to the use of household surveys in the migration domain – mainly the problem of small migrant samples.<sup>11</sup> Other issues concern a paucity of questions (consistent over space and time) to accurately identify types of migration (internal/international, permanent/temporary, return/circular) and correlate them with climate shocks, as well as <mark>limited ability in tracking international migrants and cross-border mobility.</mark> <mark>There is increasing agreement among practitioners that no single data source can resolve all migration data gaps; this could not be truer in the case of climate migration, a research field that, by definition, comes at the intersection of many different disciplines (Parrish et al., 2020). Leveraging innovative, non-conventional data can help attenuate methodological issues that come with the use of more traditional sources (Lohr and Raghunathan, 2017). For example, ‘</mark> big data’ or ‘digital traced data’ are emerging as new sources for migration data and currently shaping revolutionary ways to measure migration (Laczko and Rango, 2018).<sup>12</sup> These innovative data sources possess some characteristics that more conventional sources lack and therefore bear great potential for complementing traditional migration data (Kirchberger, 2021; Tjaden, 2021). To illustrate one example, by tracking the approximate location of individuals, digital trace data make it possible to provide objective (rather than self-reported) measures of migration over a large share of the population and on a continuous, almost real-time basis. In principle, these data can allow the identification of different types of migration (both in terms of length of staying and geographic destination) with great accuracy and at a relatively low cost. This inherent flexibility makes this new data source particularly valuable for detecting and analyzing (im)mobility responses that are triggered by rapid-onset climate or extreme nature events that, for example, could evolve into short-term, transitory moves that would be difficult or impossible to capture in a timely manner using data from traditional sources. Despite their recognized potential, big data remain vastly untested and only sporadically adopted in official statistics. Applications that try to combine new data sources are still in their early stages and important ethical and privacy challenges connected to their use are yet to be resolved. Nascent “data collaboratives” ( <mark>Young and Verhulst, 2018) such as the Development Data Partnership, a collaboration between various international organizations and technology companies for an efficient use of third-party data for international development,</mark><sup>13</sup> <mark>offer concrete promises in this respect, while also supporting the argument that the solution to the migration data problem must come from increasing integration and interoperability of different data sources.</mark><sup>14</sup>

> <mark>that have attempted to track individuals (or a subset of them) beyond country borders, some examples include the tracking of international migrants between Mexico and the US as part of the Mexican Family Life Survey (Rubalcava and Teruel, 2007), between Albania and Greece as part of the Albania Panel Survey 2004 (INSTAT, 2005), and from Tonga to New Zealand as part of a migration lottery experiment (McKenzie et al., 2006).</mark>

> 11 Further discussion on this is provided in section 2.4.

> 12 <mark>For a comprehensive review of the existing digital data sources and their recent application for measuring migration, see Tjaden (2021).</mark>

> 13 For additional information on the Development Data Partnership, see https://datapartnership.org/.

> 14 In a similar manner, establishing linkages between household and mobility surveys can help investigating with greater detail the potential transmission channels through which climate change can lead to changes in the labor market and subsequent cross-border migration (see Section 3.1 for a more detailed discussion on this issue).

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