empirically document it is still very limited.

# **2.3 The main data challenges**

The above discussed unresolved aspects are amplified when combined with the issue of severe data gaps at the microeconomic level. First and foremost, migration – especially international migration – is generally a ‘rare event’, leading most often to small sample sizes in household surveys or administrative sources (Lucas, 2021). This condition is further exacerbated in the case of climate-induced migration, as climate change impacts are normally concentrated at specific hotspots (which is especially true when the push factor is a fast-onset event). Proposed solutions to this problem are not straightforward to implement – at least in the short-run – as they imply alterations to existing samples and availability of adequate sampling frames (or, in their absence, the adoption of second-best alternatives to obtain representative samples) (McKenzie and Mistiaen, 2009; De Brauw and Carletto, 2012; Bilsborrow, 2016). In addition to this, migration is a dynamic phenomenon which can be captured only through repeated observations over a sufficiently long time span. Yet, there is a widespread dearth of such data because collecting panel data on migrants is costly and complex, whether through direct respondents or proxy ones.<sup>17</sup> These are some of the issues explaining the general paucity of migration-related longitudinal microdata from developing countries. When the focus is on climate migration, the challenges become even more problematic. Available evidence shows that contextual factors and transmission channels play a decisive role in determining the direction of the climate-migration nexus, thus rich information regarding all the potential intervening mechanisms is absolutely essential. However, finding a micro dataset combining longitudinal information on adequate samples of migrants and non-migrants with multi-topic and multi-purpose data can be a hard task. Furthermore, even if these data are available, there are still endogeneity issues related to the non-random nature of the migration decision that should be addressed through sound identification strategies or, ideally, experimental approaches (McKenzie and Yang, 2010; McKenzie et al., 2010).

In the context of climate migration, to understand the relationship of sequential interactions between the decision to migrate and other _in situ_ adaptation strategies, data is needed on coping strategies, agricultural practices, climate risk perception, and risk aversion and preferences. If the aim is to shed light on whether, in a given setting, the prevailing response will be mobility or immobility, it is necessary to combine data on migrants with information on potential migrants, the intention to migrate in response to shocks and changes, climate-induced liquidity constraints, stated preferences, and other similar information.

Further complicating matters are complexities tied to the nature of the climatic events themselves. In the case of fast-onset shocks, standard data sources are unlikely to capture the migratory responses of the affected population, and _ad hoc,_ swiftly implemented post-disaster surveys are needed. On the other hand, slow-onset

> 17 One well-known exception is the Mexican Family Life Survey (MxFLS) managed by the Iberoamerican University and the Center for Economic Research and Teaching, which has been collecting information on a wide range of socioeconomic and demographic indicators for more than 7,500 households over a 10-years period as well as on the individuals or households that grew out from the - original sample, including those who migrated within Mexico or to the US. For details about the MxFLS, see http://www.ennvih <u>mxfls.org/english/index.html.</u>

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