Population trends 1950 – 2100: globally and within Europe

Assessment versions

Published (reviewed and quality assured)

• No published assessments

Rationale

Justification for indicator selection

Trends in the size and structure of human population play a key role in the environmental impact of countries and regions (UN, 2013a). Demographic trends strongly influence economic activities, which in turn determine demands on ecosystems and levels of pollution (UNFPA, 2012). Consequently, trends in population size and structure may provide an indication of likely pressures on natural resources, such as freshwater abstraction, land use, soil degradation, resource use, landscape fragmentation and pollution.

Population size and structure also influence the consumption patterns and resilience of communities. For example, the current trend across Europe and some other developed regions towards an 'ageing society' may increase the vulnerability of society to adverse environmental change (e.g. climate change) and new patterns of disease (EEA, 2014b).

Migration, which is a major determinant of population change, can be a response to changing environmental, social or economic circumstances or a cause of such changes (ICMPD, 2011; EEA, 2014a). Understanding possible changes in migration patterns between countries is crucial for forward planning of the potential demand on countries’ infrastructure – including natural capital, health and education (IOM, 2009).

This indicator may help decision makers to understand potential demographic trends for Europe and other world regions, thus assisting in the development of sustainable development policies and measures.

Scientific references

• No rationale references available

Indicator definition

Population is defined as the “de facto population in a country, area or region as of 1 July of the year indicated” (UN, 2015b). Population changes from one year to another are due to natural population change (the difference between the number of live births and deaths during a given time period) (Eurostat, 2015) and net migration (the number of immigrants minus the number of emigrants) (UN, 2015b) in a given country or region.

The figures presented in this indicator include population estimates for 1950-2015 and eight projection variants for 2015-2100. These projection variants have been developed by the United Nations Department of Economic and Social Affairs (UN DESA) on the basis of varying assumptions related to fertility rates, mortality rates and international migration (for details see the Methodology section). The table below summarises the eight projection variants as defined in the World Population Prospects: The 2015 Revision (UN, 2015a).

Assumptions

Time horizon: 1950-2100

Geographical coverage: global, with specific attention to world regions and Europe. World regions are specified as defined by the United Nations Population Division - Department of Economic and Social Affairs: Asia, Africa, Europe, Latin America and the Caribbean, Northern America and Oceania.

References

Eurostat, 2015, 'Glossary: Natural population change'

UN, 2015a, 'World Population Prospects: The 2015 revision' 

UN, 2015b, 'World Population Prospects: The 2015 Revision — Glossary of demographic terms' 

Units

Number of people; percentage of people

Policy context and targets

Context description

Global policies

Although population is not subject to direct international policy, it is recognised that population growth is central to many policy issues that the international community seeks to address. International organisations have put in place policies, programmes and funding to provide greater awareness and choice about birth control and demographic change. Examples include the United Nations Population Fund (UNFPA), which aims to “[deliver] a world where every pregnancy is wanted, every birth is safe and every young person's potential is fulfilled” (UNFPA, 2015).

European Policies

There are no EU policies directly regulating Europe’s population size and structure. There are, however, a number of relevant initiatives. For example, in light of the ongoing demographic changes in Europe (such as an ageing population, low fertility rates, changing family structures and migration), the European Commission issued a Communication, "The demographic future of Europe – From challenge to opportunity" (COM(2006) 571 - final), which identified five key policy responses to manage demographic change

1. Supporting demographic renewal through better conditions for families and improved reconciliation of working and family life;
2. Boosting employment – more jobs and longer working lives of better quality;
3. Raising productivity and economic performance through investing in education and research;
4. Receiving and integrating migrants into Europe; and
5. Ensuring sustainable public finances to guarantee adequate pensions, health care and long-term care.

This strategy was renewed in 2009 with a new Communication, “Dealing with the impact of an ageing population in the EU” (COM(2009) 180 final). Aspects of this were included in Europe 2020, the European Commission’s growth strategy (COM(2010) 2020 final). One example of the Europe 2020 umbrella activities aimed at addressing European demographic trends is the Ambient Assisted Living Joint Programme (AAL JP), intended to support industry in providing services and products for an ageing population (EC, 2013).

Furthermore, the European Demography Forum, held by the European Commission every two years since 2006, provides policymakers, stakeholders and experts with opportunities for knowledge sharing and discussion around demographic change. 

In 2014, the European Commission issued a five-point plan on immigration including measures to secure Europe’s borders and to control and facilitate asylum and legal migration (EC, 2014). It recognises the need to develop a common legal migrati­on policy to meet the increasing demand for skills and talents likely to arise as a result of the forecast demographic trends in the EU by 2060.

At the national and European regional level, additional initiatives and policies exist to address the specific demographic challenges.

Targets

There are no global, pan-European or EU objectives and targets relating to population.

Related policy documents

• COM(2006) 571 final - The demographic future of Europe – From challenge to opportunity
  (EC, 2006)
• COM(2009) 180 final - Dealing with the impact of an ageing population in the EU (2009 Ageing Report)
  For the first time in history, the vast majority of Europe's citizens are able to lead active, healthy and participative lives well into old age. At the same time, ageing societies bring new opportunities to innovative firms through the demand for new or adapted goods and services.However, the combination of ageing and low birth rates also poses major economic, budgetary and social challenges.
• COM(2010) 2020 final, Europe 2020: A strategy for smart, sustainable and inclusive growth
  European Commission, 2010. Europe 2020: A strategy for smart, sustainable and inclusive growth. COM(2010) 2020 final. 
• EC, 2013 - Tackling the demographic challenge
  Tackling the demographic challenge to create growth and jobs - Commission proposal for a renewed Ambient Assisted Living Joint Programme (AAL JP)' (ec.europa.eu//digital-agenda/en/news/tackling-demographic-challenge-create-growth-and-jobs-commission-proposal-renewed-ambient) accessed 23 January 2015.

Methodology

Methodology for indicator calculation

The UN DESA World Population Prospects (WPPs) are produced every two years. The 2015 Revision, presented here, was published on 29 July 2015 (UN, 2015a). The dataset includes past estimates covering 1950–2015 and outlook data (projections) for 2015–2100.

Estimates based on historical data for 1950–2015

Past estimates for 233 countries and areas were produced employing the cohort-component method, using population counts from national censuses as benchmarks. The components of population change (fertility, mortality and international migration) were taken from national statistical sources (censuses, demographic surveys, registries of vital events, population registers and refugee statistics), or were estimated when only partial or poor-quality data were available.

Necessary adjustments were made for deficiencies in age reporting, under-enumeration, or under-reporting of vital events. This was the case for most countries in less developed regions, where the estimation of past trends is usually more complex. In numerous cases, therefore, consistency could only be achieved by making use of models in conjunction with methods of indirect estimation.

The key task was, therefore, to ensure that for each country, past trends of fertility, mortality and international migration are consistent with changes in the size of the population and its distribution by age and sex. The overall analytical approach used in the 2015 Revision consisted of four major steps: (i) data collection, evaluation and estimation to derive a trend line; (ii) further evaluation and adjustments in relation to geographical completeness and demographic plausibility; (iii) consistency checking and cross-validation of each population component against the other population components; and (iv) consistency checks across countries (UN, 2015c).

Projections for 2015-2100

The UN population projections are based on the cohort-component projection method, which provides a framework to account for the three demographic components of change – fertility, mortality and international migration.

Regarding the projection of total fertility, the 2015 Revision used the same probabilistic method as the 2012 Revision, with two amendments. The model takes into account the latest census data from newly available surveys, and employs an improved approach to project the age pattern of fertility consistently across all countries of the world. For details on the different fertility variants see below.

Mortality was projected by means of probabilistic methods for projecting life expectancy at birth. The approach is based on a Bayesian Hierarchical Model (BHM) and is similar to the one used in the 2012 Revision, modelling the mortality transition from high to low mortality (for details see ‘normal mortality’ below)

Data on international migration are recognised by the UN as often being sparse or incomplete. Therefore, several pieces of information were used as input for the projections, such as net international migration or its components (immigration and emigration) as recorded by countries, data on labour migration flows, estimates of undocumented or irregular migration and data on refugee movements (UN, 2015c).

Specific assumptions for projection variants

The following specific assumptions and combinations thereof have been used to generate the eight projection variants of the 2015 Revision (UN, 2015c, page number(s) for further details):

• Medium fertility – The assumption is based on the general consensus that the historical evolution of fertility includes three broad phases: (i) a high-fertility, pre-transition phase; (ii) a fertility transition phase; and (iii) a low-fertility, post-transition phase. The transition phase was modelled for each country individually based on historical country-specific information. Countries that have reached a low-fertility post-transition phase were modelled separately using the empirical evidence of all low-fertility countries but also country-specific historical information. This allowed the fact that some countries are assumed to stay at very low fertility levels rather than experiencing a slight fertility recovery (the “low fertility trap hypothesis”) to be taken into account (pp. 15-20).
• High fertility – Fertility was projected to remain at 0.5 children above the fertility in the medium variant over most of the projection period. To ensure a smoother transition between the baseline period (2010-2015) and the high variant, fertility in the high variant is initially +0.25 children in the first projection period (2015-2020), +0.4 children in the second projection period (2020-2025), and +0.5 children thereafter. By 2025-2030, fertility in the high variant is therefore half a child higher than that in the medium variant. That is, countries reaching a total fertility rate of 2.1 children per woman in the medium variant have a total fertility rate of 2.6 children per woman in the high variant (p. 20).
• Low Fertility – Fertility was projected to remain at 0.5 children below the fertility in the medium variant over most of the projection period. To ensure a smoother transition between the baseline period (2010-2015) and the low variant, fertility in the low variant is initially -0.25 children in the first projection period (2015-2020), -0.4 children in the second projection period (2020-2025) and -0.5 children thereafter. By 2025-2030, fertility in the low variant is therefore half a child lower than that of the medium variant. That is, countries reaching a total fertility rate of 2.1 children per woman in the medium variant have a total fertility rate of 1.6 children per woman in the low variant (p. 21).
• No-change – fertility and mortality were assumed to be constant as of 2010–2015 and international migration is ‘normal’.
• Normal migration – The future path of international migration was extrapolated on the basis of past international migration estimates and consideration of the policy stance of each country with regard to future international migration flows. Overall, projected levels of net migration were generally kept constant until 2045-2050 with some exceptions (e.g. large recent fluctuations in migration numbers or refugee flows). After 2050, it is assumed that net migration will gradually decline and reach 50 % of the projected level of 2045-2050 by 2095-2100. Moreover, it is assumed that refugees return to their country of origin within 5 to 10 years. Where a country has experienced both international migration and refugee movements, the two processes are combined to predict overall net migration (pp. 30-31).
• Zero migration – Under this assumption, for each country, international migration was set to zero starting in 2010-2015 (p. 31).
• Normal mortality – Life expectancy was generally assumed to rise over the projection period. This was modelled in two separate steps. In the first step, the mortality transition from high to low mortality is modelled for each country, and is individually informed by historic rates of increase in female life expectancy both at the individual country and global levels. The second step of the mortality projection process models the gap between female and male life expectancy at birth in order to derive projections of male life expectancy. Some adjustments to the general approach were introduced for countries with either much faster or much slower improvements than typically experienced by other countries. This applies in particular to countries affected by the HIV/AIDS epidemic. Also, for countries with high current levels of life expectancy (e.g. the EU member countries), a slower pace of improvement has been modelled (pp. 21-29).

• Constant fertility – For each country, fertility remains constant at the level estimated for 2010-2015 (p. 21).
• Instant replacement – For each country, fertility was set to the level necessary to ensure a net reproduction rate of 1, starting in 2015-2020. Fertility varies over the rest of the projection period in such a way that the net reproduction rate always remains equal to unity thus ensuring the replacement of the population over the long-term (p. 21).
• Constant mortality – Mortality over the projection period was maintained constant for each country at the level estimated for 2010-2015 (p. 29).

Methodology for gap filling

Recent (i.e. 2010 or later) population data from censuses or official estimates based on censuses and population registers were available only for 172 of the 233 countries or areas included in the analysis. For 54 countries, the most recent population data available were from 2000-2009. For the remaining seven countries, the year of the most recent data available ranged from 1975 in Somalia, to 1984 in the Democratic Republic of Congo and Eritrea and to 1998 in Pakistan. Thus, depending on the availability of the most recent information in a given country, estimates for 2010-2015 or for the year 2015 were, in many cases, based on a short-term forward projection from earlier time periods with available data.

Reliable historic time series of the three main components of population change (fertility, mortality and international migration) are ideally based on complete, sex-specific, annual time series of age-specific fertility rates, life tables and age-specific rates of net international migration from official statistics. However, in many cases, such information was either outdated or completely lacking. In those cases where information was lacking entirely, estimates were derived from other surveys, especially for countries lacking a civil registration system or one without sufficient coverage of all vital events (UN, 2015c).

Methodology references

No methodology references available.

Data specifications

EEA data references

• No datasets have been specified here.

External data references

• World Population Prospects, the 2015 revision

Data sources in latest figures

Uncertainties

Methodology uncertainty

Both past estimates (1950–2015) and outlook data (2015–2100) are associated with a number of uncertainties (UN, 2015c).

With regard to historical estimates, the absence or poor quality of demographic information, in particular in developing countries, and, as a consequence, the need to employ gap-filling methods to derive internally consistent estimates, is the most important source of uncertainty. Concerning information on fertility and child mortality, the percentage of countries or areas with lacking or outdated data was small. However, data on adult mortality were lacking for countries and areas totalling 16 % of the global population. Thus, a major weakness faced in producing the 2015 base-year estimates of the population by age and sex for each country was the lack of recent data on adult mortality.

Moreover, the UN acknowledges that even by combining the best available information sources on international migration (e.g. by the Office of the United Nations High Commissioner for Refugees), the production of comprehensive and consistent estimates of net migration remains a limiting factor to producing more accurate historical population estimates.

Concerning future demographic trends modelled for the 85-year period 2015-2100, the spread across the eight projection variants illustrates the uncertainty inherent in such probabilistic projections. The different projection variants also convey the sensitivity of the projections to changes in the underlying assumptions, such as changes in fertility.

The statistical modelling approach using the BHM also attempts to capture some of the uncertainty. For example, uncertainties in relation to the pace of future fertility decline (i.e. phase two of the fertility transition - for details see the ‘Methodology’ part above) is addressed by generating 600 000 double-logistic curves for all countries that have experienced a fertility decline. By systematically sampling one in ten of the 600 000 simulated trajectories produced by this process, the end result was 60 000 projected trajectories of total fertility for each country. The median of these 60 000 trajectories is used as the medium fertility variant projection.

Other uncertainties relate to temporal and geographic scale. As the time period increases so does the uncertainty inherent in the projections, meaning that, for example, the estimates for 2050 are more uncertain than those for 2020. Uncertainty also increases with geographical detail, and is higher at the individual country level than, for example, at continental level. Detailed information on the uncertainty bounds for different components at the country level is available on the website of the UN DESA Population Division.

References

UN, 2015c, World Population Prospects: The 2015 Revision — Methodology of the United Nations population estimates and projections, Working Paper No. ESA/P/WP.242, United Nations Department of Economic and Social Affairs (DESA), New York, US.

Data sets uncertainty

As described in the methodology for gap filling, there are challenges relating to the quality of individual country data so the main recorded dataset uncertainty is at the level of individual countries.

Rationale uncertainty

N/A