- The high population growth rate of \(3.32\%\) correlates with the highest fertility rate of \(6.06\).
- High fertility is the most direct cause of rapid population increase.
- While life expectancy is low (\(54\) years), it does not drive population growth in the same way as high birth rates.
- The best explanation is \(\boxed{\text{high fertility rate}}\).
Explanation
- Analyze the data table
To determine the best explanation for Uganda's high population growth rate, we first analyze the provided data in the table. We identify the column representing Uganda, which corresponds to the highest population growth rate. Comparing the values:
- Population growth rates: \(-0.10\%\), \(3.32\%\), \(1.07\%\), \(1.10\%\). The value \(3.32\%\) is clearly the highest.
- Associated Fertility rate for this column: \(6.06\).
- Associated Life expectancy for this column: \(54\) years.
- Compare fertility and life expectancy across populations
We now compare these figures to the other provided data:
- Fertility rates: \(1.39\), \(6.06\), \(2.25\), \(2.45\). The value \(6.06\) is the highest, indicating a significantly high birth rate compared to other populations.
- Life expectancies: \(84\), \(54\), \(77\), \(68\) years. The value \(54\) is the lowest, suggesting lower life expectancy.
- Evaluate the factors and reach a conclusion
In demography, population growth is primarily driven by the difference between birth rates and death rates. A high fertility rate (the number of children born per woman) is the most direct factor contributing to rapid population expansion. While a low life expectancy is noted in the data for this population, it acts as a limiting factor on population size rather than a driver of high growth. Therefore, the high fertility rate is the primary reason for the observed high growth rate of \(3.32\%\). Comparing this to the given options, 'high fertility rate' is the most accurate explanation.
Examples
This analysis helps students understand demographic transitions, where countries often experience high population growth when birth rates (fertility) remain high while mortality rates start to decline. By interpreting tables, we can identify which factors most significantly influence societal changes, providing a foundation for studying sociology, geography, and economics. For example, if a region has a fertility rate of \(f = 6.0\) and a death rate of \(d = 1.0\), the population growth can be approximated as \(g \approx f - d\), demonstrating how fertility acts as the engine of growth.