Unit 4 - Essays - DTM UPDATED Flashcards
“Assess the extent to which the demographic transition model (DTM) is useful in predicting population growth in LICs/MICs.”
Paragraph 1: Cultural and Political Differences Limit the DTM’s Usefulness
Point 1: The DTM is based on Western experiences and does not account for cultural diversity.
The DTM assumes that as countries develop, birth rates will naturally decline.
However, in Sub-Saharan Africa, cultural norms encourage large families regardless of economic growth or education improvements.
Example: Niger – Birth rate of 44 per 1,000 in 2020, despite some economic progress.
The persistence of high fertility rates due to traditional values makes the DTM less reliable in predicting demographic changes in such regions.
Point 2: Government policies can disrupt the expected progression of the DTM.
The DTM assumes a natural and gradual decline in birth and death rates, but state interventions can alter demographic patterns.
Pro-natalist policies (e.g., Nigeria encouraging higher birth rates) vs. anti-natalist policies (e.g., India’s sterilization campaigns in the 1970s led to an artificially rapid decline in birth rates, which the DTM could not predict)
Evaluation: Because political interventions vary by country and time, the DTM is not always a reliable predictor of population growth.
Paragraph 2: High Starting Birth Rates and Changing Death Rates
Point 1: Many LICs start with much higher birth rates than historical Western countries.
The DTM assumes that all countries go through the same stages, but many LICs/MICs begin their demographic transitions with much higher birth rates.
Example: Niger (44 per 1,000 in 2020) vs. 19th-century Europe (~35 per 1,000).
These higher birth rates mean that transitions to lower fertility levels take longer and do not always follow the DTM’s predicted timeline.
Point 2: Death rates have fallen much faster than in the past due to medical advancements.
The DTM assumes a gradual decline in mortality rates, but in many LICs/MICs, modern medicine has dramatically accelerated this process.
Example: Bangladesh – Child mortality dropped from 87 per 1,000 in 2000 to 34 in 2020 due to vaccinations and better healthcare.
Evaluation: Because death rates are falling so quickly, the DTM’s traditional progression is often disrupted. Instead of following predictable stages, countries experience rapid shifts in population dynamics.
Paragraph 3: The DTM Still Has Some Use – Falling Birth Rates and Urbanization
Point 1: The DTM correctly predicts declining birth rates as countries develop.
Despite its limitations, the DTM is still useful in predicting lower fertility rates as economies grow and education improves.
Example: Kenya – Birth rate dropped from 45 per 1,000 in 1980 to 28 per 1,000 in 2020 due to improved female education and family planning.
This aligns with the DTM’s idea that as societies become wealthier, fertility rates decline.
Point 2: Urbanization leads to lower birth rates, as the DTM predicts.
Urban areas generally have lower fertility rates than rural areas due to higher costs of living, career opportunities, and access to contraception.
Example: India – Urban birth rates significantly lower than rural birth rates.
The DTM is still useful in predicting these spatial variations, as urban areas move through the transition more quickly than rural areas.
Evaluation: While the DTM does not apply perfectly to every country, it remains helpful in explaining trends in urban areas.
Paragraph 4: The Pace of Change Challenges the DTM’s Predictions
Point 1: The speed of demographic transition today is much faster than in the past.
The DTM assumes that changes in birth and death rates happen gradually, but many LICs/MICs are experiencing rapid transitions.
Example: South Africa – AIDS-related deaths fell by 60% between 2004 and 2020 due to medical advancements.
This rapid decline in mortality rates is much faster than what the DTM predicts.
Point 2: Contraceptive access and changing attitudes accelerate transitions.
The DTM was based on a time when contraception was not widely available, but today, many LICs/MICs have rapid access to birth control.
Example: Southeast Asia – Contraceptive use rose from 38% in 1990 to 61% in 2020, leading to faster declines in birth rates than expected.
Evaluation: Because modern population changes happen much faster than historical transitions, the DTM often struggles to make accurate long-term predictions.
Conclusion
The DTM is useful for explaining general population trends, particularly in urban areas and wealthier MICs, where birth rates tend to decline as expected.
However, it has clear limitations, as it does not account for cultural influences, political interventions, or rapid medical advancements that can drastically alter population growth.
Final judgement: The DTM can be a helpful guide but must be adapted and modified to remain relevant in predicting population growth in today’s LICs and MICs.
