The Implications of Labour Productivity and Labour Costs on the South African Economy
Bibliographic record
Abstract
The research has shown that labour productivity growth has been slowing down.This trend is suggesting that the gains in the quality of employment in several regions of the world might be difficult to sustain.Furthermore, the South African workers were found to have the greatest amount of unproductive time and they are said to be having one of the lowest employee productivity stats in the world.The purpose of this study was to investigate the implications of labour productivity and labour costs on the South African economy.The Ordinary Least Square (OLS) based Autoregressive Distributed Lag (ARDL) approach was employed to analyse the quarterly time series data from 1998 to 2018.Since South Africa is faced with several challenges such as high levels of unemployment, higher wage bills and high levels of poverty; this study is envisaged to provide an empirical evidence to policymakers and union leaders alike to begin to recognise more fully the importance of labour productivity and labour costs towards economic growth.The results indicate that labour productivity has a significant positive impact on economic growth however labour costs have a significant negative impact on the economy of South Africa.Policy formulation should focus on policies that can help to improve the quality of the labour force in order to achieve desired economic growth levels that can help to increase the levels of employment and the reduction of poverty.Similarly, both the workers and the labour unions should be cautious not to milk the cash cow until it dies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".