Urban food insecurity in the context of high food prices: a community based cross sectional study in Addis Ababa, Ethiopia
Bibliographic record
Abstract
BACKGROUND: High food prices have emerged as a major global challenge, especially for poor and urban households in low-income countries such as Ethiopia. However, there is little empirical evidence on urban food security and how people living in urban areas are coping with sustained high food prices. This study aims to address this gap by investigating the food insecurity situation in urban Ethiopia -a country experiencing sustained high food prices, high rates of urban poverty, and a growing urban population. METHODS: A community based cross-sectional study was conducted from January 18 to February 14, 2012. A total of 550 households were selected from three sub-cities of Addis Ababa using three-stage sampling technique. Data were collected using questionnaire based interview with household heads. Items in the questionnaire include, among others, basic socioeconomic variables, dietary diversity and coping strategies. Food security status of households was assessed by a Household Food Insecurity Access Score. Data analysis was done using SPSS software and both univariate and bivariate analysis were done. RESULTS: The study found that 75% of households were food insecure and 23% were in a state of hunger. Households with higher food insecurity scores tend to have lower dietary diversity and are less likely to consume high quality diets. Reduction in meal size and shifting to poor quality/less expensive/food types were among the common coping strategies to high food price used by households. Household incomes, occupational and educational status of household heads were significant determinants of food security. CONCLUSION: Food insecurity in Ethiopia is not only a rural problem. Urban food insecurity is a growing concern due to the toxic combination of high rates of urban poverty, high dependency of urban households on food supplied by the market, and fluctuating food prices. Household food insecurity was particularly high among low income households and those headed by uneducated, daily wagers and government employed household heads. Therefore, policy makers should work on stabilization of the food market and creating opportunities that could improve the livelihood and purchasing power of urban 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 | 0.018 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".