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Simple versus composite indicators of socioeconomic status in resource allocation formulae: the case of the district resource allocation formula in Malawi.

Manthalu G, Nkhoma D, Kuyeli S - BMC Health Serv Res (2010)

Bottom Line: District proportions of national population weighted by both the simple and composite indicators were then calculated for all districts and compared.District allocations were also calculated using the two approaches and compared.However, the single variable indicator is favourable for its ease of computation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Planning and Policy Development, Ministry of Health, P,O Box 30377, Lilongwe 3, Malawi. gmanthalu@yahoo.com

ABSTRACT

Background: The district resource allocation formula in Malawi was recently reviewed to include stunting as a proxy measure of socioeconomic status. In many countries where the concept of need has been incorporated in resource allocation, composite indicators of socioeconomic status have been used. In the Malawi case, it is important to ascertain whether there are differences between using single variable or composite indicators of socioeconomic status in allocations made to districts, holding all other factors in the resource allocation formula constant.

Methods: Principal components analysis was used to calculate asset indices for all districts from variables that capture living standards using data from the Malawi Multiple Indicator Cluster Survey 2006. These were normalized and used to weight district populations. District proportions of national population weighted by both the simple and composite indicators were then calculated for all districts and compared. District allocations were also calculated using the two approaches and compared.

Results: The two types of indicators are highly correlated, with a spearman rank correlation coefficient of 0.97 at the 1% level of significance. For 21 out of the 26 districts included in the study, proportions of national population weighted by the simple indicator are higher by an average of 0.6 percentage points. For the remaining 5 districts, district proportions of national population weighted by the composite indicator are higher by an average of 2 percentage points. Though the average percentage point differences are low and the actual allocations using both approaches highly correlated (rho of 0.96), differences in actual allocations exceed 10% for 8 districts and have an average of 4.2% for the remaining 17. For 21 districts allocations based on the single variable indicator are higher.

Conclusions: Variations in district allocations made using either the simple or composite indicators of socioeconomic status are not statistically different to recommend one over the other. However, the single variable indicator is favourable for its ease of computation.

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District allocations made using formulae that employ either simple or composite indicators of socioeconomic status.
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Figure 2: District allocations made using formulae that employ either simple or composite indicators of socioeconomic status.

Mentions: District allocations made using formulae that contain the single variable and composite indices are depicted in figure 2. The three dimensional graph shows on the left vertical axis allocations in Malawi kwacha made using the two approaches and on the right vertical axis differences in the allocations expressed in percentage terms. For 21 districts, allocations made using the formula that employs the single variable indicator are higher. Percentage differences in actual allocations exceed 10% for 8 districts namely; Blantyre, Dedza, Kasungu, Lilongwe, Machinga, Mchinji, Ntcheu and Phalombe and have an average of 4.2% for the remaining 17. A spearman rank correlation test of the allocations made using the two approaches shows high correlation, a ρ of 0.96 significant at the 1% significance level.


Simple versus composite indicators of socioeconomic status in resource allocation formulae: the case of the district resource allocation formula in Malawi.

Manthalu G, Nkhoma D, Kuyeli S - BMC Health Serv Res (2010)

District allocations made using formulae that employ either simple or composite indicators of socioeconomic status.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2823741&req=5

Figure 2: District allocations made using formulae that employ either simple or composite indicators of socioeconomic status.
Mentions: District allocations made using formulae that contain the single variable and composite indices are depicted in figure 2. The three dimensional graph shows on the left vertical axis allocations in Malawi kwacha made using the two approaches and on the right vertical axis differences in the allocations expressed in percentage terms. For 21 districts, allocations made using the formula that employs the single variable indicator are higher. Percentage differences in actual allocations exceed 10% for 8 districts namely; Blantyre, Dedza, Kasungu, Lilongwe, Machinga, Mchinji, Ntcheu and Phalombe and have an average of 4.2% for the remaining 17. A spearman rank correlation test of the allocations made using the two approaches shows high correlation, a ρ of 0.96 significant at the 1% significance level.

Bottom Line: District proportions of national population weighted by both the simple and composite indicators were then calculated for all districts and compared.District allocations were also calculated using the two approaches and compared.However, the single variable indicator is favourable for its ease of computation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Planning and Policy Development, Ministry of Health, P,O Box 30377, Lilongwe 3, Malawi. gmanthalu@yahoo.com

ABSTRACT

Background: The district resource allocation formula in Malawi was recently reviewed to include stunting as a proxy measure of socioeconomic status. In many countries where the concept of need has been incorporated in resource allocation, composite indicators of socioeconomic status have been used. In the Malawi case, it is important to ascertain whether there are differences between using single variable or composite indicators of socioeconomic status in allocations made to districts, holding all other factors in the resource allocation formula constant.

Methods: Principal components analysis was used to calculate asset indices for all districts from variables that capture living standards using data from the Malawi Multiple Indicator Cluster Survey 2006. These were normalized and used to weight district populations. District proportions of national population weighted by both the simple and composite indicators were then calculated for all districts and compared. District allocations were also calculated using the two approaches and compared.

Results: The two types of indicators are highly correlated, with a spearman rank correlation coefficient of 0.97 at the 1% level of significance. For 21 out of the 26 districts included in the study, proportions of national population weighted by the simple indicator are higher by an average of 0.6 percentage points. For the remaining 5 districts, district proportions of national population weighted by the composite indicator are higher by an average of 2 percentage points. Though the average percentage point differences are low and the actual allocations using both approaches highly correlated (rho of 0.96), differences in actual allocations exceed 10% for 8 districts and have an average of 4.2% for the remaining 17. For 21 districts allocations based on the single variable indicator are higher.

Conclusions: Variations in district allocations made using either the simple or composite indicators of socioeconomic status are not statistically different to recommend one over the other. However, the single variable indicator is favourable for its ease of computation.

Show MeSH