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Sunday Tunde OMOYENI

International Organization for Migration (IOM) & Obafemi

Awolowo University

somoyeni@iom.int; tunde.omoyeni@yahoo.com

Migration and Family Formation Dynamics in Nigeria: An Exploration of

Linkages between Migration and Reproductive Behaviour

Session 021 Internal migration and family dynamics

Tuesday, August 27th 2013

10:30 am - 12:00 pm

Room 105, Convention Hall, 1st Floor

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Abstract

Migration process has implications for changing fertility behaviour through adaptation,

disruption, and selection processes. Despite this, only few available studies have made

recourse to providing empirical evidence on linkages between migration and fertility

behaviour of women in Nigeria. Using data from the 2008 Nigeria Demographic and Health

Survey (NDHS, 2008), the study analyzed differentials in fertility levels of 15,756 migrant

and 7,417 non-migrant currently married women respectively and factors associated with

these. The analysis was done at three levels of univariate, bivariate and multivariate analyses.

Findings of the study found evidence of substantial variations in the fertility levels of

migrants and non-migrants. The mean children ever born for migrants and non-migrants were

estimated at four and five children respectively. In the multivariate analysis, the odds of

reporting five or more children increased by 27% among non-migrants compared to migrants

counterparts (OR=1.27, S.E=0.09). Age, age at marriage, educational level, wealth index,

employment status, ethnicity, religious affiliations and partners’ level of education were the

variables predicting fertility differentials among migrants and non-migrants. Among these

variables, age at first marriage, education, women in high wealth index from Yoruba tribe and

partners’ education exercised greater effects on lowering fertility among migrants than they

did among non-migrants. The study raises policy issue on the implications of migration

process for fertility reduction in Nigeria and need for profound focus on the factors sustaining

high fertility among the respondents.

Key words: Migration, fertility, migrants, non-migrants, Nigeria

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Background to the study

Nigeria remains a high fertility regime country compared with other countries of the

world and prospects for decline is still remote because factors sustaining high fertility

behaviour are prevalent in the country. Recent estimate from the Population Reference Bureau

(2010) shows that TFR of about 6 children per woman for Nigeria is higher than the African

average of less than 5 children per woman. The total fertility rate (TFR) in the country,

however high, varies by regions. The TFRs for some regions in the country are as high as 7

children per woman (North East and North West) while some record a rate of less than 5

children per woman (South East, South South and South West) (NPC and ICF Macro, 2009).

Even for some regions in Nigeria with relatively lower fertility rates, the rates are far higher

than those obtained in many countries in Africa and developed countries. Coupled with this is

the low rate of contraceptive utilization in Nigeria.

Evidence from the Demographic and Health Surveys (DHS) over the years in Nigeria

showed that the contraceptive prevalence rate (CPR) in Nigeria peaked at 15 percent in 2008

with wide variations of about 32 percent and 3 percent CPR in South West and North West

respectively. Descriptively, regions in Nigeria with low contraceptive utilization show

significant high fertility rates. For instance, South Western region has about 32 percent CPR

and TFR of less than 5 children per woman compared with North East and North West

regions with low CPR of about 9 percent and 3 percent and TFR of more than 7 children per

woman respectively (NPC and ICF Macro, 2009). Given the above scenario, questions about

the prospects for fertility decline in high fertility regime Nigeria and at sub-regional levels

become a germane question.

Researchers have devoted considerable efforts to understanding the fertility and

contraceptive behaviour of people as well as fertility and contraceptive use differentials.

Many of these research efforts were devoted to understanding the factors underlying sustained

high fertility and low contraceptive use in Nigeria (Odimegwu, 1999; Moronkola, 2006;

Nwakaeze, 2008; Ogunjuyigbe and Ojofeitimi, 2009; Okezie 2010). Studies have confirmed

that fertility and family planning vary by individual and community characteristics as well as

by demographic and other social factors. For instance, women with low levels of education

(Bongaarts, 2010), in lower wealth quintile (Oye-Adediran et al. 2006) who reside in rural

areas (Avidime et al. 2010) and from Muslim societies or religious orientations (Nwakaeze,

2008) are generally less likely to use contraception and more likely to have higher fertility

behaviour than others from different socio-demographic background and societal

characteristics.

Similarly, a major growing challenge in Nigeria is the population mobility. In

particular, internal migration is growing steadily and more people, especially young people

have continued to move as a result of desire for improved socio-economic status and

associated reasons such as employment, quality education and health care facilities etc., and

major movements have always been from relatively low developed social settings to more

developed areas as well as from a more conservative social milieu to a more permissive

environments.

Also, in a similar development, the volume and configuration of internal migration is

changing in sub-Saharan African countries, particularly Nigeria from the traditional male and

rural-urban dominance to increasing rate of female migration and emergence of other

migration streams (Adepoju, 2004; Awumbila, 2007). This changing dynamic of internal

migration has implications not only for socio-economic development and population

redistributions in Africa but for changing fertility behaviour and contraceptive use. However,

as significant as the need for research on the interrelationships between migration and other

population variables of fertility and family planning are, migration process and reproductive

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behaviour nexus has been and still remains the least researched component of population

dynamics in Sub-Saharan Africa (Omondi and Ayiemba, 2005).

Migration process has implication for changes in reproductive behaviour and attitude

towards contraception as well as HIV diffusion in Africa and particularly in Nigeria. Studies

have consistently documented evidence of large element of population mobility for changes in

fertility proximate determinants such as behaviour within marriage and use of traditional and

modern methods of contraception (Hung et al. 2009) as well as higher sero-prevalence levels

of HIV in urban than rural areas (United Nations, 1994). Despite the above scenario,

explanation of fertility and contraceptive use within the context of internal migration remains

one unexplored research area in sub-Saharan Africa. Studies on migration in Africa have been

linked more to development issues and problems than to demographic issues and problems of

fertility and contraceptive use.

A great concern however, is the utilization of nationally representative data to

simultaneously assess the interplay between internal migration vis-à-vis rural-urban, rural-

rural, urban-rural, rural-rural, urban and rural non-migrants and demographic phenomena of

fertility in Africa using Nigeria as a case study. After extensive search of literature, barring

Makinwa-Adebusoye (1985) study on migrant/non-migrant fertility differentials in Urban

Nigeria and Adewuyi (1986) study on interrelations between duration of residence and

fertility in a Nigerian primate city, documented evidence on the linkages between migration

and fertility is almost non-existent in Nigeria. This is the crux of this current research effort.

Some theoretical considerations

Four models have been developed to explain the mechanisms driving migrants’ fertility

behaviour. These models have also been used by some scholars to explain migrants and non-

migrants fertility differentials and their findings have been documented in the literature

(Chattopadhyay and White, 2003).

The first model, socialization is premised on the idea that people’s value and beliefs

concerning reproduction and fertility behaviour are formed at an early age and become deeply

instilled in them. As a result, when people move to a different social context they do not

immediately adopt the norms and attitudes of the host population.

Conversely, proponents of adaptation hypothesis argue that as migrants move to a new

environment, they are more likely to imbibe the prevailing norms and values of place of

destination on reproduction. Also, selection hypothesis posits that migrants are a non-random

group of people who already possess various observed characteristics (age, educational level,

religion attributes etc. and unobserved characteristics (desire for upward mobility in life,

aspiration etc.) similar to that place of destination that make them prone to exhibit either low or

high fertility behaviour as they move. The proponents argue that fertility behaviour of migrants is

influenced by their characteristics (observed and unobserved) at the place of destination.

Finally, disruption hypothesis assumes that migrants’ fertility and contraceptive behaviour

is influenced by the disruptive process of migration itself such as spousal separation and reduced

fecundity resulting from the stress associated with changing place of residence as well as lack of

knowledge of where to get contraceptive methods. Therefore, migrants experience reduced

fertility levels in the temporary.

The present study will be supported by selection models. Migrants and non-

migrants differ in terms of contextual and socio-economic factors motivating desire to either

migrate or stay behind. Given the patterns of internal migration in Nigeria from a relatively

low developed areas to areas where there are improvements in socio-economic indicators and

evidence of better education, access to employment opportunities, access to serene and

conducive environment among migrants, it is expected that the contextual and observed

characteristics of migrants will differ from that of non-migrants. The study will explore how

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variances in the key contextual factors motivating decision to migrate contribute to migrants’

and non-migrants’ differentials in fertility and contraceptive use outcomes, rather than

disruptive and adaptive effects of migration process on fertility and contraceptive use.

In the multivariate analysis, important socio-demographic variables of migrants and

non-migrants such as age, age at marriage, educational level, employment status, wealth

status and partners’ educational level will be included in the model in order to have a better

understanding of migrants’ and non-migrants’ differentials in fertility behaviour.

Our hypotheses of the study are: first that migrant woman is less likely to have higher

number of children than women with no migration experience. The second hypothesis is that

there is an association between socio-demographic factors and fertility behaviour of migrants

and non-migrants.

The objectives of the paper are to (i) compare fertility behaviour of migrant and non-

migrant currently married women in Nigeria and (ii) examine socio-demographic predictors

of fertility of migrant and non-migrant married women in Nigeria.

Source of data and sample size

The study utilized secondary and primary data. The secondary data is obtained from

the 2008 Nigeria Demographic and Health Survey, (NDHS) datasets. NDHS is a nationally

representative stratified, self-weighting probability sample of women aged 15-49 years. A

unique feature of the 2008 NDHS is that it presented information on all the 36 states in

Nigeria including the Federal Capital Territory (FCT).

Sample design adopted in the collection of NDHS data involved multi-stage sampling

technique. The procedures involved the division of the country into states. Each state was sub-

divided into local government areas (LGAs), and each LGA was divided into localities and

each locality was further sub-divided into different census enumeration areas (EAs). Each EA

was further classified as urban or rural based on a defining criterion, where individual

households were randomly sampled and successfully interviewed (NPC and ICF Macro,

2009).

A total of 33,385 women of reproductive age (15-49) were interviewed in the 2008

NDHS. Out of the total number of women interviewed, the study employed samples of 15,756

and 7,417 for migrant and non-migrant currently married women respectively. The DHS

collected information on socio-economic and demographic characteristics of the respondents

as well as fertility and family planning variables. The unique feature of the DHS, integral to

this study, is that it collects information on previous place of residence, current place of

residence and years lived at current place of residence. Since there is no direct question on

migration, information collected on this lifetime mobility pattern was used as proxies for

measuring migration. Women migration status was identified based on their responses to

questions on “years lived in the current place of residence”. Women who responded “always”

(lived in this place) to question was classified as non-migrants. Others who responded in

terms of number of years lived in the current residences were classified as migrants. Visitors

were excluded from the analysis. Information on previous place of residence, current place of

residence and duration at place of residence was used to construct migration status (migrants

and non-migrants).

Independent variables included in the study were women’s background characteristics

(age, age at marriage, educational level, wealth index, religious, employment status, ethnicity,

region) and those of their partners (age, education, occupation and living arrangement).

The dependent variable in the study is the self-reported number of children ever born.

The response to this question were categorized into three levels – 2 or fewer children, 3-4

children and 5 or more children and coded with 0, 1 and 2 respectively for analysis.

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Limitation of using DHS datasets for measuring migration

Defining women migration status based on evidence from the DHS, 2008 posed a lot

of challenges due to indicators used in measuring and defining women migration status. Since

migration involves more than ordinary change in the usual place of residence, questions on

previous place of residence, current place of residence and years lived at current place of

residence as used in the DHS and this study to measure women migration experience cannot

sufficiently define the respondents’ migration status.

However, due to the limitation and constraint associated with these indicators (current

place of residence and years lived in the place of residence) as used in the DHS datasets in

defining person’s migration status, the term migration status only refers to the lifetime

mobility pattern of the sampled respondents. The focus, however, is to see if the respondents

have at any point in time changed their place of residence prior to the time of the survey. The

study overlooked occurrence of intervening and seasonal movements that might have taken

place prior to the time of the survey. Definition of Migration within this context was used not

as a perfect measure of migration status but as a proxy for measuring migration.

Despite the above stated limitation associated with DHS dataset used, the study

provides a robust analysis of the nexus between migration and women fertility behaviour.

This was consistent with earlier studies conducted by Omondi & Ayiemba (2005) and Lekha

(2007) on migration and fertility behaviour in Kenya and migration and contraceptive use in

Peru respectively.

Method of analysis

Data was analyzed at three levels of univariate, bivariate and multivariate levels using

STATA 10 data analysis software. At univariate level, frequency distributions were made to

describe women’s background characteristics and those of their partners by migration status.

One-way Analysis of Variance (ANOVA) was used at the bivariate level to assess variations

in respondents’ characteristics and mean children ever born for migrant and non-migrant

married women. A p-value of less than 0.05 indicated a statistically significant variation in

independent and dependent variables. Multivariate analysis measured the independent effects

of explanatory factors on the dependent variable (children ever born) using multinomial

logistic regression. Multinomial logistic regression was used because the dependent variable,

children ever born was categorized into three levels- 2 or fewer children, 3-4 children and 5 or

more children. Only the values of odds ratios (ORs) and the standard error were tabulated.

The analysis also included application of DHS appropriate survey weighting procedures to

handle biases that may result from over/under sampled of respondents and also for the results

to reflect overall sample proportion. Weighting procedure used was v005/1000000 being the

weighting number for the Demographic and Health Survey female recode datasets.

Operational definition of variables

Migration status: Migration status is used in this study to mean movement of people

from one place of residence to another over the course of lifetime. It was defined from

DHS, 2008 based on information on previous place of residence, current place of

residence and years lived at the current place of residence.

Rural-rural migrants: This is defined as those who had previously lived in rural

locations prior to the time of the survey and found to have changed their place of

residence to another rural location at the time of the survey.

Rural-urban migrants: This is defined as those who previously lived in rural

locations prior to survey time and later changed their place of residence to urban

locations at the time of the survey.

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Urban-rural migrants: This is defined as those who had previously lived in urban

locations prior to the time of the survey and later changed their place of residence to

rural location at the time of the survey.

Urban-urban migrants: This is defined as those who had previously lived in urban

locations prior to the time of the survey and later changed their place of residence to

another urban location at the time of the survey different from the one they have lived

in previously.

Non-migrants: This refers to respondents who have not moved away from their place

of birth at the time of the survey.

Educational level: Respondents with no education and primary education were

merged to generate low level of education whereas those who reported to be have

secondary and tertiary education were merged to generate high level of education:

Wealth quintile: This refers to various category of wealth status the respondents fall

into and it was measured based on the availability of some household items such as

radio, television, type of building, cooking equipment etc. NDHS, 2008 categorized

respondents’ wealth index into five vis-à-vis poorest, poor, medium, rich and richest.

For the purpose of this study, wealth quintile was collapsed into three vis-à-vis low,

medium and high. Respondents in the poorest and poor categories were merged to

form low wealth quintile whereas those in rich and richest wealth categories were

merged to generate a high wealth quintile.

Children ever born: This refers to the total number of births (living or dead) reported

by a woman to have had in her lifetime prior to the time of the survey: This was used

to measure overall fertility levels of the woman. However, this did not seem to be a

perfect measure of fertility level due to limitation associated with misreporting of

births and lapses in memory with respect to number of children ever had.

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RESULTS

Distribution of respondents’ socio-demographic characteristics by migration status

The distributions of respondents’ socio-demographic characteristics as presented in

table 1 show that more than half of the respondents (57%) are in age group 30 years or more.

About 54% of migrant and 60% of non-migrant women are age 30 years and over. On

average, migrant women tend to be younger (31 years) than non-migrant women (33 years).

Slightly above three-fifth of the respondents had their first marriage at age 15-24 years.

Comparison of mean age at first marriage across migration status indicates that non-migrants

(17 years) initiated marriage at earlier age than non-migrants (18 years). A larger percentage

of the respondents live in rural areas. About 66% and 77% of migrant and non-migrant

women live in rural areas respectively. With respect to educational attainment, almost half of

the respondents (48%) had no formal education. Distributions across migration status indicate

that non-migrants (59%) are more likely to be uneducated compared to migrants counterparts

(46%). Similarly, higher proportion of migrants (33%) than non-migrants (22%) reported that

they have secondary or higher educational attainment.

Wealth status as measured by wealth index is low among the respondents. Almost half

of the respondents (45%) are in low wealth index category. It can be observed that the

proportion of migrant women in low wealth index category (42%) is lower than the non-

migrant women (53%) in the same category. About 7 out of every 10 respondents are

working, with more than three fifth of migrant and non-migrant women working. There is no

so much difference in the proportion of migrant (33%) and non-migrant (34%) women who

are unemployed and those who reported having worked in the last one year preceding the

survey (68% for migrants, 66% for non-migrants)

With regard to religious affiliations, more than half of the respondents (55%) reported

that they are Muslim. Also, more non-migrant women (57%) than migrant women (55%)

belong to Muslim religious affiliation. Almost 2% of the respondents reported to be practising

traditional/other religion (2% and 3% for migrant and non-migrant women respectively).

Higher proportion of the respondents is from Hausa/Fulani ethnic groups (36% and 41% for

migrants and non-migrants respectively). The distributions of respondents across six geo-

political zones in the country indicate higher proportion of migrants (30%) and non-migrants

(33%) from North East region than any other region. The results for the South West region

show that the proportion of migrants (22%) is twice that of non-migrants (11%). Similarly,

more migrants (13%) than non-migrants (7%) in South South region can be observed.

However looking at the distributions of the respondents by living arrangement make it clear

that majority of respondents (90%) are living with their partners. Between the two groups,

migrants have higher proportion of women (91%) who reported to be living with their

partners than non-migrants counterparts (87%).

About four fifth of respondents’ partners were above 30 years of age (81% and 82%

for migrant and non-migrant women respectively). The comparisons of mean age for

respondents’ partners across migration status indicate that non-migrant women tend to have

older partners (43 years) than migrants counterparts (41 years).

As it was observed in the earlier distributions across education, more than three-fifth

of respondents reported that their partners had primary or less education. When compared

across migration status, almost 3 out every 5 migrant women compared with almost 2 out of

every 5 non-migrant women reported that their partners had primary or less education. Only

12% of respondents reported tertiary education for their partners (13% and 10% for migrant

and non-migrant women respectively).

With regards to partner’s occupational status, almost 29% of the respondents reported

that their partners were unemployed (29% for migrants, 30% for non-migrants) while the

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majority of women reported one form of employment or the other for their partners (72% for

migrants, 69% for non-migrants). Out of the respondents’ partners who are employed,

majority of them 35% (36% and 32% for migrants and non-migrants respectively) engage in

trading activities. Almost 18% and 10% engaged in agricultural (16% for migrants, 16% for

non-migrants) and artisan works (10% for migrants, 11% for non-migrants) respectively.

TABLE 1: Distribution of respondents’ socio-demographic characteristics by

migration status

Migration status Migrants (n=15,756) Non-migrants (n=7,417) Both

Age in categories

<20years 8.1 6.9 7.8

20-29years 38.2 32.8 36.6

30 years or more 53.7 60.3 55.6

Mean age 30.9 32.5 31.1

Age at marriage

<15 years 26.2 30.5 27.4

15-24 years 62.6 61.9 62.4

25 or more years 11.3 7.6 10.2

Mean age at marriage 18.0 17.2 17.8

Current place of residence

Urban 34.4 23.4 31.2

Rural 65.6 76.6 68.8

Educational level

No education 45.8 53.9 48.2

Primary 20.9 23.8 21.7

Secondary 25.1 18.1 23.1

Tertiary 8.2 4.2 7.0

Wealth quintile

Low 41.8 52.6 45.0

Medium 16.9 21.2 18.1

High 41.3 26.1 36.9

Employment status

Not working 32.5 34.4 33.1

Working 67.5 65.6 66.9

Religion

Catholic 9.4 8.1 9.0

Other Christians 33.7 32.6 33.4

Muslim 54.8 56.5 55.3

Traditionalist/others 2.1 2.8 2.3

Ethnicity

Hausa/Fulani 36.2 41.2 37.7

Igbo 12.2 11.0 11.8

Yoruba 18.2 10.9 16.1

Others 33.4 37.0 34.4

Regions in Nigeria

North Central 12.8 17.7 14.2

North West 14.4 18.2 15.5

North East 29.5 35.6 31.3

South East 8.3 10.5 9.0

South South 13.0 7.30 11.4

South West 21.9 10.7 18.6

Partner’s living arrangement

Lived elsewhere 9.3 12.7 10.3

Lived together 90.7 87.3 89.7

Partner’s age

30 years or less 19.5 17.6 18.9

31-44 years 41.3 35.3 39.6

45 years or more 39.2 47.1 41.5

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Mean age 41.4 years 43.3 years 41.8 years

Partner’s level of education

No education 38.4 45.4 40.5

Primary 20.6 22.7 21.2

Secondary 27.7 22.1 26.1

Tertiary 13.3 9.8 12.3

Partner’s occupation

Unemployed 28.5 30.1 29

Trading 35.8 31.9 34.6

Agriculture 16.3 21 17.7

Artisans 9.9 11.1 10.2

Others 9.5 6.0 8.5

Source: Omoyeni’s work, 2011 (Data from the 2008 NDHS)

BIVARIATE ANALYSIS

Distribution of mean children ever born by migration status and socio-

demographic characteristics

Analysis of fertility behaviour using mean children ever born (CEB) gives a better

understanding of the dynamics of fertility in a population. Mean number of children ever born

to women represents the childbearing experience of a real age cohort and reflects current and

past fertility behaviour. Table 5.1 presents the findings of the analysis of mean CEB as a

measured of lifetime fertility levels of respondents (migrants and non-migrants) at the time of

the survey across varied background characteristics.

The mean children ever born for all the respondents are estimated to be four children.

When segregated by migration status, non-migrant women tend to have higher number of

children ever born than migrants counterparts at the time of the survey. Distribution of mean

CEB across different migration streams could help to gain insights into the effects of place of

origin and destination on migrants’ fertility behaviour. Fertility of rural native women is the

highest among categories of migration streams whereas urban-urban migrant women reported

lowest fertility level. Comparisons of higher fertility behaviour of rural natives (5 children)

than rural-rural migrants’ (4 children) and higher fertility of urban natives (4 children) than

urban-urban migrant women (3 children) could reflect disruption in fertility behaviour

associated with migration process. Similarly, comparing high fertility of rural native women

with low fertility of rural-urban and urban-urban migrants could suggest adaptation of rural-

urban migrant women to low urban fertility behaviour.

As expected, women in older age group reported average of six children ever born

compared to women in younger age groups, with migrant and non-migrant women aged 30

years or more reported about six children each. Women who initiated first marriage below age

15 years are more likely to have higher number of children ever born. Higher age at first

marriage is associated with decreased number of children a woman will have in her life time.

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Expectedly, rural non-migrants (five children) are more likely to have higher number

of children ever born than urban migrants and non-migrants (four children each). There is no

difference between children ever born among rural migrants when compared with urban

migrants (four children each). Similarly, for both migrants and non-migrants, uneducated

women reported more children ever born than those with higher educational attainment.

Consistent with other previous results, children ever born are lower among migrant than non-

migrant women across all levels of education. With respect to wealth status, fertility is higher

among women in low wealth status (four children for migrants, five children for non-

migrants) than women in other wealth categories. Distributions for migrants in different

wealth category show no significant difference in number of children ever born for migrants

in low and medium wealth categories.

Surprisingly, working women reported more children than non-working women

counterparts. Similarly, there is no much variation in the number of children ever born by

migrant (four children each for non-working and working women) and non-migrant (four

children for non-working and five children for working women) in employment status

category. The mean children ever born for Protestant Christian Migrants (about four children)

and non-migrants (more than four children) is lower than the number reported by women

from other religious group. Evidence of ethnicity differentials in children ever born can be

observed. Hausa/Fulani women are more likely to have higher fertility than women from

other regions. Among non-migrant categories, fertility is high among non-migrant women

from other ethnic groups (about six children) whereas Hausa/Fulani migrants (more than four

children) reported high fertility among migrant women. Distributions of mean children ever

born across regions show that fertility is higher among respondents from North West region

(five children), followed by North East (four children) than women from other regions.

Respondents in South West region reported lowest number of children ever born (three for

migrants and four for non-migrants). Comparisons across migration status show that fertility

is highest among migrants in North West region (five children) whereas non-migrants in

South East region reported highest number of children ever born (five children).

A common pattern observes from these distributions is that fertility is lower among

migrants than non-migrants across all levels of selected background characteristics considered

in this study. This suggests that the results may be speaking more of the effect of disruption in

fertility behaviour resulting from migration process than that of respondents’ background

characteristics.

TABLE 2: Distribution of mean children ever born by migration status and socio-

demographic characteristics

Migrants Non-migrants Both

Mean F-value Mean F-value Mean F-value

Migration streams n/a n/a 77.5***

Rural natives 4.57

Rural-urban migrants 4.13

Rural-rural migrants 4.20

Urban-rural migrants 3.84

Urban-urban migrants 3.43

Urban natives 4.30

Current age 4349.6*** 2362.4*** 6753.7***

<20years 0.73 0.68 0.72

20-29years 2.56 2.69

2.60

30 years or more 5.50 6.05

5.69

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Age at marriage 394.4*** 157.5*** 560.9***

<15 years 4.85 5.41

5.04

15-24 years 3.74 4.26

3.91

25 or more years 2.75 3.35

2.90

Place of residence 136*** 11.60*** 136.7***

Urban 3.60 4.30 3.77

Rural 4.10 4.60

4.28

Educational level 267.3*** 100.6*** 390.0***

No education 4.39 4.89

4.56

Primary 4.25 4.71

4.41

Secondary 3.08 3.40

3.17

Tertiary 2.69 3.25

2.80

Wealth quintile 157.9*** 12.7*** 184.2***

Low 4.27 4.65

4.41

Medium 4.20 4.58

4.34

High 3.41 4.22

3.60

Employment status 196.6** 99.1*** 291.9***

Not working 3.51 4.06

3.69

Working 4.18 4.79

4.38

Religion

33.0*** 8.1*** 31.5***

Catholic 3.71 4.72

4.01

Other Christians 3.66 4.38

3.91

Muslim 4.13 4.56

4.27

Traditionalist/others 4.24 5.29

4.65

Ethnicity

71.1*** 16.5*** 89.6***

Hausa/Fulani 4.20 4.62 4.34

Igbo 3.58 4.80 3.96

Yoruba 3.27 3.79 3.39

Others 4.06 5.57 4.23

Region 54.1*** 15.1*** 63.9***

North Central 3.64 4.35

3.91

North West 4.46 4.56

4.50

North East 4.14 4.75

4.34

South East 3.72 4.83

4.15

South South 4.01 4.80

4.16

South West 3.36 3.71

3.43

Partners’ education 778.0*** 296.4*** 1140.8***

Primary or less 4.35 4.84 4.52

Secondary or less 3.00 3.37 3.08

Partners’ living arrangement 10.45* 70.92*** 50.42***

Lived elsewhere 3.71 3.74 3.72

Living together 3.97 4.64 4.18

SUMMARY OF CEB (3.95) (4.54) (4.14)

Source: Omoyeni’s work, 2011 (Data from the 2008 NDHS)

F-value significant at***p<0.001 **p<0.01 *p<0.05; n/a: not applicable

13

MULTIVARIATE ANALYSIS

Multinomial Logistic Regression Model Predicting Children Ever Born, Controlling Proximate

Variables

Three models of multinomial logistic regression analysis were simulated iteratively.

Model 1 predicts fertility behaviour of migrant married women only. Model 2 examined

significant factors influencing fertility behaviour of non-migrant married women. Model 3

combined data for migrants and non-migrants.

Model for migrants (model 1)

The distribution for predictors of fertility for migrants (model 1) showed that seven

variables are statistically significant in predicting the probability of having 3-4 and five or

more children. These variables include age, age at marriage, education, employment status,

living arrangement and fertility preference. In comparison 1 and comparison 2, age is a

significant predictor of fertility for migrants (model 1). The odds of having 3-4 children and 5

or more children are higher for women age 30 years or more relative to those in the reference

category. For both comparisons 1 and 2, migrant women with older age at first marriage are

significantly less likely to have 3-4 and 5 or more children compared to those who initiated

first marriage at a younger age. Similarly, migrants with higher education are less likely than

those with low education to have 3-4 and 5 or more children.

Wealth status variable is only significant for comparison 2. The odds of having 5 or

more children decrease by 22% for women in high wealth status compared to those in low

wealth categories. Women in medium wealth level are more likely than to in low wealth

category to have 5 or more children. Regarding employment status, for comparison 1 and 2,

the odds of having 3-4 and 5 or more children increase by 56% and 73% respectively for

working migrants compared to those who are not working. Regarding ethnicity, for

comparison 2 only, being a Yoruba migrant has significant effect on fertility behaviour.

Yoruba migrants are less likely to have 5 or more children compared to those from

Hausa/Fulani/Kanuri ethnic tribes.

For comparison 1, fertility is lower among migrants whose partners have higher

education than their counterpart whose partners have low education. Odds indicate that those

with higher education are less likely to have 5 or more children compared to whose partners

have low education. Migrants who are co-residing with their partners have more likely to have

3-4 and 5 or more children.

With respect to proximate variables of ideal number of children, migrant women who

desire to have 4 or more children are 3.32 times and 8.01 times as likely as those in the

reference category to have 3-4 and 5 or more children respectively. Interestingly, fertility

preference decreased the probability of having higher fertility. The odds of having 3-4 and 5

or more children among women who showed preference for having another children decrease

by 82% and 95% respectively. Desiring additional number of children is significant for

comparison 2 only. With regard to the number of wanted children, those who reported that

their partners wanted more children are more likely to have 5 or more children compared to

those in the reference categories.

Model for non-migrants (model 2)

The estimated odds for non-migrant model in predicting fertility behaviour as shown

in Table 6.5 reveals eight statistically significant predictor variables (age, age at first

marriage, educational level, employment status, living arrangement, ideal number of children,

fertility preference and number of children wanted.) for both comparison 1 and comparison 2.

14

For comparison 1, the probability of having 3-4 children versus 2 or fewer children is

compared. Women in age group 30 years or more compared to those in younger age groups

are significantly more likely to have 3-4 and 5 or more children. For both comparison 1 and

comparison 2, non-migrant women who initiated marriage at older age group are significantly

less likely to have high fertility compared to women who initiated marriage at younger age

group.

Educational level of non-migrants is another significant factor influencing fertility

behaviour in comparisons 1 and 2. Women with higher education are less likely to have 3-4

and 5 or more children compared to women with less education. Working migrants for both

comparison 1 and comparison 2 have higher odds of having 3-4 children (OR=1.30) and 5 or

more children (OR=1.52) compared to those who are not working. Similarly, religious

affiliation is significantly related to fertility behaviour. In comparisons 1, those who belong to

non-Muslim religious affiliations reported higher odds of having 3-4 (OR=1.54) more

children compared to Muslim counterparts. As it was found in the migrant model, non-

migrant women from Yoruba ethnic origin reported lower odds of having 5 or more children

compared women from Hausa/Fulani/Kanuri ethnic tribes. Fertility is high among non-

migrants who are co-residing with their partners. The odds show that women who are living

with their partners have higher odds of having 3-4 and 5 or children compared to those in the

reference categories.

Regarding ideal number of children, for comparisons 1 and 2, fertility is significantly

higher among non-migrants who wanted 4 or more children as ideal number children than

those who wanted 3 or less children. The odds of having high fertility decrease among those

who showed preference for having another child. For comparisons 2, women who reported

that their husbands wanted more children are 1.23 times as likely as those in the reference

category to have 5 or more children. This category is not significant for comparison 1.

Model for all the respondents pooled together (model 3)

Model 3 combined migrant and non-migrant datasets. Migration status is an important

predictor of fertility level. Non-migrants are more likely (1.27) than migrants to have 5 or

more children compared to migrants. As indicated in Table 6.5, seven variables were

statistically significant for both comparisons in predicting fertility behaviour in both

comparisons 1 and 2. These include age, age at first marriage, educational level, employment

status, living arrangement, ideal number of children and fertility preference. The odds of the

significant factors showed that women in older age group, those who are working, those who

are co-residing with their partners and women who desire to have 4 or more children are more

likely to have 3-4 and 5 or more children versus 2 or fewer children compared to women in

the reference categories. Conversely, women who initiated marriage at younger age, those

who have high education and women who prefer another child are less likely to have 3-4 and

5 or children.

Some other important variables predicting fertility behaviour are wealth index,

ethnicity and partners’ education. The odds of these variables showed that women in high

wealth group, those from Yoruba ethnic tribe and women whose partners have high education

are less likely to have 5 or more children compared to those in the reference categories.

Women in polygamous family type are more likely to have 5 or more children compared to

women in the monogamous home.

In general, for comparisons 1 and 2 in model 1 through model 3, age, age at first

marriage, educational level, employment status, living arrangement, ideal number of children

and fertility preference remain consistent in predicting fertility behaviour of the respondents.

Comparing model 1 and model 2, wealth index and partners’ education were significant for

model 1 (migrant model) but not for model 2 (non-migrant model). Similarly, religious

15

affiliation and number of wanted children were significant for model 2 (non-migrant model)

but not significant for model 1 (migrant model).

16

Table 3: Multinomial Logistic Regression Model Predicting Children Ever Born, Controlling Proximate Variables

MODEL 1 MODEL 2 MODEL 3

3-4 children 5 or more 3-4 children 5 or more 3-4 children 5 or more

Odd ratio S.E Odd ratio S.E Odd ratio S.E Odd ratio S.E Odd ratio S.E Odd ratio S.E

Migration status

Migrants RC RC

Non-migrants n/a n/a 1.09 0.06 1.27*** 0.09

Current age

Less than 30 years RC RC RC RC RC RC

30 years or more 4.10*** 0.31 30.25*** 2.81 3.29*** 0.38 28.05*** 3.47 3.86*** 0.24 29.58*** 2.22

Age at first marriage

Less than 25 years RC RC RC RC RC RC

25 or more years 0.28*** 0.03 0.11*** 0.13 0.41*** 0.07 0.12*** 0.02 0.31*** 0.02 0.11*** 0.01

Educational level

Primary or less RC RC RC RC RC RC

Secondary or higher 0.70*** 0.06 0.39*** 0.04 0.68** 0.09 0.43*** 0.07 0.70*** 0.05 0.40*** 0.04

Family type

Monogamy RC RC RC RC RC RC

Polygamy 1.17 0.08 1.14 0.11 1.07 0.12 1.20 0.14 1.09 0.07 1.16* 0.84

Wealth index

Low RC RC RC RC RC RC

Middle 1.17 0.12 1.32* 0.15 1.03 0.13 1.03 0.16 1.12 0.09 1.22* 0.12

High 1.11 0.10 0.78* 0.09 1.13 0.15 0.80 0.13 1.11 0.09 0.78* 0.08

Employment status

Not working RC RC RC RC RC RC

Working 1.56*** 0.10 1.73*** 0.16 1.30** 0.13 1.52*** 0.18 1.50*** 0.08 1.67*** 0.12

Religious affiliation

Muslim RC RC RC RC RC RC

Non-Muslim 1.16 0.11 0.87 0.10 1.54*** 0.20 1.26 0.22 1.25*** 0.10 0.98 0.10

Ethnicity

Hausa/Fulani RC RC RC RC RC RC

Igbo 1.03 0.14 1.00 0.17 1.05 0.22 1.02 0.26 1.04 0.12 1.00 0.15

Yoruba 0.98 0.12 0.28*** 0.04 1.40 0.26 0.39*** 0.09 1.07 0.11 0.30*** 0.04

Others 1.04 0.11 0.95 0.12 1.11 0.15 0.79 0.14 1.06 0.09 0.89 0.10

Partners’ education

Low RC RC RC RC RC RC

High 1.02 0.08 0.83* 0.08 0.87 0.10 0.92 0.13 0.98 0.07 0.86* 0.07

Living arrangement

Partner living elsewhere RC RC RC RC RC RC

17

Living with partner 1.35*** 0.14 1.43*** 0.18 1.57*** 0.22 1.90*** 0.32 1.41*** 0.11 1.58*** 0.16

Ideal children

3 or less RC RC RC RC RC RC

4 or more 3.32*** 0.38 8.01*** 1.57 2.21*** 0.40 6.70*** 0.07 3.03*** 0.29 7.70*** 1.25

Additional children

No RC RC RC RC RC RC

Yes 0.18*** 0.02 0.05*** 0.01 0.30*** 0.07 0.09*** 0.02 0.20*** 0.02 0.06*** 0.01

Undecided 0.34*** 0.06 0.14*** 0.02 0.43*** 0.14 0.23*** 0.07 0.36*** 0.05 0.17 0.03

Children wanted

Both want the same RC RC RC RC RC RC

Husband wants more 1.08 0.08 1.23 0.11 1.40*** 0.14 1.34* 0.16 1.16* 0.07 1.26*** 0.09

Husband wants fewer 0.83 0.12 0.97 0.18 1.17 0.37 1.09 0.42 0.88 0.11 0.98 0.15

Source: Omoyeni’s work, 2011 (Data from 2008 NDHS)

Significant at ***p<0.001 **p<0.01 *p<0.05, n/a- not applicable

RC- Reference Category; SE-Standard Error

18

Discussion The findings of the study have been able to highlight the significant influence of

migration status on demographic phenomenon of fertility behaviour. It also revealed some

important individual and societal factors influencing migrants’ and non-migrants’ fertility

behaviour. This section, however, deals with the validation of two (2) hypotheses put forward in

this study and discussion of some major crucial findings of the study.

The first hypothesis that migrant women are less likely to have higher number of children

than women with no migration experience can be asserted in the study. Data on mean children

ever born and findings from the multivariate analyses showed that non-migrant women tend to

have higher number of children than migrants. The odds of having 5 or more children increased

significantly for non-migrant women compared to migrant counterparts. The possible explanation

of low fertility among migrants could be that migrants deliberately delay childbearing, perhaps,

as a result of disruption in fertility associated with migration process such as separation from

partners, difficulties in adjusting to a new life in the area of destination or reduced fecundity

resulting from stress associated with changing place of residence. This finding was also

consistent with Brockerhoff (1995) where he found lower fertility behaviour among new arrivals

in cities than long-term residents of similar age and parity

Also, improvements in educational attainment and higher age at marriage among migrants

could be part of the factors fuelling lower fertility among migrants than non-migrants. Contrary

to the evidence that process of migration could have inhibiting effect on migrants’ fertility

behaviour through disruption in fertility, the study find a strong negative effect of educational

level, higher age at marriage, women in high wealth index, Yoruba women and partners’

educational attainment on children ever born among migrants that is actually slightly larger than

that found among migrants. This finding is in consonance with several studies that have found

similar relationship between migration and fertility behaviour (Omondi and Ayiemba, 2005;

Hung et al. 2009). The results of the findings highlight the importance of migrant and non-

migrant personal attributes in explaining the observed differentials in fertility and contraceptive

use.

The study also showed evidence of association between socio-demographic characteristics

and fertility behaviour. Regardless of women migration status, older women, women in middle

wealth category, working women and women who are co-residing with their partners are

significantly related to high fertility behaviour. Higher age at marriage, women with higher

education and women from Yoruba ethnic tribe have negative association with fertility

behaviour. However, it is stunning to see that the probability of having 5 or more children is low

among women who reported preference for another child. The possible reason for this could be in

congruencies between fertility reported behaviour and actual fertility outcomes.

Beyond the validation of the study hypotheses, there are some important findings of the

study that are necessary for in-depth discussions. The findings of the study on low age of

migrants compared to non-migrants corroborated earlier studies on age selectivity of migration.

Decision to migrate is usually made by young people due to increased opportunities for quality

education, employment and ability to endure stress and difficulties associated with migration

process. Evidence of increase migration of young married women to join their migrated partners

was documented in Adepoju (2006). Also, it was found in the study that migrant women are more

likely to delay initiation of first marriage. The likely explanation for this may be that migrants

delay marriage in order to fulfil aspirations necessitating migration decision such as attainment of

higher education and socio-economic status in the place of destination.

19

The findings that educational levels significantly influence fertility behaviour suggest the

importance of education in explaining the observed migrants and non-migrants differentials in

fertility behaviour. Evidence of low educational attainment among non-migrants compared to

migrants in this study could be used to explain why non-migrants have higher fertility than

migrant counterparts. Hence, qualitative education and other behavioural changed programmes

should target non-migrants more at indigenous locations if fertility targets are to be achieved.

The number of children wanted by partners has significant effects on number of children

ever born. Women whose husbands wanted more children were more likely to have higher

fertility. This could be explained from the perspective of dominance of men folk in determining

actual family size in most developing countries documented by some studies (Odu et al. 2005).

The finding reiterates the need for addressing men’s dominance in women reproductive

behaviour, particularly as it relates to the determination of actual family size.

However, the observed pattern of low fertility among women with higher education and

significant effect of partners’ education on fertility of migrants continues to underscore the

important of women empowerment and need to focus on improving girls’ and men’s education in

the on-going efforts towards fertility reduction in Nigeria.

Also, significant association between ethnicity and demographic outcomes of fertility in

this study supported already documented evidence of cultural diffusion employed in explaining

the demographic transition, particularly in fertility decline in some countries in Europe. The study

found evidence of low fertility behaviour among Yoruba migrant and non-migrant women

compared to those from the Hausa/Fulani/Kanuri ethnic tribes. Fertility reduction mechanisms

vary across ethno-religious groups in Nigeria. In view of this, any significant pragmatic effort

and policy towards contraceptive acceptance and fertility reduction should be implemented

within the context of cultural dynamically sub-population groups in Nigeria.

The substantial evidence of low odds of having 5 or more children among rural and urban

migrants compared to non-migrants and the significant effects of migration status on fertility after

controlling for selected socio-demographic characteristics highlights the effects contextual factors

influencing fertility outcomes rather than migration process itself. Fertility behaviour of the

respondents is influenced mainly by their personal and contextual factors. This finding confirmed

the validity of selection hypothesis as earlier proposed in the study.

Conclusion/Recommendations

Using data from the 2008 NDHS, the study examined socio-demographic predictors of

fertility behaviour of migrants and non-migrants in Nigeria. The study found evidence of eleven

(11) and ten (10) factors predicting migrants and non-migrants’ fertility behaviour respectively.

Among these variables, predicting factors such as age at marriage, educational attainment, and

women from Yoruba tribe exercised greater effects on lowering fertility among migrants than

they did among non-migrants. Four variables – wealth index, religious affiliation, partners’ level

of education and number of wanted children showed variations in their significant effects on

fertility for migrants and non-migrants. The study also found evidence of selection effects on

migrants’ fertility behaviour.

In designing responses to reducing high fertility level in Nigeria, differentials in migrants

and non-migrants fertility behaviour should be factored in. Alternative to other fertility

management strategy in Nigeria could be a policy formulation on internal migration in Nigeria

and promoting orderly and humane movement of the people across the country. Since migrants

exhibited lower fertility behaviour that non-migrants due to access to more education,

employment and other social factors, establishment of framework for easy access to socio-

economic factors will go a long way in checking current rate of fertility in Nigeria. It is also

20

important for the government to develop a framework and mechanism for promoting access to

contraceptives and quality education for non-migrants in the local communities. Most non-

migrants in the study reported higher fertility behaviour due to lack of access to family planning

services and low level of education. Fertility can be reduced by investments in educational

opportunities especially for women, reproductive health and family planning information and

services, and by reducing maternal and child mortality. The timing of these investments is critical

to offsetting current fertility momentum in Nigeria. Slowing population growth sooner than later

could reduce the future population.

Based on the findings of the study that fertility is lower among migrants than non-

migrants, the study concludes that the current tempo of internal migration configurations could be

a momentum for achieving fertility reduction in Nigeria and efforts should be concentrated at

addressing high rate of family planning unmet needs among non-migrants population,

particularly among rural dwellers. Also, in furtherance of achieving overall low population

growth, study emphasis the need for behavioural change programmes directed at discouraging

higher fertility behaviour among the population with a special focus on non-migrant married

women.

21

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