Determining the predictors of living standards in South Africa : a real world econometric approach
Bibliographic record
Abstract
B S T R A C T This study aims to determine the strength of various demographic and socioeconomic variables in predicting changes in living standards over the period 1997 to 2006. Data for analysis and modelling purposes ± from the All Media and Products Surveys (AMPS) for the period 1997 to 2006 ± were used in an econometric model that made use of regression analysis to determine the optimal mix of variables predicting living standards as well as the individual strengths of such variables in predicting living standards. This was done in order to gain a comprehensive understanding of the individual variables that impact on living standards in the South African context and the ways in which such variables conjointly enhance or inhibit improvements in living standards. To determine whether the different variables predict living standards separately or conjointly, a co-integration analysis was done as part of the regression analysis. From the regression analysis, it appears that six predictor variables (three variables exogenous to the equation and three variables endogenous to the equation) jointly succeeded in predicting 93.1 % of the variance in living standards. These variables were `income', `employment ' and `education ' (endogenous variables) and `province', `race ' and `type of area ' (exogenous variables). It was determined by means of the co-integration analysis that the social variables conjointly predict living standards. The strongest predictor of living standards found in this study was the variable `income', which predicted a quarter of the variance in living standards in the analysis conducted. The second strongest predictor of living standards in South Africa was a variable exogenous to the equation, namely `race', which is not surprising in the light of the fact that the broader black population group (comprising Africans, Asians and Coloureds) have made considerable headway in improving their living standards driven by a range of labour market segmentation legislation, policies and practices. It appears from the findings of this
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".