Teacher Value-Added and Economic Agency
Bibliographic record
Abstract
Teacher value-added is not a technological 'primitive.' Instead, it increases within-teacher when accountability incentives are strengthened, as we show. This evidence motivates a framework in which teacher value-added depends on two unobserved inputs to education production: teacher ability and incentive-varying teacher effort. We present a strategy to identify these two inputs and their distinct effects on test scores for the first time, using exogenous incentive policy variation and rich longitudinal data from North Carolina. The estimates indicate that both the ability component of teacher value-added and teacher effort raise current and future scores. We also find that effort responds systematically to incentives, underlining the agency of the teaching force as an important factor in education production. To explore the policy implications of this economic agency, we then use our estimates to compare the cost effectiveness of incentiveoriented education reforms with policies that target teacher ability. Incentive reforms come out ahead in a variety of plausible cases, given their potential to influence all teachers rather than a subset.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".