Growth in the Number of Firms and the Economic Freedom Index in a Dynamic Model of the U.S. States
Bibliographic record
Abstract
INTRODUCTION Freedom indices of the world have established themselves as fixtures in the social sciences literature, especially in the growth literature. (e.g., Atukeren, 2005; Berggren and Jordahl, 2005; Gwartney, Lawson and Clark, 2005; Powell, 2005; Gwartney, Holcombe and Lawson, 2004; Nieswiadomy and Strazichich, 2004; Cole, 2003; Gwartney and Lawson, 2003; Gwartney, Block and Lawson, 1996) Across the literature, the consistent finding is that freedom, as measured by the various indices, is significantly and positively related to well-being. Citizens of nations with more enjoy higher incomes, and as an economy becomes freer, incomes rise. Of course, some may object that the term economic freedom is not value neutral. Though true, the advocacy component of the indices creators does not alter the indices' proven research usefulness in summarizing a broad variety of activities. One could choose to think of the indices in terms of market liberalism, or government non-interventionism. Karabegovic, Samida, Schlegel and McMahon (2003) introduced a conceptually similar index, the Freedom of North America index (EFNA) featuring differences among U.S. states and Canadian provinces. Karabegovic, et al, used their index to explain income differences among the states, offering evidence that the EFNA is significantly, positively related to state levels and growth of activity. Various researchers have used the EFNA (e.g., Ashby, 2005; Kreft and Sobel, 2005; Wang, 2005) to address questions of income differentials between states, income growth, entrepreneurship, and other research questions. Similar to Kreft and Sobel (2005), Gohmann, Hobbs & McCrickard (2008), Sobel (2008), and others, we apply the EFNA to questions of entrepreneurship. Specifically, we ask whether the political outcomes summarized by the EFNA are significantly related to growth in the number of businesses. Karabegovic, et al, argue that the EFNA measures in states; furthermore, they argued that greater results in higher income levels for state residents because greater consists of greater opportunity to seek and exploit opportunities; that is, to pursue entrepreneurial activity. Freedom to exploit opportunities is also the to create new businesses, so should lead to more business births. However, such is a double-edged sword. The to start a business is also the for that business to fail. Indeed, it is business births that create the raw material for business failures. Therefore, the impact of on growth in the number of businesses is ambiguous, although the impact on society--higher incomes--is not. This paper contains two innovations not found elsewhere in this stream of the literature. The first is the dependent variable, the measure of businesses. We use the annual growth rate in the number of firms, approximated by the annual difference in the natural log of the number of firms. Therefore, this measure implicitly includes firm births and firm deaths, and captures the full range of firm launches, whether partnership, corporation, etc. The second innovation is the use of a particular dynamic panel data estimator (Arellano and Bond, 1991) not found in this literature outside of a working paper. (1) ENTREPRENEURSHIP, ECONOMIC FREEDOM, AND ECONOMIC PERFORMANCE Promoting entrepreneurship has emerged as a significant policy tool for regional growth and job creation. The relevant policy question becomes which policies best promote entrepreneurship. A literature has developed around the concept that the appropriate policies are those will increase freedom. Economic freedom may be conceptualized as: Policies are consistent with when they provide an infrastructure for voluntary exchange, and protect individuals and their property from aggressors seeking to use violence, coercion, and fraud to seize things that do not belong to them. …
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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 | 0.006 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".