Measuring the Socio-economic Status of Adopters of Indigenous Chicken in Mwala and Machakos Central, Kenya: Application of Principal Component Analysis
American Journal of Theoretical and Applied Statistics
Volume 9, Issue 6, November 2020, Pages: 267-271
Received: Sep. 27, 2020;
Accepted: Oct. 24, 2020;
Published: Nov. 4, 2020
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Ngetich Titus, Department of Mathematics, Multimedia University of Kenya, Nairobi, Kenya
Karanjah Anthony, Department of Mathematics, Multimedia University of Kenya, Nairobi, Kenya
Cheruiyot Kipkoech, Department of Mathematics, Technical University of Kenya, Nairobi, Kenya
This study focused on impact assessment of indigenous Chicken (KALRO Improved Chicken) in terms of the Socio-economic Status of the beneficiaries. Data analyzed comprised of household assets owned and housing characteristics. Studies have been done to assess the impact of new agricultural technologies to the beneficiaries, however, the measurement of the impact indicator (Socio-economic Status) has been a challenge. Studies rely on monetary data (reported income and expenditure), however the collection of high quality (precise and accurate) income data and expenditure is difficult and requires more resources particularly for household surveys, this approach is usually affected by unreliable reportage and measurement error, high-quality income data and expenditure will still produce biased estimates of household socio-economic status because they measure economic flows which are stochastic and include temporary income shocks. This study used principal component analysis model (PCA) to create an asset index to measure Socio-economic status. It was concluded that PCA is reliable in creating an asset index for measuring Socio-economic status, the results showed that about 40% of the households in Machakos County were poor which implies a small decline compared to 42.6% reported on  conducted by Kenya National Bureau of Statistics.
Measuring the Socio-economic Status of Adopters of Indigenous Chicken in Mwala and Machakos Central, Kenya: Application of Principal Component Analysis, American Journal of Theoretical and Applied Statistics.
Vol. 9, No. 6,
2020, pp. 267-271.
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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