Content uploaded by Francisco Rowe
Author content
All content in this area was uploaded by Francisco Rowe on Feb 12, 2018
Content may be subject to copyright.
Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=rpst20
Population Studies
A Journal of Demography
ISSN: 0032-4728 (Print) 1477-4747 (Online) Journal homepage: http://www.tandfonline.com/loi/rpst20
How is internal migration reshaping metropolitan
populations in Latin America? A new method and
new evidence
Jorge Rodríguez-Vignoli & Francisco Rowe
To cite this article: Jorge Rodríguez-Vignoli & Francisco Rowe (2018): How is internal migration
reshaping metropolitan populations in Latin America? A new method and new evidence, Population
Studies, DOI: 10.1080/00324728.2017.1416155
To link to this article: https://doi.org/10.1080/00324728.2017.1416155
© 2018 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group
View supplementary material
Published online: 30 Jan 2018.
Submit your article to this journal
Article views: 507
View related articles
View Crossmark data
How is internal migration reshaping metropolitan
populations in Latin America? A new method and
new evidence
Jorge Rodríguez-Vignoli
1
and Francisco Rowe
2
1
Population Division of the Economic Commission for Latin America and the Caribbean (ECLAC),
2
University
of Liverpool
Internal migration is a key driver of patterns of human settlement and socio-economic development, but little
is known about its compositional impacts. Exploiting the wide availability of census data, we propose a
method to quantify the internal migration impacts on local population structures, and estimate these
impacts for eight large Latin American cities. We show that internal migration generally had small
feminizing, downgrading educational, and demographic window effects: reducing the local sex ratio,
lowering the average years of schooling, and raising the share of working-age population due to an
increased young adult population. Over time, a rise in the proportion of males and a drop in the share of
the young adult population moving into cities reduced the feminizing and demographic window effects.
Concurrently, a rise in the average years of schooling associated with people moving into cities attenuated
the downgrading impact of internal migration on local education levels.
Supplementary material for this article is available at: http://dx.doi.org/10.1080/00324728.2017.1416155
Keywords: internal migration; Latin America; socio-demographic composition; spatial impact; migration
analysis
[Submitted March 2016; Final version accepted September 2017]
Introduction
Many countries have seen migration replacing ferti-
lity and mortality as the main agent of population
change. Alongside international mobility, internal
migration is now the primary demographic process
shaping national patterns of human settlement. It
underpins differences in population change and
structure across subnational areas. Understanding
and determining how internal migration changes
the population composition of local areas is critical
for responding to housing, healthcare, educational,
and transportation needs; delivering more accurate
population forecasts; and assessing the spatial distri-
bution of skills, knowledge, and labour.
Migration research has focused on understanding
the factors that trigger migration. Less progress has
been made on quantifying the effects of migration
on changing the socio-demographic composition of
local areas (Rowe, Bell, et al. 2017). This dearth can
be traced partly to the absence of a comprehensive
methodological approach to estimate these effects.
Prior work has typically used three sets of approaches
to quantify the spatial impacts of migration: compara-
tive socio-demographic profiles, net migration-based
measures, and population growth equations.
However, these approaches have failed to quantify
the migration impact of multiple population sub-
groups into a single indicator effectively, and do not
assess the impact of migration on a wide range of
socio-economic indices, such as the dependency ratio
or Gini coefficient, at a fine geographical scale.
To redress these limitations, the research described
in this paper aimed to develop a new method to esti-
mate the impacts of internal migration on the socio-
demographic composition of local areas, and to quan-
tify these impacts on eight large Latin American
(LA) cities in Ecuador, Panama, and Mexico. These
cities were selected because of their importance to
their national urban, migration, and economic
Population Studies, 2018
https://doi.org/10.1080/00324728.2017.1416155
© 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/
licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
systems (ECLAC 2012) and because we argue they
provide valuable insights into the impacts of internal
migration on the population composition of areas
within LA countries.
The proposed method relies on census-based
migration matrices, which provide information on
local populations at the census date and some
earlier year. In the context of our method, infor-
mation at the census date is considered to capture
the spatial distribution of population attributes
after internal migration has occurred, and infor-
mation at some earlier year is used to represent a
hypothetical scenario of no internal migration.
Given the reliance on census data, a key assumption
of the method is the time invariability of population
attributes. While this assumption is reasonable for
time-invariant population attributes (e.g., sex) and
those that change in a predictable way over time
(e.g., age), it is less adequate for time-varying attri-
butes (e.g., employment status). Our method,
however, has the advantage of effectively isolating
the impacts of internal migration by removing the
effects of international migration, births, and
deaths, and providing a way to decompose the
overall change in the socio-demographic composition
of areas due to internal migration into the effects
from in-migration and those from out-migration. It
thus provides an effective tool for progressing our
understanding of the relative contributions of in-
and out-migration to overall change in the socio-
demographic composition of areas.
The paper is structured as follows. The next section
reviews the literature on patterns of internal migration
in LA countries and highlights existing theoretical dis-
cussion on the impacts of these movements on the
socio-demographic composition of large cities. We
then discuss the key limitations of existing approaches
for quantifying internal migration impacts. In the
‘Methodological framework’section, we propose a
method that addresses these shortcomings and pro-
vides a summary statistic that exploits the use of
census data. The following section discusses our esti-
mates of the impact of internal migration on the age,
sex, and educational population compositions of
large cities in Panama, Mexico, and Ecuador. Finally,
we summarize our main conclusions and suggest
ways in which the proposed method could expand
internal migration research.
Background
The internal migration systems in LA countries have
experienced major transformations over the last
century. Between the 1930s and 1970s, rural-to-
urban migration dominated national migration pat-
terns, spurring significant population redistribution
(Firebaugh 1979). Fostered by the introduction of
import substitution policies after the Second World
War (Brea 2003; Rowe 2013a), net rural-to-urban
migration accelerated the urbanization process in
LA countries, accounting for over 45 per cent of
urban growth between 1950 and 1970 (Lattes 1995;
Lattes et al. 2004). Urban growth was concentrated
in a few urban centres, particularly in the largest
cities, resulting in a pronounced population imbal-
ance between the primate city and the rest of each
country.
Rural-to-urban migration during the 1930s to
1970s was characterized as a two-step process: first,
moves from rural areas to small towns, and then
moves from small towns to urban areas (Herrick
1965). Relative to the destination population, these
flows were driven by large proportions of young
people, females, and the less educated (Elizaga
1972; Herrera 2013). Like in European and North
American countries, the out-migration of young indi-
viduals and less educated people from rural areas
was underpinned by a lack of educational and
employment opportunities (ECLAC 2012), but out-
migration flows were reported to be more geographi-
cally concentrated, being directed to a limited
number of urban centres (Zlotnik 1994), reflecting
the prominent concentration of service provision in
national capitals (ECLAC 2012).
The predominance of females in rural-to-urban
migration was a result of the greater migration pro-
pensities of women than men in LA countries, with
a ratio of three women for every two men moving
to cities during the 1940s to 1960s (Simmons et al.
1978; Lawson 1998). This gender selectivity reflected
the patriarchal society and a growing urban service
economy (Germani 1971; Gilbert 1974; Jelin 1977).
Reflecting patriarchal values, some women migrated
as part of the family unit, following the male head of
household. But, unlike in Africa, large numbers
moved to cities to take up informal service jobs, as
men dominated agricultural employment and job
opportunities in rural areas (Elizaga 1966; Hugo
1993). The emergence and concentration of affluent
classes in cities increased the demand for domestic
service workers, who were primarily rural in-
migrant women (Elizaga and Macisco 1975; Szasz
1995; Chant 1999).
Given the large volume of rural-to-urban
migration characterizing the 1930s to 1970s period,
LA scholars have long argued that the selectivity of
migration flows has shaped the socio-demographic
2Jorge Rodríguez-Vignoli and Francisco Rowe
composition of large cities in the region (Villa and
Rodríguez 1998; Rodríguez and Busso 2009). While
its quantification has been prevented by lack of
appropriate data, rural-to-urban migration is
claimed to have generated three main effects: (1) a
demographic window effect, indicated by a rise in
the share of working-age population (15–59); (2) a
feminizing effect, as shown by a decrease in the
local sex ratio; and (3) a downgrading educational
effect, that is, a decline in the local levels of education
in large cities.
In contrast, during the period from the mid-1980s
to the 2010s, LA countries experienced significant
economic and political changes that reshaped
internal migration patterns and are likely to have
altered the selectivity of internal migration flows
into major urban centres (Gilbert 1993). Stimulated
by a transition to an open market economic system
(based on trade liberalization, privatization, and
development of natural resource-based export-
oriented activities), the attraction of major LA
cities to migrants diminished (Gilbert 1993; Brea
2003; Chavez et al. 2016). Increased foreign direct
investment in mining and agriculturally rich regions
promoted greater geographical dispersal of employ-
ment growth, with large cities reporting increasing
out-migration to distant areas (Rodríguez 2011a;
Rowe 2014). Between the 1990s and 2000s, capital
cities in many countries experienced net migration
losses for the first time since the 1900s (Rodríguez
2008,2011b). This was coupled with a shift in the
primary source of in-migrants, with rural areas no
longer the main suppliers of migrants to large cities
(Rodríguez and Busso 2009). Small and intermediate
urban areas are now the primary sources (Rodríguez
2011b), which is an inherent consequence of the high
degree of urbanization in LA (United Nations 2015;
Bernard et al. 2017).
These shifts in the migration network of LA
countries do not appear to have affected the prefer-
ence of young migrants for large cities (Rodríguez
and Busso 2009;Rowe2013b), but evidence suggests
that the sex and educational composition of migration
flows into large cities changed (Rodríguez 2004).
Internal migration in LA during the 1990s and 2000s
appears to have been selective of males and highly
educated individuals. Rodríguez (2004) estimated a
male-dominated migrant sex ratio and a larger percen-
tage of migrants with university degrees relative to
non-migrants in the region of origin, pointing to an
over-representation of males and university-educated
people in the migration system.
Taken together, these changes suggest that
countries’transitions from dominant rural economies
to more industrialized, service-based systems appear
to have led to a migration network dominated by
urban-to-urban migration, with more educated indi-
viduals moving to jobs. Internal migration thus
appears to have reshaped the socio-demographic
composition of LA cities. However, these compo-
sitional impacts have not been examined and are
yet to be measured and understood. Based on the
work reviewed in this section, we conjecture that
the over-representation of males and university-edu-
cated individuals in the composition of migration
flows would have reduced the feminizing and down-
grading educational effects that characterized the
1930s to 1970s period. At the same time, we believe
that the continuation of the migration selectivity of
young adults continues to have a demographic
window effect on the population of large cities,
increasing the local share of working-age population.
The next section reviews the most commonly used
existing approaches for measuring the impacts of
internal migration, discussing their strengths and
limitations.
Approaches to measuring internal migration
impacts
A primary impact of migration is redistributing popu-
lation, contributing to population growth in some
areas while leading to declines in others. A crude
approximation for measuring the impact of internal
migration on local areas involves estimating the mag-
nitude of inflows to a destination and movements in
the reverse direction, and quantifying their resulting
net balance. Yet, the magnitude of inflows and out-
flows is not the only dimension that should be con-
sidered. To effectively quantify the impacts of
internal migration, accounting for origin and destina-
tion differentials in migration selectivity is also
important: there are significant variations in out-
migration rates across population subgroups (i.e.,
origin differentials). Out-migrants tend to be
younger and more educated than the origin popu-
lation, and more likely to be single and living in
rental housing. Similarly, there are systematic vari-
ations in in-migration rates across population sub-
groups, when comparing the in-migrant and non-
migrant populations (i.e., destination differentials).
Accounting for these differentials is important in
measuring the impacts of internal migration, as they
can produce significant compositional changes in
origin and destination regions. Migration may influ-
ence the local human capital base, accelerate popu-
lation ageing, and alter the local sex balance. To
Internal migration impacts on metropolitan areas 3
quantify such compositional impacts, it is important
to capture four key population components: (1) the
magnitude of in- and out-migration flows; (2) the
size of the non-migrant population; (3) the selectivity
of migration flows; and (4) the composition of the
non-migrant population.
As mentioned earlier, three sets of approaches
have been used to quantify the impact of internal
migration on the composition of local areas: (1) com-
parative analysis of socio-demographic profiles; (2)
‘population growth equation’approaches; and (3)
net migration-based indicators. While useful, these
approaches suffer from a series of shortcomings:
they fail to provide a single statistical indicator that
integrates the four key components, to capture the
combined migration impact of multiple population
subgroups, or to assess the migration impact on a
wide range of socio-economic indicators.
The comparative socio-demographic profiles
approach involves analysis of the frequency distri-
butions of in-migrants and non-migrants with
respect to a specific characteristic (e.g., Massey and
Parr 2012). These distributions are examined to
determine the selectivity of migration to a destina-
tion region. They provide a visual inspection of
migration data, but do not produce a statistical
measure that quantifies the impact of internal
migration. Moreover, this approach is based on
ratios of population to migration inflows, but it
does not directly quantify changes in the non-
migrant population due to migration outflows. As a
result, this approach only captures a part of the
changes in local populations caused by internal
migration.
Population growth equations provide a more com-
prehensive approach to quantifying internal
migration impacts. This method involves measuring
the components of population change: fertility, mor-
tality, and net migration using a cohort survival
model (e.g., Green 1994; Gavalas and Simpson
2007). Survival probabilities are applied to derive
net internal migration estimates as the residual
between population estimates and projections.
These net migration estimates are used to assess
the impacts of internal migration. This approach,
however, does not consider the size or selectivity of
non-migrant populations.
Using five-year census transition data, Table 1
illustrates this deficiency. Based on the population
aged 5–14, it displays net migration estimates for
the city of Quito and the rest of Ecuador, revealing
a net migration gain of over 3,600 people in Quito,
and a corresponding loss in the rest of Ecuador
during the 1996–2001 census period. Based on
these balances, misleading conclusions could be
drawn—indicating that the scale of the impact of
internal migration on Quito and on the rest of
Ecuador were of a similar order—if the sizes of the
non-migrant populations in Quito and the rest of
Ecuador were not considered. However, an examin-
ation of net migration rates, which take into account
this population component, reveals that the impacts
of internal migration for Quito are much larger
than for the rest of Ecuador. Thus, while the ‘popu-
lation growth equation’approach provides an idea
of the direction of impact of internal migration (i.e.,
population gain or loss), it does not produce a
direct estimate of the resulting change in population
composition.
A second limitation of the ‘population growth
equation’approach is the stringent data requirement
of the cohort method. It requires data on births,
deaths, internal, and international migration disag-
gregated by geographic areas. Such data are rarely
available in less developed countries. An additional
limitation is that this method only returns net
migration estimates. This precludes any decompo-
sition of the overall change in population compo-
sition in an area into the contributions of in- and
out-migration. This is a major constraint to under-
standing the underlying ways in which internal
migration shapes the local population structure.
The third approach is the estimation and compari-
son of net migration rates based on transition
matrices. Net migration rates effectively summarize
the overall impact of internal migration flows by
Table 1 Population change and net internal migration for Quito and the rest of Ecuador, over the 1996–2001 census interval,
people aged 5–14
Place of residence Net migration Population at risk Non-migrant population Net migration rate (%)
Quito 3,613 308,389 292,499 1.2
Rest of Ecuador −3,613 2,384,420 2,364,917 −0.2
Notes: The population at risk is the total population at the start of the census interval (1996). The staying population is the population at risk
minus out-migration. The net migration rate is net migration divided by the population at risk.
Source: Authors’elaboration based on five-year migration data from the 2001 Ecuadorian Census.
4Jorge Rodríguez-Vignoli and Francisco Rowe
balancing net region-specific gains and losses due to
internal migration (Thomas 1941). Computed for a
population subgroup, net migration rates can also
offer an assessment of the selectivity effects of
internal migration, in addition to its effects on the
size and composition of the non-migrant population
(e.g., Voss et al. 2001; Champion and Fisher 2003).
A negative value for these rates indicates population
losses in a particular subgroup due to net out-
migration, whereas a positive score points to popu-
lation gains due to net in-migration.
Net migration rates, however, do not provide a
direct estimate of the impact of internal migration
on the socio-economic composition of local areas.
They only indicate the change experienced by a par-
ticular population subgroup as a result of internal
migration. Yet, a population subgroup can increase
its share of the population at the destination,
despite recording net migration losses, if its corre-
sponding net migration rate is lower than that of
other population subgroups. For this reason, if we
seek to estimate the impact of internal migration on
the population composition of an area, we need to
compare the net migration rates for all subgroups
into which a population can be divided in relation
to a particular attribute (e.g., age). For instance, if
we seek to assess the impact of internal migration
on the sex composition of an area, a comparison of
net migration rates for males and females is required.
While this approach is useful, its implementation is
computationally intensive and complex, as the
number of comparisons increases by a combinatorial
factor with the number of population subgroups
under analysis. For example, if we seek to compare
the changes in age composition across eleven age
groups, the number of comparisons required would
be 55.
The net migration approach suffers from two key
additional limitations. First, it does not return a
single summary indicator, so the overall impact of
internal migration of multiple population subgroups
on area cannot be effectively quantified or easily
interpreted. Second, the approach cannot be used
to estimate the effects of internal migration on popu-
lation-based socio-demographic indicators, such as
average years of schooling or the dependency ratio;
measures commonly used by local government
agencies and transnational organizations, including
the United Nations (UN), to assess the developmen-
tal status of areas.
In this paper, we propose a method that produces a
summary statistical indicator to quantify the impact
of internal migration on the population composition
of local areas. The proposed approach overcomes
the limitations of existing methods by integrating
the four key population components outlined
earlier, and exploits the availability of census data,
the most commonly available source of internal
migration data around the world (Bell et al. 2015).
Methodological framework
Methods
Our proposed approach provides a summary statisti-
cal index, the Compositional Impact of Migration
(CIM), that quantifies the impact of migration on
the socio-demographic composition of local areas.
It has been devised to take advantage of census-
based origin–destination matrices of migration
flows and to capture the interrelated effects of the
four key components identified in the previous
section. Thus, this approach overcomes some of the
key limitations of existing methods for measuring
the spatial impact of migration.
The CIM is a counterfactual approach that
involves a comparison between a Factual Value
(FV) and a Counterfactual Value (CFV). These
values are derived from the row and column margin-
als of a migration matrix based on a statistical indi-
cator and labelled the Migration Impact Indicator
(MII) matrix. It is not a matrix of migration flows.
Each element in this matrix represents a statistical
indicator that measures the socio-demographic com-
position of the local population, such as the sex ratio
or mean years of education. Like in any standard
migration matrix, the diagonal elements of the MII
matrix relate to the non-migrant population in an
area i; off-diagonal elements relate to the migration
flow from a region ito a region j; and the row and
column marginals relate to the total population in
region iat time t, and in region iat the earlier time
t–x, respectively.
FVs correspond to the row marginals of the MII
matrix, which are based on the population distri-
bution at the census date. Thus, they provide a rep-
resentation of the socio-demographic structure of
regions observed at the census, that is, after
migration. CFVs correspond to the elements in the
column marginals of the MII matrix, which are
based on the population distribution at an earlier
year: one, five, or ten years previously, as recorded
by censuses. Hypothetically, CFVs could be thought
of as the expected population composition if there
were no internal migration; in other words, if
migration had not happened, what would the local
socio-demographic structure have been? Subtracting
Internal migration impacts on metropolitan areas 5
CFVs from FVs provides a measure of change
between the start and end of a census interval and
represents our proposed summary statistic, the
CIM. The CIM measures the estimated percentage
change in the local population structure, as captured
by a MII, resulting from net migration redistribution.
A positive CIM indicates that internal migration con-
tributed to increase a given MII, for example, the
local sex ratio. A negative value denotes that internal
migration reduced the MII. The index is computed
as:
CIMi=FVi−CFVi=MIIt
i−MIIt−5
i.(1)
Using five-year transition census-based migration
data, tand t−5 correspond to the census date and
five years earlier, respectively. A detailed example
of the calculations is provided in the supplementary
material.
To create the MII matrix, any statistical indicator
can be adopted, including percentages, ratios,
averages, or medians, as well as more complex com-
posite metrics, such as the Duncan index of dissimi-
larity or Gini coefficient. In this section, we use the
sex ratio to generate our MII, in order to provide a
complete exposition and mathematical formalization
of the method. For our analysis in the next section,
we also use the share of population by age band
and the average years of schooling, to examine the
impacts of internal migration on the sex, age, and
educational composition.
Using the sex ratio P(m)/P(f) to quantify the
impact of internal migration on the local sex compo-
sition, the CIM
i
is:
MIIt
i−MIIt−5
i=P(m)t
i
P(f)t
i
−P(m)t−5
i
P(f)t−5
i
(2)
where P(m)t
iand P(f)t
idenote the local male and
female populations at the census date (t) in region
i; and P(m)t−5
iand P(f)t−5
irepresent those popu-
lations five years earlier (t−5). Equation (2) can be
decomposed into four elements, as shown by
Equation (3), to demonstrate that our method effec-
tively accounts for changes in the effect of the four
key components that determine the impact of
internal migration on the local population compo-
sition. These components are: (1) the magnitude of
in- and out-migration flows (M
ij
and M
ji
); (2) the
size of the non-migrant population (P
ii
); (3) the
selectivity of migration flows (Mconditional on
gender (fand m)); and (4) the composition of the
non-migrant population (Pconditional on gender):
MIIt
i−MIIt−5
i=
P(m)ii +n
i=1M(m)ji
P(f)ii +n
i=1M(f)ji
–
P(m)ii +n
j=1M(m)ji
P(f)ii +n
j=1M(f)ji
; where i=j.
(3)
We can use equation (3) to decompose the overall
impact of migration into the impacts of in- and out-
migration. The CIM index can be divided into two
component indices: an index for inflows (CIM
I
) and
an index for outflows (CIM
O
). The CIM
I
is computed
by comparing the MII for a region after migration
with the MII for the wider system of migration
flows. It captures the migration inflows from every
other area in the system and that of the non-
migrant population for that region. The CIM
O
is
measured by subtracting the MII for a region at the
earlier year before the census date—accounting for
all outflows to all other zones in the system—from
that of the non-migrant population. For the sex
ratio, these indices are:
CIMI
i=
P(m)ii +n
i=1M(m)ji
P(f)ii −n
i=1M(f)ji
−P(m)ii
P(f)ii
(4)
CIMO
i=P(m)ii
P(f)ii
−
P(m)ii +n
j=1M(m)ji
P(f)ii −n
j=1M(f)ji
.(5)
A key consideration in the implementation of the
method is the way in which internal migration is
measured. We require a measure that isolates the
impact of internal migration. A common problem in
defining internal migration relates to the population
at risk. The population at the start of a census interval
includes both people who die and people who emi-
grate during the interval. Rates based on this popu-
lation are confounded by the risks of mortality,
emigration, and internal migration. Rees et al.
(2000) recommended the use of the population at
the end of the census interval, which includes people
who were in the country at the start of the census inter-
val, survived, and were enumerated at the census. The
resulting count of internal migrants excludes the influ-
ences of mortality, emigration, immigration, and ferti-
lity. We adopt this definition, with the advantage of
effectively isolating the impact of internal migration
from other key components of population change.
Figure 1 illustrates the age–time classification used
in the method; that is, the age at the end of the
census interval. For example, it shows that persons
who were aged 15–19 at the time of the census and
aged 10–14 at the start of the interval are included in
6Jorge Rodríguez-Vignoli and Francisco Rowe
the calculation, whereas persons aged 0–4 at the
census are excluded, as they were not alive at the
start of the interval. Figure 1 also shows that persons
aged 60+ comprised the group of people who were
aged 55+ at the start of the interval. It is important
to note that this definition omits moves occurring
between census intervals (e.g., return and onward
migration).
Another important consideration relates to the
way the results from our method must be interpreted.
There is a temptation to take a cohort approach,
assuming for example that internal migration
impact estimates for people aged 5–9 at the end of
the census interval are those of the cohort aged 0–4
at the start of the interval. This interpretation is inap-
propriate for two reasons: first, it adopts a
longitudinal view of the impacts of internal
migration; and second, it provides the misleading
idea that cohort effects are being captured. Our
method does not capture cohort effects. It is a coun-
terfactual approach that measures the impacts of
internal migration on local areas at the end of a
census interval. It compares the factual population
distribution in an area at the census date with a coun-
terfactual distribution, representing what the popu-
lation distribution would have been if a particular
population subgroup had not migrated. The results
must be interpreted in a ‘what if’fashion: for
example, what if internal migration of the 5–9 age
group had not occurred? What would the age compo-
sition of the destination population be? We recognize
that people aged 5–9 at the end of the interval corre-
spond to people aged 0–4 at the start of the interval;
however, our method measures internal migration
impacts at the end of the interval when this popu-
lation was aged 5–9 at the destination region.
In relation to existing approaches, the proposed
method offers four key advantages: (1) it quantifies
the internal migration impact on measures of popu-
lation composition (e.g., gender) in a single index
for each; (2) it can be used on a range of socio-demo-
graphic statistical measures; (3) it provides an oppor-
tunity to contribute to theory development and guide
policy design; and (4) it enables us to expand our
understanding of structural relationships in the
national migration system at fine geographical
scales. We elaborate these points in Table 2.
A key limitation of the proposed method is
imposed by the assumption of time-invariance of
population attributes. Our method relies on a coun-
terfactual distribution, assuming that the observed
population characteristics at the census remain
stable over time. While this assumption is reasonable
for socio-economic characteristics that do not
change, or change in a predictable way over time, it
is less appropriate for time-varying attributes, such
as employment. We note, however, that this limit-
ation is shared by the wide range of analytical
approaches based on census data. Despite this, the
method has been extensively embraced by migration
scholars, as census data only provide information on
individual characteristics at the census date.
Data
We applied the proposed methods to measure the
impacts of internal migration on the population com-
position of eight large LA cities: Quito, Guayaquil,
and Cuenca in Ecuador; Mexico City, Guadalajara,
Figure 1 Lexis diagram illustrating age–time obser-
vation plan for five-year transition migration data
Internal migration impacts on metropolitan areas 7
Monterrey, and Tijuana in Mexico; and Panama City
in Panama. The metropolitan areas of each of these
cities is home to over 1 million people. We used
data from the 2000 and 2010 Census rounds extracted
from the online census microdata platform ‘REtrie-
val of DATa for small Areas by Microcomputer’
(REDATAM) and city administrative boundaries
from the ‘Spatial Distribution of Population and
Urbanization in Latin America and the Caribbean’
database, both hosted by the Latin American and
Caribbean Demographic Centre (CELADE). The
geographical boundaries used are temporally consist-
ent and correspond to those for the 2010 Census
round. These boundaries reflect administrative
areas of suburban expansion, accounting for the
effects of urban population growth.
We used data on the full population for Ecuador
and Panama. For Mexico we drew on data from the
extended census questionnaire, equating to a 10 per
cent population sample: 10,099,182 and 11,938,402
individuals from the 2000 and 2010 Censuses,
respectively. The sampling design for the Mexican
extended questionnaire is rigorously tested and
samples are carefully assessed following data collec-
tion by Mexico’s national statistical office (INEGI
2012). Sample weights are available and were
applied to make the samples statistically represen-
tative of the full census population. We also
tested differences in age, educational level, and
gender population subgroups between census
periods for Mexico. All differences were statistically
significant at a 99 per cent confidence level, except
for the 45–59 and 60+ age groups in Guadalajara
for the analysis of age composition. We believe
our results are robust and provide an adequate rep-
resentation of the way internal migration has con-
tributed to shape large Ecuadorian, Panamanian,
and Mexican cities.
The five-year transition data we used to measure
internal migration cover the second halves of the
1990s and 2000s (i.e., 1995–2000 and 2005–10). We
measured the impact of internal migration on three
key population dimensions: sex, age, and educational
composition, using three indicators: the sex ratio to
measure changes in sex composition; the share of
population in five age bands to estimate changes in
Table 2 Key advantages of the proposed CIM method
(1) Captures the internal migration impact of multiple population subgroups in a single index. The proposed method produces
a single indicator of the estimated percentage change in the population structure of local areas, resulting from the net
migration redistribution of multiple population subgroups. By using the median age or the dependency ratio to build the
Migration Impact Indicator (MII) matrix, for example, our method can summarize the overall impact of migration on local
age structures. No similar outcome can be achieved through existing approaches. A comparison between net migration
rates for multiple population subgroups, for instance, provides a measure of estimated net migration balance for each
subgroup, but does not deliver an estimate of how these balances together alter the population structure of local areas.
(2) Allows estimation of internal migration impacts using a wide range of socio-demographic measures. While existing
approaches produce migration rates, flows, and percentages to measure the impacts of internal migration (see ‘Approaches
to measuring internal migration impacts’section), they only provide an indirect estimate of the change in population
resulting from internal migration. By contrast, the proposed method produces a direct estimate of the expected percentage
change in multiple socio-demographic indicators due to internal migration, such as the sex ratio, the dependency ratio, the
average years of schooling, and the share of the population aged 60+. This enables us to make a straightforward assessment
of the impacts of internal migration across different dimensions of the local population.
(3) Aids theory and guides policy development. A key additional advantage of the proposed method is its potential to
contribute to migration theory and guide policy development. Through the decomposition of the total change in
population due to internal migration into the effects of in- and out-migration, the method can be used to determine the
leading agent of population change associated with internal population movements. Additionally, through the
measurement of the socio-demographic composition of migration, the method can be used to estimate and understand the
impacts of migration selectivity. Together, these outcomes represent valuable information for formulating a comprehensive
policy framework that targets both in- and out-migration. This is in contrast with existing national policy practices, which
focus on influencing in-migration levels (United Nations 2010).
(4) Enables understanding of structural relationships in the national migration system at fine levels of geography. The proposed
method uses an indicator matrix (MII) which uses the full origin–destination matrix and allows us to compare the
selectivity of internal migration inflows, their counterflows, and the staying population. This comparison produces a
representation of the qualitative relationships between each of these components by identifying, for example, which of
these components is associated with higher sex ratios or higher average years of schooling. Thus, in addition to estimating
the overall impact of internal migration on the local population composition, the MII can be used to quantify the impact of
migration flows between selected origins and destinations. We do not exploit this advantage in the current paper, as we
focus on a small origin–destination migration two-by-two matrix, capturing flows between our chosen cities and the rest of
the country.
8Jorge Rodríguez-Vignoli and Francisco Rowe
age structure; and the average years of schooling for
three age groups to quantify changes in educational
levels. These indicators comprise our MII (Equation
(1)) and are described in Table 3.
As already noted, given our reliance on census
data, a key assumption of our method is the time-
invariance of population attributes. Information on
each individual’s situation is only available at the
census date and this may differ from their circum-
stances five years earlier. This creates major difficul-
ties with characteristics such as education that may
change over time, especially at young ages, because
it is unclear if a rise in the local average years of
schooling is the result of (a) the in-migration of
highly skilled people; (b) less educated individuals
acquiring formal education in the destination after
migration; or (c) educational changes in the non-
migrant population.
We considered this issue as an integral part of the
interpretation and tested the robustness of our
results. We measured the internal migration impacts
on education for three age groups (Table 3). Consist-
ent with the UN’s human development index and edu-
cational attainment statistics, we first focused on the
average years of schooling for people aged 25+,
because it provides a comparable measure across
populations and countries (UNDP 2015) and pro-
duces more accurate estimates of the impact of
internal migration by removing the effect of individ-
uals obtaining education after arrival in the destina-
tion. Second, because the in-migration of young and
less educated people may still be argued to reduce
local average years of schooling at the destination,
we conducted a robustness check by comparing the
consistency of our results for the 25+ age group with
those for two older age groups: 34–44 and 45–49.
Results and discussion
We computed sex, age, and education CIMs by
implementing the method outlined in the previous
subsection and the measures reported in Table 3.
These indices quantify the impact of internal
migration and provide empirical evidence on the
feminizing,demographic window, and downgrading
educational effects that relate to the tentative find-
ings of previous studies on the impacts of internal
migration on the population structure of large LA
cities. Negative sex and education CIMs would
suggest that internal migration had both a feminizing
and a downgrading educational effect on local popu-
lation structures by reducing the relative share of
male relative to female population and the average
years of schooling. Coupled with positive age CIMs
for the 15–59 population, negative age CIMs for
the 5–14 and 60+ populations would suggest that
internal migration had a demographic window
effect by reducing the local dependency ratio, that
is, decreasing the share of local population aged 5–
14 and 60+ and simultaneously increasing the share
of younger people in productive ages 15–59. We
also report three additional CIM statistics: the rela-
tive CIM, which measures the relative percentage
change (rather than the absolute change) in the
CIM over the five-year census interval; and the
CIM
I
and CIM
O
, which quantify the separate contri-
butions of in- and out-migration to the overall impact
of migration. The FVs and CFVs used for our calcu-
lations are reported in the Appendix (Table A1).
Sex ratios
Table 4 reports the CIMs for the sex ratio. The results
show that, except for Cuenca, all cities in the sample
display a negative CIM for the 1995–2000 period,
suggesting that internal migration operated to
reduce the local sex ratio by increasing the share of
the female population. These reductions were par-
ticularly pronounced in the Ecuadorian cities of
Quito and Guayaquil, showing a decrease of 0.7 in
the sex ratio. Consistent with previous work (e.g.,
Elizaga 1966; Alberts 1977), these results indicate
Table 3 Statistics used to measure the impact of internal migration on sex, age, and educational composition
Statistic Formula Description
Sex ratio P(m)
P(f)The sex ratio of men per 100 women
Share of population by age band P(a)
PThe share of people in a particular age band (a); where amay be 5–14, 15–29,
30–44, 45–59, or 60+
Average years of schooling s(a)
PSum of schooling years of the local population (s) divided by the total
population (P); where aindicates an age band and may be 25+, 34–44, or
45–49
Internal migration impacts on metropolitan areas 9
that internal migration continued to have a feminiz-
ing effect on the demographic structure of large LA
cities during the second half of the 1990s.
Table 4 also reveals pronounced cross-city differ-
ences in the main factor contributing to this feminiz-
ing effect. While in-migration appears to have been
the main contributing force in Panama, Mexico
City, and Guadalajara, out-migration was the main
driving force in Tijuana and Quito, and both in-
and out-migration in Guayaquil and Monterrey.
These differences in contribution were paralleled
by differences in the direction of their influence,
with both acting to shape the reductions in the sex
ratio in Panama City, Mexico City, Monterrey, Gua-
dalajara, and Guayaquil, while they operated in
opposite directions in Tijuana and Quito. In the
latter cities, out-migration appears to have reduced
the local sex ratio, while a larger share of males in
the in-migration flows—relative to the local non-
migrant population—acted to increase the local sex
ratio. In absence of this in-migration effect, out-
migration would have led to a 0.37 reduction in the
sex ratio in Tijuana and a 0.77 reduction in Quito.
Thus, a consistent over-representation of men in
out-migration flows relative to the non-migrant
population has been the main driver underpinning
this feminizing effect.
In contrast to 1995–2000, the 2005–10 period
showed a reduction in or reversal of the feminizing
effect of internal migration. Quito, Guayaquil, and
Mexico City registered reductions in their corre-
sponding CIMs, from −0.70, −0.71, and −0.53 in
1995–2000 to −0.66, −0.23, and −0.24 in 2005–10,
respectively. In contrast, Panama City and Guadala-
jara saw shifts from a negative to a positive CIM
(−0.29 to 0.02 and −0.11 to 0.22, respectively). In
Panama City and Guadalajara, these changes point
to a major shift in the impact of internal migration
on the sex composition, shifting to have a masculiniz-
ing effect on the local populations.
As revealed by Table 4, these patterns appear to
be largely driven by over-representation of males in
in-migration flows relative to the local non-migrant
population, offsetting the impact of out-migration,
which acted to increase the share of females in
the local population. These effects were particularly
pronounced in Quito where out-migration reduced
the local sex ratio by 0.92. Outflows exceeded an
offsetting increase of 0.26 due to in-migration and
reduced the local representation of males in the
population, leading to an overall reduction in the
local sex ratio (−0.66).
Monterrey, Tijuana, and Cuenca represent inter-
esting cases. While the feminizing impact of internal
migration reduced in most of the cities, it strength-
ened in Monterrey and Tijuana. In Monterrey, both
in- and out-migration contributed to increasing the
local female population. In Tijuana, only out-
Table 4 Compositional Impact of Migration (CIM): sex ratio, 1995–2000 and 2005–10
City Country
Impact indicators
CIM Relative CIM (%) CIM
I
CIM
O
1995–2000 Panama City Panama −0.29 −0.30 −0.21 −0.08
Mexico City Mexico −0.53 −0.58 −0.42 −0.12
Monterrey Mexico −0.26 −0.26 −0.12 −0.14
Guadalajara Mexico −0.11 −0.12 −0.07 −0.04
Tijuana Mexico −0.30 −0.30 0.07 −0.37
Quito Ecuador −0.70 −0.75 0.07 −0.77
Guayaquil Ecuador −0.71 −0.73 −0.35 −0.36
Cuenca Ecuador 1.00 1.16 1.41 −0.41
2005–10 Panama City Panama 0.02 0.02 0.22 −0.20
Mexico City Mexico −0.24 −0.26 −0.02 −0.22
Monterrey Mexico −0.58 −0.59 −0.20 −0.38
Guadalajara Mexico 0.22 0.24 0.40 −0.18
Tijuana Mexico −0.52 −0.53 0.12 −0.64
Quito Ecuador −0.66 −0.71 0.26 −0.92
Guayaquil Ecuador −0.23 −0.23 0.12 −0.34
Cuenca Ecuador 0.36 0.40 1.35 −0.99
Note: The CIM statistic indicates the absolute difference between the sex ratio measured at the census date and five years earlier; relative
CIM shows the percentage change over the five-year period; and CIM
I
and CIM
O
quantify the impacts of in- and out-migration, respectively,
on the sex ratio.
Source: Authors’calculations based on census data. Data covering the 1995–2000 and 2005–10 census periods were used for Panama and
Mexico, and data covering the 1996–2001 and 2005–10 census periods were used for Ecuador.
10 Jorge Rodríguez-Vignoli and Francisco Rowe
migration expanded the female population base,
while in-migration had an increased masculinizing
effect. For Monterrey, these patterns seem to reflect
greater inflows of female migrants, partly reflecting
women escaping from homicides of females in
Ciudad Juárez. Monterrey also attracted women
from cities on the Mexican–US border (including
Tijuana) that were severely affected by the 2008
global financial crisis (Chavez et al. 2016). In
Cuenca, internal migration had a strong masculiniz-
ing effect, but this decreased between 1995–2000
and 2005–10, reflecting a greater representation of
males in out-migration flows. This may reflect econ-
omic restructuring towards female-dominated
employment sectors, such as hospitality, accommo-
dation, and trade, which reduced the labour
demand for male workers and may have enticed
women from neighbouring areas to migrate for
employment (Chavez et al. 2016).
These findings point to a key historical change in
patterns of internal migration in LA. In contrast to
the dominant female selectivity in migration inflows
to large LA cities during the 1930s to 1970s
(Elizaga 1966; Herold 1979; Herrera 2013), a
greater sex balance in these flows implies a reduction
in this feminizing effect. The female selectivity during
the 1930s to 1970s reflected the mass migration of
women from rural areas to take low-skilled jobs in
service activities in response to a shrinking agricul-
tural sector (Useche 2013). As countries experienced
rapid urbanization and agricultural decline, rural
female migrants employed as domestic servants in
high-income households were a common feature of
the mobility system in LA countries up to the 1980s
(Rodríguez and Busso 2009). Many rural towns
have now evolved into small and intermediate
urban areas and diversified their economies, devel-
oping tourism, accommodation, and food industry
sectors, and expanding local job opportunities for
women (Drentea 1998). Intermediate cities have
also seen growing investment in university and voca-
tional infrastructure, enlarging the range of local
post-secondary education opportunities (Bulmer-
Thomas 2003). Together, these developments may
have promoted the retention of the local female
population in small and intermediate cities, balancing
the sex ratio in population movements to and from
large LA cities.
Age structure
Table 5 and Figure 2 report the age CIMs. For the
1995–2000 period, they show negative CIMs for the
populations aged 5–14 (children), 30–44 and 45–59
(working age), and 60+ (older people), indicating
that internal migration reduced the share of these
age groups in the local populations. There were
large variations in the extent of these reductions.
Internal migration appears to have generated the
largest reductions in Panama City and Tijuana, as
indicated by the relative CIM, leading to a reduction
of nearly 5 per cent in the share of children in
Panama City, and over 7 per cent in the share of
older people in Tijuana. Reductions were marginal
in Mexico City, with internal migration producing
changes of less than 1 per cent. There were also
exceptions to this downward trend. Internal
migration acted to expand the 60+ population in
Guadalajara and Guayaquil but these expansions
were tiny.
The reductions in the shares of children and older
people were reflected in a concomitant expansion in
the share of the working-age population (i.e., ages
15–59). This was driven by a rising population aged
15–29 (see Figure 2), as internal migration acted to
reduce the local populations at ages 30–44 and 45–
59. Panama City and Tijuana experienced the
largest increases in the share of working-age popu-
lation, of 1.9 and 1.7 per cent, respectively, while
Mexico City, Monterrey, Guadalajara, and Guaya-
quil saw the smallest increases (under 0.2 per cent).
The biggest percentage increases can be observed
in Tijuana, Quito, Cuenca, and Panama City, where
internal migration operated to augment the local
share of population aged 15–29 by over 4 per cent.
Table 5 reveals that in-migration was the main
factor underpinning the reduction in the shares of
people aged 5–14 and 60+ during 1995–2000, as indi-
cated by consistently larger CIM
I
s than correspond-
ing CIM
O
s. These negative CIM
I
s indicate that the
percentages of children and older people in in-
migration flows to cities tended to be smaller than
in the non-migrant population, and as a result, in-
migration contributed to reduce the share of these
groups in the local population. In Panama City, in-
migration accounted for 93 per cent of the contrac-
tion in the share of the local population aged 5–14.
While out-migration tended to have a smaller
impact than in-migration across all age groups and
cities, we note that out-migration tended to have dif-
ferentiated impacts: in some instances, amplifying the
impacts of in-migration flows, and in others, counter-
balancing these effects. The counterbalancing effects
of out-migration were notable for older age groups
(45–59 and 60+), as indicated by positive CIM
O
s:
out-migration tended to involve a smaller share of
people aged 60+ compared with the non-migrant
Internal migration impacts on metropolitan areas 11
population, amplifying the presence of this age
group. This finding points to the fact that the
overall impact of internal migration on reducing the
local proportion of older people was not because of
older people leaving large cities, but because of
working-age people moving in, particularly those in
the 15–29 age group. This result is consistent with
observed low propensities to migrate among older
people (Rogers et al. 1978), and also reflects the
absence of a double bulge in the age distribution of
migration in LA countries (ECLAC 2012). In
industrialized countries, migration age profiles tend
Table 5 Compositional Impact of Migration (CIM): share of population aged 5–14, 15–29, 30–44, 45–59, and 60+, 1995–2000
and 2005–10
City Country
Impact indicators: 1995–2000 Impact indicators: 2005–10
CIM
Relative
CIM (%) CIM
I
CIM
O
CIM
Relative
CIM (%) CIM
I
CIM
O
Population aged
5–14
Panama City Panama −1.08 −4.99 −1.00 −0.07 −0.91 −4.47 −0.82 −0.09
Mexico City Mexico −0.16 −0.75 −0.10 −0.07 −0.10 −0.55 −0.03 −0.07
Monterrey Mexico −0.21 −0.97 −0.22 0.01 −0.12 −0.62 −0.13 0.01
Guadalajara Mexico −0.18 −0.75 −0.14 −0.04 −0.25 −1.17 −0.22 −0.03
Tijuana Mexico −0.73 −2.95 −0.62 −0.11 −0.15 −0.67 −0.07 −0.07
Quito Ecuador −0.55 −2.49 −0.58 0.03 −0.34 −1.67 −0.29 −0.05
Guayaquil Ecuador −0.19 −0.88 −0.31 0.11 −0.07 −0.34 −0.15 0.08
Cuenca Ecuador −0.51 −2.14 −0.56 0.06 −0.38 −1.81 −0.41 0.03
Population aged
15–29
Panama City Panama 1.49 4.91 1.60 −0.11 1.21 4.48 1.29 −0.08
Mexico City Mexico 0.51 1.57 0.54 −0.04 0.37 1.32 0.36 0.01
Monterrey Mexico 0.73 2.22 0.88 −0.15 0.57 2.06 0.78 −0.20
Guadalajara Mexico 0.39 1.18 0.47 −0.08 0.49 1.65 0.59 −0.10
Tijuana Mexico 2.32 7.08 2.43 −0.12 0.75 2.57 0.95 −0.20
Quito Ecuador 1.90 6.15 2.21 −0.31 1.32 4.41 1.68 −0.36
Guayaquil Ecuador 0.69 2.22 0.98 −0.29 0.40 1.37 0.65 −0.24
Cuenca Ecuador 1.83 5.72 2.14 −0.31 1.28 3.99 1.79 −0.51
Population aged
30–44
Panama City Panama 0.22 0.89 0.23 −0.01 0.24 0.97 0.26 −0.02
Mexico City Mexico −0.24 −0.94 −0.16 −0.08 −0.18 −0.70 −0.01 −0.17
Monterrey Mexico −0.29 −1.17 −0.22 −0.07 −0.17 −0.66 −0.06 −0.12
Guadalajara Mexico −0.20 −0.88 −0.03 −0.17 −0.23 −0.93 −0.02 −0.20
Tijuana Mexico −0.67 −2.70 −0.61 −0.06 −0.35 −1.28 −0.18 −0.16
Quito Ecuador −0.78 −3.23 −0.59 −0.19 −0.60 −2.52 −0.32 −0.28
Guayaquil Ecuador −0.33 −1.38 −0.25 −0.08 −0.22 −0.94 −0.11 −0.11
Cuenca Ecuador −0.56 −2.58 −0.44 −0.11 −0.46 −2.08 −0.25 −0.20
Population aged
45–59
Panama City Panama −0.39 −2.72 −0.50 0.12 −0.28 −1.71 −0.40 0.12
Mexico City Mexico −0.08 −0.64 −0.18 0.10 −0.06 −0.33 −0.20 0.14
Monterrey Mexico −0.14 −1.08 −0.26 0.12 −0.16 −0.99 −0.34 0.18
Guadalajara Mexico −0.03 −0.24 −0.19 0.16 −0.03 −0.20 −0.20 0.17
Tijuana Mexico −0.46 −4.14 −0.65 0.19 −0.20 −1.35 −0.45 0.25
Quito Ecuador −0.38 −2.81 −0.63 0.25 −0.27 −1.74 −0.63 0.35
Guayaquil Ecuador −0.17 −1.29 −0.29 0.12 −0.11 −0.67 −0.26 0.16
Cuenca Ecuador −0.45 −3.57 −0.62 0.17 −0.28 −1.96 −0.62 0.34
Population aged
60+
Panama City Panama −0.25 −2.78 −0.32 0.07 −0.26 −2.35 −0.33 0.07
Mexico City Mexico −0.02 −0.28 −0.10 0.08 −0.04 −0.34 −0.12 0.09
Monterrey Mexico −0.09 −1.18 −0.18 0.09 −0.12 −1.18 −0.25 0.13
Guadalajara Mexico 0.03 0.34 −0.11 0.13 0.02 0.18 −0.15 0.17
Tijuana Mexico −0.45 −7.07 −0.55 0.10 −0.06 −0.85 −0.25 0.19
Quito Ecuador −0.19 −2.07 −0.41 0.22 −0.10 −1.01 −0.43 0.33
Guayaquil Ecuador 0.01 0.08 −0.13 0.14 0.00 −0.02 −0.12 0.12
Cuenca Ecuador −0.32 −3.19 −0.52 0.20 −0.16 −1.51 −0.50 0.35
Note: The CIM statistic indicates the absolute percentage point change in the population shares measured at the census date and five years
earlier; relative CIM shows the percentage change over the five-year period; and CIM
I
and CIM
O
quantify the impacts of in- and out-
migration, respectively, on the population shares.
Source: Authors’calculations based on census data. Data covering the 1995–2000 and 2005–10 census periods were used for Panama and
Mexico, and data covering the 1996–2001 and 2005–10 census periods were used for Ecuador.
12 Jorge Rodríguez-Vignoli and Francisco Rowe
to peak first around the mid-20s to early 30s, and
peak for a second time around retirement age,
reflecting the mobility of older people to rural and
coastal areas (Hugo and Bell 1998). In LA countries,
this second peak does not occur, as retirees living in
large cities tend to stay put to take advantage of the
high quality of local healthcare facilities (López-
Calleja and Morejón 2015).
As in 1995–2000, Table 5 indicates that internal
migration continued acting to reduce the shares of
children and older people in Panama City and large
Mexican and Ecuadorian cities during 2005–10.
However, smaller CIMs in 2005–10 than in 1995–
2000 reveal that this effect diminished in size. The
average CIMs (not shown) for the shares of people
aged 5–14 and 60+ across cities reduced by around
Figure 2 Relative Compositional Impact of Migration (CIM) index: share of population aged 5–14, 15–59, and
60+, eight Latin American cities, 1995–2000 and 2005–10
Source: Authors’calculations based on census data. Data covering the 1995–2000 and 2005–10 census periods were used for
Panama and Mexico, and data covering the 1996–2001 and 2005–10 census periods were used for Ecuador.
Internal migration impacts on metropolitan areas 13
30 and 10 per cent, respectively, between 1995–2000
and 2005–10. Among the child population, this
reduction was primarily due to a lessening impact
of in-migration, while among older people it was
driven by a strengthening of the impact of out-
migration. For the former, this points to the fact
that, as in 1995–2000, in-migrants continued to
include a smaller share of people aged 5–14 than in
the local non-migrant population, although this
difference was less pronounced in 2005–10, generat-
ing a smaller reduction in the local child population.
For the latter, this result indicates that the percentage
of people aged 60+ in outflows from large cities
decreased between 1995–2000 and 2005–10, with
out-migration thus acting to increase the local share
of older people.
Taken together, these patterns concurrently reflect
an increasing share of people of working age, specifi-
cally young adults aged 15–29, moving into large
Panamanian, Mexican, and Ecuadorian cities. This
evidence is consistent with the pattern of young
people in industrialized countries moving into large
cities in pursuit of better education, employment
opportunities, and a more vibrant lifestyle (William-
son 1988; Fielding 1992; Rowe, Corcoran, et al.
2017). However, the rejuvenating effect of internal
migration on the age structure of cities in our
sample declined and this seems to be linked to an
overall reduction in the share of young migrants to
large cities in response to the dispersal of employ-
ment opportunities, which expanded to medium-
sized cities (Gilbert 1996). This decline in the rejuve-
nating effect of internal migration is expected to con-
tinue as LA countries move to more advanced stages
of the demographic and urban transition, at which
time an acceleration of population ageing and a
strengthening of medium-sized cities’economies
are expected (Gilbert 1996; Rodríguez 2011a).
Educational levels
In contrast to the impacts of internal migration on
boosting the working-age population, examining the
education CIMs for the population aged 25+ reveals
that internal migration tended to have a downgrading
effect on education by reducing the average years of
schooling in local populations (Table 6 and Figure
3). The CIMs of 0.1 or less, however, reveal that this
effect was marginal, indicating decreases of less than
0.1 years of schooling on average. While there was
variation in the main source contributing to this
effect across cities, out-migration appears to have con-
sistently eroded the local human capital base, as
indicated by negative CIM
O
s, except for Panama
City. In Ecuadorian cities, both in- and out-migration
contributed to generate the largest reductions in the
local average years of schooling. In Mexican cities,
the downgrading educational effect produced by out-
migration was offset by in-migration of people with
higher education than the non-migrant population in
both census periods.
These results are illuminating in two ways. First,
they reveal a tendency for more educated people to
leave major cities in Ecuador, Mexico, and Panama.
This is contrary to what might be anticipated, as
large cities concentrate educational and career devel-
opment opportunities. This pattern appears to
resemble the ‘escalator region’process that character-
izes the internal migration system in the UK (Fielding
1992), with young people moving to the South-East
region of England to develop their careers, and then
‘stepping off’the escalator and moving down the
urban hierarchy once they achieve a high occupational
status and family-related reasons gain importance.
Second, the results reveal that in-migration into
large cities does not necessarily lead to a reduction
in the average years of schooling. We find that in-
migration increased local levels of education in
Mexican cities, in contrast to the traditional assump-
tion that large cities attract a disproportionate
number of less educated people to take up low-
skilled jobs and education.
As pointed out earlier, a key assumption of our
method is the time invariability of population attri-
butes. As formal education tends to increase over
time, we checked the robustness of our results by com-
paring the CIMs for the population aged 25+ with
those for two older age groups: 30–44 and 45–49.
The results were consistent. Although there were vari-
ations in magnitude, the CIMs for the 30–44 and 45–49
age groups were predominantly negative. They indi-
cate that internal migration contributed to reduce
the average years of schooling and that the out-
migration of more educated people tended to be the
main factor underpinning this reduction.
Additionally, Table 6 reveals that the downgrading
effect of migration on education has weakened over
time. In the 2005–10 period, while migration contin-
ued to reduce the average number of years of school-
ing, the associated reductions were 0.06 years or less.
In part, this weakening in the overall impact of
migration tended to reflect a lessening of the effects
of both in- and out-migration on lowering the
average years of schooling, but it may also relate to
a decline in the propensity to move among highly
educated people. This decline in migration probabil-
ities is a common feature of mobility patterns in
14 Jorge Rodríguez-Vignoli and Francisco Rowe
many countries across the globe (Bell and Charles-
Edwards 2013; Rowe 2018), and LA countries are
no exception (Rodríguez 2011a). In Chile, Rowe
(2013b) found that the odds ratio of migration for ter-
tiary-educated individuals declined from 2.63 in the
1977–82 census period to 1.93 in the 1997–2002
period and that these individuals were more likely
to live in the main metropolitan area of the
country, Santiago. An increasing ‘rootedness’of
highly educated individuals, coupled to long-distance
commuting, has been linked to this pattern (Cooke
2011; Rowe and Bell 2018) and it may explain the
declining effects of migration on the educational
composition of large LA cities.
National pictures
Thus far, we have discussed the results according to
individual characteristics but what do they mean for
the countries studied? This subsection links the
observed patterns of internal migration impact to
socio-economic and demographic processes in each
national setting, acknowledging that a comprehen-
sive analysis of the factors underpinning these pro-
cesses is beyond the scope of this paper.
In Panama City, in-migration consistently operated
to enlarge the population in the 15–29 and 30–44 age
groups, reduced local educational levels, and transi-
tioned from having a feminizing to a masculinizing
effect on population composition. Over the last
25 years, Panama City has experienced rapid econ-
omic growth due to the expansion of trade, real
estate, and professional service activities associated
with the Panama Canal. These activities have
attracted domestic and international migrants, and
unlike in many LA nations, they form part of the
formal economy (Chavez et al. 2016). They tend to
employ a larger share of individuals aged 15–29
and 30–44 than aged 45–59, thus attracting a
Table 6 Compositional Impact of Migration (CIM): average years of schooling for population aged 25+, 30–44, and 45–49,
1995–2000 and 2005–10
Cities Country
Impact indicators: 1995–2000 Impact indicators: 2005–10
CIM
Relative
CIM (%) CIM
I
CIM
O
CIM
Relative
CIM (%) CIM
I
CIM
O
Population aged
25+
Panama City Panama −0.08 −0.79 −0.08 0.00 −0.04 −0.40 −0.05 0.01
Mexico City Mexico −0.02 −0.28 0.00 −0.03 −0.02 −0.17 0.02 −0.03
Monterrey Mexico −0.02 −0.25 0.03 −0.05 −0.02 −0.16 0.03 −0.04
Guadalajara Mexico −0.01 −0.13 0.05 −0.06 −0.02 −0.20 0.05 −0.07
Tijuana Mexico −0.03 −0.32 0.00 −0.03 0.00 0.02 0.01 −0.01
Quito Ecuador −0.10 −0.96 −0.06 −0.03 −0.04 −0.39 −0.02 −0.03
Guayaquil Ecuador −0.10 −1.13 −0.08 −0.02 −0.06 −0.67 −0.02 −0.05
Cuenca Ecuador −0.08 −0.90 −0.05 −0.03 −0.06 −0.57 0.00 −0.06
Population aged
30–44
Panama City Panama −0.09 −0.82 −0.09 0.00 −0.07 −0.57 −0.08 0.01
Mexico City Mexico −0.03 −0.29 0.00 −0.03 −0.02 −0.22 0.01 −0.03
Monterrey Mexico −0.02 −0.24 0.02 −0.04 −0.01 −0.11 0.02 −0.03
Guadalajara Mexico −0.01 −0.08 0.05 −0.05 −0.02 −0.18 0.05 −0.07
Tijuana Mexico −0.05 −0.60 −0.03 −0.02 0.00 0.02 0.01 −0.01
Quito Ecuador −0.10 −1.01 −0.06 −0.04 −0.03 −0.28 −0.03 0.00
Guayaquil Ecuador −0.09 −0.96 −0.08 −0.02 −0.07 −0.67 −0.02 −0.05
Cuenca Ecuador −0.11 −1.07 −0.10 −0.01 −0.05 −0.41 −0.03 −0.02
Population aged
45–49
Panama City Panama −0.08 −0.86 −0.09 0.01 −0.05 −0.44 −0.06 0.01
Mexico City Mexico −0.01 −0.13 0.00 −0.01 −0.01 −0.11 0.01 −0.02
Monterrey Mexico −0.01 −0.14 0.01 −0.02 −0.02 −0.19 0.00 −0.02
Guadalajara Mexico 0.00 −0.06 0.03 −0.03 0.00 0.05 0.02 −0.02
Tijuana Mexico −0.11 −1.63 −0.10 −0.01 −0.02 −0.24 −0.03 0.01
Quito Ecuador −0.10 −1.17 −0.08 −0.02 −0.03 −0.32 −0.03 −0.01
Guayaquil Ecuador −0.08 −1.00 −0.08 0.00 −0.04 −0.47 −0.02 −0.03
Cuenca Ecuador −0.06 −0.69 −0.07 0.01 −0.02 −0.18 −0.02 0.00
Note: The CIM statistic indicates the difference between the average number of schooling years measured at the census date and five years
earlier; relative CIM shows the percentage change over the five-year period; and CIM
I
and CIM
O
quantify the impacts of in- and out-
migration, respectively, on average years of schooling.
Source: Authors’calculations based on census data. Data covering the 1995–2000 and 2005–10 census periods were used for Panama and
Mexico, and data covering the 1996–2001 and 2005–10 census periods were used for Ecuador.
Internal migration impacts on metropolitan areas 15
disproportionate percentage of non-local labour in
the two former age groups (Chavez et al. 2016).
These activities are also male dominated and this
may explain the masculinization of migration flows
to Panama City.
In Ecuador, a series of factors have contributed to
shaping migration patterns during the end of the
twentieth and start of the twenty-first centuries;
these include a socio-economic crisis, dollarization
of the local consumer market, and a larger public
administration sector (Calderón et al. 2016). These
factors encouraged international emigration, consoli-
dated Quito (the capital) as the main national
migration destination, and resulted in a positive net
migration balance in Guayaquil. Concurrently, our
results indicate that the impact of internal migration
on the local population compositions by age and edu-
cation was similar across large Ecuadorian cities:
Figure 3 Relative Compositional Impact of Migration (CIM) index: average years of schooling for population
aged 25+, 30–44, and 45–49, eight Latin American cities, 1995–2000 and 2005–10
Source: Authors’calculations based on census data. Data covering the 1995–2000 and 2005–10 census periods were used for
Panama and Mexico, and data covering the 1996–2001 and 2005–10 census periods were used for Ecuador.
16 Jorge Rodríguez-Vignoli and Francisco Rowe
increasing the local share of people aged 15–29 and
reducing average years of schooling. However, its
impact on the sex composition exhibited spatial vari-
ations, feminizing the local population in Quito and
Guayaquil, but masculinizing that of Cuenca. The
in-migration of male workers to replace local men
migrating overseas appears to be underpinning this
pattern in Cuenca (Herrera 2008).
In Mexico, as in other LA countries, the transition
to an export-oriented economic development strat-
egy has transformed patterns of internal migration,
resulting in net migration losses in Mexico City
(Useche 2013). Historically, Mexico City has been
the most attractive destination for migrants but the
development of new poles of economic activity,
driven by the maquiladora sector, has promoted
population movement to cities on the Mexican–US
border (Gilbert 1996). In the maquiladora industry,
young women are a desirable source of labour as
they typically work longer hours than men and
display a greater dexterity in performing repetitive
assembly tasks (Chavez et al. 2016). Women are
thus attracted to Mexican–US border cities for
employment and this can be linked to an augmented
feminizing effect of internal migration in Tijuana
during the 2005–10 period—an effect that may be
representative of Mexican–US border cities in
general. This effect was also seen in Mexico
City, Monterrey, and Guadalajara, where it was
coupled with a consistent erosion of local educational
levels (reduction in the average years of schooling),
driven by the out-migration of people with
higher educational qualifications than the locals
(Table 6).
Conclusions
Despite its wide-ranging implications, little progress
has been made in understanding and measuring
the impacts of internal migration on the socio-demo-
graphic composition of local areas. A key obstacle
has been the lack of an approach to capture the
simultaneous influences of key population com-
ponents that determine the spatial impact of internal
migration: the size, balance, and selectivity of
migrant and non-migrant populations. In this paper,
we proposed a method that captures the dynamics
of these components, exploits the availability of
census data, and returns a single summary measure,
the Compositional Impact of Migration (CIM). We
showed how this index can be decomposed to
measure the relative contributions of in- and out-
migration.
We applied our method to quantify the impact of
internal migration on the sex, educational, and age
compositions of eight large LA cities. The estimated
impacts were generally small, echoing the inertia of
human settlement patterns in large cities, but sys-
tematic patterns emerged. Internal migration
tended to reduce the local sex ratio and average
years of schooling, and to increase the percentage
of young adults (aged 15–29) in the local population,
namely, having feminizing,downgrading educational,
and demographic window effects on local population
structures.
We also showed that the strength of these effects
diminished between 1995–2000 and 2005–10. A
greater representation of males and a smaller share
of people aged 15–29 in the in-migration flows to
these cities reduced the feminizing and demographic
window effects, while a larger proportion of highly
educated people moving in diminished the down-
grading educational effect. We also highlighted that
the estimated impacts of migration were small in
terms of the aggregate impacts on metropolitan
populations. They are likely to be more acute in par-
ticular zones within metropolitan regions, in places
where net migration gains and losses are concen-
trated. Future research is required to determine the
extent of these impacts at a sub-metropolitan scale.
It is important to recognize that our method
focuses on quantifying the migration effect of time-
invariant attributes, not time-varying characteristics.
Our work contributes to advancing migration
research in three ways. First, it provides tools to
expand our knowledge of the impacts of internal
migration. To date, existing scholarship has assessed
the impacts of migration by examining flows and
net migration balances. The proposed method
enables us to complement this knowledge by quanti-
fying and examining the compositional effects of
migration on local populations.
Second, our work has the potential to guide policy
design. Measurement and an understanding of the
compositional impacts of internal migration are key
inputs for policy development. Existing migration pol-
icies tend to focus on restricting in-migration (United
Nations 2010). Yet, our findings revealed that internal
migration may lead to a demographic window effect,
expanding the local working-age population, which
may lead local governments to promote policies that
focus on in-migration. Concurrently, our findings
revealed that migration tended to reduce local levels
of education, which could justify restrictive in-
migration policy measures. Yet, they also indicated
that out-migration is the main mechanism for such
loss in educational levels and, taken together, these
Internal migration impacts on metropolitan areas 17
results invite us to move away from traditional policy
approaches focused on in-migration to adopt a more
comprehensive framework that considers both in-
and out-migration, as well as the size, balance, and
composition of migration flows.
Third, our findings also have implications for
further research on migration within LA countries.
Prior work has documented a gradual reduction in
female selectivity in the internal migration systems
of LA countries (Rodríguez 2004), but to date, little
is known about how this change has shaped metropo-
litan populations. Our results revealed that migration
continues to have a feminizing effect on these popu-
lation structures, but it is diminishing, and in certain
cities—including Panama City, Guadalajara, and
Cuenca—a masculinizing effect has emerged. This
finding motivates a fruitful avenue of future research:
to develop a better understanding of the socio-econ-
omic changes and explain the shifting sex selectivity
of migration flows in LA countries. This would
involve examining changes in the labour market of
cities, specifically changes in the domestic sector,
which has been a major employer of women histori-
cally, but has experienced a gradual downsizing
(Chant 1999). Understanding the changing sex selec-
tivity of migration would also require an investi-
gation of the socio-economic changes in places of
origin. In the 1970s and 1980s, these places were pre-
dominantly rural and highly dependent on agricul-
tural activity. They are now small, vibrant urban
environments, but little is known about their indus-
trial structure or employment patterns.
Notes and acknowledgements
1 Please direct all correspondence to Francisco Rowe,
Department of Geography and Planning, University of
Liverpool, Roxby Building, 74 Bedford St S, Liverpool
L69 7ZT, UK; or by E-mail: F.Rowe-Gonzalez@
liverpool.ac.uk
2 We acknowledge the comments from the editor of Popu-
lation Studies and two anonymous referees that helped
to improve the quality of our manuscript.
ORCID
Francisco Rowe http://orcid.org/0000-0003-4137-
0246
References
Alberts, J. 1977.Migración Hacia Áreas Metropolitanas de
América Latina [Migration to Metropolitan Areas in
Latin America]. Un Estudio Comparativo. Santiago:
CELADE.
Bell, M. and E. Charles-Edwards. 2013.Cross-national
Comparisons of Internal Migration: An Update on
Global Patterns and Trends. Technical Paper No. 2013/
1. New York: United Nations Department of
Economic and Social Affairs: Population Division.
Bell, M., E. Charles-Edwards, D. Kupiszewska,
M. Kupiszewski, J. Stillwell, and Y. Zhu. 2015. Internal
migration data around the world: assessing contempor-
ary practice, Population, Space and Place 21(1): 1–17.
Bernard, A., F. Rowe, M. Bell, P. Ueffing, and E. Charles-
Edwards. 2017. Comparing internal migration across the
countries of Latin America: a multidimensional
approach, PLoS ONE 12(3): e0173895.
Brea, J. 2003. Population dynamics in Latin America,
Population Bulletin 58(1): 3–36.
Bulmer-Thomas, V. 2003.The Economic History of Latin
America since Independence. Cambridge: Cambridge
University Press.
Calderón, Á., M. Dini, and G. Stumpo. 2016.Los desafíos
del Ecuador para el cambio estructural con inclusión
social [The Challenges in Ecuador for a Structural
Change towards Social Inclusion]. Santiago: CEPAL,
LC/L.4281.
Champion, T. and T. Fisher. 2003. The social selectivity of
migration flows affecting Britain’s larger conurbations:
an analysis of the 1991 Census Regional Migration
Tables, Scottish Geographical Journal 119(3): 229–246.
Chant, S. 1999, Population, migration, employment and
gender, in R. Gwynne and C. Kay (eds), Latin
America Transformed: Globalization and Modernity.
London: Arnold, pp. 226–269.
Chavez, A. M., J. Rodríguez, M. Acuña, J. Barquero,
D. Macadar, J. Cunha, and J. Sobrino. 2016. Migración
interna y cambios metropolitanos: ¿qué está pasando
en las grandes ciudades de América Latina? [Internal
migration and metropolitan changes. What is
happening in the large cities of Latin America?],
Revista Latinoamericana de Población (RELAP) 10
(18): 7–41.
Cooke, T. 2011. It is not just the economy: declining
migration and the rise of secular rootedness,
Population, Space and Place 17(3): 193–203.
Drentea, P. 1998. Consequences of women’s formal and
informal job search methods for employment in
female-dominated jobs, Gender and Society 12(3):
321–338.
ECLAC (Economic Commission for Latin America and
the Caribbean). 2012.Population, Territory and
Sustainable Development. Santiago: ECLAC-United
Nations.
Elizaga, J. C. 1966. A study of migration to Greater
Santiago (Chile), Demography 3(2): 352–377.
18 Jorge Rodríguez-Vignoli and Francisco Rowe
Elizaga, J. C. 1972.Migraciones Interiores, el Proceso de
Urbanización, Movilidad Social [Internal Migration,
Urbanization and Social Mobility]. Santiago: Centro
Latinoamericano de Demografía (CELADE), Serie A
N° 117.
Elizaga, J. C. and J. Macisco. 1975.Migraciones Internas.
Teoría, Método y Factores Sociológicos [Internal
Migration, Theory, Methods and Social Factors].
Santiago: Centro Latinoamericano de Demografía
(CELADE), Serie E, N° 19.
Fielding, A. 1992. Migration and social mobility: South
East England as an escalator region, Regional Studies
26(1): 1–15.
Firebaugh, G. 1979. Structural determinants of urbaniz-
ation in Asia and Latin America, 1950–1970,
American Sociological Review 44(2): 199–215.
Gavalas, V. and L. Simpson. 2007. Segregation of ethnic
minorities in two districts of Greater Manchester,
Genus 63(1): 119–148.
Germani, G. 1971.Sociología de la Modernización
[Sociology of Modernization]. Buenos Aires: Paidos.
Gilbert, A. 1974.Latin American Development.
Harmondsworth: Penguin Books.
Gilbert, A. 1993. Third world cities: the changing national
settlement system, Urban Studies 30(4/5): 721–740.
Gilbert, A. 1996.The Mega-City in Latin America.
New York/Tokyo: United Nations University Press.
Green, A. 1994. The role of migration in labour-market
adjustment: the British experience in the 1980s,
Environment and Planning A 26(10): 1563–1577.
Herold, J. 1979. Female migration in Chile: types of moves
and socioeconomic characteristics, Demography 16(2):
257–277.
Herrera, G. 2008.Ecuador: La migración internacional en
cifras [Ecuador: International Migration in Numbers].
Quito: Fondo de Población de las Naciones Unidas—
Facultad Latinoamericana de Ciencias Sociales
(Ecuador).
Herrera, G. 2013. Latin America: gender and migration, in
I. Ness (ed), The Encyclopaedia of Global Human
Migration. Chicester: Blackwell Publishing Ltd, pp. 1–6.
Herrick, B. 1965.Urban Migration and Economic
Development in Chile. Cambridge, MA: MIT Press.
Hugo, G. 1993. Internal migration of women in developing
countries, in United Nations, Proceedings of the United
Nations Expert Meeting on the Feminization of Internal
Migration (Aguascalientes, Mexico, 22–25 October
1991), New York: United Nations, pp. 54–80.
Hugo, G. and M. Bell. 1998. The hypothesis of welfare-led
migration to rural areas: the Australian case, in P. Boyle
and K. Halfacree (eds), Migration into Rural Areas—
Theories and Issues, West Sussex: John Wiley and
Sons, pp. 107–133.
INEGI (Instituto Nacional de Estadística y Geografía).
2012.Diseño de la muestra censal 2010 [Sample
Census Design 2010]. México: INEGI.
Jelin, E. 1977. Migration and labor force participation of
Latin American women: the domestic servants in the
cities, Signs: Journal of Women in Culture and Society
3(1): 129–141.
Lattes, A. 1995. Urbanización, Crecimiento Urbano y
Migraciones en América Latina. Población y desarrollo:
tendencias y desafíos [Urbanization, urban growth and
migration in Latin America. Population and develop-
ment: trends and challenges], Notas de población 62:
211–260.
Lattes, A., J. Rodriguez, and M. Villa. 2004. Population
dynamics and urbanization in Latin America: concepts
and data limitations, in T. Champion and G. Hugo
(eds), New Forms of Urbanization. Beyond the Urban–
Rural Dichotomy, Aldershot, UK: Ashgate, pp. 89–112.
Lawson, V. 1998. Hierarchical households and gendered
migration in Latin America: feminist extensions to
migration research, Progress in Human Geography 22
(1): 39–53.
López-Calleja, C. and B. Morejón. 2015.Envejecimiento
Demográfico y Migración Interna de Personas con 60
años y más de Edad en Cuba y Países de América
Latina [Population ageing and the internal migration of
the population aged 60+ in Cuba and Latin American
countries], Población y Saluden Mesoamérica 12(2): 1–27.
Massey, S. and N. Parr. 2012. The socio-economic status of
migrant populations in regional and rural Australia and
its implications for future population policy, Journal of
Population Research 29(1): 1–21.
Rees, P., M. Bell, O. Duke-Williams, and M. Blake. 2000.
Problems and solutions in the measurement of
migration intensities: Australia and Britain compared,
Population Studies 54(2): 207–222.
Rodríguez, J. 2004.Migración Interna en América Latina y el
Caribe: Estudio Regional del Período 1980–2000 [Internal
Migration in Latin America and the Caribbean: Regional
Study, Period 1980–2000]. Serie Población y Desarrollo,
No. 50 (LC/L.2059–P), Santiago: CEPAL.
Rodríguez, J. 2008. Migración interna de la población
joven: el caso de América Latina [Internal migration
of young population: the case of Latin America].
Revista Latinoamericana de Población 2(3): 9–26.
Rodríguez, J. 2011a.Migración Interna y Sistema de
Ciudades en América Latina: Intensidad, Patrones,
Efectos y Potenciales Determinantes, Censos de la
Década de 2000 [Internal Migration and the Latin
American System of Cities: Intensity, Patterns, Impacts
and Potential Determinants, 2000 Census round]. Serie
Población y Desarrollo N° 105, (LC/L.3351), Santiago:
CEPAL.
Internal migration impacts on metropolitan areas 19
Rodríguez, J. 2011b. The spatial distribution of the popu-
lation, internal migration and development in Latin
America and the Caribbean, in United Nations, in
Population Distribution, Urbanization, Internal
Migration and Development: An International
Perspective. New York: United Nations, pp. 54–80.
Rodríguez, J. and G. Busso. 2009.Migración interna y
desarrollo en América Latina entre 1980 y 2005, un
estudio comparativo con perspectiva regional basado
en siete países [Internal Migration and Development in
Latin America between 1980 and 2005. A Comparative
Study with a Regional Focus based on Seven
Countries]. Santiago: CEPAL, (LC/G.2397–P).
Rogers, A., R. Raquillet, and L. J. Castro. 1978. Model
migration schedules and their applications,
Environment and Planning A 10(5): 475–502.
Rowe, F. 2013a. The geography and determinants of
regional human capital in eight LA and Caribbean
countries, in J. Cuadrado-Roura and P. Aroca (eds),
Regional Problems and Policies in Latin America.
Berlin/Heidelberg: Springer-Verlag, pp. 379–405.
Rowe, F. 2013b.Spatial Labour Mobility in a Transition
Economy: Migration and Commuting in Chile. Ph.D.
thesis, University of Queensland.
Rowe, F. 2014.The Effects of Economic Liberalisation on
Inter-Regional Labour Migration in a Transition
Economy, Chile. Conference paper, 2014 Tinbergen
Workshop: real people in virtual space, Amsterdam,
The Netherlands.
Rowe, F. 2018. Establishing the extent and pace of the con-
temporary trend of migration decline in Europe,
Regions E-Zine. Forthcoming.
Rowe, F. and M. Bell. 2018. The drivers of long-distance
commuting in Chile: The role of spatial distribution of
economic activities and population, in J. Poot and M.
Roskruge (eds), Regional science perspectives on popu-
lation change and impacts in Asia and the Pacific.
Springer. Forthcoming.
Rowe, F., M. Bell, A. Bernard, E. Charles-Edward, and
P. Ueffing. 2017.Measuring the Impact of Internal
Migration on Population Redistribution in Europe.
European Regional Science Conference, Groningen,
the Netherlands.
Rowe, F., J. Corcoran, and M. Bell. 2017. The returns
to migration and human capital accumulation
pathways: non-metropolitan youth in the school-to-
work transition, The Annals of Regional Science 59(3):
819–845.
Simmons, A., S. Díaz-Briquets, and A. Laquian. 1978.
Cambio Social y Migración Interna: una Reseña de
Hallazgos Investigativos en América Latina [Social
Change and Internal Migration: A Summary of
Research Findings in Latin America]. Bogotá: Centro
Internacional de Investigaciones para el Desarrollo.
Szasz, I. 1995. Mujeres y Migrantes: Desigualdades en el
Mercado Laboral de Santiago de Chile [Women and
Migrants: Labour Market Inequalities in Santiago,
Chile]. Revista de la CEPAL 56.
Thomas, D. 1941.Social and Economic Aspects of Swedish
Population Movements, 1750–1933. New York:
Macmillan.
United Nations. 2010.World Population Policies 2009.
New York: United Nations, Department of Economic
and Social Affairs, Population Division, ST/ESA/
SER.A/293.
United Nations. 2015.World Urbanization Prospects: The
2014 Revision. New York: United Nations,
Department of Economic and Social Affairs,
Population Division, ST/ESA/SER.A/366.
UNDP (United Nations Development Programme). 2015.
Human Development Report 2015: Work for Human
Development. New York: UNDP.
Useche, P. 2013. Latin America: agriculture and
migration, in I. Ness (ed), The Encyclopaedia of
Global Human Migration. Chicester: Blackwell
Publishing Ltd, pp. 1–8.
Villa, M. and J. Rodríguez. 1998. Distribución espacial de la
población, urbanización y ciudades intermedias: hechos
en su contexto [Spatial distribution of population,
urbanization and intermediate cities: facts in context],
in R. Jordán and D. Simioni (eds), Ciudades intermedias
de América Latina y el Caribe. Santiago: CEPAL,
pp. 25–68.
Voss, P., R. Hammer, and A. Meier. 2001. Migration analy-
sis: a case study for local public policy, Population
Research and Policy Review 20(6): 587–603.
Williamson, J. 1988, Migrant selectivity, urbanization, and
industrial revolutions, Population and Development
Review 14(2): 287–314.
Zlotnik, H. 1994. Expert group meeting on population dis-
tribution and migration, International Migration Review
28(1): 171–204.
20 Jorge Rodríguez-Vignoli and Francisco Rowe
Appendix
Table A1 Factual Values (FV) and Counterfactual Values (CFV) for sex, age, and education, 1995–2000 and 2005–10
Sex ratio
Percentage by age group Average years of schooling by age band
Population
aged 0–14
Population
aged 15–29
Population
aged 30–44
Population
aged 45–59
Population
aged 60+
Population
aged 25+
Population
aged 30–44
Population
aged 45–59
City FV CFV FV CFV FV CFV FV CFV FV CFV FV CFV FV CFV FV CFV FV CFV
1995–2000
Panama City 96.3 96.6 20.5 21.6 31.9 30.4 25.0 24.8 13.8 14.2 8.7 9.0 10.1 10.2 10.9 11.0 9.5 9.6
Mexico City 91.9 92.4 21.9 22.1 32.6 32.1 24.7 25.0 12.9 13.0 7.8 7.8 8.8 8.8 9.6 9.6 7.8 7.8
Monterrey 97.5 97.8 21.5 21.7 33.7 32.9 24.5 24.8 12.7 12.8 7.7 7.8 8.9 8.9 10.0 10.0 7.8 7.8
Guadalajara 92.8 92.9 24.5 24.6 33.3 32.9 22.6 22.8 12.1 12.1 7.6 7.5 8.3 8.3 9.2 9.2 7.3 7.3
Tijuana 100.4 100.7 24.1 24.8 35.0 32.7 24.3 25.0 10.6 11.1 5.9 6.4 7.9 7.9 8.6 8.7 6.6 6.7
Quito 92.6 93.3 21.5 22.1 32.8 30.9 23.5 24.2 13.0 13.4 9.2 9.4 9.8 9.9 9.5 9.6 8.1 8.2
Guayaquil 95.3 96.0 22.0 22.2 31.7 31.0 23.8 24.2 12.9 13.1 9.5 9.5 8.7 8.8 9.7 9.8 8.1 8.2
Cuenca 87.7 86.7 23.2 23.7 33.9 32.1 21.1 21.6 12.1 12.6 9.7 10.0 8.9 9.0 10.2 10.3 8.2 8.2
2005–10
Panama City 96.4 96.4 19.5 20.4 28.3 27.1 26.0 25.0 16.3 16.6 10.7 11.0 11.1 11.1 11.8 11.8 11.1 11.1
Mexico City 91.7 92.0 18.4 18.5 28.5 28.1 25.1 25.3 17.3 17.3 10.8 10.8 9.7 9.8 10.7 10.7 9.4 9.4
Monterrey 98.1 98.7 19.8 20.0 28.5 27.9 25.8 26.0 16.1 16.3 9.8 9.9 9.9 9.9 10.7 10.7 9.9 9.9
Guadalajara 92.0 91.8 21.4 21.5 30.2 29.7 24.0 24.3 14.8 14.8 9.6 9.6 9.4 9.5 10.3 10.3 9.2 9.2
Tijuana 97.7 98.3 22.0 22.2 30.0 29.2 26.9 27.3 14.2 14.4 6.9 6.9 9.1 9.1 9.8 9.8 8.5 8.5
Quito 93.2 93.9 20.1 20.4 31.2 29.9 23.2 23.8 15.5 15.7 10.1 10.2 10.7 10.8 11.5 11.5 10.6 10.7
Guayaquil 96.5 96.8 21.6 21.7 29.9 29.5 23.5 23.7 15.8 15.9 9.2 9.2 9.5 9.6 10.2 10.3 9.4 9.4
Cuenca 89.0 88.7 20.7 21.1 33.3 32.1 21.5 21.9 14.2 14.5 10.3 10.4 10.1 10.1 11.1 11.2 10.0 10.0
Note: FV and CFV correspond to the Factual and Counterfactual Values, respectively, as described in the ‘Method’subsection. The FV indicates the score of a statistic at the census date and the CFV
indicates the score of the same statistic measured five years ago.
Source: Authors’calculations based on census data.
Internal migration impacts on metropolitan areas 21
