Quatro ensaios em Economia da Educa ção
Bibliographic record
Abstract
This thesis comprises four essays in economics of education. The first two essays are focused on the returns of accumulated human capital, in which the Quadros de Pessoal dataset is used. The first essay examines the evolution of returns to education for Portuguese workers during the period 1986{2009. In order to address the possible estimate bias due to the endogeneity, the education is instrumented by the quarter of birth and by the changes in the Portuguese compulsory schooling laws. In addition, a new instrumental variable is proposed: average education by region in the year in which individual entered school. Also in the context of the returns to education, in the second essay, the existence and size of human capital spillovers at different levels of analysis (firm, regional and inter-regional) is investigated, allowing to understand how the interactions among Portuguese workers can be determinant of differences in individual wages. Furthermore, the aggregated human capital is measured in three different ways: average education, share of high qualified workers and an adjusted multi-dimensional skill index. To control to the imperfect substitutability across workers, two qualification groups of workers are considered. Using a matched employer-employee dataset for the period 2002-2009 complemented by variables at county level from other sources, augmented Mincerian wage equations are estimated using different estimation methodologies. The third and fourth essays focus on the human capital accumulation process and the MISI and JNE Statistics datasets are used. The third essay analyses the determinants that influence the students' achievement of secondary education and, in addition, estimates the school value-added. It is given greater emphasis to the analysis of the class size e effect on students' achievement. For these purposes, a multilevel variance component model, with 3 levels: student, class and school, in a value added perspective, is applied. Additionally, the results obtained from the methodologies that are commonly used in academic work on performance assessment schools, Data Envelopment Analysis (DEA) and multilevel modelling, are compared. Finally, the fourth essay examines which observable teachers' characteristics, who teach Mathematics and Portuguese in the secondary education, influence the achievement gains of their students, taking into account a whole set of other factors that influence its progression, such as student's characteristics and class size. A value-added approach that adjusts for teacher fixed-effects is applied.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".