Bibliographic record
Abstract
This final chapter discusses several other public policies which affect entrepreneurship. The first section discusses the taxation of entrepreneurs. It starts by reviewing theory and evidence about the effects of income and payroll taxes, before turning to corporation, wealth and inheritance taxes. The content of the first section complements the analysis of capital gains tax provided in the previous chapter. The second section discusses evidence relating to three types of public policy which affect entrepreneurs' labour and product market activities. The first type of policy is employment assistance schemes, designed to move unemployed workers off the welfare rolls and into independent business ownership. The second type is targeted government procurement of entrepreneurs' products, including affirmative action policies for small business owners. The third type of policy is the provision of assistance and advice to entrepreneurs. The third section of this chapter deals with an increasingly important topic: government regulation. Regulations affect entrepreneurial entry, exit, production, hiring and corporate governance decisions, as well as the availability of bank credit. These topics are all analysed in turn. The emphasis in this section is mainly on presenting empirical findings, although some theoretical arguments are also discussed. The fourth section discusses some remaining, mainly macro, policy issues such as the welfare state and minimum wages – and how they affect entrepreneurship. It also discusses the effects of several institutions which, while not directly under government control, are shaped by government policies, including the roles of ‘enterprise culture’ and trade unions in the 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".