IB ESS Topic 8.1 SL Notes
8.1 Human populations
Guiding questions (conceptual):
How can the dynamics of human populations be measured and compared?
To what extent can the future growth of the human population be accurately predicted?
Inputs to a Human Population
Births and immigration are inputs to a human population.
Crude birth rate (CBR): number of live births per 1,000 people per year.
Immigration rate: number of immigrants per 1,000 population per year.
These inputs can be measured at various scales (town, country, region, global).
Outputs from a Human Population
Deaths and emigration are outputs from a human population.
Crude death rate (CDR): number of deaths per 1,000 people per year.
Emigration rate: number of emigrants per 1,000 population per year.
These outputs can be measured at multiple scales (town, country, region, global).
Total Fertility Rate (TFR)
Total fertility rate (TFR): the average number of births per woman of childbearing age.
If fertility rates > 2.0, population tends to increase; if < 2.0, population tends to decrease, because two parents are ideally replaced by two children to maintain a stable population.
The replacement level is around 2.0 (on average) but actual replacement fertility is higher due to mortality.
Replacement Fertility and Mortality (Reality)
Replacement fertility ranges from about 2.03 in more developed regions to 2.16 in less developed regions due to infant/child mortality.
Symbolically:
ext{Replacement Fertility} \in [2.03, 2.16]
Life Expectancy
Life expectancy: the average number of years a person is expected to live, usually from birth, assuming demographic factors remain unchanged.
Calculating Natural Increase Rate (NIR)
Natural Increase Rate (NIR) can be expressed in two ways:
Per 1,000 population: NIR = CBR - CDR
As a percentage: NIR ext{(\%)} = \frac{CBR - CDR}{10}
Example interpretation: if CBR = 6 and CDR = 2, then
NIR (per ext{ }1{,}000) = 6 - 2 = 4
NIR = \frac{6 - 2}{10} = 0.4\% per year
Calculating Doubling Time (DT)
Doubling time is the number of years it would take a population to double at the current growth rate (NIR).
Formula: DT = \frac{70}{NIR}
Note: use NIR in percent for this calculation.
Example: If NIR = 4.8\%, then
DT = \frac{70}{4.8} \approx 14.5 \text{ years}
Population Calculations (Watch example concept)
Example given: a population grows by 0.4\% per year.
If 2010: CBR = 6, CDR = 2, population = 5{,}000{,}000
NIR = (6 - 2)/10 = 0.4\% per year
This demonstrates how NIR links inputs (CBR) and outputs (CDR) to growth.
Population Prediction (UN Scenarios)
Global population follows a growth curve; models predict future levels.
UN projection models indicate three scenarios linked to future fertility rates:
High Fertility Scenario: fertility remains higher than the median; slower declines than expected; higher peak population.
UN Probabilistic Median: considered most likely; central estimate for peak population and timing; accounts for current trends in fertility, mortality, and migration.
UN Low Fertility Scenario: fertility declines faster than expected; earlier peak population and a lower overall peak.
Uncertainty about future fertility drives scenario differences.
Factors Influencing Population Predictions (Discussion prompts)
Students discuss factors that could push populations toward each UN scenario (e.g., education, health, policy, migration, culture).
UN Fertility Scenarios in Detail
UN High Fertility Scenario: slower declines in fertility than the median; potentially larger peak population.
UN Probabilistic Median: most likely projection; central estimate for peak and timing.
UN Low Fertility Scenario: faster declines in fertility; earlier and lower peak.
Models to Predict Population Change: Benefits and Problems
Benefits:
Simple, generalised tools to highlight key drivers.
Useful for planning investments in education, healthcare, and infrastructure.
Help governments forecast population numbers for planning.
Problems:
Can be complex and hard to use.
Other factors (wars, disease, disasters) can influence populations.
Why Do People Have Large Families? (Non-economic drivers)
The decision to have children is not necessarily correlated with GDP or personal wealth.
Why Do People Have Large Families? (Common drivers)
High infant and childhood mortality
Security in old age
Children as an economic asset in agricultural societies
Status of women
Unavailability of contraception
Population and Migration Policies
Population policies can directly manage growth rates (anti-natalist or pro-natalist) via birth rates, or they can influence immigration/emigration.
Policies may rely on cultural, religious, economic, social, and political factors.
Directly Influencing Population Growth
Direct financial incentives to have more children (e.g., tax breaks, parental leave) can increase growth.
Examples: large child benefits, extended parental leave, tax breaks for families.
Providing sex education in schools can decrease growth.
Improve access to contraception can decrease growth.
Cultural/Religious Influences on Contraception Usage
Cultural or religious norms can raise or lower contraception use, affecting fertility.
In some cultures, boys may be valued more, influencing desired family size.
Family size can be linked to old-age security or expectations of family support.
Anti-natalist Case Study – China (Policy and Effects)
One-child policy: 1979–2015. Limited most families to one child to curb population growth.
Claimed the policy prevented roughly 400 million births (a commonly cited figure).
Policy relaxed in 2015 to allow two children; later moves to three children (2021–2022 policy shifts).
Critics argue that fertility declines could have occurred due to urbanization, female education, and more women in the workforce, even without the policy.
China’s Fertility and Policy Shift
China’s TFR in 2022 was 1.18, reflecting aging population pressures.
As a result, policy moved toward a three-child policy to bolster a younger workforce and fund pensions.
Pro-natalist Case Study – Singapore
Singapore shifted from anti-natalist to pro-natalist policies in the 1980s to 1990s.
Policies included incentives to have more children, but fertility rates remained low.
Factors: high cost of living, women remaining active in the workforce, choices about childbearing.
Indirectly Influencing Population Growth
Indirect strategies shown to reduce growth:
Increased investment in education (especially for girls).
Gender equality in the workforce.
Improved public health care reducing death rates.
Result: higher education and better employment opportunities tend to reduce birth rates.
Urbanisation and Population Growth
After urbanisation, birth rates often fall as women enter jobs in formal/informal sectors.
Awareness and use of family planning can reduce birth rates.
Policies that target female education and female participation in the job market are effective at reducing population pressure.
Immigration as a Population Strategy
Policies can encourage immigration to fill labor market gaps in countries with falling birth rates.
Migrant workers attracted by employment, pay, healthcare, and education can support the economy.
Migration can also rise due to political instability or conflict.
Immigration Policies: Effects and Examples
Positive impacts on destination country: migrants join workforce, contribute taxes, and support pensions; can send money home.
Destination countries with aging populations often use immigration to balance demographics (e.g., many European countries like the Netherlands).
Some countries restrict immigration to protect labor markets (e.g., some Asian economies prefer limited inflows to protect domestic jobs).
Age–Sex Pyramids and Population Structure
Age–sex pyramids model and compare population structure by age group and sex.
Pyramids can be measured in absolute numbers or as a percentage of total population.
They illustrate the proportion of each gender in each age group.
Age–Sex Pyramids (Examples and Interpretations)
Nigeria: typically a broad base indicates a young population with high birth rates.
China: shifts in base due to past one-child policy and changing fertility; younger cohorts smaller than older cohorts in some years.
Japan: an aging society with a top-heavy pyramid; shrinking younger cohorts and increasing older population.
Indonesia: pyramid shape changing with development and education; diversification in age structure over time.
Demographic Transition Model (DTM)
The DTM describes changing births and deaths in populations through stages of development.
It links fertility and mortality to economic and social development.
DTM Stages (Overview)
Stage 1 – High stationary (pre-industrial):
High birth rates, high death rates, little population growth.
Stage 2 – Early expanding (LEDCs):
Death rates fall due to sanitation/health improvements; birth rates remain high; rapid population growth.
Stage 3 – Late expanding (Wealthier LEDCs):
Birth rates fall due to contraception, education, women’s emancipation; population growth slows; smaller families.
Stage 4 – Low stationary (MEDCs):
Low birth and death rates; stable population sizes.
Stage 5 – Declining (MEDCs):
Birth rates fall below death rates; aging population; potential population decline.
DTM Figures and Concepts
Stage indicators (birth/death rates per 1,000):
Stage 1: birth high, death high
Stage 2: birth high, death falls
Stage 3: birth falls, death falls more slowly
Stage 4: birth low, death low
Stage 5: birth very low, death low
Population trajectory and natural increase vary by stage.
Limitations of the DTM
Based on patterns observed in a subset of industrialised countries; not universal.
Some countries skip stages (e.g., Asian tiger economies) and move quickly to higher development.
The 5th stage was added after original models; death rates may not fall as steeply as predicted, especially with urban migration and outbreaks.
Assumes contraception and female education are available; not universal across all contexts.
Population Pyramids and DTM Relationship
There is a relationship between age–sex pyramids and DTM stages.
Changes in birth/death rates alter the pyramid shape over time.
Indonesia: 1970–2010 and projections to 2050 (DTM context)
Diagrammatic interpretation: changes in age–sex pyramid reflect development and fertility trends; projections indicate shifting age structure toward older cohorts by 2050.
Task prompt: discuss where Indonesia sits on the DTM and implications for society and policy.
DTM Task and Activities
Task: Use the DTM simulation and slides to complete the questions on stages, birth/death rates, population change, and pyramids.
Secondary Data Analysis (Education and Fertility)
Task: Analyze how years spent in education by women affect the total fertility rate (TFR) of a country.
Method: follow provided instructions to conduct a secondary data analysis using existing datasets.
Key Formulas and Concepts Recap
Natural Increase Rate (NIR):
NIR = CBR - CDR\quad(\text{per 1,000 per year})
NIR\% = \dfrac{CBR - CDR}{10}
Doubling Time (DT):
DT = \dfrac{70}{NIR}
Replacement Fertility (R):
\text{Replacement Fertility} \approx 2.0, \quad \text{range due to mortality: } [2.03, 2.16]
Demographic Transition Model (DTM) stages and characteristics (high to low fertility/death, population growth patterns).
Notes on policy effects:
Direct policies can be pro- or anti-natalist (e.g., incentives for more children vs. contraception access).
Indirect policies focus on education, gender equality, and healthcare to influence birth rates.
Immigration can offset aging populations and labor shortages in some countries.
Observations on population dynamics:
Large family size can stem from multiple factors beyond GDP, including mortality, old-age security, and cultural norms.
Fertility decline trends are influenced by urbanisation, female education, and economic conditions as much as by formal policy.
Examples and case studies mentioned:
China: One-child policy (1979–2015) and later shifts to three-child policy; 2022 TFR ~ 1.18.
Singapore: Pro-natalist history with incentives but persistent low fertility.
Netherlands (example of immigration-friendly policy in Europe).
Indonesia, Japan, Nigeria: interpreting pyramids and stage implications.