sampling frame does not necessarily substitute the existing sampling frames but could complement them, particularly the national census frame, and can be used as an alternative.

Finally, this approach creates demand for public datasets like OpenStreetMap. The existence of such highquality, public, geospatial data can return tens to hundreds of millions of dollars in societal value, even without counting their indirect benefits (Hansen and Schrøder, 2019).

# **2 Existing and Accessible Sampling Frames**

The household survey is one of the most popular methods for collecting population and socioeconomic indicators. An accurate sampling frame is essential to carry out household and other surveys at the national level. These frames are frequently created and updated during the national housing and population census. Some countries may not have a digital national sampling frame because their census was not digitized and more seriously some might not even have a physical map of the census frame, or it might be not accessible. Additionally, the frames might be outdated because demographic factors change quickly, and most censuses are conducted every ten years. In Armenia, there is no workable and accessible map or cartographic information that can be used for a national sampling frame. It is a significant obstacle to conducting nationally representative socioeconomic surveys. There is also no up-to-date digitalized national sample frame in the country.

The last census completed in Armenia was conducted in 2011 (ArmStat, 2024), and there are currently no up-to-date enumeration areas that can be employed as national sampling frames for representative socioeconomic surveys. The spatial resolution of the census results in the current use is restricted to the provincial or district level (2<sup>nd</sup> and 3<sup>rd</sup> administrative units), making it difficult to understand how people are distributed at the facility, sub-district, or neighborhood levels—the levels where policy interventions are typically provided. In developing countries, creating a sampling frame for surveys using representative community samples usually involves manually delineating small geographic areas (or enumeration areas) on high-resolution satellite imagery. This is a standard method employed by multiple National Statistical Offices (NSOs) but is logistically complex, and requires substantial material resources, Geographic Information System (GIS) experts, and training (Qader et al., 2020, 2021). In addition, the process is extremely timeconsuming and expensive and often leads to postponing the survey. For example, it can take 2-3 years to carry out a survey (Grosh and Glewwe, 1995). These factors demand a fast and inexpensive sampling frame and population enumeration methodologies.

In this paper, a digital national sampling frame has been created using a semi-automatic and cost-effective approach. However, background on the existing sampling frames in the country is provided before discussing the new sampling frame. Three existing datasets that can serve as a potential sampling frame for household surveys include the 2022 Census with addresses from the State Population Register (SPR), the 2011 Census with settlements (villages in rural areas, towns in non-Yerevan urban regions, and districts in Yerevan), and 2023 election data with electoral precincts.

The ArmStat conducted a Population Census in November 2022 using a combined approach based on administrative data from the State Population Register (SPR) and a sample of 25% of the addresses in the SPR (ArmStat, 2022). In this dataset, primary sampling units (PSUs) are the workload of SPR addresses for each enumerator during the census. Although these PSUs do not have identifiable boundaries like the traditional census enumeration areas (EAs), this dataset can be used as a sampling frame since they cover approximately 93% of all addresses in the country. The household listing in this dataset is as of October 5