Population characteristics vary significantly across the world. Understanding their spatial distribution and the associations between them is essential for interpreting population data and applying the Demographic Transition Model.
| Characteristic | Definition | Formula | Global Average (approx.) |
|---|---|---|---|
| Birth rate (CBR) | Number of live births per 1,000 people per year | (Births ÷ Population) × 1,000 | ~17–18‰ |
| Death rate (CDR) | Number of deaths per 1,000 people per year | (Deaths ÷ Population) × 1,000 | ~7–8‰ |
| Natural increase rate (NIR) | Net population growth from births minus deaths | CBR − CDR (÷ 10 = %) | ~1.0% |
| Infant mortality rate (IMR) | Deaths of children under 1 year per 1,000 live births | — | ~28‰ |
| Total fertility rate (TFR) | Average number of children a woman would have in her lifetime | — | ~2.3 |
| Life expectancy at birth (LE) | Average years a newborn is expected to live | — | ~73 years |
Replacement-level fertility = ~2.1 children per woman (higher in high-mortality contexts)
Birth rate and fertility rate
- Highest in sub-Saharan Africa: Niger TFR ~6.9; Mali ~5.9; Somalia ~6.1 (2022)
- High in parts of South Asia (Afghanistan ~4.6), Middle East (Yemen ~3.8)
- Low in Europe: Italy, Spain, South Korea TFR ~1.1–1.3 (well below replacement)
- Intermediate in South/Southeast Asia (India ~2.0, Indonesia ~2.2)
Death rate
- Paradox: high-income countries often have higher crude death rates than middle-income countries because their populations are older
- Low in Gulf states and Southeast Asia (young age structures → low CDR despite limited healthcare)
- Highest in very low-income countries: Sierra Leone, Chad, Central African Republic (~14–16‰)
- High in Eastern Europe (older populations, lifestyle diseases): Bulgaria, Serbia (~15‰)
Infant mortality rate
- Very low in high-income countries: Finland, Japan, Singapore <2‰
- Very high in sub-Saharan Africa: Sierra Leone ~78‰, Nigeria ~58‰ (2022)
- Strong negative correlation with GDP per capita, healthcare access, female education
Life expectancy
- Highest: Japan (84), Switzerland (84), Australia (83)
- Lowest: Central African Republic (55), Sierra Leone (54), Chad (55)
- Global gap between highest and lowest: ~30 years
Spatial association means that two or more distributions tend to co-occur in the same locations. Key associations:
| Positive Associations (co-occur) | Negative Associations (inverse) |
|---|---|
| High birth rate & high IMR | High birth rate & high GDP per capita |
| High IMR & low life expectancy | High LE & high IMR |
| High TFR & low female education | High female education & high TFR |
| Low LE & high CDR | High income & low CDR |
Why? These associations reflect the relationship between development level and demographic characteristics. Countries at earlier stages of demographic transition tend to have high fertility, high mortality and low life expectancy; advanced economies have low fertility and low mortality.
VCAA exams often present data tables, graphs, or choropleth maps requiring:
1. Identification of the pattern (which countries/regions are high/low?)
2. Description using spatial language (concentrated in, higher in, inverse relationship with)
3. Explanation using geographic factors (income, healthcare, education, culture, conflict)
KEY TAKEAWAY: Population characteristics are spatially associated with each other and with development level. Sub-Saharan Africa has the highest birth rates, IMRs and TFRs; Europe and East Asia have the lowest. Life expectancy and IMR are strongly inversely correlated with income.
EXAM TIP: Know the formulas and be able to calculate rates from raw data. Be precise with units: birth rate and death rate are per 1,000; NIR can be expressed as a percentage (subtract CDR from CBR and divide by 10).
STUDY HINT: Link each characteristic to its position in the Demographic Transition Model (KK 17). High CBR + high CDR = Stage 1/2; low CBR + low CDR = Stage 4/5. This framework connects all the characteristics.
