# **_Reconstructing the climate migration chain_**

Recognizing that human mobility happens along a continuum of needs, possibilities, and choices that are shaped by direct or indirect push and pull factors, implies there can be many steps in the migration process possibly culminating in cross-border movements (Malmberg, 1997). However, internal and international migration are almost always seen as separate processes and rarely analyzed jointly (Cirillo et al., 2022), and climate-related migration is no exception. In the context of climate migration, reconstructing a potential chain and assessing the existence of indirect and secondary climate migration effects is a complex analytical task requiring an extensive amount of information that household surveys alone cannot provide. While priorities such as the need to better understand potential climate immobility traps can easily be accommodated in the framework of LSMS-type surveys through the means discussed above, there is little that can be done to improve the traceability of international migrants: first, because the ‘rare event’ issue gets even more severe when it comes to international migration, in light of its higher costs; second, because although household surveys are often well-equipped to track internal movements, tracking cross-border migration is much harder. This hinders a thorough investigation of the full climate migration chain which potentially leads from internal to cross-border mobility. Yet again, some concrete possibilities may be offered by enhancing the integration of LSMS surveys with other traditional and non-traditional data sources. In general, integrating these different sources would enhance the ability of LSMS surveys at all levels but, as depicted in Figure 1, would be especially critical for shedding better light on the intermediate and final steps of the climate migration chain. Towards this end, we propose the following suggestions:

- LSMS-type surveys are crucial for the investigation of internal rural-urban movements triggered by initial climatic shocks and should be used to cover data needs for this type of empirical analysis which pertain to the first steps of the climate migration chain. For subsequent steps, _i.e._ , crossborder migration out of overcrowded urban areas triggered by pressures on urban labor markets generated by the initial internal flow, household survey data must be complemented by other data sources, such as labor mobility data from labor force surveys, bilateral migration flows, indirect estimates retrieved from population censuses, and also non-conventional data such as citizengenerated information. Understanding how such diverse sources can be consistently and thoroughly integrated with each other should be a priority area for future work, and one which would unleash an untapped potential for sound research on policy-relevant issues for which we currently have little to no evidence. Regarding the climate migration chain, for example, the few available studies (e.g., Marchiori et al., 2012) use country-level data based on direct and indirect estimates from various country-level sources to proxy rural-urban migration caused by climate-related events using the weather-induced increase in the share of urban population at the country level. Going micro by

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