Forecasting Population in an Uncertain World: Approaches, New Uses, and Troubling Limitations
Based on:
Journal Article (2024)
Investigates the challenges and advancements in population forecasting, emphasizing the integration of probabilistic models and Bayesian methods to address uncertainties in demographic trends.
Brief by:
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Population forecasting has become increasingly complex due to unexpected changes in mortality, fertility, and environmental conditions. In the United States, mortality stagnated and life expectancy declined from 2014 to 2017, while fertility rates dropped to a total fertility rate (TFR) of 1.665 in 2022. Globally, countries like South Korea and Thailand have seen TFRs as low as 0.72 and 1.2, respectively. These shifts raise questions about the sustainability of such low fertility rates and their implications for society and the economy. Traditional forecasting methods, which often rely on past demographic trends without considering economic or environmental factors, are now challenged by rapid changes in the natural environment, such as increased frequency of droughts, wildfires, and extreme weather events due to global warming. These changes suggest a reevaluation of the ”business-as-usual” approach in population projections, highlighting the need for new methods that incorporate environmental feedback and uncertainties. While demographic transition theory has provided a framework for understanding historical patterns of fertility and mortality, it does not adequately address the endpoints of fertility and mortality declines. Statistical and probabilistic models for forecasting have helped improve the accuracy and relevance of population forecasts. They also have other useful applications in stress-testing different public pension designs and as a foundation for the new Longevity Swap industry.
Key findings
The use of probabilistic models and Bayesian methods has improved the accuracy and reliability of population forecasts by incorporating some uncertainties, although not reflecting the unknown impacts of climate change.
Evidence
The study highlights new methods and promising directions. Microsimulation combined with aggregate forecasting methods can incorporate socioeconomic heterogeneity. The United Nations Population Division has adopted (UNPD) has adopted Bayesian probabilistic population projection methods, draw information from all countries to inform each country's forecast.
What it means
These advancements enable more nuanced and flexible population forecasts, which are crucial for addressing the challenges posed by unexpected demographic changes and environmental factors.
Traditional demographic theories are insufficient for current population forecasting needs
Evidence
Probabilistic forecasting models can also generate a collection of possible demographic futures that can be used to test the performance of different public pension designs that are intended to maintain fiscal sustainability automatically, like the Swedish system.
What it means
The limitations of traditional demographic theories highlight the need for more comprehensive models that integrate socioeconomic and environmental variables to improve population forecasts.
The development of stochastic population forecasts has enabled stress-testing of public policies, such as pension systems, for fiscal stability and intergenerational equity.
Evidence
The study describes how random sample paths are used to generate stochastic forecasts, which can be applied to test the fiscal sustainability of public pension designs. For example, the Swedish Notional Defined Contribution (NDC) system was analyzed using stochastic methods, revealing ways to improve fiscal stability and intergenerational fairness.
What it means
These applications demonstrate the practical value of probabilistic forecasts in informing policy decisions and ensuring the long-term viability of public programs.
The emergence of the longevity swap industry illustrates the commercial applications of probabilistic mortality forecasts.
Evidence
The study notes that probabilistic mortality forecasts are used in finance and insurance, with the longevity swap industry emerging as a means to de-risk pension funds. This industry, expected to conduct $25 billion in business in 2023, helps manage the uncertainty of future mortality trends and their impact on pension obligations.
What it means
The growth of this industry underscores the economic significance of accurate mortality forecasts and their potential to influence financial markets and risk management strategies.
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Forecasting Population in an Uncertain World: Approaches, New Uses, and Troubling Limitations
Cite this brief: Lee, Ronald. 'Forecasting Population in an Uncertain World: Approaches, New Uses, and Troubling Limitations'. Acume. https://www.acume.org/r/forecasting-population-in-an-uncertain-world-approaches-new-uses-and-troubling-limitations/
Brief created by: Professor Ronald Lee | Year brief made: 2025
Original research:
- Lee, R., ‘Forecasting Population in an Uncertain World: Approaches, New Uses, and Troubling Limitations’ 00(0), pp. 1–28. –
Research brief:
Investigates the challenges and advancements in population forecasting, emphasizing the integration of probabilistic models and Bayesian methods to address uncertainties in demographic trends.
Population forecasting has become increasingly complex due to unexpected changes in mortality, fertility, and environmental conditions. In the United States, mortality stagnated and life expectancy declined from 2014 to 2017, while fertility rates dropped to a total fertility rate (TFR) of 1.665 in 2022. Globally, countries like South Korea and Thailand have seen TFRs as low as 0.72 and 1.2, respectively. These shifts raise questions about the sustainability of such low fertility rates and their implications for society and the economy. Traditional forecasting methods, which often rely on past demographic trends without considering economic or environmental factors, are now challenged by rapid changes in the natural environment, such as increased frequency of droughts, wildfires, and extreme weather events due to global warming. These changes suggest a reevaluation of the ”business-as-usual” approach in population projections, highlighting the need for new methods that incorporate environmental feedback and uncertainties. While demographic transition theory has provided a framework for understanding historical patterns of fertility and mortality, it does not adequately address the endpoints of fertility and mortality declines. Statistical and probabilistic models for forecasting have helped improve the accuracy and relevance of population forecasts. They also have other useful applications in stress-testing different public pension designs and as a foundation for the new Longevity Swap industry.
Findings:
The use of probabilistic models and Bayesian methods has improved the accuracy and reliability of population forecasts by incorporating some uncertainties, although not reflecting the unknown impacts of climate change.
The study highlights new methods and promising directions. Microsimulation combined with aggregate forecasting methods can incorporate socioeconomic heterogeneity. The United Nations Population Division has adopted (UNPD) has adopted Bayesian probabilistic population projection methods, draw information from all countries to inform each country’s forecast.
These advancements enable more nuanced and flexible population forecasts, which are crucial for addressing the challenges posed by unexpected demographic changes and environmental factors.
Traditional demographic theories are insufficient for current population forecasting needs
Probabilistic forecasting models can also generate a collection of possible demographic futures that can be used to test the performance of different public pension designs that are intended to maintain fiscal sustainability automatically, like the Swedish system.
The limitations of traditional demographic theories highlight the need for more comprehensive models that integrate socioeconomic and environmental variables to improve population forecasts.
The development of stochastic population forecasts has enabled stress-testing of public policies, such as pension systems, for fiscal stability and intergenerational equity.
The study describes how random sample paths are used to generate stochastic forecasts, which can be applied to test the fiscal sustainability of public pension designs. For example, the Swedish Notional Defined Contribution (NDC) system was analyzed using stochastic methods, revealing ways to improve fiscal stability and intergenerational fairness.
These applications demonstrate the practical value of probabilistic forecasts in informing policy decisions and ensuring the long-term viability of public programs.
The emergence of the longevity swap industry illustrates the commercial applications of probabilistic mortality forecasts.
The study notes that probabilistic mortality forecasts are used in finance and insurance, with the longevity swap industry emerging as a means to de-risk pension funds. This industry, expected to conduct $25 billion in business in 2023, helps manage the uncertainty of future mortality trends and their impact on pension obligations.
The growth of this industry underscores the economic significance of accurate mortality forecasts and their potential to influence financial markets and risk management strategies.