Disaggregating Poverty Estimates to Sub-County Level using Small Area Model: Application of EBP

Topic: Risk, prevention and care in population studies

The results demonstrate similar poverty patterns as per county data findings on Kenya poverty report, 2022. The coefficients of variation (CV) for Small Area Estimation (SAE) estimates were consistently lower across most sub-counties. The bootstrap-based measures of uncertainty, including CV and mean squared error (MSE), confirmed that the Empirical Best Predictor (EBP) estimates were more precise. Unique Contribution to Theory, Policy and Practice: The study recommended that National Statistical Organizations (NSO) implement SAE to routinely produce disaggregated poverty and development indicators at the lowest administrative levels, including Enumeration Areas. By adopting this methodology, the high costs associated with large-scale surveys currently required to achieve sufficient sample sizes for such estimates can reduce significantly. Furthermore, the study emphasizes that strengthening capacity in SAE through targeted training is essential to ensure methodological rigor, maintain data quality, and uphold high statistical standards.

Key findings

• The results demonstrate similar poverty patterns as per county data findings on Kenya poverty report, 2022.
• The coefficients of variation (CV) for Small Area Estimation (SAE) estimates were consistently lower across most sub-counties.
• The bootstrap-based measures of uncertainty, including CV and mean squared error (MSE), confirmed that the Empirical Best Predictor (EBP) estimates were more precise.
• Unique Contribution to Theory, Policy and Practice: The study recommended that National Statistical Organizations (NSO) implement SAE to routinely produce disaggregated poverty and development indicators at the lowest administrative levels, including Enumeration Areas.
• By adopting this methodology, the high costs associated with large-scale surveys currently required to achieve sufficient sample sizes for such estimates can reduce significantly.

Why it matters

This study used a small area estimation method to produce more precise poverty estimates at the sub-county level in Kenya. It provides no information on treating, curing or preventing Alzheimer's disease.

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Study details

• Evidence strengthLow

Read the original

• DOI: 10.47941/ijpid.3494
• Original source →
• Free full text: International Journal of Poverty Investment and Development →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{onchomba2026-1f783a9e,
  author  = {Onchomba, Denis O. and Kamanu, Timothy K. K.},
  title   = {{Disaggregating Poverty Estimates to Sub-County Level using Small Area Model: Application of EBP}},
  journal = {International Journal of Poverty, Investment and Development},
  year    = {2026},
  doi     = {10.47941/ijpid.3494},
  url     = {https://doi.org/10.47941/ijpid.3494}
}

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