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Multivariate Statistical Analysis of Economic Shocks Perception in A Growing Economy (Case Study – Abia State)
Subject area: Science,Engineering and Technology · Area of research: Multivariate Analysis
DOI: 10.64388/IREV9I12-1719081
Abstract
A sample-based procedure for selecting an optimal subset of variables (shocks) for a cross-sectional survey is studied. Often, researchers are faced with the problem of selecting optimum subsets of variables in the midst of enormity of variables that will very best predict a model. Such inquiry traverse through biological taxonomy, medical diagnosis, marketing, personal behavior and attitude studies which either by necessity or design are categorical multivariate data and will transform to informative data which are dichotomous or qualitative. This study analyzed 17 economic shock variables across a sample of 750 households. Utilizing the expansion of [2], the study modeled the joint probability distribution of binary shock responses. The Kullback-Leibler Divergence statistic (2NI), based on the framework by [7], the hypothesis of shock independence was tested. Linear Discriminant Analysis (LDA) was subsequently used to determine misclassification probabilities and validate a parsimonious model.The findings reveal a significant departure from independence (2NI = 143.79, p < 0.01), indicating that shocks such as Herdsmen Vandalization (X15) and Crop Failure (X11) are highly covariant (ρ = 0.31). Although Death of Household Head (X1) had the highest individual prevalence (20%), the parsimonious model focusing on X1, X11, and X15 yielded an Apparent Error Rate (APER) of 0.13%, suggesting these three variables are nearly perfect predictors of household vulnerability. The study concludes that economic vulnerability in Abia State is driven by a "syndrome of shocks.", and recommends policy interventions to prioritize security-linked agricultural insurance and social safety nets for bereaved households to mitigate the most severe impacts effectively.
Keywords
Agricultural Production, Economic Growth, Shocks, Variable selection, Households.
References
[1] Ajibefun, M. B. (2018). Social and Economic Effects of the Menaace of the Fulani Herdsmen Crisis in Nigeria. Journal of Educational and Social Research. Vol 8 No 2 133 - 139
[2] Bahadur, R. R. (1961). A representation of the joint distribution of responses to n dichotomous items. In H. Solomon (Ed.), Studies in Item Analysis and Prediction (pp. 158-168). Stanford University Press.
[3] Cox, D. R (1972). The Analysis of Multivariate Binary Data. Journal of Applied Statistics. 21,
[4] Dercon, S. (2002). Insurance against poverty. Oxford: Oxford University Press.
[5] Fisher, R.A. (1936)” the use of Multiple Measurements in Taxonomic problems” Ann. Eugerics, 7, 179-188
[6] Friedman, M. (1957). A Theory of the Consumption Function. Princeton University Press.
[7] Goldstein, M., & Dillon, W. R. (1977). A Cluster-Analysis Test for Independence. Journal of the American Statistical Association, 72(357), 180-183.
[8] Hills, M. (1967) “Discrimination and Allocation with Discrete Data” J. Roy. State Soc. C16,237-250
[9] James, G., Witten, D., Hastie, T., &Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.
[10] Kullback, S., &Leibler, R. A. (1951). On Information and Sufficiency. The Annals of Mathematical Statistics, 22(1), 79-86.
[11] Morduch, J. (1999). Between the State and the Market: Can Informal Insurance Patch the Safety Net? World Bank Research Observer, 14(2), 187-207.
[12] Nigeria National Bureau of Statistics (NBS). (2024). Nigeria Poverty and Inequality Survey: Abia State Profile.
[13] Onyemachi, C. U., Onyeagu, S. I and Achusim, C. O (2014). A Procedure for Making Optimal Selection of Input Variables for Discrete Multivariate Discrimination. Paper presented at the Annual Conference of the Nigerian Statistical Association, Katsina
[14] Osuji G.A (2010), Evaluation of some Classification Procedures for Binary Variables PhD. Dissertation. Nnamdi Azikiwe University Awka, Nigeria.
How to cite this paper
@article{1719081,
author = {Chikezie David Chidi, Akpanta, Anthony Chukwudi, Onyemachi Uchechi Chris},
title = {Multivariate Statistical Analysis of Economic Shocks Perception in A Growing Economy (Case Study – Abia State)},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {12},
pages = {3828-3839},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719081.pdf},
abstract = {A sample-based procedure for selecting an optimal subset of variables (shocks) for a cross-sectional survey is studied. Often, researchers are faced with the problem of selecting optimum subsets of variables in the midst of enormity of variables that will very best predict a model. Such inquiry traverse through biological taxonomy, medical diagnosis, marketing, personal behavior and attitude studies which either by necessity or design are categorical multivariate data and will transform to informative data which are dichotomous or qualitative. This study analyzed 17 economic shock variables across a sample of 750 households. Utilizing the expansion of [2], the study modeled the joint probability distribution of binary shock responses. The Kullback-Leibler Divergence statistic (2NI), based on the framework by [7], the hypothesis of shock independence was tested. Linear Discriminant Analysis (LDA) was subsequently used to determine misclassification probabilities and validate a parsimonious model.The findings reveal a significant departure from independence (2NI = 143.79, p < 0.01), indicating that shocks such as Herdsmen Vandalization (X15) and Crop Failure (X11) are highly covariant (ρ = 0.31). Although Death of Household Head (X1) had the highest individual prevalence (20%), the parsimonious model focusing on X1, X11, and X15 yielded an Apparent Error Rate (APER) of 0.13%, suggesting these three variables are nearly perfect predictors of household vulnerability. The study concludes that economic vulnerability in Abia State is driven by a "syndrome of shocks.", and recommends policy interventions to prioritize security-linked agricultural insurance and social safety nets for bereaved households to mitigate the most severe impacts effectively.},
keywords = {Agricultural Production, Economic Growth, Shocks, Variable selection, Households.},
month = {June},
doi = {https://doi.org/10.64388/IREV9I12-1719081}
}