SMALL BUSINESS DETERMINANTS OF PERFORANCE IN MEXICO: AN EMPIRICAL STUDY
Bibliographic record
Abstract
ABSTRACT What determines performance among small businesses with five employees or less in Mexico? Based on a conceptual framework already used in Argentina and on previous research, a sample of 174 Mexican entrepreneurs from two different states (Jalisco and Nuevo León) was used to test a set of nine hypotheses. The dependent performance variables tested were an objective one, sales, and a subjective one, the personal assessment of performance (or success) of entrepreneurs. The independent variables considered included personal, sociological, and organizational characteristics. Results were obtained from two linear regression models on the two dependent variables. In terms of personal characteristics, variables that were positively related to sales included three Human Capital components (Education level, Business experience, and Weekly hours worked), having been pushed into self-employment by economic necessity, and belonging to the male gender. Regarding organizational variables, entrepreneurs with higher sales had obtained bank loans and had purchased their business (by opposition to starting it from scratch) and had economic necessity (extrinsic) reasons to be in business. Respondents who worked long hours and had obtained government support were more likely to be more satisfied of their own performance than others.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".