Received: 24 February 2022 | Revised: 1 May 2022 | Accepted: 3 May 2022 DOI: 10.1002/psp.2578 RESEARCH ARTICLE Understanding patterns of internal migration during the COVID‐19 pandemic in Spain Miguel González‐Leonardo1 | Antonio López‐Gay2,3 | Niall Newsham4 | 2,3 4 Joaquín Recaño | Francisco Rowe 1 International Institute for Applied Systems Analysis, Wittgenstein Centre for Abstract Demography and Global Human Capital, Existing empirical work has analysed the impacts of COVID‐19 on mortality, fertility Vienna, Austria 2 Department of Geography, Universitat and international migration. Less is known about the ways in which the COVID‐19 Autònoma de Barcelona, Barcelona, Spain pandemic has influenced the patterns of internal migration. Anecdotal reports of 3 Centre d'Estudis Demogràfics, Barcelona, mass migration from large cities to less populated areas have emerged, but lack of Spain 4 data has prevented empirically assessing this hypothesis. Drawing on geographically Department of Geography and Planning, University of Liverpool, Liverpool, UK granular administrative population register data, we aim to analyse the extent of change in the patterns of internal migration across the urban hierarchy in Spain Correspondence during 2020. Our results show a decline of 2.5% in the number of internal migration Francisco Rowe, Department of Geography and Planning, University of Liverpool, moves, particularly during the early stages of the pandemic, returning to pre‐ Liverpool, UK. pandemic levels in late 2020. Results also reveal unusually large net migration losses Email: F.Rowe-Gonzalez@liverpool.ac.uk in core cities and net migration gains in rural areas. Net migration losses in cities and Funding information gains in rural areas particularly accumulated following the elimination of the strict Ministerio de Ciencia e Innovación, Grant/Award Number: PID2020‐113665RB‐ lockdown measures in June. Yet, these net losses and gains trended to pre‐pandemic I00; Universitat Autònoma de Barcelona: levels in late 2020, and movements between cities, and between cities and suburbs, TalentProgram; “la Caixa” Foundation, Grant/Award Number: LCF/PR/SR21/ continued to dominate the internal migration system. Thus, while the COVID‐19 52560007; Alan Turing Institute, pandemic exerted notable changes in the geographic balance of internal migration Grant/Award Number: 1162533 flows, these changes appear to have been temporary and did not significantly alter the existing structures of the national migration system. KEYWORDS COVID‐19, internal migration, rural areas, Spain, urban exodus, urban hierarchy 1 | INTRODUCTION (Bell et al., 2015). Internal migration underpins the efficient functioning of the economy by bringing knowledge and skills to As fertility and mortality stabilise, migration has become the main the locations where they are needed and is essential to social well‐ driver of population change shaping patterns of human settlement being by enabling individuals to pursue their goals and aspirations both between and within countries (Bell et al., 2015). Together (Van Ham et al., 2001). Internal migration is widely regarded as with international migration, internal migration is the primary an integral component of human development (Klugman, 2009; agent redistributing population between subnational boundaries Skeldon, 1997). This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2022 The Authors. Population, Space and Place published by John Wiley & Sons Ltd. Popul. Space Place. 2022;28:e2578. wileyonlinelibrary.com/journal/psp | 1 of 13 https://doi.org/10.1002/psp.2578 2 of 13 | GONZÁLEZ‐LEONARDO ET AL. In the wake of the COVID‐19 pandemic, government stringency work. At the same time, more remote locations may not have the measures globally resulted in a major disruptive shock to the human digital infrastructure and diversity of services required to cater for mobility system of countries, constraining international travel city residents—and initiatives are already underway to make cities (Guadagno, 2020; IOM, 2020) and local daily mobility patterns more resilient to future pandemics (Eltarabily & Elghezanwy, 2020; (Duque et al., 2021). The extent to which these interventions have Moreno et al., 2021; Sharifi & Khavarian‐Garmsir, 2020). impacted internal migration movements is less well understood. Thus, while anecdotal evidence has emerged about large out‐ Reflecting on the postpandemic future of big cities, hypotheses have migration flows from big cities, little is known about the extent and emerged on the ways in which COVID‐19 may have impacted durability of these patterns. The lack of suitable data has prevented internal migration patterns. the analysis of internal migration movements across the rural‐urban During early phases of the pandemic, reports of an ‘urban continuum at a national scale. We had exclusive access to controlled exodus’ emerged with rampant speculation that this trend would micro‐data covering all reported changes in residential address in persist post‐COVID‐19 (e.g., Marsh, 2020; Nathan & Overman, 2020). Spain between January 2016 and December 2020. Drawing on these As COVID spread across the world and we started learning about its data, we analysed the extent and persistence of changes in internal severity, mounting fear and anxiety resulted in a need to minimise migration patterns in Spain during 2020. Spain was one of the first social contact and avoid crowded spaces. Globally, big cities became hardest hit countries by the COVID‐19 pandemic in 2020, with one the first epicentres of infection (Pomeroy & Chainey, 2020). High of the highest excess mortality records across Europe (Euro- population density, strong air connectivity and spatial concentration stat, 2022), and a strict lockdown from mid‐March to late April. We of public‐facing jobs serve as conducive mechanisms for the specifically seek to address the following set of questions: clustering of COVID‐19 cases in large cities (Bhadra et al., 2021; Florida et al., 2021). These factors created dense networks of social 1. To what extent did people leave cities, and moved to less interaction facilitating community transmission and geographical populated areas? spread of COVID‐19 (Brandén et al., 2020). 2. How did these patterns vary across cities during the pandemic? Emerging anecdotal evidence indicates that big city residents 3. What have been the main destinations for migration flows from moved to second homes, vacation towns, suburban areas and other cities? smaller cities (e.g., Hughes, 2020; Marsh, 2020; Paybarah et al., 2020). 4. How have the patterns of internal migration changed over the The pandemic has brought about changes that have allowed nonwork course of the pandemic? Have the observed changes been short‐ related considerations to become more prominent in the residential term? Did they persist over 2020? decision making process (Hernández‐Morales et al., 2020; Nathan & Overman, 2020). In addition to the existing urban pressures from air The rest of the paper is structured as follows: Section 2 discusses pollution, housing affordability and crime, big city residents were anecdotal evidence indicating how COVID‐19 may have impacted confined to small living spaces, with limited capacity to accommodate internal migration and the contemporary patterns of internal remote work, home‐schooling and other daily activities during migration before the pandemic across the Spanish urban hierarchy. lockdowns. Business shutdowns and social distancing measures Section 3 described the data and method used for the analysis. stripped away the effervescence of social interaction, vibrant urban Section 4 presents the results before discussing the key findings and spaces and many were pushed into unemployment (Blustein offering some concluding remarks in Section 5. et al., 2020; Honey‐Rosés et al., 2020). Additionally, telework reduced the need for frequent commuting, and hence, living in close proximity to work. These changes are believed to have reduced 2 | B A C K GRO U N D attachments to big cities in preference for larger, cheaper and less crowded residential areas during the pandemic, or seek for a refuge in 2.1 | Emerging evidence on internal migration family homes (Florida et al., 2021). patterns during COVID‐19 While these changes are expected to alter micro‐level household decision choices, they are not expected to significantly reshape the As COVID‐19 expanded throughout the world in February and March existing macro‐level patterns of national population settlement and of 2020, anecdotal evidence of an ‘urban exodus’ from big cities economic systems (Florida et al., 2021). Big cities have successfully emerged in many western societies (Davies, 2021; Marsh, 2020; weathered previous pandemics (Glaeser, 2020) and are likely to Matheson et al., 2020). In early stages of the pandemic, little was remain attractive places to live. Agglomeration economies are a known about the virus, and globally connected cities were hit hardest fundamental feature of cities. Cities facilitate the clustering of talent (OECD, 2020; The Economist, 2022). By November 2020, approxi- and economic assets, consumer base, face‐to‐face interaction and mately 95% of all the reported infections and fatalities had occurred diversity that are key to fostering innovation, creativity and economic in cities (Pomeroy & Chainey, 2020). Rampant speculation was linked growth (Storper & Venables, 2004). New forms of hybrid work are a to newspapers’ headlines claiming ‘the end of cities’ (e.g., likely permanent outcome of the pandemic (Alexander et al., 2021). Cavendish, 2020; Kimmelman, 2020). In the United Kingdom, the Telework is a poor substitute for high‐contact, knowledge‐based number of online inquiries from residents in the 10 largest cities GONZÁLEZ‐LEONARDO ET AL. | 3 of 13 looking for a village property was reported to increase by 126% in migration from cities of similar size (Davies, 2021). In both June–July 2020 relative to the same period in 2019 based on data countries COVID‐19, does not seem to have fundamentally altered from the property website Rightmove (Marsh, 2020). Increases in real the pre‐existing national structure of the net internal migration estate transactions outside cities were also linked to city residents balances. However, it seems to have accelerated relocation moving to smaller towns or villages in France (Sagnard, 2021). In the decisions that were already in motion prepandemic (Davies, 2021; United States, a rise of 30 percentage points in the number of Kolko et al., 2021). households moving from large metropolitan areas was reported Persuasive cases have been made against headlines speculating based on data from mail‐forwarding requests and credit report data about the end of cities. Past pandemics wreaked havoc and (Hughes, 2020; Whitaker, 2021). Australian Bureau of Statistics data substantially influenced medical, cultural, political and urban design indicate a net loss of 11k people from Australian capital cities in the changes, but they have not dented the key role that cities play in September quarter of 2020 (Davies, 2021). Recent empirical society (Glaeser, 2020). For instance, the Black Plagues of the 14th evidence also saw an increase of net‐migration losses in some cities century killed one‐third of the population in Europe and the Middle of Japan, Germany, Sweden and the United Kingdom (Fielding & East (Dols, 2019; Pamuk, 2007). The Cholera outbreaks of the 19th Ishikawa, 2021; Rowe et al. 2022; Stawarz et al., 2022; Vogiazides & century decimated major cities across the world, including, London, Kawalerowicz, 2022). Paris, Moscow, Hamburg, New York and Madrid (Evans, 1988). Yet, Various factors contributed to the ‘urban exodus’ narrative with large cities have continued to be important gravitational forces of COVID‐19 exposing key shortcomings of living in big cities. population concentration. Facilitated by high air‐travel connectivity, job density and spatial Cities are critical engines of innovation, economic growth and concentration of public‐facing jobs, big cities became early global prosperity. They enable the emergence of agglomeration economies. epicentres of COVID‐19 infections during the early phases of the Concentration in cities facilitates the exchange of goods, knowledge, pandemic (Florida et al., 2021; Rodríguez‐Pose & Burlina, 2021). information and ideas by reducing transportation and communication Prepandemic housing affordability and poor housing conditions have costs, offering abundant critical mass, and fostering strong firm been consistent urban challenges in big cities. Coupled to these linkages (Glaeser, 2010). A fundamental ingredient underlying these challenges, lockdowns, social distancing, remote work and home- benefits is the face‐to‐face interaction that urban concentrations schooling have reportedly added greater pressure for families living in enable (Storper & Venables, 2004). While routine, codified activities small and crowded living spaces, to move out of cities in the look for can be more easily communicated and performed virtually from more space and affordable housing (Hughes, 2020; Marsh, 2020). remote locations, complex, innovative and less familiar tacit Teleworking and increased familiarity, and use of online shopping, knowledge, tasks and ideas are facilitated by face‐to‐face contact reduced the need for commuting and living in proximity to work (Storper & Venables, 2004). This is a fundamental reason why the locations and shops. Business closures removed the effervescence of advent of internet communication has not led to the geographic urban entertainment, leisure and social spaces, and triggered a rapid dispersal of urban agglomerations and ‘the death of distance’, despite spike in unemployment in many countries during 2020 as non- enabling various forms of complex communication to occur at a essential, public‐facing work shut down (King, 2020; Smith distance (Tranos & Nijkamp, 2013). et al., 2021). At the same time, rural and remote areas may lack the COVID‐19 is claimed to have fuelled migration from big cities to infrastructure and services needed. They do not offer the vibrancy rural areas, suburbs and smaller cities (e.g., Hughes, 2020; and sophistication of entertainment, cultural and convenient services Sagnard, 2021). Technologies to facilitate remote work, such as that urbanites are used to. Additionally, telework is likely to be a video conferencing, shared documents, instant messaging and cloud permanent outcome of the COVID‐19 pandemic. Yet, poor broad- computing are now widely available. Virtual services, like video band connectivity in rural and remote locations has remained a key streaming and social media platforms offer access to some of the challenge across most countries in the world (Chen & Wellman, 2004). cultural effervescence and community that has drawn people to big Not all forms of work can be done remotely, including high‐touch, cities. Online shopping platforms, such as Amazon and Ebay provide public facing work providing essential (e.g., healthcare and education an opportunity to buy and ship products from distant locations. services) and nonessential (e.g., restaurants, bars and clothing stores) However, internal migration movements during the pandemic services; essential, nonpublic facing work related to construction, seem to have been over relatively short distances. Preliminary infrastructure and maintenance; and knowledge‐intensive activities evidence from the United States and Sweden indicates that the vast requiring high‐level abstraction and cognitive capacity, such as majority of movements from big cities during the pandemic has teaching and networking (Florida et al., 2021). Online work fatigue been towards the suburbs of those very cities, as opposed to has become a new phenomenon, with the widespread use of ‘Zoom smaller, remote cities and towns (e.g., Hughes, 2020; Ramani & fatigue’ (Wiederhold, 2020). Rather than full‐time remote work, Bloom, 2021; Vogiazides & Kawalerowicz, 2022), though urban hybrid forms of work are more likely to stay post‐pandemic, requiring residents have also moved to neighbouring areas, second resi- flexibility to combine office and online presence (Haag, 2021). This dences, holiday destinations and other cities (Quealy, 2020). In may entail a need for reliable broadband connectivity and accessibil- Australia, larger cities have been the primary destination of ity to employment centres. 4 of 13 | GONZÁLEZ‐LEONARDO ET AL. Thus, speculations during early phases of the pandemic pointed towards cities (Collantes & Pinilla, 2011; Recaño, 2020). In this paper, to an ‘urban exodus’ as COVID‐19 cases and deaths surged in large we analyse how COVID‐19 has impacted these pre‐existing patterns cities. Emerging evidence suggests that the effects from the of internal migration across the urban hierarchy. pandemic have reverberated through the internal migration system of countries across the world prompting residential relocations from cities (Fielding & Ishikawa, 2021; Stawarz et al., 2022; Vogiazides & 3 | D A T A A N D ME T H O D Kawalerowicz, 2022). Yet, these reverberations are not expected to have significantly redrawn the internal migration system (Brown & We used register population data to capture internal migration Tousey, 2021). Rather, they may have accelerated existing migration measuring changes in the municipality of residence. We analysed trends, and cities are expected to bounce back and remain major internal migration during 2020 and used averaged indicators across centres of agglomeration postpandemic. Thus far, however, existing the 2016–2019 period as a benchmark to determine deviations from evidence remains scarce. We therefore seek to offer empirical pre‐pandemic trends. We analysed the data in four phases. First, we evidence assessing the ways in which the Spanish internal migration explored out‐, in‐ and net‐migration flows across the urban hierarchy system has weathered during the pandemic in 2020. to explore the extent of changes in internal migration from cities to less populated areas. Second, we examined out‐, in‐ and net‐ migration outcomes for five core cities which experienced large 2.2 | Contemporary trends of internal migration population losses during 2020 (Madrid, Barcelona, Valencia, Zaragoza across the Spanish urban hierarchy and Bilbao) to identify their key destinations along the urban hierarchy and extent of variations across cities in 2020. Third, we Spain stands out as one of the least mobile countries in Europe (Bell analysed the spatial patterns of internal migration at the municipal et al., 2015; Bernard & Vidal, 2020; Rowe et al., 2019). According to geographical scale to identify key changes across the internal Estadística de Variaciones Residenciales (EVR) data, an overall rate of migration system and the extent of these variations in less populated internal migration of 3.3% indicates that only around 3% of the areas. Finally, we explored monthly changes in the levels of internal Spanish population changed municipality of residence during the migration flows during 2020 to determine the durability of the 2016–2020 period. This is similar to the low migration intensities internal migration patterns observed during the COVID‐19 pandemic. observed in other countries in Southern and Eastern Europe, including Italy, Greece, Poland, Romania, Russia and Czech Republic (Alvarez et al., 2021; Rowe et al., 2020). But, it contrasts with the high 3.1 | Data levels found in Nordic and Western European countries, with internal migration rates of over 18% in Iceland and France (Rees et al., 2017). We had access to a unique and large administrative data set Spain also displays one of the lowest levels of internal migration recording all individual reported changes in place of residence in efficiency; that is, the extent to which the national population is Spain. We drew on data from official microdata collected by the redistributed through internal migration. Spain reports an internal (EVR) of the Instituto Nacional de Estadística (INE) from 2016 to migration efficiency of 6.3%, which is below the European mean of 2020 for 8131 municipalities. An open access version of this data set 11.4% (Rowe et al., 2020). Such low levels of migration efficiency in is available to researchers, but it does not include information for the Spanish internal migration system reflects very low levels of municipalities with less than 10,000 inhabitants, which equate to population redistribution resulting from migration inflows being 90.7% of all Spanish municipalities. The data set we used includes closely balanced by outflows. data for all municipalities. During the lockdown, mainly in the early Precoronavirus low levels of internal migration efficiency and stages, EVR registrations were affected by underreporting, but the intensity in Spain resulted in a migration system characterised by a effect of this influence is unclear. On the one hand, closure of some pattern of spatial equilibrium (Rowe et al., 2019; Rowe & in‐person EVR registration offices may have resulted in under‐ Patias, 2020). This is a system in which migration flows across the reporting, with EVR capturing a lower number of changes in national urban settlement are closely balanced, resulting in limited residential locations than those occurring. On the other hand, population redistribution (López‐Gay, 2017; Recaño, 2020). Even so, telematic services were rapidly deployed across the country, enabling population redistribution occurs with three distinct processes: digital registration. Additionally, selective local and regional mobility urbanisation, suburbanisation and counterurbanisation (Gil‐Alonso restrictions on nonessential activities were implemented during the et al., 2021; González‐Leonardo, 2021; López‐Gay et al., 2020; last 4 months of 2020 where COVID‐19 cases increased, mostly in Torrado et al., 2020). Movements from major core cities to suburbs cities (La Moncloa, 2021). It may have led to over‐reporting in rural and population exchanges between cities are a prevalent form of areas caused by potentially untruthful residential registrations to mobility in the Spanish migration system (Bayona & Pujadas, 2020; avoid mobility restrictions in cities. Estimating the balance between González‐Leonardo et al., 2019; Rowe & Patias, 2020). Pre‐ these registration challenges is not possible as we have no data on coronavirus, the migration system in Spain was also characterised the levels of under‐ and over‐reporting. While we do not think these by population losses in inland rural areas due to internal migration issues invalidate our conclusions, they make the identification of the GONZÁLEZ‐LEONARDO ET AL. | 5 of 13 (Ii − Oi ) causes of the observed changes in residence more challenging. It is NRi = × 1000, (3) Pi difficult to determine if EVR registrations indicate a real or a convenient change of residence. Yet, we believe that the data still where Oi is the number of migrants moving out of the origin area i; Ii provides a relatively accurate representation of the internal migration is the number of migrants moving to the destination area i; and Pi is trends during 2020. the middle population. Stage 3. We analysed the spatial patterns of internal migration using municipality‐specific ORs, IRs and NRs to identify the location 3.2 | Method of key internal migration winners and losers during COVID‐19 in 2020 and quantify the demographic impact of out‐migration flows As we explained below, our analysis consisted of four stages: from these cities to less populous areas. Stage 1. To analyse the extent of internal migration from cities, Stage 4. We analysed monthly changes in the level of internal we measured variations in migration intensity and impact across the migration to assess the continuity of internal migration patterns urban hierarchy. To this end, we classified municipalities into core during COVID‐19 and shed light into future patterns. To establish the cities, suburbs, towns and rural areas. Core cities and suburbs extent of changes in internal migration patterns during COVID‐19, are classified based on the official Spanish urban area classification— we compared monthly internal migration counts in 2020 to those the Átlas Estadístico de las Áreas Urbanas of the Spanish Ministerio de recorded in prepandemic years between 2016 and 2019. This Transportes, Movilidad y Agenda Urbana (MITMA). This source only comparison captures monthly seasonal variations in internal migra- includes urban areas, therefore core cities and their suburbs are tion patterns and enables determining the extent to which observed listed, but not towns and rural areas. It used several variables to variations deviate from the long‐term trajectory in internal migration classify municipalities as urban areas, such as administrative criteria, intensity. We also analysed these trends across the urban hierarchy, population density, size, trends and structure, urban morphology, to assess the extent of variation in the volume of internal migration housing market, transport infrastructures or labour market charac- during COVID‐19 across core cities, suburbs, towns and rural areas. teristics. Core cities represent central municipalities of urban areas and are classified based on administrative criteria, including most Spanish capital cities. We classified municipalities which are not 4 | RESULTS considered as urban areas into towns or rural areas if they are larger or smaller than 10,000 inhabitants, respectively. Table 1 in the 4.1 | Changes in internal migration across the Supporting Information Material shows the distribution of munici- urban hierarchy: Cities as losers and rural areas as palities across these areas. In this stage, we used system‐wide winners indicators to quantify changes in mobility patterns across the national migration system. To capture migration intensity, we estimated the Taken together, the number of moves decreased from 1.56 million to number of people moving in and out of core cities, suburbs, towns 1.52 million (2.5%) between the prepandemic 2016–2019 average and rural areas. To measure the spatial impact of migration, we and 2020. Although there was not a significant difference in the total measured net migration which captures the net balance between in‐ number of movements, Figure 1 reveals changes in the patterns of and out‐migration flows. internal migration across the urban hierarchy, particularly in core Stage 2. We analysed variations in internal migration patterns cities and rural areas. Before the pandemic, a status of spatial across core cities. Focusing on a selection of five core cities (Madrid, equilibrium dominated the national internal migration system, with Barcelona, Valencia, Zaragoza and Bilbao), we examined changes in limited population redistribution as in‐migration flows were closely out‐, in‐ and net‐migration rates from these cities to their suburbs offset by out‐migration movements. (according to the MITMA), other urban areas (core cities and During the pandemic in 2020, a pattern of population suburbs), towns and rural areas. We chose these cities because they deconcentration emerged reflecting net migration losses in high‐ represent key population agglomerations in the Spanish settlement density areas and net migration gains in more sparsely populous system with populations of over 300k, recorded large overall locations. Core cities recorded a rise in out‐migration by 6.0% increases in net migration losses during the COVID‐19 pandemic in (515k–546k) in 2020, and a drop in in‐migration by 15.4% (494k to 2020 (higher than 50% compared to 2016–2019) and provide a 418k). These changes resulted in a population loss due to internal diverse overview of changes in local regional internal migration migration of 127k in 2020, representing an increase in annual systems. For our analysis, we used city‐specific out‐migration rates population loss in core cities. Annual prepandemic net‐migration (ORs), in‐migration rates (IRs) and net‐migration rates (NRs): during the 2016–19 period averaged −20k. By contrast, rural areas 0i recorded a decrease in out‐migration by 12.6% (296k–258k), which ORi = × 1000, (1) Pi was met by a rise in in‐migration by 20.5% (288k–347k), leading to an overall positive net‐migration balance of 88k in 2020. In rural areas, Ii this represents a reversal of prepandemic population losses due to IRi = × 1000, (2) Pi internal migration, and a trend of rising counterurbanisation. Yet, 6 of 13 | GONZÁLEZ‐LEONARDO ET AL. F I G U R E 1 Internal out‐, in‐ and net‐migrations: 2016–2019 (annual average) and 2020. Note: The black lines at the top of the bars represent confidence intervals. Source: Own elaboration using data from the Estadística de Variaciones Residenciales (INE). movement between core cities and suburbs continued to dominate Zaragoza. Net‐migration with suburbs was negative for all cities in the internal migration system in 2020. the pre‐pandemic period and further reductions were recorded in 2020 due to a decay in the number of arrivals from suburbs to core cities, particularly in Madrid, Barcelona and Valencia. 4.2 | Variation on internal migration across cities: Increasing out‐migration from large cities to rural areas 4.3 | Rural areas and coastal towns in close proximity to core cities as key destinations We next analyse out‐, in‐ and net‐, migration outcomes for a selection of five core cities. Figure 2 shows out, in‐ and net migration We then explored the spatial patterns of internal migration at the rates in Madrid, Barcelona, Valencia, Zaragoza and Bilbao with their municipal level to understand the general structure of internal suburbs, other urban areas, towns and rural municipalities. All five migration flows. Figure 3 reports municipality‐specific out‐, in‐ and core cities display overall negative net migration rates during net‐migration rates for 2020 and the average of the 2016–19 period. the pandemic in 2020. These negative net balances exacerbated in Prepandemic, net‐migration rates are predominantly moderate, with the largest core cities, Madrid, Barcelona and Valencia, with rates net migration gains concentrated in the suburbs of large urban declining from −0.5%, −5.8% and −2.3% in 2016–19 to −12.4%, agglomerations, and net migration losses concentrated in rural areas, −14.0% and −9.0%, respectively (see Table 2 in the Supporting particularly in east and north of Madrid. During COVID‐19 in 2020, Information Material). In Zaragoza and Bilbao, positive net migration Figure 3 shows the spatial structure of population deconcentration balances in 2016–19 were reversed, declining from 1.4% to −4.9% with people away from core cities to suburbs and rural areas. and 0.7% to −6.5%, respectively. These more pronounced negative Additionally, it reveals that this pattern of population net balances were due to a decline of in‐migration and a rise in out‐ deconcentration is particularly related to movements from the large migration, except for Valencia, where the main cause seems to be an urban agglomerations, such as Madrid and Barcelona, to specific overall decline in in‐migration. locations involving mountain rural areas and certain coastal towns. Although rising declines in net migration rates are observed for These areas are known to be popular holiday destinations and population exchanges with all types of areas for our sample of cities, concentrate second home residences (Alario et al., 2014; López‐Colás the largest drops consistently occurred for migration interactions & Módenes, 2004), including areas such as the neighbouring with rural municipalities, reflecting an acceleration in counter‐ mountain villages of Madrid as a consequence of the arrivals from urbanisation, especially in Madrid and Barcelona. Figure 2 shows the capital of Spain (see also Figures 2 and 3 in the Supporting that net migration rates to other urban areas were reversed from Information Materials); those in the Pirineo Catalán mountain and positive to negative in the global cities of Madrid and, to a lesser coastal towns in the north of the Mediterranean, caused by internal extent in Barcelona, preliminary as a result of declining in‐migration, migrations from Barcelona; the Pirineo Aragonés mountain due to but also caused by an increase of out‐migration. Yet, suburbanisation inflows of residents from the city of Zaragoza; and mountain villages is the dominant pattern in the national migration system, except in in the north of Burgos where in‐migration from Bilbao increased. In GONZÁLEZ‐LEONARDO ET AL. | 7 of 13 F I G U R E 2 Out‐, in‐ and internal net‐migration rates in selected core cities by type of origin and destination areas (x‐axis): 2016–2019 (annual average) and 2020. Note: Urban areas include core cities and suburbs within other urban areas; suburbs include municipalities of the own urban area of each core city; confidence intervals are represented at the top of the bars. Source: Own elaboration using data from the Estadística de Variaciones Residenciales and Cifras Oficiales de Población (INE). these rural areas, increasing arrivals from cities had a great this drop occurred during March–May in 2020, as COVID‐19 spread demographic impact during the pandemic. and a national lockdown was enacted. Following the end of the lockdown in June, the number of internal migrants increased, exceeding the prepandemic levels until November. In December, 4.4 | Internal migration over the pandemic. Are however, values returned to prepandemic levels. changes likely to endure after COVID‐19? These changes conceal distinctive temporal patterns across core cities, suburbs, towns and rural areas. While all areas As indicated in Section 4.1, the overall number of internal migrants recorded a notable drop in in‐migration and out‐migration during dropped by 2.5% between 2016–19 and 2020. Figure 4 shows that March–May, variations in the intensity and recovery of these 8 of 13 | GONZÁLEZ‐LEONARDO ET AL. F I G U R E 3 Out‐, in‐migration and net‐migration rates by municipalities (‰): 2016–2019 (annual average) and 2020. Source: Own elaboration using data from the Estadística de Variaciones Residenciales and Cifras Oficiales de Población (INE). GONZÁLEZ‐LEONARDO ET AL. | 9 of 13 outcomes during June and November resulted in distinctive net recorded continuing population losses, converging to prepandemic migration balances (Figure 5). Core cities enced a continuing loss of levels in December. Taken together, this evidence suggests that population through internal migration from May to November, changes in internal migration patterns during COVID‐19 may have driven primarily by out‐migration post April. Relative to prepan- been temporary with trends returning to prepandemic levels in demic levels, in‐migration registered little variation with levels late 2020. similar to those observed for the 2016–2019 period. Net migration balances in suburbs and towns were virtually zero with in‐ migration closely balancing out‐migration. In the case of suburbs, 5 | D IS CU SS IO N A N D CO N CL US I O N this resulted from comparable drops and rises in out‐ and in‐ migration levels, while out‐migration and in‐migration levels in During the early phases of the COVID‐19 pandemic, anecdotal towns converged to pre‐pandemic levels following a decline during evidence of an urban exodus emerged. This hypothesis suggests that March and April. As we know, the key gainers of internal migration the concentration of COVID‐19 in cities, teleworking, lockdowns in losses in core cities were rural areas. Figure 5 reveals that net poor housing conditions and the shutdown of urban vibrancy led to migration gains occurred during May–November as core cities large migration outflows from cities to less populated areas (Davies, 2021; Marsh, 2020; Matheson et al., 2020). Our results revealed that sudden changes in internal migration patterns occurred across the Spanish urban hierarchy during the COVID‐19 pandemic in 2020. In core cities, out‐migration increased by 6% and in‐migration decreased by 15.4%, resulting in a population loss due to internal migration. Conversely, rural areas recorded a 12.6% decrease in out‐ migration and a 20.6% increase in in‐migration, leading to a positive net‐migration balance. Minor changes were observed across suburbs and towns. While these net migration outcomes have a modest impact on the population structure of cities, they resulted in significant expansions in the local population of sparsely populated rural areas. Despite these changes, most migration movements continued to occur between core cities and suburbs, or between different urban areas. These results indicate that whilst the pandemic F I G U R E 4 Internal migrations by months in 2016–2019 (annual disrupted urban lifestyles and increased urban‐to‐rural migration, average) and 2020. Note: Grey lines represent years from 2010 to 2019. Source: Own elaboration using data from the Estadística de shocks to the internal migration system do not seem to have led to an Variaciones Residenciales (INE). urban exodus. F I G U R E 5 Internal out‐, in‐ and net‐migrations by months and territorial typology: 2016–2019 (annual average) and 2020. Source: Own elaboration using data from the Estadística de Variaciones Residenciales (INE). 10 of 13 | GONZÁLEZ‐LEONARDO ET AL. We provided evidence revealing that the largest Spanish cities with telework capacity. As a consequence of the stringency measures experienced the highest increase in migration outflows and declines and telework, second residence tenure and socioeconomic status in migration inflows, especially Madrid and Barcelona, as it has been seem to have been key factors underpinning changes in the direction also observed in Stockholm in Sweden (Vogiazides & of migration flows. Understanding and establishing the causal effect Kawalerowicz, 2022), Tokyo in Japan (Fielding & Ishikawa, 2021), of these factors on internal migration is important to guide policies the chief cities of Germany (Stawarz et al., 2022) and densely targeting to increase the attractiveness of rural locations as places of populous areas in Britain (Rowe et al. 2022). Out‐migration away residence. Drawing on the 2021 Spanish Census and the EVR 2021 from these cities may have been linked to the fact that large cities data when they become available, future research should also extend became the main early epicentres of COVID‐19 infections and our analysis to examine the continuity or change in the patterns of mortality (Florida et al., 2021; Rodríguez‐Pose & Burlina, 2021). High internal migration flows, to establish the durability of the shifts over population density in cities has been linked to high spreading of the COVID‐19 pandemic in 2020. While mass vaccination has COVID‐19 infections (Bhadra et al., 2021; Wong & Li, 2020). Out‐ reduced mortality and risk of infection, new variants have continued migration from cities has also been connected to high income to cause anxiety, social discontent and rises in COVID‐19 cases. The households relocating to second and vacation residences elsewhere net impact of these potentially conflicting effects on internal (Hughes, 2020; Paybarah et al., 2020). migration are to be established. Our findings indicated that out‐migration flows from cities to less We used administrative data from the Spanish population populated areas headed towards rural destinations with a particular register (the EVR) to capture internal migration movements. As set of attributes. These destinations tended to comprise key touristic indicated in Section 3, these records were impacted by under‐ locations, including coastal towns, mountain areas and localities in reporting of changes in residence during the early stages of the close proximity to cities and natural parks. High concentration of pandemic due to closure of registration offices, and they may have second residences seem to have been a key factor underpinning the been affected by over‐reporting due to untruthful residential attractiveness of these locations (Alario et al., 2014; López‐Colás & registrations to avoid selective local and regional mobility restrictions. Módenes, 2004). While we believe that the data provide a relatively accurate Despite remarkable changes, we presented evidence suggesting representation of the internal migration trends during 2020, the that these alterations have been temporary. The scale of internal spatial distribution of these reporting issues remained unknown and migration appeared to have returned to prepandemic levels over difficult to unpack. Future work could examine the spatial patterns of 2020. While internal migration flows and rates recorded pronounced internal migration drawing on nontraditional data sources, such as changes during the early phases of the pandemic, as lockdowns were mobile phone location data from applications (Rowe 2022; Rowe, imposed and COVID‐19 rapidly spread across the country, they Arribas‐Bel, et al., 2022), to substantiate the evidence provided here. tended to converge to the levels observed during the four preceding years during the late months of 2020. ACKNOWL EDGEM ENTS Taken together, we presented evidence of marked changes in the This study was supported by the Talent Program of the Autonomous patterns of internal migrations during 2020. Yet, the COVID‐19 University of Barcelona and the Center for Demographic Studies; ‘La pandemic does not seem to have significantly altered existing Caixa’ Social Research (LCF/PR/SR21/52560007); Ministry of Sci- structures in the internal migration system. Spanish cities have ence and Innovation (PID2020‐113665RB‐I00); Alan Turing Institute remained and are likely to remain key centres of attraction for in the United Kingdom (1162533). migration flows. Agglomeration economies will probably continue to facilitate economic prosperity and thus promote the spatial concen- CONFLIC T OF INTEREST tration of population and businesses (Storper & Venables, 2004). At The author declares no conflict of interest. the same time, rural areas may lack the necessary infrastructure and services to support hybrid work arrangements. Telework is likely to ORC I D become a more predominant form of interaction. Poor broadband Miguel González‐Leonardo http://orcid.org/0000-0003- connectivity, transport accessibility and the remoteness of rural 2508-7977 locations are likely to represent major challenges to enable hybrid Antonio López‐Gay http://orcid.org/0000-0001-8892-2816 working (Chen & Wellman, 2004). Francisco Rowe http://orcid.org/0000-0003-4137-0246 We analysed how the COVID‐19 pandemic led to changes in the spatial patterns of internal migration. Future research is needed to RE F ER EN CES establish the profile of internal migrants and key socioeconomic Alario, M., Molinero, F., & Morales‐Prieto, E. (2014). Nuevos usos factors underpinning these trends. The pandemic has had a very residenciales en el espacio rural de Castilla y León. Boletín De La Asociación De Geógrafos Españoles, (66), 397–422. https://doi.org/ differentiated impact across the population and only privileged 10.21138/bage.1794 groups have reportedly been able to migrate. Particularly, these Alexander, A., Cracknell, R., De Smet, A., Langstaff, M., Mysore, M., & groups likely included wealthy individuals with second homes away Ravid, D. (2021). What executives are saying about the future of hybrid from cities, and people in nonpublic facing and highly flexible jobs work. McKinsey & Company. https://www.mckinsey.com/business- GONZÁLEZ‐LEONARDO ET AL. | 11 of 13 functions/people-and-organizational-performance/our-insights/ statistics#Excess_mortality_in_the_European_Union_between_ what-executives-are-saying-about-the-future-of-hybrid-work January_2020_and_February_2022 Alvarez, M., Bernard, A., & Lieske, S. N. (2021). Understanding internal Evans, R. J. (1988). Epidemics and revolutions: Cholera in nineteenth- migration trends in OECD countries. Population, Space and Place, century Europe. Past & Present, 120, 123–146. https://doi.org/10. 27(7), e2451. https://doi.org/10.1002/psp.2451 1093/past/120.1.123 Bayona, J., & Pujadas, I. (2020). Las grandes áreas metropolitanas en Fielding, T., & Ishikawa, Y. (2021). COVID‐19 and migration: A research España: del crecimiento y la expansión residencial al estancamiento note on the effects of COVID‐19 on internal migration rates and poblacional. Documents d'Anàlisi Geogràfica, 66(1), 27–55. https:// patterns in Japan. Population, Space and Place, 27(6), e2499. https:// doi.org/10.5565/rev/dag.576 doi.org/10.1002/psp.2499 Bell, M., Charles‐Edwards, E., Ueffing, P., Stillwell, J., Kupiszewski, M., & Florida, R., Rodríguez‐Pose, A., & Storper, M. (2021). Cities in a post‐ Kupiszewska, D. (2015). Internal migration and development: COVID world. Urban Studies, 004209802110180. https://doi.org/ Comparing migration intensities around the world. Population and 10.1177/00420980211018072 Development Review, 41(1), 33–58. https://doi.org/10.1111/j.1728- Gil‐Alonso, F., López‐Villanueva, C., Bayona‐i‐Carrasco, J., & Pujadas, I. 4457.2015.00025.x (2021). Towards an even more spatially diversified city? New Bernard, A., & Vidal, S. (2020). Does moving in childhood and adolescence metropolitan population trends in the post‐economic crisis period. affect residential mobility in adulthood? An analysis of long‐term Urban Science, 5(2), 41. https://doi.org/10.3390/urbansci5020041 individual residential trajectories in 11 European countries. Glaeser, E. (2020). Cities and pandemics have a long history. City Journal. Population, Space and Place, 26(1), e2286. https://doi.org/10.1002/ https://www.city-journal.org/cities-and-pandemics-have-long-history psp.2286 Glaeser, E. L. (2010). Agglomeration economics. University of Chicago Press. Bhadra, A., Mukherjee, A., & Sarkar, K. (2021). Impact of population González‐Leonardo, M. (2021). Declive demográfico y envejecimiento en density on Covid‐19 infected and mortality rate in India. Modeling las capitales de provincia. Cuadernos Geográficos, 60(3), 168–191. Earth Systems and Environment, 7), 623–629. https://doi.org/10. https://doi.org/10.30827/cuadgeo.v60i3.17719 1007/s40808-020-00984-7 González‐Leonardo, M., López‐Gay, A., & Recaño, J. (2019). Brain drain Blustein, D. L., Duffy, R., Ferreira, J. A., Cohen‐Scali, V., Cinamon, R. G., & and the second wave of depopulation. Perspectives Demogràfiques, Allan, B. A. (2020). Unemployment in the time of COVID‐19: A 16, 1–4. https://doi.org/10.46710/ced.pd.eng.16 research agenda. Journal of Vocational Behavior, 119, 103436. Guadagno, L. (2020). Migrants and the COVID‐19 pandemic: An initial https://doi.org/10.1016/j.jvb.2020.103436 analysis. International Organization for Migration. https://www.iom. Brandén, M., Aradhya, S., Kolk, M., Härkönen, J., Drefahl, S., Malmberg, B., int/sites/default/files/documents/mrs-60.pdf Rostila, M., Andersson, G., & Mussino, E. (2020). Residential context Haag, M. (2021). Remote work is here to stay. Manhattan may never be and COVID‐19 mortality among adults aged 70 years and older in the same. The New York Times. https://www.nytimes.com/2021/03/ Stockholm: A population‐based, observational study using 29/nyregion/remote-work-coronavirus-pandemic.html individual‐level data. The Lancet Healthy Longevity, 1(2), 80–88. Hernández‐Morales, A., Oroschakoff, K., & Barigazzi, J. (2020). The death https://doi.org/10.1016/S2666-7568(20)30016-7 of the city. Politico. https://www.politico.eu/article/the-death-of- Brown, J., & Tousey, C. (2021). How the pandemic influenced trends in the-city-coronavirus-towns-cities-retail-transport-pollution- domestic migration across US urban areas. Economic Review, 106(4), economic-crisis/ 39–58. https://www.kansascityfed.org/documents/8524/Economic Honey‐Rosés, J., Anguelovski, I., Chireh, V. K., Daher, C., ReviewV106N4BrownTousey.pdf Konijnendijk van den Bosch, C., Litt, J. S., … Nieuwenhuijsen, M. J. Cavendish, C. (2020). The pandemic is killing the attraction of megacities. (2020). The impact of COVID‐19 on public space: An early review of Financial Times. Opinion Smart. https://www.ft.com/content/ the emerging questions–design, perceptions and inequities. Cities & e30b16e2-9604-11ea-899a-f62a20d54625 Health, 1–17. https://doi.org/10.1080/23748834.2020.1780074 Chen, W., & Wellman, B. (2004). The global digital divide–within and Hughes, C. (2020). Coronavirus escape: To the suburbs. The New York Times. between countries. IT & Society, 1(7), 39–45. http://www.ifs.tuwien. https://www.nytimes.com/2020/05/08/realestate/coronavirus-escape- ac.at/%7Edieter/teaching/GmA/Chen2004.pdf city-to-suburbs.html Collantes, F., & Pinilla, V. (2011). Peaceful surrender: The depopulation of IOM. (2020). COVID‐19 Analytical Snapshot #3: Travel restrictions & rural Spain in the twentieth century. Cambridge Scholars Publishing. mobility. Understanding the migration & mobility implications of https://doi.org/10.1002/psp.1745 COVID‐19. International Organization for Migration. https://www. Davies, A. (2021). Has COVID really caused an exodus from our cities? In iom.int/sites/default/files/our_work/ICP/MPR/covid-19_analytical_ fact, moving to the regions is nothing new. The Conversation. https:// snapshot_3_-_travel_restrictions_and_mobility.pdf theconversation.com/has-covid-really-caused-an-exodus-from-our- Kimmelman, M. (2020). Can city life survive coronavirus? The New York Times. cities-in-fact-moving-to-the-regions-is-nothing https://www.nytimes.com/2020/03/17/world/europe/coronavirus- Dols, M. W. (2019). The black death in the Middle East. Princeton city-life.html University Press. King, B. (2020). Unemployment rate: How many people are out of work? Duque, R., Torrado, J. M., & Mesa‐Pedrazas, A. (2021). Lockdown and BBC News. https://www.bbc.co.uk/news/business-52660591 adaptation: Residential mobility in Spain during the COVID‐19 crisis. Klugman, J. (2009). Human development report 2009. Overcoming barriers: European Societies, 23(1), 759–776. https://doi.org/10.1080/ Human mobility and development. United Nations. Development 14616696.2020.1836386 Programme. https://hdr.undp.org/en/content/human-development- Eltarabily, S., & Elghezanwy, D. (2020). Post‐pandemic cities‐the impact of report-2009 covid‐19 on cities and urban design. Architecture Research, 10(3), Kolko, J., Badger, E., & Bui, Q. (2021). How the pandemic did, and didn't, 75–84. https://doi.org/10.5923/j.arch.20201003.02 change where Americans move? The New York Times. https://www. OECD. (2020). Economic resilience and regional economic disparities. In: nytimes.com/interactive/2021/04/19/upshot/how-the-pandemic- P. Veneri OECD regions and cities at a glance 2020 (pp. 47–66). did-and-didnt-change-moves.html OECD Publishing. https://www.oecd.org/cfe/oecd-regions-and- López‐Colás, J., & Módenes, J. A. (2004). Vivienda secundaria y residencia cities-at-a-glance-26173212.htm múltiple en España: una aproximación sociodemográfica. Scripta Eurostat. (2022). Excess mortality—statistics. https://ec.europa.eu/ Nova. Revista Electrónica de Geografía y Ciencias Sociales, 8(178), eurostat/statistics-explained/index.php?title=Excess_mortality_-_ 1621–1638. http://www.ub.edu/geocrit/sn/sn-178.htm 12 of 13 | GONZÁLEZ‐LEONARDO ET AL. López‐Gay, A. (2017). Hacia un patrón territorial complejo de la movilidad Rowe, F., Bell, M., Bernard, A., & Charles‐Edwards, E. (2020). Internal residencial. El caso de la Región Metropolitana de Barcelona. Papers. Migration Intensity and Impact in Europe. European Demographic Revista de Sociologia, 102(4), 793–823. https://doi.org/10.5565/rev/ Datasheet 2020. Vienna Institute of Demography (VID), International papers.2420 Institute for Applied Systems Analysis (IIASA) and Wittgenstein López‐Gay, A., Andújar‐Llosa, A., & Salvati, L. (2020). Residential mobility, Centre for Demography and Global Human Capital. www. gentrification and neighborhood change in Spanish cities: A post‐ populationeurope.org crisis perspective. Spatial Demography, 8(3), 351–378. https://doi. Rowe, F., Bell, M., Bernard, A., Charles‐Edwards, E., & Ueffing, P. (2019). org/10.1007/s40980-020-00069-0 Impact of internal migration on population redistribution in Europe: Marsh, S. (2020). Escape to the country: How COVID is driving an exodus Urbanisation, counterurbanisation or spatial equilibrium? Comparative from Britain's cities. The Guardian. https://www.theguardian.com/ Population Studies, (44), https://doi.org/10.12765/CPoS-2019-18 world/2020/sep/26/escape-country-covid-exodus-britain-cities- Rowe, F., Calafiore, A., Arribas‐Bel, D., Samardzhiev, K., Fleischmann, M., pandemic-urban-green-space (2022). Urban exodus? Understanding human mobility in Britain Matheson, J., Nathan, M., Pickard, H., & Vanino, E. (2020). Why has during the COVID‐19 pandemic using facebook data. Preprint. coronavirus affected cities more than rural areas? Economics Observa- Rowe, F., & Patias, N. (2020). Mapping the spatial patterns of internal tory. Nations, Regions & Cities. https://www.economicsobservatory. migration in Europe. Regional Studies, Regional Science, 7(1), com/why-has-coronavirus-affected-cities-more-rural-areas 390–393. https://doi.org/10.1080/21681376.2020.1811139 La Moncloa. (2021). El Gobierno decreta el estado de alarma para hacer Sagnard, A. (2021). Aujourd‘hui, les enfants ont tout le temps le rose aux frente a la expansión de coronavirus COVID‐19. La Moncloa. https:// joues: Ces Français qui quittent la ville pour changer d'air. L'OBS. www.lamoncloa.gob.es/consejodeministros/resumenes/Paginas/ https://www.nouvelobs.com/societe/20211203.OBS51732/ 2020/14032020_alarma.aspx aujourd-hui-les-enfants-ont-tout-le-temps-le-rose-aux-joues-ces- Moreno, C., Allam, Z., Chabaud, D., Gall, C., & Pratlong, F. (2021). francais-qui-quittent-html Introducing the “15‐ minute city”: Sustainability, resilience and place Sharifi, A., & Khavarian‐Garmsir, A. R. (2020). The covid‐19 pandemic: identity in future post‐pandemic cities. Smart Cities, 4(1), 93–111. Impacts on cities and major lessons for urban planning, design, and https://doi.org/10.3390/smartcities4010006 management. Science of the Total Environment, 749, 142391. https:// Nathan, M., & Overman, H. (2020). Will coronavirus cause a big city doi.org/10.1016/j.scitotenv.2020.142391 exodus? Environment and Planning B: Urban Analytics and City Science, Skeldon, R. (1997). Migration and development: A global perspective. 47(9), 1537–1542. https://doi.org/10.1177/2399808320971910 Routledge. Pamuk, S. (2007). The Black Death and the origins of the ‘Great Smith, S., Edwards, R., & Doung, H. (2021). Unemployment rises in 2020, as Divergence’ across Europe, 1300‐1600. European Review of the country battles the COVID‐19 pandemic. Monthly Labor Review. Economic History, 11(3), 289–317. https://doi.org/10.1017/ https://www.bls.gov/opub/mlr/2021/article/unemployment-rises- S1361491607002031 in-2020-as-the-country-battles-the-covid-19-pandemic.htm Paybarah, A., Bloch, M., & Reinhard, S. (2020). Where new yorkers moved Stawarz, N., Rosenbaum‐Feldbrügge, M., Sander, N., Sulak, H., & to escape coronavirus. The New York Times. https://www.nytimes. Knobloch, V. (2022). The impact of the COVID‐19 pandemic on com/interactive/2020/05/16/nyregion/nyc-coronavirus-moving- internal migration in Germany: A descriptive analysis. Population, leaving.html Space and Place, e66. https://doi.org/10.1002/psp.2566 Pomeroy, R., & Chainey, R. (2020). Has COVID killed our cities? Forum. Storper, M., & Venables, A. J. (2004). Buzz: Face‐to‐face contact and the World Economic. https://www.weforum.org/agenda/2020/11/ urban economy. Journal of Economic Geography, 4(4), 351–370. cities-podcast-new-york-dead/ https://doi.org/10.1093/jnlecg/lbh027 Quealy, K. (2020). The richest neighborhoods emptied out most as The Economist. (2022). Covid‐19 data. Tracking covid‐19 excess deaths coronavirus hit New York City. The New York. https://www. across countries. The Economist. https://www.economist.com/ nytimes.com/interactive/2020/05/15/upshot/who-left-new-york- graphic-detail/coronavirus-excess-deaths-tracker coronavirus.html Torrado, J. M., Calvache, R. D., & Susino, J. (2020). El retorno de la ciudad. Ramani, A., & Bloom, N. (2021). The donut effect of covid‐19 on cities. La tesis de la recuperación material y simbólica de las ciudades Technical report. National Bureau of Economic Research. https://doi. centrales. Biblio3W. Revista Bibliográfica de Geografía y Ciencias org/10.3386/w28876 Sociales, 25(1299), https://doi.org/10.1344/b3w.25.2020.31038 Recaño, J. (2020). Cambio y continuidades en las migraciones internas Tranos, E., & Nijkamp, P. (2013). The death of distance revisited: Cyber‐ en España. In J. D. Sempere (Ed.), Población y territorio. España place, physical and relational proximities. Journal of Regional Science, tras la crisis de 2018 (pp. 229–265). http://rua.ua.es/dspace/ 53(5), 855–873. https://doi.org/10.1111/jors.12021 handle/10045/115404 Van Ham, M., Mulder, C. H., & Hooimeijer, P. (2001). Spatial flexibility in Rees, P., Bell, M., Kupiszewski, M., Kupiszewska, D., Ueffing, P., job mobility: Macrolevel opportunities and microlevel restrictions. Bernard, A., Charles‐Edwards, E., & Stillwell, J. (2017). The impact Environment and Planning A, Economy and Space, 33(5), 921–940. of internal migration on population redistribution: An international https://doi.org/10.1068/a33164 comparison. Population, Space and Place, 23(2036), e2036. https:// Vogiazides, L., & Kawalerowicz, J. (2022). Urban exodus in Covid times: Who doi.org/10.1002/psp.2036 moves out of the inner city of Stockholm and where do they go? Stockholm Rodríguez‐Pose, A., & Burlina, C. (2021). Institutions and the uneven Research Reports in Demography, 2022:4. Stockholm University. geography of the first wave of the COVID‐19 pandemic. Journal of https://su.figshare.com/articles/preprint/Urban_exodus_in_Covid_ Regional Science, 61(4), 728–752. https://doi.org/10.1111/jors.12541 times_Who_moves_out_of_the_inner_city_of_Stockholm_and_where_ Rowe, F., (2022). Big data and human geography. In: D. Demeritt & L. Lees do_they_go_/19248190 (Eds.), Concise Encyclopedia of Human Geography. Encyclopedias in the Whitaker, S. D. (2021). Did the COVID‐19 pandemic cause an urban exodus? Social Sciences Series. Edward Elgar. In press. https://doi.org/10. District Data Briefs. https://doi.org/10.26509/frbc-ddb-20210205 31235/osf.oi.phz3e Wiederhold, B. K. (2020). Connecting through technology during the Rowe, F., Arribas‐Bel, D., Calafiore, A., Samardzhiev, K., MacDonald, J., & coronavirus disease 2019 pandemic: Avoiding “Zoom Fatigue”. Fleischmann, M. (2022). Mobility data in urban science. Report. Alan Cyberpsychology, Behavior and Social Networking, 23(7), 437–438. Turing Institute. https://doi.org/10.13140/RG.2.2.16202.24009 https://doi.org/10.1089/cyber.2020.29188.bkw GONZÁLEZ‐LEONARDO ET AL. | 13 of 13 Wong, D. W., & Li, Y. (2020). Spreading of COVID‐19: Density matters. PLOS One, 15(12), e0242398. https://doi.org/10.1371/journal.pone. How to cite this article: González‐Leonardo, M., López‐Gay, 0242398 A., Newsham, N., Recaño, J., & Rowe, F. (2022). Understanding patterns of internal migration during the S UP P O R T I N G I N F O R M A T I O N COVID‐19 pandemic in Spain. Population, Space and Place, 28, Additional supporting information can be found online in the e2578. https://doi.org/10.1002/psp.2578 Supporting Information section at the end of this article.