The Role of Small and Medium Enterprises (SMEs) in Employment Generation and Economic Growth: A Study of Marble Industry in Emerging Economy
Bibliographic record
Abstract
This study is undertaken to find out how SMEs contribute to the economy in terms of employment generation and its impact on the economic growth of the country. Small and Medium Scale Enterprises (SMEs) is accepted globally as a tool for empowering the citizenry and economic growth. In Pakistan efforts have been made by successive governments to increase employment opportunities, reduce poverty and accelerate economic growth by increasing foreign direct investment, diversifying the economy, enacting policy frameworks which favor small business ownership and entrepreneurship programs. Specifically, this study tends to figure out: how SMEs contribute to employment generation, whether a significant number of people is employ within the SME sector; whether the SMEs increase the income level of people. The total number of employees was 255 being selected randomly from Swat marble industries. A questionnaire was constructed and distributed to the selected respondents. The responses were collected and analyzed using the Statistical Package for Social Sciences (SPSS) analytical tool. The study exposes that SMEs play a vital role in employment generation. There is a positive relationship between SMEs and unemployment reduction. The result also shows that there is a positive relationship between SMEs and increase in income level. This study may be beneficial both for practitioners and academicians. For practitioners, the current study may help to devise policies and strategies concerning SMEs to generate employment opportunities. The current study may lead to the generalizability of existing research in the same field as for academic aspect is a concern.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".