al., 2020) or to augment traditional data sources to provide better measures of international migrant stocks (Rampazzo et al., 2021). Developing sound approaches to detect migration events in big data (see for example Chi et al., 2020) can create great opportunities for using this type of data for sampling purposes. One possibility could be, for instance, to use these data to identify primary sampling units or to combine them with data on climate change exposure for determining ‘climate migration hotspots’ to be oversampled in addition to the usual, nationally representative survey sample. All this comes, of course, with non-trivial privacy and ethical issues that need to be addressed as well as with technical challenges, which opens room for new, promising methodological streams of research. However, as the value of leveraging this new data to fill many of the gaps of more traditional data sources gets increasingly recognized by social scientists, it is possible that progress on these fronts will speed up.<sup>35</sup>

- While oversampling is the recommended (and perhaps only) direct method to get around the ‘rare event’ problem, a complementary solution can come from **looking at ‘surrogate outcomes’** (McKenzie, 2022) – that is, at immobility as opposed to only mobility responses. As already pointed out, the existing climate migration literature shows that climate-induced immobility is at least as important as climate-related migration (Letta et al., 2023). For that reason, when considering the reference statistical population for climate migration studies, ‘trapped’ or ‘potential’ migrants should be included to supplement the (typically small) ‘actual’ migrant sample. Multi-topic household surveys such as the LSMS hold many comparative advantages for investigating the causal links between climatic shocks and the ‘failure to migrate’, especially as they contain rich information on a variety of topics that allow for precisely assessing the transmission mechanisms and contextual factors at play. To maximize the research potential for studying climate migration traps, survey instruments should be improved and expanded to include modules that systematically collect reliable data on **migration aspirations, intentions, plans, and failed attempts** to migrate, including aspects related to climate-induced liquidity constraints, as well as detailed information on household adaptation and coping strategies and climate risk perceptions. From an empirical perspective, this additional information would substantially improve the ability of LSMS-type surveys to investigate key issues such as immobility _vs._ mobility or the role of liquidity constraints and key mediating channels, and the contextualization of the migration phenomenon within a broader adaptation framework.

> 35 In this regard, the recent COVID-19 pandemic has constituted an important landmark as, for the first time, big tech and mobile network companies started sharing their data for public global good. This has also created incentives for the scientific community to develop analytical techniques for big data and advance the research agenda in many different domains, including human mobility (Letta et al., 2023).

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