Explaining socioeconomic inequality in food consumption patterns among households with women of childbearing age in South Africa
Bibliographic record
Abstract
The changing food environment shifts peoples' eating behaviour toward unhealthy food, including ultra-processed food (UPF), leading to detrimental health outcomes like obesity. This study examines changes in socioeconomic inequalities in food consumption spending between 2005/06 and 2010/11 in South African households with women of childbearing age (15 to 49) (WCBA). Data come from the 2005/06 and 2010/11 Income and Expenditure Surveys. The distribution of spending according to the NOVA food classification system groupings (unprocessed or minimally processed foods, processed culinary ingredients, processed and UPF products) was analysed using standard methodologies. Changes in spending inequalities between 2005/06 and 2010/11 were assessed using the concentration index (C), while the factors explaining the changes in spending inequalities were identified using the Oaxaca decomposition approach. The Kakwani index (K) was used to assess progressivity. Results show that average real spending on all food categories, including UPF, increased between 2005/06 and 2010/11. Socioeconomic inequality in UPF consumption spending decreased (C = 0.498 in 2005/06 and C = 0.432 in 2010/11), and spending on processed foods (C = 0.248 in 2005/06 and C = 0.209 in 2010/11). Socioeconomic status, race, and urban residence contributed to overall socioeconomic inequality and changes in UPF consumption inequality between 2005/06 and 2010/11. Spending on all food categories was regressive in 2005/06 (K = -0.173 for UPF and -0.425 for processed foods) and 2010/11 (K = -0.192 for UPF and -0.418 for processed foods) because such spending comprises a larger share of poorer household's income than their wealthier counterparts. The government should address these contributors to inequality to mitigate the risks associated with UPF consumption, especially among less affluent households.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".