3 to ensure that questionnaires are designed to comply with the international standards laid out by the Expert Group on Refugee, IDP and Statelessness Statistics (EGRISS).<sup>7</sup> Our findings are relevant to the literature on knowledge gaps on FDP. Though few, there are prior studies undertaken to review existing evidence and data gaps around migrants and displaced populations (e.g., Baal 2021; Rico and Camilo 2022; USAID 2021; Berretta et al. 2023; EGRISS 2023). Berretta et al. (2023) provide a systematic review of existing studies on the causes of migration. Most recently, the _Compilers’ Manual on Forced Displacement Statistics_ by the Expert Group on Refugee, Internally Displaced Persons and Statelessness Statistics (EGRISS 2023) features some of the recent attempts to collect micro-level data on refugees and IDPs through censuses and household surveys.

This paper develops a systematic approach to identify publicly available micro-level datasets on FDP. While there are several previous studies that attempt to assess data gaps on FDP,<sup>8</sup> they are limited in scope in terms of both geographical and temporal coverage and often lack a rigorous and systematic approach to guard against reviewer selection bias (Sida 2020; James et al. 2016; Campbell Collaboration 2015; Clapton et al. 2015; Gough et al. 2012; Rico and Camilo 2022).<sup>9</sup> This current analysis systematically reviews all datasets on FDP from the UNHCR and WB MDLs. While our paper has its limitations, the proposed methodology is the first attempt to develop a replicable and scalable approach to identify the universe of publicly available FDP microdata.

Our approach is innovative in that we leverage the entire collection of datasets found in the UNHCR and WB MDLs with the aid of a natural language processing tool and text mining. This involves scraping metadata from thousands of publicly available datasets in those databases, evaluating whether they include a representative sample of refugees and IDPs, and assessing various attributes of each dataset (e.g., geographical coverage, thematic focus, kinds of questions available for identification of displacement status). The innovation in our approach lays the groundwork for future data gap mapping exercises to be more objective, replicable, and scalable.<sup>10</sup> Additionally, this paper contributes to highlighting specific data gaps that have yet to be filled. To make further progress in filling data gaps, it is critical that we understand where those gaps exist today. This paper does exactly that by analyzing what and where data exists, so policy makers and development practitioners alike can gain a broad knowledge of the current data landscape on the forcibly displaced and aid their decision making based on available data.

# 2. Data

For this analysis, we leverage a rich catalogue of micro-level datasets from the UNHCR and World Bank MDLs. The WB MDL includes datasets from the World Bank, other international organizations, statistical agencies and other agencies in low- and middle-income countries. These datasets may also originate from population, housing or agricultural censuses or through an administrative data collection processes. This

> 7 See more information on EGRISS at https://egrisstats.org/.

> 8 See, for instance, Baal 2021; S 2020; Rico and Camilo 2022; USAID 2021; Berretta et al. 2023.

> 9 For instance, Rico and Camilo (2022) assess the coverage of information on migrants by the censuses and regular household surveys, but their analysis is geographically confined to Latin America.

> 10 All analysis for this report is performed in R. The replication codes are all available from <u>https://github.com/takaakimasaki/DisplacementDataGap.</u>