Determinants of participation in non-farm activities and household food security in rural communities: a case of Mnquma Local Municipality, Eastern Cape Province.
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Date
2024-01
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Publisher
University of Fort Hare
Abstract
Non-farm income generating activities enable rural households to buy food for subsistence while still allowing their land to be used for food crops. This increases both the number of inputs that are purchased and the number of inputs that are important for enterprise operations, growth and expansion. However, agricultural activities continue to improve rural households’ quality of life, especially the poorest, in a way that is sustainable in terms of the economy, society, and the environment of most less developed countries including South Africa. In order to achieve the development of the rural non-farm economy, the involvement of farmers cannot be ignored. The study seeks to analyse the non-farm income generating activities and household food security in rural parts of Mnquma Local Municipality in the Eastern Cape Province. More specifically, the study seeks to analyse various income diversification activities and their contribution on rural households’ income, the factors affecting the choice of rural households’ participation in income generating (livelihood) activities as well as their effects on household food security.
A multistage random sampling technique was used to select a sample of 398 rural households. Survey participants were dispersed among Butterworth, Centane and Ngqamakwe towns of Mnquma Local Municipality. Two wards in each town were chosen to reflect the range in town standard of living. Five administrative areas that represented the wards average level of living were chosen in each ward. Survey households were selected at random to participate in each administrative area. The dataset represents a well representative sample of households in Mnquma given the administrative areas stratification and household sampling scheme. Data was collected using structured questionnaires. To analyse the data collected on socio-economic and demographic characteristics of households, the study made use of descriptive statistics such as mean, standard deviation, percentages and frequency of occurrence. One-way analysis of variance (ANOVA) was used to analyse the data collected on various income diversification activities. ANOVA was used to test the difference in mean income of the three groups of farmers namely: farm, non-farm and both farm and non-farm income generating activities.
Indices on access to infrastructure and rural constraints were constructed using Principal Component Analysis (PCA) and these were variables in the multinomial regression analysis. The factors affecting the income diversification choice decision of rural households were estimated using the Multinomial Logistic Regression (MLR). National Poverty Line was employed to determine whether a household is food secure or food insecure. The effects of income diversification choice decision on household food security were examined using the Binary Logistic Regression (BLR).
The results reveal an almost equal gender representation with 51% males and 49% females. The average family size was 7, with 40% of the household head having secondary education and having completed at least an average of 10 years of formal education. The average monthly household income was R8 872, 88. The most observed occupation was found to be a combination of both non-farming and farming income activities with (49%); non-farm activities (33%) and farming (18%). Among revenue from enterprises, old age grants, pensions, child support grants, remittances as well as the combination of other sources of income, salaries and wages accounted for 37% of the household heads' non-farm income. Combined income sources represent the largest contributor and pension as the least contributor. The income of surveyed households is primarily derived from a combination of farm and non-farm incomegenerating activities.
One- way analysis of variance (ANOVA) was carried out to determine if there are significant differences between the mean incomes of farm, non-farm and both farm and non-farm income generating activities. The F-statistic showed a significant difference in the means of the groups compared to the variations within the groups. The findings revealed a statistically insignificant difference in the mean income of farm, non-farm, and both farm and non-farm incomegenerating activities at the 10% probability level, which was bigger than the significant P-value of 0.05. Therefore, there was insufficient data to conclude that the means of the households' income diversification activities differed in a way that was statistically significant. The study is unable to reject the null hypothesis. The Principal Component Analysis (PCA) model for rural constraints kept three (3) variables with Eigenvalues greater than one. The results showed that the three variables kept in the model explained 62.51% of the variance. These variables are lack of infrastructure, access to markets and lack of productive assets. PCA results on infrastructure accessibility revealed three (3) variables that were preserved, described 82.34% of the variance namely roads, electricity as well as water and sanitation.
The Multinomial Logistic Regression (MLR) revealed that number of years spent in school, employment status of the household head and rural constraints clearly show a significant relationship on the choice of rural households to engage in farming activities. The results reveal a significant relationship between gender, age, employment status, access to extension services, and infrastructure accessibility and rural households' decisions to engage in non-farm income activities. These variables were statistically significant at p < 0.05 level. The marginal effects reveal that male-headed households are less likely to diversify their income through farming and non-farming activities, with a decrease in this likelihood. Age also affects this decision, with higher educational attainment leading to an increase in farming activities. Employment status also affects this decision, with a 26% increase in the likelihood of both income activities.
Rural constraints and Infrastructure accessibility also affects this decision of household participation in both farming and non-farming activities. The National Poverty Line demonstrated that the vast majority of respondents (47%) fall within the upper bound poverty line, food poverty line (38%) and lower bound poverty line (15%). Upper bound poverty line households have access to enough food and non-food products. On the other hand, households with incomes below the upper limit of the poverty level are considered to be food insecure. The poverty line findings reveal that households within the food poverty line and lower bound poverty line account for 53% of the total, indicating a higher level of food insecurity.
The Binary Logistic Regression show that household income and household size have an impact on household food security and are statistically significant at 1% probability level. The marginal effects revealed that household income and household size significantly impact food security, with income increasing by 0.005% and size decreasing by 6% for larger households, thereby affecting food availability to each member. According to the study, lack of infrastructure, access to markets, a lack of productive assets, government regulations, high competition from other enterprises, job insecurity, insufficient access to credit facilities, and a lack of awareness and trainings all impede the effectiveness of income diversification activities in improving food security in Mnquma Local Municipality. Therefore, local, provincial and national governments should invest in the development and evaluation of rural economic development policies that support farm, non-farm, and food security income.
Description
MSc Dissertation
Keywords
Income, Food security, Principal components analysis
Citation
Mvango, S., 2024. Determinants of participation in non-farm activities and household food security in rural communities: a case of Mnquma Local Municipality, Eastern Cape Province. MSc dissertation. University of Fort Hare