Three essays on entrepreneurship and alternative economic development policies
Bibliographic record
Abstract
This study examines the effect of rurality on early-stage necessity and opportunity entrepreneurship among women and men in America from three rural typology perspectives.To achieve this objective, I build a dataset that combines GEM U.S. individual data for 2005-2010 and county economic characteristics from the Bureau of Labor Statistics and the Census Bureau.I use three typologies to define rurality and compare the results, the OMB metro-nonmetro classification system (2003), Isserman (2005) and county population density.I further analyze this data in subsamples by gender using cross-section time-series rare events logistic regression with clustered robust errors and year fixed effects.Key findings indicate the three rural typologies show similar results in magnitude, direction and significance, although population density shows sensitivity to the rurality variable and subsamples.Also, compared to women in OMB metro counties in America, women who live in OMB nonmetro counties have a higher probability of engaging in opportunity entrepreneurship.This probability increases with college education and decreases if the woman lives alone or is retired.Among men, living in OMB nonmetro or Isserman rural counties also increases their probability of engaging in opportunity entrepreneurship.College education and being African American also increases this probability.Predictors of necessity entrepreneurship are having an income below 50,000 among women and being employed part time among men.x Essay 2 This study uses the resource-based view of the firm in the context of neoclassical economics and the concept of additionality to determine the effect of public sources of start-up capital on entrepreneurial performance at the business and state levels.To attain this objective, the study develops a dataset that combines the 2007 Survey of Business Owners (SBO) Public Use Microdata Sample (PUMS) (released August 2012) with state data from the Census Bureau's Business Dynamics Statistics.The final dataset contains over one million observations from firms across the US that were operational in 2007, and is analyzed using OLS and two-stage least squares (2SLS) with two alternative instrumental variables.Public sources of start-up capital include government loans, government guaranteed loans and grants, and are combined into one indicator.Results indicate that public intervention in the provision of start-up capital has a marginal negative effect on business employment, and a positive effect in the long term (once the firm is established) on the state establishment entry rate compared to using private sources of capital.This comparative study fills a gap in the literature by providing strong theoretical and empirical evidence on the effect at the business level and the additionality effect at the state level of offering public sources of start-up capital to firms across the US.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".