the movement of families, opportunities in a shifting world piece - domestic... · opportunities in...
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B e n t a A t i e n o A b u y a 2 0 1 7
The movement of families, households, and individuals within countries and its relationship with
education: challenges and opportunities in a shifting world
This paper was commissioned by the Global Education Monitoring Report as background information to assist in drafting the 2019 concept note. It has not been edited by the team. The views and opinions expressed in this paper are those of the author(s) and should not be attributed to the Global Education Monitoring Report or to UNESCO. The papers can be cited with the following reference: “Paper commissioned for the Global Education Monitoring Report 2019 Consultation on Migration”. For further information, please contact [email protected].
ED/GEMR/MRT/2019/T1/2
Think piece prepared for the 2019 Global Education Monitoring Report Consultation
Migration
08 Fall
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1. Table of Contents
1 Introduction ............................................................................................................................. 6
1.1 General Migration Theories and Trends .......................................................................... 6
1.2 Key suppositions from previous research ...................................................................... 11
1.3 The link between Migration, social capital and schooling ............................................. 12
1.4 Migration, strain and stress theory and schooling .......................................................... 12
1.5 Migration and internal displacement in Kenya .............................................................. 13
1.6 Migration, Displacement and educational outcomes ..................................................... 15
1.7 Proposed analytical framework ...................................................................................... 16
2 Results from the NUHDSS and KDHS case study ................................................................ 18
2.1 Context and data ............................................................................................................. 18
2.2 Reasons for migration .................................................................................................... 19
2.3 Type of movements for those aged 24 years and below ................................................ 24
2.4 Migration status and schooling outcomes ...................................................................... 26
2.5 Education attainment ...................................................................................................... 29
2.6 Utilization of private school for the poor ....................................................................... 32
2.7 Transition to secondary school ....................................................................................... 34
2.8 Internal displacements .................................................................................................... 35
3 Conclusion ............................................................................................................................. 37
3.1 Key issues and outstanding questions ............................................................................ 37
3.2 Take home message ....................................................................................................... 39
4 References ............................................................................................................................. 40
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List of figures Figure 1: Proposed framework to analyse the relationship between migration and education .... 17
Figure 2: Reasons for migrating among individuals aged between 6 and 24 years, NUHDSS .... 20
Figure 3: Proportion of individuals’ migrating to join school by movement type and age group,
NUHDSS ...................................................................................................................................... 21
Figure 4: Reasons for migration among individuals aged above 24 years stratified by type of
migration and gender, NUHDSS .................................................................................................. 23
Figure 5: Forms of migration among individuals aged above 24 years stratified by age and
migration status, NUHDSS ........................................................................................................... 25
Figure 6: Reason for movement and schooling status among individuals aged 6-14 years,
NUHDSS ...................................................................................................................................... 28
Figure 7: Years schooling among individuals aged between 20 and 24 years, KDHS 2008/9 .... 31
Figure 8: Proportion of primary school going age enrolled in LFPS by migration type, NUHDSS
....................................................................................................................................................... 33
Figure 9: Transition to secondary school by migration type, NUHDSS ...................................... 34
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List of tables Table 1: Intensity of Lifetime Migration between Zones (percent): ............................................... 9 Table 2: Reasons for migrating among those aged above 25 years by migration status, NUHDSS....................................................................................................................................................... 22 Table 3: Migration status of households with individuals aged 6 to 24 years, KDHS 2008/9 ..... 26 Table 4: Current schooling status on year of migration, NUHDSS .............................................. 27 Table 5: Current schooling enrolment using KDHS 2008/9 ......................................................... 29 Table 6: Average years of schooling for individuals age 6 to 24 years, KDHS 2008/9 ............... 30 Table 7: Transition to secondary school by migration type, KDHS 2008/9 ................................. 35 Table 8: Reasons for migrating among those aged above 24 years by migration status, gender and year, NUHDSS .............................................................................................................................. 36
Abstract
Migration remains an important event in most people’s lives in Kenya, shaping economic outcomes
while also portending a life of hardship for others, especially internally displaced persons. Research has
shown that migration may impact education negatively through its disruptive nature on social capital;
the latter has been shown to reinforce positive educational outcomes. This work assesses the
relationship between different migration streams and educational outcomes in Kenya. We use data from
the Kenya Demographic and Health Survey and the Nairobi Urban Demographic and Health
Surveillance System to characterise migration and assess its relationship with education. Our findings
point to age and sex differentiated reasons for migrating into the city (Nairobi) with family reunion
pushing women and those below 24 years while economic reasons were important to males and those
24 years and older. Grade repetition and enrolment in low quality schools was higher among migrants.
Rural-urban migrants also have lower educational attainment compared with urban-rural migrants.
Dropout rates are higher among rural-urban migrants compared with urban-rural migrants. We
conclude that migration type is important in various educational outcomes.
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1 Introduction Human movement has existed as long as man has inhabited the planet, enabling the conduct of trade,
the discovery of new lands and the creation of new cultures. In contemporary times, migration and
mobility continue to play a key role in the social, economic, and cultural development of societies.
While most human movements are voluntary and undertaken after rational decisions, increasingly,
movements are becoming involuntary due to conflict or natural disasters such as droughts and floods.
The African continent has increasingly been contributing to the migrant streams as people move in
search of better opportunities. The United Nations Department of Economic and Social Affairs
(UNDESA), estimates that in 2013, there were 18.6 million international migrants from the African
region (UNDESA, 2013). On the other hand internal migrant numbers from five-year estimates indicate
that Africa had 39.7 migrants far exceeding the estimated for international migrants (Bell & Charles-
Edwards, 2013).
In this paper, we focus on internal migration in Kenya and categorise internal human movements into
two broad forms namely: forced movements (internal displacement) and voluntary migration. The latter
category includes sub-categories based on the direction of movement and nature of the origin and
destination namely: rural-urban migration, inter and intra-urban movements, rural-rural as well as
urban-rural migration. In this paper, we focus on the relationship between movements of families and
education. We argue that individuals in these two broad movement streams are dissimilar in the
educational outcomes due to factors motivating the decision to move, and the type and quality of
educational facilities at the destination. Following this, we propose a framework to understand the
drivers of migration in Kenya and how it affects education of school going children.
1.1 General Migration Theories and Trends
Several theories have been put forward to explain migration (both internal and international). Classical
economic theories of migration indicate the migrant as a rational agent making rational decisions to
move in order to maximise his utility at destinations where opportunities and wages are better than at
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the origin (Harris & Todaro, 1970; M P Todaro, 1969). The human capital theory posits that migration
is fuelled by [international] differences in the average returns to labour and human capital in the source
and destination countries, and the migrant is driven by the opportunity to build his own human capital
(Borjas, 1989). The New Economics of Labor Migration (NELM) on the other hand asserts that
migration is a calculated strategy for households to spread their risk by distributing members between
economically depressed places of origin and better off destinations. They further note that there is a
flow of remittances between the destination and origin to cushion against production and market
constraints at the origin (Stark & Bloom, 1985). Other theories exist, such as the gravity theory that
assumes that “the volume of migration between two places is directly proportional to the product of the
populations of the origin and destination and inversely proportional to the distance between the two.”
This theory allows for the inclusion of contextual factors such as political, environmental and economic
factors determining migrant flows (Ramos, 2016).
While most of these theories came about in efforts to explain the “push-pull” factors in international
migration, they can also be applied to internal migration. This is especially relevant in the SSA context
where urbanization is rapidly changing the economic landscape with development investments
concentrated in urban areas which are home to 33% of the population (African Development Bank,
2012). This uneven development, coupled with changing climatic conditions has led to high mobility
between under-developed rural areas and better off urban centres. In addition, the region is
characterised by localised conflicts due to scarce resources and politically instigated conflicts that have
led to both internal displacements and refugee situations. Further, the region has increasingly been
experiencing climate related natural disasters such as flooding and droughts that have led to internal
displacement of populations (International Organization for Migration, 2014).
Most of Sub-Saharan Africa’s (SSA) development since pre-independence has laid emphasis on
investing in capital cities and other economically strategic towns and rural areas for instance those with
agricultural and mineral wealth. This bias led to few cities/regions being the points of attraction for
rural populations. The post-independence era brought about the relaxation of anti-mobility laws that
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had previously prohibited natives from migrating into cities. Consequently, there was an influx of
migrants from under-developed rural areas into cities and well-endowed rural areas in search of better
livelihoods (Oucho, Oucho, & Ochieng', 2014). This economic driven migration continues to the
present, given uneven development within countries as well as natural disasters such as droughts that
have rendered agriculture a non-viable source of livelihood in some areas (Adepoju, 2011). Research
on the determinants of internal migration in SSA has found that education is both a driver and a
consequence of migration, while gender and contextual factors play a role. Internal migrants have been
found to be positively selected on educational attainment while increasingly, across the region, more
females are in the migrant streams either as an economic move or due to marriage. (Ginsburg et al.,
2016). The region’s vulnerability to climate change has led to the movement of populations from areas
where it is no longer life supporting to more favourable areas. Thus migration in the region lies along
the following typologies: rural-urban, rural-rural and urban-rural as well as urban-urban (Oucho et al.,
2014; Reed, Andrzejewski, & White, 2010). While in high-income countries, migrants tend to leave the
place of origin and permanently settle at the destination, this is not largely the case in SSA where
circular migratory tendencies are dominant. This is because migrants retain links to their places of
origin where they may make investments as a retirement destination (White, Mberu, & Collinson,
2008).
The SSA region has a highly mobile population in intra-country and international migration streams.
Bell and Charles-Edwards (2013) used the Integrated Public Use Microdata Series (IPUMS) to analyse
internal migration data and found the intensity to increase in a number of African countries. The
following table shows mixed intensities of internal lifetime migration in several countries in the region,
based on census data.
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Table 1: Intensity of Lifetime Migration between Zones (percent): Select Developing Countries Country No. of Regions Intensity (%) Botswana 28 30.7 Egypta 27 7.5 Ghana 110 27.8 Guinea 34 15.8 Kenya 69 20.3 Mali 47 11.5 Malawib 24 18.7 Rwanda 12 10.4 Senegal 34 21.9 South Africaa 9 17.7 Sudana 25 9.9 Tanzania 26 14.1 Uganda 56 14.6 Zambia 72 29 Zimbabwe 10 28.9
Source: (Bell & Charles-Edwards, 2013) All Data based on 2000 round of census; abased on 2010 census; b based on 1990 census
The United Nations (2008), has argued that by 2050, nearly one half of the migrants in Africa will be
urbanized – with continued rise of rural to urban migration. In Kenya, the devolved system of
government is hypothesized to accelerate rural to urban migrations and very critical therefore to
examine using existing data, the effects of migration on education. This is because the 46 new seats of
government, which are within medium-sized urban areas, are likely to create primate cities within the
Counties. With the push for each County to encourage investments modelled after the old system of
government investment that concentrated resources in major urban centres, there is a likelihood that
County capital cities will be the new ‘magnets’ attracting the mainly rural populace to take up job
opportunities created through the devolution of government functions as well as through these
investments.
Extant research evidence shows the association between migration and other development indicators
such as fertility and economic status of households through remittances. However, the assumptions are
that migration is occurring amidst growing opportunities for migrants, yet in sub-Saharan Africa, it is
occurring amidst limited growth (Ravallion, Chen, & Sangraula, 2007). The United Nations (2004),
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notes that rural to urban migration and natural growth are a challenge for many governments in sub-
Saharan Africa. This could stem from the nearly half of the urban population growth that rural-urban
migration contributes to; which in the context of poor economic performance would have implications
for city authorities capacity in meeting an increased demand for housing, services and infrastructure to
support this increased demand (Tacoli, McGranahan, & Satterthwaite, 2014; Michael P Todaro &
Smith, 2012).
The forms of migration have shifted from the past when only individuals were migrating and leaving
their families behind, to also include movement of entire households. However, split migrants persist in
which an individual, usually the male head of household (in the Kenyan context), moves to an urban
area leaving behind his wife and children in the place of origin (de Laat, 2005). Other studies suggest
that larger families are likely to adopt the split migration strategy to reduce the migration cost (Agesa
& Kim, 2001).
There is limited empirical evidence in the region on the impact of migration on education. Specifically,
while education achievement continues to characterize adult migrants, the evidence on the impact of
the migration on the education of school going age children is not well understood. Understanding the
relation between migration and education is important given that in SSA, most migration is happening
into areas that Potts (2013) refers to be characterized by “extreme levels of informality, in economic
activities as well as in the production and consumption”. For example, trends in urbanization in Africa
indicate that slums are home to more than half the urban population. It is to these informal areas of
cities that many migrants find themselves. Studies have shown the precarious nature of slum life which
heavily relies on unsteady sources of income mainly in the informal sector of the city’s economy and
where unemployment is quite high (The World Bank, 2006; Zulu et al 2006). The informality can
predispose migrants to vulnerabilities that can be detrimental to the education of their children. This is
true, given limited capacities of receiving areas to provide for social and economic needs of the
migrants.
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1.2 Key suppositions from previous research
A classical study conducted in 1975 posits that the mechanisms through which migration, and
particularly long distance movement interferes with schooling of children include: the differences in
school facilities, the way pupils adjust to the new curricula, the teachers and the teaching practices in
the destination schools, the regulations and practices of schools in various states, how well the pupils
are able to adjust and the influence that their peers will have on them (L. H. Long, 1975). Moreover,
this classical study does suggest that if the parents are well educated, their education can reduce the
negative effects of migration on their children’s school progress (Straits, 1987). Moreover, scholars
agree that the effects of migration on the progress of children in school are positively associated with
the degree to which the differences in cultures between the destination and the original places of
residence can be minimized. Therefore, it could be possible that the reason for the perceived
differences in student performance, particularly for those who move, is that the movers and the non-
movers are different from the onset (Pribesh & Downey, 1999). However, researchers have also
established an association between the distance covered in any migratory move and educational
outcomes (Hango, 2006). For example, in the context of the USA, children who do interstate
movements are less likely to lag behind in school (L. H. Long, 1975), while other piece of evidence
suggests that when migration is greater than 50 miles it is less likely to harm children and more likely
to reduce problems in school(Straits, 1987) .
Moreover, research shows that children’s progress in school is positively related to the head of family’s
education, the education level of the spouse, income of the family, while it is negatively related to
being raised in a female headed household, residing in a large family consisting of younger siblings
(Straits, 1987). It would appear that migration would negatively affect children’s school performance,
if they reside in the households that are already disadvantaged in one way or the other (Pribesh &
Downey, 1999). For instance, research shows that those children who live in families that are poor, and
who do not live with biological parents are more likely to move (L. Long, 1992; Pribesh & Downey,
1999).In which case, such moves are associated with poor performance which is a manifestation of the
existing characteristics of the households of origin.
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1.3 The link between Migration, social capital and schooling
The link between social capital and schooling lies in the fact that migration destroys and cuts the most
significant social ties that are inherent in communities, families and organizations that are important for
social and cognitive development (James S Coleman, 1990). According to this school of thought,
school performance improves when the social connections are solid between and within the families
(Pribesh & Downey, 1999). Children who are close to their parents benefit from closer parental
monitoring, reinforced expectations about school, and are provided with guidance on school related
matters. Similarly, children whose parents talk with the parents of their schoolmates, gain new insights
of monitoring their children, in addition to sharing new information and gaining access to new
resources.
In the context of Kenya’s urban informal settlements, migrants tend to move into areas where their kin
or ethnic group have already settled. This provides a sense belonging and additional security to the new
household. In some instances, these enclaves are given names that are similar and familiar to the origin
of the migrant stream. Therefore, such parents, and families will have forged stronger ties, which will
thereby enhance norms relating to school performance (J. S. Coleman, 1988; Hango, 2006; Pribesh &
Downey, 1999). From a social capital lens, migration interferes with the ties already established at
school, community and within the respective families. Therefore, migration interrupts ties within
families, and cuts off their community ties with teachers, school administrators and community
members, and eventually affects schooling (Hango, 2006; Pribesh & Downey, 1999).
1.4 Migration, strain and stress theory and schooling
The stress and strain theory has been used as an additional lens to explain migration and educational
attainment. Within this framework, when families move and change residences, it is a source of stress
in the lives of children (Hango, 2006). This is because when families move they destroy the
relationships already established and thereby interfere with positive stimuli (Agnew, 1992; Hango,
2006). However, research shows that in some instances, there are positive effects of family mobility on
educational attainment, although this may be observed after a long time. For instance, Hango (2006)
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suggest that those children who move with their families when they are aged upto to fifteen years, have
higher chances of completing high school that those who do not migrate. Moreover researchers argue
that the added strain may lead to young people hating the idea of migrating, particularly if they leaving
their peers behind. This may predispose to the youth to engage in deviant behaviour, directed towards
their parents (Hango, 2006; Simpson & Fowler, 1994), who they may perceive as being responsible for
the mobility and the stress thereof accruing from the mobility.
1.5 Migration and internal displacement in Kenya
In Kenya, rural-urban migration remains a key factor in shaping the urbanization process, as more and
more individuals make decisions to leave the predominantly agricultural rural areas to move into
cities/towns where they hope to find better job opportunities. Other movements happen between
cities/towns, between rural areas as well as urban to rural migration, usually at the end of an
individual’s working life or due to failure to get employment (Oucho et al., 2014). In most of these
movements, the destination is usually better economically compared to the origin, as most of the
movements are economically motivated (Oucho et al., 2014). A common factor in these movements is
the rational decision that is taken by the individual and/or his family to move. Therefore, there is an
element of preparation for the move, both financially and emotionally, and perhaps also anticipation of
a better life at the destination.
The decision to move is mainly based on imperfect market information at the destination, where the
migrant or his family perceives an existing opportunity and wage advantage compared to the origin,
and where the migrant may use existing networks to help in getting market information and support
during the initial stages of settling at the destination (Epstein, 2008). The decision to move is mostly
made by a household (Hagen-Zanker, 2008) which contributes towards offsetting the migration cost
and in supporting the migrant to settle at the destination while the migrant will make remittances to the
household to help with consumption and investments. While the rationalization to move is possible for
adult migrants, it is not the case for under-age migrants accompanying or left behind by their
caregivers.
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On the other hand, forced movements within Kenya have been part of the country’s history since
colonial times when colonial powers resettled many indigenous people in an effort to acquire land for
agriculture. Internal displacements are triggered by natural disasters, development projects, inter-tribal
conflicts, pastoralist displacements, cattle rustling and political violence (Internal Displacement
Monitoring Centre (IDMC), 2014; Kiama & Koome, 2014). Displacements due to political violence
have become more pronounced since the 1990s when a multi-party political system was introduced.
Along with this new political structure came cyclic political related violence where individuals
belonging to other ethnic groups but living in areas where they are not the dominant group, were
dispossessed of their land and forced to move back to their ‘homelands’. Worth noting is the multi-
ethnic nature of Kenya with about 47 tribes living in the country, there is usually a sense of belonging
to one’s ethnic group- a fact that has been exploited politically to rally people to ‘claim’ their land back
from ‘strangers’ (Kamungi, 2013).
The most notable event was the 2007 post-election violence that happened after disputed presidential
elections in which an estimated 600,000 people were displaced (Internal Displacement Monitoring
Centre (IDMC), 2014). Though the government indicates that most of these IDPs had been resettled,
according to the IDMC, in 2015 Kenya had 309,000 internally displaced persons due to conflict and
105,000 newly displaced due to disasters (Internal Displacement Monitoring Centre (IDMC), 2015), a
figure that might rise owing to an on-going drought in most parts of the country in 2016/2017. IDPs in
Kenya are sheltered in camps though considerable proportions remain outside these shelters. This latter
fact has excluded these IDPs from government interventions targeting IDPs, usually those living in
camps or other shelters. A distinguishing feature of displacement is the involuntary nature of the
movement and the stressful circumstances under which the movement is undertaken. In addition, there
is potential separation of families as people disperse due to the conflict or disaster, adding to further
stress especially to children.
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1.6 Migration, Displacement and educational outcomes
Research into the effect of parental migration on the educational outcomes of children left behind
indicates mixed results. On the one hand there are studies that have found positive effects of parental
migration on children’s education (Lu & Treiman, 2011; Macours & Vakis, 2010); ostensibly through
remittances that are invested in children’s education at the origin. On the other hand, parental migration
has been shown to have negative effects on children’s education (Halpern-Manners, 2011; Lu, 2014;
Mazzucato & Cebotari, 2016; Nobles, 2011) due to its disruption on the social fabric of family,
especially where both parents have migrated. Context was found to matter in some of these studies in
terms of government investment in the education sector where if limited, then migration might bear
positive results due to the benefits of remittances (Lu, 2014). In addition, in countries where child
upbringing is culturally structured around extended family and not on the parents, educational
outcomes were not negatively impacted by parental migration. However contexts where parents are the
sole caregivers of children had results indicative of an adverse effect on the education of children
(Mazzucato & Cebotari, 2016). Most of the impacts reported were noted for international migration
while internal migration had a negative impact but not of the same magnitude as the former (Lu, 2014).
Kenya is committed to the provision of education for all and the government has made efforts to be
inclusive in this effort. While voluntary movers would have access to education at the destination,
displaced persons especially those in camps have to rely on government programs aimed at providing
education/support towards the education of IDPs. The Kenyan government has programs such as
bursaries targeting disadvantaged groups including IDPs (Ministry of Education Sciencee and
Technology, 2014). In addition to bursaries, the government has adult and continuing education for
individuals learning under special circumstances such as displaced persons.
There are, however, concerns about poor educational outcomes among IDPs due to children being
absent from school due to home related challenges or their failure to register in schools at the
destination, or parental inability to pay school fees. In addition schools in receiving areas become
overcrowded, leading to poor quality of education (Internal Displacement Monitoring Centre (IDMC),
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2014). Further, conflicts and natural disasters lead to school closures, disrupting learning and the
subsequent performance of pupils/students (Ministry of Education Sciencee and Technology, 2014). In
conflict prone areas, teaching staff remain reluctant to go and work there, further leading to poor
outcomes among students. Studies conducted elsewhere in communities with conflict have shown
negative educational outcomes for children impacted by conflict (Due~nas & Sanchez, 2007; Wharton
& Oyelere, 2011).
1.7 Proposed analytical framework
The proposed framework is an adaptation of the migration and environmental framework as proposed
in the migration and global environmental change report (Black et al., 2011) which was used to
investigate the drivers of migration and its influence on environmental change. The environmental
change framework postulated migration to be as result of the push-pull factors and household and
individual characteristics as intervening variables. Moreover, the framework recognized social
networks, legal framework, and cost of moving as other factors that can facilitate migration. In this
adapted and simplified framework (Figure 1), we propose to understand the drivers and type of
migration, and characteristics of the migrants. Moreover, the proposed framework will help understand
the education and schooling outcomes of the different types of migrants especially those aged between
6 and 24 years.
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Figure 1: Proposed framework to analyse the relationship between migration and education
The drivers of migration adopt a macro neoclassical theory of push and full factors (King, 2013). The
framework characterizes the different migrants, with an important indication as to whether migrants
moved as individuals or households. The latter, is of importance in understanding the education and
schooling outcomes for migrant households with school going children or with children who attain
school going age after migration. We use data from the African Population and Health Research Center
(APHRC) and the Kenya Demographic and Health Survey (KHDS) to 1) examine types, reasons of
migration and movement types, and 2) understand the effects of migration on schooling of individuals
aged between 6 and 24. The schooling outcomes include enrolment, transition, and type of school
enrolled.
Drivers: Push and pull factors: Political, economic, social, environmental, and demographic
Who is migrating?
Type of migrations: rural – urban, inter-urban, internal displacement)
Characteristics of migrants
Schooling outcomes of those aged 6 to 24 years:
Attainment; Transition; Type of
school enrolled
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2 Results from the NUHDSS and KDHS case study 2.1 Context and data
The NUHDSS is a demographic and surveillance system operated by APHRC. The NUHDSS, which
has been in operation since 2003, operates in two informal settlements of Nairobi. Through the
NUHDSS, longitudinal data on births, deaths, migration – both into and out of the surveillance is
collected prospectively. Further, the system provides a platform for investigating other outcomes such
as education, fertility, early childhood development and testing of interventions among others (Beguy
et. al, 2015). By 2012, the NUHDSS was tracking at least 65,000 individuals in 24,000 households.
Migration forms part of the routine data collection. Information on individuals who move in and out of
the two surveillance sites is recorded after every four months. An individual is migrated after
continuously staying within the study sites for at least 120 days, and out-migrated after continuously
spending out of the study sites a similar number of days. Information collected from the migrants
include gender, place of origin, education, reasons for migrating, the formation of movement ( the
whole of previous households or part of the household) and whether moving to join, form or relocate as
a household. In this study, we use data for the years 2003 to 2015, involving about 27,600 migrants, of
which about 12,800 were aged between 6 and 24 years – at entry.
In addition to the NUHDSS data, we use the 2008/9 Kenya Demographic and Health survey datasets
(KDHS) to understand whether there are differences in schooling patterns between the different type of
residents. The KDHS is a national survey conducted after every five years, with an aim of providing
information for monitoring population and health status in Kenya (Kenya National Bureau of Statistics
(KNBS) & ICF Macro, 2010). In addition to the health, population, and fertility data, it collects
detailed education data for household members in sampled households. The Kenya National Bureau of
Statistics and ICF Macro collected the 2008/9 KDHS in collaboration with various development
partners.
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The KDHS consists of various modules: The household, children, men, women, and births among
others. The women module of KDHSS included a migration item, which captured the childhood place
of residence besides the current residence; similar information was not collected for the household
module, which collects background information on household members and the household. Following
this, we use the women migration status when the relationship to the household head is either the head
or the wife to construct a proxy for migration status of a particular household. In total 8444 females,
aged between 15 and 49 participated in the women module out of which 65% (5461)1, where 34% were
the household heads and 66% were spouses to the household head. The data were merged with the
household membership of persons aged between 6 and 24 years. The household membership module
collected detailed information for each household member such as age, gender, schooling information
(both level and grade). In total, 17060 persons were aged between 6 and 24 years; 120172 merged with
the women module dataset. A further relationship inclusion criterion of the household member to the
household head was imposed – to limit the data to the relationship where the member is a child. The
final data included 9016 individuals aged between 6 and 24 years in 3590 households.
2.2 Reasons for migration
Figure 2 shows the reasons for migration for individuals aged between 6 and 24 years, stratified by age
group using data from the NUHDSS for the two informal settlements in Nairobi, Kenya. Overall, the
main driver for migration among the individuals aged between 6 and 24 years is family reunification.
However, the proportion citing family reunification as the main reason decreases with increased age
when the reasons for migration are stratified by age group. That is, 91% and 78% of those aged
between 6 and 9 and 10 and14 years respectively migrated to join their families, while only 34%
among those aged between 20 and 24 years migrated for the same reason. At the young ages, the
1 The other proportion of women were related to the household head in a number of ways including being daughters, in-‐laws, sisters, non-‐related, relatives among others form of relationships.
2 The households that did not merge had women participating in the women module; however, the woman relationship to the household head was not the head or spouse to the head.
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migrants are not independent, and most likely will migrate to existing households that can provide for
their basic needs including education.
Between ages 6 and 19, about 13% migrated to join school, with the highest proportion being those
aged between 10 and 14 years (18%). The primary school going age in Kenya is 6 to 14 years, and
from the reasons for migration, family reunification potentially includes joining school, though not
explicitly stated. From ages 15, better job and business opportunities, an economic driver of migration
becomes apparent, with 60% of those aged between 20 and 24 years migrating in search of economic
opportunities. Other social drivers include better and cheaper housing and better security. Affordability
in terms of both housing and cost of living, and better housing featured as economic pull factors.
Figure 2: Reasons for migrating among individuals aged between 6 and 24 years, NUHDSS
91 78
49 34
35 60
9 18 12 3
0
10
20
30
40
50
60
70
80
90
100
6 to 9 10 to 14 15 to 19 20 to 24
Per
cent
Age group
To be with family Better security Better housing Better job or business opportunities Low cost of living Cheaper rent Illness Pregnant, to join husband Place is accessible To attend school For a change
21
Figure 3 shows the proportion of individuals aged 6 and 24 years who migrated due to schooling
related reasons, stratified by age group and type of movement. The type of movement is either inter-
urban or rural to urban. The inter-urban migrant refers to those migrating from another town to
Nairobi’s two informal settlements under surveillance, while the rural-urban refers to those migrating
from the rural to the two study sites.
A significant proportion of rural-urban migrants aged between 6 and 19 years migrated to ‘join school’
compared to the inter-urban migrants; the difference is pronounced when comparing those aged
between 10-14 years by movement type. We postulate the reason for this pattern overlaps with family
reunion, in which young family members move to join their parents. In such cases, school enrolment
for these young family members who move to join their parents is inevitable for many households.
Moreover, perceived better quality of education provided in urban schools could potentially drive
parents to move their children from rural to the urban.
Figure 3: Proportion of individuals’ migrating to join school by movement type and age group,
NUHDSS
2 10 10
2 [VALUE]*
[VALUE]*
12
3 0
5
10
15
20
25
6 to 9 10 to 14 15 to 19 20 to 24 6 to 9 10 to 14 15 to 19 20 to 24
Inter Urban Rural Urban
Per
centa
ge
* P<0.05
22
Table 2 shows reasons for migration among individuals aged above 24 years in the NUHDSS stratified
by the type of movement. The main reasons for migration among this group of individuals are
economic-related reasons, with the main one being better job opportunities (an average of 71.3%). This
reinforces the trend seen in Figure 2, in which economic factors featured with increased age. The
second cited reason was a social pull factor, to join family. When the results are disaggregated by
movement type, we observed significant differences in the reasons for migration between the two
groups. For instance, 26% and 71% of those migrating from rural to urban were to join their families
and better opportunities compared to 19% and 76% of inter-urban respectively. Very few individuals
aged above 24 years moved to join schools. At older ages, school might mean tertiary education rather
than basic schooling (primary and secondary). Older ages are associated with seeking for economic
opportunities and independence (Statistics NZ, 2007). Moreover, it comes with increased
responsibilities not only to provide for oneself but also to the family.
Table 2: Reasons for migrating among those aged above 25 years by migration status, NUHDSS
Reason for migrating Inter-urban Rural-urban
Number % Number %
To be with family 579 19.3 6,031 25.5*
Better security 10 0.3 35 0.1
Better housing 8 0.3 122 0.5
Better job or business opportunities 2,275 75.7 16,693 70.7*
Low cost of living 28 0.9 117 0.5
Cheaper rent 17 0.6 56 0.2
Illness 22 0.7 169 0.7
Pregnant, to join husband 1 0.0 7 0.0
Place is accessible 30 1.0 46 0.2
To attend school 13 0.4 125 0.5
For a change 22 0.7 211 0.9
* Significant at p<0.05
23
A closer examination of the key reasons for migration stratified by type of migration and gender of the
migrant shows significant differences by gender (Figure 4). Other than joining family and job
opportunities, all other reasons for migration were collapsed. In both types of migration, men tend to
migrate because of economic reasons while the females move mainly to join their families. Overall, a
slightly higher proportion of men who migrate from the rural is in search of a job compared to those
who move from another town to Nairobi. Though among females we see slight differences in reasons
for migration by movement types, the differences are not statistically significant. That is among
females, the inter-urban migration is split between joining family and job opportunities; among the
rural-urban, significantly more women were migrating to join families (55%) than for job opportunities
(39%). This reinforces the gender norms, that exists to influence migration whereby women’s decision
to move may be influenced by the family especially the spouse (O’Neil, Fleury, & Foresti, 2016).
Figure 4: Reasons for migration among individuals aged above 24 years stratified by type of migration
and gender, NUHDSS
50
7
55
6
45
89
39
92
0
10
20
30
40
50
60
70
80
90
100
Female Male Female Male
Inter Urban Rural-Urban
PER
CEN
T
Join family Job opportunity other
24
2.3 Type of movements for those aged 24 years and below
When individuals or households3 (HH) move, they take different forms. In this study, we use the
conventional definition of a household as composed of one or more people who stay under the same
roof and share meals. Following this definition, a household can consist either of family members
(persons related by blood) and non-family members or both (Beaman & Dillon, 2012). The
composition of households can change over time. That is, people move to exit or join existing
households, others dissolve, or composition changes due to events such as births and deaths. In terms
of migration, households can move as “a whole” or as “a part” – where some of the household
members exit the household either to form a new household or to join an existing household under a
different dwelling unit or whole. A household is said to move as a whole when it relocates in its
original composition to a new location. Formation of new households can be interpreted to mean that
the original composition of the household was disrupted and it is only part of it that moved to form a
new household.
Figure 5 shows different household formation by age group and movement type for individuals aged
between 6 and 24 years. The inter-urban movement is related to both formation of new household or
join existing households, while the rural to urban is to join existing households. Across the age group,
the migrations were to join existing households, which speaks to the main reason for the trend shown in
Figure 2. We, however, see an increase in the proportion of those migrating to form their own
households with increased age. Those reporting to form new households, and are in the lowest age
groups implies that when the household that they belong in moved, it did so to form a new one.
3 Households can also refer to families. However, households can extend the composition beyond relationships to include composition of convenience, even in cases where people are not related. In this study, we define households using the extended composition, which largely overlaps to a family.
25
Figure 5: Forms of migration among individuals aged above 24 years stratified by age and migration
status, NUHDSS
We further explored migration using the KDHS 2008/9 data. The KDHS migration status is constructed
using the current place of residence and childhood place of residence for women aged between 15 and
49 years. The childhood and current place of residence for the women categorized as household heads
or spouses to the household head are used as a proxy for the household migration status. Five
categories of migration status (Table 3) were constructed for households with individuals aged between
6 and 24 years. Rural – where the current and childhood place of residence are both rural. Urban –
where the childhood and current place of residents are urban (major town or a city as defined in
KDHS). Rural-urban – where the childhood place of residence is rural and the current residence is
urban. Urban-rural – where the childhood place of residence is urban and current residence is rural.
International – those reporting their childhood place as abroad and currently living in Kenya
irrespective of the current residence status (urban or rural). The rural and urban migration status imply
that the household did not move and if it did, this happened within the same residence (urban or rural).
This nature of categorization allows detailed comparison of schooling outcomes by migration type with
74
80
78
58
50 6
9
0
10
20
30
40
50
60
70
80
90
100
6-9 10-14 15-19 20-24 Inter Urban Rural Urban
Age group Migrattion Type
PER
CEN
TAG
E
HH=HOUSEHOLD Form HH Join HH Relocation
26
a national representation. The KDHS did not collect data on reasons for migration and therefore are not
presented.
Table 3: Migration status of households with individuals aged 6 to 24 years, KDHS 2008/9
Migration status Overall
Women relationship to household head
Head Spouse N % N % N %
Rural 2,360 65.74 827 66.96 1,533 65.10
Urban 304 8.47 103 8.34 201 8.54
Rural-Urban 348 9.69 128 10.36 220 9.34
Urban-Rural 479 13.34 157 12.71 322 13.67
International 99 2.76 20 1.62 79 3.35
The distribution of women migration status, a proxy for household migration status shows most of the
households participating in the KDHS were mainly rural. About 10% of the households had a rural-
urban migration status. Interestingly, 13% of the households are categorized as urban-rural migrants –
meaning the childhood resident of a woman was urban but current residence is rural.
2.4 Migration status and schooling outcomes
We linked the NUHDSS migration data to a longitudinal education study conducted in the same
settlements. The education study collected information on enrolment, level, and grade as well as school
location and type4. In total, 8587 individuals aged between 6 and 24 years merged to their education
data. Of these individuals, 5531 have education data for the same year they migrated, the others’
education data exists at least one year after migrating into the two study sites. We first present
schooling outcomes of those with education data on the year of migration and thereafter conduct a
4 School type was collected for those studying within Nairobi, since confirmation of the information was easier, to ensure its accuracy.
27
longitudinal follow-up to establish progression and transition to secondary school. In addition, we use
KDHS data for the 9016 individuals aged between 6 and 24 as described above.
Table 4 & Table 5: Current schooling enrolment using KDHS 2008/9 show the schooling status of
individuals aged between 6 and 24 years using the NUHDSS and KDHS data respectively; while
Figure 6 shows schooling status stratified by reasons for migration. The schooling status for the
NUHDSS is based on the year of migration while that of KDHS is based on the year of interview. The
NUHDSS data shows that the majority of migrants were in school, and the proportion was high at
younger ages as anticipated. When the information is stratified by type of migration, we observe a
slightly higher proportion of inter-urban migrants were not in school compared to rural-urban migrants,
though the difference is not statistically significant. While the reasons are not clear for the small
proportion of migrant children who are not in school ,the potential one could be barriers such limited
spaces in the existing schools, especially in government schools and related costs of joining a new
school – in both government and low-fee private schools.
Table 4: Current schooling status on year of migration, NUHDSS
Total Inter-urban Rural-Urban
6-9 94.0 91.9 94.1
10-14 93.3 91.3 93.4
15-19 33.3 35.6 33.1
20-24 7.0 6.4 7.0
The schooling status by reason for migration show high uptake among those migrating to join school;
this group is used as the reference point to test whether there are significant difference in the schooling
status by the reasons of movement. Among the inter-urban, enrolment rates for those migrating due to
better opportunities or family reunification were significantly lower than those migrating to join school.
Among the rural-urban, we see significant difference between those migrating to join school and for
better opportunities but not between schooling and family reunification. This clear pattern clearly
28
shows a positive association between schooling enrolment and the reason of migration being either to
join family or school in the NUHDSS.
Figure 6: Reason for movement and schooling status among individuals aged 6-14 years, NUHDSS
The KDHS data shows that at lower ages (6-9 years), school enrolment for the rural residents is low
and higher among urban residents and urban-rural migrants. The difference between the urban-rural
migrants and the rural residents is significantly different at 5%. This can be attributed to late school
enrolment among rural populations (Akyeampong, Djangmah, Oduro, Seidu, & Hunt, 2007). Between
ages 10-14, we do no observed significant differences though the rural-urban migrants exhibit the
highest school enrolment than the rural residents. Overall, school enrolment between rural-urban and
urban-rural migrants does not significantly differ across the age groups. Ages 15 to 19 reflect the
secondary school going population, and the results show significantly lower enrolment among urban
residents compared to the other groups. The result seems contrary to research evidence that suggests
high school enrolments in urban than rural settings (Mugisha, 2006).
[VALUE]*
[VALUE]*
100
94
[VALUE]*
96
0 10 20 30 40 50 60 70 80 90 100
Join family
Better opportunities
Schooling
Join family
Better opportunities
Schooling
Inte
r-U
rban
R
ura
l-U
rban
Percent * P<0.05
29
Table 5: Current schooling enrolment using KDHS 2008/9
Age group Rural Urban Rural-Urban Urban-Rural International
6-9 83.90 93.85 90.60 94.82 83.7
10-14 92.28 95.61 97.49 96.34 96.25
15-19 82.32 72.45 85.81 85.56 88.37
20-24 44.21 42.55 50.91 46.67 52.38
2.5 Education attainment
The cross-sectional nature of the KDHS data gives the opportunity to understand education attainment
of individuals. In our case, education attainment is a measure of the number of schooling years
completed. Table 6 shows the mean and standard deviation of the average years completed by migration
status and age group. We do not see significant differences in the average schooling years for those
aged between 6 to 9 years by migration type, however, the rural residents have the lowest average years
of schooling in this age group. Among those aged between 10 to 14 years, the urban residents had the
highest average years of schooling, which was significantly different from that of the rural residents.
This can be attributed to the early school entry exhibited by those in the urban settings. The differences
in the average years of schooling persist, with the rural residents and rural-urban migrants showing
fewer years of education attainment compared to both the urban and urban-rural migration statuses,
with increased age.
In Kenya, the average schooling years is about 12 for persons aged 18 years and above, assuming
everybody completes secondary school. We therefore in detail examine the number of schooling years
for the 20-24 age group, in order to establish if there are significant differences in educational
attainment. The age group also provides an opportunity for adjusting late school entry, especially
among rural residents by two years from the conventional 18 to 20 years. Examining the data, we see
an average of 8.6 years of schooling among the rural residents, which translates to primary level
30
education, which is 8 years of schooling. Among the urban and urban-rural – the average schooling
years are 12 and 11 respectively – which are significantly different from those of the rural residents.
This means, those migrating to the rural population, insignificantly lose the urban advantage in terms of
schooling, compared with the urban residents, as exhibited by the small difference - one year - in
average schooling years. The difference between the rural and rural-urban average years of schooling is
one year in favour of the latter. This means that those migrating from the rural to urban areas still do
not catch up with their counterparts, who are primarily urban; however, the rural-urban migrants reap
from the urban advantage in terms of an additional year of schooling compared to their rural
counterparts.
Table 6: Average years of schooling for individuals age 6 to 24 years, KDHS 2008/9
Age group Mean & Std. Dev Rural Urban Rural-Urban Urban-Rural
6 -9 Mean 0.93 1.22 1.09 1.24
Std. Dev 1.13 1.26 1.20 1.23
10-14 Mean 3.81 5.12 * 3.86 4.76
Std. Dev 2.10 1.98 1.88 2.08
15-19 Mean 6.92 8.17 7.04 8.69 *
Std. Dev 2.72 3.14 2.70 2.67
20-24 Mean 8.60 11.73 * 9.67 11.28 *
Std. Dev 3.53 3.00 2.74 3.84
Rural = reference for comparison; * significant at P<0.05; Std. Dev=Standard deviation
We further explore the distribution of education attainment among individuals aged 20 to 24 years
stratified my migration status (Figure 7). The figure shows two peaks, at eight (8) years and at twelve
(12 years). The first peak is pronounced for the rural residents, implying low transition rates into
secondary school; and highest for urban-rural migrants, while the urban and the rural-urban exhibit the
same rate. The proportion of individuals with nine (9) to eleven (11) years of schooling is small
showing low rates of secondary school dropouts among those who transit from primary to secondary.
31
However, the pattern for the rural and rural-urban is consistently high between nine and eleven years
indicating more dropouts as compared to the urban and urban-rural migration status. The second peak,
at 12 years reflects secondary school completion in Kenya, which is highest among the urban residents.
Though the curve for the rural peaks at 12 years, it is the second highest, the low primary to secondary
transition and dropouts before 12 years pull the average mean years of schooling to 8.6 for the rural
residents as shown in 7. Moreover, after 12 years, it appears the rural and urban-rural do not progress to
tertiary education. The tertiary education for the urban and urban-rural explains the high average years
of schooling seen in 7.
Figure 7: Years schooling among individuals aged between 20 and 24 years, KDHS 2008/9
Overall, we find the rural and rural-urban migration statuses to exhibit low education attainment
compared to the urban and urban-rural. Poverty levels are high in rural areas besides late school entry,
which could contribute to the observed patterns (Akyeampong et al., 2007). Moreover, in SSA rural-
urban migration is associated with urbanization of poverty (Ravallion et al., 2007), which means the
-5
0
5
10
15
20
25
30
35
40
<3 4 5 6 7 8 9 10 11 12 13 14 15+
PR
OP
OR
TIO
N
NUMBER OF SCHOOLS YEAR
Rural
Urban
Rural-Urban
Urban-Rural
32
migrants are not better off to afford education for their children; this could potentially explain why the
education attainment of the rural-urban are no different from those of the rural, though not conclusive5.
2.6 Utilization of private school for the poor
The Kenya urban informal settlements are characterized by limited government provision of social
services such as schools. The absence of such investments and the demand provided by the residents
gave an opportunity for the low fee service providers (LFPS). By 2012, APHRC research showed that
almost two in every three children in primary schools in Nairobi’s informal settlements attend the
LFPS (Ngware et al., 2013). The LFPS are privately owned and charge low fees, which in most cases is
paid on flexible terms such as on a monthly basis. The LFPS fill the supply gap left out by the few
government schools serving the school going population in the informal settlements. We investigate
whether the patterns of the type of school enrolment over time are related to the migration status of the
individuals (Figure 8).
5 The KDHS data does not indicate when the person migrated and therefore the rural-‐urban could have migrated to urban areas after moving out of school, hence making them no different from the rural residents.
0
10
20
30
40
50
60
70
80
90
2003 2004 2005 2006 2007 2008 2009 2010
Per
cem
t
Year
Resident Inter-Urban Rural-Urban
33
Figure 8: Proportion of primary school going age enrolled in LFPS by migration type, NUHDSS
The results show that migrants (inter-urban & rural-urban) in 2003, when the NUHDSS began, were
more likely to be enrolled in low-fee private schools (LFPS) schools than the residents; with the inter-
urban migrants having the highest proportion of utilization. We think the reason for this is that most of
the inter-urban primary school going age children as shown earlier moved to form new households-
meaning only part of the original household moved- unlike the rural-urban who moved to already
existing households. New households will have difficulties negotiating to join government schools
which are few, their fixed calendar of events in which potential learners sit for entry exams at the
beginning of the year, and other related costs and requirements such as school uniforms. The LFPS,
given they are private entrepreneurial entities provide an opportunity for these households given they
enrol throughout the year, have relaxed rules such as on school uniform and provide opportunities for
parents to discuss on the modalities of paying related school costs (Ngware et al., 2013). In essence, the
LFPS provide flexibility than is absent in government schools. The new households are therefore
disadvantaged when they migrate than those moving to join existing households. The effect of this is
the inter-urban moved into LFPS, which in 2003, the year free primary education was introduced, were
overcrowded. The only option left for the inter-urban migrant was therefore to join the LCPS.
Overtime, we see a shift into government schools by the inter-urban migrants between 2003 and 2006
from 78% to 50% and thereafter a gradual increase. We think that after getting their children in school,
the next step was to reduce costs incurred at LFPS, by enrolling children in government schools with
the clamour of free primary education, and therefore very enthusiastic to negotiate their way into these
schools. In 2006, the data also shows an increase of enrolment into LFCS schools by the residents and a
bit of stability by the rural-urban migrants. Research evidence highlight perceptions of quality in the
LFPS few years after the introduction of FPE policy in Kenya, which could explain the shift into these
schools starting from 2007 (Oketch, Mutisya, Ngware, & Ezeh, 2010). The fact that the majority of
rural-urban migrated to join existing households implies that they already have existing links and
34
networks and therefore had increased likelihood of finding a space within government schools (Kadigi,
Mdoe, & Ashimogo, 2007).
2.7 Transition to secondary school
Further, we check on the transition to secondary school using both NUHDSS and KDHS. The inter-
urban migrants were few and were combined with the rural-urban category. The analysis on transition,
therefore, compares migrants versus residents (Figure 9). We observe insignificant differences in
transition between the migrants and residents. Slightly more migrants repeated grade 8 than did the
residents. Those coded as non-transits were those who never repeated and were not in school in the
subsequent year, and could potentially be referred to as primary school dropouts.
Figure 9: Transition to secondary school by migration type, NUHDSS
Table 7 shows transition rates by migration status using the KDHS data. In total, 265 individuals were
in grade 8 the year preceding 2008/9 data collection and were, therefore, eligible to transit to secondary
school during the year of data collection. The numbers when stratified by migration status are small;
however, the transition was lowest among the urban-rural migrants.
12%
56%
32%
15%
53%
32%
Repeated Transit No transit
Resident Migrant
35
Table 7: Transition to secondary school by migration type, KDHS 2008/9
Migration status N % Transited
Rural 203 62.07
Urban 15 66.67
Rural-Urban 20 60.00
Urban-Rural 23 56.52
International 4 100.00
Total 265 62.26
2.8 Internal displacements
Internal displacements can catalyse migrations besides creating a class of internally displaced persons.
Displacements can happen due to a myriad of reasons including war, violence, and political instabilities
among others. In 2007, Kenya experienced the post-election violence, which was a result of disputed
election results by then ruling party and the main opposition. The violence was in parts of rural areas,
mainly in areas with a mix of ethnic and political affiliations and in the Nairobi urban informal
settlements (Dercon & Gutiérrez-Romero, 2011) and it led to displacement of estimated 600,000
people (Internal Displacement Monitoring Centre (IDMC), 2014). Following this, we explore using the
NUHDSS data whether the reasons for movement changed after 2007. We do this by examining the
reasons for migration in 2005 and 2006 and 2008 and 2009, to give us a picture of what happened two
years before and two years after the violence. We also include the year 2007 to observe whether there
are peaks during the period. Moreover, during this period, we examine the reasons for out-migration.
The importance of examining such patterns is to explore the short-term impacts of the violence on
livelihoods of the residents living in informal settlements. The results are shown in Table 8 stratified by
year, gender, and type of migration.
36
Table 8: Reasons for migrating among those aged above 24 years by migration status, gender and year,
NUHDSS
Gender Year
Inter Urban Rural Urban
Join family Job other Join family Job other
Female 2005 61.1 36.1 2.8 64.3 32.2 3.5
2006 64.2 29.9 6.0 64.9 33.4 1.7
2007 50.0 47.2 2.8 57.3 40.3 2.4
2008 55.7 41.0 3.3 53.3 43.4 3.3
2009 44.0 52.3 3.7 52.1 45.4 2.4
Male 2005 8.3 90.0 1.7 5.2 92.7 2.1
2006 5.5 92.0 2.5 4.0 94.2 1.9
2007 3.4 91.7 4.9 4.0 93.5 2.5
2008 7.8 87.7 4.6 4.8 93.2 2.0
2009 6.7 88.1 5.2 4.4 92.7 2.9
Overall, we do not observe large shifts in the distribution of the reasons for migrating into the two
informal settlements among men. Among women, we see an increase in the proportion of women
reporting to migrate in search of job opportunities and a decrease among those migrating to join their
families. This shift occurs in 2007 and thereafter it is sustained. We think the reason for this are
twofold: 1) more families moving together, hence no need for re-unification like observed in earlier
years and 2) women moving to independent households, where economic opportunities become an
integral reason for movement. There is a need for further examination of the potentiality of the two
reasons, though. The reasons for moving out of the two study sites (results not shown) show that more
women migrated out due to poor job opportunities and businesses after 2007 than were reported in
2005 and 2006. For instance, 31%, 43%, and 47% of women who migrated out of the two informal
settlements in 2006, 2007 and 2008 did so because of lack of or poor job and businesses respectively,
an increase of at least 10% percentage points. Among men, there was not an apparent shift in the
reasons of outmigration. The aftermath of the post-election was increased inflation rates and a drought
37
in 2008, which reduced the purchasing power of households. This had huge negative effects especially
the informal economy, which is the main livelihood for informal settlement residents (Kirimi &
Njuguna, 2014).
3 Conclusion 3.1 Key issues and outstanding questions
As we developed the framework to explain the relationship between migration and education, we
noticed that there is a lot of research that tries to explain this relationship in the context of the West,
and particularly in the context of the USA. There is very limited research in the context of SSA in so
far as establishing the relationship between migration and education. The GEM Report would benefit
from looking at the political, economic, social, environmental and demographic indicators that
influence migration in selected countries in Africa, and Kenya in particular. It would also be
informative to see L. H. Long (1972); (1975) classical studies of how migration influences education,
thereby determining who moves replicated in the context of Kenya. Our sense is that the classical
studies would be a starting point to investigate consistently the processes of migration, and thereby
generate the much needed data as acknowledged by contemporary migration researchers (Bakewell,
2009). Having documented the reasons why people move, much of what is not explained is related to
the distances that people move internally. This would explain the distances that movers cover between
urban centres in Kenya, for intercity migration, and distances covered between the specific rural areas
and urban centres and how this influences education. What would be of interest to the GEM report is
the identification of the quality of the movements of individuals and households and how this affects
education. This may be measured by educational attainment of children, transition to secondary school,
and type of school attended (whether private or public school). Such a focus may extend the work of
Hango (2006) by looking at moving into better and quality residential areas and the impact of such
moves on the education of children. More so, a key priority should be the exploration of the whole
notion of social capital and how this construct links to education of the children aged between 6-24
years in SSA in general, and Kenya in particular.
38
A key issue that is still outstanding for both the internal and external migration and its effects on
education is the idea of using a social capital lens to explain the effects of migration on younger
elementary school children. This is particularly important since the research on Early Childhood
Education (ECD) is becoming a key area of focus than ever before in Kenya. More critical is being able
to effectively define the key constructs that comprise social capital and how it can be used to explain
various migration types. What might be more interesting to look at is whether the decline of social
capital necessarily leads to lower academic achievement among school going population? The
supposition would be that individuals and families migrating to better environments could compensate
the loss of social ties with improved academic achievement. This would lend itself to the study of how
individuals are affected by the characteristics of the destination and origin communities.
In addition, the critical issue for internal migration is for the GEM to focus on the effects that migration
has on the parents of the youth. Hardly did we see any papers that were focused on the impact of
individual and residential mobility on the parents of young people. We presuppose that parents as
caregivers, if impacted negatively by migration, pass on these negative effects to the young people. For
instance, if parents are affected negatively in terms of their education level as a result of moving, they
may not be in a position to mitigate the negative effects that migration may have on their schooling
progress.
Further, the GEM report should focus on the relationship between forced migration and education,
especially in the context of a changing climate that is bringing along more frequent natural disasters
and consequent displacement of people. In addition, localised conflicts continue to displace thousands
of families with implications for the educational outcomes of children and youth. These focal areas
would be important especially for Kenya whose government has put in place some initiatives aimed at
enabling schooling among internally displaced persons. However, what is unclear is to what extent all
displaced persons have been reached by these initiatives as data on the number of displaced persons
and the attendant challenges is unreliable or missing.
39
Finally, in Kenya there are no studies that seek to document the effects of migration on the school
system, on schools and communities. Moreover, all school going children in Kenya are exposed to a
similar local curriculum despite the migration status. Even though there are schools that offer
internationally accepted curriculum, such schools are often expensive and out of reach of the ordinary
child, whether a migrant or a resident. It would be interesting in Kenya, and indeed in the urban
informal settlements where most of these migrants end up to explore the community perceptions to
what has been described as poorly performing, poor students moving to an area. The GEM team could
initiate qualitative studies to understand these community perceptions, what key government officials
in the respective African Countries think in terms of the relationship between migration and education,
and to the school leadership, what are some of the effects of migration, if any on the school systems.
3.2 Take home message
The review of existing literature provides compelling reasons to focus on internal migration and
education, especially the disparate streams of voluntary migration and displacements which are bound
to have contrasting relationships with educational outcomes of school going children and youth. On the
one hand, voluntary migration is usually associated with a move to better destinations and lives;
however, there are instances where such movements are to lesser endowed areas such as urban slums,
with implications for educational outcomes of accompanying children. The results from two urban
slums provide evidence of a negative impact of certain migration types on education. For example, the
NUHDSS data shows that migrants were more likely to use the low fee private schools, which are often
characterized by low quality education. In addition, migration to the rural areas reduces the average
schooling years by one year, thereby losing out on the impact of the urban advantage on schooling. .
The two streams need to be the focus of future studies to understand the pathways to educational
outcomes and explain any noted differences in these outcomes. Theoretical propositions have
reinforced the need to have local context studies that look at the relationship between migration and
education. This focus would inform on-going efforts to ensure the education for all goal does not leave
any segment of the population behind. In the development of future surveys, the DHS should strive to
40
have a consistent way of defining the migration status and make it consistent across countries. In so
doing, data can also be compared across countries.
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