Microtheory and recent developments in the study of economic institutions through economic history
Bibliographic record
Abstract
INTRODUCTION Adam Smith ([1776] 1937) argued that the “propensity to truck, barter, and exchange” is in human nature (p. 13) and proceeded to examine how institutions impact the efficiency implications of this tendency. Arguably, institutions impact efficiency since they influence the allocation of resources to and by economic agents and the set of exchange relations that these agents are willing to assume. The diversity of economic environments and institutions utilized throughout history provides a unique source for examining the nature and implications of institutions. Indeed, a distinctive feature of economic history has always been its concentration on the examination of institutions, their origins, natures, and implications. This chapter provides a brief survey of the three approaches within economic history - the neoclassical (section 1), the new institutional economic history (section 2), and the historical institutional analysis (section 3) - that utilize microeconomic theory for the study of institutions and their efficiency implications. Each of these complementary approaches focuses on different sets of institutions, utilizes different theoretical frameworks to analyze them, and advances different methodologies to integrate theoretical and historical analyses. Owing to space limitation this chapter concentrates on highlighting the methodology of and interrelations with microeconomic theory adopted by these approaches, and gives some of their insights with respect the study of economic institutions. Since historical analysis is the most recent development in the study of economic institutions through economic history, most of the survey is devoted to this approach.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".