data collections are more infrequent, with the COVID-19 pandemic contributing to further disruptions (United Nations & World Bank, 2021).

Although efforts have been made in analyzing gaps and limitations of existing data sources to provide recommendations for improving methodologies and tools (Bilsborrow et al., 1997; Lucas, 2000; De Brauw and Carletto, 2012; Bilsborrow, 2016; Global Migration Group, 2017; Kirchberger, 2021; Tjaden, 2021), at present, the lack of accurate, meaningful data on migration flows – especially on international migration – at the global, regional and, in some contexts, national level is still one of the main constraints to study different types of mobility patterns. Moreover, much of the emphasis for improving migration data has focused on the quantification aspect, positing census data as the gold standard for computing reliable migration rates. Qualifying migration, that is, determining its drivers and impacts at a fine scale, should be equally important and an essential complement to accurately estimate “how much” migration is happening and is expected to happen.

A detailed review of the issues with defining migration goes beyond the scope of this paper.<sup>7</sup> However, it is clear that old and new challenges apply when it comes to producing definitions and measurements in the domain of “climate migration”, which is itself an umbrella term encompassing different circumstances such as forced displacement, planned relocation, or even involuntary immobility.<sup>8</sup> Multiple (push and pull) mediating factors contribute to determining different types of mobility responses – and associated impacts – in the face of climate change-induced shocks. To be able to disentangle these dynamics and assess the effects on different groups of individuals (e.g., women, marginalized groups, etc.), rich, granular, contextual data and accurate weather and climate information are needed, in addition to data on actual movements of people. This type of information is generally not available in population censuses or administrative records/registries and has only limited availability in most of the existing household surveys that are typically used to source data on migration such as Demographic and Health Surveys (DHS), National Labor Force Surveys (LFS) or even specialized migration surveys.<sup>9</sup> Conversely, multi-topic household surveys, by their very nature, collect data on a multiplicity of topics that go beyond household demographics and basic socioeconomic characteristics, making them an attractive prospect for studying complex, multidimensional phenomena such as the interrelationship between climate change and migration. Such surveys are common in many countries around the world, including in low- and lower-middle-income countries, where they are typically implemented on a regular basis, therefore providing more up-to-date data compared to censuses. Oftentimes these surveys are also longitudinal in nature (i.e., follow the same households and individuals over time) and georeferenced, which are both essential features for analyzing temporally and spatially dynamic processes such as climate-induced mobility and migration more generally.<sup>10</sup> Despite these appealing elements, however, there are recognized limitations

> 7 <mark>For a detailed discussion about concepts, definitions, and issues in measuring migration see Bilsborrow (2016).</mark>

> 8 <mark>This conundrum has also been fueling the (unresolved) debate among international human rights lawyers as to whether the definition of “refugees” should be expanded to include “climate refugees” (Brown, 2008). Indeed, addressing this controversy will have strong implications for policies as well as for the collection of data on cross-border mobility.</mark>

> 9 <mark>For a detailed description of the most common sources of migration data, their limitations and prospects, see Bilsborrow (2016).</mark>

> 10 <mark>Longitudinal household surveys track movers to minimize the attrition rate and address potential selectivity biases in non-random attrition (Witoelar, 2011). This type of tracking, however, is generally done within domestic boundaries. There is a dearth of studies</mark>

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