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Record W3125773055

Match Quality, Worker Productivity, and Worker Mobility: Direct Evidence from Teachers

2010· article· en· W3125773055 on OpenAlexaboutno aff
C. Kirabo Jackson

Bibliographic record

VenueRePEc: Research Papers in Economics · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityQuality (philosophy)Quarter (Canadian coin)Teacher qualityLabour economicsDemographic economicsEstimatorInstrumental variableEconometricsEconomicsTurnoverExplanatory powerBusinessStatisticsOperations managementMathematicsManagementEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

I investigate the importance of the match between teachers and schools for student achievement. I show that teacher effectiveness increases after a move to a different school, and I estimate teacher-school match effects using a mixedeffects estimator. Match quality can "explain away " a quarter of, and is as economically important as, teacher quality. Match quality is negatively correlated with turnover, and increases with experience. This paper provides the first estimates of worker-firm match quality using output data as opposed to inferring productivity from wages or employment durations. Because teacher wages are essentially unrelated to productivity, this is the compelling evidence that workers may seek high quality matches for reasons other than higher pay. The productive quality of the match between a worker and the firm plays a central role in canonical models of worker mobility (Jovanovic 1979, Mincer and Jovanovic 1981, Neal 1999, Burdett 1978, Mortensen 1998, Johnson 1978). One of the key roles of the labor market is to allocate workers to firms in the most efficient manner. The hypothesized mechanism through which this efficient allocation emerges is through workers either leaving jobs where the productivity match between the worker and the firm is low or seeking jobs where match quality is high, or both. Match quality is also used to explain the stylized facts that changing jobs is associated with rapid earnings growth (Bartel and Borjas 1981, Altonji and Shakotko 1987,

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.070
GPT teacher head0.396
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2010
Admission routes1
Has abstractyes

Explore more

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