Panel Data in Research on Mobility and Migration: A Review of Recent Advances

Vidal, Sergi; Lersch, Philipp M. Article — Published Version Panel Data in Research on Mobility and Migration: A Review of Recent Advances Comparative Population Studies Provided in Cooperation with: German Institute for Economic Research (DIW Berlin) Suggested Citation: Vidal, Sergi; Lersch, Philipp M. (2021) : Panel Data in Research on Mobility and Migration: A Review of Recent Advances, Comparative Population Studies, ISSN 1869-8980, Federal Institute for Population Research, Wiesbaden, Vol. 46, pp. 187-214, https://doi.org/10.12765/CPoS-2021-07 This Version is available at: https://hdl.handle.net/10419/235708 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Lersch Abstract: Panel data has become the gold standard for causal assessments of complex human behaviour in quantitative social science. The objective of this review is to examine and discuss how panel data and related methods contribute to the identifi cation of causal relationships in spatial mobility research. We illustrate this by providing a succinct overview of recent progress in spatial mobility research, drawing on panel data. The review outlines research from a number of scholarly disciplines that maps patterns, establishes determinants and assesses the impact of spatial mobility for a range of outcomes. Studies presented in this article are used to decipher complex interdependencies over the life course, scrutinise the selectivity of migrants, and shed light on the interplay between individual agency, social embeddedness and socio-structural contexts. The article concludes with a set of critical issues for future research. Keywords: Panel data · Longitudinal methods · Residential mobility · Internal migration · Life course 1 Introduction Spatial mobility is a major driving force underlying demographic and social change, and a fundamentally important experience for many people. In 2019, 272 million people (3.5 percent of the world’s population) were international migrants living in a country other than their country of birth (International Organization for Migration 2019). Mobilit y within national borders is more common, but with signifi cant variation Comparative Population Studies Vol. 46 (2021): 187-214 (Date of release: 28.06.2021) Federal Institute for Population Research 2021 URL: www.comparativepopulationstudies.de DOI: https://doi.org/10.12765/CPoS-2021-07 URN: urn:nbn:de:bib-cpos-2021-07en3 * This article belongs to a special issue on "Identifi cation of causal mechanisms in demographic research: The contribution of panel data". • Sergi Vidal, Philipp M. Lersch 188 across countries.1 For instance, about 19 percent of the population in Iceland, around 9 percent in Germany, and roughly 3 percent in Spain changes residence each year (Bell et al. 2015). Changing the place of residence within countries, however, is becoming less common in many countries (Bell/Charles-Edwards 2013; Champion et al. 2017). Instead, recurrent mobility, such as commuting or circulating between multiple homes, is increasing with advances in transportation and communication technology, and with changes in the way work and families are organised (Lück/ Schneider 2010). All of these types of spatial mobility can have a profound infl uence on individuals’ wellbeing and life chances (Aybek et al. 2015). Spatial mobility is studied by a multidisciplinary research community at the intersection of demography, economics, geography, psychology, sociology and other related disciplines. This community has made important advances in recent decades in our understanding of the patterns, determinants, and outcomes of spatial mobility. Many of these advances have been made possible through the increasing availability of panel data. This review aims to provide insights into the use of panel data and methods for the identifi cation of causal relationships in spatial mobility research. This is illustrated through an overview of recent advances in selected research fi elds. Panel data offers two key advantages for the study of spatial mobility through repeated observations of the same individuals. Firstly, panel data improves measurement. Observing individuals repeatedly is necessary if we are substantively interested in how changes occur within individuals, and to answer questions about long-term wage profi les of movers compared with non-movers. Importantly, panel data allows us to examine not only change but also persistence in mobility practices and immobility. Repeated observations are also necessary to track the various stages of the mobility process, from intentions and plans to the realisation of mobility (Kley 2011). Arguably, panel data is also superior in its accuracy of measuring mobility as prospective data collection is less likely to be subject to recall bias compared with retrospective data. Secondly, panel data improves modelling. Repeated observations allow us to eliminate unobserved time-constant heterogeneity by analytically focusing on within-individual change. In addition, repeated observations allow us to longitudinally model individual-specifi c trajectories and other substantively relevant heterogeneity in multi-level models. However, panel data is not a panacea. Issues such as unobserved time-variant heterogeneity may still hinder causal inference based on panel data. These and other key advantages (and limitations) of panel data and methods are discussed in Section 2. Building on these advantages, panel data has prompted major advances in spatial mobility research. Firstly, analyses of panel data enable the adoption of a temporal view on spatial mobility, which operationalises core concepts of the life course approach in empirical research, including individuals’ practices, transitions, 1 Bell and Charles-Edwards (2013) estimate that in 2005, about 12 percent of the world’s population was living in its origin country but outside the region of birth. Panel Data in Research on Mobility and Migration: A Review of Recent Advances • 189 and trajectories. This acknowledged the diversifi cation and fl exibility of individuals’ lives, moving away from outdated life cycle views of spatial mobility. Diverse patterns of spatial mobility, repeated mobility practices and complex interdependencies with central life domains have thus become traceable. Secondly, analyses of panel data shed more light on inequalities and differences observed between movers and nonmovers over time. In this context, the selectivity of mobile and migrant populations is not only a methodological issue to address when assessing the impacts of spatial mobility, but is also a central feature of the migration process itself that deserves further scrutiny. Thirdly, panel data with multi-actor designs (e.g. gathering information from several household members) deepens our knowledge as regards the relational dimension of spatial mobility, thereby acknowledging power relations and social resources from interpersonal relationships and the embeddedness in larger communities. Fourthly, panel data enables us to establish micro-macro links and address the interplay between individual agency and socio-structural conditions. These are the main themes around which our review of selected literature in Section 3 is organised. We use the general term spatial mobility to refer to movements in geographic space that either involve a change of primary place of residence and place of daily activity or that are circular with a fi xed primary (or several habitual) address(es), e.g. commuting (Aybek et al. 2015). Changes in place of residence can be across national borders (international migration) or within borders (internal migration, residential relocation). The latter type of mobility is most often covered in panel data. We include commuting and circular mobility around multiple homes as alternatives to relocation, but our working defi nition of spatial mobility does not include temporary, one-off mobility, such as travel for leisure, and daily routine mobility, such as shopping. Because the research area of spatial mobility is so vast, we necessarily have to limit ourselves in what we cover in this review. From our search of literature conducted on major databases,2 we selected sets of topics and studies to exemplify the use of panel data in solving analytical problems and addressing new research questions. We only considered studies using panel data (including survey and register sources) which allow us to observe individuals before and after a move, or variation in recurrent mobility practices over time. This means that we excluded other important areas of research, such as those which followed immigrants after their migration over time. Notably, this excluded the bulk of studies on integration of migrants using panel data, which has become more relevant in recent decades. 2 We conducted a literature search between March and June 2020 on Scopus and Google Scholar using the search terms “(panel | longitudinal | register) & (migration | mobility | move | residential)”. We also searched the databases of the British Household Panel Survey (BHPS), Household, Income and Labour Dynamics in Australia (HILDA) Survey, Understanding Society – The UK Household Longitudinal Study (UKHLS), Panel Study of Income Dynamics (PSID), and the Socio-Economic Panel (SOEP) Study. We added further studies that we considered relevant but which were not covered by the search terms. The full database resulting from our literature search can be accessed at https://osf.io/nzbj8/. • Sergi Vidal, Philipp M. Lersch 190 Also note that although we covered studies from all over the world, relevant panel data is collected more often in developed countries. 2 Panel data in spatial mobility research Panel data has become the gold standard for causal assessments of complex human behaviour in quantitative social science. There are several main advantages (and current issues) of panel data for measuring and modelling spatial mobility. 2.1 Measurement A range of theories and conceptual frameworks of spatial mobility revolves around temporal processes, such as the dynamics of change among mobile populations, the dynamics of stability and persistence in place, and sequential processes of mobility decision-making. In the absence of longitudinal data, none of these processes can be empirically examined to an adequate extent. Compared with cross-sectional data, longitudinal measurement is also advantageous because it enables us to establish the time order of cause and effect, e.g. to address whether it is moving to a certain place (in the event of migration) or in being a migrant (thus a person with certain characteristics) that better explains employment outcomes. Panel data presents several advantages over other sources of longitudinal data in terms of measurement quality, although researchers should also be aware of some problematic issues. Firstly, the longitudinal analysis of spatial mobility that is based on retrospective information collected as part of cross-sectional data designs is susceptible to left truncation bias (i.e. bias from omitting those who have previously moved), as samples are extracted from surviving populations that have not moved away from the study context at the time of data collection. With panel data, truncation bias in spatial mobility studies is minimised as the prospective design enables mobile individuals to be followed over time. However, left censoring, where the event of interest occurs before the observation period, can still be an issue. The researcher should be aware, however, that prospective data collections are affected by attrition (i.e. the drop-out after a realised interview with unit nonresponse), which can lead to misleading results when respondents who drop out of the panel systematically differ from those who stay in the panel (Frees 2004: 11). Mobility (and even more so migration) increases the chances of attrition, partly because it is diffi cult to follow households if they change addresses. While prior research on spatial mobility fi nds little evidence that substantial conclusions are distorted by selective attrition (Washbrook et al. 2014), it is important that researchers think carefully about the potential implications of selective attrition for their research questions and adjust their empirical strategy accordingly. Secondly, reports of spatial mobility practices and events are less likely to be subject to recall bias in panel data collections, particularly when time intervals between interviews are short. Recall bias in retrospective migration histories can be particularly harmful when it comes to events that occurred far in the past; that were Panel Data in Research on Mobility and Migration: A Review of Recent Advances • 191 not paired with other salient life events (e.g. marriage, childbirth, or job change); for local moves; and for shorter residential episodes (Smith/Thomas 2003). Even with panel data, however, recall bias may occur, e.g. when respondents report events more than once. Also, the granularity of data on spatial mobility in extant panel data collections is limited. Since most panel data is not specifi cally designed for the study of spatial mobility (see Section 2.3), measurements are typically restricted to one change of residence since the last interview, and ignore repeated mobility between interviews. Since it is often the case that only annual data is collected, it is not always possible to clearly establish the time order of events such as mobility and their outcomes within a given yearly interval. Thirdly, information that is collected retrospectively may be contaminated by more recent experiences, such as the outcomes of spatial mobility or immobility – what is also known as post-hoc rationalisation bias. This type of bias is particularly pernicious for subjective measures such as intentions, motivations, or attitudes, and should not be asked in reference to the past, particularly the distant past. While spatial mobility (and in particular subjective evaluations) is preferably collected prospectively, the extent to which learning effects and panel conditioning (i.e. response patterns infl uenced by prior interviews) compromise measurement deserves more attention in panel survey analysis. While the prospective measurement of spatial mobility within countries is very common, panel data on international migration – with observations of individuals before (at origin) and after (at destination) a move – is limited. This is because most panel surveys focus on the representative nature of a dynamic population within national borders and do not re-interview original respondents after emigration even if they are traceable, considering them as a population that is not eligible for interview. A prominent exception is the Mexican Family Life Survey (as well as certain other projects in developing countries), which has made large efforts to track and re-interview migrants to the United States. In the absence of large migration fl ows, such as the case of Mexico-US migration, the investment in following individuals might be too great in relation to the small number of migrants to study (Liu et al. 2016). In this case, retrospective studies linking individuals across origins and destinations are more cost-effective sources of longitudinal data for the study of the international migration process. Nevertheless, information in panel studies on migrant networks, resources (e.g. remittances), intentions to move (abroad), migration histories, or survey metadata indicating whether respondents emigrated are all useful in addressing questions relating to the migration process. In addition, panel data in the receiving countries could be complemented with information from (non-migrant) populations in the origin countries to help us understand the extent to which migrants’ outcomes are driven by migrant selectivity (Feliciano 2020). 2.2 Modelling Theoretical and conceptual models of spatial mobility highlight a multiplicity of stressors, self-selective processes, and complex and recursive associations with the correlates of spatial mobility. Given this, and in the absence of (quasi-)experimental • Sergi Vidal, Philipp M. Lersch 192 research designs, methods for the analysis of cross-sectional data may lead to biased and inconsistent estimates of the antecedents and consequences of spatial mobility since key model assumptions are unlikely to hold. For example, spatial mobility is an intermediary variable for many other life course processes, and omitting these processes might lead to a misestimation of the impact of spatial mobility. With panel data and its associated methods, it is possible to identify causal effects under weaker assumptions. In particular, the fi xed effects (FE) estimator – and related estimators that exploit within-individual variation in panel data – eliminates all individual-specifi c (time-constant) unobserved heterogeneity, thereby relaxing some strong assumptions in regression models, such as there being no correlation between explanatory variables and the stochastic error term. (See Brüderl/Ludwig 2014) for features and assumptions of major panel data estimators.) Accordingly, the FE estimator has become very popular in research that estimates spatial mobility and associated outcomes, such as employment status, income, and subjective wellbeing (Cooke et al. 2009; Nowok et al. 2013; Scheffel/Zhang 2019). Despite their advantages, most within estimators still assume no time-varying unobserved heterogeneity, but this assumption arguably does not hold true in many situations (see below). Also, among estimators that only exploit within-individual variation (and compared to random effects estimators that exploit between-variation and withinvariation) effi ciency is reduced, and inference (which cannot be made beyond the groups in the sample) is compromised when studying processes or contexts where spatial mobility is rare. Methods such as hybrid panel models aim to overcome issues of effi ciency and inference (Allison 2009), but these introduce new model assumptions. The main argument for the use of within estimators is that spatial mobility is a self-selective process. Mobile populations are often younger, healthier, and more qualifi ed than the general population. Panel data is particularly helpful because it contains information on the populations of origin and destination, which enable us to study the selective nature of spatial mobility, how it has an impact on individuals’ outcomes, and whether it leads to structural changes in the populations of origin and destination (see e.g. Brimblecombe et al. 2000; Norman et al. 2005). The fact that mobile individuals are also selected on unobservables (or factors that are diffi cult to measure, such as ability, personal traits, or motivation) is problematic because it induces individual unobserved heterogeneity in spatial mobility models and limits the ability of researchers to obtain unbiased estimates. Within estimators and instrumental variable approaches can be used to obtain unbiased estimates under the presence of self-selection on unobservables. They are, however, not without problems. On the one hand, fi nding a valid instrument can be cumbersome; on the other hand, within-estimator approaches rely on the parallel trend assumption, i.e. the expectation of similar temporal trends in outcomes for the mobile and the non-mobile populations in the absence of spatial mobility. To relax the parallel trend assumption, fi xed effects models with individual slopes can be used, but have rarely been deployed in mobility research (Kratz/Brüderl 2013), in part because data requirements are high. Panel Data in Research on Mobility and Migration: A Review of Recent Advances • 193 Questions about causal relationships in spatial mobility research also concern temporality, including the timing of events, individual change and stability over time. The timing of mobility in relation to its trigger events is often addressed with event history analysis, which accounts for censoring and truncation biases relating to incomplete information on events and their timings as they occur outside the study observation window (Blossfeld et al. 2016). Growth curve models can be used to examine variability in outcome change rates across mobile and immobile populations (Curran et al. 2010). Dynamic panel models include lagged panel variables to account for “true” state dependence, by which prior experiences infl uence current experiences to explain processes such as persistence in place or neighbourhood disadvantage (Baltagi et al. 2015). Despite their usefulness in modelling temporality, most of these models are not immune to individual unobserved heterogeneity and so researchers need to be aware of potential issues relating to omitted variables, sample selection, or measurement error. Additionally, complex time dependencies between spatial mobility and its triggers/outcomes have been proposed (see Section 3.1), by which the time order of events does not necessarily refl ect the causal order and brings up the issue of reverse causality. Recent advances allow us to address these issues more convincingly with panel data (Allison et al. 2017; Steele 2008). For instance, cross-lagged panel models with fi xed effects or extensions of the event-history model to the simultaneous analysis of multiple correlated processes enable individual unobserved heterogeneity to be controlled for and reciprocal causation to be assessed. These models require the observation of repeated outcomes per individual, however, and are not immune to time-varying unobserved heterogeneity. Spatial mobility can be considered as a process that is wider than an event or practice observed at one discrete point in time. Adopting a holistic view to spatial mobility, trajectories consisting of multiple mobility events and immobility episodes have been examined using descriptive sequence analysis methods. Related studies established typical long-term mobility pathways, often in interplay with occupational and family trajectories (e.g. Stovel/Bolan 2004; Vidal/Lutz 2018; Impicciatore/Panichella 2019). With panel data, it is also possible to address more explanatory questions within holistic approaches, although these have not yet been considered in spatial mobility research. For example, researchers can use mixture hidden Markov models to predict transition probabilities between life stages in trajectories combining spatial mobility with events in employment, family and other life domains (Helske et al. 2018). Additionally, machine learning methods for variable selection, such as Boruta or LASSO, can be used to identify the most relevant properties (i.e. sequencing, timing and duration) of migration trajectories associated with a life outcome (Bolano/Studer 2020). Also, Brüderl et al. (2019) propose the triangulation of statistical tools, including descriptions of long-term trajectories and panel methods to shed light on underlying causal processes. Many theoretical approaches highlight social relations and structural factors infl uencing spatial mobility behaviour and outcomes. Although panel data is largely used in empirical analyses informed by these approaches, a relatively small number of these applications use appropriate modelling strategies that deal • Sergi Vidal, Philipp M. Lersch 194 with heterogeneity at supra-individual levels. Multi-level modelling frameworks acknowledge complicated clusters of individuals in social relationships or structures, which enable us to explicitly address the infl uence of the spatial areas where individuals are nested in their mobility behaviour and outcomes. Dyadic models, a sub-type of multi-level models, have been used recently in the study of family migration in order to address the joint infl uence of couple members on mobility decisions and associated outcomes (see Section 3.2). 2.3 Types of panel data There are increasing numbers of panel datasets which are regularly used for the study of spatial mobility. These data collections are diverse but rarely are they specifi cally designed to study spatial mobility. As a result, extant types of panel data display different strengths and limitations for the study of spatial mobility in relation to key aspects, such as sample size and attrition, length of observation, quality of measurement, or availability of key study variables to address intermediate or spurious relations. Recent research often used household panel surveys such as the UK Household Longitudinal Study, the Socio-Economic Panel in Germany, or the Panel Study of Income Dynamics in the USA. These data collections follow nationally representative samples of households, where all (adult) household members have been interviewed on a regular basis (often annually). These data sources are particularly advantageous in that their multi-actor designs enable the collection of a wealth of information in a household context, and their broader target populations enable us to study heterogeneity in a society, in terms of age groups, mobile/nonmobile populations, etc. Many of these surveys follow individuals over long periods of time, even those that abandon or join original sample households. This not only helps to maintain the representativeness of the population in the sample but also enables the examination of a rich set of household dynamics, such as changes in family arrangements or the role of power relations within households for spatial mobility (see Section 3.2). Cohort studies such as the UK Millennial cohort study, the German Family Panel, or the Survey of Health Ageing and Retirement in Europe (SHARE) follow individuals from a given cohort defi ned by age or an event such as birth, leaving school, or entering retirement. Since samples are drawn for homogeneous groups (cohorts), the data design already controls for a great deal of context heterogeneity and allows for better causal assessments than general population surveys. The ability to make inferences to other groups of society, however, is rather limited. Given the focus on the stages of childhood (and early adulthood), birth cohort studies have become particularly popular when studying the role of spatial mobility for childhood developmental processes and (later) outcomes (e.g. Vidal/Baxter 2018). Rotating panel surveys such as the European Living Conditions Survey or the European Labour Force Survey follow different sets of individuals for shorter periods of time. For example, the longitudinal part of the Labour Force Survey interviews the same individuals in each quarter of a year, but only for six consecutive quarters. Panel Data in Research on Mobility and Migration: A Review of Recent Advances • 201 Household panel studies usually include interviews with all household members, thus allowing us to directly model the couple level, which is par ticularly relevant in the study of family migration. Against this backdrop, sophisticated models that exploit both temporal and multi-actor components of panel household data have been deployed to address the joint infl uence of couple members on mobility decisions and associated outcomes. Using a dyadic approach (actor-partner interdependence model) which also capitalises on the household panel structure, Lersch (2016) shows that in Britain, women with more egalitarian partners are less likely to leave employment after family migration. However, even adjusting for egalitarian gender ideology, family migration still has different outcomes among men and women. The model contains partner-specifi c random variables which are allowed to correlate to account for non-independence within couples and to limit bias due to couple-level heterogeneity. Recently, dyadic models specifi c to residential mobility have been proposed. Kern and Stein (2018) put forward a dyadic modelling framework that specifi es actor and partner effects on the mobility dispositions of each couple member, which in turn have an impact on joint couple mobility decisions. They empirically illustrate the model using a multi-level structural equation model set-up (also accounting for regional context heterogeneity) and SOEP geo-coded data. Results show strong similarities in couple members’ conditional mobility disposition, and that couple members infl uence household moving behaviour through their partner’s dispositions. This supports the notion of family migration as a by-product of couple bargaining. Steele et al. (2013) propose panel data models that acknowledge the infl uence of both partners in couple decisions and are fl exible enough to address partnership dynamics, i.e. that individuals can change partners and incur periods of singlehood. Compared to previously used panel data models, the proposed new models were found to improve the residual structure of an application to residential mobility as they capture unaccounted partnership dynamics.5 Although the authors fi nd differences in estimated effects across models, these were not large enough to affect the substantive conclusions. We note that despite their usefulness, the level of sophistication and the high data requirements of these modelling approaches might hamper their wider application. Although the number of panel studies following international migrants before and after migration is small, the consequences of migration for sending households can be examined in panel studies for countries with high emigration rates. Migration and mobility do not only affect those who are mobile, but also those left behind, and it is important to account for selectivity in migration. Most studies along these lines have deployed cross-sectional data, which does not enable us to assess whether study outcomes are due to differences between households (with members abroad or not), or changes before and after a member moved abroad that better refl ect the 5 These are “multiple-membership-consensus” models that include a weighted combination of the random effects for each partner, and a “head-of-household-joint” model that specifi es distinct but correlated random terms for single and partnered individuals. • Sergi Vidal, Philipp M. Lersch 202 causal ef fect of migration. In this regard, novel collections of panel data in developing countries have been instrumental in obtaining more accurate estimates of the impact of emigration. For instance, in contrast to earlier cross-sectional analyses, Acosta (2020) fi nds no effects of remittances or international migration on labour supply in origin households when using fi xed effects and controlling for agricultural income shocks as important sources of time-varying heterogeneity in rural areas of El Salvador for men. For women, remittances and migration increase their participation in agricultural activities while reducing non-agricultural and domestic labour, but the effect sizes are small (see also Arouri and Nguyen (2018) and also Murard (2020)). Using a similar analytical strategy, Cuong and Linh (2018) fi nd that in Vietnam, remittances increase both household earnings and consumption and reduce poverty, while also reducing labour supply. The overall effect of migration on these outcomes is minor, most likely because migrants already contributed substantially to households’ economic resources before migration. Similar results have been found for the Philippines (Ducanes 2015). In a related manner, the consequences of migration on left-behind children have received growing attention over recent years. Binci and Giannelli (2018) use Vietnam Living Standards Surveys and fi xed effects regression to show that remittances after domestic migration reduce child labour and increase school attendance for origin households in the country. In contrast, applying cross-sectional methods remittances from international migration seem to be more important than remittances from domestic migration. The authors argue that unobserved migration networks of families may drive these differences across methods. Lu (2015) examines the consequences of internal and international migration on the physical growth (height and BMI) of left-behind children in Indonesia (Indonesian Family Life Survey) and Mexico (Mexican Family Life Survey) using fi xed effects regression. While there is no effect on BMI, the study fi nds that internal migration impacts positively on height in Indonesia while the effect of international migration is negative in Mexico (other effects are not statistically signifi cant). These cross-country differences were attributable to the different developmental status and nutritional profi les of both countries. Yue et al. (2020) study maternal, internal out-migration for children below the age of 2 in China with a two-way fi xed effects regression, i.e. a difference-in- difference approach. Maternal out-migration leads to worse mental development and cognitive delay. There is no association of out-migration with social-emotional delay, anaemia, weight, or frequency of illness. Earlier out-migration has particularly negative effects. Results for pooled OLS would lead to substantially different conclusions, sometimes with opposite signs. Intermediate variables that can explain the association between out-migration and children’s outcomes are reduced activity with children and a reduction in the quality of food. 3.3 Context-level conditions and outcomes Spatial mobility allows individuals and households to change their economic, social, environmental, and geographical context, e.g. by moving into a different neighbourhood or labour market region. At the same time, dynamic contexts Panel Data in Research on Mobility and Migration: A Review of Recent Advances • 203 have an impact on the probability of spatial mobility. Because individuals (partly) select their contexts, it is often diffi cult to examine the causal consequences of contexts in terms of behaviour and outcomes. The aggregation of individual decisions also shapes population compositions and can lead to social change. Panel data has several major advantages when studying the relationship between spatial mobility and context. Firstly, panel data allows us to observe and model selection in context. Secondly, because place is not static (Baker et al. 2016), panel data is also particularly suited to investigating how changing contextual conditions infl uence spatial mobility and how spatial mobility creates changes in context. Thirdly, panel data allows us to study the persistence in contextual exposure, e.g. the exposure to poor neighbourhoods. In addition, deploying multi-level models on repeated observations of individuals clustered in different contexts enables correct inferences to be drawn where there is context-level heterogeneity, and allows us to estimate context-level effects on individuals’ mobility behaviour and outcomes. A central context for spatial mobility at the macro level is the housing market, which is often conceptualised at the regional level. As theorised in the institutional approach of spatial mobility (Flowerdew 1982), aspects such as tenure structure (where more rental accommodation facilitates mobility), housing demand, transaction costs, and housing costs can all have important implications for mobility. Panel data is particularly suited to understanding the consequences of changing housing markets for spatial mobility. In considering changes in supply and demand in regional housing markets by drawing on panel data, Lersch (2014: 156f) shows that population growth is positively associated with increases in crowding in Britain and Germany, but regional tenure structure is not associated. The author deploys fi xed effects regression, and replicates the analyses using multi-level models with random intercepts at the individual and housing market level to yield unbiased standard errors for the housing market variables. Results from the application are broadly similar across both models, but standard errors are greater for the fi xed effects models. The bulk of micro-level research on the role of context characteristics for individual migration decisions has focused on those of origin context only. Such models implicitly assume that the characteristics of potential destinations do not play any role in migration decisions, and is inconsistent with theory and empirical evidence from macro-level research that considers that such destinations do matter. Incorporating information about potential destinations for movers in Britain, Rabe and Taylor (2012) fi nd that differentials in house prices between origin and potential destinations matter for migration decisions; in particular, these deter migration for homeowners. The study innovatively combines BHPS with the British Labour Force Survey, register data on regional mobility, and regional house price data to estimate random effects regression models predicting mobility. Several sources of migrant selectivity were modelled additionally, since random effects models are not immune to individual unobserved heterogeneity. The choice of the random effects model is in response to the interest in examining differences in opportunities across individuals rather than within-individual variation over time. Overall, the origin-destination comparison approach enriches our understanding of context- • Sergi Vidal, Philipp M. Lersch 204 level migration incentives. Thomas et al. (2015) also consider origin and destination contexts in a study of the distance moved by residential movers in England and Wales. They assess the relative importance of individual and place-based variations, employing a multi-level cross-classifi ed statistical framework in which respondents can be nested in distinct higher level units. The modelling strategy enables them to assess the role of place-based attractiveness, net of average socio-demographic context compositions. The results show that migrants are pulled towards rural and coastal amenity-rich destination environments, which indicates the persistence and strength of counterurbanisation processes. With more and more people worldwide affected by climate change, a timely and relevant additional line of research studies the infl uence on spatial mobility of weather as an environmental and regional condition. For instance, Sedova and Kalkuhl (2020) link household panel data with weather forecasts at the district level for India and utilise panel regression models, including fi xed effects at the household and district level. They fi nd that temperature and precipitation extremes in rural India affect mobility. Here, adverse weather shocks reduce international migration and rural-rural mobility but instead push migrants to cities in richer Indian states. These climate migrants are mostly unskilled agricultural workers. Using similarly combined data pooled for Ethiopia, Malawi, Tanzania, and Uganda, Mueller et al. (2020) show that weather extremes reduce out-migration from urban areas but are not a predictor of rural migration using fi xed effects models. Confl ict can also be a contextual condition that pushes people to leave their places of residence and is in fact a major reason for international migration (International Organization for Migration 2019), but individual-level evidence of the relationship between confl ict and mobility is scarce when drawing on panel data. A notable exception is a study by Bohra-Mishra and Massey (2011) who rely on panel data from Nepal to study the likelihood of local, internal, and external migration due to civil confl ict using an event history framework. The study provides evidence in support of a threshold model of migration, where violence must reach a high level to increase the likelihood of migration. Moving from a regional to a local scale, a large body of literature is concerned with spatial mobility and neighbourhoods. Baker et al. (2016) use HILDA to study the relationship between housing affordability, mobility, and neighbourhood quality. In so doing, they use a dynamic random effects panel model to demonstrate that the context of where an individual lives is causally infl uenced to a signifi cant degree by the prior context, which is often of the same type. The authors augment the model with the Mundlak approach (i.e. includes as an explanatory variable the means of the time-variant variables), which allows for potential correlation between the individual-specifi c effects and the explanatory variables, and ensures unbiased and consistent model estimates in the context of random effects regression. Results show that those persons facing housing affordability issues are more likely to move and are more likely to move to disadvantaged neighbourhoods. Vaalavuo et al. (2019) use Finnish register data with fi xed effects regression to show that native Finns are more likely to translate income growth into improvements in neighbourhood quality Panel Data in Research on Mobility and Migration: A Review of Recent Advances • 205 when compared with immigrants. Not applying a fi xed effect regression framework would underestimate the difference between the native population and immigrants. 4 Conclusion After decades of collection (which can be costly) and use of panel data, it is imperative to monitor research advances in the fi eld of spatial mobility and to evaluate the capacity to build an evidence base that not only informs policy but also contributes to theoretical development. The aim of this article is to establish the benefi ts of panel data for spatial mobility research and to illustrate this by providing an overview of recent progress in research. The necessarily limited selection of themes and studies presented in this overview does not do justice to the vast literature that exists, but it should show the strong level of engagement among spatial mobility researchers with panel data and illustrate the value of panel data in the study of spatial mobility. Furthermore, this overview has explained how spatial mobility is a multi-faceted phenomenon. Panel data can be extremely useful in establishing the diverse and complex patterns of mobility and immobility, isolating key relationships in a range of outcomes for mobile and immobile populations, and deciphering the causal mechanisms underlying these processes. The overview also revealed that some limitations in extant data collection and the under-utilisation of the data available might be slowing down further progress. There are several critical issues which we believe to be essential in order to continue making progress. The key advantage of panel data is the repeated observation of individuals, but this is often not fully exploited in empirical research. Among researchers who study spatial mobility, it is important to increase expertise in methods devised to examine data with longitudinal and panel structures (e.g. fi xed effects models, event history analysis, multi-level models). Many of these methods enable more accurate inferences than cross-sectional analyses, pooled cross-sections, or time series analyses by isolating within-individual change from between-individual differences. Additionally, they enable the modelling of complex associations that better characterise spatial mobility phenomena by uncovering temporal relationships or acknowledging complicated clusters of individuals in social relationships or structures (among others). We still largely ignore how the increasing fl uidity and complexity of the life course, with multiple and differentiated life transitions, is affecting how spatial mobility is (re-)negotiated throughout individuals’ lives. To shed light on these matters, research should take advantage of long-running, multi-purpose panel studies to examine changes and continuities in space for long life segments, from childhood to older age, and across generations, while accounting for problems of attrition. Such analyses should deepen the role of spatial mobility at different life course positions as well. This would allow us to shed more light on the links between aspirations, diverse motivations, and outcomes – including subjective wellbeing, health and lifestyle, environmental quality, climate and green spaces, social relationships and solidarity, and political attitudes. Greater knowledge about these links would enable • Sergi Vidal, Philipp M. Lersch 206 us to gain new insight into the role of spatial mobility as a boost or a bottleneck to advancement throughout the life course. Panel datasets increasingly (but not suffi ciently) include sets of questions that enable relevant intersections to be established among diverse forms of spatial mobility. Very limited progress has been made in terms of unveiling how relocation mobility (which has been declining in many countries) as well as commuting and other recurrent mobility practices (which are on the rise) are intertwined and have an impact on life outcomes. At the same time, immobility is simply considered to be the absence of mobility, the causes and consequences of which remain largely understudied (Schewel 2020). The same can be said of the links between internal and international mobility, although this is a general gap in spatial mobility research (Kin/Skeldon 2010). While the vast majority of panel data collections are not devised to examine the entire process of international migration, creative research designs that combine complementary data sources from registers, surveys, social media, and other digital sources across origin and receiving countries can be valuable solutions. For example, Panichella (2018) combined German and Italian household panel data to assess the social mobility of southern Italians who move internally (to northern Italy) and internationally (to Germany). Panel data is also better able to explain mobility-related phenomena beyond individuals and their properties. As this overview has shown, panel data is largely used in empirical analyses informed by approaches that emphasise social relations and structural factors. However, a relatively small fraction of these applications use advances in dyadic, multi-level, and other modelling strategies to deal with heterogeneity at supra-individual levels. Understanding the social embeddedness of spatial mobility also requires us to deepen our knowledge of social relationships beyond the household (Mulder 2018) and to consider mobility as relational practices that link lives together; one example of this is children circulating around the homes of their separated parents (Coulter et al. 2016), even though there are only a small number of panel studies, such as pairfam, which collect detailed information on ties outside the household. Evidence from extant research also calls for further assessments of the links across micro-, meso- and macro-levels of analysis, and for the combination of multiple levels to be taken into consideration. For instance, some studies found relevant context-level variation on individual-level, partner-specifi c outcomes of decisions about household relocations taken at the couple level (Lersch 2016; Nisic/ Melzer 2016). Advancing knowledge on the role of geographic, economic, cultural, or socio-political conditions for spatial mobility processes can be achieved by exploiting variation across temporal and spatial contexts, including cross-national comparisons. There is also untapped potential in panel data in the form of large surveys and registers, to understand the micro-level foundations of context-level change resulting from differential movement of individuals by features such as age, gender, education, class, or race. Selective mobility can reshape population compositions in origin and destination areas, and, if substantive, both drive social change and reconfi gure the inequality structures of cities, regions, and countries. Stratifi ed Panel Data in Research on Mobility and Migration: A Review of Recent Advances • 207 mobility patterns cause a myriad of local and urban processes such as gentrifi cation, ethnic segregation, or socio-economic polarisation, as well as broader spatial processes such as rural depopulation, social diffusion, or economic redistribution. These merit further study but cannot be fully understood without assessing the features and behaviours of individuals over time. Acknowledgements We thank Kilian Rüß for research assistance. We thank guest editors, Johannes Huinink and Josef Brüderl, and two anonymous reviewers for thoughtful comments and suggestions. Sergi Vidal acknowledges fi nancial support from the Spanish Ministry of Economy and Competitiveness (grant number: RYC-2015- 18254) and from the CERCA programme of the Government of Catalonia. Philipp M. 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