Comparative analysis of households' socioeconomic and demographic characteristics and food security status in urban and rural areas of Kwara and Kogi States of north-central Nigeria
Bibliographic record
Abstract
Food security is a critical issue in Nigeria today as the country struggles with high rates of food prices and poverty. This study analysed the socioeconomic and demographic characteristics of Household Heads (HHH) and classified them according to food security status. Household level data from the cross-sectional survey was employed in November 2006 to February 2007through a well-structured questionnaire to 396 HHH with a multi-stage sampling procedure. Data were analysed through a descriptive statistics and Rasch model. Average age of the HHH was 42.45years with Standard Deviation (SD) of 9.57 years in Rural Areas (RA) against 43.29 years and SD of 9.83 years in Urban Areas (UA). The HHH level of education was much higher in UA compared to RA. The Household Size (HSZ) was 5.88 with SD of 2.29 in RA against 5.91 and SD of 2.17 in UA, and monthly income of N9, 244.86 with SD of N11, 071.77 in RA against N10, 194.15 and SD of N14, 936.30 in UA. The results from Rasch Model for classifying households according to food security status show that differences exist between households’ food security status in rural and urban areas of Kwara and Kogi States. While 15.6% HHH were food secure (FS) in RA of Kogi State, only 11.1% were FS in the RA of Kwara State. On the other hand, 20.7% HHH were FS in UA of Kogi State compared to 17.1% in UA of Kwara State. Disaggregating food security status of adults and children in households separately revealed that, 25.8% adults in RA of Kogi State were FS compared to 19.2% in Kwara, while 24.4% urban adults were FS in Kogi against 23.2% in Kwara. In addition, 40.6% children in RA of Kogi State were FS against 32.3% in Kwara, while only 29.9% Kogi urban children were FS against 46.3% in Kwara. In general, households were more FS in Kogi State compared to Kwara and more FS in UA compared to RA. The rural children in Kogi State were also more FS compared to the urban, while urban children in Kwara were more FS when compared to rural children. In order to improve households’ food security status in both rural and urban areas, there is the need to take into account some significant variables such as reduction in household size through birth control, and increase in household heads’ participation in agricultural activities especially those residing in urban areas through urban agriculture.
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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 | 0.000 | 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".