metadata: authors: 0: author: Carletto,Calogero
1: author: Letta,Marco
2: author: Montalbano,Pierluigi
3: author: Paolantonio,Adriana
4: author: Zezza,Alberto
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docna: 0: docna: Too Rare to Dare ? Leveraging Household
            Surveys to Boost Research on Climate Migration
docty: Policy Research Working Paper
abstracts: cdata!: Reliable microeconomic data to
            understand the climate-migration nexus are virtually
            nonexistent. Nationally representative multitopic household
            surveys are rarely, if ever, explicitly designed for
            studying migration issues. Despite this limitation, most
            countries have no alternatives to the use of household
            surveys when it comes to analyzing complex multidimensional
            phenomena such as the interrelationship between climate
            change and migration. This paper offers a critical
            reflection on current challenges faced by multi-topic
            household surveys in responding to this need, but also, more
            importantly, on the many opportunities embedded in their
            use. Specifically, using the Living Standards Measurement
            Study as a case study, a conceptual framework, practical
            empirical guidance, and a methodological agenda are proposed
            to address data gaps and contribute to a more solid
            understanding of the climate-migration nexus.
display_title: Too Rare to Dare ? Leveraging
            Household Surveys to Boost Research on Climate Migration
listing_relative_url: /research/2023/11/34200675/too-rare-dare-leveraging-household-surveys-boost-research-climate-migration
topicv3: Climate Change m1326829 567,Household Surveys m1504071 2761,Migration Policies and Jobs m1504161 2836
publishtoextweb_dt: 2023-11-21T00:00:00Z
dois: 10.1596/1813-9450-10613
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
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model_extractions: Policy Research Working Paper 10613

# Too Rare to Dare?

Leveraging Household Surveys to Boost Research on Climate Migration _Gero Carletto Marco Letta Pierluigi Montalbano Adriana Paolantonio Alberto Zezza_ Development Economics Development Data Group November 2023
Policy Research Working Paper 10613

# **Abstract**

Reliable microeconomic data to understand the climate-migration nexus are virtually nonexistent. Nationally representative multitopic household surveys are rarely, if ever, explicitly designed for studying migration issues. Despite this limitation, most countries have no alternatives to the use of household surveys when it comes to analyzing complex multidimensional phenomena such as the interrelationship between climate change and migration. This paper offers a critical reflection on current challenges faced by multi-topic household surveys in responding to this need, but also, more importantly, on the many opportunities embedded in their use. Specifically, using the Living Standards Measurement Study as a case study, a conceptual framework, practical empirical guidance, and a methodological agenda are proposed to address data gaps and contribute to a more solid understanding of the climate-migration nexus.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at apaolantonio@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Too Rare to Dare? Leveraging Household Surveys to Boost Research on Climate Migration**

Gero Carletto _Development Data Group, The World Bank_ <u>gcarletto@worldbank.org</u> Marco Letta _Sapienza University of Rome and Global Labor Organization (GLO)_ <u>marco.letta@uniroma1.it</u> Pierluigi Montalbano _Sapienza University of Rome and University of Sussex_ <u>pierluigi.montalbano@uniroma1.it</u> Adriana Paolantonio<sup>*</sup> _Development Data Group, The World Bank_ <u>apaolantonio@worldbank.org</u> Alberto Zezza _Development Data Group, The World Bank_ <u>azezza@worldbank.org</u>

**Keywords:** migration, climate change, household surveys, LSMS

# **JEL classification:** O15, Q54, R23, C83

**Acknowledgments** : We are grateful to Mauro Testaverde for valuable comments and suggestions on an earlier draft of this work. We also thank participants at the IFAD 2022 Conference on “Jobs, Innovation and Value Chains in the age of Climate Change” where this work for presented for the first time.

* Corresponding author.
# **1. Introduction**

Migration<sup>1</sup> constitutes a fundamental mechanism for reallocating labor across economic sectors and geographies to adapt to shocks, stresses, and imbalances. If managed well, migration can bring important gains at different levels and scales: it can improve the well-being and make more resilient the livelihoods of the families and communities of origin; it can ease labor market constraints in destination economies while at the same time alleviating un(der)employment pressures in places of origin; it can contribute to developing skills that migrants may bring back when they return home; and it can help people surviving violence, conflicts, natural catastrophes and environmental disasters caused by climate change. Achieving this requires implementing effective policies in all sectors, which in turn requires a thorough understanding of migration, its many nuances, and underlying causes and mechanisms at both the national and supranational levels (World Bank, 2023).

Although migration happens predominantly within borders, the number of international migrants in the world increased by more than 83 percent between 1990 and 2020, according to estimates from the United Nations Department of Economic and Social Affairs (UN DESA). However, the proportion of international migrants in the world’s overall population remains at less than 4 percent, with greater concentrations in high-income countries and most of these immigrants come from middle-income countries, with only a small share from low-income countries (UN DESA, 2020).<sup>2</sup> On the other hand, a lot of migration is currently happening within and across developing countries. For example, more than 50 percent of African migrants move within Africa, and this tendency is even more pronounced within regions such as West Africa where about 64 percent of migrants move to another destination within West Africa (UN DESA, 2020). Such movements have serious development and political implications and are currently animating public debate in many developed and developing countries, bringing renewed attention to the study of migration both in the scientific research community and among policy makers.

Despite this revamped interest, however, systematically measuring migration and its underpinnings remains a challenge, as also highlighted in the recent Global Compact for Safe, Orderly, and Regular Migration.<sup>3</sup> This is due to multiple factors but, above all, is the result of a paucity of accurate and regular data on migration, especially in low-income settings, and difficulties in reconciling information collected at different temporal and spatial scales regarding potential drivers and other important contextual factors (Parrish et al., 2020).

In 1990 the IPCC argued that the biggest impact of the changing climate might be on human displacement. Since then, the role of climate change in driving – directly or indirectly – migration has been progressively

> 1 Throughout this paper, the term “migration” is used in a broad sense, referring to all people who have moved away from their home (or usual place of residence) to live temporarily or permanently in another area in the same country or in another country, unless otherwise specified (e.g., internal migration, international migration, etc.).

> 2 Based on UN DESA data on international migrant stock in 2020, 65 percent of international migrants worldwide have high-income countries as their destination. In terms of origin, 65 percent of all immigrants in high-income countries come from middle-income countries, 25 per cent from other high-income countries, and only 6 percent from lower-income countries.

> 3 See: https://www.iom.int/resources/global-compact-safe-orderly-and-regular-migration/res/73/195.

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recognized and increasingly studied (Hoffmann, 2020), gaining more prominence, and possibly also spurring more controversy, in the development research agenda.

Climate migration is not a new phenomenon. Human populations have historically used migration as a strategy to adapt and respond to changes in the climate (IOM, 2009). However, as these changes become more severe, widespread, and frequent, more and more people are expected to move as a consequence of both slow onset climate events and sudden climate shocks (Clement et al., 2021). As a result, the field of climate migration modeling has been evolving fast in recent years, allowing researchers to start quantifying this new phenomenon. The latest projections indicate that more than 210 million people will be forced to migrate within their own countries by 2050 due to climate change (Clement et al., 2021) with the poorest countries bearing the greatest burden (World Bank, 2021). While these figures have become accepted in the media (Lustgarten, 2020) and among specialized international institutions, it is also recognized that they come with caveats and do not often have empirical support in the literature (Letta et al., 2023). This is because, at present, climate migration models are based on fairly general assumptions about migration behavior due to a lack of reliable global datasets on climate-related human movements (McLeman, 2013).<sup>4</sup> Moreover, too little is known about the impact of climate change on international migration. Empirical efforts have focused mostly on modeling internal migration and evidence from the literature seems to support the view that most of the movements induced by changes in climate are currently occurring within national borders (rural-rural or rural-urban). While it is reasonable to assume that in the short-run climate-related migration is mainly a domestic issue, the latent potential for large-scale, international movements is often referenced in public discourse (Lustgarten, 2020) although poorly studied and substantiated by empirical evidence.<sup>5</sup> In particular, there is an absence of rigorous evidence on the interconnection between internal and international migration in the medium- to long-term (Cirillo et al., 2022) and the linkages with different types of climate change occurrences (slow-changes _vs._ rapid-onset shocks or their cumulative effect). Datasets on international migrants (especially on flows) are rare and outdated and, even when available, they rarely include the type of information that can be used to reconstruct the chain of migratory events potentially triggered by distinct climate change events or to understand behavioral responses and impacts at the micro level. Recognizing that repeated droughts, floods, progressive warming, sea level rise and desertification are much more important as indirect (rather than direct) drivers of cross-border mobility yields new, context-specific empirical questions – and associated data demands – regarding the channels through which this indirect mechanism operates. This suggests a change of perspective is needed to overcome the traditional distinction between “survival” migration and economically-driven migration, and consider the continuum (World Bank, 2023) and related intermediate outcomes that can act as push factors in determining international movements.

> 4 The study of climate migration involves the use of a variety of approaches many of which can be simplistic as they do not account for any behavioral aspects, or they do only to a limited extent (e.g., exposure mapping, gravity modeling). More refined approaches, such as agent-based models, that build on statistical and econometric analysis to model the behavior of different decision makers to climate-induced changes are still scarcely utilized as they require data on migration and key determinants at the micro level.

> 5 See section 2.2 for a more detailed discussion on this issue.

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Another often overlooked yet complementary aspect of climate migration studies is climate-induced immobility traps. This refers to the same climate shocks possibly creating the opposite outcome by reinforcing pre-existing vulnerabilities and aggravating liquidity constraints, forcing poorer households to stay due to a lack of necessary resources to cover the costs of migration. Under this alternative scenario, climate change would reinforce existing and generate new local poverty traps and vulnerability pockets in the most affected (especially rural) areas, which should make climate-related immobility as much of a concern as mobility (Findlay, 2012; Letta et al., 2023).

Gaining a firm understanding of these complex dynamics requires clarity about what we need to measure, at what scale and how and, more importantly, filling data gaps on climate migration at the micro level. This not only involves providing more accurate quantification of migration inflows and outflows, but also improving the ability to properly characterize the phenomenon and empirically assess the ultimate socioeconomic and welfare impacts and how they differ across population subgroups (e.g., What type of migration does climate change induce? How do those migrating differ from those who stay? What channels and contextual factors mediate different migration responses and the realization of different outcomes, and how do they interact with each other?). Conducting these measurements requires an adequate amount of sufficiently disaggregated information on multiple socioeconomic aspects and living standards. It also requires this information to be collected in a consistent and systematic way, made available and accessible in a timely manner, and updated regularly. Addressing these data needs in the immediate future is instrumental to building the evidence base needed to inform policies and development interventions aimed at improving climate change adaptation and promoting better forms of mobility.

Household surveys are one of the data sources traditionally used to measure and study migration. Despite some recognized limitations, _multi-topic_ household surveys hold great potential when it comes to analytically addressing complex, multidimensional phenomena such as climate and migration, which require data on multiple livelihood dimensions and possibly over extended periods of time. When georeferencing is used in household surveys it allows for integration with other data sources, offering even greater potential to achieve higher levels of accuracy and granularity at lower cost as well as to increase the relevance of survey data production and their downstream reuse (e.g., for ground-truthing and calibration purposes).<sup>6</sup> In the last few years there has been a progressive push towards repositioning household surveys in the face of an evolving data environment and new demands for policy-relevant data in different fields (Carletto and Gourlay, 2019; Carletto, 2021; United Nations Statistical Commission, 2021). The COVID-19 pandemic accelerated this process and proved that household surveys are flexible enough instruments to respond effectively to modern data collection challenges. New modes of data collection as well as integration with alternative data sources have been tested and validated. Leveraging technological innovations, more can be done in the near future to improve survey methodologies and data collection methods (Zezza et al., 2022;

> 6 This, of course, must be done in compliance with adequate security standards to protect confidentiality (United Nations Statistical Commission, 2021).

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Kirchberger, 2021). These recent experiences, coupled with the increasing demand for more and better data on migration and the growing interest of scholars and policy makers in the climate-migration relationship, make this a very opportune time to rethink the role of household surveys in this domain and identify potential areas of improvement.

Building on recent insights from Letta et al. (2023), this paper proposes a three-part solution for advancing the climate migration agenda by leveraging the use of household surveys. First, it offers a critical reflection on the current challenges and opportunities for household surveys to fill data gaps and contribute to shedding light on some crucial aspects of the climate-migration nexus and its implications for household and individual welfare. Second, it proposes a conceptual framework that identifies key relationships, focusing on burning issues such as the direct and indirect contribution of climate change to cross-border mobility, and assesses the corresponding data needs to operationalize this empirically and the potential of multi-topic household surveys to address these needs. Third, the paper provides practical empirical guidelines for researchers interested in leveraging existing household surveys to investigate the climate-migration nexus, with special emphasis on how to assess and reduce the rare event problem, explore the migration-adaptation relationship, and address issues related to migration definition, variable construction, and identification strategy. For the sake of concreteness, we tailor our recommendations to one of <mark>the largest and longest-running household survey programs worldwide,</mark> namely, the Living Standards Measurement Study (LSMS) of the World Bank. Among academics, LSMS surveys are often referred to as vested candidates for contributing to an increasing demand for migration data (Bilsborrow, 2016). Using LSMS surveys as an example, the paper identifies priority entry points for enhancing the value of household surveys as a source for accurate and reliable climate migration data and proposes some concrete actions that can be undertaken for achieving this goal in the short- and medium-term.

The remainder of the paper is organized as follows. Section 2 provides essential information on data sources and characteristics of climate-related migration, summarizes the most recent findings of the empirical literature, and assesses the main remaining data gaps hindering further understanding of the climate-migration nexus. Section 3 proposes an empirically-oriented conceptual framework on climate migration and outlines a methodological agenda that serves as a guide to maximize and boost the potential of household surveys for the study of climate related migration. Section 4 puts forward empirical guidelines for the use of existing surveys. Section 5 concludes.

# **2. Assessing the climate migration data landscape: Gaps, limitations, and the quest for doing better**

# **2.1 Definitions, metrics, and data sources: Navigating an imperfect (but promising) world**

Measuring migration is a long-standing thorny issue. In general, data on any type of migration is difficult to collect precisely. The intrinsic complexity associated with the concept of migration, its dynamic nature (across space and time), and the lack of universally-accepted definitions represent a challenge in the development of appropriate data collection systems on migration. This applies especially where national statistical systems are weak, which is more common in low- and lower-middle-income countries. In such cases, regular, systematic 5
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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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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<mark>The next two sections s</mark> ummarize the empirical literature on climate change and migration and identify the main evidence and data gaps that could be closed by leveraging existing multi-topic household surveys as a starting point.

# **2.2 The status of the literature on climate change and migration**

In a recent data-oriented synthesis of the current state of empirical climate migration literature, Letta et al. (2023) notice that while the literature has grown exponentially in the last decades, this growth has not resulted in definitive evidence regarding the nature and magnitude of climate-migration nexus, nor in a clear-cut identification of transmission channels. There are two main reasons for this lack of correspondence between research output and conclusive evidence. The first is the nature of the subject matter itself: migration is per se an irreducibly complex and context-specific phenomenon and, when coupling it with the vast uncertainties surrounding the effects of climate change, it becomes clear how difficult it is to distinguish climate migrants from other types of migrants. The second, as already stated, is the existence of severe data gaps in developing contexts, especially at the microeconomic, longitudinal level. While precise figures of migratory flows induced by climate change are not likely possible, the literature equips us with four important and policy-relevant qualitative insights.

First, **evidence-based projections are needed** . Currently, there is no empirical evidence to support projections of sudden and massive migratory flows as a response to climate change, especially when referring to international migration. Instead, the available evidence suggests that climate-induced immobility, _i.e._ , a _decrease_ in migration in response to climatic shocks, is at least as important as climate-related migration. Immobility appears to be driven by the existence of liquidity constraints generated or aggravated by climate change, which makes it impossible for the poorest households to migrate in response to the shock. From this perspective, better-off households can choose who migrates in order to diversify risk and, as such, can be considered more resilient than those who are forced to stay. Furthermore, when weather shocks and climate volatility do trigger mobility responses, the literature recognizes that these are predominantly internal, ruralurban movements rather than costlier, longer-distance cross-border flows. As already pointed out, this is in stark contrast with the predominant attention international climate migration usually receives in the policy arena.

Second, **slow-onset changes matter more than fast-onset shocks** . Slow-onset changes such as repeated droughts, progressive warming, and desertification are more likely to induce longer-term or permanent migration compared to fast-onset shocks such as hurricanes, storms, and floods. Slow-onset events are also more closely linked with possible immobility traps in rural and agriculture-dependent areas in poor countries.<sup>15</sup> The bifurcation of the migration response in the face of slow-onset changes – immobility _vs._ mobility – seems

> 15 In principle, fast-onset shocks may also lead to immobility, as they can quickly deplete households’ financial resources (Kaczan & Orgill Meyer, 2020). In practice, however, the most widespread consequence of sudden events is displacement or forced relocation. 8
to stem predominantly from contextual factors and differences in income, wealth, and financial liquidity constraints.

Third, **heterogeneity is irreducible** . The heterogeneity of (im)mobility outcomes in the empirical literature is due to a host of mediating channels, including, among others, socioeconomic and demographic characteristics, assets and resources, risk preferences, risk management and coping strategies, gender and other cultural perceptions, and context-specific or geographic factors. These channels are so important that they can alter not just the magnitude but even the direction of the climate-migration link.

Fourth, **migration is adaptation** . It is increasingly evident that migration is just another form of adapting to weather shocks and climate change – or more aptly, the outcome of an inability to adapt locally to global climate-induced changes. From this perspective, migration is one among many in the pool of potential coping strategies households can adopt in response to change.<sup>16</sup> Placing migration within the broader spectrum of climate adaptation allows two fundamental types of migration to be distinguished: migration as adaptation, _i.e._ , a risk-management strategy that is primarily a prerogative of better-off households, and ‘survival’ or lowreturn migration, implemented by the poorest households when _in situ_ adaptation has not worked or is not feasible. Although shifts are observed along a continuum rather than a clear-cut binary reality, establishing this key distinction between forced (or involuntary) and discretionary (voluntary) migration helps to reconcile the heterogeneous and apparently contrasting findings on the type of migratory responses induced by weather shocks and climate change.

The above insights provide a more nuanced and realistic picture of the climate-migration relationship, especially when compared with traditional narratives on the matter. In essence, the irreducible heterogeneity and complexity of the phenomena involved imply that the sign and size of climate change impacts on human (im)mobility are ultimately a context-specific empirical question. Additionally, though it is theoretically possible to identify _a priori_ the key transmission channels, the conceptual toolbox needed to unveil the main sources and consequences of such heterogeneities remains incomplete, mainly due to the current lack of a unified and comprehensive theoretical framework on climate adaptation and migration.

Regarding empirics, we are a long way from disentangling the role of the many contextual factors and mediating channels at play. Similarly, the relationship between (im)mobility and liquidity constraints (due to weather shocks, wealth, and resources) is still not fully understood.

Lastly, we reiterate the scarcity of studies trying to reconstruct a potential climate migration ‘chain’, that is, the cumulation of direct and indirect effects on both internal and international migration. Think, for instance, of a not-implausible scenario in which climate change induces internal migration in the form of rural-urban movements, which, in turn, triggers subsequent cross-border migration by placing pressures on urban labor market dynamics. Such a course of events may already be taking place in parts of the world, but the ability to

> 16 Depending on the specific type of migration response, this can be conceived as either a “proactive” (or ex-ante) or “reactive” (or expost) risk management strategy. One common example of the first in the context of agricultural livelihoods is off-season migration to diversify income sources and mitigate the potential risk of weather-related agricultural income losses.

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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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events require sufficiently long panels to track the effects of such gradual changes.

Lastly, considering the key role played by agriculture as the main income-generating activity for households living in rural areas, linking detailed information on agricultural output with high-resolution, crop- and areaspecific, growing-season climatic time series is required. In sum, when combined all together, the existing data gaps and needs represent one of the main obstacles that currently prevent further advancements in applied research.

# **3. Shedding light on the climate-migration nexus: An opportunity for innovation**

# **3.1 An empirically-oriented conceptual framework on the climate migration nexus**

An empirically-grounded conceptual framework on the climate-migration nexus, depicted in Figure 1, highlights data gaps that can be addressed through multi-topic household surveys. This framework draws inspiration from theoretical insights provided by the extensive literature on asset-based and shock-driven poverty traps (Barrett and Carter, 2013; Carter and Barrett, 2006; Carter et al., 2007 and 2008; Jalan and Ravallion, 2002; Letta et al., 2018) and incorporates the analysis of mobility patterns into the broader context of resilience and adaptation.

The framework starts by distinguishing migratory responses depending on the nature of the climatic event: slow-onset changes _vs._ fast-onset shocks. We include as slow-onset changes events such as desertification, progressive warming, droughts, and permanent changes in rainfall patterns.<sup>18</sup> Fast-onset events refers to devastating floods, torrential rains, hurricanes, cyclones, and storms. As discussed in subsection 2.2, this is a standard distinction in the literature (Cattaneo et al., 2019), which is rooted in the different nature of the climatic events: in the face of slow-onset changes and cumulative, repeated shocks, the outcome is typically either long-distance, (semi-)permanent migration or immobility, depending on how the shock interacts with the mediating channels and contextual factors. In the case of fast-onset shocks, which typically involve forced physical relocation from the place of living, the prevailing outcome is usually a type of migratory response which is temporary and over short distances (Letta et al., 2023). Given the strictly ‘involuntary’ aspect of movements triggered by sudden shocks, the mediating channels and contextual factors play a much more minor role in shaping the climate-migration relationship, as the displacement brought about by such shocks typically leaves less if any room for aspects such as _in situ_ adaptation or the decision to send a household member away to diversify risk (economically driven migration), regardless of household wealth, assets, and resources.

Unlike the response to fast-onset shocks which is intrinsically immediate, when there is a migratory response to slow-onset events, it tends to be a long-run response, in the form of gradually progressive migratory flows. These events are more likely to activate a range of alternative “adaptation” pathways, both on and off the farm.

> 18 We do not include sea-level rise among slow-onset changes because its impact on migration is expected to materialize only in the (very) long run (Duijndam et al., 2021). Also, the nature of sea-level rise related hazards that can unfold in the shorter term is mixed as they can comprise both more gradual changes such as soil and groundwater salinization or erosion as well as sudden occurrences such as flooding from storm surges (Duijndam et al., 2021), and these events are in fact covered in the conceptual framework.

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Building on the original conceptual framework proposed by Carter and Barrett (2006), the potential for mobility in the case of slow-onset events is mediated by _in situ_ adaptation, assets, and liquidity constraints. _In situ_ adaptation encompasses various on-farm practices, including the use of fertilizers, pesticides, improved seeds, irrigation, and the adoption of sustainable land management practices. The availability of assets is crucial for identifying dynamic thresholds, above which households are expected to maintain a smoother consumption path (through asset decumulation) over time and experience less pressure to migrate. Liquidity constraints can significantly impede mobility, especially when compounded by adverse weather shocks induced by climate change. The “+” and “-” signs above the arrows (in Figure 1) represent the signs of the depicted relationships and impacts.

This conceptual framework provides valuable insights for empirically analyzing climate-induced mobility. First, it emphasizes that the relationship between weather shocks and mobility is complex and non-linear, mediated by several key factors that depend on household-specific values and contextual elements. Second, it underscores the need to identify heterogeneous effects of climate shocks, where outcomes are not binary but instead include mobility, immobility, and traps that perpetuate immobility. Third, it highlights the importance of estimating critical thresholds that determine households’ migration responses based on their assets, welfare, and liquidity constraints.

Understanding whether this mobility is temporary or long-term and assessing its impact on local labor markets or other domains, potentially even across borders, requires further investigation and the identification of additional transmission channels. However, while these departures can also be associated with cross-border movements, the existing literature tells us that this is less likely: international migration as a response is rare as its costs are prohibitive for most households living in rural developing contexts. Internal migration is much more common and usually takes the form of rural-urban movements.<sup>19,20</sup> The initial stage of migration is typically a choice of relatively better-off households who aim to diversify risk and income and can sustain the costs associated with the departure of one of its members. For the poorest households, however, the most likely outcome is an immobility trap, in which the slow-onset changes further deplete their already limited assets and resources and aggravate their liquidity constraints, forcing them to stay even if they are unable to adapt. As household members migrate internally in response to slow-onset events, there is a potential domino effect: flows into cities may lead to pressures on urban labor markets through wages and urbanization effects. This, in turn, may trigger economically motivated cross-border movements from these overcrowded cities to other countries and regions.<sup>21</sup> In such cases, climate change would be an _indirect_ rather than _direct_ push factor. From an empirical perspective, this means that internal migration can result in

> 19 Note that rural-rural movements can also be comprised within the rural-urban migratory response if progressive arrivals in the same rural destination gradually lead to a reclassification of this rural area into an urban one.

> 20 Indeed, international patterns go beyond the scope of this appraisal, as our main objective is to assess the utility of household surveys in shedding light on the initial stage of the depicted framework, while addressing the inherent challenge of the “rare event” issue. 21 We remark that the channel leading to secondary cross-border migration may not be necessarily an economic one but could also consist of social unrest or political instability in urban areas due to problems such as discrimination, labor exploitation, unequal access to basic services.

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intermediate outcomes associated, for example, to growing pressure on labor markets in destination areas, with international migration as a possible final outcome.

Importantly, the relationships illustrated in this framework are based on the implicit assumption that, in response to both slow-onset changes and sudden shocks, the decision-maker is the household: sending away one or more of its members as migrants to dampen the effects of slow-onset changes or moving entirely if forced to do so by a fast-onset disaster. This implies the household, rather than the individual migrant, as the key unit of analysis in empirical studies, a point we will return to in Section 4.

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**Figure 1: A literature-based, empirically-oriented conceptual framework on the climate-migration nexus**

<!-- Start of picture text --> POTENTIAL POTENTIAL POTENTIAL<br>MEDIATING<br>TYPE OF EVENT OUTCOMES 1 INTERMEDIATE OUTCOME 2 FINAL IMPACT<br>CHANNELS<br>(direct effect) OUTCOME (indirect effect)<br>Pressures on<br>R-U internal<br>migration, urban labor + Cross-<br>mostly markets: wages border<br>and urbanization<br>permanent, mobility<br>effects<br>Wealth, assets, econ. driven<br>liquidity constraints,<br>in situ adaptation,<br>climate risk<br>-<br>(mis)perception, - Welfare<br>Slow-onset context-specific Immobility<br>events factors = (Lack of) traps impact<br>adaptation/risk in O-D<br>management places<br>strategy<br>Cross-border<br>mobility<br>(rare, costlier)<br>Short-distance,<br>Welfare<br>+ temporary -<br>Fast-onset impact<br>movements;<br>shocks in O-D<br>‘survival’<br>places<br>migration<br><!-- End of picture text --> 14
# **3.2 Can household surveys help bridge existing climate migration data gaps?**

<mark>The conceptual framework presented here suggests that, despite existing limitations,</mark> multi-topic household surveys <mark>are an essential source of information</mark> for studying the key elements of the climate-migration nexus, and that even greater gains could be achieved if efforts are made to enhance their fitness for purpose in this domain. For example, standard household surveys are clearly disadvantaged in assessing the impact of fastonset shocks: because these appear suddenly and tend to be localized, nationally-representative surveys are unlikely the ideal tool to study their impact. However, coupling them with _ad hoc_ , swiftly implemented post-disaster surveys – perhaps in the form of (high frequency) phone surveys, light/rapid “response” surveys, or community-resident-based surveys – may improve their usefulness for addressing such shocks. Having established, well-functioning, regular national survey programs in place makes these types of data collection workable as there are higher chances that national survey data can be used as pre-shock baselines in quasi-experimental research designs (see for example McCarthy et al., 2018) and, more broadly, can facilitate the design of such instruments.<sup>22</sup> When considering slow-onset events, the framework as well as the literature more generally, implies that the migratory outcome should not be the only outcome of interest for researchers and policy makers. This suggests that broader reference statistical population for climate migration studies should be considered – one which encompasses ‘potential migrants’ and people ‘trapped’ in immobility due to the formation of local vulnerability pockets and poverty traps triggered by climate change. From a practical perspective, the study of immobility and the identification of such populations would benefit from the collection of additional information within multi-topic household surveys, such as migration aspirations and intentions, and household adaptation and coping strategies in the face of climatic shocks. Strengthening the focus on climate-induced immobility traps can also represent a complementary solution to the ‘rare event’ problem since the ‘trapped’/‘potential’ migrant sample supplements the ‘actual’ migrant sample.<sup>23,24</sup> Exploring concrete possibilities for integrating multi-topic household survey data with mobility data or labor data in destination places, and enhancing the interoperability between these different data sources, will be key for expanding the knowledge base on migration chains; for example, it would allow assessing

> 22 Note that this applies to all kinds of unforeseen shocks. A very recent example is the High-Frequency Phone Surveys on COVID19 (HFPS) supported by the Living Standards Measurement Study (LSMS) team at the World Bank which leveraged existing LSMS (face-to-face) survey systems in seven Sub-Saharan countries for rapid and timely implementation of monthly phone survey rounds to track responses and the socioeconomic impacts of the pandemic in those countries (Gourlay et al., 2021).

> 23 Other ways for addressing or attenuating the ‘rare event’ problem are discussed in section 4.

> 24 By routinely collecting accurate information on migration intentions, actual migration episodes and characteristics of migrants as part of longitudinal household surveys, it will be also possible to assess if and to what extent intentions are able to predict future migratory flows. This can be particularly relevant for policy makers as, if it is shown that migration intentions are good predictors of future moves, this type of data can help in identifying possible emigration hotspots and characterizing the different profiles of the potential migrants, thus anticipating the future challenges the sending and recipient countries may face.

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whether climate-induced rural-urban movements lead to pressures on urban labor markets of destination cities, and whether/how that in turn leads to international movements. Apart from the more accurate and detailed information on urban market dynamics available in labor surveys, this integration would also provide adequate temporal data coverage for sequential analysis, which is currently difficult to achieve when only using longitudinal household surveys that often consist of a limited number of waves. To facilitate this integration, efforts should be made to improve the accessibility of data sources, fostering data interoperability by design, establishing total quality frameworks for data integration, and maintaining ethical standards and data confidentiality (United Nations Statistical Commission, 2021). To study the potential outcome of cross-border movements, integration with non-traditional data such as remote sensing, mobile phone traces, social media, and other types of big data is also desirable, given the intrinsic difficulty in capturing international migration in national household surveys. This should be combined with improvements in panel tracking protocols within and across borders for individuals and whole households. This can include using mobile phones to track migrants, relying on CATI/CAWI for data collection to assess ultimate impacts on welfare, and so on.

Based on the above, we identify the following key areas where the return on household surveys for filling climate migration data gaps appears to be higher in the short- and medium-run:

- a. Improving the design of existing survey tools including modifications to questionnaires, updates of tracking protocols, and adoption of mixed data collection modes (e.g., the use of CATI/CAWI in combination with face-to-face interviews).

- b. Exploring and enhancing integration and interoperability with non-conventional data sources (e.g., mobile phone traces, social media, and other types of big data) and other traditional data sources (e.g., census data, administrative records and registries, labor mobility data).

- c. Broadening the perspective for some key research questions to make better use of existing household survey data and provide complementary solutions to the ‘rare event’ problem (e.g., look at climate-induced immobility other than mobility).

In an ideal scenario all these tools and integrations would be operationalized and the longitudinal and multitopic nature of household surveys would: i) allow investigating the prevailing outcomes of slow-onset changes, namely migration _vs._ immobility, and their interactions with contextual factors; ii) shed light on the leading mediating channels at play; iii) facilitate assessing the potential existence of relevant migration chains and the role of climate change as an indirect push factor for cross-border mobility; and iv) gauge final impacts on welfare and how these are distributed across different types of households and individuals.

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# **3.3 The Living Standards Measurement Study (LSMS) survey program as a candidate for studying climate migration**

<mark>The Living Standards Measurement Study (LSMS) is the most widely known household survey program of the World Bank and has been at the forefront of methodological research on data collection in low- and middle-income countries since its start in the early 1980s. LSMS surveys have several distinct strengths that make them a solid source of data for studying the effects of climate change at the micro level. Their multi-purpose, multi-topic nature and their nationally representative samples allow tracing the impacts of climate change on a wide range of outcomes including poverty, mobility, agriculture, and health, and assessing these impacts for different groups of households and individuals, especially the poor and most vulnerable ones. These surveys regularly collect data on ownership of various asset types; financial inclusion; employment; access, usage, and management of natural resources, energy, and water access; consumption; health; and anthropometrics. This rich contextual information is essential for accurately understanding the main transmission channels through which climate migration impacts materialize and how they may disproportionately affect different population groups.</mark> <mark>LSMS leads the production of detailed plot-level data on agricultural productivity, inputs, and production practices including those that are relevant for climate change adaptation and mitigation.</mark><sup>25</sup> <mark>This is critical information in the context of climate change, given that most of the global poor are engaged in agriculture and that this sector is highly vulnerable to climatic variability – especially in low-income countries where smallholder farmers rely on rainfall as the primary source of irrigation. In these settings, agriculture is possibly the leading channel to explain climate migration and, as such, detailed agricultural data collected at the household level are key information for studying this nexus. The community survey complementing the household level data collection in the LSMS survey system is also an important source of information for better understanding changes and events affecting entire communities, such as climate shocks, environmental shifts, and migration inflows and outflows.</mark> <mark>The longitudinal nature of many LSMS surveys is clearly a fundamental feature for the study of climateand migration-related issues as it allows individuals, households, and communities to be tracked across time and space. In recent years, LSMS has been advancing the methodological frontier in the collection of quality</mark> intra-household, self-reported, and <mark>individually-disaggregated data in low- and middle-income countries.</mark><sup>26</sup> <mark>This is particularly relevant from a climate migration perspective as it allows for capturing</mark>

> 25 This is done through the <mark>LSMS Integrated Survey on Agriculture (LSMS-ISA) project in collaboration with national statistical offices of client countries. F</mark> or more information about the LSMS-ISA project, see: <u>https://www.worldbank.org/en/programs/lsms/initiatives/lsms-ISA.</u>

> 26 This is part of the Living Standards Measurement Study – Plus (LSMS+) program, established in 2016. For more information about the LSMS+ program see https://www.worldbank.org/en/programs/lsms/initiatives/lsms-plus.

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<mark>intra-household heterogeneity in the response to – and the impacts of – climate change based on individual characteristics (including gender, age, and education) and endowments. In the case of panel surveys, such as many of those implemented by the LSMS team, this also provides a unique opportunity to study intergenerational relationships that may influence climate-induced migration.</mark> <mark>Another key feature of LSMS surveys is the georeferencing of community, household, and plot locations which facilitates integration with high-resolution third-party geospatial data products which have greatly increased the scope of microeconomic research on climate change adaptation and impacts. This specific feature is a central element for enabling data integration and therefore instrumental to increasing the relevance of household survey data for climate-migration analysis, given the current data landscape characterized by the spreading availability of novel data products such as big data.</mark> <mark>Lately, the LSMS team has been testing and validating new modes for collecting national household survey data with the goal of increasing the adaptability of survey systems in the context of unforeseen shocks so they are better positioned to provide rapid responses to shifting data collection needs, such as those that emerged during the COVID-19 pandemic crisis (Zezza et al., 2022; Gourlay et al., 2021).</mark><sup>27</sup> <mark>This kind of innovative solution can play a critical role in increasing the availability of timely and high-frequency data to monitor and assess the socioeconomic impacts of different types of shocks, including those triggered by slow- and rapid-onset changes in climate.</mark> <mark>Leveraging these comparative advantages (over other traditional migration data sources), LSMS has</mark> great potential for increasing its fitness for purpose in climate migration research. To achieve this objective, there are methodological shortcomings that need to be resolved. The next subsection identifies the priority areas of a potential methodological agenda for unleashing the potential of LSMS-type surveys in the climate migration research domain over the short- and medium-term.

# **3.4 Priority areas and actions going forward: A tentative methodological agenda**

This proposed agenda focuses on improvements and innovations that can help tackle key research questions of the climate-migration nexus, as suggested by the conceptual framework presented in subsection 3.1 and for which multi-topic household surveys in general – and LSMS in particular – if properly tweaked, hold a significant comparative advantage (as discussed in the previous subsection).

> 27 Specifically, the LSMS has been supporting High-Frequency Phone Surveys (HFPS) in a number of low-income countries to track responses to and socioeconomic impacts of COVID-19. For more information about the LSMS-supported HFPS see <u>https://www.worldbank.org/en/programs/lsms/brief/lsms-launches-high-frequency-phone-surveys.</u>

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# **_Improving migration measurement in household surveys_**

The first and most straightforward issues when using household surveys to study migration are the limited amount of adequate migration information that is generally collected as well as the lack of consistency in survey-based measures of migration. As already discussed, this is in part due to the absence of standardized, internationally recognized definitions of different migrant typologies, including the new category of “climate migrants”. ‘Rare event’ issues aside, this paucity of information translates into a limited ability of household surveys to capture (present and past) migrants in the original household sample even when they actually exist. To tackle these challenges, we recommend the following improvements:

- Starting with the few available official definitions and looking at best practices in the design and collection of migration data, efforts should be devoted to developing **state-of-the-art migration questions and modules** that can be embedded within multi-topic household surveys with sufficient consistency across space and time.

- As migration is not a unimodal concept, it is important that the information collected through the survey instruments allows for **differentiating categories of migrants** (permanent, temporary, seasonal, circular, return, etc.) **and types of migration** (rural, urban, internal, international, economic/voluntary, forced, etc.) with the highest possible precision and, at the same time, in an efficient way (i.e., without overburdening the questionnaire).<sup>28</sup> This implies testing and validating alternative approaches for collecting key migration information such as the length and frequency of the migration episodes, the geography of the migration moves, and the motives of migration.<sup>29</sup>

- Special attention must be devoted to identifying innovative methods for reducing well-known issues related to the **use of proxy respondents and recall bias** . The adoption of mixed-mode survey designs leveraging the use of new technologies, such as mobile phones and widely used free messaging apps or social media information (e.g., Facebook data), is a promising avenue to explore in the context of international migration, as demonstrated by recent studies (Ndashimye et al., 2022). Combining remote data collections and big data with face-to-face household surveys can be an effective way to improve the quality of migration information as it allows for direct reporting of the subject of interest (i.e., the migrant), rather than relying on a proxy respondent, and for recording migration-related events at a higher frequency (e.g., in between panel waves or even survey visits) while also minimizing potential recall bias.

> 28 It is important to note that the possibility of clearly defining different categories of migrants matters as well for the ‘rare event’ problem as the rarity of the event also depends upon the definition adopted and the type of migrant being studied given that certain types of migration might not be as infrequent in the context of a household survey (De Brauw and Carletto, 2012).

> 29 For example, Gillespie et al. (2021) discuss the main methodological issues in the measurement of migration motives focusing on the use of open-ended questions and the strengths and weaknesses of this method.

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 The higher flexibility offered by remote data collection tools should also be exploited to **investigate migration-related outcomes of different types of climate shocks** that might interest survey locations at large-scale, including fast-onset ones. For example, these data collection tools can be quickly adapted to conduct a partial survey focused only in the areas of a given disaster, which can then be merged with other data waves without compromising the implementation or representativeness of the core survey. This work should build upon the lessons learned from recent experiences with the COVID-19 pandemic,<sup>30</sup> which have provided actionable tools such as rapid phone-survey modules that may outlive the pandemic itself.

  As the use of remote data collection technologies comes with multiple statistical challenges (i.e., non-representativeness, attrition, non-response, and selection bias) more research should be conducted to **assess the extent of statistical challenges and validate methods for overcoming them** . Again, starting with and moving beyond the COVID-19 experience may be the key to solving challenges related to overly specific migration-related survey tools (for instance, by complementing standard face-to-face representative surveys with _ad hoc_ followup phone-survey interviews for selected subgroups, and then analyzing differences with the full face-to-face sample to econometrically address selection bias issues).

Improving **tracking** in longitudinal household surveys is also key for more accurate migration measurement, especially for internal migration. The primary benefit of an effective tracking strategy is to reduce attrition in panel surveys. But if the right information is collected as part of tracking (for example, about the motives of moving<sup>31</sup> ), it can also provide direct observation of migratory flows of households and individuals within the original survey sample. This is also an area where the use of new technologies and data sources can be highly beneficial. In general, augmenting panel survey data with new types of data like digital trace data would allow the collection of information in domains that are normally arduous to cover in household surveys due to respondent burden and recall issues, such as geographic moves and migratory events.<sup>32</sup>  Complementary to (longer-term) efforts for more and better tracking, the collection of (full or partial) **migration histories** of selected household members is an effective – and more immediate – solution for increasing the availability of migration data in panel household

> 30 This includes the adoption of new best survey practices introduced as part of these experiences such as the systematic elicitation of contact information of all household members which is a key prerequisite for implementing remote follow-up surveys. 31 In a recent working paper exploiting LSMS-ISA panel data to study the effects of cumulative climate shocks on long-term migratory flows in Sub-Saharan Africa, Di Falco et al. (2022) point out that the information about the motives of moving is only available for two of the five countries included in their analysis ( _i.e._ , Ethiopia and Nigeria).

> 32 This type of applications would require consent from survey participants and possibly participation in additional tasks with the risk of increasing panel dropouts (Trappmann et al., 2022), a risk that, however, could be outweighed by the potential gains in using these data.

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surveys, as recommended by De Brauw and Carletto (2012). As of now, there is no best practice in the household surveys realm for collecting this data, whose quality is likely to be compromised by different types of bias (e.g., recall bias, respondent fatigue bias). Testing and validating alternative methods for collecting information on past migration episodes in the context of household surveys is another area of development for migration-related methodological research.<sup>33</sup>

# **_Assessing and overcoming sampling issues_**

Not having a sufficiently large sample of migrants remains the biggest limitation preventing the use of household surveys for studying any type of migration-related issues. As already discussed, this problem originates from the ‘rare event’ nature of the migration decision which is even more pronounced in the case of climate-induced migration. Given that internal migration is much less rare than international migration, LSMS-type surveys have often been used to study internal movements but – despite some exceptions such as Albania (e.g., Mendola and Carletto, 2012) – hardly ever to look at cross-border migration. An easy way to diagnose the magnitude of the ‘rare event’ problem in household surveys is to look at data on the prevalence of migrants (or households with migrants) in the national population relative to the size of the existing sample for the specific survey, as suggested by Bilsborrow (2016). The larger the sample size and the higher the prevalence, the less severe the ‘rare event’ problem.<sup>34</sup> However, should this type of preliminary diagnostics confirm the ‘rare event’ issue, there could still be options for overcoming it. Some of the main options, and related potential innovations going forward, are:

- The use of specialized sampling techniques as proposed by De Brauw and Carletto (2012) remains the most effective solution to the issue. This essentially consists in **oversampling** areas with a high prevalence of migrants which, however, poses challenges due to the limited availability of adequate sampling frames, particularly in developing contexts. This is yet another area where the **integration of household surveys with other data sources** can bring significant advantages by considerably cutting down the costs associated with more traditional updating methods (i.e., door-to-door listing operations). For example, using multi-source frames or leveraging auxiliary information from nonconventional sources (e.g., Earth Observations and geospatial data) can facilitate the implementation of alternative sampling designs that can be used to oversample migrant households (United Nations Statistical Commission, 2021). There are already quite a few examples of studies trying to link survey data with digital trace data to recruit difficult-to-reach respondents (Iannelli et

> 33 Alternative approaches to be tested can include the adoption of roster of moves for shorter recall periods, or relying on household roster updates to capture household members’ moves in-between waves.

> 34 For an application and further practical reflections on the ‘rare event’ issues in household surveys, see subsection 4.1.

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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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# **_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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replacing these types of macro estimates at the first step of the chain with data and evidence from detailed household survey information, such as that of LSMS, would allow much more precision and nuance, but, to the best of our knowledge, has yet to be attempted.

- Through integration with other data sources, the analytical value and relevance of LSMS-type survey data can be considerably enhanced. To achieve this, however, efforts must be made to increase data accessibility (while at the same time finding solutions to maintain ethical standards and data confidentiality) and enhance interoperability in data collection methods. In operational terms, this means being able to link different data sources through, for example, the use of common identifiers for individuals, households, or administrative areas, geospatial coordinates, time stamps, and common classification standards (Carletto et. al., 2022).

# **4. Empirical guidance on existing surveys**

Based on both the conceptual framework and the methodological agenda set out in the previous sections, we now provide some practical empirical guidelines for interested researchers along with some simple illustrations of why and how to use household surveys to study climate migration. We start with descriptive statistics, drawing on a case study, and carry out some simple back-of-the-envelope calculations about the ‘rare event’ issue; then we provide an empirical checklist for undertaking the research effort.

# **4.1 An illustrative case study to assess ‘rare event’ issues and the migration-adaptation link**

The first step for a researcher interested in leveraging a specific household survey to study climate migration is to understand whether this is feasible in the first place. As we have repeatedly mentioned throughout the paper, in standard surveys which are not _ex ante_ planned for the _ad hoc_ tracking of migrants, there is often a ‘rare event’ issue which can undermine research efforts: small migrant samples compared to the overall sample size. But, since the ‘rare event’ issue depends on the unit of analysis, we argue that apparent ‘rare event’ issues can be drastically attenuated by shifting from the individual to the household as the statistical population of interest and the unit of analysis. To illustrate why this is the case, we leverage the Nigeria Universal Panel Data (NUPD) from the World Bank’s LSMS-ISA collection, which is currently composed of four waves from 2010-2019.<sup>36</sup> To keep things simple, we adopt a very broad and generic definition of migration. We classify an individual as a migrant – regardless of the reasons for the move – if he/she meets the following criteria: i) was present in the previous interview round but not in the current one; ii) his/her current place of residence is either in

> 36 The data are publicly available and can be downloaded here.

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a different Local Government Area (LGA) within the country or in a different country.<sup>37</sup> Table 1 reports simple descriptive statistics that show how to perform some preliminary back-of-theenvelope calculations to assess and address the ‘rare event’ issue in household surveys. A ‘migrant individual’ is an individual that was present in the previous interview but has now moved either internally (at least to a different LGA) or internationally. A ‘migrant-sending household’ is a household where there is at least one migrant individual in a given survey wave. In the case of the NUPD there is a mere 3 percent of migrant individuals, implying that migration is a statistically rare event in the individual sample. Conversely, almost 20 percent of households sent at least one migrant, meaning household-level migration is roughly 7 times greater than individual-level migration. The household-level share cannot be considered a ‘rare event’ outcome if we use a rule of thumb of 10 percent threshold for the underrepresented class.

**Table 1: Migration-related descriptive statistics for Nigeria Universal Panel Data (2010-2019)**

|**Variable name**|**Mean**|**SD**|**Min**|**Max**|**Obs**|
|---|---|---|---|---|---|
|Migrant individual|0.030|0.170|0|1|121,690|
|Migrant-sending household|0.195|0.396|0|1|19,249|
|Household size|6.322|3.451|1|34|19,249|

This simple exercise makes an important point: it is almost always true that there is a ‘rare event’ issue in standard household surveys for the study of (climate) migration, but only when looking at _individual_ migrants. Shifting to the household as the unit of analysis, the ‘rare event’ issue will be much attenuated (and possibly even eliminated), allowing the achievement of adequate statistical power for estimating household-level migration responses to climatic stress. This technical case for using households as the statistical population of interest compounds with the theoretical reasons provided in the conceptual framework, which suggest that the mobility response to weather and climate shocks is a decision mostly taken at the household rather than at the individual level, both in the case of economically-motivated migration in the face of slow-onset changes, and for ‘survival’ migration in response to fast-onset shocks.

> 37 LGAs are the second administrative level in Nigeria, and we use them to capture internal migration and exclude absent individuals who simply went to live on their own nearby. Even though our definition encompasses international migration other than internal mobility, the number of international migrants is minimal: 3.1 percent of the migrant sample and 0.0009 percent of the full sample.

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Another important issue emerging from the conceptual framework of Figure 1 revolves around the exploration of the dynamic relationship between (im)mobility and adaptation outcomes, which has important implications for rural development and local adaptation policies. Exploring this nexus presupposes the availability of both mobility and adaptation outcomes. While we have just illustrated that household surveys are generally well-equipped to look at household-level migration, a key question is whether it is also possible to build a reliable adaptation indicator using their data. The ideal adaptation indicator we have in mind is a household-level indicator of _in situ_ adaptation – which excludes _ex situ_ adaptation via migration – able to capture both on-farm adaptation through the use of modern agricultural practices and investment in inputs and the ability of the household to diversify income away from agriculture. For this purpose, having access to data from an agricultural questionnaire is essential, meaning that building such an index is currently feasible only for household survey collections – such as the LSMSISA – with a specific focus on agriculture. In practice, this index can be built using standard econometric tools such as factor or principal component analysis starting from raw data variables. To illustrate this point, we stick with the Nigeria dataset employed above and show, in Table 2, descriptive statistics for a type of _in situ_ adaptation score built out of the Nigeria Universal Panel Data. We also report descriptive statistics for the raw data variables that enter the estimation model.

**Table 2: Adaptation statistics using the Nigeria Universal Panel Data (2010-2019)**

|**Variable name**|**Mean**|**SD**|**Min**|**Max**|**Obs**|
|---|---|---|---|---|---|
|_In situ_adaptation score|31.371|17.264|0|100|11,262|
|Share of irrigated plots|0.020|0.127|0|1|11,389|
|Share of plots on which inorganic fertilizers were used|0.380|0.461|0|1|11,503|
|Share of plots on which organic fertilizers were used|0.133|0.326|0|1|11,503|
|Share of plots on which herbicides fertilizers were used|0.292|0.436|0|1|11,474|
|Share of plots on which pesticides were used|0.157|0.347|0|1|11,474|
|Number of plots owned by the household|2.311|1.403|1|13|11,536|
|Non-farm enterprise (yes=1)|0.568|0.495|0|1|11,564|

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We employed standard principal factor analysis using the seven variables listed in the table after the aggregate index. Six of these variables capture on-farm adaptive capacity, whereas the non-farm enterprise dummy is an indicator of whether the household is totally dependent on farming or can also count on diversification of livelihoods. Clearly, the choice of the variables employed – as well as of the method used to generate the score – is subjective and purely illustrative.<sup>38</sup> In order to ease interpretability, the adaptation index has been rescaled to have a range between 0 and 100. First, note the drastic loss in sample size compared to the household-level data reported in Table 1: this is due to the fact that the agricultural questionnaire is only administered to households that are engaged in agriculture. This reduction in sample size is thus the first element to consider when investigating household _in situ_ adaptation. Second, there is a trade-off between the number of raw variables that enter the estimation model and the final number of observations for which the score will be available. Considering the observations in this sample, 11,564 farming households, only the non-farm enterprise dummy has no missing values. Limiting the index to just seven variables still results in more than 300 missing values in the final adaptation score. Keeping these caveats in mind, once the index has been built, it can be employed either as an alternative outcome variable or as a conditional variable in a mediation analysis or causal machine learning approach in order to investigate issues related to substitution _vs_ . complementarity of adaptation and migration, immobility, and the investigation of transmission channels illustrated in the conceptual framework of Figure 1.

# **4.2 Empirical checklist**

Finally, we provide some simple empirical guidelines for researchers interested in leveraging existing household survey data to shed light on the climate-migration nexus. These can serve as a starting point to evaluate the preliminary feasibility of a research study as well as to orient identification strategy and sort out issues related to variable definition and construction. This empirical checklist is reported in Box 1 and encompasses the key points we have touched upon throughout the paper. Note that the checklist refers to the case in which a researcher intends to use only existing household surveys as the migration data source.

# **Box 1: Empirical checklist for using household surveys to study climate migration**

# **1) Do you have repeated cross-sections or panel data?**

If you have repeated cross-sections, you can consider the adoption of a pseudo-panel approach (Deaton, 1985). If the data are representative at the national and subnational level, you can consider the aggregation of micro information at the spatial level to obtain panel data at that level of aggregation.

> 38 Similarly, not all the raw agricultural variables included in the construction of the score have necessarily to be strictly related to climate change impacts, as their purpose is to proxy the willingness and capacity of households to invest in and implement modern agricultural practices.

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If you have panel data, continue to the next questions.

# **2) What type of migration information do you have access to?**

If you have information on the motives, timing, and destination of migrant individuals, then you can reasonably distinguish between the different types of migration – short- _vs_ . long-term migrants; internal _vs_ . international migrants; voluntary (economically-motivated) _vs_ . involuntary (survival/low-return) migration, etc. In this case, you can use these different migration phenomena as dependent variables and compare the results across different categories and against existing evidence and theoretical predictions. In any case, be aware that when using nationally-representative surveys it is unlikely that you will observe a sufficiently large sample size of international migrants.

If you only have partial access to this information, stick with general definitions of migration. For example, if information regarding timing and reasons of migration is unavailable or unreliable, but you have information on destination, build migration outcomes by imposing a ‘spatial threshold’ after which moving individuals are considered as migrants rather than as simply absent (e.g., an individual could be considered a migrant if he/she has at least moved to another district or subnational administrative unit, even if you do not have data on where or when they moved).

# **3) Do you have a ‘rare event’ issue at the individual level?**

If yes (e.g., migrant individuals are less than 10 percent of the sample), collapse the individual information at the household level and use households as the main unit of analysis. If you still prefer to work at the individual level, adopt data-driven techniques to tackle the data imbalance issue (e.g., Chawla et al., 2002) and divide your original sample into disjointed training and testing sets to assess treatment effects on the original real-world (skewed) data rather than on the artificially rebalanced dataset.

If not, carry out the analysis at both the individual and household level, and compare the key findings. Remember, however, that aside from technical issues, household-level analysis should be preferred for both risk-diversification migration in response to slow-onset changes and ‘survival’ migration in response to fast-onset shocks.

# **4) Does the survey include an agricultural questionnaire?**

If yes, use agriculture-related variables as the most important transmission channels and to explore treatment effect heterogeneity. In addition to the migration outcome, also build an _in situ_ adaptation outcome (e.g., via factor analysis starting from raw agricultural data on inputs, practices, and assets) which you can use as an alternative outcome or as a conditioning variable in order to study the dynamic relationship between migration and local adaptation or the relevance of climate-induced liquidity constraints and immobility traps.

If not, stick with the main migration outcome but be aware that, due to the predominant importance of the agricultural linkage, you may not be able to thoroughly explore treatment effect heterogeneity and mediating channels. In practice, looking only at _average_ treatment effects may be misleading if there is substantial heterogeneity whose underlying signal you are unable to detect (e.g., simplistic case: half sample trapped in immobility, half sample responding positively by sending migrants, null average effect).

# **5) Are you more interested in slow-onset or fast-onset events?**

If you want to explore responses to slow-onset changes, focus on treatment effect <u>heterogeneity, as the literature indicates that average treatment effects can mask substantial</u> 28
<mark>heterogeneity in response to slow-onset changes, ranging from resource-constrained</mark> immobility to permanent long-distance migration. If you have rich covariate data, consider the use of machine learning algorithms suited for a data-driven search for heterogeneity, such as causal trees and causal forests (Athey & Imbens, 2016; Wager & Athey, 2018), properly adapted for their use in an observational panel data setting, in order to estimate conditional average treatment effects.

If you are interested in fast-onset localized shocks or other specific events/disasters, remember that nationally representative household surveys are usually ill-suited for the purpose due to both timing and sample size issues, and that you should seek access to _ad hoc_ post-disaster surveys that oversample the most affected areas. Regarding identification, stick with difference-in-difference types of estimators based on pre- _vs_ . post-shock comparisons. If there is ‘staggered adoption’, here meaning non-simultaneous exposure to the shock, consider the adoption of modern difference-in-difference estimators for settings with differential treatment timings (Roth et al., 2023).

# **5. Conclusions**

The focus of this paper was to propose a conceptual framework and a methodological agenda and provide empirical guidelines for the investigation of climate-induced migration leveraging the use of multi-topic household surveys such as those implemented under the Living Standards Measurement Study (LSMS) program of the World Bank. The agenda we propose is ambitious but is meant to be the first step in this direction. If pursued, we deem it would deliver large benefits in terms of closing existing data gaps and provide a more accurate and granular measurement of the complex and highly heterogeneous phenomenon that is climate migration. In the meantime, however, we showed that there is a lot that researchers can do – and do in better ways – even with currently existing instruments. Undertaking microlevel climate migration research in a more systematic way and based on improved, more homogeneous methods and tools, will contribute to shedding decisive light on the nexus itself, its underlying mechanisms, and the socioeconomic implications for the main actors involved.

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