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In 2022, post COVID-19 pandemic, more than 1 million[1] registered Bangladeshi migrants abroad, contributing 21.2[2] billion USD in remittances to back home – which is equivalent to 5.1 per cent of the country’s entire GDP[3].  The size of Bangladesh’s diaspora and its subsequent remittances is a critical tool of development for the country, which is currently the sixth largest origin country[4] for international migration and has seen 275 billion USD[5] in remittances since independence. The volume of irregular migration from Bangladesh is not known which is a big data gap affecting migration services and assistance to migrants. Internal migration trend in Bangladesh is equally huge; According to the 2011 census, 10 per cent of Bangladeshis reported having migrated internally. Over half of those migrated to cities, the most common of which were the urban hubs of Dhaka and Chittagong.[6] Data in 2021 also shows urbanization on the rise, with an urbanization rate of 38.95 per cent[7], 4.65 per cent higher than in 2015 (34.3%). As natural disaster and climate change related incidences increase, rural to urban displacement and migration are predicted to become progressively more prominent in Bangladesh in coming year. Despite the persistence of international and internal migration, the onset of the COVID-19 pandemic has seen an increase in yet another direction of population mobility: more than 400,000 Bangladeshis returned to the country between April and November 2020 according to the Probashi Kollyan Desk (PKD) at Hazrat Shahjalal International Airport (HSIA) in Dhaka. The size, dynamic nature and impact of Bangladesh’s mobile populations underscores the need for a comprehensive evidence base and sustainable data mechanism to observe, analyse and holistically understand Bangladeshi migration. This would ultimately result in better-targeted migration programming and policies for safe migration and humanitarian assistance delivered in Bangladesh. Some of the key gaps vis-à-vis migration and mobility data can be clubbed as below:

IOM in Bangladesh, realising the need to fill in the data gap piloted a series of data related interventions and has already laid the groundwork to strengthen the evidence-based formulation and implementation of humanitarian and development policy and programming on migration and displacement due to natural disaster in Bangladesh. IOM’s Displacement Tracking Matrix (DTM), an information system and set of tools developed to gather and analyse data to disseminate critical multi-layered information on the mobility, vulnerabilities, and needs of displaced and mobile populations are being implemented in Bangladesh in two districts – Kurigram and Sathkhira. Some of the data initiatives focused on exploring drivers and flows of regular and irregular international migration and return; types and volume of internal mobility; migration facilitation, social networks in migration processes and other migration experiences. Primary research by IOM has also provided longitudinal data on the socioeconomic recovery and reintegration of returnees as well as the causes of human mobility related to labour, climate change, within Bangladesh and abroad. Few of the key interventions that IOM initiated to address some mobility related data gap. in past couple of years are below:
Baseline Mobility Assessment (BMA)
The Baseline Mobility Assessment in Bangladesh has been piloted in Bangladesh to track mobility, provide information on population estimates, locations and geographic distribution of migrants and returnees. Data is collected at the village level from key informants and direct observations. Under this method enumerators collect data through two-layered assessments:

Between 2019 and 2022, IOM piloted this key-informant-based survey in Kurigram and Satkhira district. A total of 4,686 villages were assessed through 16,817 interviews.
Target Mobile Population Group

 
Survey on Drivers of Migration (SDM)
A quantitative approach was adopted to conduct the Survey on Drivers of Migration and to analyse specific thematic areas: socio-economic profiles, drivers and reasons for migration, challenges and access to services in Bangladesh and migration networks and aspirations. A quantitative approach was preferred among other methods because it allowed for systematic, standardised comparison of factors that contribute to different migration choices and aspirations. This could then be used to find a pattern in migration in Bangladesh, looking for differences between the geographical origin of potential migrants, their intended destinations and in the decision making of regular and irregular potential migrants.
 
The research focused on potential migrants in Bangladesh in 2019 who planned to migrate within the next six months. 11,415 potential migrants from all 64 districts were interviewed. They were categorized as regular or irregular, based on whether they had registered their intention to move with the government or not.
 
Potential migrants were surveyed on demographic information, household information, education and previous employment. Individuals were also asked if they had registered their intention to migration with the government and if they had paid a migration facilitator for support. The survey included questions about how much they had paid, who they
paid it to and what services they had paid for. The study also looked at migration intentions and the reasons behind the
decision to migrate, as well as respondents’ challenges and access to services, and the type of support and information used to get information about migration. Long term aspirations were also explored.
 
Returnee Longitudinal Survey (RLS)
Based on the Reintegration Sustainability Survey (RSS)[8], developed by IOM in 2017, the Returnee Longitudinal Survey (RLS) aims to better understand return migrants’ profiles, the living conditions of returnees and their reintegration process over a longer period. The purpose of RLS is to strengthen the information-base on the sustainability of reintegration and the gaps and needs within Bangladesh for future programming and policy making.
The RLS focuses on returnees who returned to Bangladesh through AVRR and VHR programmes[9]. Based on the sampling frame of all the AVRR and VHR returnees in Bangladesh, the sample was constructed based on three criteria:

 
Between October 2020 and January 2021, the DTM team in Bangladesh interviewed 635 returnees in-person, located in 17 districts for the first round of RLS. The largest share of respondents returned from Libya (553 respondents), and 82 respondents returned from Greece.
 
COVID-19 Response and Other Activities
Returnee Rapid Assessment (RRA) 
 
In May and June 2020, IOM, supported by the European Union under the Displacement Tracking Matrix (DTM) Regional Evidence for Migration Policy and Analysis (REMAP) project and in coordination with the Research and Policy unit of the Bangladesh Ministry of Expatriates’ Welfare and Overseas Employment (MOEWOE), along with the NPM team based in Cox’s Bazar, conducted a rapid assessment  among 2,765 Bangladeshi international and internal return migrants in order to y assess their immediate needs and vulnerabilities in the wake of COVID-19. This sample was made up of 1,486 international return migrants and 1,279 internal return migrants. Based on the findings of the first Round 1, IOM conducted a second round of rapid assessment in August and September 2020 among the same respondents to further enhance the understanding of the pandemic’s economic impacts and challenges using longitudinal analysis. The sample included 875 international return migrants and 709 internal return migrants who had also participated in Round 1 (the sample size reduced due to participant drop out).
 
The pilots and studies mentioned above have given a good base to expand the proven methodology which cheap and reliable to whole of Bangladesh. In coming years IOM will undertake various data related capacity building interventions and will continue to support BBS and other relevant ministries to mainstream migration and mobility data into regular data interventions.

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