Estimation Of Long-Run Probability Of Zero Offspring Using Branching Processes In Varying Environment
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Date
2022-04
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Publisher
University Of Ghana
Abstract
There are many challenges associated with both young and ageing population.
If a country experiences a younger population, there’s a tendency for high
unemployment rates and social vices. On the other hand, an ageing population
typically results in a low labour force and high dependency ratios. Countries
that tend to solve the problem of a young population initiate policies to control
birth rates. However, these policies gradually lead to an ageing population before
being revised, due to high costs associated with regular monitoring of population
dynamics. Therefore, there is a need to develop a less costly method to monitor
population dynamics and estimate the expected time to revise population policies.
This study employed a more general theorem and a corollary based on ideas
of probability generating functions in a branching process to come out with a
method to solve the problem. The method was applied to both hypothetical and
empirical data in the branching processes. The empirical data were obtained from
Demographic and Health Surveys (DHS) for seven selected countries. The results
from the study revealed that under certain closeness conditions, both constant and
random environments yield similar results. Hence, using the method under the
constant environment, which is easier, is a step in the right direction; otherwise,
the proposed method for the random environment should be used. Burkina Faso
recorded the youngest population, while Philippines recorded the least country
with younger population. Results from the spectral analysis estimated that
population policy for the selected countries should be revised between 34 to
40 years. The study recommended that the proposed model should be used
to monitor population dynamics regularly. Also, population policies should
be guided by appropriate time frame depending on the country’s demographic
characteristics.
Description
PhD. Statistics
Keywords
Offspring, Branching Processes