longtable

# Reduced mobility? Urban exodus? Medium-term impacts of the COVID-19 pandemic
on internal population movements in Latin American countries

Francisco Rowe  Geographic Data Science Lab, Department of Geography and
Planning, University of Liverpool, Liverpool, United Kingdom  Carmen Cabrera-
Arnau  Geographic Data Science Lab, Department of Geography and Planning,
University of Liverpool, Liverpool, United Kingdom  Miguel González-Leonardo
Centre for Demographic Urban and Environmental Studies, El Colegio de México,
Ciudad de México, México  International Institute for Applied Systems
Analysis, Wittgenstein Centre, Vienna, Austria  Andrea Nasuto  Geographic Data
Science Lab, Department of Geography and Planning, University of Liverpool,
Liverpool, United Kingdom  Ruth Neville  Geographic Data Science Lab,
Department of Geography and Planning, University of Liverpool, Liverpool,
United Kingdom

###### Abstract

The COVID-19 pandemic has impacted the national systems of population movement
around the world. Existing work has focused on countries of the Global North
and restricted to the immediate effects of COVID-19 data during 2020. Data
have represented a major limitation to monitor change in mobility patterns in
countries in the Global South. Drawing on aggregate anonymised mobile phone
location data from Meta-Facebook users, we aim to analyse the extent and
persistence of changes in the levels (or intensity) and spatial patterns of
internal population movement across the rural-urban continuum in Argentina,
Chile and Mexico over a 26-month period from March 2020 to May 2022. We reveal
an overall systematic decline in the level of short- and long-distance
movement during the enactment of nonpharmaceutical interventions in 2020, with
the largest reductions occurred in the most dense areas. We also show that
these levels bounced back closer to pre-pandemic levels in 2022 following the
relaxation of COVID-19 stringency measures. However, the intensity of these
movements has remained below pre-pandemic levels in many areas in 2022.
Additionally our findings lend some support to the idea of an urban exodus.
They reveal a continuing negative net balances of short-distance movements in
the most dense areas of capital cities in Argentina and Mexico, reflecting a
pattern of suburbanisation. Chile displays limited changes in the net balance
of short-distance movements but reports a net loss of long-distance movements.
These losses were, however, temporary, moving to neutral and positive balances
in 2021 and 2022.

##  1 Introduction

The COVID-19 pandemic triggered major changes in the patterns of human
population movement across the world. Nonpharmaceuthical interventions to
contain the spread of COVID-19, such as social distancing, lockdowns, business
and school closures contributed to sharp declines in internal population
movements within countries (Nouvellet et al. 2021). As the virus spread
globally in early 2020, COVID-19 infection and mortality rates were
disproportionately higher in large and dense metropolitan areas (Pomeroy and
Chiney 2021), remote work reduced the need to commute (Nathan and Overman
2020) and diminished retail activity decreased the attractiveness and vibrancy
of these areas (Florida, Rodríguez-Pose, and Storper 2021). In a gloomy
prospect for urban centres, media headlines suggested the death of cities
gathering anecdotal evidence on an “urban exodus”, claiming sudden rises in
the number of people moving from large cities to suburbs and rural locales
(Paybarah, Bloch, and Reinhard 2020; Marsh 2020).

Empirical evidence have examined these claims focusing on two key dimensions
of internal population movements i.e. its level (or intensity), and its
spatial impacts on population redistribution. Existing evidence reveals a
sharp reduction in levels of overall mobility, including short- and long-
distance movements, and some support for an urban exodus during the early days
of COVID-2019 in 2020 as the pandemic was unfolding and strict
nonpharmaceuthical measures were enacted (Rowe, González-Leonardo, and
Champion 2023). Population losses through internal population movements were
documented for large core metropolitan areas in Spain (González-Leonardo et
al. 2022; González-Leonardo and Rowe 2022), Germany (Stawarz et al. 2022),
Sweden (Vogiazides and Kawalerowicz 2022), Japan (Tønnessen 2021), Australia
(Perales and Bernard 2022), the United States (Ramani and Bloom 2021) and
Britain (Rowe et al. 2022). Suburban and outer parts of metropolitan areas (as
in the ‘donut effect’ noted for US cities, see Ramani and Bloom (2021)), as
well as more distant rural areas, were identified as gaining population.

While this research has contributed to advancing our understanding of the
impacts of COVID-19 on internal population movements in the Global North, less
is known about the extent and durability of population movements to and from
large cities during the pandemic in the Global South and extending beyond
2020. Existing work has focused on countries in the Global North and
restricted to the immediate effects of COVID-19 data during 2020. Only two
studies have analysed data extending to 2021 and they offer mixed evidence.
For Spain, González-Leonardo and Rowe (2022) report continuing population
losses through internal migration in large cities, such as Madrid and
Barcelona. In Britain, however, Rowe et al. (2022) record a recovery in the
levels of internal movements in low and high density areas converging to pre-
pandemic levels.

A major limitation to capture national-scale changes in the patterns of
internal population movement across the rural-urban hierarchy in the Global
South during the pandemic has been the lack of suitable data. Traditionally
census and survey data are used to study human mobility patterns at such
scale. But, these data systems are not frequently updated and suffer from slow
releases, with census data for example being collected over intervals of ten
years in most Latin American countries and with a considerable lag for the
release of information (CEPAL 2022). These systems thus lack the temporal
granularity to analyse population movements over short-time periods (Green,
Pollock, and Rowe 2021). Digital footprints (DFs) left as a result of social
interactions on digital platforms offer an unique source of information to
capture human population movement in the unfolding data revolution (Rowe
2023). Particularly DF data from mobile phone applications have become a
prominent source to sense patterns of human mobility at higher geographical
and temporal resolution over in real time, and especially in data-scarce
environments in less developed countries.

Drawing on Meta-Facebook users’ mobile phone location data, we aim to examine
the extent and persistence of changes in the levels (or intensity) and spatial
patterns of internal population movement across the rural-urban continuum in
Argentina, Chile and Mexico over a 26-month period from March 2020 to May
2022. Specifically, we seek to address the following set of questions:

  * •

How have the levels of mobility changed across the rural-urban hierarchy over
time?

  * •

To what degree have large cities experienced population loss through internal
migration movements during the pandemic? What have been the main destinations
for people leaving large cities?

  * •

To what extent have the intensity and potential negative balance of population
movement from large cities have persisted over time?

We primarily focus on national capital cities. In Latin American countries,
these cities have dominated the national system of internal population
movement since the 1930s, although they have recorded continuing balance of
moderate population losses due to internal migration over the last three
decades (Bernard et al. 2017; Rodríguez-Vignoli and Rowe 2018). We could
expect that the magnitude of these population losses to have accelerated
during the COVID-19 pandemic. We distinguish between short- and long-distance
movement to identify the potential sources of the patterns underpinning
observed changes in population movements during the pandemic.

Our work contributes to expanding existing knowledge by offering new empirical
evidence on the medium-term impacts of COVID-19 on internal population
movement. First, it provides a systemic understanding of the overall
population movement system in Latin America. While prior work has focused on
daily mobility or internal migration, our study provides a more complete
representation analysing how short- and long-distance mobility have evolved
during COVID-19. Second, we capture the evolution of the internal population
movements from 2020 to 2022. Data limitations have prevented previous studies
from extending their analysis to understand the patterns of internal
population movements beyond 2020. Third, we contribute some of the first
empirical evidence documenting the patterns of internal population movement in
a set of countries in the Global South.

The rest of the paper is structured as follows. The next section provides a
brief review of recent research on internal population movements during the
COVID-19 pandemic, before providing an overview of the main patterns of
population movements within Latin American countries. Section 3 describes the
data, and Section 4 discusses the methods used in this study. Section 5
presents the key results from our analyses before they are discussed in light
of the existing literature in Section 6, which also identifies key limitations
and potential avenues for future work. Section 7 provides some concluding
remarks.

##  2 Background

###  2.1 The impact of COVID-19 on internal population movements

Globally, there is evidence that the COVID-19 pandemic constrained both short-
and long-distance movements within national boundaries (Nouvellet et al. 2021;
González-Leonardo, Rowe, and Fresolone-Caparrós 2022; Wang et al. 2022; Rowe,
González-Leonardo, and Champion 2023). Declines were documented across the
Global North during the first year of the pandemic in the United States
(Ramani and Bloom 2021), a number of European countries, Japan and Australia
(Rowe, González-Leonardo, and Champion 2023). The magnitude of these declines
vary widely from 2.5% in Spain (González-Leonardo et al. 2022) to 8.5% in
Australia (Perales and Bernard 2022). The shapest drops occurred when
nonpharmaceutical interventions, such as stay-at-home requirements, travel
restrictions, mobility restrictions, business and school closures were
enacted.

These drops did not last very long. Existing evidence suggests that volume of
human mobility recovered pre-pandemic levels in most countries following the
ease of stringency measures, particularly lockdowns (Rowe, Robinson, and
Patias 2022). In Australia, declines in the intensity of population movement
were attributed to lockdowns, as well as increased teleworking and a loss of
labour market dynamism resulting from the COVID-19-driven economic recession
due to business closures and supply shortages (Perales and Bernard 2022). In
Latin America and the Caribbean, evidence indicates that the implemention of
social distancing policies resulted in 10 percenge decrease in human mobility
levels 15 days after they were enacted (Aromí et al. 2023). The highest
declines were recorded in Bolivia (19%), Ecuador (17%) and Argentina (16%).
Smaller drops of 3% were registered in Paraguay and Venezuela (Aromí et al.
2023).

In Global North countries, evidence indicates that the COVID-19 pandemic
reshaped the spatial patterns of internal population movements. Primarily the
pandemic is believed to have trigger movements away from large cities to
sparsely populared areas, including suburban, coastal and rural locations
(Rowe, González-Leonardo, and Champion 2023). Consistent findings have been
reported from the United States (Ramani and Bloom 2021), the United Kingdom
(Rowe et al. 2022; Wang et al. 2022), Spain (González-Leonardo et al. 2022;
González-Leonardo, Rowe, and Fresolone-Caparrós 2022), Germany (Stawarz et al.
2022), Sweden (Vogiazides and Kawalerowicz 2022), Norway (Tønnessen 2021),
Australia (Perales and Bernard 2022) and Japan (Fielding and Ishikawa 2021;
Kotsubo and Nakaya 2022). In the United States, Germany, Norway, Sweden and
Japan the predominant pattern was net migration losses in large cities during
2020 but increases in their suburban areas. In the United States, Ramani and
Bloom (2021) labelled this phenomenon as “donut effect”, reflecting an
increase net population loss through population movement in metropolitan areas
(urbanisation) and simultaneously a rising net population gain in suburban
rings (suburbanisation) and rural areas (counterurbanisation).

In the United Kingdom, Spain and Japan, there is no evidence of a “donut
effect” (Fielding and Ishikawa 2021; Rowe et al. 2022; González-Leonardo,
Rowe, and Vegas-Sánchez 2023). In these countries, net population balances in
suburbs did not show significant increases during 2020 and 2021. Yet,
population movements to large cities did decline and movements to rural,
sparsely populated areas increased, leading to unusual large population gains
in these areas. In Spain, Sweden, Japan and Germany, attractive touristic,
holiday and coastal areas were popular destination for large city residents
(González-Leonardo, Rowe, and Fresolone-Caparrós 2022; Vogiazides and
Kawalerowicz 2022; Stawarz et al. 2022). These areas tend to be locations with
a prominent number of second and holiday homes, suggesting that wealthy
households who were able to work remotely left large cities and settled in
these areas (Haslag and Weagley 2021; Tønnessen 2021).

The cumulative evidence has also suggested that while the patterns of
population movement were altered during the pandemic, the preexisting
predominant trends remained mostly unchanged. Most movements continued to
occur within and between urban areas, and the existing evidence suggests that
the observed sudden rises in movements from large cities to rural location are
not likely to endure the height of the pandemic (Rowe, González-Leonardo, and
Champion 2023). In the United Kingdom, mobility levels had almost returned to
prepandemic levels by August 2021 (Rowe et al. 2022; Wang et al. 2022). In
Australia, changes in the patterns of internal population movements due to
COVID-19 had virtually dissapered towards the end of 2020 (Perales and Bernard
2022). In Spain, however, large cities such as Madrid and Barcelona have
continued to loss population through internal migration from 2020 to 2021
(González-Leonardo and Rowe 2022). While Barcelona had a record of net
migration losses prior to the pandemic, population losses due to internal
migration occurred during COVID-19. At the same time, unusually high levels of
counterurbanisation in Spain persisted over 2021 (González-Leonardo, Rowe, and
Fresolone-Caparrós 2022). Thus, while the levels and spatial patterns of
internal population movements seem to have resumed pandemic trends, key
changes seem to have endure the height of the COVID-19 pandemic. The long-term
effects of COVID-19 remain to be established.

Overall, existing work has contributed to better understand how internal
population movements across the rural-urban hierarchy were affected by the
COVID-19 pandemic in the Global North. However, less is known about COVID-19
impacts on movements between cities, suburbs and rural areas in the Global
South and the durability of resulting changes. Yet, that is not to say that
evidence does not exist. Evidence based on small surveys carried out in India
(Irudaya Rajan, Sivakumar, and Srinivasan 2020) and South Africa (Ginsburg et
al. 2022) in 2020 suggests that flows from large cities to less populated
areas increased as a result of the return of workers to their hometown as
business closures and lockdowns were enacted, At the same time, movements to
cities diminished (Irudaya Rajan, Sivakumar, and Srinivasan 2020). Both
studies suggest that the economic downturn caused by nonpharmaceutical
interventions during the pandemic (Ghosh, Seth, and Tiwary 2020) resulted in a
reduction in inflows of workers to cities, as well as an increase in the
number of return moves due to a rise in unemployment. This evidence suggests
that vulnerable populations in the Global South seem to have played a key role
in shaping the movements to and away from large cities during the pandemic.

Yet, this evidence differ from recent work which points to an increase in
population movements made by wealthy individuals from large cities to less
densely populated areas in Global South countries during the first wave of
COVID-19. Lucchini et al. (2023) provide evidence analysing data from Brazil,
Colombia, Indonesia, Mexico, Philippines and South Africa. They estimated that
residents from high-wealthy neighborhoods were 1.5 times more likely to leave
cities compared to those from low-wealthy areas. This pattern is consistent
with evidence from Chile indicating an over-representation of high-income
individuals in movements out of the Greater Santiago Area during the pandemic
(Elejalde et al. 2023). Wealthy individuals are believed to have the resources
to move, be able to work remotely, and own second residences in popular
holiday destinations. Low-income households, on the other hand, may be less
movable as they may lack the financial resources to move. Also, a large
proportion of low-income families work in service or informal sectors of the
economy and jobs in these sectors require in-person, face-to-face
interactions.

Overall, there is little evidence analysing the patterns of internal
population movement in Global South countries. Though, evidence suggests
distinctive patterns in the selectivity of movements occurring in Latin
American countries. Yet, we are still to better understand the magnitude and
durability of changes to internal population movements across the urban-rural
hierarchy. Here, we use mobile phone data to analyse the effect of COVID-19 on
the patterns of internal population movements in Argentina, Chile and Mexico.
Before, we review literature on internal population movements in Latin
America, to offer some broader context about the predominant patterns of
mobility that were at work before the unfolding of the COVID-19 pandemic.

###  2.2 Preexisting patterns of population movement

Currently, Latin America has the highest urbanisation rate in the world after
North America, totaling 81% (Nations" 2019). In Latin American countries,
population is highly concentrated in a few urban centres, particularly in
large cities with more than one million inhabitants, where half of urban
residents (Pinto da Cunha 2002; A. E. Lattes, Rodríguez, and Villa 2017). High
urbanisation rates have been the result of high levels of population
redistribution from rural settlements to cities, mostly during a fast phase of
industrialisation between the early 1950s to late 1970s (Firebaugh 1979; A.
Lattes 1995). During these decades, considerable population gains were mainly
recorded in capital cities (J. Sobrino 2012).

Since the 1980s, however, movements to metropolitan areas in Latin America
have diminished (Chávez Galindo et al. 2016). This has been a natural
consequence as rural population stocks depleted (Chávez Galindo et al. 2016),
coupled with a transition from an industrial substitution economic strategy to
free-market model led to a trend of population deconcentration away from large
cities, such as Santiago in Chile (González Ollino and Rodríguez Vignoli 2006;
Rowe, 2013.) or Mexico City (Jaime Sobrino 2006). This transition has resulted
in a reduction in the volume of long-distance movements to capital cities,
leading to population losses in some cities, like Santigo (Rowe, 2013.).

As a result, middle-sized cities became more attractive population centres.
They benefitted from increasing domestic and foreign investment in export-
oriented industries and tourism activities, leading to geographic economic
dispersal and subsequent migration inflows (Brea 2003; Pérez-Campuzano 2013;
Chávez Galindo et al. 2016). Over the two past decades, movements between
cities have dominated the internal migratory system in Latin American
countries (Bernard et al. 2017; Rodríguez-Vignoli and Rowe 2018; Nations"
2019). About 80% of internal migrants moved between cities, according to the
2010-11 census round (Rodríguez Vignoli 2017). Medium-sized cities from
500.000 to 1 million residents record the largest population gains due to
internal migration (Rodrííguez Vignoli 2017). Large cities with more than 1
million residents registered balanced rates, while small cities with less than
500.000 inhabitants recorded population losses through internal migration
(Rodríguez-Vignoli and Rowe 2018).

Latin American cities have shown significant growth in terms of land
development in their urban peripheries. Since the 1970s, large cities, such as
Santiago de Chile, Buenos Aires and Mexico City have experienced rapid
suburbanisation (Graizbord and Acuña 2007; Chávez Galindo et al. 2016).
Suburbanisation flows comprise middle- and high-class families moving from
cities to highly segregated areas on the periphery (Borsdorf 2003; Rodríguez
Vignoli 2019). Though, some peripheral areas have also served home for low-
income households settling in informal housing (Janoschka 2002; Rodríguez
Vignoli and Rowe 2017). Peripheral areas have thus become major residential
areas for metropolitan residents serving as satellite neighbourhoods for daily
commuters to core areas of capital cities (Chávez Galindo et al. 2016). Next
we describe the data we used to examine the extent to which the existing
patterns of population movement have been altered during the COVID-19
pandemic.

##  3 Data

###  3.1 Meta-Facebook Data

To capture population movements during the COVID-19 pandemic, we used
anonymised aggregate mobile phone location data from Meta users for Argentina,
Chile and Mexico, covering an 26-month period from March 2020 to May 2022. We
used two datasets Facebook Movements and Facebook Population created by Meta
and accessed through their Data for Good Initiative
(https://dataforgood.facebook.com). The data are built from Facebook app users
who have the location services setting turned on on their smartphone. Prior to
releasing the datasets, Meta ensures privacy and anonymity by removing
personal information and applying privacy-preserving techniques (Maas et al.
2019). Small-count dropping is one of these techniques. A data entry is
removed if the population or movement count for an area is lower than 10\. The
removal of small counts may mean that population counts in small sparsely
populated areas are not captured. A second technique consists in adding a
small undisclosed amount of random noise to ensure that it is not possible to
ascertain precise, true counts for sparsely populated locations. Third,
spatial smoothing using inverse distance-weighted averaging is also applied is
applied to produce a smooth population count surface.

The Facebook Movements dataset offers information on the total number of
Facebook users moving between and within spatial units in the form of origin-
destination matrices. The Facebook Population dataset offers information on
the number of active Facebook users in a spatial unit at a given point in
time. Both datasets are temporally aggregated into three daily 8-hour time
windows (i.e. 00:00-08:00, 08:00-16:00 and 16:00-00:00). The datasets include
a baseline capturing the number of Facebook user population or movement before
COVID-19 based on a 45-day period ending on March 10th 2020. The baseline is
computed using an average for the same time of the day and day of the week in
the period preceding March 10th. For instance, the baseline for Monday
00:00-08:00 time window is obtained by averaging over data collected on
Mondays from 00:00 to 8:00 for a 45 day period. Details about the baseline can
be found in Maas et al. (2019).

The Bing Maps Tile System developed by Microsoft (Microsoft) is used a spatial
framework to organise the data. The Tile System is a geospatial indexing
system that partitions the world into tile cells in a hierarchical way,
comprising 23 different levels of detail (Microsoft). At the lowest level of
detail (Level 1), the world is divided into four tiles with a coarse spatial
resolution. At each successive level, the resolution increases by a factor of
two. The data that we used are spatially aggregated into Bing tile levels 13.
That is about 4.9 x 4.9km at the Equator (Maas et al. 2019).

###  3.2 COVID-19 stringency data

We used the stringency index as a measure of the level of nonpharmaceutical
interventions to COVID-19, such as social distancing and lockdowns. We used
these data to understand how the level and patterns of population movement
changed in response to the implementation of various nonpharmaceutical
interventions during COVID-19. The stringency index ranges from 0 to 100, with
100 being the value corresponding to the most strict scenario. The stringency
index were retrieved from the COVID-19 government response tracker
[https://www.bsg.ox.ac.uk/research/research-projects/covid-19-government-
response-tracker](https://www.bsg.ox.ac.uk/research/research-
projects/covid-19-government-response-tracker). See Hale et al. (2021), for
more information.

###  3.3 WorldPop population data

We also used data from WorldPop (Tatem 2017) to measure the degree of
population density of local areas. WorldPop offers open access gridded
population estimates at a resolution of 3 and 30 arc-seconds approximately
100m and 1km at the Equator, respectively. WoldPop produces these gridded
datasets using a top-down (i.e. dissagregating administrative area counts into
smaller grid cells) or bottom-up (i.e. interpolating data from counts from
sample locations into grid cells) approaches. The data are in raster and
tabular data formats. We gridded population data at 1km resolution in raster
format. Data at this level of resolution are needed to spatially integrate our
Facebook population and movement data.

##  4 Methods

###  4.1 Population density classification

We sought to analyse the extent and durability of changes in internal
population movements across the rural-urban continuum. We thus moved away from
a rural-urban binary framework to understand population movements and capture
the diversity of contemporary human settlement systems. Champion and Graeme
(2004) and Rowe et al. (2019) serve as excellent references summarising these
arguments. We focused on understanding the patterns of population movement
across the rural-urban continuum, particularly population exchanges between
dense urban cores, suburbs, medium-size areas and rural locations. Following
Rowe et al. (2019), we measured population density to capture the rural-urban
continuum.

For individual countries, we spatially aggregated these the WorldPop data into
the Bing tiles to correspond with our Facebook population data using spatial
intersection operators and area weighted centroids. Area weighted centroids
are particularly useful to allocate grids on coastal regions. We then
classified the WorldPop population into population density deciles,
identifiying population density classes, with 1 capturing the least dense
areas to 10 representing the most dense areas. To this end, we employed the
Jenks natural breaks classification method (Jenks 1967).

Figure 1 displays a map with our classification of Bing tiles coloured
according to the population density classes, and a bar plot reporting the
number of Bing tiles in each population classes. The bar plot also shows
example areas in bracket of the areas included in each category, to provide an
idea of the type of places that they include. A key observation to recall for
the remainder of the paper is the fact that the most dense class 10
exclusively includes the core metropolitan areas of the national capitals
(i.e. Buenos Aires in Argentina, Santiago in Chile, and Mexico City in
Mexico). Class 9 areas tend to represent highly dense, suburban metropolitan
areas around these national capitals, and class 1 areas tend to be small,
sparely populated areas on coastal regions or around natural parks, natural
reserves, agricultural and forest areas. Also note that the population density
ranges for each density class differ by country. The logic of this
classification is that we expect tiles belonging to the same category in
individual countries have a similar function in the national settlement
system.

Figure 1: Population density classes at Microsoft Bing tile level and number
of grids in each population density class.

###  4.2 Tile-based mobility metrics

_Intensity of movement_

We sought to measure two key dimensions of population movements: its level or
intensity, and spatial impact (M. Bell et al. 2002). We measured changes in
the intensity of movement by quantifying the variation in the number of
population movements by population density category in relation to the
baseline period. We focused on two distinctive months: May 2020 and March 2022
representing a period during the height of the COVID-19 pandemic when a range
of stringency measures were in place, and a period at a very advanced stage of
the pandemic after mass vaccination has been rolled out and stringency
measures had been removed. Specifically, we used the percentage change in the
intensity of a population movement in reference to the reference period, as
provided in the Facebook Movement datasets (Maas et al. 2019):

$\hypertarget{eq-
eq1}{}{Change(\%)=\Biggl{(}\frac{n_{crisis}}{n_{baseline}}-1\Biggl{)}\times
100}$ (1)

where $n_{crisis}$ corresponds to the number of people moving; and,
$n_{baseline}$ corresponds to the number of people that would be expected to
move in the baseline pre-pandemic period at the same time of the day, on the
same day of the week, and between the same origin and destination tiles. A
positive score indicates an increase in the extent of population movement
relative to baseline levels. A negative score represents a decrease, while a
zero score denotes no change.

We grouped the resulting percentage change metrics by population density class
for places of origin, and generate boxplots to capture the distribution of
changes across these classes. We also considered the relationship between
these changes in mobility and degree of stringency during the pandemic.

_Spatial impact of movement_

We also sought to examine the degree of population redistribution through
mobility. Particularly we aimed to explore the extent to which people have
moved from capital cities down in the rural-urban hierarchy. To this end, we
computed the net balance between inflows and outflows:

$\hypertarget{eq-eq2}{}{netflows=inflows-outflows}$ (2)

where: $inflows$ and $outflows$ represent the total number of movement in and
out of an area. We computed and aggregated the net balance by each tile to
produce an overall metric for individual population density class. Thus, the
net balance for population density class 10 areas represent the overall net
balance in capital cities. Also, because we computed the net balance for
individual areas at three windows during a day, we avoid focusing on daily
mobility. Over the course of a day, positive and negative net balances of
daily mobility would cancel out, and hence capture moves involving one-night
stay.

###  4.3 Local and long-distance movement

To further mitigate the effect of daily and short-distance moves, we
distinguish between short- and long-distance movements. In the literature,
short-distance moves are often used to capture residential mobility, while
long-distance moves are used to represent internal migration (Niedomysl 2011).
Short-distance moves are associated primarily with housing reasons, while
long-distance moves are driven primarily by labour market reasons. For the
metrics described above, we therefore considered only flows with a Euclidean
distance between origins and destinations greater than 0km. We defined short-
distance moves as trips of less 100km, and long-distance moves as trips
covering 100km or more.

In terms of expectations, we expected to observe an overall decrease in
mobility as a consequence of stringency measures especially during the early
stages of the COVID-19 pandemic. During this period, we also expected to find
a decrease in movement in and to metropolitan cores and an increase in
mobility to suburban areas or satellite cities, reflecting changes in
commuting patterns. We expected to observe negative net balances of movements
in big cities and positive balances down the rural-urban hierarchy,
particularly in less dense, coastal towns, rural and lake areas.

##  5 Results

###  5.1 Changes in mobility in May 2020 and in March 2022

We analysed changes in the intensity of movements across our population
density classes across Argentina, Chile and Mexico in May 2020 and March
2022\. These two months represent two pivotal points in the pandemic. May 2020
provides a good representation of the early days when a series of strong
stringency measures were enacted following the WHO’s declaration of COVID-19
on the 11th of March 2020 as a global pandemic. March 2022 captures the later
days of the COVID-19 pandemic about six months after most of the COVID-19
restrictions had been relaxed in the countries in our analysis. Figure 2 and
Figure 3 show boxplots of the distribution of the percentage change in the
number of movements by areas according to their population density for under
and over 100km. The boxplots report the percentage change in movements for
each population density category during May 2020 and March 2022. The baseline
levels are represented by the dotted line at $y=0$. Positive values indicate
increases in mobility relative to pre-pandemic levels, while negative values
indicate a reduction in mobility.

Figure 2: Changes in mobility flows during May 2020 and March 2022 by
population density class, relative to baseline period. Short-distance (<100km)
movements. Figure 3: Changes in mobility flows during May 2020 and March 2022
by population density class, relative to baseline period. Long-distance
(<100km) movements.

_Short-distance movements_

Figure 2 shows a consistent decline in movement across all population density
classes for movements under 100km. This decline is consistent across all
countries in our analysis and particularly pronounced in the early days of the
pandemic. Figure 2 also shows a consistent gradient of decline. The most
densely populated areas, comprising locations within Buenos Aires, Santiago
and Mexico City, tended to record the sharpest declines in the early stages of
the pandemic. Some of these areas registered drops of over 50%, notably in
Chile. Declines were less pronounced in the least densely populated areas.

While Figure 2 shows that the predominant trend in May 2020 was a decline in
the levels of mobility, it also reveals that there is a great degree of
variability. While most areas experienced a decline in May 2020, others
recorded larger volumes of movement in relation to the prepandemic period, and
this occurs predominantly in the least density class areas. Some doubled their
levels of mobility. This, however, does not necessarily mean large population
numbers as least density class areas house very small populations, and small
variations in population translate into large percentage changes. Yet, these
percentage change provide an idea of the degree of impacts on these sparsely
populated locations.

Additionally, Figure 2 reveals that an overall recovery of mobility levels.
While some areas continued to record levels of mobility well below their
prepandemic patterns, Figure 2 shows that average levels of mobility have
moved closer to prepandemic levels in most areas in March 2022 with Chile
displaying the most modest intensity of recovery. This evidence suggests that
the pandemic may have resulted in durable changes in the patterns of short-
distance mobility, particularly in highly dense areas. Such change may be the
result of the adoption of remote or hybrid working as a main way of operation.

_Long-distance movements_

Figure 3 displays similar trends for long-distance movements to those observed
for short-distance trips. It shows a consistent decline in movement across all
population density classes in May 2020, with the most densely populated areas
experiencing the largest drops, and small average changes in the least dense
areas. Similarly, Figure 3 shows a large degree of variability within
population density classes, yet the extent of increases was less pronounced
than for short-distance movements. These findings indicate that the enactment
of stringency measures was very effective in reducing mobility levels and
potentially the extent of COVID-19 transmission in the early days of the
pandemic.

Figure 3 also reveals that the average intensity of long-distance movement
were virtually back to prepandemic levels in Argentina and Mexico. This trend
is evidence by the boxes overlapping the baseline 0. However, Figure 3
suggests that the mobility levels were still below prepandemic volumes in
Chile, particularly in moderate and high population density areas. Our
estimates indicate that long-distance mobility levels were 50% below
prepandemic in the highest density areas covering the Santiago metropolitan
area.

###  5.2 Spatio-temporal patterns of population redistribution during COVID-19

We also analysed the spatial impact of mobility on redistributing population
across the country during the COVID-19 pandemic. Specifically, we examined the
evolution of changes in the net balance between mobility inflows and outflows
across the rural-urban hierarchy during the pandemic. We calculated the
monthly net movement balance as the difference between mobility inflows minus
outflows for individual population density classes during March 2020 to May
2022. Figure 4 and Figure 5 show the changes in net balance for movements
under 100km and over 100km, respectively.

Figure 4: Total number of net movement by population density class. Short-
distance (<100km) movements. Figure 5: Total number of net movement by
population density class. Long-distance (>100km) movements.

_Short-distance movement_

Figure 4 reveals a persistent pattern of fluctuating negative net balances of
movements under 100km in the highest population density class areas in
Argentina and Mexico for most of March 2020 to May 2022. The extent of these
balances becomes more notably pronounced after July 2020, but they vary widely
by country. Mexico displays the largest negative balances, probably reflecting
the fact that it is the second most populated country in Latin America
following Brazil. The trend of negative balances indicates that the highest
population density class areas in Argentina and Mexico tended to record a
larger number of outward movements than inward movements over distances of
less than 100km. As indicated in Section 4.1, these locations comprise highly
dense metropolitan cores predominantly in capital cities, including the
Central Business District. In Mexico, consistently large negative net balances
also occurred in areas of class 9. These areas include citie such as
Monterrey, Puebla and Guadalajara. The spatial concentration of negative net
balances in high density class areas mirrors the pattern of population loss
identified in metropolitan areas in developed countries during the COVID-19
pandemic (Rowe, González-Leonardo, and Champion 2023).

At the same time, Figure 4 shows a relatively consistent positive net balance
of movements in specific types of areas along the rural-urban hierarchy.
Argentina records a consistent pattern of greater inward movement than outward
movement in medium density areas of class 5 type, including small towns and
cities such as Ushuaia, Trelew and San Luis, and a trend of irregular net
positive balances in the least dense areas of class 1-3 and high density
locations of class 9. Areas of class 1-3 in Argentina correspond to rural and
sparsely populated regions across the country, while class 9 areas
predominantly represent suburban metropolitan and large city areas, such as
Cordoba, Mendoza and Moreno. Negative net losses also tended to occur in areas
of class 7 and 8 over 2021 and 2022, predominantly including medium-sized
cities such as Salta and Resistencia. In Mexico, a pattern of consistently
positive net balance of movements occurred in the range of less dense areas of
class 1 to 5 and 8. These balances were particularly pronounced in areas of
class 2 and 4, encompassing touristic, forest and natural reserve places such
as Rio Verde, Chilcota and Ciudad Valles. In Mexico, the observed patterns
suggest mobility out of high density areas in Mexico City, including
metropolitan core areas and suburbs, to nearby sparsely and rural areas within
a radius of 100km.

Chile displays a different pattern. The most densely populated areas in the
country record net balances around zero for movements under 100km. This
pattern suggests limited population redistribution from and to metropolitan
core areas in Santiago over short distances during the pandemic. Yet, areas of
class 8 display large net losses, particularly from July 2021 following less
stringent COVID-19 restrictions. At the same time, areas of class 9 report
more pronounced net gains since July 2021\. These findings suggest a pattern
of movement up the rural-urban hierarchy from urban areas, such as Valparaíso,
La Serena and Viña del Mar areas to more dense locations in the Santiago
Metropolitan Area.

_Long-distance movements_

Figure 5 reveals a systematic pattern of net balances across Argentina, Chile
and Mexico. It shows small net balances of movements over 100km in the most
densely populated areas from the start of the COVID-19 outbreak to December
2020 but sizable net gains during 2021 and 2022. The pace of these gains,
however, differs across countries. Argentina and Chile report rapid a rise in
net gains of long-distance movements in metropolitan core areas, while the
trend of increase in positive balances in Mexican highly dense areas is less
marked. These patterns seem to relate to the impact of stringency levels of
COVID-19 restrictions. These impacts are particularly visible in metropolitan
core areas. Limited population redistribution appears to have occurred early
in the pandemic when severe COVID-19 restrictions were enacted, and enlarged
as the stringency level of these measures decreased. Also, comparatively,
Argentina and Chile implemented stricter restrictions and reductions in
stringency levels seem to have resulted in larger expansions in the net
balances of metropolitan core areas than in Mexico.

Unlike other countries, Chile displays a pattern of negative net balances of
movements over 100km in the highest density areas of the Santiago metropolitan
area during the first year of the pandemic (Figure 5). These balances are not
very large but suggest a steady flow of people moving away from high density
areas of Santiago to destinations over 100km. Net positive balances of
movements over 100km in less dense areas of class 1 to 3 indicate that these
were the primary destinations of flows from the capital. Yet, the duration of
these balances suggests that movements from metropolitan core areas were
temporary. Figure 5 reveals an irregular pattern of large net positive
balances in these areas from May 2021 as COVID-19 restrictions were relaxed.
At the same time, we observe a continuous trend of negative net balances in
highly dense areas of class 9, reflecting increasing net losses in north-west
areas of the Santiago Metropolitan Area, including Quilicura and Lampa, as
well as Reñaca.

###  5.3 Movements from and to capital cities

To understand better the nature of movements involving areas of population
density class 10, we analysed the spatial patterns of movement of under and
over 100km from capital cities. In our analysis, movements from population
density class 10 areas exclusively represent mobility patterns from
metropolitan core areas of capital cities. Figure 6 displays the number of
movements out from metropolitan core areas in Buenos Aires, Santiago and
Mexico City for March 2020 and April 2022. As anticipated, areas around the
national capitals represent the main destinations for short-distance
movements. In April 2020, movements away from capital were limited but had
expanded in volume and geographical extent by March 2022.

Figure 6: Short-distance (<100km) and long-distance (>100km) movements from
core areas in capital cities. April 2020 and March 2022.

Figure 6 reveals the main destinations for the movements underpinning the
persistent losses of short-distance movement observed for class 10 areas in
Argentina and Mexico. Our data revealed that nearby urban and suburban areas,
such as Moreno, La Matanza and Tigre in Argentina and Ecatepec de Morelos,
Aztizapán de Zaragoza and Chicoloapan de Juárez in Mexico absorbed over 80% of
short-distance moves from Buenos Aires and Mexico City in March 2022. To a
lesser extent, these moves have been directed to rural areas. Though, as we
described in the previous section, these areas recorded a relatively large net
balance of short-distance moves particularly in Mexico. In Chile, short-
distance movements were quite balanced in metropolitan core areas throughout
from 2020 and 2022 (Figure 4). Figure 6 reveals that these movements were
predominantly to mostly rural, sparsely populated areas, such as Cordillera
and Maipo.

Figure 6 also reveals a similar pattern to the donut effect documented in the
US with large cities hollowing out (Ramani and Bloom 2021). The maps for
short-distance moves in Figure 6 show that suburban areas closer to
metropolitan core areas absorbed larger counts of short-distance moves, with
neighbouring rural locations and towns attracting smaller numbers. In
Argentina and Chile, this pattern seem to have been relatively uniform with
areas closer to core metropolitan areas of Buenos Aires and Santiago absorbing
large numbers. In Mexico, areas on the west and north-west, such as Lerma,
Metepec and Chiautla have tended to record the largest numbers from Mexico
City.

Figure 6 identifies the destination areas underpinning the negative balance of
long-distance movements in the early months of the pandemic observed for
Santiago, Chile. The 2020 bottom map for long-distance in Figure 6 reveal that
key destinations in Chile were central-southern areas in the provinces of
Talca, Curico and Colchagua. Movements to these destinations are thought to
have been moves from affluent households, with second or holiday homes in
these areas and jobs which could be done remotely. Additionally, Figure 6
shows that the provinces of Antofagasta and El Loa also attracted a relatively
large number of moves. The main economic sector in these areas is mining
suggesting that movements to these regions may have been long-distance
commuting moves, rather than migratory moves.

Figure 5 showed that metropolitan core areas in the three countries in our
analysis have tended to record consistent positive balances of long-distance
movements from early 2021. Figure 7 reports the top ten areas of origins for
these flows in March 2020, revealing that over 25% of all inflows over 100km
were originated from coastal areas in Argentina and Chile and from the small
central state of Querétaro in Mexico. These are known as popular tourism
destination suggesting that these flows may reflect temporary moves made to
second or holiday homes by capital city residents during the pandemic.
Contrary to early speculations of the death of cities, this pattern also
highlights the resilience of cities to endure crises and as a key engine of
economic growth, innovation and prosperity, attracting population from long
distances.

Figure 7: Long-distance (>100km) movements to metropolitan core areas in
capital cities. March 2022. Top ten largest flows.

##  6 Discussion

###  6.1 Key findings

Emerging evidence has indicated that the COVID-19 pandemic resulted in a urban
exodus from large cities in developed countries. Anecdotal evidence has
suggested that a similar pattern occurred in Latin American countries.
However, lack of access to suitable data has prevented the analysis of changes
in internal population movements over the pandemic. Drawing on location data
from Meta-Facebook users, we sought to analyse the extent and durability of
changes in internal population movements across the rural–urban hierarchy in
Argentina, Chile and Mexico during the COVID-19 pandemic from March 2020 to
May 2022. We found evidence of an overall systematic decline in the level of
population internal movement during the enactment of nonpharmaceutical
interventions in May 2020 across all three countries. We showed that declines
occurred across the rural-urban continuum for movements over both short and
long distances. The largest reductions occurred in the most dense areas of
Argentina, Chile and Mexico. Average levels of declines in less dense areas
were less pronounced as a large number of them recorded rises mobility levels.
We also revealed that the intensity of mobility bounced back closer to pre-
pandemic levels in May 2022 following the relaxation of COVID-19 stringency
measures. Yet, the intensity of movement remained below pre-pandemic levels in
many areas.

Additionally, we presented evidence of sustained negative net balances of
movements under 100km in the highest density areas of Argentina and Mexico
during 2020 and 2022, particularly since July 2020. At the same time, we
identified a positive net balance of movements for distances under 100km in
areas of low and moderate population density. In Chile, population
redistribution for movements under 100km is limited across the urban
hierarchy, except for moderately high density areas. These patterns differ
from those observed for movements over 100km. In all three countries, the most
dense areas registered overall positive net balances of movements over 100km
particularly from early 2021. Only in Chile, we found evidence of a negative
net balance for movements over 100km in the hightest density areas during
2020, when COVID-19 began spreading throughout the country and stringency
measures were enforced. The patterns of population redistribution at lower
levels of the urban hierarchy varied by country. In Argentina, we observed
moderate negative net balances of movements over 100km in dense areas. In
Chile, a pronounced and growing negative net balance for these movements
occurred in highly dense areas around the Santiago Metropolitan Area and
neighbouring cities. In Mexico, large negative net balances were recorded in
low density areas.

###  6.2 Interpretation

Our findings suggest that levels of internal population movement over both
short and long distances declined in 2020 and remained below pre-pandemic
levels in May 2022. In Chile, seasonality in mobility patterns may prompt
these conclusions as the pre-pandemic baseline coincides with the national
period of summer holidays when levels of internal movements over long
distances are typically higher than during the rest of the year. Yet, we argue
that such seasonality produces a substitution effect by trading short-distance
commuting moves for long-distance travel, rather than increasing overall
levels of mobility across the country. Additionally, we argue that hybrid
forms of working that emerged during the height of the pandemic may have had a
continuing effect on internal population movements in the three countries in
our analysis. Hybrid working may have precipitated a secular trend of internal
migration decline which was in motion before the pandemic (Martin Bell et al.
2017), and led to reduced rates of mobility in many countries during COVID-19
(Rowe, González-Leonardo, and Champion 2023). Furthermore, global analyses
indicate that hybrid working is thus expected to reduce the need for daily
commuting travel and living in close proximity to work places (Barrero, Bloom,
and Davis 2021; Aksoy et al. 2022). Hybrid working has become a new future of
our working life as stigma associated with working from home has diminished
(Aksoy et al. 2022). Large investments on working environments to enable
hybrid work have been made; and, working-from-home experiences in terms of
productivity and work-life balance have been better than expected (Barrero,
Bloom, and Davis 2021; Aksoy et al. 2022).

Additionally, our study lends some support for the theory of an urban exodus
providing evidence of negative net balances of movements in the most dense
areas of capital cities. These patterns are consistent with ‘the donut effect’
identified by Ramani and Bloom (2021) to describe net population losses in
dense city centres in most US metropolitan areas during ealy stages of the
COVID-19 pandemic. In Argentina, Chile and Mexico, distinct processes seem to
have underpinned these patterns of negative net balances. In Argentina,
sustained negative balances in highly dense areas were associated with short-
distance movements, likely reflecting a pattern of suburbanisation in the
metropolitan area of Buenos Aires. In Mexico, the pattern of negative balances
were also associated with short-distance moves but extended to a wider set of
highly dense areas around the metropolitan area of Mexico City, and generated
positive balances of moves in sparsely populated areas, suggesting a pattern
of counterurbanisation. In Chile, negative balances in highly dense areas of
the metropolitan area of Santiago were underpinned by long-distance moves.
These losses were however temporary, switching to neutral and positive
balances in 2021 and 2022.

Overall, we contributed to expanding existing evidence on the impact of
COVID-19 on the levels and spatial patterns of internal population movement.
Existing work has focused on developed countries and restricted to the
immediate effects of COVID-19 analysing data from 2020\. The cumulative
evidence suggest that the levels of human mobility declined in 2020, that
large cities lost population through internal migration, and that suburbs and
rural areas experienced population gains. To our knowledge, only two studies
have analysed data extending to 2021 for Spain (González-Leonardo and Rowe
2022) and Britain (Rowe et al. 2022). In Spain, large cities continued to
report population losses through internal migration. In Britain, low and high
density areas returned to display net balances of movement similar to those
observed before the COVID-19 pandemic.

We expanded the existing evidence by investigating the patterns of internal
population movement in Latin America over a period stretching from March 2020
to May 2022. Our evidence suggests that negative balances of moves in highly
dense areas and large cities recorded for various countries in 2020 may have
persisted over 2021 and 2022, as we documented for short-distance moves in
Argentina and Mexico. They suggest a structural change from a core city
preference for suburban and less populated areas. Though, this seemed to be
shifting back in Argentina. Alternatively, our findings suggest that
population losses due to long-distance movement in highly dense areas in the
metropolitan area of Santiago were short-lived, shifting to neutral and
positive balances over 2021 and 2022. Contrary to the initial expectations of
apocalyptic headlines - ‘empty cities’, ‘dead cities’, ‘ghost cities’,
‘deserted cities’ and ‘silent cities’- on the media, the evidence emerging
from Chile attests the attraction of cities to absorb population. Economies
agglomeration in dense urban spaces are likely to continue to facilitate and
foster knowledge exchange, innovation and economic growth (Storper and
Venables 2004). At the same time, digital infrastructure in rural locales of
Latin American countries remain deficient (Pick, Sarkar, and Parrish 2020),
representing a major challenge to permanently relocate to these areas.

###  6.3 Limitations and future work

We distinguished between short- and long-distance moves. Typically short-
distance moves are typically used to capture local residential and routine
moves, and long-distance moves are used to account for more permanent and
infrequent moves, such as internal migration (Owen and Green 1992). However,
we cannot identify moves involving permanent changes of residential address.
Our data capture these movements but also include frequent and daily travel
behaviour, such as commuting and shopping trips. Future work could extend and
validate our work identifying changes to moves relating permanent and
temporary displacements, and triangulating alternative DFD and traditional
data sources, such as censuses and representative surveys collecting
information on residential location.

We focused on analysing the levels and spatial patterns of internal population
movement. Available evidence from the Global North suggests systematic
differences in the mobility patterns of more and less socio-economically
advantaged individuals during the early stages of the pandemic (Gauvin et al.
2021; Long and Ren 2022; Santana et al. 2023). While empirical evidence is
lacking, existing findings suggest that more socio-economically advantaged
individuals seem to have been able to relocate more “permanently” away from
large and dense urban areas during the height of the pandemic (Gauvin et al.
2021). They are more likely to have second or holidays homes and have jobs
which can be done remotely. Less socio-economically advantaged individuals
were less able to relocate away from their home as they are more likely to
work on less well paid service jobs requiring face-to-face interaction
(Santana et al. 2023). Drawing on area-level population attributes, future
work could investigate the occurrence and persistence of these patterns in
Global South countries where socio-economic inequalities are more acute
(Qureshi 2023).

Further research is also needed to establish the cause of the observed changes
in the spatial patterns of internal population movement since the start of the
COVID-19 pandemic. Understanding these causes can assist in anticipating long-
term structural changes in the direction of mobility flows in the coming
years. A combination of factors, including social distancing, business
shutdowns, school closures, telework and unemployment, have all been cited as
key forces recasting preexisting patterns of population movement in the early
stages of the pandemic. While some of these factors vanished as COVID-19
restrictions were relaxed, others such as telework has endured the pandemic
(Aksoy et al. 2022). Determining the extent to which forms and workers have
and will continue to adopt remote work will be key to understand future
residential and mobility choices within and away from large and dense urban
areas.

Additionally, accessing and working with DFD to measure human mobility and
migration is challenging. DFD are known to suffer from biases and issues of
statistical representation reflecting differences in technology usage and
accessibility (Rowe 2023). While concerted efforts to develop data services
and methodological frameworks to leverage on DFD exist in the Global North
[REF], limited progress has been done in Latin America. National government
and statistical agencies have a prime position to promote the creation of data
sharing partnerships with private companies and create the digital
infrastructure to ensure equal and secure access to DFD sources for the social
good. With academia, these agencies could play a major role in promoting the
creation of a methodological framework for the use and analysis of DFD,
setting common definitions, metrics, and standard approaches to measure and
correct data biases and validate data analyses. Location data have been proven
key to monitor and develop appropriate policy responses to population
displacement and movement during natural disasters, wars and epidemics (Green,
Pollock, and Rowe 2021). We envision resources like those created by the
European Commission Joint Research Centre (2022) and the UNSD (2019),
identifying sources of non-traditional data and establishing methodological
guidelines for the use of mobile phone data for official mobility statistics.
Existing resources have a global character, we call for initiatives with a
regional focus accounting for differences in the local landscape of data
availability and digital technology usage.

##  7 Conclusion

The COVID-19 pandemic significantly altered civil liberties and the ways
people move in 2020. Over the last two years, research has been undertaken to
understand the immediate impacts of the COVID-19 pandemic on human mobility
particularly in the Global North. Limited research has focused on
understanding the persistence of the impacts of the COVID-19 pandemic beyond
2020 in the Global South context, primarily due to the lack of suitable data.
Drawing on aggregate anonymised mobile phone data, we examined the extent and
durability of changes in the levels and spatial patterns of internal
population movement across the rural-urban continuum in Argentina, Chile and
Mexico over a 26-month period from March 2020 to May 2022. We presented
evidence of an overall systematic decline in the level of short- and long-
distance movement during the enactment of nonpharmaceutical interventions in
2020, with the largest reductions occurring in the most dense areas. While
levels of movements recovered and moved closer to pre-pandemic levels in 2022,
they have remained below pre-pandemic levels. This trend aligns with a broader
pattern of declining migration intensities in many industrialised countries
(Martin Bell et al. 2017). Additionally we also showed evidence offering some
support for the idea of an urban exodus. We found a continuing negative net
balance of short-distance movements in the highly dense areas of capital
cities in Argentina and Mexico, reflecting a pattern of suburbanisation. In
Chile, we recorded very limited changes in the net balance of short-distance
movements but we found a net loss of long-distance movements. However, these
losses were temporary. Yet, our findings point to long-lasting effects of
COVID-19 in Argentina and Mexico, resulting in sustained losses of movements
in core metropolitan areas of capital cities and gains in suburban locations.
These patterns are contrary to population gains through internal migration in
cities over 1 million people in the region of Latin America and Caribbean
(Rodríguez Vignoli 2017), and may reflect the enduring effect of remote work
and differences in housing costs as centrifugal forces.

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