Three Essays in the Economics of Education
Bibliographic record
Abstract
In principal, public education provides a child's key means of skill accumulation, irrespective of background. In practice, however, the actual performance of public schooling is a disappointment, with stakeholders concerned that the current state of public education heightens inequality and prepares students inadequately for the workforce or higher learning. This thesis develops and applies novel econometric techniques to highlight education policies that may increase student achievement and reduce the pervasive test score gaps that plague public education today. Chapter 1 sets out a new approach that enables me to credibly identify dynamic interactions among school inputs for the first time. Such an approach is rarely adopted in empirical research due to stringent requirements: in an observational setting, identifying dynamic interactions requires period-by-period randomization. I overcome this challenge by combining rich administrative data with a rule whereby students are held accountable only if there are forty or more students in their demographic group. The rule provides useful year-to-year variation, supplying the backbone of my identification strategy. I then estimate the technology structurally and consider the efficacy of alternative accountability schemes: conditioning on initial test scores rather than prior test scores can increase average achievement and reduce inequality. Chapter 2 proposes an approach that allows researchers to identify separate treatment components from a single discontinuity. As an application, I consider the discontinuity associated with class size caps -- a widespread education policy used to reduce class sizes. My approach exploits the asymmetry between school-grades entering versus exiting treatment to distinguish the pure effect of changes in class size from the effect of a newly-hired teacher. Using data from New York City, I find that class size reductions increase student achievement, though these gains are counteracted by the newly-hired teacher. Chapter 3 uses a regression discontinuity design to investigate the merits of decentralized public goods provision in the context of Title I, the largest U.S. federal education funding program. My results indicate that the negligible impact of Title I is caused by its centralized nature: in a decentralized form, Title I generates a substantial improvement in student achievement, particularly for the socioeconomically disadvantaged.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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 source (direct Gemma or distilled Codex), 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".