Bibliographic record
Abstract
Our purpose in this paper is to present a class of convex endogenous growth models, and to analyze their performance in terms of both growth and business cycle criteria. The models we study have close analogs in the real business cycle literature. In fact, we interpret the exogenous growth rate of productivity as an endogenous growth rate of human capital. This perspective allows us to compare the strengths of both classes of models. In order to highlight the mechanism that gives endogenous growth models the ability to improve upon their exogenous growth relatives, we study models that are symmetric in terms of human and physical capital formation -- our two engines of growth. More precisely, we analyze models in which the technology used to produce human capital is identical to the technologies used to produce consumption and investment goods, and in which the technology shocks in the two sectors are perfectly correlated. We find that endogenous growth models can generate levels of labor volatility close to those observed in the data, as well as positively correlated growth rates of output. We also find that these models outperform a related exogenous growth version in most dimensions. Larry Jones Rody Manuelli Department of Economics Department of Economics University of Minnesota University of Wisconsin Minneapolis, MN, 55455 Madison, WI, 53706 and NBER and NBER lej@econ.umn.edu manuelli@ssc.wisc.edu Henry Siu Department of Economics Northwestern University Evanston, IL, 60208 hankman@nwu.edu Growth and Business Cycles 3 1.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".