•   Adanma Ngozi Ohia

  •   Joel Chiadikobi Nwaubani

  •   Uchechi Mgbafor Ezeji

  •   Uzokwe Chinwe Adaugo

  •   Christiana Uzoma Ezechukwu

  •   Opara Peace


Statistics on population change and the structure of population are increasingly used to support policymaking and to provide the opportunity to monitor demographic behaviour within political, economic, social and cultural contexts. Specifically, this concerns demographic developments that focus on a likely reduction in the relative importance of the working age population and a corresponding increase in the number of older persons. These statistics may be used to support a range of different analyses, including studies relating to population ageing and its effects on the sustainability of public finance and welfare, the evaluation of fertility as a background for family policies, or the economic and social impact of demographic change. This research aims to highlight the population change in twenty-five countries of the European Union. We consider the use of categorical data analysis to estimate the population change in EU25: absolute numbers and crude rates from 2003-2017. The data used in this study are from the Eurostat/World population prospects and estimated on actual base year from. Since the main focus is to have a better understanding of the population change in EU25, the analysis of association table (ANOAS) is given in order to ascertain the percentage of the data which is covered by each model. We find and estimate the association model with the best fit and in conclusion we find out that the Row-Column Effects Association Model (RC) of the multivariate model (M=4) has the best fit among all - covering a total of 99.9% of the data observed.

Keywords: Association models, Log-linear and non-linear models, population and EU25


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How to Cite
Ohia, A. N., Nwaubani, J. C., Ezeji, U. M., Adaugo, U. C., Ezechukwu, C. U., & Peace, O. (2020). Using Categorical Data Analysis to Estimate Population Change in EU25: Absolute Numbers and Crude Rates. European Journal of Business and Management Research, 5(5). https://doi.org/10.24018/ejbmr.2020.5.5.481

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