Entrepreneurship and urban growth: dimensions and empirical models
Bibliographic record
Abstract
Purpose The purpose of this paper is to look at various dimensions of entrepreneurship and the empirical models that try to explain the relationship between entrepreneurship and growth in cities for both developed (USA and Europe) and developing countries. Design/methodology/approach This paper provides an in‐depth and extensive review of the existing literature on entrepreneurship and economic growth in cities. In most empirical studies, the growth rate of employment or unemployment rate is used as the dependent variable to analyze the effect of entrepreneurship on development. The important independent variables other than entrepreneurship (new start‐ups) are localization, urbanization, level of education, age, industry structure (specialization vs competition), monopoly or competition. The economic units considered for cities are labor market areas (LMAs), standard metropolitan areas (SMAs) and consolidated metropolitan statistical areas (CMSAs). The majority of studies have utilized discrete dependent variable models such as Tobit or Probit to calculate the probability of the effect of entrepreneurship on economic growth. Other studies have applied ordinary least squares estimation to find the cross‐sectional variation of employment growth that accounts for entrepreneurial activities. Panel data are employed in a number of models to control for region‐specific and country‐specific fixed effects. Findings In this paper, four important dimensions of entrepreneurship are identified. First, for entrepreneurial studies on economic growth, cities are considered to be appropriate economic units rather than states or countries. Second, there are several definitions and measurements of entrepreneurship available in the literature. Hence, empirical models and their results may vary depending on the model specification. Third, the relationship between employment growth (a proxy for economic growth) and innovative activity is dynamic in nature and thus the problem of endogeneity needs to be addressed. And, finally, entrepreneurship has a spatial dimension and that characteristic must be incorporated into the urban and regional models of entrepreneurship. Three different types of urban models are chosen to reflect these four central dimensions of entrepreneurship. All three urban models confirm the hypothesis that there exists a statistically significant and positive relationship between entrepreneurship and growth in cities. However, the causality of the relationship is not well established. Originality/value A critical and in‐depth summary of existing quantitative work on entrepreneurship and economic growth in different cities is the original contribution of the paper.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".