Acemoglu Meets Lucas: Institutions, Human Capital and Economic Growth
Bibliographic record
Abstract
This paper proposes a reinterpretation of Lucas endogenous growth model (1988), once we add an institutional component as one of its determinants. Firstly, the paper develops a theoretical model that links human capital and institutions. Our modelling strategy establishes the human capital accumulation function as being derived from an endogenous process in which the institutional performance is a booster for the economy’s growth. The essay uses a 40–country panel data of the years 2000, 2005 and 2010 and implements a Pooled Ordinary Least Squares (POLS) analysis – alongside instrumental variables (IV) – aiming to validate empirically the model proposed. We verify that Lucas’ model overestimates the human capital contribution as we evaluate the significant impact that economic and political institutions have on the capability of human capital foment growth. Additionally, our estimations also suggest that human capital is, effectively, institutionally driven and works as a channel for the institutions.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".