Population estimates are essential for planning. They are, however, politically sensitive. Population figures, including those for displaced persons, can become a bargaining tool for resources or a source of embarrassment. If grossly inaccurate, they can lead to shortage or waste in relief efforts, and may cost many lives.
Different methods are used for populations in closed areas (e.g. camps) and for moving populations or areas with difficult access. There are also particular considerations in each case for arriving at population breakdowns by age and gender.
1. Measure the surface of the area (example is 6 sq. km.)
2. On this map, select three or more random points spread out.
Draw a 100 m square around each point (10,000 sq. m of
each area)
3. Mark these squares on the ground and count the number of
persons living within each of them.
4. The average number of people living in one square (10.000
sq. m) total no. of people living in the selected squares
= number of squares
The average population/10.000 sq. m can be used to calculate the total population in the area. The approximation of this method is

15 – 20%
(Adapted from MSF: Mission Exploratoire/ Mission d’Évaluation MSF, 1989.)
100 m
Total surface: 6 km 2
This method may be used for a more ordered site than the one above: e.g., an IDP camp with lines of dwellings.
1. Count the number of dwellings in the area
2. Decide on the sample size: for example, 40 dwellings for a camp with fewer than 5,000 persons, up to 100 dwellings for camps with over 20,000
3. Divide the total number of dwellings by the desired sample size (for example, 1700 / 60 = 28)
4. Go to every x th
(28th in this case) dwelling and count the
number of people living in it
5. Add the number of people in the 60 dwellings and divide it by 60, thus estimating the average number of persons per dwelling
6. Multiply this average number by the total number of
dwellings in the camp
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Headcounts
Headcounts are only good for fairly localised populations and require co-ordination with leaders from within the population. People are called to an open area and organised in some form of social or administrative unit for counting. This may be perceived as very offensive treatment of primary stakeholders. It is often difficult to organise this in a way that is comfortable for those being counted, protecting them from sun, rain etc.
Counts can be inflated by an influx of people from surrounding areas. Such inflation is a risk particularly where the exercise is associated with the distribution of some benefit. (Without such a promise of benefit, participation is likely to be limited.)
Dwelling counts
Dwelling counts can be used once a population has settled, even if only temporarily. Dwelling counts rely on a tested estimate for the number of people per dwelling. The estimate of people per dwelling can be drawn from previous surveys with probabilistic sampling (e.g. nutrition status survey) combined with referencing in ethnographic studies. Where time is severely limited, dwelling counts can be used instead of counting individuals in the "Quick and Dirty" method. Similarly, dwelling counts based on aerial surveys or photos may be the only method possible where ground access is limited. Like headcounts, this is subject to manipulation where population figures are sensitive. Age and sex breakdowns obviously must be extrapolated from standard distribution figures or could be carried out for a sampling of dwellings (see below).
Checkpoint counts
Organisations or institutional services on site can sometimes get a realistic assessment of a moving population by having a team do a head count at a point along the migration route (total/day or hour, depending on flow). With more people, this can be disaggregated, particularly for young children below a height cutoff. In making final estimates of affected populations based on a headcount of a moving population, it is important not to neglect those who are left or stay behind.
Traditional structures
In some African countries, the local administrative structure represents a systematic division of the population. Each local chief will represent approximately the same number of families or will represent the same number of intermediate leaders, e.g. a "headman", who in turn represents a set number of families; similarly higher rank leaders will control a fixed number of local chiefs, and so on. By knowing the average size of a family and the number of leaders at each level of the structure, it is therefore possible to estimate the total population. This method has been employed by UNICEF Sudan among others.
Bird's-eye view
Many relief organisations and journalists make very rough estimates based on visual assessments. In practical terms, this means an educated guess and it can only be done with some kind of visual picture of what, for example, 1,000 people looks like. It should be noted that in northern countries where this technique has been used for assessing the size of public demonstrations and crowds, different sources often come up with figures that differ by 100 percent or more. This is not a refined science.
Key informant's estimate
Estimates by people from the area and community leaders can also be used. The margin of error may be no greater than with the bird’s-eye view. At the initial stages of an emergency, this can be particularly useful, before vested interests can become consolidated. (See Telford 1997: p 60.)
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On desired age breakdowns, see core content sheet "Gender and age in crisis and unstable contexts"
While collecting data for population estimates for closed populations, one may also obtain a rough breakdown by age and gender. Accuracy of estimates will depend on sampling. Using probabilistic sampling methods (see core content sheet "Sampling"), a sample size of about 400 individuals is required for estimating the proportion of under-fives with a margin of error of 3 percentage points, with 90 percent confidence. Smaller samples may be used, but the margin of error will be wider.
Note that obtaining exact age in some less-developed populations may be difficult, but only a generic, approximate breakdown is required to guide programme response during emergencies. Also note that finer age breakdowns may be used (for example, children under one year) but the relative margin of error will be greater. Unaccompanied children may also be counted (margin of error depending on prevalence).
(Adapted from MSF: Mission Exploratoire/Mission d’Évaluation, Philip Autier MSF, 1989.)
Family structure, particularly breakdown by age and sex, is usually included in nutrition or immunisation surveys. This means that for each unit surveyed, e.g. the household, the sex and age of all living members currently residing is recorded. This will yield good reference figures both on the overall age/sex distribution, for verification if this differs from the norm (see below), as well as the average number per family or household, for use with dwelling counts or counts based on traditional structure (see above).
Sample sizes for standard cluster surveys on nutrition and immunisation usually allow for the collection of data on age and sex breakdown that is accurate enough for planning purposes. The added cost of getting data on population age/sex distribution will therefore usually be associated with time costs (training, time in the interview and additional data for processing) as opposed to the costs of expanding the sample size.
In some contexts, the challenge for collecting data of this type lies in defining the unit of analysis in a way that fits easily with local understanding and realities. For example, terms such as household and family must be clearly understood. Similarly, a consistent handling of household members temporarily residing with the family, such as internally displaced persons, must be defined.
New data collection on population distribution by age and sex should be compared to standard figures, as new data might reveal imbalances in the population distribution due to displacement, high mortality or departure of family members to join military activities, for example. The standard distribution in nonemergency, less-developed country situations is as follows:

About 17% of the total population are expected to be under five years of age

About 22% of under-fives will be less than one year old


About 45% of the population will be under 15 years of age
About 50% of the women will be of childbearing age (15 – 44 years)

Ratio of women to men will be1.02 to 1.0
— ratios greater than 1.10 or lower than 0.94 suggest gender heterogeneity
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All of the above methods require cross-checking with other sources. Important sources for cross-checking include:
 Old census data, DHS
 Other survey results (cluster surveys such as MICS, immunisation, nutrition status), as well as sampling frames
 Regionally appropriate population growth estimates (applied to most recent, best national data)
 Key informants knowledgeable of the area
For recurring natural disasters (earthquake, flooding, hurricane, monsoon), for immediate estimates, it may also be useful to compare with previous figures on damage and fatalities, taking into account the intensity of the incident and the comparability of the region hit.
In using different data, consider reliability of the source, bias, precision of methodology (potential sources and margin of error), plausibility (Does this make sense?), and urgency (Will this estimate do for now? Can we afford the time to get a better estimate?").
Methods that provide exact figures, for example headcounts or dwelling, can be misleading in terms of accuracy. Take care to provide statements or estimates on possible error whenever this data is used.
The most difficult task with all of these techniques is going from localised estimates (e.g. from headcounts, dwelling counts, and more accurate estimates for camps) to aggregate figures for regions or countries.
Great care must be taken in aggregating estimates from different methods.
Source
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