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Record W1519361556

Age Effects and Information Shocks: A Study of the Impact of Education Policy on Student Outcomes

2008· dissertation· en· W1519361556 on OpenAlexaboutno aff
Justin C. Smith

Bibliographic record

VenueMacSphere (McMaster University) · 2008
Typedissertation
Languageen
FieldSocial Sciences
TopicGlobal Educational Reforms and Inequalities
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyDemographic economicsPolitical scienceMathematics educationEconomics
DOInot available

Abstract

fetched live from OpenAlex

This thesis studies the impacts of school entry policy and information revelation on student outcomes using a sample of students from the province of British Columbia (BC), Canada. The questions examined by the first two essays arise from a policy used by many industrialized countries, whereby students born within a 1-year time span all begin school at the same time. This policy creates large differences in age among students in the same class, which are thought to affect their academic performance along a number of dimensions. In the first essay, I contribute to the literature by establishing the persistence in test score differentials among students in the same class who differ in age. I show that in grade 4 older students outperform younger students by a large margin in numeracy, reading and writing, an effect that persists to a lesser magnitude until grade 10. The persistence is strongest for the writing skill, and it is also much stronger for girls than for boys. The strength of the test score differential in grade 10 suggests that the effects of age could have more lasting effects on cognitive and labour market outcomes. In the second essay, I take a closer look at how age affects outcomes, by disentangling the entry age effect from the test age effect. Nearly all studies in this literature interpret age-related differences in student outcomes as the result of entry age, but because students who enter later are also older at every stage in compulsory schooling, the entry age effect has not been separated from the test age effect. Using a set of students entering school at the time of BC's dual entry experiment, I show that test age is largely responsible for age-related differences in the probability of repeating grade 3, and entry age is largely responsible for age-related differences in grade 10 numeracy and reading scores. I show further that having an extra year of schooling reduces the likelihood that a student repeats grade 3, but has a negligible impact on grade 10 test scores. Both the entry age and test age effects are stronger for boys than they are for girls. The final essay examines whether school choices change when parents are exposed to a new source of information on school quality. I model the effect of new information on choices using a simple expected utility framework and show that parents will use the new information to make different choices if they do not perceive it to be too noisy and if they have poor prior information on school quality. Furthermore, they make increasing use of the new information as more observations become available, since it becomes a more accurate predictor of true quality. Using the sudden release of BC's new standardized testing regime, I then study whether there is empirical support for the model. I show that the likelihood of switching out of a school increases when a school performs worse on the test, and that enrollment into kindergarten responds positively to increases in test scores. The response becomes stronger when more test score observations are available. Finally, I show variance in the response among parents living in less-educated neighbourhoods and among those who do not speak English at home, suggesting that prior information does play a role in the information use.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.291
Threshold uncertainty score0.578

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.013
GPT teacher head0.315
Teacher spread0.301 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2008
Admission routes1
Has abstractyes

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