Revolutionizing Data Collection: Armenia’s First Digital Pre-Census Sampling Frame

Armenia, in collaboration with the World Bank and WorldPop, has developed its first digital pre-census sampling frame using geospatial technology to address outdated survey methods and demographic shifts. This innovative approach ensures precise, cost-effective data collection and sets a global benchmark for modern survey methodologies.

Armenia has addressed one of its most pressing statistical challenges in a groundbreaking collaboration between the World Bank’s Poverty and Equity Global Department, WorldPop at the University of Southampton, and other international research institutes. The country has long lacked a reliable national sampling frame for conducting representative household surveys, a critical tool for policymaking and socio-economic reforms. Armenia’s need for updated data collection methods has been exacerbated by the internal displacement of populations caused by the Nagorno-Karabakh conflict, which has significantly altered demographic distributions. This first-ever digital pre-census sampling frame offers an innovative and accessible solution to these challenges, leveraging geospatial technologies to produce compact, precise sampling units.

A Revolutionary Approach to Data Collection

At the heart of this transformation lies the concept of "pre-census enumeration areas" (pre-EAs), designed using a semi-automated process that combines advanced geospatial tools and publicly available datasets. Pre-EAs were constructed using WorldPop’s gridded population data, OpenStreetMap’s infrastructure details, and the Global Human Settlement Layer. These sampling units, with populations ranging from 100 to 1,000 people, are delineated by natural and administrative boundaries, providing a manageable and consistent framework for surveyors. This innovative approach contrasts sharply with Armenia’s existing methodologies, which rely on outdated census settlements or electoral precincts. The limitations of these older systems such as large geographic units, incomplete boundary data, and outdated population estimates have rendered them inefficient and costly for household surveys.

Testing the Framework: "Listening to Armenia"

The effectiveness of the new sampling frame was tested in the World Bank’s “Listening to Armenia” survey, aimed at understanding public sentiment on social and economic reforms. The survey employed a two-stage stratified cluster sampling method, with pre-EAs serving as the primary sampling units. A total of 400 primary units were proportionally allocated across Armenia’s urban and rural regions, ensuring a balanced representation of its population. In the first stage, pre-EAs were selected based on population size, while in the second, households were randomly chosen within these units. This method not only ensured equitable representation but also optimized resource allocation and survey logistics. The successful implementation of this survey demonstrated the practicality and accuracy of the pre-EA framework, proving its potential for replication in similar resource-limited settings.

Addressing Challenges with Geospatial Precision

While the pre-EA framework has shown tremendous promise, it is not without challenges. A reliance on gridded population data introduces potential biases, as these estimates may not always reflect real-time demographic shifts. For instance, the data used for the project was drawn from WorldPop’s 2020 gridded population dataset, which may not fully capture recent changes in population distribution. Similarly, incomplete or poor-quality digital boundaries sometimes resulted in oversized or irregular sampling units, requiring manual adjustments to refine outputs. Despite these limitations, the pre-EA approach represents a significant improvement over traditional methods, which often require years of manual digitization and extensive financial resources. In Armenia’s case, the automated process, supplemented by minor manual corrections, was completed in just two months by a single specialist.

A Blueprint for Global Application

The success of this project has far-reaching implications beyond Armenia. The methodology’s scalability and efficiency make it particularly relevant for other developing nations, especially those grappling with conflict, displacement, or outdated census systems. In many countries, national statistical offices are reluctant to share census data with international organizations, further complicating survey efforts. Even when census data is accessible, enumeration areas often cover vast populations, increasing the cost and complexity of survey implementation. The pre-EA framework offers a transformative alternative, enabling researchers to construct precise, cost-effective sampling units that adhere to international standards. Its utility extends to both developed and developing regions, providing a replicable model for modern survey design.

Armenia’s Leap into the Future

Armenia’s adoption of the pre-EA methodology marks a turning point in its approach to survey design, offering a replicable and scalable framework that addresses long-standing challenges in data collection. By integrating advanced geospatial tools with resource-efficient techniques, the project not only modernizes Armenia’s survey infrastructure but also sets a benchmark for other nations. The pre-EA framework ensures a level of precision and cost-efficiency previously unattainable, enabling policymakers to make informed decisions based on accurate and representative data. As researchers continue to refine the methodology and explore its applications globally, Armenia’s initiative stands as a model of innovation in public data collection. This groundbreaking effort signals a brighter future for survey methodology, particularly in regions where reliable data is essential for equitable and effective governance.