Would You like to Work More Hours?—An Investigation on South Africa
Bibliographic record
Abstract
To begin with, Sustainable Development Goals are of tremendous importance in all areas, being seen as vital aims in all domains, which makes them indispensable when it comes to addressing the particularities of the labour market these days. Subsequently, human resources occupy a distinctive and unique position when referring to the implications derived from targeting Sustainable Development Goals, especially in the context represented by the period specific to the COVID-19 pandemic and the international events that followed immediately after that. This study investigates the work motivation of individuals, and whether they would be willing to work more hours if they are paid. Motivation and attitudes towards working more hours might be affected by several factors, and they are important contributors to business performance. Not only business performance is to be affected, but this is also a part of Sustainable Development Goals where labour market conditions and productivity concerns are addressed, along with several other factors. Using the Quarterly Labour Force Survey from 2017 to 2022 that is conducted by Statistics South Africa, this study attempts to shed light on individual preferences for working more hours in the case of South Africa. Considering the dichotomous dependent variable, a binary response model is utilised to explore the determinants of such behaviour. Findings of the probit model reveal that socio-demographic factors such as gender, marital status, education level, and work experience are important indicators to explain this preference. More precisely, being female increases the likelihood of willingness to work more hours if paid by 1.1 percentage points, and being never married increases that probability by 2.7 percentage points. Within education categories, the highest coefficient in magnitude, having tertiary education decreases the probability of willingness to work more hours by 8.2 percentage points. As an important labour market indicator, one more year to commence working increases the probability of willingness to work more hours by 0.4 percentage points.
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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) | 0.005 | 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".