Difference in levels and predictors of food insecurity among urban and rural households of Kombolcha district of east Harerge zone

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1 Difference in levels and predictors of food insecurity among urban and rural households of Kombolcha district of east Harerge zone Asnake Ararsa (MPH), Negga Baraki (MPH, Ass. Professor), Haji Kedir (MPH, PHD) ANH Academy Week, JUNE 24 Addis Ababa, Ethiopia

2 Introduction Urbanization is rapidly increasing in developing countries. Rural population in Ethiopia was 80.97% in 2014 (82.7% in 2012). Unless well planned, this will exacerbate poverty, including food insecurity (Global monitoring Report, 2013). Making urbanization a national priority will accelerate Ethiopia s progress towards reaching middle-income status. Need to be balanced with the right to adequate food: "fundamental right (The Sphere Project, 2004; UNHCHR, 2010).

3 According to Ethiopia s agricultural sector policy and investment framework (PIF), over 12,000,000 people are chronically or sporadically food insecure (PIF, 2010). Unique characteristics of urban and rural life affect the nature and severity of food insecurity. Food security and vulnerability assessments in Ethiopia have traditionally focused on rural areas (WFP-Ethiopia, 2009). Therefore, this study compare levels and predictors of urban and rural households food insecurity.

4 Material and methods Kombolcha district of eastern Harerge Zone from January 7 to 16. Community based crossectional study design was used 1 urban (144 HHs)& 5 rural (570 HHs) kebeles Rural divide in to 3 livelihood zones

5 Data collection method Two tools, HFIAS (Deitchler et al, 2010) and HDDS (Kennedy and Ballard, 2013) was used. Hierarchical multiple regression was used after assumptions were satisfied.

6 Result Mean HFIAS was 4.9±4.4 (SD) for urban and 8.14±5.6 (SD) for rural households. The nine items into three major domains: Feelings of uncertainty or anxiety of household food supplies was 336 (59.8%) for rural and 80 (55.6%) for urban Perceptions of insufficient quality and food type preference ; and was 272(48%) for rural and 58 (40%) for urban Insufficient food intake and its physical consequences was 238 (41.08%) for rural and 22 (15%) for urban.

7 Percentage of households HFIAS food insecurity across residence FOOD SECURE MILDLY FOOD INSECURE MODERATLY FOOD INSECURE SEVERE FOOD INSECURE Food security category Urban Rural Total Figure 1: Percentage of household food security category of urban and rural households of Kombolcha district, (n: urban=144, rural=562, total=706)

8 0.0% 0.0% 4.3% 1.7% 13.9% 11.6% 25.8% 27.4% 47.7% 48.7% 52.3% 66.6% HFIAS food insecurity across rural livelihood zones Chat-vegetable Sorghum-coffee Maize-sorghum F O O D S E C U R E M I L D L Y F O O D S E C U R E M O D E R A T E L Y F O O D I N S E C U R E S E V E R L Y F O O D I N S E C U R E Figure 2: Percentage of HDD category of rural households across livelihood zones of Kombolcha district, (Rural=562, (Rural=562, Maize-Sorghum=117; Sorghum-Coffe=65; Chat-Vegetables=380)

9 12.60% 9.20% 16.90% 13.70% 28.20% 44.50% 42.90% 58.10% HDDS: Mean HDDS was 5.48±2.0 (SD) for rural and 7.77 ±1.97 (SD) for urban households. HDDS across residence Rural urban Total 73.6 HDD across rural livelihood zones Chat-vegetable Sorghum-coffee Maize-sorghum LOW DIETARY DIVERSITY MEDIUM DIETARY DIVERSITY HIGH DIETARY DIVERSITY LOW DIETARY DIVERSITY MEDIUM DIETARY DIVERSITY HIGH DIETARY DIVERSITY Figure 4: Percentage of HDD category of rural households of Kombolcha district, (Rural=562, Maize- Sorghum=117; Sorghum-Coffe=65; Chat-Vegetables=380)

10 9.4% 6.8% 17.6% 17.6% 31.3% 41.5% 18.2% 52.3% 27.3% 51.7% 64.7% 54.5% 29.9% rural and 8.3 % of urban HHs have vegetable garden. Chat-vegetable Sorghum-coffee Maize-sorghum Have Home garden Sell all of it Sell part of it Use all for HH consumption Figure 5: Percentage of rural households owing and using vegetable gardens across livelihood zones of Kombolcha district, (Rural=562, Maize-Sorghum=117; Sorghum-Coffe=65; Chat-Vegetables=380)

11 Mean score of HFIAS across variables Table 1. Difference in mean scores of HFIAS of Kombolcha district households, 2014 (n=706) Grouping Variable HH head Rural Urban Mean(SD) t-test P- value Mean (SD) t-test P- value Male 8(5.542) (3.718) Female 8.91 (5.844) 7.83 (5.819) Vegetable garden No 7.97 (5.332) (4.513) Yes 8.54 (6.193) 4.33 (3.939) Domestic animals No 8.51 (5.241) (4.728) Yes 8.08 (5.645) 4.07 (4.025)

12 Mean score of HDDS across variables Table 2. Difference of mean scores of HDDS of Kombolcha district Households, 2014 (n=706) Categorizing variables Rural Urban Mean(SD) t-test P- value Mean (SD) t-test P- value HH head Male 5.5 (2.002) (1.899) Female 5.39 (2.035) 6.83 (2.001) Vegetable garden No 5.33 (1.990) (1.903) Yes 5.84 (2.003) 9.25 (2.17) Domestic animals No 5.54 (2.082) (1.925) Yes 5.47 (1.996) 8.21 (1.944)

13 In determining predictors of food insecurity in rural areas by HFIAS and HDD First block variables: Demographic control variables Second block: Vegetable garden ownership, wealth index, livelihood zones, source of income food for purchase (predictors of interest)

14 Predictors of food insecurity among rural HHS HFIAS 1 st block variables :F (16, 484) = 4.544; p < 001, R 2 =13.1 % 2 nd block variables :F (41, 459) = ; p <.001, R 2 =52.0%, R 2 =38.9%, F (25,459) =14.871; p<0.001 In the final model significant variables were: Wealth index (lowest tercile, β = 0.2) Women education (grade 1-12, β = ,p<0.05) HDDS 1 st block variables :F (16,484) = 4.821; p<.001, R 2 =13.7% 2 nd block variables: F (38,462) = 6.643, p <.001, R 2 =34.5% R 2 =20.7%, F (22,462) =6.643; p< In the final model significant predictors variables were Wealth index (Lowest, β = -.356) Livelihood zones (Maize-sorghum,β = -.180, p <.001)

15 Predictors of food insecurity among urban HHS HFIAS 1 st block variables : F (14, 129) = 4.943; p <.001, R 2 = 34.9 % 2 nd block variables: (F (30, 113) = 5.212; p <.001, R 2 = 58% R 2 = 23.1%, F (16,113) =3.895; p< In the final model significant predictor variables were Partner occupation (daily laborer, β = -.183, p <.05).) Wealth index (β =.229, p <.0 1) HDDS 1 st block variables : F (14, 129) = 1.627; p >.05, R 2 = 15.2 % 2 nd block variables: (F (29, 114) = 2.181; p <.01), R 2 = 35.7% R 2 = 20.5%, F (15,114) =2.425; p<0.01 In the final model significant predictor variables were Ownership of vegetable garden (β =.287, p <.01) Livestock owned(β =.273, p <.05)

16 Conclusion and recommendation Levels and severity of food insecurity was greater among rural households when measured using both tools. Contextualized interventions such as income generating activities at local level, integrating home gardening in routine health extension workers and Agricultural developmental workers Urban agriculture education services and training at HH level

17 References Addis Continental Institute of Public Health.2009.Community-Based Sub-Component of Ethiopian National Nutrition. Baseline Survey Report. Addis Continental Institute of Public Health, Addis Ababa. ADONG Patterns of food consumption and expenditure among households in the Limpopo province, South Africa. Ghent University. Amsalu M., Bekabil F. and Beyene T Emperical analysis of the determinants of rural households food security in Southern Ethiopia: case of Shashemene Distric.Basic Research Journal of Agricultural Science and Review ISSN Vol. 1(6), Asogwa BC. and Umeh J Food Insecurity Determinants among Rural Farm Households in Nigeria. International Conference on Ecology, Agriculture and Chemical Engineering (ICEACS'2012) December 18-19, Phuket (Thailand). Bashir, M.K., Schilizzi, S. and Pandit, R.2012.The determinants of rural household food security: The Case of Landless Households of the Punjab, Pakistan, Working Paper 1208,. Crawley,Australia: School of Agricultural and Resource Economics, University of Western Australia. Birhane, T., Shiferaw, S., Hagos S. and Mohindra K.S. (2014). Urban food insecurity in the context of high food prices: a community based cross sectional study in Addis Ababa, Ethiopia. BMC Public Health. 14:680 DOI: / Bremner, J Population and food security: Africa s challenge Connecticut Ave., NW Washington, DC USA: population reference bureau.

18 Thank you