By Hansika Mehra
Abstract: Population estimation, projection, and forecasting are key tools in demography and urban/regional planning. Estimation refers to determining the current population size (typically between censuses) by adjusting the last census count with recent data on births, deaths, and migration. Projection involves creating future population scenarios under specified assumptions (e.g. fertility or migration trends). It is not a definitive prediction but a “what-if” extrapolation. Common projection methods include mathematical growth models (arithmetic or geometric extrapolation), the cohort-component method, and economic models. Forecasting goes a step further by integrating expert judgment and contextual factors to give the most likely future population. This essay reviews each concept in detail, outlines the main techniques (including India’s experience with cohort-component projections), and highlights their importance for policy and planning. Reliable population estimates and projections are essential for planning services (schools, hospitals, housing, etc.) and making informed policy decisions.
Introduction
Understanding population dynamics is critical for effective planning and development. Governments and planners must know how many people currently reside in an area and how that number may change over time. Between censuses, population projections and forecasts are the only practical means to track demographic change. In India and elsewhere, the last full population counts occur only once a decade. In the intervening years, demographers produce estimates of the current population and projections of future population sizes to guide resource allocation and infrastructure planning. For example, after the 2011 census, India’s Registrar General’s office projected future populations for states up to 2036. As Aryal (2020) notes, “accurate and consistent information [on population] are inevitable to planners, policymakers, administrators… for effective decision-making”. This essay explains what population estimation, projection, and forecasting mean, how they differ, and what methods are used for each.
Population Estimation
Population estimation refers to calculating the present size of a population when a recent census count is not available. It fills the “gap” between census enumerations. Estimation typically uses known demographic events or indicators since the last census to infer the current population. For example, one definition states that population estimation is based on “direct components of population change such as the actual number of births and deaths occurring between the date of the previous census and the date of the estimation”. In other words, we start with the last census count and add births, subtract deaths, and account for net migration to approximate today’s population. When complete vital statistics are lacking, indirect indicators may be used: for instance, changes in school enrollment numbers or vehicle registrations can signal how many people have been born or moved in an area.
Common techniques for estimation include mathematical interpolation or extrapolation and the use of administrative records. Mathematical methods might simply apply a constant growth rate (arithmetic or geometric) to estimate population between census years. Administrative records — such as civil registration of births and deaths, voter rolls, or ration card data — provide another source of information. Demographers may also conduct sample surveys (e.g. a Demographic and Health Survey) to estimate fertility and mortality levels and then apply those rates to update the population count. For example, one source notes estimating India’s population in 2024 by taking the 2011 Census figure and adjusting it with registered births, deaths, and migration data in the interim.
In practice, population estimates are often made yearly or quarterly by national statistical offices. These estimates inform current policy: for instance, districts use them to track progress on health indicators or to allocate budgets. However, estimation methods assume that trends continue uniformly in the short term and often overlook sudden events. Their accuracy depends on the quality of input data (e.g. completeness of birth/death registration) and may degrade rapidly if conditions change.
Population Projection
Population projections are calculations of future population size and structure under explicit assumptions. Unlike estimation, a projection concerns a future date: it answers, “what if” scenarios, not “what actually is”. One definition describes projection as “an estimation of the number of people expected to be alive at a future date that is made based on assumptions of population structure, fertility, mortality and migration”. In other words, we take current data and assume certain rates of births, deaths, and migration to compute the population at a future time. It is important to note that a projection is conditional: it shows what will happen if the assumptions hold, rather than a guaranteed outcome.
A key distinction often made is that projections are scenario-based and not firm predictions. Track2Training explains that projection is “not a prediction, but a ‘what if’ scenario based on specified conditions”. For instance, we might project a population under “high-fertility” and “low-fertility” variants to see a range of possible outcomes. Users should interpret projections with this in mind: they illustrate possible futures, not certainties. As the US Census Bureau notes, population projections are estimates for future dates usually based on assumptions about future fertility, mortality and migration. In contrast, estimates describe the population that has already occurred.
The most sophisticated and widely used projection method is the cohort-component method. This method advances each age-sex cohort of the population year by year, applying survival (mortality) rates and adding births for the youngest cohort based on fertility rates. In practice, the cohort-component method projects change in each five-year age group separately, accounting for mortality and age-specific fertility. Both the United Nations and India’s Registrar General rely on this method for long-term projections. As one analysis states, “Both UN and RGI [Registrar General of India] projections are based on the cohort component model, in which the components of population change (fertility, mortality, and net migration) are projected separately for each birth cohort or age-group”. This method provides detailed outcomes by age and sex, making it valuable for planning needs such as school enrollment or pension requirements. Its drawback is data intensity: it requires reliable estimates of current age structure, fertility rates by age, mortality rates, and migration flows. In countries with good vital statistics and census data, it yields the most credible projections.
In addition to cohort-component, demographers use several mathematical (often called “growth”) methods, especially when data are scarce or only short-term forecasts are needed. These include:
- Arithmetic (Linear) projection: Assumes the population will grow by a constant absolute amount each period. For example, if a town added 10,000 people each decade in the past, one might project +10,000 each decade going forward.
- Geometric projection: Assumes a constant percentage growth rate. For example, if the population has been increasing by ~5% per decade, the projection applies that fixed growth rate to each future period.
- Exponential (Compound) projection: Similar to geometric but treats growth as compounding continuously. It uses the formula Pt=P0ertPt =P0 ert where r is the continuous growth rate.
- Logistic and other curves: In some cases, analysts use logistic or Gompertz curves to model a decelerating growth as the population approaches a ceiling. These methods can capture the “S-shaped” growth seen when fertility is declining. However, logistic models require estimating a population “cap” or slowing parameter and are less commonly used for national forecasts.
- Share-of-growth or Ratio methods: For sub-national areas (cities, provinces), forecasters sometimes assume that local population will change in proportion to a larger area’s growth. For example, if a state is projected to grow 20%, a city that was 10% of the state may be projected to grow similarly. One source lists “ratio method” among common techniques.
These mathematical methods are relatively simple and transparent, but they have limitations. They implicitly assume that past growth trends will continue unchanged (same birth/death rates) and usually cannot account for sudden shifts or age structure effects. For short periods (less than a decade), simple arithmetic or geometric interpolation between known census points may be acceptable, but for longer-range forecasts they often become unrealistic. As Aryal (2020) warns, mathematical methods assume an “unchanging socio-economic setting” and ignore irregular fluctuations. Such methods do not produce age-specific projections, only total population. Thus, they are often used when detailed demographic data are lacking or for quick checks, while longer projections rely on cohort-component.
A third category is the economic method of projection. This approach attempts to relate population change to economic factors. It operates on the principle that changes in birth, death, or migration rates are partly driven by economic development and social conditions. For example, economic growth may lead to lower fertility or change migration patterns. In practice, the economic method might involve regression or simulation models where demographic rates are functions of GDP growth, employment, or urbanization. Aryal (2020) explains that the economic method “tries to describe the way how economic factors influence the demographic factors i.e. birth, death and migration”. It recognizes that simple trend extrapolation ignores dynamic influences (e.g. a boom attracting migrants). In India’s context, however, this method is less often used at the national level. Aryal notes it is “less applicable” for country-wide projections, although it may be useful for regional or sectoral analyses (for example, forecasting urban migration in response to economic development). Overall, the economic approach is more complex and depends on accurate data about socioeconomic trends; it complements rather than replaces demographic methods.
Example (India): India’s official population projections (for 2011–2036) illustrate these methods in action. The Registrar General’s Technical Group used a mix of methods: for several small Northeastern states (together only ~1% of India’s population), they applied a simple mathematical (“arithmetic”) method due to sparse data. For the remaining states, they used the full cohort-component method, projecting each cohort by fertility, mortality and migration assumptions. These projections showed India’s population rising from about 121.1 crore in 2011 to roughly 152.2 crore by 2036. This example highlights how different methods may be applied to different contexts within one country.
Population Forecasting
Population forecasting refers to predicting the most likely future population, often for planning purposes. Unlike a bare-bones projection, which simply applies preset assumptions, forecasting incorporates expert judgment, policy knowledge, and consideration of uncertainty. Track2Training defines forecasting as “a prediction of the most likely future population based on past trends, present data, and expert judgment”. In other words, forecasters take projection results and adjust them using current information about policies, technological changes, or possible disruptions.
The key difference between projection and forecast is that a projection shows possible outcomes given assumptions, whereas a forecast attempts to state the expected outcome. For example, a projection might present scenarios where fertility is high or low; a forecast will select one scenario as the “best estimate” based on what experts believe will actually happen. In practice, forecasters often produce a single forecast (or a most-likely variant) and may provide high/low alternative scenarios around it. As the US Census Bureau notes, projections can come in multiple series (high, medium, low), but a forecast is usually interpreted as the “most likely” one among them.
Forecasting relies heavily on the forecaster’s judgment. A classic planning report observes: “Population forecasting is essentially a matter of judgment…This should be an informed judgment, backed up by the most complete and thorough analysis of the particular problem”. Forecasters must evaluate recent demographic trends and the factors behind them – such as changes in education, healthcare, or migration policy – and decide how these will play out. They may adjust projections to account for known upcoming changes (e.g. planned family programs, new immigration laws) or plausible shocks (e.g. a recession). For example, during the COVID-19 pandemic, forecasters needed to revise assumptions about mortality and migration in many countries.
In formal practice, a population forecast often starts with a baseline projection and then applies expert adjustments. The forecaster might consider demographic momentum, potential changes in fertility preferences, or government targets for birth rates. Sometimes forecasts are presented as a range: a central forecast plus optimistic/pessimistic variants. A US planning handbook explains that well-founded projections “are the best obtainable guides, but they are not infallible,” and cautions users that even a thorough forecast “may prove off the mark”. This humility is necessary because unforeseen events can alter trends.
Population forecasts are crucial for planning infrastructure and services. Planners use forecasts to answer questions like: How many schoolchildren will there be in ten years? Will we need to build new hospitals? How much housing will the city require? By integrating demographic projections with social and economic context, forecasts aim to inform such decisions. For example, a government might forecast the number of households to plan electricity grids or forecast the working-age population to model labor markets. In urban planning, accurate forecasts of city growth help in land-use and transportation planning. Although this essay is at the national scale, the same principles apply at regional or city levels, with perhaps greater uncertainty for smaller areas.
Conclusion
In summary, population estimation, projection, and forecasting are related but distinct tasks. Estimation determines the current or very recent population (usually using census data plus intervening birth/death records). Projection computes future population under specified assumptions, producing scenarios of what the population could be. Forecasting goes further by integrating expert judgment to predict the most probable future outcome, given policies and anticipated trends. Each of these tools serves planners: estimates update our picture of today’s population, projections outline possible futures under different demographic paths, and forecasts give a best-guess baseline for planning.
Across these tasks, the cohort-component method remains the gold standard for national projections, because it explicitly models births, deaths, and migration by age. Simpler mathematical methods (arithmetic/geometric) can be useful for short-term estimates or in data-poor settings. The economic method reminds us to consider broader drivers of change, though its practical use is limited by data availability. Forecasters must remember that all projection methods rely on assumptions about fertility, mortality, and migration. As noted in a US planning report, planners should always recognize that forecasts, however well-founded, “are not infallible”.
Nevertheless, having reliable estimates and projections is vital. Aryal emphasizes that population estimates and projections “provide accurate and consistent information” and are “essential tools for projecting to the future size and structure of population at national, provincial, [and] local” levels. In India, for instance, projected population figures guide everything from health service expansion to education enrollment targets. Globally, organizations like the UN use projections to track progress towards goals (e.g. sustainable development). In planning, these demographic tools help ensure that resources – schools, hospitals, housing, jobs – are matched to future needs. In conclusion, while no forecast can be perfectly certain, systematic estimation and projection techniques form the backbone of evidence-based planning. Keeping assumptions transparent and updating projections as new data arrive are key to improving their usefulness for society.
References
- Aryal, G. R. (2020). Methods of Population Estimation and Projection. Journal of Population and Development, June 2020, pp. 54–60nepjol.infonepjol.info.
- Track2Training (2025). Population Estimation, Projection, and Forecasting. (Blog article by Dr. Kavita Dehalwar)track2training.comtrack2training.com.
- U.S. Census Bureau (2024). Population Projections. Census Academy Data Gem. Retrieved from Census.govcensus.govcensus.gov.
- U.S. Federal Highway Administration (1967). Population Forecasting Methods: A Report on Forecasting and Estimating Methods. (Taylor and Hudson, Office of Planning, U.S. Department of Commerce)fhwa.dot.govfhwa.dot.gov.
- People’s Archive of Rural India (2020). Population Projections for India and States, 2011–2036. PARI Library (Summary of RGI technical report)ruralindiaonline.orgruralindiaonline.org.
- Bhattacharya, Pramit & Mishra, Nandlal (2024). Population projections and their track record. DataForIndia, Nov 26, 2024dataforindia.comdataforindia.com.
- Census of India (2019). Population Projections for India and States 2011–2036. Technical Group report (see PARI summary)ruralindiaonline.orgruralindiaonline.org.
- Aryal, G. R. (2020). Methods of Population Estimation and Projection (continued). Journal of Population and Development, June 2020nepjol.infonepjol.info.
- Aryal, G. R. (2020). Methods of Population Estimation and Projection (conclusion). Journal of Population and Development, June 2020nepjol.info.
- Census Academy (2024). Population Projections – How are they done?. U.S. Census Bureau info sheetcensus.govcensus.gov.
