Determinants of Household Poverty Dynamics in Rural Regions of the Eastern Cape Province, South Africa

Poverty has always been studied in a world of certainty. However, if the aim of studying poverty is not only improving the well-being of households who are currently poor, but also preventing people from becoming poor in the future, a new forward looking perspective must be adopted. For thinking about appropriate forward-looking anti-poverty interventions (i.e. interventions that aim to prevent or reduce future poverty rather than alleviate current poverty), the critical need then is to go beyond a cataloging of who is currently poor and who is not, to an assessment of households’ vulnerability to poverty. This study analyses a panel dataset on a representative sample of 150 rural households interviewed in 2007 and 2008 in the Amathole District Municipality of the Eastern Cape Province to empirical assess the dynamics of poverty and estimate the determinants of households’ vulnerability to poverty. The result of the study indicates that the number of vulnerable households is significantly larger than for the currently poor households; the vulnerability index was found to be 0,62 compared to 0,56 headcount index in 2008. This implies that while 56 percent of the sampled households are poor (ex post) in 2008, 62 percent are vulnerable to becoming poor (ex ante) in future. The result of the Probit model shows that the age, level of education and occupation of the household head, dependency ratio, exposure to idiosyncratic risks and access to credit are statistically significant in explaining a households’ vulnerability to poverty.Poverty, vulnerability, poverty dynamics, risks, rural households, Food Security and Poverty,

Determinants of Household Poverty Dynamics in Rural Regions of the Eastern Cape Province, South Africa

Poverty has always been studied in a world of certainty. However, if the aim of studying poverty is not only improving the well-being of households who are currently poor, but also preventing people from becoming poor in the future, a new forward looking perspective must be adopted. For thinking about appropriate forward-looking anti-poverty interventions (i.e. interventions that aim to prevent or reduce future poverty rather than alleviate current poverty), the critical need then is to go beyond a cataloging of who is currently poor and who is not, to an assessment of households’ vulnerability to poverty. This study analyses a panel dataset on a representative sample of 150 rural households interviewed in 2007 and 2008 in the Amathole District Municipality of the Eastern Cape Province to empirical assess the dynamics of poverty and estimate the determinants of households’ vulnerability to poverty. The result of the study indicates that the number of vulnerable households is significantly larger than for the currently poor households; the vulnerability index was found to be 0,62 compared to 0,56 headcount index in 2008. This implies that while 56 percent of the sampled households are poor (ex post) in 2008, 62 percent are vulnerable to becoming poor (ex ante) in future. The result of the Probit model shows that the age, level of education and occupation of the household head, dependency ratio, exposure to idiosyncratic risks and access to credit are statistically significant in explaining a households’ vulnerability to poverty

Adoption of striga (striga hermonthica) Management Technologies in Northern Nigeria

This study examined the adoption of Integrated Striga Management (ISMA) technologies among maize farmers in Bauchi and Kano states of northern Nigeria. It employs a double-hurdle approach to analyse the factors influencing adoption and intensity of ISMA technologies among households, using cross-sectional data of 643 farmers from the two states. The results show that the estimated coefficients of exogenous income and proximity to extension office are negatively significant (P < 0.05), while higher total farm income, polygamous households, past participation in on-farm trials, awareness of the technology, contact with extension agents and access to cash remittances are positive and significant (P < 0.01), and are the most significant factors likely to influence ISMA technologies adoption. Marital status, household size, farm size and access to cash remittances are most significant factors influencing adoption intensity. Maize farmers in the study area who adopted ISMA technologies obtained higher output than the non-adopters, which resulted in a positive and significant effect on their total farm income. Hence, policies targeted at increasing maize productivity through Striga management need to include ISMA technologies as a potentially feasible option. The study recommends actions to improve farmers’ access to financial services to increase their liquidity. Nevertheless, the most immediate action will be improvement in farmers’ access to extension services as they have proved to be a reliable source of information in the rural areas