time expertise (arXiv:2410.03017v2) [Preprint]. arXiv. link C Limited

Every source for this factor is listed on the educational factor page.