James Tobin on Overlapping Generations Models and the Microeconomic Foundations of Macroeconomics Reconsidered
Bibliographic record
Abstract
The American Keynesian economist and Nobel laureate James Tobin entitled his contribution to the inaugural issue of Journal of Money, Credit and Banking “A General Equilibrium Approach to Monetary Theory” (1969) yet he was sharply critical of the version of general equilibrium analysis used in the now-prevalent dynamic stochastic general equilibrium (DSGE) approach to macroeconomic modeling. He also rejected claims that overlapping generation (OLG) models provided a choice-theoretic foundation for the holding of fiat money, denying that the assumption that people held fiat money because no other asset existed was less arbitrary than allowing costs of transactions between assets to be non-zero. Yet, while not accepting OLG as the explanation of why people hold fiat money, Tobin, in a series of articles from 1967 to 1983 (some joint with Walter Dolde), placed the life-cycle model of consumption and saving (whose origins he attributed to an earlier Yale economist, Irving Fisher) into an OLG setting. This article examines Tobin’s critiques of DSGE and OLG modelling in macroeconomics, what he meant by “a general equilibrium approach to monetary theory,” and his contribution placing the life-cycle model in an OLG setting.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".