Millions of people in rural areas are missing from global population data

Governments, international organizations, and researchers depend on global population data for essential decision-making. These datasets help allocate resources, plan infrastructure, track disease epidemiology, and manage disaster risks.

However, recent research reveals that rural population figures have been systematically underestimated, raising concerns about the accuracy of decades’ worth of data that inform global policy and planning.

A new study from Aalto University in Finland exposes the severe extent of this undercounting.

Rural populations are vastly underreported

“For the first time, our study provides evidence that a significant proportion of the rural population may be missing from global population datasets,” said postdoctoral researcher Josias Láng-Ritter from Aalto University.

The study compared five of the most widely used global population datasets – maps that break the world into high-resolution grid cells based on census data – with resettlement figures from more than 300 rural dam projects across 35 countries.

The results were striking. Depending on the dataset, rural populations were underestimated by 53% to 84% over the study period.

“We were surprised to find that the actual population living in rural areas is much higher than the global population data indicates,” said Láng-Ritter.

“The results are remarkable, as these datasets have been used in thousands of studies and extensively support decision-making, yet their accuracy has not been systematically evaluated.”

Dam resettlement data offers a comparison

To assess the accuracy of global datasets, the team turned to an unconventional but reliable source – resettlement data from dam projects.

“When dams are built, large areas are flooded and people need to be relocated,” noted Láng-Ritter. “The relocated population is usually counted precisely because dam companies pay compensation to those affected.”

“Unlike global population datasets, such local impact statements provide comprehensive, on-the-ground population counts that are not skewed by administrative boundaries. We then combined these with spatial information from satellite imagery.”

The discrepancies in rural population data were particularly noticeable in China, Brazil, Australia, Poland, and Colombia, where more independent data was available for comparison.

How population censuses create biases

One major reason for these discrepancies is that population censuses – the primary data source for global datasets – often fail to capture accurate numbers in rural areas.

Some countries lack the resources for comprehensive data collection, and vast rural landscapes make travel difficult for census workers. Sparse populations spread across large areas further complicate the process.

To assess the extent of the issue, the researchers examined data from 1975 to 2010, as dam resettlement records were available for these years.

The analysis showed that, while accuracy improved slightly over time, even the 2010 datasets missed between one-third (32%) and three-quarters (77%) of rural residents.

Newer datasets from 2015 and 2020 likely still contain similar biases, but no dam resettlement records were available for these years.

“While our study shows accuracy has somewhat improved over decades, the trend is clear:  global population datasets miss a significant portion of the rural population,” said Láng-Ritter.

“With the same basic practices in place, it’s unlikely that slightly improved input data could correct for this level of bias. And even if the most recent population maps reflected reality, earlier datasets have influenced decision-making for decades and are still used to monitor change, for instance providing a distorted picture of movement over time from the countryside to urban areas.”

The consequences of underreporting

Currently, about 43% of the world’s 8.2 billion people live in rural areas, according to official estimates. However, these figures may be misleading if rural populations have been significantly undercounted.

Global decision-makers, including the UN and the World Bank, rely on the same flawed census data used to create population maps. This means essential services, from healthcare to transportation, may not be adequately planned for rural areas.

“In many countries, there may not be sufficient data available on a national level, so they rely on global population maps to support their decision-making: Do we need an asphalted road or a hospital? How much medicine is required in a particular area? How many people could be affected by natural disasters such as earthquakes or floods?” Láng-Ritter explained.

The need for improved rural population data is especially pressing in crisis-hit regions, where transitioning to digital population records could take years.

“For example here in Finland, the population data is nowadays very reliable even in rural regions, as we were the second country in the world to start keeping digital population records already in 1990. But especially in crisis-hit countries, the shift towards digital population registers could take years, even decades,” Láng-Ritter added.

“To provide rural communities with equal access to services and other resources, we need to have a critical discussion about the past and future applications of these population maps.”

Rethinking how we count rural populations

Accurate population data is essential for governments and international organizations to make informed decisions about resource allocation, infrastructure, and disaster preparedness. This new study highlights the urgent need to reassess how rural populations are counted to ensure that global datasets reflect reality.

If rural communities continue to be undercounted, they risk being overlooked in critical planning efforts, from healthcare access to climate adaptation strategies. The findings emphasize the importance of improving data collection methods, particularly in areas where traditional census methods fall short.

Without change, the missing millions in rural areas will remain invisible to decision-makers worldwide.

The full study was published in the journal Nature Communications.

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