The Market Process: Entrepreneurship, Intervention, and the Role of the State
Bibliographic record
Abstract
The market process is driven by the entrepreneur, hindered by the intervention of the state, and made possible by institutions that minimize transaction costs. This dissertation addresses these components of economic growth through the application of the theory of entrepreneurship, the dynamics of intervention, and institutional analysis. Chapter one demonstrates the power of the entrepreneur to drive the market using Henry Ford’s Five-Dollar Day initiative as an illustration. I offer an alternative to the existing literature that interprets Henry Ford’s five-dollar day as an efficiency wage. In contrast, I argue that the theory of entrepreneurship provides a more robust understanding of the motives and purpose behind Ford’s novel labor policy. It also demonstrates how entrepreneurial action shapes the contours of the market. Chapter two applies the dynamics of intervention and public choice theory to international trade barriers. Trade barriers lead to systematic distortions of the market process, hindering growth. The unintended consequences of using trade policy to bolster a nation's economy include an unanticipated and undesired market structure, as well as the prevention of efficient resource allocation. Even if a government manages to improve the terms of trade or protect an infant or favored industry, the inability to use economic calculation to form policy and the disruption of entrepreneurial discovery will prevent the allocation of resources to their highest-valued use. The Chicken War (1963) and the US-Canada softwood lumber disputes (1982–Present) illustrate the theory. Chapter three is an analysis of the institutional structure of the English tenth-century market economy. After the Viking conquest of the late ninth century upset the political structure of Anglo-Saxon England, the change in bargaining strengths of key political actors led to institutional change. King Alfred’s reliance on the rule of law and existing custom to make these changes built predictable sticky institutions. The drive to establish a legal means for the transfer of power at the king’s death increased the king’s time horizon and thus his encompassing interest in society. An analysis of Alfred’s written law code, bureaucratic system, and cultural reforms illustrate the self-interested action of the autocrat, Alfred, and the consequence of a flourishing market economy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".