Assignment name:

 

Identification of household characteristics relevant for poverty targeting in the Multi-Category SCT Programme in Luwingu, Serenje and Zambezi

 

Approx. value of the contract (in current US$):

 

USD 0 (Corporate Social Responsibility)

Country: Zambia

Location within country: Lusaka.

 

Duration of assignment (months):

1 Months

 

 

Name of Client:

Ministry of Community Development, Mother and Child Health

 

Total No. of staff-months of the assignment:

1.0

 

Contact Person:

Stanfield Michelo, Director, Department of Social Welfare, Ministry of Community Development, Mother and Child Health, Lusaka, Zambia. Tel. No. +260-97-780-9900

 

 

Start date (month/year): 2010

Completion date (month/year): 2011

 

No. of professional staff-months provided:

1.0

 

Name of associated Consultants, if any:

 

None

 

 

 

Name and designation of senior professional staff:

Gelson Tembo (Project Coordinator); Liseteli Ndiyoi (IT Manager)

Description of Project:

 

Use data from the 2006 Living Conditions Monitoring Survey (LCMS), and Principal Components Analysis (PCA) and a poverty-based vulnerability index analysis to help the Ministry of Community Development, Mother and Child Health (MCDMCH) to identify household socio-economic characteristics that are highly correlated with poverty in the Multi-Category SCT Program (MCP) districts of Luwingu, Serenje and Zambezi. The results of this analysis were then used to identify the potential beneficiaries of the SCT intervention. One of the characteristics identified by this analysis was hosting og orphaned and vulnerable children (OVCs). Thus, this variant of the SCT program is sometimes referred to as the OVC model.

 

Description of actual services provided by your staff within the assignment:

•     Sourced LCMS data and questionnaires from the Central Statistical Office

•     Computed the relevant variables

•     Constructed a poverty-based vulnerability index, and a more qualitative vulnerability index based on principal components analysis (PCA)

•     By comparing the two indices, through correlation analysis, confirmed that the two indices were highly correlated

•     Equiped with this proof, designate the variables contributing significantly to the PCS index to be most important determinants/indicators of poverty. Thus, targeting could proceed based on this observable characteristics of the household