The impact of national social capital on business creation rates in the formal vs informal sectors
Bibliographic record
Abstract
Purpose Through utilizing social capital as an overarching concept, the purpose of this article is to investigate cross-country rates of business formation in the formal vs informal sectors. Plus, empirically assess the impact of social capital constructs on the national rates of entrepreneurship. Design/methodology/approach Adopting a regression-oriented methodology, partial least squares (PLS), the study used a sample comprising 50 nations. National rates of registered and nonregistered business creation were utilized as endogenous variables. To determine the indigenous variables, constructs of social capital were measured which is consistent with the World Value Survey (WWS). Findings The results of this study show that in the formal and the informal sectors, social networking enables business creation with varying levels of impact. It establishes that institutional trust has a negative effect on informal business creation and a positive effect on business registration; interpersonal trust drives entrepreneurship in the informal sector but has less impact on business registration; norms of trustworthiness are related to business registration than informal business creation. Practical implications The findings of this research have theoretical and practical implications. They stimulate academic debate on the application of social capital constructs at the national level. The indications that social capital promotes business formation in both the informal and formal sectors can influence entrepreneurship policy development in many countries. Originality/value The originality of the results of this study lies in how it conceptualizes social capital as having direct impact on business creation in the informal vs formal sector. Thus, the findings elevated the conceptualization of social capital to the national level thereby enhancing knowledge on the entrepreneurship process as well as developmental economics.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".