Updated: November 2025
The accuracy of population data varies widely across countries.
The most comprehensive data on the number of people living in a territory and their demographic profile, a vital component for public sector economic and social planning and also for private sector needs, is usually only available from the result of a census.
Over a sample of 155+ countries, the average census age at the end of 2022 was 10 years. 62 countries had census data older than the recommended 10 -year interval between censuses.
Most countries conduct a regular population and household census every ten years and some even use a five year cycle. National statistics offices produce only estimates of total population numbers and the demographic breakdown for the intervening years. The accuracy of these estimates depends on the coverage of the last census and the elapsed time since the census, the data and assumptions about births, deaths and net migration and a host of other factors related to the capacity of the national statistical office and its ability to carry out its functions unimpaired by political interference.
There are a number of problems which limit the accuracy of these between census population estimates. National censuses require a large amount of resources to carry out and often vary in accuracy even for developed countries. The Brookings Institution, for example, has expressed fears that political interference and budget restrictions may seriously impair the accuracy of the 2020 United States population census. In many developing countries there are large gaps in terms of the years between holding a census. This means that population estimates made become less and less accurate as time elapses.
Over a sample of 155+ countries, the average census age at the end of 2022 was 10 years. 62 countries had census data older than the recommended 10 -year interval between censuses. The problem is particularly serious in Africa. For example the census age in Namibia is 11 years and 38 years in the Democratic Republic of Congo. The problem is not confined to Africa. Lebanon, for example, has not conducted a census since 1943.
Estimating the level and distribution of population between censuses requires accurate data for live births, deaths and net migration. In developed countries the registration of births and deaths is usually comprehensive. In many developing countries, in contrast, lower registration rates, particularly of births and especially in rural areas means that the data available to national statistics offices is often widely inaccurate. According to the World Bank the registration of children under 5 years old is less than 50% in rural sub-Saharan Africa.
This is a major handicap for national statistics offices in most emerging markets many of which have limited resources available to undertake surveys and it is compounded by long gaps between censuses. The same problem exists in many emerging markets for estimating net migration statistics, particularly for countries that have seen uncontrolled economic and war-induced migration. The accuracy of net migration data is also subject to wide errors in some developed countries. A committee of Members of Parliament in the UK once described official net migration data as ‘little better than a guess.”
Four primary components make up the Population Data Quality Ratings:
- Last Census Date (years out of date) - Statistical Resources of National Statistics Office - Percentage registration of births - Corruption Levels
This factor is based on data from the most recent census and the number of years that have elapsed between that census and 2025. Some countries, such as Switzerland, Germany, and Denmark, conduct what is referred to as an "annual census". This methodology relies on the integration and analysis of multiple administrative data sources rather than a traditional population enumeration involving direct surveys of households. These countries are assigned the highest rating because their data is considered more current.
Data on the number of years out of date are provided in the Population Data Quality Ratings Tables along with the source data.
The ability of a country to produce accurate data is a function of its statistical capacity which depends on the resources – human, financial and technical – of its national statistics office.
The score for this component is derived from the World Economics Statistical Resource Index (SRI) for Population data.
The SRI is a composite indicator developed by World Economics to measure the level of resources available to national statistical systems (specifically for Population data).
It is constructed from primary data drawn from two sources: the World Bank Statistical Performance Indicators (SPI) and the Open Data Watch Open Data Inventory (ODIN). For each underlying indicator, country-level z-scores are calculated to standardise distributions across variables with differing scales and units. These z-scores are subsequently transformed onto a 0–100 scale (where 100 represents the highest observed performance). The standardised scores from the two source datasets are then aggregated through an equally weighted combination to yield two distinct sub-indices: one specifically targeted at resources supporting the production of GDP-related statistics and another focused on resources underpinning population and demographic statistics.
We use this later index as a proxy for assessing the availability of economic resources in national statistics offices for population data. In theory, the larger the resources devoted to statistics offices, the better the quality of statistics.
The third factor employed is estimates of the percentage of births registered by country using UNICEF data available from a World Bank dataset. This variable is available in percentage terms and can be converted into an index varying between 0 and 100 and is used as a proxy for the accuracy of population estimates produced between censuses. Data on live births registered by country are provided in the online tables.
Corruption can affect the independence and accuracy of national statistical offices. It may lead to political interference in census processes, misallocation of resources, or inaccuracies in vital registration systems. Incorporating this factor, allows the rating to better reflect the institutional environment in which population data are produced.
Each of the three component indices is first transformed into standardised z-scores to ensure comparability across components with different units, scales, and distributional properties. This standardisation is achieved by subtracting the global mean of each component from the country’s raw score and dividing by the global standard deviation of that component. For components where a lower raw value indicates better performance (in particular, years elapsed since the last census), the raw score is multiplied by −1 prior to standardisation so that higher z-scores consistently reflect superior population data quality.
The standardised z-scores for the three components are then aggregated into a composite Population Data Quality Index using a simple unweighted average, assigning equal weight (25%) to each dimension. This equal-weighting approach reflects the view that none of the three core pillars, timeliness of census coverage, institutional resources, and the reliability of vital registration systems, can be fully substituted by the others when evaluating the overall accuracy and usability of population data.
The resulting composite index is rescaled to a uniform 0–100 range, where higher scores indicate higher population data quality and reliability. This rescaling is performed using the observed minimum and maximum composite z-scores in the current year (or a fixed historical reference range where greater year-to-year stability is preferred), facilitating intuitive interpretation and consistent comparisons across countries and over time.
Countries are assigned summary ratings of A through E based on their final 0-100 composite scores using the same bell-curve grading approach employed for World Economics Indexes. Rather than dividing countries into fixed quartiles or equal-sized groups, the rating boundaries are determined dynamically each year by the actual spread of composite scores as measured by the standard deviation from the global mean. This method ensures that grades reflect genuine deviations from average global performance rather than imposing arbitrary equal distribution across categories.
The rating categories are defined as follows: