A Partial Identification Approach to Identifying the Determinants of Human Capital Accumulation: An Application to Teachers
Bibliographic record
Abstract
ABSTRACT This paper views career growth in teacher quality through the lens of human capital theory to understand the roles of on‐the‐job training (OJT) and learning by doing (LBD) in human capital formation. If OJT is the primary determinant of human capital, incentive pay policies could create a dynamic multitasking problem, leading teachers to reduce their human capital investments, thereby lowering future student achievement. In contrast, teacher human capital and future achievement would both increase if LBD were the dominant force. To explore this, I develop explicit bounds on components of a human capital production function allowing for both channels, which I estimate using experimental variation from publicly available data from a teacher incentive pay experiment in Kenya. I find that LBD is present and also estimate an informative upper bound on the OJT component. This suggests that dynamic multitasking, while theoretically relevant, may have limited practical significance, at least in this context.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; a candidate call from one teacher head, not a consensus.
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".