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 gemreport@unesco.org.

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.

   

               

19  

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.  

   

               

20  

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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