A new study from Aalto University in Finland has uncovered that global population datasets may underestimate rural populations by as much as 53% to 84%. Published in Nature Communications, the research highlights the systemic biases in these datasets, which are widely used by governments, international organizations, and researchers for decision-making in areas ranging from resource allocation to disaster risk management.
The study, led by postdoctoral researcher Josias Láng-Ritter, compared five widely used global population datasets with resettlement data from over 300 rural dam projects across 35 countries. The findings revealed that rural populations were significantly underrepresented, with discrepancies ranging from 32% to 77% in 2010 datasets. Although newer datasets from 2015 and 2020 show some improvement, the researchers believe that a significant portion of the global rural population is still missing from these estimates.
Láng-Ritter explained that the root cause of these biases lies in the reliance on national censuses, which are often incomplete, especially in rural areas. “Not all countries have the resources for precise data collection, and rural regions can be difficult to access,” he said. In contrast, resettlement data from dam projects provide more accurate population counts, as dam companies meticulously record the number of people displaced to compensate them.
The study also found that the underestimation of rural populations has far-reaching implications. With 43% of the world’s 8.2 billion people living in rural areas, inaccurate data can lead to inadequate resource allocation, such as insufficient healthcare or transportation infrastructure. “The needs of rural populations have been underrepresented in global decision-making,” Láng-Ritter noted.
Dr. Josias Láng-Ritter, the lead author of the study, stated, “Our findings show that global population datasets have been missing a significant portion of the rural population for decades. This has likely influenced decisions that affect millions of people, particularly in rural areas.”
The study underscores the urgent need for more accurate and comprehensive population data, especially in rural regions. As global population datasets continue to play a critical role in planning and development, the researchers call for a reevaluation of how these datasets are compiled and used. “To ensure equal access to resources and services, we must address the biases in these datasets and improve their accuracy,” Láng-Ritter concluded.
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