DEMOGRAPHIC RESEARCH
VOLUME 39, ARTICLE 44, PAGES 1181,1226
PUBLISHED 11 DECEMBER 2018
https://www.demographic-research.org/Volumes/Vol39/44/
DOI: 10.4054/DemRes.2018.39.44
Research Article
Contemporary female migration in Ghana:
Analyses of the 2000 and 2010 Censuses
Samantha R. Lattof
Ernestina Coast
Tiziana Leone
Philomena Nyarko
© 2018 Lattof, Coast, Leone & Nyarko.
This open-access work is published under the terms of the Creative Commons
Attribution 3.0 Germany (CC BY 3.0 DE), which permits use, reproduction,
and distribution in any medium, provided the original author(s) and source
are given credit.
See https://creativecommons.org/licenses/by/3.0/de/legalcode.
Contents
1 Introduction 1182
2 Background 1183
2.1 Migration in Ghana 1183
2.2 Gender and migration 1185
2.3 Data sources for analysing migration in Ghana 1187
3 Data and methods 1188
3.1 Data 1188
3.2 Methods 1191
4 Results 1192
4.1 Identification of migrants 1192
4.2 Demographic structure of internal migrants 1196
4.3 Interregional female migration 1201
5 Discussion 1203
6 Acknowledgements 1206
References 1207
Appendices 1212
Demographic Research: Volume 39, Article 44
Research Article
Contemporary female migration in Ghana:
Analyses of the 2000 and 2010 Censuses
Samantha R. Lattof1
Ernestina Coast2
Tiziana Leone3
Philomena Nyarko4
Abstract
BACKGROUND
Knowledge of female migration patterns is scant despite increased recognition and
reporting of the feminization of migration. Recent data on female internal migration in
Ghana challenges historical assumptions that underestimated female migration.
OBJECTIVE
This study presents the first detailed comparative analyses of female migration using
microdata from Ghana’s censuses (2000 and 2010) and exploits this national data to
understand the gendered dimensions of migration.
METHODS
Secondary analyses use direct and indirect methods to describe the scale, type, and
demographic structure of contemporary female migration; assess the distribution of
female migrants across age and geography; and estimate net internal female migration.
RESULTS
Excluding international migrants, census microdata identified 31.1% of females as
internal migrants in 2000 and 37.4% of females as internal migrants in 2010. Working-
age migration was particularly pronounced in 2010, reinforcing economic opportunity
as a likely driver of migration for both sexes. Female migrants were significantly more
likely than female nonmigrants to reside in urban areas and work for pay, profit, or
family gain. By 2010, married women were less likely to migrate than peers who had
1 London School of Economics and Political Science, London, UK. Email: lattof@post.harvard.edu.
2 London School of Economics and Political Science, London, UK.
3 London School of Economics and Political Science, London, UK.
4 University of Ghana, Legon and Ghana Statistical Service, Accra, Ghana.
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Lattof et al.: Contemporary female migration in Ghana: Analyses of the 2000 and 2010 Censuses
never married. Net out-migration exceeded net in-migration in eight of Ghana’s ten
regions.
CONTRIBUTION
Our analyses expand the evidence base on contemporary female migration and refute
the outdated stereotype that girls and women do not participate in migration. The
prominence of the Greater Accra and Ashanti Regions as destinations for female
migrants suggests that interventions are needed in Ghana’s more rural regions to reduce
poverty and develop greater economic opportunities for girls and women.
1. Introduction
Due to population growth and urbanization, projections suggest that two-thirds of the
world’s population will reside in urban areas by 2050, with most of this increase
occurring in Asia and Africa (UNDESA 2014). Planning for and managing this
changing population distribution will require better understanding of new migration
patterns and the impacts of internal migration. This includes a better understanding of
female migration, which has been historically underestimated, with analyses focused on
male migrants or assumptions that migrants were male (Caldwell 1969; Zlotnik 1995).
Knowledge of female migration patterns is scant despite increased recognition and
reporting of the feminization of internal migration (Hofmann and Buckley 2012; Beegle
and Poulin 2013). Research from South Africa challenges the assumption that females
represent the residentially stable population, finding women in rural areas to be highly
mobile (Camlin, Snow, and Hosegood 2014). In Malawi, where young women now
migrate more than young men, assumptions of traditional patterns of matrilocal
residence following marriage no longer hold (Beegle and Poulin 2013). As evidence
reveals changes in the sex composition of migrants, it also reveals changes in the
reasons for migrating.
While both sexes may attribute their migration decisions to factors such as the
need to seek employment or a lack of independence at the place of origin, gender-
specific factors emerge. In South Africa, girls experience an increased risk of moving
out of the household following a parent’s AIDS-related death compared to boys;
families experiencing a death may expect girls to perform caring duties elsewhere or
may prefer to keep boys (Ford and Hosegood 2005). In Ghana, girls and women
attribute their migrations to the need to accumulate property for marriage; to avoid
harm, including female genital mutilation; and to avoid forced or arranged marriages
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that may be polygamous (Anarfi and Agyei 2009).5 These factors influence both the
decision to migrate and the choice of destination.
Data from Ghana’s two most recent Population and Housing Censuses (2000 and
2010) indicates that there are more female than male internal migrants, particularly at
younger ages (GSS 2013c). The growing number of younger migrants puts increasing
pressure on social services and employment opportunities in urban areas. Some
migrants move to Ghana’s urban areas independent of available resources or
employment opportunities (Agyei and Ofosu-Mensah Ababio 2009).
This study analyses Ghana’s 2000 and 2010 Censuses using census microdata
disaggregated by sex to provide a comprehensive picture of internal female migration at
all ages. We use direct and indirect techniques to analyse the patterns, trends, and
determinants of contemporary female migration. Our comparative analyses are the first
to exploit national data from the 2000 and 2010 Censuses with a view to understanding
the gendered dimensions of migration in Ghana.
2. Background
2.1 Migration in Ghana
Migration has historically been a way of life in West Africa and migration within
Ghana is no exception. Ghana’s internal migration is primarily a north–south
phenomenon established well before the census started officially recording migration
data in 1960 (Agyei and Ofosu-Mensah Ababio 2009). Since 1960, each census has
recorded large out-migration streams from Ghana’s northern regions and significant in-
migration streams into the Greater Accra Region, with Ghana’s 2010 Census recording
an intercensal in-migration rate of 40.72% for Greater Accra (GSS 2013c). Nearly one-
third (32.2%) of the Greater Accra Region’s population is between the ages of 15 and
29 years, due to a high rate of age-selective in-migration and rapid natural increase
(GSS 2013b). Migrants residing in Accra also tend to be long-term migrants, with only
about one in ten having moved in the 12 months prior to the 2010 Census (GSS 2013b).
As a result, Ghana’s urban centres (Figure 1) are facing growing challenges brought on
by unemployment, inadequate sanitation, and the development of shanty towns. Of the
1.6 million migrants residing in the Greater Accra Region during the 2010 Census,
about 10% originated from Ghana’s three northern regions (GSS 2013b).
With growing social acceptance of female independence and mobility, girls and
women are now the majority of Ghana’s internal migrants. Among adolescents, females
5 Polygamy is illegal under Ghanaian civil law, but it is common in northern Ghana.
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migrate from Ghana’s rural areas to the country’s urban areas at greater rates than males
(GSS 2013a). The same pattern exists among youth aged 25 years and younger, with
girls and young women comprising 60.5% of migrant youth (Anarfi and Appiah 2009).
Girls frequently migrate before completing their education. Depending on the estimates,
between 50% and 80% of female migrants have no formal education (Agyei and Ofosu-
Mensah Ababio 2009; Frempong-Ainguah, Badasu, and Codjoe 2009; Quartey and
Yambilla 2009).
Figure 1: Map of Ghana by region with differentiated urbanization levels
(2010)
Note: Map created by the authors.
There is debate about whether independent child migrants decide to migrate
primarily as a result of poverty or for economic reasons (Anarfi and Agyei 2009).
Commonly cited causes of child migration include deteriorating agricultural land,
drought, poor market facilities, poor transport networks, lack of employment
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opportunities, and lack of desire to participate in the agricultural industry (Frempong-
Ainguah, Badasu, and Codjoe 2009). Urban-pull factors and rural-push factors also
influence children’s migration decisions. Push factors for child migration include
parental inability to cater for their children’s needs, ethnic conflicts, lack of privacy and
money, lack of interest in schooling from parents and/or children, maltreatment by
family members, desire to prevent being given away in marriage, and lack of
independence (Frempong-Ainguah, Badasu, and Codjoe 2009). Pull factors for child
migration include assisting a sibling with work, schooling, learning a trade, working for
money, experiencing city life, and staying with a relative (Frempong-Ainguah, Badasu,
and Codjoe 2009).
Child migrants experience a number of problems related to either their work or
their young age: for instance, a decline in business, cheap prices for migrant services,
harassment from city guards, financial problems, physically demanding work, work that
is too difficult, no/insufficient work, no place to sleep, and high taxes (Kwankye and
Addoquaye Tagoe 2009). Given these challenges, child migrants frequently return to
their place of origin (Addoquaye Tagoe and Kwankye 2009). A survey conducted in
northern Ghana among returned child migrants found that other reasons for children’s
return included continuing their education, changed marital status, and being needed at
home (Addoquaye Tagoe and Kwankye 2009). As children (and their families) appear
to constantly weigh the costs and benefits of migrating to and from their place of origin,
repeated migrations may occur (Anarfi and Kwankye 2009).
2.2 Gender and migration
Defining the roles of girls and women as daughters, wives, and mothers has failed to
recognize women’s work beyond reproductive labour (e.g., caregiving, household
labour, unpaid work). This narrow view of female roles is present in the literature on
migration. Migrant girls and women may be classified as ‘dependent’ or ‘independent’
based on whether they migrate as daughters and wives or as members of the workforce
(Llácer et al. 2007: ii4). Similarly, the migration literature has referred to girls and
women who migrate with fathers and husbands as “passive” rather than “active”
migrants (Findley 1989). These labels are absent from the literature on migrant boys
and men. Male migrants are not classified according to their relationship to their
mothers and wives.
In addition to using different language to describe the migration of girls and
women, the migration literature has historically overlooked the roles of female
migrants. Girls’ and women’s forms of migration and their migration-related
employment have often been invisible and unrecognised, especially with regards to
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migrant domestic work (Elias 2010). This invisibility stems from research in the 1960s
and 1970s in which researchers often assumed migrants were male, focusing analyses
on male migrants and historically underestimating female migration (Caldwell 1969;
Zlotnik 1995). Sex-disaggregated census data increasingly shows growing mobility
among girls and women, with migration rates frequently balanced between the sexes
(Beegle and Poulin 2013; GSS 2013c; Camlin, Snow, and Hosegood 2014). While
census data is limited to sex-disaggregated analyses, examining differences between the
migration patterns of women and men is the first step in advancing our understanding
of gender and migration.
Migration increasingly allows girls and women to challenge traditional social roles
in rural societies (Guo, Chow, and Palinkas 2011). In Ghana, girls challenge these roles
by independently deciding to migrate (70% of girls vs. 54% of boys) and by personally
financing their migrations (57.6% of girls vs. 34.9% of boys) (Anarfi and Agyei 2009).
Research from the Democratic Republic of Congo and Senegal finds that, in patriarchal
settings, women’s access to and support from migrant networks is crucial in order for
women to migrate (Toma and Vause 2014). Upon migrating, migrant women develop
and strengthen community ties by strategically giving gifts, sharing food, caring for
children, and participating in reciprocal labour (Tufuor et al. 2015).
Evidence suggests that gender-specific factors may influence girls’ and women’s
choice of destination. Based on a survey of 450 child migrants residing in Accra and
Kumasi in 2005, researchers found that migrant girls were occasionally pursued and
recaptured by their families; this finding may illustrate one of the reasons why many
females decide to move to Accra, the urban centre that is furthest from the northern
regions (Anarfi and Agyei 2009). In addition to choice of destination, gender may
influence where migrants work. In Accra, public spaces have historically been
gendered: markets are associated with female entrepreneurship, whereas bus stations
are associated with male entrepreneurship (Thiel and Stasik 2016).
When mothers migrate, it can lead to restructuring of the parent–child relationship
as well as paradoxes pertaining to mothers’ caregiving role (Resurreccion 2009;
Contreras and Griffith 2012). With economic support now a key component of
‘superior motherhood,’ this type of support comes at a cost for migrant mothers:
mothers may be absent from their children’s lives and unable to provide their children
with emotional support and care from afar (Contreras and Griffith 2012: 62). Migration
can enhance the value of motherhood, as mothers provide increased resources and
improved material conditions for their children; however, migration can also diminish
motherhood, as other family members are called upon to provide childcare
responsibilities in the mother’s absence (Contreras and Griffith 2012). In this regard,
mothers migrating independently without their children are in fact dependent upon
family members’ ability to fulfil the daily caregiving role.
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2.3 Data sources for analysing migration in Ghana
Ghana’s internal migration data comes primarily from the decennial censuses and ad
hoc population surveys, as Ghana has no population register or administrative data
suitable for migration analyses. While census data provides limited depth of
information on female migration, it provides the most comprehensive source of
evidence on female migration at all ages that can be exploited using demographic
techniques. Ad hoc subnational surveys and research on female migration in Ghana are
localized and small-scale, precluding national-level analyses (Awumbila and Ardayfio-
Schandorf 2008; Anarfi and Kwankye 2009). These studies address important aspects
of migration, such as push- and pull-factors underlying independent child migration,
childcare practices among young migrants, and migrants’ livelihood strategies. National
migration data comes from the Ghana Migration Study (1991–1992), “Development on
the Move” migration study (2008–2009), Ghana Demographic and Health Surveys
(conducted in 1988, 1993, 1998, 2003, 2008, and 2014), Ghana Living Standards
Survey (conducted in 1987, 1988, 1991–1992, 1998–1999, 2005–2006, and 2013), and
post-independence censuses (1960, 1970, 1984, 2000 and 2010). Each of these data
sources has strengths and limitations for national-level analyses of migration.
The 1991–1992 Ghana Migration Study (GMS), developed in response to
inadequate migration data in prior censuses, provided a depth of migration data
unparalleled by more recent surveys. It collected evidence on the processes,
mechanisms, and effects of internal migration; however, this survey has not been
repeated (Twum-Baah, Nabila, and Aryee 1995). Despite its relative depth of migration
data, the 1991–1992 GMS has significant limitations: exclusion of child migrants
younger than 15 years of age; documented implementation challenges, such as
inaccessible enumeration areas (i.e., resulting from floods, ethnic conflicts, and broken
transportation); and lack of technical assistance required to implement the survey
(Twum-Baah, Nabila, and Aryee 1995).
To fill evidence gaps in migration’s developmental impacts and policy that were
unaddressed in the GMS, the Regional Institute for Population Studies at the University
of Ghana and the Global Development Network collaborated in 2008–2009 on a
nationally representative survey entitled “Development on the Move: Measuring and
Optimising Migration’s Economic and Social Impacts” (Yeboah et al. 2010). This study
focused on international migration and its socioeconomic impacts on households and
individuals remaining in Ghana.
Ghana’s Demographic and Health Surveys (GDHS) (1988, 1998, 2003, and 2008)
have each asked the same single question about migration – “How long have you been
living continuously in (NAME OF CURRENT PLACE OF RESIDENCE)?” – and
defined migrants based on how long they have lived in the enumeration area (GSS and
IRD 1989; GSS and Macro International 1999; GSS, NMIMR, and ICF Macro 2004
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Lattof et al.: Contemporary female migration in Ghana: Analyses of the 2000 and 2010 Censuses
2009). This question has several drawbacks for measuring migration. It precludes
identification of types of migrants (e.g., internal, international) and calculation of
subnational interregional migration flows. The 1993 GDHS included a five-question
migration module that went beyond birthplace to include whether or not the respondent
had lived elsewhere for at least six months, age at first migration, and reason for first
migration (GSS, GHS, and ICF Macro 1994). Most recently, the 2014 GDHS asked
respondents how many times in the last 12 months they had been away from home for
one  or  more  nights  and  whether  they  had  been  away  from  home  for  more  than  one
month at a time (GSS, GHS, and DHS Program 2015). These questions have not been
repeated, preventing comparative analyses across GDHS. Furthermore, GDHS
sampling in Ghana excludes girls and women outside 15–49 years of age.
The Ghana Living Standards Survey (GLSS) assesses living conditions in
Ghanaian households using a nationally representative sample. In the household roster,
the 2012–2013 GLSS6 captures region/country of birth (question 11) and how many
months during the past 12 months the person (aged six months and older) has been
away from this household (question 22). The survey also contains a ten-question
module on migration (Section 5A) that collects data such as timing of move/return,
intentions to stay, occupation and industry of migrant labour, and reason for migrating.
The GLSS6 is a valuable source of migration data since this migration data is linked to
detailed individual- and household-level sociodemographic data; however, the ten-
question module is asked only of household members aged seven years or older.
3. Data and methods
3.1 Data
Through the Ghana Statistical Service (GSS), we obtained a 10% random sample for
both the 2000 and 2010 Censuses along with all available questionnaires, manuals,
codebooks, and reports. To assess data quality, we reviewed the post-enumeration
surveys conducted to assess coverage and content errors (GSS 2003, 2012). Three
months after the 2000 Census, the post-enumeration survey sampled 200 out of 26,716
enumeration areas to collect data on eight selected census questions, including place of
usual residence (GSS 2003). The post-enumeration survey data was matched to the
census data and reconciled where necessary. Unfortunately, planning for the 2000 post-
enumeration survey was more effective than its data management; the 2000 post-
enumeration survey data is physically missing, preventing analysis of whether or not
the final census results required adjustment.
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Implementation was greatly improved for the 2010 Census post-enumeration
survey, which sampled 250 out of 37,488 enumeration areas seven months after the
census (GSS 2012). The post-enumeration survey found an omission rate of 3%, the
erroneous  inclusion  of  1.3%  of  the  population  in  the  census,  and  a  greater  chance  of
males (3.3%) being omitted from the census than females (2.8%) (GSS 2012). Based on
the low net coverage error of 1.8% at the national level, it was unnecessary to adjust the
2010 Census results for our analyses. However, some populations, such as migrant
kayayei (female porters who carry loads on their heads at markets and transportation
centres), proved challenging to enumerate in the 2010 Census since they are highly
mobile and occasionally homeless; this population reportedly exceeded estimates and
required additional time to enumerate in Accra (Daily Express 2010). Comparing key
variables between the microdata and censuses reveals that the microdata sample from
the 2010 Census more accurately reflects the complete census than the microdata
sample from 2000, in which the age structure differs slightly (Table 1).
Table 1: Comparison of microdata samples to the 2000 and 2010 Censuses
2000 2010
Census Sample (10.0%) Census Sample (10.0%)
Total population 18,912,079 1,891,158 24,658,823 2,466,289
Sex
     Female 9,554,697 (50.5%) 955,504 (50.5%) 12,633,978 (51.2%) 1,262,598 (51.2%)
     Male 9,357,382 (49.5%) 935,654 (49.5%) 12,024,845 (48.8%) 1,203,691 (48.8%)
Enumeration locality
     Rural 10,637,809 (56.2%) 1,063,732 (56.2%) 49.1% 49.1%
     Urban 8,274,270 (43.8%) 827,426 (43.8%) 50.9% 50.9%
Age structure
     Median age 19.4 19.0 20.0 20.0
     Dependent population† 8,965,233 (47.4%) 880,031 (46.6%) 10,617,930 (43.1%) 1,060,608 (43.0%)
Regional population distribution
     Highest share Ashanti (19.1%) Ashanti (19.1%) Ashanti (19.4%) Ashanti (19.3%)
     Lowest share Upper West (3.0%) Upper West (3.0%) Upper West (2.8%) Upper West (2.9%)
Note: † Respondents aged <15 and >64 years.
The 2000 and 2010 Censuses both included four questions to measure migration.
However, the phrasing of these questions differed (Table 2), affecting cross-census
comparability. Given these changes to the phrasing of migration questions between the
2000 and 2010 Censuses, the 2010 Census National Analytical Report acknowledges
that the census data underestimates people’s actual mobility and does “not provide
enough and adequate information on patterns and differentials of migration in a
country” (GSS 2013c: 205). Several response categories also changed between the 2000
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and 2010 Censuses. Changes to response categories between censuses (e.g., additions,
removals, or changes in definitions), and their analytic implications, are explored in the
results.
Table 2: Criteria for classifying migrants and nonmigrants by Ghana census
questions on migration
2000 Census 2010 Census
Census question Migrantdetermination Nonmigrant Census question
Migrant
determination Nonmigrant
P06a BORN IN Person who is Ghanaian by Person who is P05 BIRTHPLACE: Person enumerated in Person
THIS birth and enumerated in a place Ghanaian by Was (NAME) born a place different from enumerated
TOWN/VILLAGE: different from the place where birth and in this town/village? the place where s/he in the place
Was (NAME) born in s/he was born enumerated in If Yes, go to P07. was born where s/he
this town or village? A NO answer is a lifetime the place A NO answer is a was born
If Yes go to P07. migrant. where s/he migrant. A YES
[Note: Only asked of was born answer is a
respondents who International migrant = person
for whom this answer is missing A YES answer nonmigrant.were Ghanaian by is a
birth.] (implying that they are a foreign
citizen) nonmigrant.
P06b BIRTHPLACE Person who is Ghanaian by – P06 BIRTHPLACE: Person enumerated in –
OUTSIDE THIS birth and enumerated in a place In what region or a place different from
TOWN/VILLAGE: In different from the place where country was the place where s/he
what region or s/he was born (NAME) born? was born
country was (NAME) Internal migrant = person who is Internal migrant =
born? Ghanaian by birth and born in person born in Ghana
[Note: Only asked of one of Ghana’s nine regions outside the place of
respondents who outside the region of enumeration
were Ghanaian by enumeration
birth.] International migrant =
International migrant = person person born outside
who is Ghanaian by birth and Ghana
born outside Ghana All respondents are
All respondents answering are migrants.
lifetime migrants.
P07 USUAL PLACE Person enumerated in a place Person P07 LIVING IN Person who has not Person who
OF RESIDENCE: In different from her/his usual enumerated in THIS lived in the place of has lived in
what district is place of residence her/his usual TOWN/VILLAGE: enumeration for her/his the place of
(NAME’S) usual Internal migrant = person who district of Has (NAME) been entire life enumeration
residence? usually resides in one of residence living in this town A NO answer is a for her/his
Ghana’s districts outside the or village since migrant. entire life
district of enumeration birth? If Yes, go to A YES
P09.
International migrant = person answer is a
who usually resides outside nonmigrant.
Ghana
P08 PLACE OF Person whose place of Person whose P08 NUMBER OF Person who has lived Person who
RESIDENCE FIVE residence at the 2000 Census district of YEARS LIVED IN in the place of has lived in
YEARS AGO IF differs from her/his place of residence at THIS enumeration for a the place of
(NAME) IS FIVE residence in 1995 the 2000 TOWN/VILLAGE: period less than her/his enumeration
YEARS OR OLDER: Internal migrant = person who Census is the For how long has age for her/his
In what district was usually resided in 1995 in one of same as that (NAME) been living entire life
(NAME’S) usual districts outside the district of in 1995 in this town or
place of residence enumeration village?
five years ago?
International migrant = person
who usually resided outside
Ghana in 1995
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Definitions in this paper are consistent with those used by the GSS. “Lifetime
migrants” are people whose residence at the census differs from their birthplace (GSS
2013c), with “birthplace” defined as “the town or village (locality) of usual residence of
the [infant’s] mother at the time of birth” (GSS 1999: 37). “District of usual residence”
refers to the district in which the respondent usually resides and may be the place where
s/he was enumerated; however, in cases where respondents maintain multiple
residences (e.g., students, military personnel), “usual residence” refers to “where the
person spends most of his/her days or time” (GSS 1999: 38). A respondent may also be
considered a “usual resident” if s/he has “lived there for at least six months or has the
intention of staying for the next six months” (GSS 1999: 38).
3.2 Methods
Secondary analyses of the 2000 and 2010 Census microdata were conducted using
SPSS Statistics 22.0 and Microsoft Excel 2011 software. We used direct and indirect
demographic techniques (UNDESA 1970; Moultrie et al. 2013) to describe the scale,
type, and demographic structure (e.g., age, religion, marital status) of contemporary
female migration in Ghana and to assess the distribution of female migrants across age
and geography. We detail these methods and their assumptions in a technical appendix
(Appendix 1).
In order to represent typical age patterns of migration, we fitted a Rogers–Castro
multiexponential model migration schedule to observed female migration data (Rogers
and Castro 1981; Little and Dorrington 2013) (Appendix 1, Section A-1.1). These
schedules, which range from 7 to 13 parameters depending on the model’s complexity,
depict the dependency between age and migration for use in population projections and
in understanding migration dynamics (Little and Dorrington 2013). While not all data
will produce a shape compatible with the multiexponential model migration schedule,
researchers have successfully fitted the schedule to migration flows in North America,
Europe, Asia, and Africa (Little and Dorrington 2013). To examine the effects of
demographic indicators on the likelihood of a girl or woman migrating internally in
2000 and 2010, we conducted logistic regression analyses (Appendix 1, Section A-1.2).
Binary logistic regression modelled the effects of selected independent variables on
whether  or  not  a  girl  or  woman was  identified  in  the  census  as  ever  having migrated
internally. Selection of the independent variables was based on a literature review of
push- and pull-factors of migration. Finally, we generated estimates of net internal
female migration between subnational regions from place of birth data (Dorrington
2013) (Appendix 1, Section A-1.3). While we considered estimates produced using the
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Lattof et al.: Contemporary female migration in Ghana: Analyses of the 2000 and 2010 Censuses
cohort component method (Spoorenberg 2015), our estimates of net internal migration
from place of birth data appear more robust (Appendix 1, Section A-1.4).
4. Results
After first identifying all migrants in the census data, we present analyses of the
demographic structure of internal migrants. We then explore the demographic
characteristics of female migrants, using regression analyses to explain internal
migration status, with “internal migrant” as the dependent variable (yes/no). After
examining who migrates, we analyse their migration destinations. The results conclude
with analyses of interregional migration, including patterns and trends in the geographic
distribution of internal migrants and estimates of interregional female migration
between 2000 and 2010.
4.1 Identification of migrants
Migrants in the 2000 and 2010 Censuses were identified and classified according to the
criteria in Table 2. The 2000 Census microdata identified a total of 359,960 female
internal and international migrants (37.7% of the female population) and 371,577 male
internal and international migrants (39.7% of the male population) (Appendix 2, Table
A-7). In the 2010 microdata, the questions identified 487,376 female internal and
international migrants (38.6% of the female population) and 447,485 male internal and
international migrants (37.2% of the male population).
Of the female migrants identified in the 2010 microdata, international migrants
comprised 3.1% of the sample (15,123). The 2000 Census permitted more refined
identification of international migrants, since it collected data on place of usual
residence at the time of the census and place of usual residence five years prior to the
census. In the 2000 microdata, female migrants can be split into 62,929 international
migrants (13.5%) and 402,146 internal migrants (86.5%). Between 2000 and 2010, the
proportion of lifetime internal migrants increased for both females and males (28.7% to
35.6% and 28.1% to 34.2% respectively). The relative increase in lifetime migration
was greater for females during this period.
At the subnational level, we identified interregional lifetime migration for both
sexes using region of birth and region of residence at enumeration (Tables 3 and 4).
This identification ignores any interim migration and captures only migration between
region of birth and region of residence at enumeration.
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Demographic Research: Volume 39, Article 44
Table 3:  Female population classified by region of birth and region of
enumeration, Ghana, 2000 and 2010
Region Region of enumeration
of birth Western Central Greater Volta Eastern Ashanti Brong Northern Upper Upper Total
Accra Ahafo East West
a) Region of birth by region of enumeration at 2000 Census
Western 642,460 16,760 28,380 2,920 8,000 21,060 5,560 1,880 1,600 1,410 730,030
Central 62,770 676,570 89,760 3,260 29,500 42,480 7,160 2,840 1,000 740 916,080
Greater
Accra 11,700 15,640 809,900 13,850 27,230 17,310 6,220 3,420 2,230 1,420 908,920
Volta 22,260 13,250 125,930 725,740 54,130 23,840 13,520 8,610 780 810 988,870
Eastern 29,300 21,540 162,960 11,400 858,730 37,760 8,970 2,120 1,420 930 1,135,130
Ashanti 44,500 15,970 78,680 5,070 19,850 1,304,400 36,120 7,360 8,830 5,340 1,526,120
Brong
Ahafo 28,420 3,300 16,980 2,130 5,150 35,620 683,910 5,640 2,310 3,390 786,850
Northern 8,870 3,020 23,010 14,910 5,600 31,620 27,290 821,860 4,020 2,660 942,860
Upper
East 19,410 2,550 12,680 960 4,480 42,890 23,720 10,410 422,900 1,440 541,440
Upper
West 12,370 1,890 9,710 810 3,860 22,890 40,210 12,700 2,200 264,120 370,760
Total 882,060 770,490 1,357,990 781,050 1,016,530 1,579,870 852,680 876,840 447,290 282,260 8,847,060
b) Region of birth by region of enumeration at 2010 Census
Western 909,160 30,970 43,610 3,640 11,730 40,980 10,090 1,210 1,600 1,540 1,054,530
Central 71,810 945,810 136,770 4,840 35,330 58,510 8,150 1,880 590 650 1,264,340
Greater
Accra 15,150 43,100 1,188,210 19,930 37,770 25,650 7,480 3,620 2,510 1,480 1,344,900
Volta 23,340 22,980 180,300 1,000,130 63,580 26,720 15,900 8,660 880 710 1,343,200
Eastern 28,610 38,450 245,430 15,380 1,123,500 46,750 10,290 1,830 1,030 1,000 1,512,270
Ashanti 41,350 29,580 125,150 7,230 28,910 2,011,670 44,260 7,620 12,740 5,230 2,313,740
Brong
Ahafo 27,870 7,730 32,930 3,850 8,780 77,220 943,410 6,700 2,550 5,170 1,116,210
Northern 18,190 6,950 49,480 17,280 10,890 61,570 40,740 1,190,720 5,970 3,620 1,405,410
Upper
East 21,250 3,850 20,530 910 6,610 66,430 29,680 9,560 500,400 2,230 661,450
Upper
West 13,370 2,050 9,910 610 4,170 28,600 50,520 11,820 2,770 334,880 458,700
Total 1,170,100 1,131,470 2,032,320 1,073,800 1,331,270 2,444,100 1,160,520 1,243,620 531,040 356,510 12,474,750
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Lattof et al.: Contemporary female migration in Ghana: Analyses of the 2000 and 2010 Censuses
Table 4:  Male population classified by region of birth and region of
enumeration, Ghana, 2000 and 2010
Region Region of enumeration
of birth Western Central Greater Volta Eastern Ashanti Brong Northern Upper Upper Total
Accra Ahafo East West
a) Region of birth by region of enumeration at 2000 Census
Western 613,470 14,430 26,760 2,620 7,390 19,710 5,580 1,750 1,870 1,440 695,020
Central 62,760 593,640 85,470 3,460 25,960 43,890 8,380 3,470 910 520 828,460
Greater
Accra 13,890 15,600 769,250 14,930 27,750 19,980 7,480 3,620 2,480 1,200 876,180
Volta 25,450 13,360 122,100 665,010 52,970 26,210 14,590 9,030 1,090 780 930,590
Eastern 33,250 21,020 151,680 10,780 804,890 39,620 9,700 2,330 1,540 790 1,075,600
Ashanti 48,040 15,600 80,840 4,170 18,940 1,222,970 34,200 7,190 8,850 4,610 1,445,410
Brong
Ahafo 30,760 3,690 17,350 2,210 5,170 35,070 647,860 5,340 2,530 2,600 752,580
Northern 10,710 3,630 23,200 14,170 7,260 35,630 32,400 796,510 3,680 2,510 929,700
Upper
East 23,880 2,890 14,600 1,070 6,230 49,060 29,090 8,390 372,130 1,040 508,380
Upper
West 13,780 1,940 8,700 1,060 5,310 27,470 49,760 12,530 2,090 242,230 364,870
Total 875,990 685,800 1,299,950 719,480 961,870 1,519,610 839,040 850,160 397,170 257,720 8,406,790
b) Region of birth by region of enumeration at 2010 Census
Western 874,870 25,780 38,060 2,790 10,360 37,300 11,550 1,070 1,730 1,640 1,005,150
Central 72,240 850,070 117,280 4,790 31,750 54,310 9,030 1,880 800 810 1,142,960
Greater
Accra 20,080 41,520 1,137,810 20,680 36,550 27,510 9,220 3,800 3,370 1,700 1,302,240
Volta 27,770 25,350 164,370 922,570 63,920 31,140 18,380 8,050 1,240 700 1,263,490
Eastern 34,700 37,390 211,150 14,320 1,071,690 46,210 11,210 2,130 1,600 910 1,431,310
Ashanti 50,080 31,680 123,980 6,700 27,270 1,868,170 47,390 7,400 12,710 5,840 2,181,220
Brong
Ahafo 32,480 9,420 29,570 3,330 9,300 66,940 895,440 6,250 2,480 4,430 1,059,640
Northern 21,890 7,840 45,020 16,990 13,680 61,050 47,070 1,172,660 5,250 4,200 1,395,650
Upper
East 26,540 5,250 20,180 910 7,460 65,630 33,050 7,150 471,290 1,610 639,070
Upper
West 14,880 2,650 7,240 680 6,190 27,940 55,620 10,430 1,820 315,410 442,860
Total 1,175,530 1,036,950 1,894,660 993,760 1,278,170 2,286,200 1,137,960 1,220,820 502,290 337,250 11,863,590
Figures 2 and 3 condense these migration streams by sex into noncumulative,
stacked column charts that compare the totals (i.e., net lifetime migration) and their
shares (i.e., lifetime out-migrants, lifetime in-migrants) (Appendix 2, Tables A-8 and
A-9). Four regions experienced population gains in net lifetime migration streams by
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Demographic Research: Volume 39, Article 44
both sexes in 2000 and 2010: Greater Accra, Western, Ashanti, and Brong Ahafo. The
remaining six regions experienced net losses by both sexes in 2000 and 2010.
Figure 2:  Lifetime female migration streams, Ghana, 2000 (blue) and
2010 (red)
3,000,000
2,500,000
2,000,000
1,500,000 Net lifetime migration, 2010
Lifetime out-migrants, 2010
1,000,000
Lifetime in-migrants, 2010
500,000 Net lifetime migration, 2000
0 Lifetime out-migrants, 2000
Lifetime in-migrants, 2000
-500,000
Region of origin and destination
Figure 3:  Lifetime male migration streams, Ghana, 2000 (blue) and 2010 (red)
3,000,000
2,500,000
2,000,000
1,500,000 Net lifetime migration, 2010
Lifetime out-migrants, 2010
1,000,000
Lifetime in-migrants, 2010
500,000 Net lifetime migration, 2000
0 Lifetime out-migrants, 2000
Lifetime in-migrants, 2000
-500,000
Region of origin and destination
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Number of migrants Number of migrants
Lattof et al.: Contemporary female migration in Ghana: Analyses of the 2000 and 2010 Censuses
4.2 Demographic structure of internal migrants
Disaggregating internal migrants by age and sex highlights changes between groups and
over time. Though Ghanaians migrate at all ages, the mean age of internal migrants
increased over time. From 2000 to 2010, the mean age of female internal migrants rose
from 27.39 years (s.d. 18.86) to 29.71 years (s.d. 18.69). Males showed a similar trend,
with the mean age of internal migrants increasing from 28.48 years (s.d. 19.57) to 29.71
years (s.d. 18.62) between 2000 and 2010.
Examining the distribution of migrants and nonmigrants by five-year age groups
indicates growing relative migration between 2000 and 2010. In 2000, female
nonmigrants outweighed female migrants in each five-year age group (Figure 4, top).
By 2010, the percentage of female migrants overtook female nonmigrants among
women aged between 25 and 49 years (Figure 4, bottom). For males in 2000,
nonmigrants comprised a greater percentage of each age group than migrants, with the
exception of the age group 45–49 years (Figure 5, top). By 2010, male migrants
outweighed male nonmigrants among men aged between 30 and 59 years (Figure 5,
bottom). Working-age migration was particularly pronounced in 2010 for both men and
women.
The age-related distribution of female and male regional out-migrants was
assessed in greater detail using multiexponential model migration schedules (Figure 6)
for age cohorts x‒5 to x over the period 1995–2000. Since retirement was not
concentrated among specific ages in this data and the data may exaggerate older ages
(Little and Dorrington 2013), the standard 7-parameter model fitted the observed data
better than the more complex 9-, 11-, or 13-parameter models, which account for more
complex components such as retirement peaks and post-retirement up-slopes. The mean
absolute percentage error statistic, 7% for both sexes, is within the boundaries for
achieving a reasonable fit. The R-squared values for males (92%) and females (89%)
are acceptable compared to the established threshold of 90%, indicating that the models
reasonably fit the data (Little and Dorrington 2013). T-statistics are significant at the
0.05 level for all coefficients. For both sexes, the rate of ascent of the labour force
component is greater than the rate of this component’s descent. Female migration
propensity rises sharply from the age of 10, peaking at 0.09097 at the age of 23 years.
Male migration propensity peaks several years later at 0.10204 at the age of 27 years.
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Demographic Research: Volume 39, Article 44
Figure 4:  Female population pyramid by migrant status, 2000 Census (top)
and 2010 Census (bottom)
95—99 % Internal migrants
90—94
% Nonmigrants
85—89
80—84
75—79
70—74
65—69
60—64
55—59
50—54
45—49
40—44
35—39
30—34
25—29
20—24
15—19
10—14
5—9
0—4
0—4
5—9
10—14
15—19
20—24
25—29
30—34
35—39
40—44
45—49
50—54
55—59
60—64
65—69
70—74
75—79
80—84
85—89
90—94
95—99
14 12 10 8 6 4 2 0 2 4 6 8 10 12 14
Per cent
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Age group (in years)
Lattof et al.: Contemporary female migration in Ghana: Analyses of the 2000 and 2010 Censuses
Figure 5:  Male population pyramid by migrant status, 2000 Census (top)
and 2010 Census (bottom)
95—99 % Internal migrants
90—94
% Nonmigrants
85—89
80—84
75—79
70—74
65—69
60—64
55—59
50—54
45—49
40—44
35—39
30—34
25—29
20—24
15—19
10—14
5—9
0—4
0—4
5—9
10—14
15—19
20—24
25—29
30—34
35—39
40—44
45—49
50—54
55—59
60—64
65—69
70—74
75—79
80—84
85—89
90—94
95—99
14 12 10 8 6 4 2 0 2 4 6 8 10 12 14
Per cent
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Age group (in years)
Demographic Research: Volume 39, Article 44
Figure 6: Regional out-migration by sex over the five-year interval, 1995–2000,
and fitted with a 7-parameter model schedule, Ghana, 2000 Census
10% microdata
0.12 Female obs
Female fit
0.1 Male obs
Male fit
0.08
0.06
0.04
0.02
0
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75
Age (in years)
After identifying all female internal migrants in the microdata and examining
migrant status by sex and age, we analysed the effects of demographic indicators on the
likelihood of a girl or woman being identified as an internal migrant (Table 5).
International migrants are excluded from these regression analyses. Age, in five-year
age groups, and education status were nonsignificant predictors. These variables are
excluded from the final models for 2000 and 2010, as they worsened or did not
significantly improve the models’ ability to predict internal migrant status. The model
for 2000 accurately predicts 63.5% of cases, predicting nonmigrants (85.1%) better than
internal migrants (29.7%). The 2010 model improves the accuracy of predicting internal
migrants (51.1%). It accurately predicts 65.7% of cases, including 75.5% of
nonmigrants. Difficulties in accurately determining migrant status based on census data
are likely to affect the models’ predictive abilities. Although both models have low R-
squared values, they also have statistically significant predictors that can be used to
draw conclusions about migrant status.
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Migration probability
Lattof et al.: Contemporary female migration in Ghana: Analyses of the 2000 and 2010 Censuses
Table 5:  Regression results explaining female internal migration status in
Ghana, 2000 and 2010 Census microdata, with internal migrant as
the dependent variable
2000 2010
Demographic characteristics
(Independent variables) Odds
ratio Std. Error 95% C.I.
Odds
ratio Std. Error 95% C.I.
Rural Ref – – Ref – –
Residence
Urban 1.377 0.006 1.362–1.393 1.602 0.004 1.589–1.616
Never married Ref – – Ref – –
Marital status
Married 0.999 0.009 0.982–1.017 0.981 0.007 0.967–0.994
Consensual union† 0.937 0.013 0.914–0.960 1.000 0.011 0.979–1.022
Separated 0.902 0.021 0.866–0.940 0.834 0.016 0.809–0.860
Divorced 0.758 0.014 0.737–0.780 0.827 0.012 0.808–0.847
Widowed 0.775 0.014 0.755–0.796 0.804 0.010 0.788–0.821
Worked for pay, Did not work Ref – – Ref – –
profit, or family gain Worked 1.117 0.006 1.104–1.130 1.097 0.005 1.086–1.107
Head Ref – – Ref – –
Relationship to
head of household Nonrelative 1.952 0.018 1.886–2.021 2.091 0.009 2.024–2.161
Temporary head‡ 1.355 0.018 1.309–1.403 – – –
Group quarters§ 4.468 0.074 3.861–5.169 1.320 0.015 1.283–1.358
Spouse 1.401 0.010 1.375–1.428 1.271 0.007 1.252–1.289
Child 0.519 0.011 0.508–0.529 0.356 0.008 0.350–0.361
Parent or parent-in-law 1.190 0.021 1.142–1.241 1.017 0.016 0.986–1.049
Daughter-in-law 1.055 0.022 1.010–1.102 0.758 0.020 0.729–0.789
Grandchild 0.397 0.019 0.382–0.412 0.294 0.012 0.287–0.300
Sister‡ – – – 0.787 0.011 0.769–0.804
Stepchild‡ – – – 0.547 0.025 0.521–0.574
Adopted/ foster child‡ – – – 0.724 0.031 0.681–0.769
Other relative 1.156 0.010 1.134–1.178 0.914 0.009 0.898–0.930
No religion Ref – – Ref – –
Religion
Catholic 0.918 0.014 0.893–0.944 1.178 0.012 1.150–1.206
Protestant 1.019 0.014 0.991–1.046 1.277 0.012 1.248–1.307
Pentecostal¶ 1.154 0.014 1.124–1.185 1.561 0.011 1.527–1.597
Other Christian 1.033 0.015 1.003–1.063 1.294 0.012 1.263–1.326
Muslim 0.616 0.015 0.598–0.634 0.758 0.012 0.740–0.776
Ahmadi‡ – – – 1.118 0.029 1.057–1.182
Traditional 0.397 0.017 0.384–0.410 0.516 0.015 0.501–0.532
Other 1.158 0.034 1.082–1.239 1.285 0.025 1.223–1.350
Cox & Snell R2 0.067 0.105
Nagelkerke R2 0.090 0.142
Notes: † In 2010 this category included informal unions and living together. ‡ This response category is included in only one census.
§ Group quarters included members of nonhousehold populations (e.g., nurses working the night shift) and referred to places such
as hotels, orphanages, universities, prisons, and hospitals. ¶ In 2010 the category Pentecostal included respondents who identified
as Charismatic.
Being a female migrant is significantly associated with residing in an urban area,
indicating the prominence of rural–urban migration. Residing at a residence where
relationship to the household head is group quarters, nonrelative, temporary head,
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Demographic Research: Volume 39, Article 44
spouse, or parent/parent-in-law also increases a census respondent’s odds of being
identified as an internal migrant. Female migrants are more likely than nonmigrants to
report working for pay, profit, or family gain, suggesting that economic opportunity is a
likely driver of migration. By 2010, female migrants are likelier to have never married
than be married. Female census respondents are substantially less likely to be identified
as internal migrants in 2000 and 2010 if they practise a traditional religion or Islam and
if they are the children of the household head.
4.3 Interregional female migration
Key features of Ghanaian female internal migration include the high concentration of
intraregional migration within all regions and out-migration from the Upper East, Upper
West, Northern, Volta, and Central Regions, with no significant in-migration. The
Greater Accra Region exhibited significant in-migration from all but three regions
(Upper West, Upper East, and Brong Ahafo).
The importance of the Greater Accra and Ashanti Regions as internal migration
destinations is further underscored by examination of interregional female migration
streams between 1995 and 2000. Using five-year fixed-interval data from the 2000
Census, we calculated interregional female migration streams between 1995 and 2000
in Ghana in the population aged five years and older. Table 6 depicts destination-
specific out-migration rates for each of Ghana’s regions, producing a five-year
migration rate for females who survived the period 1995–2000. Three of the five
highest migration rates are among females migrating to Greater Accra from the Volta
(0.0180), Eastern (0.0172), and Central Regions (0.0138). The highest rate is among
females in the Western Region migrating to the Central Region (0.0218). The highest
rates of migrants to the Ashanti Region are among females migrating from the Upper
East (0.0129) and Brong Ahafo (0.0119) Regions.
Regional estimates of the net number of interregional female in-migrants from
2000 to 2010 (Appendix 2, Table A-10) show that Greater Accra received the largest
number of female migrants among all age groups. Of Ghana’s estimated 804,365 total
female in-migrants (Table 7), nearly half (43.56%) migrated into Greater Accra, with
the Ashanti Region, home to Ghana’s second largest city, receiving 22.47% of female
in-migrants. The lowest levels of in-migrants are in northern Ghana, with a net number
of 662 girls and women migrating into the Northern Region (0.08%) and 6,823
migrating into the Upper East Region (0.85%). Negative numbers in Table A-10
indicate negative net in-migration. The Upper West is the only region to experience
overall net negative in-migration. Net in-migration in the Upper West Region for 2000
and 2010 is positive only among girls aged 0–4 years.
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Lattof et al.: Contemporary female migration in Ghana: Analyses of the 2000 and 2010 Censuses
Table 6:  Female interregional migration rates in 2000 as proportions of
survivors of the 1995 population, female population aged five years
and older
Region of Region of residence at census, 2000
residence,
1995 Western Central
Greater
Accra Volta Eastern Ashanti
Brong
Ahafo Northern
Upper Upper
East West Total
Western – 0.0218 0.0067 0.0025 0.0041 0.0099 0.0037 0.0007 0.0019 0.0023 0.0537
Central 0.0098 – 0.0138 0.0016 0.0052 0.0065 0.0012 0.0005 0.0005 0.0002 0.0394
Greater 0.0038 0.0080 – 0.0170 0.0086 0.0043 0.0014 0.0009 0.0011 0.0040 0.0490
Accra
Volta 0.0032 0.0028 0.0180 – 0.0081 0.0029 0.0015 0.0014 0.0006 0.0005 0.0390
Eastern 0.0032 0.0043 0.0172 0.0046 – 0.0066 0.0016 0.0005 0.0006 0.0008 0.0394
Ashanti 0.0058 0.0033 0.0072 0.0016 0.0036 – 0.0085 0.0012 0.0017 0.0062 0.0391
Brong Ahafo 0.0053 0.0015 0.0042 0.0016 0.0022 0.0119 – 0.0037 0.0023 0.0037 0.0365
Northern 0.0018 0.0007 0.0046 0.0028 0.0017 0.0058 0.0044 – 0.0018 0.0015 0.0251
Upper East 0.0079 0.0020 0.0043 0.0011 0.0021 0.0129 0.0055 0.0041 – 0.0008 0.0408
Upper West 0.0077 0.0008 0.0043 0.0008 0.0016 0.0092 0.0128 0.0058 0.0010 – 0.0441
Note: Interregional migration rates over 0.0100 are emphasized in bold.
Table 7:  Estimates of overall net female out-migrants, in-migrants, and
migration streams, Ghana, 2000 to 2010
Net in-migrants Net out-migrants Overall netRegion of origin and migration
destination Total    % Total    %
Western 42,208 5.25 55,919 6.83 –13,711
Central 91,774 11.41 107,894 13.19 –16,121
Greater Accra 350,391 43.56 50,179 6.13 300,213
Volta 8,186 1.02 109,747 13.41 –101,561
Eastern 70,757 8.80 141,887 17.34 –71,130
Ashanti 180,774 22.47 79,344 9.70 101,431
Brong Ahafo 64,635 8.04 79,573 9.73 –14,939
Northern 662 0.08 109,747 13.41 –109,085
Upper East 6,823 0.85 54,035 6.60 –47,212
Upper West –11,844 –1.47 29,890 3.65 –41,734
Total 804,365 100 818,215 100 –13,849
Regional estimates of the net number of female out-migrants (Appendix 2, Table
A-11) show that the net out-migration was highest in the Eastern Region. Of Ghana’s
818,215 total female out-migrants (Table 7), 17.34% migrated from the Eastern Region,
followed by the Northern and Volta Regions (13.41% each). Net out-migration was
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Demographic Research: Volume 39, Article 44
smallest in the Upper West Region with 29,890 female out-migrants (3.65%), followed
by Greater Accra with 50,179 female out-migrants (6.13%).
Negative numbers in Table A-11, such as among girls aged 5–14 years in the
Upper West Region, indicate negative net out-migration. Among young girls in the
Volta, Upper East, and Upper West Regions, the negative out-migration suggests that
these children are likely to be returning home with a mother or father who was working
outside the region. Among women aged 55 years and older in the Greater Accra,
Western, Northern, Upper East, and Upper West Regions, negative out-migration
suggests return migration of retiring workers.
Combining estimates of net in-migration and net out-migration reveals that net
out-migration exceeds net in-migration in eight of Ghana’s ten regions. Only the
Greater Accra and Ashanti Regions have positive net overall migration (Table 7). By
contrast, overall net migration is lowest in the Northern and Volta Regions, with more
girls and women moving out of the regions than moving into them.
5. Discussion
Our analyses reveal that the overwhelming focus of previous research on male internal
migrants is misplaced. Internal migration in Ghana involves both sexes and warrants
greater attention to sex-disaggregated analyses. Our analyses reveal that recent
migration in Ghana is sex-balanced, according to the 47%–53% typology put forward
by Donato and Gabaccia (2015). Ghanaian girls and women migrate at all ages, and
approximately 40%–50% of these migrants are within age groups excluded from
noncensus sources of national migration data (e.g., GDHS). Working-age migration is a
key feature of migration for both sexes, peaking at earlier ages for females than males.
Being a female migrant is significantly associated with residing in an urban area and
working for pay, profit, or family gain. These findings suggest that economic
opportunity is an important driver of female migration.
Advancing our understanding of gender and migration requires paying greater
attention to examining differences between the migration patterns of women and men.
The historical narrative of the “passive” female migrant has no place in today’s
evidence. The regression results indicate increased mobility and independence among
female migrants, as reflected in their living situations. Female migrants exhibit greater
odds of residing in group quarters, in a household where they are the temporary head of
household, or in a household with a nonrelative head of the household. Moving with a
spouse is no longer a precursor to female migration. By 2010, married women were less
likely to migrate than peers who had never married.
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Lattof et al.: Contemporary female migration in Ghana: Analyses of the 2000 and 2010 Censuses
Only the Greater Accra and Ashanti Regions, home to Ghana’s two largest cities,
have positive net overall migration. With net out-migration exceeding net in-migration
in eight of Ghana’s ten regions, productive female labour losses may have a negative
impact on local development efforts and local economies. The prominence of the
Greater Accra and Ashanti Regions as destinations for female migrants suggests that
interventions are needed in Ghana’s more rural regions to reduce poverty and develop
greater economic opportunities for girls and women.
Ghana’s kayayei have become a visible sign of changing internal migration
patterns. This growing population represents the face of female north–south, rural–
urban migration in Ghana, with most migrant female youth becoming porters on arrival
in Accra (Kwankye and Addoquaye Tagoe 2009). Though kayayei exist in Ghana’s
second and third largest cities, Kumasi and Tamale, their presence in the capital has
generated particular policy concerns (Parliament 2016). There is no accurate and
reliable data on the number of kayayei; estimates range from 2,300 to 160,000 in Accra
(Kearney 2013; Parliament 2016). Such variation in the estimates reveals a need for
improved data on and reporting of female internal migration if policymakers are to
address development-related issues in the sending and receiving communities.
Our analyses highlight the valuable information that census data provides on
migration’s demographic structure, patterns, and trends. Recent collaborations between
the GSS and the International Organization for Migration suggest that future data
collection activities in Ghana will pay greater attention to migration; however, existing
census data presents an incomplete picture of contemporary female migration. Resource
constraints in census offices, the expense of implementing a census, the balance of
interests among census committee members, and political priorities frequently limit the
number of migration questions in census questionnaires. Censuses also fail to capture
migrants’ underlying motivations and migration experiences.
Census analyses reveal a need for researchers to bring a gendered lens to issues
such as drivers of migration, impacts of migration, and links between migration and
health. Census data reveals nothing about migrants’ and nonmigrants’ opportunities or
their perceptions of the costs and gains of migration. Breastfeeding infants may migrate
with their mothers out of necessity, and girls from large families may be fostered out to
aunts or other relatives. Preadolescent girls may independently decide to migrate in
search  of  ways  to  pay  their  school  fees.  Censuses  also  miss  the  social  and  economic
contributions that migrants make to their families and communities. Too often the lack
of data on female migrants’ contributions reinforces the outdated stereotype that girls
and women take passive roles in migration. Ad hoc subnational surveys and more
detailed interviews can address these aspects of migration in greater depth,
complementing national-level census analyses and presenting a completer picture of
contemporary migration.
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Demographic Research: Volume 39, Article 44
The 2000 and 2010 Censuses have several limitations. Since the post-enumeration
survey data collected after the 2000 Census is unavailable, it is impossible to assess the
quality of the 2000 Census and whether the results required adjustment. Furthermore,
the microdata from the 2000 Census is less representative of the national population
than the microdata from the 2010 Census. While the post-enumeration survey
conducted after the 2010 Census revealed no need to adjust the final results, the 2010
Census reportedly struggled to enumerate highly mobile populations like the kayayei
(Daily Express 2010). It is possible that such migrant groups may be underrepresented,
particularly if enumerators attempted to enumerate them during working hours or were
unprepared to capture mobile populations’ large numbers. Additional data limitations
include possible reference period error for the question asking about place of residence
five years prior, potential uncertainty about exact geographic boundaries, and problems
reporting age.
One particular conceptual challenge is that the census questionnaires’
understanding and measuring of migration do not capture contemporary migration
patterns identified via other sources of migration data. Most movements between place
of birth and current residence are missing. The censuses fail to capture cyclical and
short-term migrations, which are commonplace in Ghana, as well as seasonal or repeat
migrations and migration histories. The censuses also struggle to capture migration
duration and meaningful data on intraregional migration, which is more common than
interregional migration. These challenges have implications for the types of migrants
and migrations that are identified and included in national analyses. Identifying these
types of migration patterns in the census would significantly strengthen the predictive
ability of regression models examining determinants of migration, as well as sex-
specific differences between migrants.
The analyses conducted in this study provide a rich source of information on
female migration across the lifespan that complements subnational migration studies
and  may  have  relevance  in  other  low-  and  middle-income  countries.  Addressing  the
measurement and impact of female migration is an issue of importance for researchers,
policymakers, and nongovernmental organizations working in the development sector.
In order to better meet the varied needs of female migrants of all ages and to plan for
changing population distribution within Ghana, we would make the following
recommendations:
∂ Data collection and analyses of female migration cannot afford to exclude
migrants outside 15–49 years of age. Female migrants have unique age-
specific  needs,  such  as  integrating  into  a  new  school  or  ensuring  that
appropriate support systems exist to assist with challenges brought on by
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Lattof et al.: Contemporary female migration in Ghana: Analyses of the 2000 and 2010 Censuses
ageing. Data is needed on female migrants of all ages, not just those of
reproductive or working age.
∂ While multiple surveys measure migration at the national level, the questions
they use infrequently permit comparative analyses across time or across
surveys. Standardizing questions on migration would allow for more
comprehensive analyses of national trends.
∂ Survey questions on migration should expand upon basic demographic data
to include migrants’ underlying motivations, migration experiences, and
economic contributions.
∂ Net out-migration in the Volta Region and northern Ghana (Upper West,
Upper East, and Northern Regions) may negatively affect local economies
and local development efforts. Policymakers concerned about the impact of
this productive female labour loss should consider focused interventions in
these rural regions to reduce poverty and develop greater economic
opportunities for girls and women.
Ultimately, female migration is a dynamic process with inextricable links to
development, affecting factors such as the development of communities, the delivery of
social services, and the impact of remittances. Should current trends continue, female
migration within Africa will rise, particularly to regions offering economic
opportunities. The planning of development programmes requires far better data
sources than those currently existing, as well as greater attention to analyses using a
gendered lens.
6. Acknowledgements
We wish to thank Professor Rob Dorrington (University of Cape Town) for providing
technical assistance on fitting the multiexponential model migration curve and
Professor Allan G. Hill (University of Southampton) for his comments on an earlier
version of this paper. We also thank the London School of Economics and Political
Science for funding this research. Working drafts of this paper were presented in
September 2017 at the annual conference of the British Society for Population Studies
in Liverpool, United Kingdom, and in October 2016 at an international Demography
and Gender workshop organized by Le Centre de recherches de l’Institut de
Démographie de l’Université Paris 1 in Paris, France.
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References
Addoquaye Tagoe, C. and Kwankye, S.O. (2009). Returning home and re-integrating as
an independent child migrant in Ghana. In: Anarfi, J.K. and Kwankye, S.O.
(eds.). Independent migration of children in Ghana. Accra: Sundel Services:
206–247.
Agyei, J. and Ofosu-Mensah Ababio, E. (2009). Historical overview of internal
migration in Ghana. In: Anarfi, J.K. and Kwankye, S.O. (eds.). Independent
migration of children in Ghana. Accra: Sundel Services: 9–44.
Anarfi, J.K. and Agyei, J. (2009). To move or not to move: The decision-making
process of child migrants from northern to southern Ghana. In: Anarfi, J.K. and
Kwankye, S.O. (eds.). Independent migration of children in Ghana. Accra:
Sundel Services: 101–131.
Anarfi, J.K. and Appiah, M. (2009). The phenomenon of independent child migration in
Ghana in the context of a globalised world. In: Anarfi, J.K. and Kwankye, S.O.
(eds.). Independent migration of children in Ghana. Accra: Sundel Services: 45–
70.
Anarfi, J.K. and Kwankye, S.O. (2009). Independent migration of children in Ghana.
Legon: Sundel Services.
Awumbila, M. and Ardayfio-Schandorf, E. (2008). Gendered poverty, migration and
livelihood strategies of female porters in Accra, Ghana. Norsk Geografisk
Tidsskrift – Norwegian Journal of Geography 62(3): 171–179. doi:10.1080/
00291950802335772.
Beegle, K. and Poulin, M. (2013). Migration and the transition to adulthood in
contemporary Malawi. Annals of the American Academy of Political and Social
Science 648(1): 38–51. doi:10.1177/0002716213481329.
Caldwell, J.C. (1969). African rural–urban migration: The movement to Ghana’s
towns. London: C. Hurst.
Camlin, C.S., Snow, R.C., and Hosegood, V. (2014). Gendered patterns of migration in
rural South Africa. Population Space and Place 20(6): 528–551. doi:10.1002/
psp.1794.
Contreras, R. and Griffith, D. (2012). Managing migration, managing motherhood: The
moral economy of gendered migration. International Migration 50(4): 51–66.
doi:10.1111/j.1468-2435.2012.00771.x.
http://www.demographic-research.org 1207
Lattof et al.: Contemporary female migration in Ghana: Analyses of the 2000 and 2010 Censuses
Daily Express (September 28, 2010). ‘Kayayei’ numbers higher than anticipated. Daily
Express. http://statsghana.gov.gh/docfiles/news/Kayayei.pdf.
Donato, K.M. and Gabaccia, D. (2015). Gender and international migration. New
York: Russell Sage Foundation.
Dorrington, R. (2013). Estimation of migration from census data. In: Moultrie, T.,
Dorrington, R.E., Hill, A.G., Hill, K., Timæus, I., and Zaba, B. (eds.). Tools for
demographic estimation. Paris: IUSSP: 376–389.
Elias, J. (2010). Making migrant domestic work visible: The rights based approach to
migration and the ‘challenges of social reproduction.’ Review of International
Political Economy 17(5): 840–859. doi:10.1080/09692290903573872.
Findley, S.E. (1989). Les migrations féminines dans les villes africaines: Une revue de
leurs motivations et expériences. In. Antoine, P. and Coulibaly, S. (eds.).
L’insertion urbaine des migrants en Afrique. Paris: ORSTOM: 55–70.
Ford, K. and Hosegood, V. (2005). Aids mortality and the mobility of children in
Kwazulu Natal, South Africa. Demography 42(4): 757–768. doi:10.1353/dem.
2005.0029.
Frempong-Ainguah, F., Badasu D.M., and Codjoe, S.N.A. (2009). North-South
independent child migration in Ghana: The push and pull factors. In: Anarfi, J.K.
and Kwankye, S.O. (eds.). Independent migration of children in Ghana. Accra:
Sundel Services: 71–100.
GSS (1999). Government of Ghana 2000 Population and Housing Census:
Enumerator’s manual. Accra: Ghana Statistical Service.
GSS (2003). 2000 Population and Housing Census administrative report. Accra: Ghana
Statistical Service.
GSS (2012). 2010 Population and Housing Census post enumeration survey report.
Accra: Ghana Statistical Service.
GSS (2013a). Children, adolescents and young people. In: 2010 Population and
Housing Census. Accra: Ghana Statistical Service.
GSS (2013b). 2010 Population and Housing Census: Regional analytical report:
Greater Accra Region. Accra: Ghana Statistical Service.
GSS (2013c). 2010 Population and Housing Census: National analytical report. Accra:
Statistical Service.
1208 http://www.demographic-research.org
Demographic Research: Volume 39, Article 44
GSS (2014). 2010 Population and Housing Census report: Fertility. Accra: Ghana
Statistical Service.
GSS, GHS, and DHS Program (2015). Ghana Demographic and Health Survey 2014.
Accra: Ghana Statistical Service, Ghana Health Service, and the DHS Program.
GSS, GHS, and ICF Macro (1994). Ghana Demographic and Health Survey 1993.
Accra: Ghana Statistical Service, Ghana Health Service, and ICF Macro.
GSS, GHS, and ICF Macro (2009). Ghana Demographic and Health Survey 2008.
Calverton: Ghana Statistical Service, Ghana Health Service, and ICF Macro.
GSS and IRD (1989). Ghana Demographic and Health Survey 1988. Accra: Ghana
Statistical Service and Institute for Resource Development.
GSS and Macro International (1999). Ghana Demographic and Health Survey 1998.
Accra: Ghana Statistical Service and Macro International.
GSS, NMIMR, and ICF Macro (2004). Ghana Demographic and Health Survey 2003.
Calverton: Ghana Statistical Service, Noguchi Memorial Institute for Medical
Research, and IFC Macro.
Guo, M., Chow, N.W.S., and Palinkas, L.A. (2011). Circular migration and life course
of female domestic workers in Beijing. Asian Population Studies 7(1): 51–67.
doi:10.1080/17441730.2011.544905.
Hofmann, E.T. and Buckley, C.J. (2012). Cultural responses to changing gender
patterns of migration in Georgia. International Migration 50(5): 77–94.
doi:10.1111/j.1468-2435.2010.00681.x.
Kearney, H. (November 22, 2013). Ghana’s female porters get family planning advice.
The Guardian. https://www.theguardian.com/global-development-professionals-
network/2013/nov/22/ghanas-female-porters-get-family-planning-advice-case-
studies.
Kwankye, S.O. and Addoquaye Tagoe, C. (2009). City life outside the home: The
experiences of independent child migrants in Ghana. In: Anarfi, J.K. and
Kwankye, S.O. (eds.). Independent migration of children in Ghana. Accra:
Sundel Services: 132–170.
Little, J. and Dorrington, R. (2013). The multi-exponential model migration schedule.
In: Moultrie, T., Dorrington, R.E., Hill, A.G., Hill, K., Timæus, I., and Zaba, B.
(eds.). Tools for demographic estimation. Paris: IUSSP: 390–402.
http://www.demographic-research.org 1209
Lattof et al.: Contemporary female migration in Ghana: Analyses of the 2000 and 2010 Censuses
Llácer, A., Zunzunegui, M.V., del Amo, J., Mazarrasa, L., and Bolůmar, F. (2007). The
contribution of a gender perspective to the understanding of migrants’ health.
Journal of Epidemiology and Community Health 61(S2): ii4–ii10. doi:10.1136/
jech.2007.061770.
Moultrie, T., Dorrington, R.E., Hill, A.G., Hill, K., Timæus I.M., and Zaba, B. (2013).
Tools for demographic estimation. Paris: IUSSP.
Parliament of the Republic of Ghana (2016). Parliamentary debates: Official report.
Accra: Parliament House.
Quartey,  P.  and  Yambilla,  E.  (2009).  The  costs  and  benefits  of  child  migration  in
Ghana: The case of child migrants from Northern Ghana. In: Anarfi, J.K. and
Kwankye, S.O. (eds.). Independent migration of children in Ghana. Accra:
Sundel Services: 248–291.
Resurreccion, B.P. (2009). Female migration and social reproduction in the Mekong
region. Asian and Pacific Migration Journal 18(1): 101–122. doi:10.1177/
011719680901800105.
Rogers, A. and Castro, L.J. (1981). Model migration schedules. Laxenburg:
International Institute for Applied Systems Analysis.
Spoorenberg, T. (2015). Population estimation: Regional workshop on the production
of population estimates and demographic indicators. Addis Ababa: United
Nations, Department of Economic and Social Affairs, Population Division.
Thiel, A. and Stasik, M. (2016). Market men and station women: Changing
significations of gendered space in Accra, Ghana. Journal of Contemporary
African Studies 34(4): 459–478. doi:10.1080/02589001.2017.1281385.
Toma, S. and Vause, S. (2014). Gender differences in the role of migrant networks:
Comparing Congolese and Senegalese migration flows. International Migration
Review 48(4): 972–997. doi:10.1111/imre.12150.
Tufuor, T., Niehof, A., Sato, C., and van der Horst, H. (2015). Extending the moral
economy beyond households: Gendered livelihood strategies of single migrant
women in Accra, Ghana. Women’s Studies International Forum 50: 20–29.
doi:10.1016/j.wsif.2015.02.009.
Twum-Baah, K.A., Nabila, J.S., and Aryee, A.F. (1995). Migration research study in
Ghana: Volume 1: Internal migration. Accra: Ghana Statistical Service.
UNDESA (1970). Manual VI: Methods of measuring internal migration.  New  York:
United Nations.
1210 http://www.demographic-research.org
Demographic Research: Volume 39, Article 44
UNDESA (2014). World urbanization prospects: The 2014 revision, highlights. New
York: United Nations.
WHO (2018). Global Health Observatory data repository: Life tables by country:
Ghana. Geneva: World Health Organization.
Yeboah, I., Dodoo, F., Kwankye, S., Nyarko, P., Badasu, D., and Biavaschi, C. (2010).
Development on the move: Measuring and optimising migration’s economic and
social impacts. Legon: Regional Institute for Population Studies (RIPS) at the
University of Ghana and Miami University.
Zlotnik, H. (1995). Migration and the family: the female perspective. Asian and Pacific
Migration Journal 4(2–3): 253–271. doi:10.1177/011719689500400205.
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Appendix 1: Demographic methods
This technical appendix justifies the methods we used to analyse female migration. It
also details the assumptions, applications, and limitations of these methods.
A-1.1 Rogers–Castro multiexponential model migration schedule
Following the instructions detailed in Tools for Demographic Estimation,  we  fitted  a
Rogers–Castro multiexponential model migration schedule to observed migration data
in order to represent typical age patterns of migration (Rogers and Castro 1981; Little
and Dorrington 2013). These migration schedules range from 7 to 13 parameters,
depending on the model’s complexity, and depict the dependency between age and
migration (Little and Dorrington 2013). Checking the “shape” or age distribution of
migrant flows by fitting a model migration schedule also permitted us to check our
estimates of net internal female migration in Section A-3.
Before applying this method, we obtained migration rates for single ages,
examined the population’s age structure, and examined the relative completeness of the
census counts. We assumed that (1) the census accurately counted the population by
subnational region and place of birth and (2) the census identified people who moved
from one region to another in the time period of interest (1995–2000).
The  first  step  in  applying  this  method  is  to  prepare  a  schedule  of  the  observed
rates. We used census data that gave the numbers of migrants who survived the five-
year migration interval 1995–2000. From this data, it is possible to calculate one-year
age propensities by backcasting census respondents to the region where they reported
living in 1995. The age-specific out-migration propensity is calculated for each one-
year age group as the ratio of migrants to the number at risk of migrating over the time
period (Little and Dorrington 2013).
The second step is to decide which multiexponential model best fits the data. As
noted earlier (Section 4.2, Demographic structure of internal migrants), since retirement
is not concentrated among specific ages in this data and the data may exaggerate older
ages (Little and Dorrington 2013), we adopted the standard 7-parameter model rather
than the more complex 9-, 11-, or 13-parameter models.
For the third step, fitting the model using Solver, we obtained an Excel Workbook
for fitting model migration schedules directly from Professor Rob Dorrington at the
University of Cape Town. Our calculations for fitting this model appear in a multipage
Excel Workbook that is available upon request.
Then, in step four, we evaluated the model’s fit using the mean absolute
percentage error statistic. At 7% for both sexes, it is within the boundaries for achieving
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Demographic Research: Volume 39, Article 44
a reasonable fit. We also calculated the R-squared values for males (92%) and females
(89%). Both values are acceptable compared to the established threshold of 90%,
indicating that the models reasonably fit the data (Little and Dorrington 2013). T-
statistics are significant at the 0.05 level for all coefficients. We also checked that the
age-specific migration rates were visually compatible with the Rogers–Castro model
and looked for extreme values that could distort the parameters in our model.
Since we employed census data for these models, they experience the limitations
of census data detailed in our article (Section 5, Discussion). Furthermore, a limitation
of this method is that without accurate, well-behaved data, it is possible that the model
may be overparameterized if it does not produce a close fit (Little and Dorrington
2013). Since the lowest-parameter model best fitted the data, we are not concerned
about overparameterization.
A-1.2 Logistic regression analyses
To examine the effects of demographic indicators on the likelihood of a girl or woman
migrating internally in 2000 and 2010, we conducted logistic regression analyses using
SPSS Statistics 22.0 software. Binary logistic regression modelled the effects of
selected independent variables on whether or not a girl or woman was identified in the
census as ever having migrated internally (see Table 2 for criteria used to classify
migrants). International migrants were excluded. Selection of the independent variables
was based on a literature review of push- and pull-factors of migration. We examined
the following independent variables: age (in one-year and five-year age groups),
education status (ever attended or attending school), marital status, religion, ethnicity,
residence (urban, rural), work status (worked for pay, profit, or family gain; did not
work), and relationship to household head.
These analyses assume that the census correctly identifies all girls and women who
have migrated within Ghana and that our dependent variable (ever having migrated
internally) can be measured on a dichotomous scale (yes/no). We know, however, that
the census questionnaires’ understanding and measuring of migration do not capture
contemporary migration patterns identified via other sources of migration data. Most
movements between place of birth and current residence are missing, leading to a likely
undercount of internal migrants. Improving the census’s ability to capture contemporary
migration patterns (e.g., cyclical migration, seasonal migration) would significantly
strengthen the predictive ability of this regression model.
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Lattof et al.: Contemporary female migration in Ghana: Analyses of the 2000 and 2010 Censuses
A-1.3 Estimates of net internal female migration from place of birth data
To generate estimates of net internal female migration from census data, we followed
the instructions detailed in Tools for Demographic Estimation for estimating
subnational regional net in- and out-migration from place of birth data (Dorrington
2013). This estimation required the number of females, in five-year age groups, by
subnational region in 2010 and by subnational region at the preceding census in 2000.
For estimating deaths in this period, we calculated survival factors using model life
tables from GSS (GSS 2013c).
Our assumptions are as follows:
1. Ghana’s censuses correctly identify region of birth and accurately count
the population by subnational region.
2. We can accurately estimate the mortality of people moving between two
regions in Ghana.
Before applying the method, Dorrington (2013) warns demographers to examine
the data’s age structure of the population and the data’s relative completeness. As noted
in our article (Section 3.1, Data), we assessed data quality and completeness by (1)
reviewing the post-enumeration surveys conducted to assess coverage and content
errors (GSS 2003, 2012) and (2) comparing key variables between the microdata and
censuses. The microdata sample from the 2010 Census more accurately reflects the
complete census than the microdata sample from 2000 in which the age structure differs
slightly (Table 1). Unfortunately, the 2000 Census’s post-enumeration survey data is
physically missing, preventing analysis of whether or not the final census results
required adjustment. The 2010 Census required no adjustments based on the low net
coverage error of 1.8% at the national level (GSS 2012). While this data is imperfect, it
is the best currently available for estimating net internal migration in Ghana.
Dorrington (2013) also warns demographers that the estimations are sensitive to
census quality: for example, inaccurately recorded place of birth (e.g., respondent may
be unaware of boundary changes or may be unaware of person’s place of birth),
inability to completely identify all migrants and from where they migrated (i.e.,
undercount), and net migration’s underestimation of migrant flows into and out of a
region.
The first step in estimating net internal migration between subnational regions
from place of birth data is to decide on survival factors. While we considered survival
factors generated by the 2005 life table for Ghana from the World Health
Organization’s (WHO) Global Health Observatory data repository (WHO 2018) (Table
A-1),  we  ultimately  used  survival  factors  derived  from  the  Urban  Females  and  Rural
Females model life tables produced by the Ghana Statistical Service (GSS) (2013c).
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Demographic Research: Volume 39, Article 44
Since we had reason to believe that mortality differed between regions, using the Urban
Females and Rural Females life tables produced by the GSS permitted us to better
match the mortality profiles of each region. Ten-year survival factors determined by the
Urban Females model life table were used to generate migration estimates for the
Greater Accra (see “5S ” in the fifth column of Table A-2) and Ashanti Regions,
where the majority of girls and women reside in urban areas (90.5% and 59.6%
respectively) (Figure 1). We used the GSS’s Rural Females model life table to generate
ten-year  survival  factors  used  in  the  estimates  for  the  other  eight  regions,  where  the
rural population exceeded the urban population (see “5S ”  in  the  fifth  column  of
Table A-3). The second step is to use these survival factors to estimate the number of
deaths that occurred between the 2000 and 2010 Censuses. The third step is to estimate
the net number of in-migrants or out-migrants.
Table A-1: Comparison of overall net migration estimates based on changes to
survival factors
Overall net migration
As estimated with constant survival As estimated with separate survival
Region factors for all regions, based on the factors for predominately rural or urban % difference
WHO 2005 life table for Ghana regions, based on Ghana’s 2010 Censuslife tables
Western –13,332 –13,711 –1.40
Central –18,117 –16,121 5.83
Greater Accra 318,278 300,213 2.92
Volta –105,237 –101,561 1.78
Eastern –74,510 –71,130 2.32
Ashanti 106,929 101,431 2.64
Brong Ahafo –12,627 –14,939 –8.39
Northern –111,108 –109,085 0.92
Upper East –47,941 –47,212 0.77
Upper West –41,916 –41,734 0.22
Table A-2 works through these steps for estimating the net number of female in-
migrants. The second and third columns show the number of girls and women living in
the Greater Accra Region who were born outside the region, as counted by the 2000 and
2010 Censuses. We calculated the ten-year survival factors (5S ) in the fifth column
using data from the GSS (2013c) Urban Females model life table. The seventh column
(Do) is the number of estimated deaths of in-migrants who were born outside that
occurred in the ten years between censuses (n). We estimated deaths of people born
outside the region (denoted by the superscript O) aged between x and x + 10 years at the
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Lattof et al.: Contemporary female migration in Ghana: Analyses of the 2000 and 2010 Censuses
time of the first census (t), 5 , of those aged A–n and older at the first census, ∞ ,
and of those born between the censuses, , as follows:
For those born between the two censuses
	=	 	(5 (2010)) 	× 	((1/ , ) − 1)	
=	 	(34,950	× 	 − 1
. 	
= 1,410		
For those aged 65 years and older at the time of the first census
∞ 	=	 	(∞ (2000) 	×	∞ , +	∞ (2010)) 	× 	((1/∞ , ) − 1)	
=	 	((6,630 + 4,260 + 9,520) 	× 	0.62448 + 14,730) 	× 	 − 1
. 	
=	8,261	
For all other age groups, such as those aged 30–34 years at the time of the first census
5 	=	 	(5 (2000) 	×	5 , +	5 (2010)) 	× 	((1/5 , ) − 1)	
=	 	(53,230	× 	0.93040 + 57,480)	× 	 − 1
. 	
=	4,002	
where 5 ( )	represents the number of people born outside the region (by age group)
according to the census at time t who were aged between x and x + 10 years.
The final column (Net M (born out)) shows the net number of female migrants into
the Greater Accra Region who were born in regions other than the Greater Accra
Region for each five-year age group. From 2000 to 2010, a total of 371,632 girls and
women born outside the Greater Accra Region moved to the Greater Accra Region
(after excluding those who moved out).
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Table A-2: Estimation of the net number of female in-migrants of those born
outside by age group, Greater Accra Region, Ghana, 2000–2010
Age 2000 2010 x 5 Age at 2nd census Do Net M (born out)
B 0.92534
0–4 30,390 34,950 0 0.98072 0–4 1,410 36,360
5–9 38,460 40,280 5 0.98272 5–9 1,625 11,515
10–14 46,270 60,730 10 0.97981 10–14 890 23,160
15–19 63,980 79,870 15 0.97245 15–19 1,034 34,634
20–24 68,690 117,250 20 0.96188 20–24 1,675 54,945
25–29 69,260 119,690 25 0.94706 25–29 2,576 53,576
30–34 53,230 93,920 30 0.93040 30–34 3,170 27,830
35–39 45,660 74,330 35 0.91571 35–39 3,910 25,010
40–44 35,430 57,480 40 0.90525 40–44 4,002 15,822
45–49 26,190 44,490 45 0.89823 45–49 3,972 13,032
50–54 19,130 39,350 50 0.88747 50–54 3,738 16,898
55–59 12,360 25,560 55 0.86645 55–59 2,781 9,211
60–64 9,170 19,100 60 0.83183 60–64 2,287 9,027
65–69 6,630 11,640 65+ 0.62448 65–69 1,722 4,192
70–74 4,260 10,740 70–74 1,857 5,967
75+ 9,520 14,730 75+ 8,261 9,211
Total 538,630 844,110 Total 44,911 350,391
Table A-3 works through the steps for estimating the net number of female out-
migrants. The second and third columns show the number of girls and women living in
regions other than Ghana’s Upper East Region who were born in the Upper East
Region, as counted by the 2000 and 2010 Censuses. We calculated the survival factors
(5S )  in  the  fifth  column  using  data  from  the  GSS  Rural  Females  model  life  table
(2013c). The seventh column (Di) is the number of estimated deaths of out-migrants
who were born inside that occurred in the ten years between censuses. It is calculated in
the same manner as the deaths of in-migrants who were born outside the region (Do).
The final column (Net M (born in)) shows the net number of female out-migrants of
those born in the Upper East Region (i.e., the number of girls and women born in the
Upper East Region who moved out, less those who have returned). From 2000 to 2010,
a  total  of  54,966  girls  and  women  born  in  the  Upper  East  Region  moved  out  of  the
Upper East Region (after excluding those who moved in).
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Table A-3: Estimation of the net number of female out-migrants of those born
inside by age group, Upper East Region, Ghana, 2000–2010
Age 2000 2010 x 5 Age at 2nd census Di Net M (born in)
B 0.92197
0–4 10,900 8,030 0 0.96465 0–4 340 8,370
5–9 12,660 9,050 5 0.98064 5–9 383 –1,467
10–14 11,270 12,680 10 0.98033 10–14 425 445
15–19 12,240 16,370 15 0.96941 15–19 284 5,384
20–24 14,640 25,790 20 0.95095 20–24 370 13,920
25–29 14,630 23,970 25 0.93235 25–29 565 9,895
30–34 11,390 17,340 30 0.92103 30–34 806 3,516
35–39 9,160 13,470 35 0.91866 35–39 984 3,064
40–44 5,900 9,240 40 0.91618 40–44 846 926
45–49 4,680 6,670 45 0.90422 45–49 668 1,438
50–54 3,330 5,570 50 0.86801 50–54 502 1,392
55–59 2,160 2,560 55 0.78906 55–59 360 –410
60–64 2,050 2,770 60 0.66829 60–64 430 1,040
65–69 1,300 1,880 65+ 0.32150 65–69 479 309
70–74 1,100 2,290 70–74 908 1,898
75+ 2,110 3,370 75+ 5,086 5,246
Total 119,520 161,050 Total 13,436 54,966
After estimating net female in-migration and out-migration for each of Ghana’s ten
regions, we combined these estimates into Table 7 of our article. While these
estimations are currently the most accurate available based on existing data, they have
several limitations. As previously mentioned, the quality of census data affects these
estimates. Censuses may not identify all migrants and may suffer from an undercount.
Additionally, place of birth data and place of residence data are affected by
misreporting if boundaries change between rounds or if respondents are ignorant of the
boundaries.
A-1.4 Estimates of net female migration using the cohort component method
To strengthen confidence in our estimates of net internal female migration from census
data (Section A-1.2), we compared these estimates to those generated by the cohort
component method (Spoorenberg 2015). This estimation required us to first forward-
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project the female population enumerated in the 2000 Census to 2005, based on
estimated levels of age-specific fertility and mortality rates. We then forward-projected
the estimated female population in 2005 to compare it with the actual female population
enumerated in the 2010 Census. Without accurate vital registration statistics on fertility
and mortality during these periods, we relied on estimations. For estimating fertility, we
used age-specific fertility rates (ASFRs) for women aged 15–49 years (in five-year age
groups) produced by the 2003 Ghana Demographic and Household Survey (GDHS)
(GSS, NMIMR, and ICF Macro 2004) and the 2008 GDHS (GSS, GHS, and ICF Macro
2009). We applied the urban ASFRs to the Greater Accra and Ashanti Regions and the
rural  ASFRs  to  the  eight  remaining  regions.  For  estimating  deaths  in  this  period,  we
calculated survival factors using WHO model life tables for Ghana (WHO 2018). For
2000–2005, we used the life table for 2003. For 2005–2010, we used the life table for
2008.
Our assumptions are as follows:
1. Life table survival rates are representative of mortality conditions during
the intercensal period, and we can accurately estimate mortality.
2. Fertility rates are representative of fertility during the intercensal period,
and we can accurately estimate fertility.
3. Female migrants have the same fertility and mortality levels as the
enumerated population.
4. The distribution of net migrants is equal across years during the
intercensal period.
5. Differences between our projected population in 2010 and the population
enumerated in the 2010 Census result from migration.
The first step in estimating net migration using this method was to forward-project
the  females  enumerated  in  the  2000 Census  five  years  to  2005 (Table  A-4).  Next,  we
estimated the total number of surviving female births from 2000 to 2005 (Table A-5).
Then, we repeated the process by forward-projecting the projected female population in
2005 to 2010 and estimating surviving female births from 2005 to 2010. Finally, we
compared our estimated female population in 2010 to the actual enumerated female
population in 2010. Differences between these figures imply in-migration or out-
migration.
Table A-4 works through the steps for forward-projecting the female population in
the projection intervals. The first column after age group shows the female population
(in five-year age groups) residing in the Upper East Region, as counted by the 2000
Census. The next column lists the five-year survival factors that we derived from the
WHO life table for Ghana in 2003. The product of these two columns is the projected
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Lattof et al.: Contemporary female migration in Ghana: Analyses of the 2000 and 2010 Censuses
population in 2005; however, there is one exception. The projected population for the
age group 0–4 years comes from Table A-5, in which we estimated female births
surviving the projection interval 2000–2005. We repeat these steps once more to project
the 2005 population forward to 2010. Finally, we estimate net female migration by
subtracting the projected population in 2010 from the population enumerated in the
2010 Census. From 2000 to 2010, the Upper East Region experienced negative net
migration, with a total of 75,346 girls and women moving out of the region.
Table A-4: Estimating net intercensal female migration by age (birth) cohorts,
according to the cohort component method, in the Upper East
Region, Ghana, 2000–2010
Five-year life Projected Five-year life
Age group Population, table survival population, table survival Projected Population, Estimated net
(in years) 2000 Census ratio 2005 ratio population, 2010 2010 Census migrants
(1) (2) (3) = (1) x (2) (4) (5) = (3) x (4) (6) (7) = (6) – (5)
0–4 66,440 0.93043 85,338 0.93923 96,152 68,450 –27,702
5–9 75,250 0.97342 61,818 0.97818 80,152 73,600 –6,552
10–14 51,260 0.98795 73,250 0.99020 60,469 64,850 4,381
15–19 40,840 0.99121 50,643 0.99181 72,532 54,020 –18,512
20–24 33,840 0.98779 40,481 0.98901 50,228 42,050 –8,178
25–29 35,770 0.97855 33,427 0.98357 40,036 37,640 –2,396
30–34 29,190 0.96822 35,003 0.97475 32,878 32,840 –38
35–39 26,830 0.96136 28,262 0.96519 34,119 29,180 –4,939
40–44 23,800 0.95851 25,793 0.96027 27,278 26,570 –708
45–49 21,870 0.95902 22,813 0.96067 24,769 20,340 –4,429
50–54 18,020 0.95498 20,974 0.95746 21,915 19,450 –2,465
55–59 11,990 0.94552 17,209 0.94846 20,081 11,510 –8,571
60–64 13,240 0.91340 11,337 0.92161 16,322 14,580 –1,742
65–69 8,980 0.85251 12,093 0.86934 10,448 9,350 –1,098
70+ 19,670 0.61137 19,681 0.62723 22,858 30,460 7,602
Total 476,990 538,121 610,236 534,890 –75,346
Notes: Figures in bold were produced using the estimation method for female births surviving the projection interval, as shown in
Table A-1.5.
Table A-5 works through the steps for estimating female births surviving the
projection intervals. The first column shows the female population aged 15–49 years (in
five-year age groups) residing in the Upper East Region, as counted by the 2000
Census. The second column shows the projected female population in 2005, based on
our calculations in Table A-4. The third column calculates the mid-period female
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population as an average of the sum of the populations in the first and second columns.
ASFRs in the fourth column come directly from the 2003 GDHS, in this example, and
are those used for rural areas. The final column, estimated births (2000–2005), is the
product of the female mid-period population and the ASFRs multiplied by five (years)
to account for the period 2000–2005. For the first interval (2000–2005), we used a sex
ratio of 105 for both urban and rural areas based on the 2000 Census report (GSS 2003).
For the second interval (2005–2010), we used rural (103.1) and urban (101.2) sex ratios
from the 2010 Census report on fertility (GSS 2014). We generated newborn five-year
survival ratios using the WHO 2003 and 2008 life tables for Ghana (WHO 2018). From
2000 to 2005, we estimated 85,338 surviving female births in the Upper East Region.
This figure goes into the first row (age group 0–4 years) of the fourth column (Projected
population, 2005) in Table A-4.
Table A-5: Estimation of female births surviving the projection interval, Upper
East Region, Ghana, 2000–2005
Female population, Female population, Female population, Age-specific Estimated births
Age group 2000 census 2005 projected mid-period fertility rates (2000–2005)
(in years) (1) (2) (3) = ((1) + (2)) / 2 (4) (5) = 5 x ((3) x (4))
15–19 40,840 50,643 45,741 0.113 25,844
20–24 33,840 40,481 37,161 0.225 41,806
25–29 35,770 33,427 34,598 0.256 44,286
30–34 29,190 35,003 32,096 0.213 34,183
35–39 26,830 28,262 27,546 0.179 24,654
40–44 23,800 25,793 24,797 0.095 11,778
45–49 21,870 22,813 22,341 0.049 5,474
Total births 188,024
Proportion of female births (sex ratio, rural = 105) 0.488
Total female births (2000–2005) 91,719
Average five-year survival ratio of newborns 0.930
Expected deaths among female births (2000–2005) 6,381
Total surviving female births 85,338
The estimates produced using the cohort component method have several
limitations beyond the quality of census data. This method is incredibly sensitive to our
estimated fertility and mortality rates. Using ASFRs from the GDHS and censuses
produced drastically different estimates (Table A-6). ASFRs from the GDHS produced
overall net out-migration in six of Ghana’s ten regions, whereas ASFRs from the
censuses produced overall net out-migration in only two of Ghana’s ten regions. Since
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Lattof et al.: Contemporary female migration in Ghana: Analyses of the 2000 and 2010 Censuses
measures between the 2008 GDHS and 2010 Census indicate misreporting of births in
the census and census fertility data of questionable reliability, we felt the GDHS ASFRs
produced more robust estimates. The mortality rates illustrated less significant swings
in the estimates produced using the cohort component method, depending on where we
generated the survival rates from. For this reason, we consider our estimations of
subnational regional net in- and out-migration from place of birth data (Section A-1.3)
to be more robust, as they are affected only by mortality estimates.
Table A-6: Comparison of estimates of net female migration in Ghana produced
using different methods
Overall net female migration
As estimated with the As estimated with the As estimated with the
cohort component cohort component method cohort component method As estimated with place
Region method using ASFRs using urban/rural ASFRs using ASFRs from the 2003 of birth data (Section
from the 2000 and 2010 from the 2003 and 2008 and 2008 GDHS with A-1.3)
Censuses GDHS additional modifications*
Western 332 –80,102 –80,102 –13,711
Central 118,650 51,291 33,360 –16,121
Greater Accra 367,656 308,633 308,633 300,213
Volta 54,411 –13,143 –13,143 –101,561
Eastern 27,725 –57,576 –57,576 –71,130
Ashanti 456,663 389,721 389,721 101,431
Brong Ahafo 42,939 –33,492 –33,492 –14,939
Northern 132,650 70,086 –44,247 –109,085
Upper East –40,570 –75,346 –75,346 –47,212
Upper West –24,367 –47,997 –47,997 –41,734
Notes: * Women in the Northern Region have the highest total fertility rate (TFR) in Ghana, with 7 children per woman in 2003 and
6.8 children per woman in 2008 (GSS, NMIMR, and ICF Macro 2004, GSS, GHS, and ICF Macro 2009). The Central Region also
experiences above average fertility with TFRs of 5 children per woman in 2003 and 5.4 children per woman in 2008 (GSS, NMIMR,
and ICF Macro 2004, GSS, GHS, and ICF Macro 2009). To improve the accuracy of our migration estimates using the cohort
component method, we adjusted the ASFR upwards when estimating births in these two regions. For estimating births from 2005 to
2010, we multiplied the rural ASFRs by a factor of 1.39 for the Northern Region and a factor of 1.1 for the Central Region. These
factors are the ratio of each region’s TFR to Ghana’s overall rural TFR of 4.9. For estimating births from 2000 to 2005, we adjusted
the Northern Region’s ASFRs upward using a factor of 1.25.
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Appendix 2: Supplementary tables
Table A-7: Migrants identified by Ghana census questions on migration, 2000
and 2010 (10% microdata)
2000 Census questions Migrants identified in 2000 (%), Migrants identified in 2010 (%),by sex 2010 Census questions by sex
P06a BORN IN THIS 335,951 of 955,504 females (35.2%) P05 BIRTHPLACE: Was 450,071 of 1,262,598 females (35.6%)
TOWN/VILLAGE: Was Ghanaian female migrants = 274,167 (NAME) born in this 412,035 of 1,203,691 males (34.2%)
(NAME) born in this town (81.6%) town/village? If Yes, go
or village? If YES go to International foreign female migrants to P07.
P07. = 61,784 (18.4%)
[Note: Only asked of
respondents who were 349,023 of 935,654 males (37.3%)
Ghanaian by birth.] Ghanaian male migrants = 262,911
(75.3%)
International foreign male migrants =
86,112 (24.7%)
P06b BIRTHPLACE 274,167 of 274,167 females (100%) P06 BIRTHPLACE: In 450,071 of 450,071 females (100%)
OUTSIDE THIS Female internal migrants = 265,153 what region or country Female internal migrants = 434,948
TOWN/VILLAGE: In what (96.7%) was (NAME) born? (96.6%)
region or country was Female (Ghanaian) international Female international migrants = 15,123
(NAME) born? migrants = 9,014 (3.3%) (3.4%)
[Note: Only asked of 262,911 of 262,911 males (100%) 412,035 of 412,035 males (100%)
respondents who were
Ghanaian by birth.] Male internal migrants = 254,048 Male internal migrants = 394,703
(96.6%) (95.8%)
Male (Ghanaian) international Male international migrants = 17,332
migrants = 8,863 (3.4%) (4.2%)
P07 USUAL PLACE OF 28,679 of 955,504 females (3%) P07 LIVING IN THIS 478,783 of 1,262,598 females (37.9%)
RESIDENCE: In what Female internal migrants = 28,329 TOWN/VILLAGE: Has 439,930 of 1,203,691 males (36.5%)
district is (NAME’S) usual (98.8%) (NAME) been living in
residence? Female international migrants = 350 this village or town since
(1.2%) birth? If Yes, go to P09.
29,797 of 935,654 males (3.2%)
Male internal migrants = 29,338
(98.5%)
Male international migrants = 459
(1.5%)
P08 PLACE OF 187,027 of 816,989 females (19.6%) P08 NUMBER OF 451,686 of 1,262,598 females (35.8%)
RESIDENCE FIVE Female internal migrants = 185,228 YEARS LIVED IN THIS 413,681 of 1,203,691 males (34.4%)
YEARS AGO IF (NAME) (99%) TOWN/VILLAGE: For
IS FIVE YEARS OR Female international migrants = how long has (NAME)
OLDER: In what district 1,799 (1%) been living in this village
was (NAME’S) usual 189,490 of 935,654 males (20.3%) or town?
place of residence five
years ago? Male internal migrants = 187,194
(98.8%)
Male international migrants = 2,296
(1.2%)
Total number of migrants identified in Total number of migrants identified in
2000 microdata, by sex: 2010 microdata, by sex:
359,960 of 955,504 females (37.7%) 487,376 of 1,262,598 females (38.6%)
Female internal migrants = 297,031 Female internal migrants = 472,253
(31.1%) of all females (37.4%) of all females
Female international migrants = Female international migrants = 15,123
62,929 (6.6%) of all females (1.2%) of all females
371,577 of 935,654 males (39.7%) 447,485 of 1,203,691 males (37.2%)
Male internal migrants = 284,269 Male internal migrants = 430,153
(30.4%) of all males (35.7%) of all males
Male international migrants = 87,308 Male international migrants = 17,332
(9.3%) of all males (1.4%) of all males
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Table A-8: Lifetime female in-migrants by region of origin, out-migrants by
region of destination, and net lifetime migration streams, Ghana,
2000 and 2010
2000 Census 2010 Census
Region of origin and Lifetime in- Lifetime out- Net lifetime Lifetime in- Lifetime out- Net lifetime
destination migrants migrants migration migrants migrants migration
Western 239,600 87,570 152,030 260,940 145,370 115,570
Central 93,920 239,510 –145,590 185,660 318,530 –132,870
Greater Accra 548,090 99,020 449,070 844,110 156,690 687,420
Volta 55,310 263,130 –207,820 73,670 343,070 –269,400
Eastern 157,800 276,400 –118,600 207,770 388,770 –181,000
Ashanti 275,470 221,720 53,750 432,430 302,070 130,360
Brong Ahafo 168,770 102,940 65,830 217,110 172,800 44,310
Northern 54,980 121,000 –66,020 52,900 214,690 –161,790
Upper East 24,390 118,540 –94,150 30,640 161,050 –130,410
Upper West 18,140 106,640 –88,500 21,630 123,820 –102,190
Total 1,636,470 1,636,470 0 2,326,860 2,326,860 0
Table A-9: Lifetime male in-migrants by region of origin, out-migrants by
region of destination, and net lifetime migration streams, Ghana,
2000 and 2010
2000 Census 2010 Census
Region of origin and Lifetime in- Lifetime out- Net lifetime Lifetime in- Lifetime out- Net lifetime
destination migrants migrants migration migrants migrants migration
Western 262,520 81,550 180,970 300,660 130,280 170,380
Central 92,160 234,820 –142,660 186,880 292,890 –106,010
Greater Accra 530,700 106,930 423,770 756,850 164,430 592,420
Volta 54,470 265,580 –211,110 71,190 340,920 –269,730
Eastern 156,980 270,710 –113,730 206,480 359,620 –153,140
Ashanti 296,640 222,440 74,200 418,030 313,050 104,980
Brong Ahafo 191,180 104,720 86,460 242,520 164,200 78,320
Northern 53,650 133,190 –79,540 48,160 222,990 –174,830
Upper East 25,040 136,250 –111,210 31,000 167,780 –136,780
Upper West 15,490 122,640 –107,150 21,840 127,450 –105,610
Total 1,678,830 1,678,830 0 2,283,610 2,283,610 0
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Table A-10: Estimates of the net number of female in-migrants of those born
outside by age group, Ghana, 2000–2010
Net in-migration by region
Age
Western Central GreaterAccra Volta Eastern Ashanti
Brong Upper Upper
Ahafo Northern East West
0–4 14,435 12,723 36,360 6,209 10,905 22,305 12,166 4,150 2,973 2,028
5–9 –4,145 4,389 11,515 –202 1,934 –2,869 383 –1,771 234 –1,753
10–14 –3,555 6,799 23,160 –338 3,252 7,882 532 –2,841 –82 –2,158
15–19 –996 12,709 34,634 –362 8,352 18,632 4,793 –275 1,324 –1,561
20–24 11,244 12,676 54,945 71 7,763 34,082 11,923 656 196 –374
25–29 8,227 10,342 53,576 747 7,080 28,664 10,100 771 825 –1,482
30–34 1,106 6,620 27,830 368 3,714 17,041 4,532 –195 324 –1,117
35–39 2,434 6,133 25,010 –255 4,713 13,139 3,773 –531 416 –1,602
40–44 1,112 4,223 15,822 633 4,970 9,440 3,183 –115 176 –1,048
45–49 3,190 3,853 13,032 –110 4,104 6,666 2,561 –383 46 –552
50–54 3,690 4,033 16,898 876 4,687 7,931 3,306 518 233 –438
55–59 –505 1,237 9,211 –404 985 2,741 –34 –459 –70 –461
60–64 1,930 2,191 9,027 475 2,364 4,056 2,304 359 208 –115
65–69 98 796 4,192 –351 189 577 –136 –141 14 –337
70–74 1,776 1,470 5,967 409 2,371 4,894 2,968 347 79 –229
75+ 2,166 1,579 9,211 419 3,624 5,592 2,280 571 –72 –643
Total 42,208 91,774 350,391 8,186 71,007 180,774 64,635 662 6,823 –11,844
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Table A-11: Estimates of the net number of female out-migrants by region of
birth and age group, Ghana, 2000–2010
Net out-migration by region
Age
Western Central GreaterAccra Volta Eastern Ashanti
Brong Upper Upper
Ahafo Northern East West
0–4 8,804 15,223 14,731 14,866 17,387 17,114 10,317 11,588 8,436 6,051
5–9 1,376 244 2,739 –1,250 1,899 577 3,340 2,173 –1,392 –1,762
10–14 4,221 4,451 2,044 3,305 6,238 2,865 4,857 5,955 380 –1,272
15–19 7,113 8,556 5,448 8,451 9,453 5,384 7,094 14,071 5,542 3,033
20–24 9,215 14,769 6,808 14,832 18,058 14,897 13,556 20,691 14,284 7,567
25–29 6,734 13,343 5,101 17,184 21,035 11,459 12,956 16,897 10,333 6,055
30–34 3,871 7,619 2,524 9,009 11,223 4,429 6,432 11,386 3,884 2,276
35–39 3,505 9,254 3,189 7,274 9,337 4,314 6,513 6,710 3,375 2,110
40–44 1,939 6,155 3,170 5,856 8,351 2,910 3,993 5,775 1,204 817
45–49 1,814 4,646 2,108 5,289 7,786 2,959 3,616 3,260 1,741 441
50–54 2,407 7,506 1,588 7,300 9,104 5,416 3,108 3,467 1,661 1,265
55–59 1,490 2,234 –79 2,327 4,471 1,080 1,273 291 –264 271
60–64 1,470 3,902 420 4,105 5,750 2,693 816 1,911 1,078 1,268
65–69 176 1,311 –332 1,511 1,843 265 454 –26 189 –95
70–74 3,006 3,362 783 3,917 4,193 1,859 694 2,202 1,463 1,037
75+ –1,221 5,320 –63 5,769 5,758 1,124 556 3,396 2,120 828
Total 55,919 107,894 50,179 109,747 141,887 79,344 79,573 109,747 54,035 29,890
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