Michel Guillot

The first demographic projections only concerned the numerical size of the total population. They were simple population extrapolations that did not yet consider population structure by age and the three demographic processes—mortality, fertility, migration—that account for that structure. Here we can site Malthus’ 1798 Essay where he formulated his celebrated hypothesis of exponential population growth. Later, Quetelet (1835), Verhulst (1838), and then Pearl in the 1920s put forward what is called the logistics law or S-shaped curve” according to which population size tends to stabilize around a limit or limiting value following a phase of rapid growth. Starting in the 1930s, these unreliable extrapolation methods were replaced by what is called the “components” method, where researchers calculate population size changes by age and sex on the basis of assumptions about mortality, fertility, and migration trends. This method, a version of which was initially proposed by Cannan (1895), was independently developed by Whelpton (1928) and is still consensual today. 

All three of the demographic processes cited above are difficult to anticipate, each in its own way. For mortality, it is difficult to anticipate epidemics such as HIV and COVID-19, or mortality crises due to conflicts. For fertility, it is difficult to predict the speed at which it will fall in countries that continue to have high rates. Conversely, in low-fertility countries (low fertility defined as below 1.6 children per woman), we currently have few empirical or theoretical guides that we can use to justify assumptions on future developments: in the first case, new upward movement, and in the second, ongoing decline. International migration, meanwhile, is often a response to economic, social, political, and environmental phenomena that are by nature difficult to predict. Of the three processes, fertility is the one that demographic projections are particularly sensitive to. A difference of a few decimals in fertility level can have a strong impact on a population’s future size. In this respect, we can say that fertility assumptions are the most difficult to formulate. Projection errors, observable only afterward by comparing population size actually attained at a given date to size projected earlier for the same date, are due mainly to error in fertility assumptions. 

While demographic projection calculations still use the components method, they have greatly evolved in recent years. They now use simulation models that enable researchers to integrate levels of uncertainty about a given population’s future size. We can expect that new technological advances will also improve our knowledge of current populations and so provide a better foundation on which to do demographic projections. However, regardless of what those technological advances are, demographic projections are by nature uncertain. 

The latest projections by the Wittgenstein Centre for Demographic and Global Human Capital include a scenario that integrates SDG 4, the goal of instituting quality universal primary and secondary education by 2030 (K. C. Samir et al. 2024). The 2020 Institute for Health Metrics and Evaluation projections integrated SDGs into education and contraception needs. However, SDGs are not directly figured in to United Nations Population Division projections. There’s no consensus on integrating such variables as education level or contraception use into demographic projections on fertility, for example. This is why the United Nations favors an approach based mainly on extrapolating from past mortality, fertility, and migration trends.