15 data – particularly representative micro-level data – on FDP will be essential. Despite the growing need for such data, however, the supply of it may not be adequately catching up.

This study leveraged a rich catalogue of micro-level datasets from the UNHCR and WB MDLs to undertake a stocktaking of all available micro-level datasets on FDP, thereby also shedding light on existing data gaps. By geography, the study finds that FDP datasets are relatively rich in Sub-Saharan Africa compared to other regions given that more than half of the publicly available FDP datasets identified through this study come from Sub-Saharan Africa. By topic, FDP datasets are relatively scarcer in topics such as labor and employment, finance, agriculture/livestock/fishery, and education.

One of the surprising results revealed from this study is an overall lack of publicly available microdata on IDPs. Of the 375 FDP datasets identified through this exercise, only 31 of them have a representative sample of IDPs. Furthermore, some countries hosting at least 1 million IDPs have zero publicly available dataset from the UNHCR and WB MDLs, including Colombia, Democratic Republic of Congo, Ethiopia, the Syrian Arab Republic, Türkiye, and Yemen.

The study also reveals how a large majority of the existing datasets did not contain a core set of questions for proper identification of refugees and IDPs. Some of the essential identification questions are often missing, such as the place or country of birth as well as reasons for and history of displacement. The omission of these core questions makes it difficult to properly identify the displacement status of sampled households or individuals. Since the international recommendations by EGRISS were established in recent years, it still remains to be seen to what extent future surveys will adapt to these recommendations.

These findings come with some caveats. First, the metadata of the datasets was extracted in December 2022. This means that our data gap map may already be missing datasets that have been added since then, calling for periodic updates in the analysis of data gaps. Second, our datasets only capture publicly available microdata from the UNHCR and World Bank MDLs. While we believe these MDLs contain the most datasets pertinent to microdata on FDPs, there may be other datasets not contained in either of these databases. Finally, as with all automated processes, it is possible that some datasets are miscoded due to the fact that the underlying metadata we use for this study is sometimes incomplete. We attempt to mitigate this concern by manual verification and making corrections in the metadata itself but there still remains more work to be done to ensure that the metadata is thoroughly populated in the UNHCR and WB MDLs.

Despite the limitations, this paper contributes to advancing our knowledge about existing data gaps on FDP and by developing a systematic process to identify them based on the information from the UNHCR and WB MDLs. Future data collection efforts may build upon the findings from this report and strategically target those geographical or thematic areas where data gaps still abound. Furthermore, the methodology developed in this study can be adopted, modified, and applied in other contexts, thereby making it easier to undertake this sort of a data gap mapping exercise.