JHASS
Estimation of the Population Size in Tanzania from 1967 to 2112 Using Logistic and Exponential Models: A Comparative Analysis
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Abstract
This study offers a comprehensive comparative analysis of Tanzania’s population growth by applying both exponential and logistic growth models to empirical data spanning from 1967 to projected figures for 2112. Reliable data were sourced from national census records and demographic projections issued by recognized authoritative agencies. The analysis aims to understand historical patterns in Tanzania's population increase and to evaluate plausible future trajectories under different growth assumptions. The exponential model, widely used for initial population studies, assumes a constant growth rate without imposing any resource constraints, thereby representing a scenario of unlimited growth. In contrast, the logistic model introduces the crucial concept of carrying capacity, setting an upper boundary to growth as determined by environmental limitations and socio-economic factors. This adjustment allows for a more realistic and nuanced prediction, especially over extended periods. Model parameters were estimated through quantitative methods, including curve fitting and regression analysis against available data sets. The results reveal that while the exponential model closely fits recent and historical trends due to sustained high growth rates, it increasingly diverges from projected realities as resource constraints and policy interventions begin to influence demographic outcomes. The logistic model, by accounting for these factors, projects a gradual deceleration of growth as Tanzania’s population approaches its estimated carrying capacity. Comparative analysis outlines the strengths and limitations inherent in each approach. The exponential model is sensitive to recent trends but risks overestimating future population sizes in the face of ecological and socio-economic constraints. Conversely, the logistic model provides a conservative forecast but may understate short-term fluctuations and policy-driven variances. Policy implications of these findings are significant for resource planning, infrastructure development, and implementation of sustainable development initiatives. Accurately modeling population growth is essential for anticipating future demands on health, education, and social services while ensuring environmental sustainability.
Keywords
Comparative analysis; logistic model; exponential growth model; population
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How to cite this paper
Estimation of the Population Size in Tanzania from 1967 to 2112 Using Logistic and Exponential Models: A Comparative Analysis
How to cite this paper: Emmanuel Deogratias, Geoffrey Karugila. (2025) Estimation of the Population Size in Tanzania from 1967 to 2112 Using Logistic and Exponential Models: A Comparative Analysis. Journal of Humanities, Arts and Social Science, 9(6), 1201-1218.
DOI: http://dx.doi.org/10.26855/jhass.2025.06.026
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