4 MDL contained 4,539 datasets at the time of this research (December 2022), some of which were also cross-listed in the UNHCR MDL.

The UNHCR MDL contains micro-level datasets that are of concern to UNHCR’s mission and mandates. These datasets often – if not always – include a sample of refugees, asylum seekers, IDPs, or stateless people. The datasets come from censuses, registration and administrative exercises, and surveys. This MDL contained 546 datasets at the time of our mapping exercise.

One of the key advantages of using these databases is that they offer detailed metadata which help us understand the main characteristics of each dataset. The metadata contains a rich set of attributes pertaining to each micro-level dataset, including the country of data collection, the producer(s) of the dataset, brief description or abstract and thematic scope of a dataset, dates of data collection, unit of analysis (e.g., households, individuals), geographic coverage (e.g., national, regions, camps), data type (e.g., sample survey data, census, administrative), questionnaire modules, as well as sampling strategy. A complete list and explanation of each attribute in the metadata can be found in Annex B: Metadata.

In terms of geographical coverage, this study covers all low-income and middle-income countries with a significant presence of refugees, other Venezuelan refugees and migrants<sup>11</sup> in need of international protection, and/or IDPs. We apply 100,000 (in terms of the number of FDP as of 2021) as an inclusion threshold for this analysis. This results in the final list of 53 low-income and middle-income countries, which altogether account for 23 million refugees and 58 million IDPs.<sup>12</sup>

# 3. Methodology

The methodology we apply to analyze micro-level datasets in the UNHCR and WB MDLs follows a procedure that is systematic, replicable, and scalable (Figure 1). First, we scrape the metadata from all the micro-level datasets found on the UNHCR and WB MDLs.<sup>13</sup> We find 412 datasets from UNHCR MDL and 1,927 datasets from WB MDL that have been collected in the sample of countries under study.

Second, we remove _false positive_ datasets from the UNHCR MDL. While a large majority of datasets in the UNHCR MDL are individual/household-level household survey datasets sampled from refugees or IDPs, some are not. Furthermore, many datasets do not have a clearly defined sampling frame from which a representative sample can be drawn, thus failing to meet our inclusion criteria for this exercise. A few datasets listed in the UNHCR MDL are indeed based on key informant interviews (instead of a 11 Venezuelan refugees and migrants are included in this analysis due to their status as Persons of Concern (PoC) under UNHCR’s mandate. The reason why migrants are included here is laid out by UNHCR: “Venezuelan [migrants] refers to persons of Venezuelan origin who are likely to be in need of international protection under the criteria contained in the Cartagena Declaration, but who have not applied for asylum in the country in which they are present. Regardless of status, Venezuelan [migrants] require protection against forced returns and access to basic services.” (UNHCR 2020). In short, since Venezuelan migrants are likely to be in refugee-like situations, they are treated like refugees and thus should be included in this analysis.

> 12 The number of refugees, Venezuelan refugees and migrants and IDPs (in 2021) is taken from World Bank _World Development Indicators_ (WDI) <u>(https://databank.worldbank.org/source/world-development-indicators), UNHCR</u> Refugee Finder, and Global Internal Displacement Database by the International Displacement Monitoring Center (IDMC) <u>(https://www.internal-displacement.org/database/displacement-data), respectively. See Annex A: List of</u> countries included in this study.

13 The extraction of metadata from the UNHCR and WB MDL was performed in December 2022. This involved scraping the JSON files of all existing datasets from both sources and compiling them into a master list.