Front Cover
 Title Page
 Table of Contents
 List of Figures
 List of tables
 Theoretical schema of the decision...
 The integrated methodology of the...
 Characteristics of migrants and...
 The process of rural-urban...
 Rurul-urban migration, the urban...
 Econometric analysis of rates of...
 Summary and policy implication...
 African rural employment paper...

Group Title: African rural economy paper ; 13
Title: Rural-urban migration in Sierra Leone
Full Citation
Permanent Link: http://ufdc.ufl.edu/UF00087469/00001
 Material Information
Title: Rural-urban migration in Sierra Leone determinants and policy implications
Series Title: African rural economy paper
Physical Description: ix, 113 p. : ill. ; 28 cm.
Language: English
Creator: Byerlee, Derek
Tommy, Joseph L. ( joint author )
Fatoo, Habib ( joint author )
Publisher: African Rural Economy Program, Dept. of Agricultural Economics, Michigan State University
Place of Publication: East Lansing
Publication Date: 1976
Subject: Rural-urban migration -- Sierra Leone   ( lcsh )
Genre: government publication (state, provincial, terriorial, dependent)   ( marcgt )
bibliography   ( marcgt )
non-fiction   ( marcgt )
Spatial Coverage: Sierra Leone
Bibliography: Bibliography: p. 111-113.
Statement of Responsibility: by Derek Byerlee, Joseph L. Tommy, Habib Fatoo.
 Record Information
Bibliographic ID: UF00087469
Volume ID: VID00001
Source Institution: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 02793158
lccn - 77621648

Table of Contents
    Front Cover
        Front Cover 1
        Front Cover 2
    Title Page
        Page i
        Page ii
    Table of Contents
        Page iii
        Page iv
    List of Figures
        Page v
    List of tables
        Page vi
        Page vii
        Page viii
        Page ix
        Page 1
        Page 2
        Page 3
        Page 4
        Page 5
    Theoretical schema of the decision to migrate
        Page 6
        Page 7
        Page 8
        Page 9
        Page 10
    The integrated methodology of the migration survey
        Page 11
        Page 12
        Page 13
        Page 14
        Page 15
        Page 16
        Page 17
        Page 18
        Page 19
        Page 20
        Page 21
        Page 22
    Characteristics of migrants and rates of migration
        Page 23
        Page 24
        Page 25
        Page 26
        Page 27
        Page 28
        Page 29
        Page 30
        Page 31
        Page 32
        Page 33
        Page 34
        Page 35
        Page 36
        Page 37
        Page 38
        Page 39
        Page 40
        Page 41
        Page 42
        Page 43
        Page 44
        Page 45
        Page 46
        Page 47
    The process of rural-urban migration
        Page 48
        Page 49
        Page 50
        Page 51
        Page 52
        Page 53
        Page 54
        Page 55
        Page 56
        Page 57
        Page 58
        Page 59
        Page 60
        Page 61
        Page 62
    Rurul-urban migration, the urban labor market, and urban unemployment
        Page 63
        Page 64
        Page 65
        Page 66
        Page 67
        Page 68
        Page 69
        Page 70
        Page 71
        Page 72
        Page 73
        Page 74
        Page 75
        Page 76
        Page 77
        Page 78
        Page 79
        Page 80
        Page 81
        Page 82
    Econometric analysis of rates of migration
        Page 83
        Page 84
        Page 85
        Page 86
        Page 87
        Page 88
        Page 89
        Page 90
        Page 91
        Page 92
        Page 93
        Page 94
        Page 95
        Page 96
    Summary and policy implications
        Page 97
        Page 98
        Page 99
        Page 100
        Page 101
        Page 102
        Page 103
        Page 104
        Page 105
        Page 106
        Page 107
        Page 108
        Page 109
        Page 110
        Page 111
        Page 112
        Page 113
    African rural employment papers
        Page 114
        Page 115
        Page 116
        Page 117
Full Text




L. Tommy**

*Assistant Professor, Department of Agricultural Economics,
Michigan State University, East Lansing, Michigan (formerly Research
Fellow, Department of Agricultural Economics, Njala University College,
Njala, Sierra Leone).

**Lecturer, Njala University College, Njala, Sierra Leone and
currently Ph.D. Candidate, Department of Agricultural Economics and
Rural Sociology, Ohio State University, Columbus, Ohio.

***Graduate Research Assistant, Department of Agricultural Economics,
Michigan State University, East Lansing, Michigan.



PREFACE . . . .



. ix



Features of the Integrated Methodology . .. 11
Rural and Urban Data Collection . .. 11
Tracing of Migrants . . . .. 11
Integration of Migration and Farm
Management Surveys . . .. 12
Complete Coverage of Urban Migration
Streams . . . . .. 12
Simultaneous Analysis of Rural-Rural
and Rural-Urban Migration . .. 12
Multi-disciplinary Research on Migration .... 13
The Sierra Leone Migration Survey in Practice .... 13
Phase 1: Rural Areas . . .. 13
Phase 2: Urban Areas . . .. 16
Phase 3: Rural Areas . . .. 18
Preliminary Analysis of the Sample of Traced
Migrants . . . . . 19


Definitional--Who Is a Migrant? . . .
Classification of the Rural Population . .
Characteristics of Migrants . . .
Demographic Characteristics . . .
Economic Characteristics . . .
Rates of Migration . . . .
Estimation Procedures . . .
Rates of Rural-Urban Migration . .
Rates of Rural-Rural Migration . .
Summary . . . . . ..


Migration Decision Making in Rural Areas . .
Moving to Town ......
Settling in Town . . .

S. 23

S. 23
S. 25
S. 27
S. 27
S. 32
S. 37
S. 37
S. 43
. 46

S. 48

S. 48
S. 54

Rural-Urban Remittances and Contacts .
Return Migration . . .
Attitudinal Characteristics of Migrants
Summary . . . .


Method of Analysis . . .
Labor Force Participation . .
Structure of Employment . .
Structure of Urban Earnings . .
Rural-Urban Earnings Differentials .
Urban Unemployment . . .. .
The Rate of Urban Unemployment .
Profile of the Urban Unemployed .
Attitudes and Expectations of the
Unemployed Migrants . .
Summary . . . .


Introduction . . . .
The Model . . . ...
Data and Estimation Procedures ..
Empirical Application of the Model .
Implications of the Analysis . .


Summary of Major Empirical Findings
in Sierra Leone .. . . .. . .
Summary of Theoretical and Methodological
Findings . . . . . .
Policy Implications . . . . .
Policies to Raise Rural Incomes . . .
Policies Affecting Urban Incomes . . .
Food Pricing Policies . . .. .
Educational Policies . . .
Distribution of Social Amenities . . .
Policies Affecting Urban Living Costs . .
Policies Affecting Information Flows . .
Policies Directly Controlling Migration . .

BIBLIOGRAPHY . . . . . .


. . 80
. . 81







A Schema of the Decision to Migrate . .

Rural Enumeration Areas and Urban
Areas of the Migration Survey . .




Table Page

1 Overview of the Sampling Procedure and
Questionnaires Used in the Sierra
Leone Rural-Urban Migration Survey ... 14

2 Distribution of Rural-Urban Migration
Sample by Type of Address, Method
of Tracing and Urban Area . ... 17

3 Distribution by Origin and Destination
of Migrants Traced to Urban Areas
Compared to Migrants Identified
in Rural Sample . . ... 21

4 Characteristics of Migrants Traced to
Urban Areas Compared to Migrants
Identified in Rural Sample . ... 22

5 Urban Groupings, Sizes and Economic
Characteristics . . ... 24

6 Disaggregation of the Rural Population
in Each Region by Nonmigrants, Rural-
Rural Migrants, Urban-Rural Migrants,
and International Migrants . ... 26

7 Education, Age and Sex of Nonmigrants,
Rural-Rural Migrants, Urban-Rural
Migrants and Rural-Urban Migrants ..... 28

8 Characteristics of Rural-Urban and
Urban-Rural Migrants by Urban
Area . . . ... .. 30

9 Education of Rural-Urban Migrants by
Rural Origin and Sex . . ... 31

10 Occupational Distribution of Migrants
and Nonmigrants Ten Years and
Older in the Rural Population ...... 33

11 Rural Per Capita Incomes of Households
with Nonmigrants Compared to House-
holds with Rural-Urban Migrants . .. 35

12 Reasons Given for Rural-Rural and
Rural-Urban Migration . . .. 36

13 Gross Cohort Specific Rates of Rural-
Urban Migration by Sex, Education
and Age for Eight Rural Regions
and Four Urban Centers . . .. 40



16 Rural-Rural Migration--Gross and Net
Aggregate Rates by Origin and
Destination Region . .

Persons Identified as Decision Maker
for Migrants by Type of Migrant
and Age at Migration . . .

Comparison of Incomes Estimated by
Rural Nonmigrants and Urban Migrants
for Four Occupations and Actual Incomes
for Migrants with Those Occupations .

Perceived Wage Rate of Rural Non-
migrants by Migration Inten-
tions and Education . . .

Support in Town, Rural-Urban Remittances
and Property Ownership for Working
Migrants by Income Group and for
Nonworking Migrants . . .

Labor Force Participation of Adult
Migrants by Sex, Education and
Age . . . . ..

Percentage Employed in Large-Scale
and Small-Scale Sectors by Sex
and Education and by Urban Area .

Analysis of Variance of Effects of
Sex, Age, Education, Employer
and Urban Area on Earnings . .

Comparison of Rural and Urban Wage
Rates . . . .

Rates of Urban Unemployment by Age
and Education for Male Migrants
Compared to Unemployment Amongst
All Urban Residents . . .

Aggregate Gross and Net Rates of Rural-
Urban Migration by Sex, Education
and Age for Four Destination
Urban Centers . . .

Ratio of Urban-Rural Migrants to Rural-
Urban Migrants Per Year for Adults
15 to 34 Years Age . .


. 42

. . 45

. 52


S. 56


S. 66


Table Page

26 Unemployment by Urban Center . ... .77

27 Profile of Urban Unemployed in
Freetown by Education . . .. 78

28 Gross Rural-Urban Migration of
Adults in Sierra Leone:
Ordinary Linear Function . ... .90



This paper has been developed as part of a three year study of rural

employment in tropical Africa financed under a United States Agency for

International Development contract (AID/csd 3625) with Michigan State

University. The research in Sierra Leone was carried out under a sub-

contract to the Department of Agricultural Economics and Extension, Njala

University College, Sierra Leone, under AID/csd 3625. The research pro-

gram at Njala University College was also supported by a grant from the

Rockefeller Foundation and the Population Council--the latter specifi-

cally to cover the field research costs of the migration study reported

in this paper.

This first report on the Sierra Leone migration survey together with

previous African Rural Employment Papers by Derek Byerlee, "Research on

Migration in Africa: Past, Present and Future," and by Sunday M. Essang

and Adewale F. Mabawonku, "Determinants and Impact of Rural-Urban Migra-

tion: A Case Study of Selected Communities in Western Nigeria," have

been developed to specifically address a major objective of the African

Rural Employment Study--that is the determinants and characteristics

of rural out-migration in Africa.

We would like to express appreciation to the many persons who con-

tributed to this study. In Sierra Leone we are grateful to our research

assistants, Ola Roberts and James Kamara; our enumerators and numerous

respondents. At Michigan State University, particular thanks are due

our computer programmer, Linda Buttel, and as always Janet Munn for her

secretarial services.


Only a decade ago rural-urban migration was regarded as a necessary

element of rapid economic development. Popular theories and economic

history depicted development as the process of moving labor from agri-

culture to industry with industrialization as the driving force of eco-

nomic growth. Moreover this labor transfer from agriculture to industry

was, and still is, widely equated with movement from rural to urban areas.

The disappointing growth rate of agriculture combined with rapid urbaniza-

tion and high urban unemployment rates has led to a questioning of this

strategy. In particular urbanization has been proceeding much faster

than industrialization and growth in industrial employment has lagged far

behind increases in industrial output.

The magnitude and importance of rural-urban migration in most African

countries including Sierra Leone is increasingly recognized as a problem

by policy makers and planners. At least three dimensions of this problem

can be distinguished: (a) the rate, (b) the concentration and (c) the

composition of migration. The rate of migration may be too high for both

economic and social reasons. Numerous authors (e.g., Eicher, et al.

[1970], Byerlee [1974], Todaro [1971]) have noted various price distor-

tions such as high urban wage rates and low agricultural prices particu-

larly for export crops which act to increase rural-urban income differ-

entials and increase migration Moreover the rapid influx of migrants

into urban areas and the stagnation of employment in urban large-scale

sectors has contributed to high rates of urban unemployment--usually in

excess of 10 percent.

The burden that migration places on the urban labor market is illus-

trated by the case of Freetown, Sierra Leone, which is estimated to be

growing at the relatively modest rate of 5.5 percent annually, while

employment in large-scale sectors is growing at most by 2 percent annually.1

Given that about half of the urban labor force is employed in large-scale

sectors, the implied growth rate of the labor force which must be absorbed

in small-scale sectors or become unemployed is of the order of 10 percent
per year. In addition to these urban problems, high rates of rural-

urban migration deplete rural labor which is a limiting factor to agri-

cultural production [Byerlee and Eicher, 1974]. In Sierra Leone, there

is evidence of a decline in export crops as well as an increase in food

imports corresponding to the "diamond rush" of the 1950s.

The problems created by high rates of migration are compounded by

the concentration of migrants in one or two large cities. As Hance [1970]

notes, most African countries have one "primate" city--usually the capital--

which is also the fastest growing city in the country. As a result urban

problems of housing shortages and unemployment are concentrated in the

largest city. In Sierra Leone, over half of the unemployed reside in

Freetown, the capital city.

The composition of rural-urban migrants is a further dimension of

the rural-urban migration problem. Rural-urban migrants are, on the

average, younger and better educated than the rural population from

which they originate. Since education represents a considerable propor-

1The distinction between small-scale and large-scale sectors follows
Byerlee and Eicher [1974]. The literature variously refers to modern
and traditional sectors, formal and informal sectors, etc., to make a
similar distinction.

2Byerlee and Tommy [1975] compute that the equivalent growth of the
labor force which must be absorbed in small-scale sectors or become
unemployed for Nairobi and Abidjan are 25 percent and 12 percent respec-

tion of total rural investment in many rural areas, rural-urban migration

embodies a substantial capital transfer to urban areas [Byerlee, 1974;

Essang and Mabawonku, 1974; Schuh, 1976]. This is a particular concern

because capital is a constraint on rural development, yet migrant school-

leavers, the product of this educational investment, form the bulk of

urban unemployment. There are also distortions in the educational system

such as the emphasis on education as a criteria for job hiring even where

education does not increase productivity in that job. In rural areas,

too, the selective migration of younger people increases the age and

the dependency ratio of the rural population intensifying the problem of

rural labor shortages.

Recently there has been concern that the composition of rural-urban

migrants leads to rural income inequalities. Lipton [1976] argues that

since urban migrants depend upon rural relatives for support while look-

ing for a job, only higher income rural households can afford to send

migrants to town. However, if these migrants are successful in their

job search they remit considerable amounts of their wages back to their

rural households thus increasing income disparities in rural areas. A

similar argument would hold if educated migrants originate in higher

income households who can afford to educate their children.

Despite the widespread recognition of rural-urban migration as a

problem in Africa, research on migration has not emphasized policy mea-

sures for dealing with the problem. As we have discussed elsewhere

[Byerlee, 1974], extensive research has been undertaken on migration but

the underlying theory and methodology of this research has been such that

its policy relevance is limited. Research has often been descriptive

in nature leading to a good knowledge of migrants' characteristics and

their processes of migration but little understanding of the determinants

of migration. Numerous studies of migration in Africa have identified

economic motives as dominant in the decision to migrate but only Sabot

[1971], Essang and Mabawonku [1974] and Rempel [1971] have carefully

measured urban incomes and none have measured incomes of rural households

from which migrants originate. As a result reducing rural-urban income

differentials has become a universal panacea for slowing rates of migra-

tion; but as we shall show in this paper, this fails to recognize the

complexity of the migration process.

Part of the reason for these deficiencies in earlier studies stems

from the methodology employed. Many studies (e.g., Beals, Levi and Moses

[1967], Harvey [1975], Mabagunje [1970]) have used census information

which is severely limited by information on current rates of migration

and which is of no value for such important variables as incomes. As

a result conflicting conclusions are often reached from census informa-


Numerous surveys of migration have also been undertaken but these

are usually partial in scope emphasizing either the rural or urban side

(but not both) and selective streams of migrants--most commonly male

adults. The difficulties of using past results of research on migration

in Africa for policy analysis thus stem from both deficiencies with re-

spect to the underlying theoretical framework for analyzing migration

processes and the methodology employed. In light of this background

of previous migration research in Africa, the basic objectives of this

1For example, Mabagunje [1970] in Nigeria finds a negative relation-
ship between migration and regional per capital income while Beals, et al.
[1967] in Ghana finds a positive relationship between the same variables.


study are (a) to develop a theoretical schema of the decision to migrate,

(b) to develop an improved methodology for testing this schema, (c) to

apply this methodology to a comprehensive analysis of rural-urban migra-

tion in Sierra Leone and (d) to formulate policy recommendations for influ-

encing the rate, direction and composition of migration in Sierra Leone.

This report details the initial results of our findings from a com-

prehensive study of migration in Sierra Leone. First a theoretical schema

of the decision to migrate is briefly presented and discussed, followed

by a description of the integrated methodology employed in the study and

some preliminary analysis of the representativeness of the sample.

The report then turns to a discussion of the survey results. The

characteristics of migrants and the magnitude and direction of migration

flows are described followed by an analysis of the migration process with

emphasis on migration decision making and intra-urban and rural-urban

income transfers associated with migration. Finally the urban labor mar-

ket in which the migrant participates is analyzed with emphasis on the

structure of urban earnings and unemployment.

The remaining sections of the report integrate the findings from the

descriptive analysis to econometrically estimate the determinants of

rates of migration. This is then used as a basis for a discussion of

policy implications of the study presented in the final section.


In Figure 1 we present a schema for viewing the decision to migrate.

Factors affecting the migration decision can be conveniently segmented

into (a) monetary costs and returns relating to incomes, moving costs and

employment and (b) nonmonetary costs and returns relating to risk, atti-

tudinal characteristics, social ties and expectations. Also a distinc-

tion is made between actual and perceived returns to migration according

to the availability of information on urban life.

The monetary benefits of migration are determined by differences

in rural and urban incomes. Measuring rural incomes to an individual is

difficult where work and income is shared by a household [Knight, 1972].

Nonetheless a useful measure of foregone income is the marginal produc-

tivity of labor which depends on the age and sex of the migrant as well

as a host of other variables such as capital stock and technology.

In urban areas the schema follows Todaro's [1969] expected income

model based on the probability that a migrant will obtain a job in the

large-scale sector with a high wage or alternatively remain unemployed.

The probability that a migrant will be absorbed in the urban traditional

sector with lower wages is however explicitly recognized in this schema.

There are also nonmonetary returns to migration particularly the bene-

fits from improved social amenities such as schools and hospitals and

attainment of higher social status.

Costs of migration include the transport costs of moving, the oppor-

tunity costs of looking for a job in the urban area and the cost of

"setting up house". This latter cost can be greatly reduced by the pre-

sence of friends and relatives in urban areas. Finally there are also


Investment Policies Distribution Media
Income Distribution of Policies
Policies, Etc. Amenities

Rural Non-Monetary
Income Returns (e.g.,
Social Amenties Information

ision ( Food Returns Expected Perceived
Prices to Present Value Value of
rfta eis

Source: Adapted from Byerlee 119741.

Urban Housing Policies

costs that cannot be readily measured in monetary units particularly the

cost of breaking old and establishing new life styles which is most acute

for older people.

Since educated migrants are of such overriding importance in the

migration stream, we emphasize education in our schema. Education enters

into the migration decision in various ways. First it may increase a

migrant's access to knowledge of urban areas. Second it may enable mi-

grants to derive additional value from urban life styles (and perhaps

devalue rural life styles). Finally and most important there is ample

evidence that despite unemployment the private returns to education are

considerably higher in urban areas compared to rural areas (e.g., Todaro

[1971], Sabot [1971], Hutton [1973]). An important and unresolved issue

is the extent to which education affects the decision to migrate through

each of these three mechanisms.

We would be remiss if we merely accepted education as a given var-

iable in the decision to migrate. It is essential for long run analysis

of migration to understand who gets educated--that is, we need to look

also at the decision to educate. Again a costs-returns framework is a

useful analytical device providing the variation of these costs and re-

turns with individuals is also considered. It is generally true that

the costs of education are relatively lower for high income families be-

cause of their ability to sacrifice present consumption for investment

in education. Thus higher income households invest more in the educa-

tion of their children [Kinyanjui, 1974; Mbilinyi, 1974].

The difference between costs and returns to migration is the ex-

pected present value of migration. However the migration decision is

based on the perceived value of migration which differs from the actual

value according to the information available on the urban labor market.

Although it is generally recognized that informal channels are the most

important sources of information for migrants there is little evidence

on the quality of this information.

The above simplified framework is useful in identifying and explain-

ing various streams of migrants. In general we can distinguish three main

types of migrants: .(1) migrants in the labor force, (2) migrants attend-

ing school and (3) women who migrate for reasons of marriage.

Migrants working or seeking work readily fit the above schema. It

is hypothesized that they perceive that expected benefits of migration

are higher than the costs. These migrants will often be young since

their time horizon for reaping the benefits of migration is longer and

the cost of breaking old and establishing new life styles are less for

young people. Moreover it is convenient to distinguish between the edu-

cated and the uneducated in this stream. The significance of this for

policy purposes is that we hypothesize that uneducated migrants are likely

to conform to the conventional notion that urban migrants originate in

poor rural households and in poor regions of the country, whereas educated

migrants tend to originate in higher income rural households and more

developed sections of the country with long established educational insti-


The decision of migrants to attend school in urban areas also follows

our framework except that the decisions to educate and migrate are taken

simultaneously but still based on perceived long-run costs and returns.

We hypothesize that there are at least three categories of migrant schol-

ars: (1) those who have to leave home to attend school because there

is no school available in the rural area, (2) those who leave because

urban education is perceived to be of higher quality than rural education

and therefore to have higher returns and (3) those who have urban rela-

tives who can support the costs of education in town.

Finally many women migrate for reasons of marriage. There are those

women who are married when they migrate and whose decision to migrate

may be made by the husband. If this is the case, she can be regarded

as a dependent and should not concern us in policy analysis. However,

a second category of women migrate to find a husband in town. This type

of migrant can be readily analyzed within our framework since it can be

presumed that the monetary and nonmonetary benefits of a urban marriage

induce this migration. Unfortunately most surveys of migration in Africa

are based on samples of male migrants and relatively little information

exists on the extent to which women migrate for marriage reasons or al-

ternatively to find work.

In summary, the theoretical schema developed here emphasizes eco-

nomic variables in the decision to migrate although the importance of

many other factors such as risk, expectations and social ties are also

recognized as affecting individual decisions. But to adequately analyze

these motives, the urban labor market must be disaggregated into large-

scale sectors, small-scale sectors and the unemployed. Furthermore it

is essential to disaggregate migration streams by educational level to

capture earnings differentials between rural and urban sectors and with-

in urban sectors.


Features of the Integrated Methodology

The survey methodology we employed in Sierra Leone was designed

to overcome some of the obstacles to policy analysis inherent in previous

methodologies employed in migration surveys in Africa. Essentially there

are six features in this methodology which lead to the generation of an

integrated set of data on rural-urban migration.

Rural and Urban Data Collection

Exclusive emphasis on studying migration in rural areas or in urban

areas alone gives only one side of the picture. In the Sierra Leone sur-

vey, data were collected in both rural and urban areas and as a result

direct comparisons can be made between rural and urban socio-economic

variables and attitudinal characteristics. Furthermore, expectations of

potential migrants in rural areas can be compared to the reality of ac-

tual migrants in urban areas. Finally both rural-urban migration and

urban-rural migration can be surveyed providing greater insights into the

migration process.

Tracing of Migrants

The rural and urban data were made more comparable by tracing migrants

from specific locations into urban areas. By focusing on migrants from

given villages or other well defined areas (e.g., census enumeration

areas), the variance of variables describing the rural environment such

as agricultural production systems, incomes, ethnic group, distance, etc.,

is greatly reduced. This may enable a reduction in overall sample size

of urban migrants, and hence a more indepth study of this smaller sample.

Integration of Migration and Farm Management Surveys

The difficulty of obtaining accurate rural income data can be over-

come if a migration survey uses the same sample as a recent or ongoing

farm management or household expenditure survey where economic data are

collected through continuous interviews over a period of time (or even

in a detailed one contact interview). Of course, this presumes that the

sampling method for the farm management survey is appropriate for the

migration survey. In Sierra Leone our migration survey was integrated

with a nationwide farm management survey. The farm management survey

provides information on various measures of rural incomes such as house-

hold incomes, returns to family labor and wages for hired labor.

Complete Coverage of Urban Migration Streams

As shown above migration can be classified into various streams,

such as migrants in the labor force, adult migrants not in the labor

force (primarily housewives and scholars) and children who are sent to

town as wards. Each of these streams was included in our survey to take

into account the various decision makers and motives involved and to

produce a more comprehensive analysis of the migration process than is

afforded by surveys which include only male adults (e.g., Rempel [1971]

in Kenya).

Simultaneous Analysis of Rural-Rural and Rural-Urban Migration

The opportunity costs of migrating to urban areas is represented

not only by the alternative of not migrating but also by the possibility

of moving to other rural areas. In Sierra Leone information was also

collected on rural-rural migrants and both rural-rural migration and

rural-urban migration were analyzed.

Multi-disciplinary Research on Migration

Since migration research is in the domain of several disciplines

a fuller understanding of the migration process can be achieved through

involving more than one discipline. In our case we are combining agricul-

tural economics and rural sociology.

The Sierra Leone Migration Survey in Practice

The migration survey was conducted in three phases in 1974/1975

beginning in the rural areas, then moving to urban areas and finally

back to the same rural areas. Details of questionnaires are shown in

Table 1.

Phase 1: Rural Areas

Since one of the features of our migration survey is its integra-

tion with a farm management survey, the rural sample for the migration

survey was based on the sample for a nationwide farm management survey

conducted by Spencer and Byerlee [1976]. The country was divided into

eight resource regions shown in Figure 2 reflecting different ecological

zones and hence farming systems. Within each resource region, three

census enumeration areas (E.A.s) were chosen at random with the exclusion

of localities exceeding a population of 2,000 (the former Sierra Leone

definition of an urban area). For the farm management survey, twenty

households were randomly chosen within each enumeration area for a total

sample size of about five hundred households. Each of these households

was visited twice weekly over a cropping year to obtain data on labor

inputs, output, expenditures, remittances and incomes.1

See Spencer and Byerlee [1976] for more details.


Question- Title of Sampling Sample Frequency Contents of Questionnaire Major Variables
naire Questionnaire Procedure Size of Derived
Number Interview

MG-1 Rural Origin All households in 24 30,000 Once Age, sex, education, fertility, Basic demographic parameters.
questionnaire enumeration areas of persons last place lived, mortality. Population of enumeration
farm level study. Names and addresses of out- areas. Population change.
migrants. Rates of rural-urban migra-

MG-2 Urban All migrants traced 800 Once Detailed information on occu- Urban incomes, unemployment.
migrants into towns 2,000 persons pation, incomes, job search, Rural-urban remittances, etc.
above, support, property, social
participation, the migration
decision, transport, contacts
with home, education, etc.

MG-3 Characteristics All villages in each 100 Once Government, communications, Description of rural envir-
of rural of 24 enumeration villages social amenities, schools, onment.
villages areas. leadership in each village

MG-4 Return Ten persons in each 150 Once Migration history, life in Determinants of return
migrants enumeration area persons town, reasons for returning migration.
who have lived in home
town and returned

MG-5 Out-migrant Heads of households 150 Once Decision making, exchange of Decision making for migration.
households with household mem- persons gifts. Use of remittances.
bers away in town.

MG-6 Nonmigrants Males in each en- 150 Once Migration intentions and per- Determinants of decision to
umeration area, persons ception of urban areas, migrate or not migrate.
15-30 years who
have not left
that enumeration

MG-7 Attitudinal Three migration 110 Once Attitudes to rural and urban Effects of migration on
characteristics streams purposely persons life style, family ties, etc. attidudes.
chosen. Both ur- Occupational prestige.
ban migrants and
rural nonmigrants

MG-8 Unemployment All unemployed 30 Once Details of job-search, Nature and causes of
migrants iden- persons support, expectations. unemployment.
tified in MG-2.

Figure 2. Rural Enumeration Areas and Urban Areas of the Migration Survey

r ." IC

f ,*' Sinkni" ",.

---. ...f"-",.
i ,. ,
1 d

.. o ... ....., *
.Ka>b^ 5 1 C'

f,- L _-"- E_ .. ; "_/-
1 ) ae.." 3 k e .''

1-.Port 5.. -, L ,.. -

... .., .. brk
Lussar oKoldu

-, (. ." M ,, / ..

.i- .* / .r' -al ..

.' ; .... *,H- s/ St--*-n I
8-..,T 1* 2

i * ** *
I la a I II r
!2 6 ,- U -

We urce Regioal Iouedary F
3 0 4 0 so 60Miles a-

EAO I Woof Areas
is. Q0 lie

Sierra Leone

The first phase of the migration survey was conducted in all house-

holds in each enumeration area (E.A.) including the five hundred selected

households in the farm management study. A census was taken of all peo-

ple in the E.A. to collect data on general demographic characteristics

of the people such as age, sex, education, occupation, etc. At the same

time, data were collected on fertility, mortality and in-migration (see

Table 1). Finally each household was asked to provide the names and

demographic characteristics of persons who had left that household.

Addresses were collected where possible for those who had gone to urban

areas. Together these data enable changes in population in an area

to be explained in terms of births, deaths and in- and out-migration.

Phase 2: Urban Areas

The collection of names and addresses of urban migrants from about

2,500 rural households in the first phase resulted in the names of about

2,000 migrants fifteen years old and above in urban areas. Of these one-

third had gone to Freetown--the capital and main city. Table 2 shows that

we were able to obtain some form of addresses for about half of all mi-

grants although this proportion is considerably lower for migrants in the

diamond mining areas (Kono-Tongo). We had little difficulty locating

migrants because as soon as we had found one or two migrants from a given

village they were able to tell us the whereabouts of other migrants from

that same area. Indeed through this process we located many migrants

1Addresses were obtained from several sources including (a) the
household head, (b) letters written home, (c) school children in the
household who often know the whereabouts of brothers and (d) return
migrants from town.


Urban Areas (By Size)





Percent urban migrants identi-
fied by:

a. Name and address 52 27

b. Name only 48 73

Total 100 100

Percent traced through:

a. Name and address from rural

b. Name only from rural sample

c. Referral from other urban
migrants (not identified in
rural areas)






________________________________________ .4 S



Sample Identified in Rur;

55 52

45 48

100 100

-Sample Located in Urban

78 54

0 2

22 45

100 100


Makeni All
al Areas








A v -*

34 40

66 60

100 100

52 52

5 6



who were not originally identified in the rural survey increasing the

total number of migrants by over a third (see Table 2).1

Migrants who were traced and located were interviewed to obtain

indepth information on jobs, migration history, initial support in town,

remittances, expectations, plans to return home and socio-cultural fac-

tors (see Table 1). The incomes of these migrants were obtained using

separate forms for wage and salary earners, self-employed traders and

workers in small industries and the unemployed. Incomes for the self-

employed which are particularly difficult to estimate are being checked

against incomes estimated separately in a small industries survey con-

ducted by Liedholm and Chuta [1976]. Overall, we traced and interviewed

over eight hundred migrants in sixteen urban areas.

Phase 3: Rural Areas

The final phase of the study involved a return to the same rural

areas to interview three groups of rural people.

Heads of Out-migrant Households. Heads of households from which

migrants have left for urban areas were interviewed to supplement the in-

terviews with migrants in urban areas. This was important since in many

cases these household heads have been heavily involved in the migration

decision of a household member. For example, the decision of school

children or wards to migrate at an early age is almost entirely made by

the rural household head. Thus the household head was interviewed to

determine the motives and reasons for sending or encouraging someone to

live in town. At the same time estimates of remittances of migrants and

Enumerators were paid a bonus of Le .20 to Le .25 in lieu of over-
night allowances, etc., for every migrant located and interviewed (le 1.00
= U.S. $1.10).

the extent to which these remittances were invested in agriculture and

other businesses were obtained.

Return Migrants. Phase 1 of the survey indicated that for every

three rural-urban migrants there were about two urban-rural migrants, many

of whom were return migrants. Hence of particular interest to us are

the determinants and consequences of return migration. A sample of urban-

rural migrants was interviewed to obtain information on their stay in

town, their reasons for returning and the impact that migration has had

on their rural social and economic status.

Nonmigrants. Nonmigrants in rural areas were interviewed to under-

stand why people do not migrate. Nonmigrants may be classified as those

not intending to migrate and those intending to migrate. In both cases

expectations of urban incomes and jobs were measured to determine the gap,

if any, between rural expectations and urban reality. The sample of non-

migrants was weighted toward those most likely to migrate, i.e., male,

young and educated persons.

Preliminary Analysis of the Sample of Traced Migrants

If rural areas are sampled randomly and all migrants identified are

traced into town the urban sample will also be random. However because of

time constraints it was not possible to trace all migrants and possible

biases in the urban sample may result if some groups of migrants are more

easily traced than others. Prior to our analysis of the data we have

The sampling for all three questionnaires in Phase 3 was drawn
such that selected farm management households were included in the sample
if they fitted one or more of the categories: out-migrant households,
return migrants and non-migrants. For these selected households accur-
ate income data are available. For other households a short questionnaire
on total output of crops was administered. This was converted to house-
hold income through correlations derived from the farm management survey.

run some checks on sample bias by comparing the characteristics of urban

migrants identified by rural residents in Phase 1 of the survey, with the

characteristics of migrants actually traced into urban areas. Table 3

gives a distribution of both samples by origin and destination. In gen-

eral there is good correspondence between the two samples although the

traced sample is clearly underrepresented in Kono in the diamond mining

areas where we had few addresses. In Table 4 some general demographic

characteristics of the two samples are compared. In the case of the per-

centage male and the average age in each sample there is a very good

correspondence in nearly all cases. However our traced sample has a con-

sistently higher level of education than the rural sample. Reasons for

this include (a) higher success in tracing scholars in the town of Bo

and Kenema (see Table 4), (b) the concentration of our good enumerators

in the better educated southern part of the country leading to higher

success in tracing and (c) likely understatement of the education of

absent migrants by rural household heads, particularly for scholars who

have acquired education in town. Overall we do not view this bias as

serious since in any event urban incomes are estimated and analyzed for

each educational subgroup. In addition the tracing provides several advan-

tages which outweigh this possible disadvantage. For example we obtained

excellent cooperation in urban areas when migrants learned we had visited

their home area and obtained their name and address (and sometimes mes-

sages for the migrants) from a relative. This cooperation was important

to obtaining accurate data on sensitive variables, such as income.


Urban Destination (j)
(By Size) Over 100,000- 20,000-100,000 2,000- Rural
200,000 200,000 20,000 Region
Rural Freetown Kono Bo Kenema Makeni All Small
Region Towns

S 2.4 1.1 0.0 0.0 .2 .7 4.4
3.6 1.1 .3 .1 .3 1.1 6.5

2. Southern 6.5 .5 2.4 .1 0.0 2.8 12.3
Coast 3.2 1.1 1.4 .4 .2 2.6 8.9

3. Northern 4.0 2.7 .i 0.0 1.1 1.3 9.2
Plains 7.5 5.6 .6 .5 1.6 4.4 20.2

4. Riverain 3.0 .5 1.8 .4 .2 2.7 8.6
Grasslands 1.5 .9 1.0 .4 .1 1.8 5.7

5. B s 13.1 1.0 1.3 0.0 2.3 2.2 19.9
5. Bolilands
9.2 1.6 .5 .4 1.8 1.9 15.4

6. M B 1.5 4.2 0.0 6.3 .2 1.5 13.7
1.9 3.9 .8 4.5 .3 3.6 15.0

7. Northern 3.4 1.9 0.0 0.0 .8 2.3 8.4
Plateau 2.4 5.5 .1 .4 .6 2.8 11.8

8. Southern 6.5 5.8 5.8 2.2 0.0 3.8 24.1
Plains 2.9 3.8 3.7 1.7 .3 3.6 16.0

Total 40.5 17.3 11.0 8.8 5.0 17.3 100.0
32.4 23.5 8.4 8.4 5.2 21.8 100.0

Key: Upper left corner:

Lower right corner:

Migrants traced from rural region, i, to urban area, j, as percent of all mi-
grants traced (total 825).

Migrants identified in urban area, j, by survey in rural region, i, as per-
cent of all migrants identified in rural sample survey (total 1,900).


Urban Area (By Size)

Over 100,000- 20,000-100,000 2,000- All
200,000 200,000 20,000 Urban
Freetown Kono Bo Kenema Makeni All

Percent male
Rural sample 64 61 58 54 59 52 59
Urban sample 68 62 59 77 65 55 65

Average age
Rural sample 28.2 25.9 25.8 22.9 27.7 28.5 27.3
Urban sample 29.0 28.9 27.8 24.0 26.5 28.7 28.3

Average education (years)
Rural sample 3.5 2.0 4.7 4.6 3.6 3.4 3.3
Urban Sample 4.9 3.6 7.2 7.2 5.1 4.2 5.0

Scholars as percent all
Rural sample 13 5 22 27 17 17 14
Urban sample 12 4 30 42 24 25 18

aSample of urban migrants identified by interview in rural areas.

Sample of urban migrants traced to urban areas.


We now turn to a presentation of the results of our Sierra Leone

migration survey beginning with a description of migrants' characteris-

tics and estimation of migration rates. However before proceeding with

this analysis we divert briefly to establish an operational definition

of categories of migrants used in this study.

Definitional--Who is a Migrant?

Migrants for the purpose of this study were defined on the basis

of both space and time dimensions. To qualify as a migrant an individual

must have crossed a chiefdom boundary, or moved to an urban area within

that chiefdom. In crossing a chiefdom boundary a migrant was classified

as a rural-rural migrant if he or she moved to another rural location.

Rural locations were defined as any location with less than 2,000 per-

sons--the size limit officially used in Sierra Leone. A rural-rural

migrant was defined as an intraregional migrant if he or she moves to

an area inside the same resource region and an interregional migrant

if he or she moves across a resource region boundary. Alternatively a

migrant was classified as a rural-urban (or urban-rural) migrant if he

or she moved to (or from) an urban area--i.e., towns above 2,000 persons.

In much of the following analysis towns are grouped by size as shown in

Table 5 with each group having characteristics related to its economic

base. Finally migrants were classified as international migrants if they

had moved across a national boundary--in this case mainly to and from

Guinea and Liberia.

The chiefdom is the basic unit of local government in Sierra Leone.


Groups Towns Estimated Total Economic
Population Population Characteristics
Size of Towns in Groups

Freetown Freetown 275,000 275,000 Capital city
and main commer-
cial and indus-
trial center

Kono All towns 100,000+ 110,000 Main diamond
in Kono mining area
and Tongo

Medium Bo 20,000- 100,000 Provincial cap-
towns Kenema 50,000 itals, educa-
Makeni tional services
and some indus-

Small Bonthe Less 130,000 Some district
towns Rokupr than capitals, large-
Segbwema 20,000 ly commercial
Kabala centers for
etc. rural areas

In the time dimension, a migrant must have resided in an area for

longer than six months to be considered a migrant to that area. This

eliminated the problem of classifying people visiting towns and school

children returning home at vacation time as migrants. For a migrant

who had left his place of birth and moved to another area and then re-

turned home again he must have resided in that place for six months or

more and have returned for six months or more to be considered a migrant.

An individual who satisfied these criteria was defined as a return mi-

grant since he had returned to his home area after a period of residence


In summary a migrant was defined as a person who had moved across

a chiefdom boundary for at least six months. A nonmigrant was defined

as an individual who had resided in his chiefdom of birth all his life

or who had not resided elsewhere for more than six months.

Classification of the Rural Population

Using the above definitions, the rural population was divided into

various groups--nonmigrants, rural-rural migrants, urban-rural migrants

and international migrants. Table 6 shows the disaggregation of the

rural population for each rural region. Nonmigrants consistently com-

prise about two-thirds of the rural population. Rural-rural and urban-

rural migrants are about equal in importance and together contribute

about 25 percent of the rural population. Each of these groups is divid-

ed into return migrants and migrants born elsewhere. Return migrants form

about half of all urban-rural migrants but a very small proportion of

rural-rural migrants. International migrants are generally unimportant

except in Region 7 which borders with Guinea and shares several ethnic



Migrant Category Percent of Rural Population in Each Regionb

1 2 3 4 5 6 7 8 All
Scarcies Southern Northern Riverain Boli- Moa Northern Northern Rural
Coast Plains Grasslands lands Basin Plateau Plains Areas

Nonmigrants 77 62 76 71 73 66 64 70 69

Rural-rural migrants 11 26 15 21 11 16 6 15 13

Return migrants 1 7 1 3 4 1 0 1 2

Migrants born
in other rural
areas 10 19 14 18 7 15 6 14 11

Urban-rural migrants 9 11 9 7 15 16 5 14 11

Return migrants 1 5 3 4 5 6 1 6 4

Migrants born
in other rural
areas 2 2 2 0 3 2 0 2 2

Migrants born
in urban areas 6 4 4 3 7 8 4 6 5

migrants 2 1 0 1 1 2 25 1 7

Total rural
population 100 100 100 100 100 100 100 100 100

aThe rural population base used here excludes people who have resided in the area enumerated for
less than six months and hence fall outside the definition of both nonmigrants and migrants.

bsee Figure 2 for location of regions.

groups in Guinea. For this reason international migrants will be ignored

in further analysis.

Rural-rural migrants and urban-rural migrants shown in Table 6 are

in-migrants to that region. The opposite streams of migrants are of

course rural-rural out-migrants and rural-urban out-migrants. Since

we had a nationwide rural sample rural-rural out-migrants to one region

are rural-rural in-migrants to another region and hence in the follow-

ing discussion only rural-rural in-migrants are analyzed.

Characteristics of Migrants

Demographic Characteristics

Table 7 summarizes the education, age and sex characteristics of

various groups of migrants. In general rural-rural migrants have char-

acteristics resembling very closely that of the rural population as a

whole which in turn is dominated by nonmigrants (see Table 6). However,

the breakdown of rural-rural migrants into return migrants and migrants

born elsewhere reveals that return migrants are substantially older and

tend to be predominantly male. Urban-rural migrants, on the other hand,

have a higher level of education and also contain a higher proportion

of males. These characteristics are most pronounced for the return mi-

grants who as in the case of return rural-rural migrants are also much

older than other groups in the population.

The higher level of education and percentage of males among urban-rural

migrants is a reflection of these characteristics among rural-urban out-

migrants. Nearly half of all adult rural-urban migrants have some educa-

tion at the time of migration as opposed to only 10 percent for the rural

adult population as a whole (Table 7). It is significant that although


Type of Migrant


Rural-rural migrant

Return migrants

Migrants born

Urban-rural migrants

Return migrants

Urban born

+I _ _


None Pri- Second-
mary ary



















15-24 25-34




















Total rural
population 90 8 2 .44 40 16 13 30 25.1 47

Rural-urban migrants 55 12 33 2.82 28 41 20 10 17.5 54

SOURCE: Migration survey, Phase 1.

aAge and education are computed
15 years old and above.

for the year when migration occurred.

Education is for persons

bTotal rural population includes nonmigrants, rural-rural migrants and urban-rural migrants.

urban-rural return migrants have a higher level of education than the

rural population, they have only about half the number of years of educa-

tion as those leaving for town despite the fact that many migrants acquire

further education while in town. Return migration is selective of older

persons with little education.

Consistent with other migration surveys in Africa, young people domi-

nate in the rural-urban migration stream. Youths aged 15 to 24 years

comprise 41 percent of all rural-urban migrants and the mean age is only

17.5 years.

The characteristics of rural-urban and urban-rural migrants are fur-

ther disaggregated by urban areas in Table 8. Medium size towns which

consist of Bo, Kenema and Makeni attract the youngest migrants and migrants

with the highest average education. To a large extent this reflects the

substantial proportion of scholars migrating to these towns. Freetown

also receives migrants with a relatively higher education while migrants

to Kono have a significantly lower education reflecting the dominance of

self-employment in diamond mining which does not require educational skills.

The larger urban centers attract a higher proportion of males than

medium and smaller towns. Nonetheless the statistic of 58 percent male

migrants to Freetown or Kono, is not unduly high when compared to statis-

tics from other countries, particularly Kenya where males comprise about

70 percent of the migrants to Nairobi.

In Sierra Leone the education of rural-urban migrants is highly re-

gion and sex specific. Table 9 shows that for the southern regions (2,

4, 6, 8) almost three-quarters of male migrants have some secondary school-

ing while for the northern regions (1, 3, 5, 7) only about one-quarter


Migrants Urban Areas All Urban
Freetown Kono Medium Small
Towns Towns

Number years of education

Rural-urban migrants *2.87 1.76 3.81 2.89 2.82

Urban-rural migrants 1.47 .82 1.58 1.04 1.23

Average age

Rural-urban migrants 18.1 18.8 15.6 17.4 17.5

Urban-rural migrants 23.9 23.0 23.5 23.7 23.5

Percent male

Rural-urban migrants 58 58 49 54 54

Urban-rural migrants 55 66 55 50 53

SOURCE: Migration survey, Phase 1.

aAge and education are computed for the year migration occurred;
education is for persons 15 years old and above.


Rural Region

1. Scarcies

2. Southern Coast

3. Northern Plains

4. Riverain Grasslands

5. Bolilands

6. Moa Basin

7. Northern Plateau

8. Southern Plains

All rural regions


No Primary Secondary
(Percent Distribution)
74 3 23

26 12 62

65 10 25

18 18 64

72 2 26

16 8 76

71 9 20

12 17 71

44 10 46












No Primary Secondary
- (Percent Distribution) --
-87- 4 9

46 15 39

77 12 11

61 19 20

98 2 0

60 18 22

84 13 3

60 20 20

70 14 16

aEducation of adults 15 years and above.











i I

have secondary schooling. Education of females is much lower but follows

a similar regional pattern.

Economic Characteristics

In addition to age, sex and educational characteristics it is in-

structive to note the occupation of migrants and nonmigrants in the rural

population. A higher proportion of rural-rural migrants are in nonfarm

occupations such as small industries (tailors, carpenters, blacksmiths),

small-scale trading and services and government jobs than is true of non-

migrants or the rural population as a whole (Table 10). This dominance

of nonfarm occupation is even more pronounced for urban-rural migrants.

Almost 20 percent of urban-rural adult migrants have a nonfarm occupation

compared to less than 5 percent for nonmigrants. These results indicate

that persons with nonfarm occupations are more mobile perhaps in part due

to lack of necessity for land and in part because many serve apprentice-

ships in town where apprenticeship fees are lower [Liedholm and Chuta,


An important hypothesis arising from our theoretical schema is that

uneducated rural-urban migrants originate in poorer rural households while

educated migrants originate in higher income households who have the re-

sources to educate their children. For a subsample of five hundred rural

households we obtained accurate data on household income in an associated

farm management survey by Spencer and Byerlee [1976]. Rural per capital

incomes were computed for adult migrants and nonmigrants in the age cate-

gory 15 to 35 years in which most migration takes place. The results,

1Occupations reported here are the stated primary occupation of rural
people. In practice the occupation may change from season to season (see
Liedholm and Chuta [1976]).



Migrant Category


Rural-rural migrants

Urban-rural migrants

Total rural population 66.7 3.4



71.1 1.8

66.9 3.7

60.9 6.0

Trade Govern- Housewives
Services ment
--- (Percent Distribution)

2.7 .7 14.7

3.9 2.1 16.7

7.6 3.7 15.5

4.0 1.6 15.5

Scholars Othera








5.6 3.2 100

aIncludes unemployed, Arabic scholars, religious workers,


reported in Table 11 do indeed support our hypothesis since incomes of

rural households are significantly lower for households with uneducated

male migrants and significantly higher for households with educated male

migrants. (Both differences are significant at the 5 percent level.)

Educated female migrants also originate in higher income rural house-

holds but uneducated females originate in households with average incomes.

This is probably in part because (as we show below) most uneducated females

migrate for reasons such as marriage rather than to seek a higher pay-

ing job. The fact that educated migrants originate in higher income

households is strongly underlined by the fact that migrants under 15 years

of age sent for schooling in town originated in households with per capital

incomes 68 percent above the average.

Differences between migrant and nonmigrant household incomes arise

in part out of a tendency for uneducated migrants to originate in some-

what poorer regions and villages and educated migrants to originate in

higher income regions and villages. However the differences in house-

hold incomes by type of migrant persist even at the village level where

households with male uneducated migrants had average incomes 8 percent

below average incomes for that village and households with male educated

migrants had incomes 6 percent above average incomes for that village.

These differences are not large in part because incomes within a village

tend to be evenly distributed.1

Finally the reasons for migration are shown in Table 12. Although

reasons for rural-urban migration will be considered in more detail in

It is possible that lower per capital household income of house-
holds with uneducated migrants is in part the result of the migration
since older persons are left behind. This is the subject of ongoing


Type of Migrantb

1. Nonmigrants

2. Uneducated rural-urban

3. Educated






Per Person Per Year)




aFor rural-urban migrants
from which migrants originate.

incomes refer to the rural household
Incomes exclude rural-urban remittances.

Includes only adults aged 15 to 35 years old.

CDifferences between all male groups and between nonmigrant and
educated female migrants are significant at the 5 percent level.


Migrants Work Marry Schooling Warda Other Total

(Percent Distribution)

aChildren sent away for upbringing.

a later section the comparison of reasons for rural-rural and rural-urban

migrants shows considerable similarities in both cases. Significantly

only about a quarter of migrants leave for work related reasons. Marriage

is equally important for rural-rural migrants while schooling is the rea-

son given for over one-quarter of rural-urban migrants. This underscores

the limitations of surveys which focus only on male migrants in the labor


Rates of Migration

Estimation Procedures

Rates of both rural-urban and rural-rural migration were computed

from our demographic survey in rural areas. Persons who had left the area

enumerated were identified and the year they departed recorded. Likewise

persons residing in the area enumerated at the time of the survey were

asked their last place of residence and the years they lived in their pre-

sent residence. Rates of migration were computed from the number who

had moved in and out of the area each year using the last five years as

a base. Two deficiencies are inherent in this approach. First even

though our total sample included 30,000 persons it was necessary to use

the last five years rather than the last year to provide a large enough

sample for measuring origin-destination specific migration rates. Hence

there is some recall lapse which tends to underestimate in- and out-migration

by about 25 percent. It is also possible that the recall lapse is less

For rural-rural migrants, work related reasons include farming.
2 -kt
Recall lapse was estimated by fitting the function, mt = m e
to the cumulative average migration rate where mt is the migration rate
estimated for t, mo is the migration rate corrected for recall lapse,
k is a constant and t is time [Som, 1968].

for certain groups of out-migrants, particularly those who have been

successful in town. Second there is likely to be a better reporting of

in-migrants who are resident at the time of the survey than out-migrants

who are absent.1 For these reasons the absolute value of both gross and

net out-migration are probably underestimated although we believe the re-

lative magnitudes of our estimates are valid.

In estimating migration rates two measures are employed. First the
aggregate rate of migration, mijk, is defined as the number of persons in

the kth age, sex, education cohort, Mijk, migrating from origin i to

destination j per thousand of the rural population N. in i. That is,
mijk = Mijk x 1,000/Ni. Second, we computed cohort-specific rates of mi-

gration, mjk by expressing the migration rate as the rate per thousand

of that specific age, sex, education cohort in the rural population, where
s th
m. = M. x 1,000/N and N. is the number of the k age, sex, educa-
ijk ijk ik Ik
tion cohort in the rural population.

These two measures--the aggregate rate and the cohort specific rate--

are both useful in analyzing migration streams. Aggregate rates are a

measure of the number of persons in a specific cohort migrating while

cohort specific migration rates measure the propensity to migrate. For

example in a given area the propensity for educated persons to migrate--

as measured by the cohort specific rate--may be high but the number of

educated persons migrating as measured by the aggregate rate may be low

simply because there are very few educated persons in that rural popula-

tion. It should also be noted that aggregate rates are additive over

Evidence that this is the case is obtained for rural-rural migrants
where the number of rural-rural out-migrants should equal the number of
rural-rural in-migrants because we had a nationwide sample. In fact, we
found that in-migrants outnumbered out-migrants by about 50 percent.

cohorts (k) and destinations (j) but cohort specific rates are only addi-

tive over destinations (j).

Finally we estimated both gross and net migration flows. Aggregate

net migration rates were computed from gross rates by the equation mjk =

(Mijk Mik )/Ni] x 1,000 where M. is the number of persons of the kth

cohort migrating from i to j and M.ik is the number of persons of the kth
cohort migrating from j to i. Cohort specific net migration rates were

similarly estimated. Gross rates are, of course, a measure of the total

movement of people while net migration rates are an indicator of changes

in population size and structure.

Rates of Rural-Urban Migration

Gross cohort-specific rates of rural-urban migration measuring the

propensity to migrate for twelve age, sex and education cohorts are shown

in Table 13. Here migrants are divided into three age groups--15 years

and younger, 15 to 34 years and 35 years and older--and two educational

levels--the uneducated with less than five years of schooling and the

educated with five years or more of schooling. Both age and education have

marked effects on the propensity to migrate to urban areas. Consequently

the 15 to 34 year age group has the highest propensity and the over 34

year age group the lowest propensity to migrate for both sexes and both

educational levels. Likewise the propensity to migrate for educated per-

sons is consistently five to ten times higher than those without educa-

tion for all ages and sexes. On the other hand, sex has relatively little

effect on the propensity to migrate although there is a slight tendency

for educated females to have a lower propensity to migrate compared to males

in the same age cohort.


Rural Regions Sex
and Male Female
Urban Centers
Uneducated Educated Uneducated Educatec

Age (Years)

<15 15-34 >34 <15 15-34 >34 <15 15-34 >34 <15 15-34 >34

By Rural Origin (Rate Per Thousand)

1. Scarcies 1.6 15.8 8.8 22.2 145.5 n.a. 11.0 9.4 3.3 100.0 100.0 n.a.

2. Southern Coast 5.1 10.5 1.9 55.6 134.9 16.7 16.1 7.7 2.8 46.2 87.0 n.a.

3. Northern Plains 3.8 37.6 6.5 23.5 248.6 75.0 5.7 14.3 3.2 120.0 428.6 n.a.

4. Riverain Grasslands 6.4 5.2 1.9 54.5 116.3 n.a. 11.9 9.2 2.1 55.6 146.7 n.a.

5. Bolilands 4.7 30.2 4.2 12.1 85.0 44.4 13.2 16.6 4.7 100.0 22.2 n.a.

6. Moa Basin 8.0 12.7 1.3 55.8 170.5 23.1 15.4 11.4 3.3 25.0 98.0 n.a.

7. Northern Plateau 5.8 3.0 3.0 133.3 107.1 50.0 3.9 11.8 3.1 n.a. 72.7 n.a.

8. Southern Plains 10.0 22.7 2.8 33.3 154.1 85.1 14.6 21.8 3.8 61.6 108.8 n.a.

By Urban Centere

Freetown .7 4.4 1.2 21.7 43.5 20.5 2.1 2.3 1.0 14.0 28.7 n.a.

Kono 1.3 10.5 .9 2.3 23.2 5.6 1.8 5.5 .7 n.a. 18.2 5.7

Medium Townsd 2.6 4.5 .3 14.5 46.2 8.2 4.6 3.9 .8 25.4 44.8 11.3

Small Towns 1.9 3.4 1.0 23.7 37.0 10.8 2.4 2.1 .9 9.2 34.3 22.0

All Rural-Urban
All Rural-Urban 6.4 22.9 3.4 62.1 149.9 45.1 10.9 13.7 3.3 49.6 125.9 39.0

Cohort specific rates of rural-urban migration are computed as the number of rural-urban migrants
per year of a particular age, sex, education cohort per thousands persons of that cohort in the rural

bThe number of educated migrants in the age category 35 years and above is sometimes too small to
estimate a cohort specific migration rate.

Computed from all rural regions weighted by population for each rural region.

dMedium size towns are Bo, Kenema and Makeni.

NOTE: n.a. = not available because sample too small for estimation.

Overall there are substantial differences in cohort-specific migra-

tion rates by rural region of origin and urban centers of destination.

As observed earlier uneducated migrants have a high propensity to migrate

to Kono while educated migrants tend toward Freetown and medium size towns.

Aggregate gross rates of migration shown in Table 13 follow a simi-

lar pattern to cohort specific rates except that the female uneducated

are more important and female educated migrants less important than males

becuase females have a much lower level of education. However, aggre-

gate net migration rates also shown in Table 14 reveal several points of

interest. First for uneducated migrants of both sexes, net rates for per-

sons 34 years and older are negative indicating that the urban-rural flow

exceeds the rural-urban flow. For males this urban-rural flow is so large

that the net rate of migration for uneducated males of all ages is nega-
tive. For educated persons, however, the net flow is always positive,

even for those above 34 years of age. In fact, educated males 15 to 34

years comprise almost exactly half of all net rural-urban migration.

A second interesting finding of Table 14 is that the most important

destination in terms of net flows to urban areas is Kono. For example,

the net migration rate for all people to Kono is 2.12 compared to 1.45

to Freetown. In fact, using (a) net rates computed here, (b) approximate

urban population figures of Table 5, (c) urban natural growth rate of 2.5

percent and (d) allowing for the underestimation bias against out-migration

reported previously, we can compute rough population growth rates for Free-

town of 4.5 percent; Kono, 9.0 percent; medium towns, 5.1 percent and small

Bear in mind, however, that we believe our out-migration figures
are an underestimate as discussed earlier.



Urban Centers Sex Total
Males Females All
Education sons

Uneducated Educated Uneducated Educated

<15 15-34 >34 <15 15-34 >34 <15 15-34 >34 <15 15-34 >34

Gross Migration Rates

Freetown .13 .49 .15 .09 .77 .09 .39 .41 .13 .04 .17 0 2.88

Kono .26 1.11 .12 .03 .47 .04 .33 1.04 .09 .01 .15 .01 3.67

Medium Towns .50 .42 .04 .19 1.17 .07 .82 .71 .12 .13 .43 .02 4.62

Small Towns .38 .36 .14 .08 .57 .09 .43 .37 .14 .05 .20 .05 2.86

All Urban Centers 1.27 2.38 .45 .40 2.98 .30 1.97 2.52 .48 .23 .96 .07 14.01

Net Migration Ratesc

Freetown -.08 .27 -.04 .05 .66 .07 .20 .18 -.02 .03 .14 -.01 1.45

Kono .03 .70 -.22 .02 .40 .02 .17 .80 .03 .01 .13 .01 2.12

Medium Townsb -.12 -.05 -.42 .12 .83 -.04 .31 -.02 -.10 .05 .26 0 .82

Small Towns -.03 .04 -.20 .06 .46 .06 .05 -.19 -.10 .05 .15 .03 .38

All Urban Centers -.20 .97 -.88 .24 2.35 .12 .73 .77 -.19 .15 .68 .03 4.77

Total all ages -.13 2.71 -- 1.31 I- .86 + 4.77
Total all ages
and education
levels 2.58 + 2.17 4.77

aAggregate rates of migration are computed as the number of migrants for a given age, sex
and education cohort per thousand total rural population.

bMedium towns are Bo, Kenema and Makeni. Small towns have less than 10,000 population.

cNet rates of migration are computed by subtracting the rate of urban-rural migration
from the rate of rural-urban migration.

towns, 3.5 percent. These growth rates are consistent with estimated

growth rates for these centers.

Finally even casual inspection of Table 14 indicates that the differ-

ence between net migration and gross migration is largest for uneducated

groups and for smaller towns. For example, gross migration is largest

for medium size towns but when net rates are computed medium towns receive

only a small proportion of the net flow of migrants. In Table 15 a mea-

sure of this difference, the ratio of urban-rural migrants to rural-urban

migrants is computed. Without exception this ratio is higher for unedu-

cated migrants than educated migrants. This is expected since return

migrants are likely to be less educated and move more freely between rural

and urban occupations with a relatively low differential in pay. In

addition the ratio is highest for small towns and least for large towns.

This implies that migration to the large towns of Kono and Freetown is

relatively permanent whereas migration to smaller towns is much more

circular in nature with more return migration. There is then consider-

able mobility of rural people, particularly uneducated, to and from

small towns usually over short distances.

Rates of Rural-Rural Migration

Gross and net aggregate migration rates for rural-rural migration

are reported in Table 16. Again gross migration rates indicate signi-

ficant flows of migrants for some regions although intraregional flows

often dominate. However, when net migration flows are computed the impact

on population changes is usually quite small. Regions 2 and 3, the South-

ern Coast and Northern Plains, are the major out-migration areas while

Region 1, the Scarcies Area, is the main recipient. The determinants

of the magnitude of these flows will be analyzed later in this report.


Towns Males Females

Uneducated Educated Uneducated Educated

Large towns: Freetown, Kono .39 .14 .32 .16

Medium and small towns 1.01 .26 1.19 .35



Region Destination Region

Scarcies Southern Northern Riverain Boli- Moa Northern Southern Total
Coast Plains Grass- lands Basin Plateau Plains Rate
lands All
1 2 3 4 5 6 7 8 Desti-

Origin Region

1. Scarcies

2. Southern Coast

3. Northern Plains

4. Riverain Grasslands

5. Bolilands

6. Moa Basin

7. Northern Plateau

8. Southern Plains

Origin Region

1. Scarcies

2. Southern Coast

3. Northern Plains

4. Riverain Grasslands

5. Bolilands

6. Moa Basin

7. Northern Plateau

8. Southern Plains












Gross Migration Rates

3.5 .4 .3

.5 .4

1.5 -- 1.6


.2 -- 3.7

-- .1 .2

.2 .1 1.7

Net Migration Rates

--6 -.3 .

2.6 -.3 .3
































aRate per thousand of origin population.

A final observation is that rural-rural migration is relatively unim-

portant compared with rural-urban migration. Our data indicate that only

about 12,500 persons or 0.5 percent of the rural population change rural resi-

dence in a year, compared to some 50,000 or about 2.0 percent of the total

population who change residence between rural and urban areas each year.


The methodology employed in our survey allows a disaggregation of

migration streams into various categories--nonmigrants, rural-rural, rural-urban

and urban-rural migrants. The finding that rural-urban migrants are young,

well educated and with a higher percentage of males is consistent with evi-

dence from other African countries [Rempel, 1971; Caldwell, 1969]. Also the

propensity to migrate is several times higher for educated persons and is also

higher for young adults 15 to 34 years old--but does not appear to differ by

sex. Furthermore in Sierra Leone there is a clear north-south dichotomy with

the southern regions producing the bulk of the educated migrants and the north-

ern regions producing most of the uneducated migrants. An important finding

was that uneducated male migrants originate in poorer rural households while

educated migrants originate in higher income rural households. The necessity

of disaggregating migration streams by educational level is clearly demon-

strated by these results.

Some important differences were noted between rural-rural and rural-

urban migration. Rural-rural migrants do not differ significantly in age,

sex and educational characteristics from the rural population as a whole.

Moreover in absolute numbers rural-rural migration is much less than rural-

urban migration and is largely confined to intraregional migration over short


Our survey provides some of the most detailed information available

in Africa on urban-rural migration. About half of urban-rural migrants are

migrants returning home. These return migrants are generally older than the

rural population as a whole. Return migrants also have a low level of educa-

tion compared to migrants who leave for urban areas. As a result the net flow

of uneducated males to urban areas is negative while educated males comprise

about half of net rural-urban flows. Also substantial back and forth mobility

exists between rural areas and small and medium urban towns as measured by

gross migration rates but migration to the large towns of Kono and Freetown

is more permanent with less return migration.

Finally a brief examination of the rural-urban migration streams

shows that migrants seeking work, housewives and scholars are about equally

important, each group comprising about 25 percent of the total number of rural-

urban migrants. These figures underscore the need to disaggregate migration

streams and not stereotype all migration as "labor" migration.


Rural-urban migration will be examined in this section with respect

to the sequential processes of (a) decision making in rural areas, (b) mov-

ing to town, (c) settling in town and entry into the labor market, (d) main-

taining ties with rural areas particularly through remittances and (e) re-

turning home again and re-entry into rural society.

Migration Decision Making in Rural Areas

Our survey revealed two aspects of rural-urban migration that were

important in migration decision making in Sierra Leone. First only a

minority of rural-urban migrants initially leave home to obtain work.

Migration for marriage and schooling are equally important as migration

for finding work. Secondly migrants leave home at a relatively young age.

In our sample, male migrants without education left home at an average

age of 18 years and educated migrants left at the age of 12 years. As a

result the decision to migrate is more often made by persons other than

the migrant--usually the head of the household--as seen in Table 17. Even

for migrants seeking to work in town almost half the decisions were made

by a parent at home or a relative in town.

Almost all educated migrants initially moved to an urban area to

attend school. Typically an educated migrant had attended school for

11 years of which 5 years were at home and 6 years were in an urban area.

Ninety percent of all migrants with education had attended a school in an

urban area. Of these who had completed school in town, only 27 percent

were working in the same town in which they attended school indicating

substantial mobility among educated persons.


Decision Maker

Type of Migrant

1. Working

2. Housewife

3. Scholar

Age at Migration

1. Below 15 years

2. 15-24 years

3. Over 24 years



Other Tow
Rural Rela

(Percent Distribution)

I 1 7

n Spouse Total







~- I

1 -

Since the household head was largely responsible for the decision

to send children to school in town we asked why they had chosen a school

in town rather than a rural school. Fifty-six percent made this decision

because there was a relative or friend in town who could help pay fees.

Thirty percent claimed that urban schools were better while 11 percent

responded that there was no school in the vicinity of their villages.

Most women gave marriage as the reason for their migration. In 20

percent of the cases the woman accompanied her husband who was moving

to town. Another 20 percent moved to town seeking a husband while most

moved to town to marry a man already in town.

Migrants who left home to seek work were primarily interested in

obtaining a higher paying job than farming, although a more interesting

job and improved social life were also mentioned. Eighty percent of un-

educated migrants and 93 percent of educated migrants in town felt they

were earning more than was possible at home. Similar beliefs were ex-

pressed by nonmigrants in rural areas although only 60 percent of non-

migrants believed that a city job would pay more.

Migrants, however, are aware of the difficulty of obtaining a job

before they leave rural areas. Among nonmigrants who were intending to

migrate only 15 percent with no education were certain they would obtain

a job. Those with education were more confident with 40 percent certain

they would obtain a job.

Job information is provided by relatives and friends in town for

two-thirds of all migrants while visits to town and friends and relatives

at home provide information to others. An effort was made to measure

the quality of this information by asking a comparable group of urban

migrants and rural nonmigrants the earnings of four occupations--government

clerk, policeman, medical doctor and driver. Results shown in Table 18

show that there is no consistent evidence that rural potential migrants

lack information about urban occupations. In fact, the difference between

perceived incomes and the actual incomes of migrants in town with that

occupation, is negligible except for a government clerk which nonmigrants

ranked much higher and which is the only occupation to show a statisti-

cally significant difference between rural and urban persons. It is appar-

ent, however, that the variance of the estimates of rural persons was

higher than urban migrants indicating that rural people as a whole do not

have unduly high perceptions of urban earnings although there is wide

variation in these perceptions.

Further evidence of rural perceptions is provided by an interview

with young adult male nonmigrants in rural areas--the group with the

highest propensity to migrate. Each person was asked to state his future

migration intentions and to estimate his earnings if he were to move to

town. The comparison of perceived earnings of nonmigrants disaggregated

by migration intentions with actual earnings of migrants already in town

is shown in Table 19. For both levels of education, intending migrants

had higher perceptions of urban earnings than nonintending migrants with

this difference being larger for educated persons. Furthermore intend-

ing migrants in both cases had perceived earnings higher than migrants

in town were actually receiving. There is therefore some evidence that

migrants who leave home have somewhat higher perceptions of urban earn-

ings than are realistic.

Finally among young male rural residents who had no intention of

migrating we found that most had some contacts in town, had in fact visit-

ed town and most believed that their earnings could be increased by














Estimated for


Actual Income
of Migrants
with That

I ____________



(Le./Mo.) -
















NOTE: n.a. not available.

aDifferences between rural nonmigrants and urban migrants are
not statistically significant at the 5 percent level except for clerks.

Le 1.00 = $1.10.





_ _ _ I




Percent Intending
to Migrate

Perceived Wage Ratesb

to Migrate

Not Intending
to Migrate

Actual Wage of
Urban Migrants
with Same Age---
and Education

(Le./Month) (Le./Month) (Le./Month)-

Mean 38 52 42 36

aSample includes only adult males, 15 years to 30 years of age.

Difference between perceived wage rates of intending and not intending migrants significant
at 5 percent level for educated migrants.


migrating. We, therefore, asked these nonmigrants why they did not intend

to move to town. The most important reason given was the need to support

parents and family, suggesting that factors such as kinship ties are im-

portant in the decision not to migrate.

Moving to Town

As Sierra Leone is a small country most rural-urban migration covers

a relatively short distance averaging only about one hundred miles. Be-

cause of this short distance and because over two-thirds move without

dependents the average cost of moving to town is only Le 2.30 and the

cost is nearly always less than Le 10.

There is considerable mobility of migrants after leaving home. The

average migrant resided in two other locations for six months or more

before arriving at his present destination, one of which was an urban lo-

cation. Educated migrants exhibit more mobility so that by the age of

twenty-five they have lived in, on an average, two other urban centers

besides their present urban residence.

Settling in Town

Our survey showed that the prior presence of relatives and friends

in town is almost essential to a migrant's successful adjustment to town

life. Almost 90 percent of migrants were initially supported by rela-

tives and friends in town. The remainder either obtained a job immediately

or had some initial savings for support. On the average a migrant was

supported through food, lodgings and sometimes money for one and a half

years on arriving in town. Nearly all of this support was provided by

urban relatives, most of whom are themselves migrants of an earlier period.

Only apprentices received significant support from other than relatives--

in this case their instructor.

The importance of this support of new migrants underscores the sub-

stantial intra-urban income transfers among migrants. In an effort to

learn who was giving and receiving support we asked each migrant to value

the food, lodging and cash gifts he gave or received to or from an adult

who was not a parent or spouse or child of the migrant.

The results reported in Table 20 show a clear division between work-

ing migrants who are providing support and nonworking migrants including

scholars and the unemployed, who are receiving support. Working migrants

on an average "transfer" Le 9.50 or about 17 percent of their income to

support relatives and friends in town. The amount transferred increases

absolutely (but not proportionally) with the income of the migrant so

that the top 5 percent in the income distribution support up to three

persons at a value of Le 30 per month.

Those who received support are predominantly scholars, apprentices

and the unemployed. Scholars receive support of about Le 16 per month

which is higher than other groups because of the cost of school fees and

books. Significantly migrants as a whole have a net intra-urban income

transfer of almost zero indicating that migrants as a group do not depend

on urban nonmigrants for support.

New migrants seeking a job require support during the period of job

search. Migrants who are currently employed on an average reported a ten

month period to obtain their first job. However, many migrants, parti-

cularly those in the lowest income group, continue to receive some

support for some time after obtaining a job. Furthermore the importance


Working Not Working All

Income (Le./Month)a All Unemployed House- Scholars Appren- Other
Income wives tices
<32 32-50 50-90 90-150 150+ Groups

Support in Town
Value given (Le./month) 7.2 10.8 18.1 24.2 30.6 12.9 3.6 2.8 .7 .5 2.9 7.0
Value received (Le./month) 4.5 3.3 4.0 -- 3.4 12.4 2.5 16.4 16.7 4.9 6.2
Net value given (Le./month) 2.7 7.5 12.1 24.2 30.6 9.5 -8.8 .3 -15.7 -16.2 -2.0 -.8
Percent giving 41 48 64 77 70 55 20 17 -- 20 28
Percent receiving 29 21 16 -- 20 55 18 80 82 30 37

Rural-Urban Remittances
Value given (Le./month) 1.6 2.8 3.0 6.1 12.0 3.1 .5 .8 .2 .1 .8 1.5
Value received (Le./month) .7 1.0 1.3 2.3 1.9 1.9 1.0 .9 1.4 .5 1.2 1.1
Net value given (Le./month) .9 1.8 1.7 3.8 10.1 1.2 -.5 -.1 -1.2 -.4 -.4 .4
Percent giving 77 80 86 100 90 82 44 62 23 41 43 57
Percent receiving 56 69 63 -- 50 66 63 63 73 41 52 64

Property at Home
Percent males owning 36 43 46 46 60 45 27 n.a. 23 29 39 30
Property income (Le./Mo.) .4 1.3 3.7 3.1 33.3 3.8 .6 1.2 4.5 2.9

aMigrants' incomes are distributed as follows: 25 perce
less than 90 Le./month and 95 percent less than 150 Le./month.

nt less than 32 Le./month, 50 percent less than 50Le./month, 90 percent

of relatives and friends is again underscored by the fact that two-thirds

of working migrants obtained their first job through a relative or friend.

Rural-Urban Remittances and Contacts

The remittances of income by urban migrants to rural areas has been

widely noted (but rarely measured) in Africa. Our survey shows that re-

mittances follow a similar pattern to intra-urban income transfers in

the form of support (Table 20). The working population remits about

Le 3.10 (about 5 percent of their earnings) to rural areas each month.

However this same group receives Le 1.90 per month so that the net trans-

fer to rural areas is only Le 1.20 per month. Both gross and net urban-

rural remittances increase with urban incomes. Urban-rural.remittances

are largely cash with some imported items such as clothing, while rural-

urban remittances are largely food.

Among the nonworking urban migrants, there is a net transfer from

rural to urban areas. These transfers are largest for scholars and the

unemployed where they could be considered a form of support by rural peo-

ple of their relatives in town. However this form of support to scholars

and the unemployed is almost negligible compared to support received from

relatives in town.

When all working and nonworking migrants are considered together

there is still a small net transfer of income to rural areas of about

40 cents per month or Le 5.00 per year. In our interviews with rural

households we obtained a figure of net remittances received of Le 2.00

per year. The difference in these two figures suggests that migrants

send money to more than one rural household. Most cash remittances re-

ceived by rural households were used for consumption purposes although

about one-third was used for hiring labor and small amounts for equipment,

school fees and medical expenses.

In addition to remittances, migrants also maintained contacts with

their home area in other ways. Visits home for vacation and special pur-

poses were frequent and averaged about one visit per year among our sam-

ple. Significantly too, migrants tended to acquire property at home--

more so than in the town in which they lived. About half of all working

migrants owned property in their village, such as land, tree crops and

houses (Table 20). They also received small incomes from ownership of

that property, particularly migrants in the highest income group. In addi-

tion over 90 percent of all migrants in town stated that they had access

to land in their village so that acquiring land is not an obstacle to

migrants returning home.

Return Migration

The importance of return migration was noted in the previous sec-

tion. When we asked urban migrants about their intentions to return home

about 65 percent stated they planned to return home although few were

very definite about when they would do so. The intentions to return home

were strongest among uneducated migrants and older migrants. For exam-

ple, only 54 percent of youths 15 to 25 with secondary schooling planned

to return while 86 percent of migrants above 45 without education planned

to return.

It is also likely that some of the difference is due to rural per-
sons understating their receipts and urban migrants overstating their gifts.

Three primary reasons were given by urban migrants for planning to

return home. First, about one-third wished to retire in their home vil-

lage. Second, another third wished to return for economic reasons believ-

ing that farming was at least as profitable as their urban job. Finally

about one-quarter felt that they may not receive support in town in the

long run and would return. When return migrants were interviewed in rural

areas over half gave reasons relating to problems in obtaining a job or

support from urban relatives suggesting that economic hardship is more

important than retirement as a motive for return migration. In fact,

25 percent of return migrants who sought jobs were unsuccessful and re-

turned without working in town.

As noted earlier, return migrants are older and with lower education

than those who leave for urban areas. On an average our return migrants

had spent fourteen years in town and had typically left at the age of

18 years and returned at the age of 33 years.

Return migrants are of potential significance to rural communities

if they bring money or new ideas acquired in town to that community.

However, our interviews with return migrants would indicate that this

role is relatively minor. Only 20 percent of return migrants had made in-

vestments in property while in town compared to a third of migrants who

were currently residing in town who had made investments in property.

On returning home most brought cash averaging about Le 32 for each return

migrant of which about Le 8 was spent in farming and the remainder con-

sumed. Some 13 percent of migrants had undergone an apprenticeship re-

flecting the fact that many of the skills for small rural industries--

tailoring, carpentry and blacksmithing--are acquired in urban areas

[Liedholm and Chuta, 1976]. Another 10 percent had acquired some educa-

tion in town but as noted previously most educated persons do not return

to rural areas. Finally almost one-third of return migrants felt that

they had not benefitted in any way from their stay in town.

Attitudinal Characteristics of Migrants

Throughout our interviews with various categories of migrants we tried

to gain a perspective on attitudes toward rural and urban residences.

Here we briefly note some of the attitudinal characteristics toward so-

cial amenities that may have a bearing on the migration decision. Both

migrants and nonmigrants attached considerable importance to social amen-

ities such as school, medical facilities and utilities in town. About

40 percent of the urban households but none of the rural households in

our sample had electricity and piped water. Both rural and urban respon-

dents cited these as important advantages of urban residence. Likewise

educational facilities in towns were considered advantages and both rural

and urban respondents felt that rural schools even when available provided

less opportunity for a good education.

When urgan migrants were asked to list disadvantages of urban living

the overwhelming response was the high cost of living in urban areas. Of

course, this was to some extent expected since it was a period of rapid

price inflation. However, among rural persons who were intending to mi-

grate, 40 percent could not think of any disadvantages of urban living

suggesting that their attitudes are changed by the experience of living

in town.


In examining the process of rural-urban migration in this section,

we have highlighted migration decision making, urban support and rural-

urban contacts through remittances and return migration. Because most

migrants leave home at a very early age decision making by parents or

other members of the rural household is more important than by the migrants

themselves. This underscores the need to conduct rural-urban migration

surveys in rural areas.

Through interviews with potential migrants in rural areas we obtained

information on rural perceptions of urban opportunities--a deficiency of

most earlier migration research in Africa. Rural nonmigrants do not appear

to have unduly high perceptions of urban wages or job opportunities. How-

ever, perceptions do vary quite widely with individuals and it was shown

that rural people intending to migrate have higher income expectations

than nonintending migrants. These income expectations of intending mi-

grants are also higher than actually realized by urban migrants in town

suggesting that high income expectations do play some role in the deci-

sion to migrate.

A particularly important part of the migration process is the support

given by friends and relatives in town. It was shown that working migrants

are transferring about 17 percent of their earnings to support nonwork-

ing scholars and the unemployed. This intra-urban transfer of income

enables migrants to acquire an education or undergo an average of one

year's job search. Significantly migrants as a group seem to be "self-

sufficient" and do not depend on urban nonmigrants or rural households

for support. In addition relatives and friends are important in helping

new migrants obtain a job.

The importance of intra-urban income transfers is in contrast to

the relatively small rural-urban remittances observed in our sample.

Whereas Johnson and Whitelaw [1974] observe in Kenya that 20 percent of

urban wages are remitted to rural areas the comparable figure for Sierra

Leone for working migrants is only 5.5 percent or Le 3 per month. Net

urban-rural remittances are a good deal smaller--about Le 5 per year--

since rural people also send remittances to urban areas and in the case

of nonworking scholars and the unemployed, these remittances exceed urban-

rural remittances. The most likely explanation for this difference between

Kenya and Sierra Leone is the practice in Kenya of maintaining a wife and

family in rural areas.

We conclude then that intra-urban income transfers are much more

important than urban-rural income transfers in migration in Sierra Leone.

This evidence does not support Lipton's [1976] thesis discussed earlier

that migrants originate in higher income rural households who support

their job search and who after the migrant is employed receive substan-

tial remittances further increasing rural income inequalities.

Finally return migration is numerically important and also contributes

some skills, particularly in small-scale industry, to rural communities;

However, migrants largely return for reasons of economic hardship and

therefore contribute little capital to rural areas. The relatively easy

access to land enjoyed by migrants even when away in town probably in

large part explains the substantial back and forth migration between rural

and urban areas existing in Sierra Leone.


Method of Analysis

An important aspect of migration to urban areas is the participation

and remuneration of migrants in the urban labor market. In this section

adult migrants 15 years and older are analyzed with respect to (a) par-

ticipation in the labor force (i.e., those working or seeking work),

(b) employment structure, (c) earnings and (d) unemployment. In this

analysis the effects of migrants' sex, age, town of residence, education

and employer are considered. Because the sample is relatively small, var-

ious aggregations are used in this analysis. Two basic age groups are

used--those between 15 and 24 and those 25 years or older. Towns are

aggregated into four size categories as in earlier sections. With respect

to education, migrants were classified as educated if they had completed

more than four years of formal education and the remainder were treated

as uneducated.1 Finally the migrant's employer was disaggregated by large-

scale and small-scale sectors where small-scale sectors consist of firms

employing less than ten persons. Large-scale sectors are further disaggre-

gated into the government sector, including public corporations and semi-

government agencies, and large private industrial and commercial firms.

Migrants employed in small-scale sectors are further disaggregated by

wage earners and self-employed.

In interpreting the results, particular caution must be exercised

for female migrants since the sample size is quite small as a result of

1In fact the educated male migrants in our sample are overwhelming-
ly secondary school-leavers since in Sierra Leone a very high proportion
of male scholars who complete primary school enter (but do not necessar-
ily complete) secondary school.

(a) the dominance of males in rural-urban migration and (b) the low fe-

male participation in the urban labor force. However, because statistical

techniques do point up significant sex differences some results are re-

ported for female migrants.

Labor Force Participation

Labor force participation rates for eight age, sex and education

cohorts are given in Table 21. Seventy-five percent of adult male mi-

grants are in the labor force. The remaining one-quarter are largely in

the 15 to 25 year age category where 56 percent of educated migrants

are still attending school or in the case of uneducated migrants 23 per-

cent are acquiring skills through apprenticeship.

Among female migrants, however, only a quarter are in the labor force.

This proportion rises with both age and education but still remains sub-

stantially lower than for males. These low participation rates are in con-

trast to the important contribution of women in rural occupations, par-

ticularly farming [Spencer, 1976]. Moreover as a result of the substan-

tial number of scholars and housewives not in the labor force overall

labor force participation rates for urban households are lower than rural

households and hence earnings for those who work will have to be higher

to offset the reduced number of workers.

Structure of Employment

The government is the dominant employer of migrants in our sample,

employing half of all migrants who currently hold a job (Table 22). Self-

employment in the small-scale sectors is also important. In contrast

wage employment in both small and large private firms together accounts

for only 20 percent of total employment.



Labor Force Sex

Males Females

Education All Education All
Males Females
Uneducated Educated Uneducated Educated

__Age -- -- Age

15-24 25+ 15-24 25+ 15-24 25+ 15-24 25+

Wage employed 33 54 25 85 51 -- 2 11 33 6
Self-employed 16 29 2 5 13 13 21 2 19 14
Unemployed 19 10 14 6 11 4 5 6 5

Total in the
labor force 68 93 41 96 75 17 28 19 52 25

Housewives -- -- -- 78 65 35 33 59
Scholars -- 56 1 20 -- 45 -- 12
Apprentices 23 2 2 1 3 1 -
Others 9 5 -- 2 2 4 6 1 14 4

Total not in
the labor force 32 7 58 4 25 83 71 81 47 75

Total 100 100 100 100 100 100 100 100 100 100



By Sex and Education By Urban Area All
Males Females Migrants

Unedu- Edu- All Unedu- Edu- All Free- Kono Med- Small
cated cated Males cated cated Females town ium Towns

Government sector 40 73 57 7 48 20 63 13 55 64 52
Large private firms 9 16 13 0 14 5 12 27 6 9 12

Total large-scale
sector 49 89 70 7 62 25 75 40 61 73 63

Small-scale wage
employed 14 4 9 0 10 3 7 10 9 0 8
Small-scale self-
employed 37 7 21 93 29 72 18 51 30 27 28

Total small-scale
sectors 51 11 30 93 39 75 25 60 39 27 36

Total 100 100 100 100 100 100 100 100 100 100 100

aIncludes local government.

The division of employment between small and large-scale sectors

differs significantly with education and sex. Over half of the employed

male migrants without education are employed in small-scale sectors but

almost all educated migrants are employed in large-scale sectors. Female

migrants with and without education have a stronger tendency than males

to be self-employed in small-scale sectors. This reflects to a large ex-

tent the dominance of women in food trading activities.

The structure of employment is quite uniform across urban centers

with the exception of Kono where diamond mining increases the share of

both large private firms, in this case the National Diamond Mining Com-

pany, and small-scale self-employment comprised of diamond diggers.

Structure of Urban Earnings

The structure of earnings of urban migrants is important in deter-

mining migration flows but at the same time serious problems occur in

the estimation of earnings. Earnings in large-scale sectors are generally

easiest to determine. However, fringe benefits such as housing and allow-

ances can be quite important. In our survey these extra benefits were

estimated and added to reported income. For migrants self-employed in

small-scale sectors two methods were used to estimate incomes. First the mi-

grant was asked to state his earnings in a normal month after subtract-

ing all his business costs except his labor. Second, for the week prior

to the interview migrants were asked to recall their transactions. For

small-scale industries respondents were asked to recall all cash transac-

tions for purchased inputs and sales. For traders we recorded wholesale

purchases of commodities, the time to sell their stock and their buying

and selling prices. An estimate of income for the previous week could

then be computed. In most cases, this second measure was used but where

this was unsatisfactory because of missing information or because the pre-

vious week's activity was abnormal, the first measure (i.e., the stated

income) was employed. Finally a high proportion of migrants in Kono were

diamond diggers whose incomes are particularly difficult to measure--in

part because of the illegal nature of much mining. Interpretation of

their incomes must therefore be treated cautiously.

Analysis of variance procedures were used to analyze the effects

of age, sex, education, employer, rural origin and urban centers on

earnings of urban migrants. Results of this analysis are shown in table

23 where the independent effects of sex, age, education, employer and lo-

cation are reported relative to the average income of all migrants. This

analysis demonstrates a wide gap between male and female incomes even

when allowance is made for the different education and employment status

of females. This parallels a similar observation that female wage rates

are lower than male wage rates in rural areas [Spencer and Byerlee, 1976].

However when self-employed persons are excluded from this analysis, sex

is no longer statistically significant. This can be explained by the fact

that many women are engaged in self-employed trading activities on a

part-time basis and receive very low monthly earnings.

Age is also a significant determinant of urban earnings. This is

expected as migrants acquire more skills and capital the longer they stay

on the job. Education has generally the largest effect on urban earnings.

A person with five or more years of education can expect to earn about

50 percent more than his uneducated counterpart.

In the case of self-employed traders and artisans, earnings in-
clude returns to capital.



Effect Due To: Percentage Change Significance
from Mean Incomea Level

1. Sex

2. Age
15-24 Years
25 Years and Above

3. Education
Less Than 5 Years
Five Years and More

4. Employer
Large Private Firms
Small Private Firms

5. Urban Center






{ .001


{ .001

{ .015

{ .292

aMean income of all migrants = Le 56.37.

Even after allowing for age, sex and education the type of employer

has a significant effect on migrants' earnings. In particular for wage

earners, large-scale private firms pay the highest wage--substantially

higher than the government. At the same time small-scale sectors pay

a wage significantly lower than the government. This is evidence of a

dual labor market with small-scale sectors paying a competitive wage

below the government and large-scale wage structure.

Self-employed workers in the small-scale sectors in our sample re-

ceived earnings above other sectors for two reasons. First, their earn-

ings include returns to capital as well as labor which in the case of

traders and small-scale industries are an important component of earn-

ings. Second this self-employed category includes diamond diggers in

Kono who sometimes have high incomes. It should also be noted that earn-

ings for the self-employed had the highest variance reflecting the hetero-

geneity of composition of this category.

The size of the urban center had some effect on the earnings of mi-

grants with earnings in large towns being above earnings in small towns.

However neither the magnitude nor significance of this effect is as large

as for other variables such as age and education. Only when the effect

of employer is omitted from the analysis does urban location become sig-

nificant. That is, earnings differences between location are largely

due to the differential structure of employment rather than wage differ-

ences per se.

The above analysis treating each effect separately is only rele-

vant if higher order interactions are not important. For example, it

could be hypothesized that there is interaction between age and educa-

tion with education having a larger effect with age. In fact all two-way

interactions were not statistically significant and the only interac-

tion that was not negligible was between education and urban size.

This reflects the fact that educated migrants to Kono received a very

small differential in earnings as a result of education.

Rural-Urban Earnings Differentials

The difficulties of comparing rural and urban earnings are well re-

cognized [Knight, 1972; Collier, 1976]. In comparing rural and urban

incomes here we compare directly the actual wage rate per hour worked

in rural and urban areas. Rural wage rates were derived from the daily

wage observations from a farm management survey reported in Spencer

and Byerlee [1976] where all payments in kind were converted to mone-

tary values and the wage per hour computed from the observation of the

number of hours worked. Urban wage rates were computed from the migra-

tion survey using the hours worked in the week preceding the interviews.

Comparison of these wage rates is given in Table 24. Wage rates

for uneducated migrants in urban sectors are on the average about Le 0.25

per hour or about three times higher than the wage rates of Le .08 per

hour in rural areas. The lowest paying urban sector--the small-scale

sector--has wages above the average rural wage rate but only slightly

above the rural wage rate in the region with the highest wage rate (i.e.,

the Scarcies region). In all cases, of course, educated migrants have

a wage rate higher than uneducated migrants.

1Significant only at the 27 percent level.


Rural Areas Urban Areas

Region Wage Employer No Educated
(Le./Hr.) (Le./Hr.) (Le./Hr.)

1. Scarcies .13 Government .19 .35

2. Southern Private large-
coast .08 scale sector .38 .37

3. Northern Small-scale
plains .07 sector .15 .21

4. Riverain .08 Average urban
wagea .25 .35
5. Bolilands .07
6. Moa basin .08 Expected wage
6. Moa basin .08 o y 15
of youth 15
to 24 given
7. Northern to 24 given
plateau .08 probability
plateau .08
of unemploy-
. Southern mentb .11 .18
8. Southern
plains .11

rural wage .08

aAverage over all employers and all age cohorts.

probability of

wage for youths 15 to 24 years of age multiplied by
employment for that age and education group.

A more relevant measure of urban wages is the expected wage of young

male migrants between 15 and 24 years taking into account the probabil-

ity that they will be unemployed. That is, the expected wage is computed
as W= (1-Uk)Wk where Uk and Wk are the unemployment rate and average

wage respectively for young male migrants. The wage rate was computed

as the average for all migrants in both small and large-scale sectors

while unemployment rates were derived from data presented in the next

section. The expected wage for uneducated migrants is only marginally

higher than the average rural wage rate and lower than or equal to the

wage rate in two rural regions. Educated migrants still maintain a

considerable wage differential over all rural regions.

These results suggest that over the long term a migrant in an urban

job can earn a considerably higher wage rate in urban areas compared

to rural areas. However in the short term given the lower wage rates

and the high unemployment rates, young uneducated migrants stand to gain


These results must be qualified by at least two factors. First

there is a cost of living differential between rural and urban areas

partly because the basic consumption item is food which includes a mar-

keting margin in urban areas. Secondly, the wage rate is not necessar-

ily the best measure for comparison since urban persons work a larger num-

ber of hours per year than rural persons due to the agricultural slack

season. Thus Spencer and Byerlee [1976] find that rural men work about

1,400 hours per year compared to urban migrants in our sample who worked

over 2,000 hours per year. Migrants may move to urban areas not only

for a higher wage but also to have the opportunity to work longer hours

than is possible in rural areas.

Urban Unemployment

The relationship between unemployment and migration is important

both because unemployment is a central variable of the well-known Todaro

model of migration and its derivatives and because urban unemployment

is aggravated by the influx of new migrants. In this section we brief-

ly examine urban unemployment rates, draw a profile of the unemployed

migrant and his job search and examine his attitudes and expectations

with respect to obtaining a job.

The Rate of Urban Unemployment

The overall rate of male unemployment of migrants in our sample was

14.7 percent (see Table 25) which is slightly higher, but very comparable

to the 13.9 percent figure for all urban residents which can be derived

from the household surveys of the Central Statistics Office [1967-1971].1

However, when migrants are disaggregated by age and education in Table

25 it is found that this unemployment rate rises to 33 percent for young

migrants in the 15 to 24 years age group. In fact, the marked difference

between age groups is common to both educated and uneducated migrants.

For the young age group the educated migrants have a higher unemployment

rate but not significantly so.

The Central Statistics Office surveys provide only a breakdown by

age and by education separately but even these estimates shown in Table

25 are surprisingly consistent with our survey--despite our relatively

small sample size. One implication of this consistency is that the

Our sample shows the rate of female unemployment is 20 percent--
somewhat higher than males. However, the number of females in the labor
force is too small to make a further disaggregation of female unemploy-






Age (Years)

15-24 I



All Urban

(Percent Unemployed)





Migrants 33 9 14.7

All urban
persons 30 9 -- 13.9

Migration Survey.

Central Office of Statistics [1967-1971].




unemployment rates of migrants are similar to the urban population as a

whole although there may be some initial adjustments. Thus for Freetown

the Central Statistics survey computed a rate of unemployment of migrants

in the first year of residence in Freetown of 19.6 percent compared to

17.3 percent for our survey of migrants (of whom a third are new migrants)

and 15.5 percent for all urban residents.

The unemployment rate also varies substantially with urban areas.

The largest urban areas tend to have the largest unemployment rate as

shown in Table 26. In absolute numbers half of all unemployed persons

reside in Freetown.

Profile of the Urban Unemployed

Although the rate of unemployment in our sample differs more with

age than with education, since most young urban migrants are also educa-

ted the dominant group numerically in our sample are young, educated males

who make up 44 percent of the unemployed. Older male adults with no edu-

cation constitute another 29 percent of the unemployed. In Freetown a

special interview was conducted with each unemployed migrant to determine

his length of unemployment, job search activities, etc., as well as his

attitudes and expectations. Although this sample is quite small (forty)

some important attributes of these unemployed migrants emerge. These are

reported in Table 27 disaggregated by education.

Contrary to the image that unemployed migrants are new arrivals in

town, only one-third of our unemployment sample were new migrants in town.

However, among educated migrants 83 percent were seeking their first job--

that is they were "school-leavers". Over half of these school-leavers

had attended school in Freetown and therefore were not new migrants.



275,000 110,000 20,000- 2,000- All
100,000 20,000 Towns

Freetown Kono Medium Small
Towns Towns

migrantsa 17.3 16.8 12.3 10.3 14.7

residents 15.5 11.6 12.2 n.a. 13.9

NOTE: n.a. = not available.
aSORCE: Migration survey.
SOURCE: Migration survey.


Central Office of Statistics [1967-1970].


Education All
Uneducated Educated

Employment and Job Search
Percent new migrants 29 36 32
Percent seeking first job 36 83 62
Years unemployed 1.0 1.1 1.1
Percent registered employment
exchange 13 50 38
Percent seeking casual work 18 19 19
Number of job applications per
month .6 1.6 1.2
Job search expenses per week
(Leone) .92 1.14 1.04

Current household income
(Leone per month) 25 62 45

Attitudes and Expectations
Expected wage (Leone per
month) 39 49
Actual wage for employed
migrants of comparable
age and education 38 44 -
Minimum acceptable wage
(Leone per month) 35 39
Percent more than half certain
of job 55 85 71
Percent risk takers 21 44 36
Years unemployed--risk takers .3 .5 .4
Years unemployed--risk neutral .5 -- .5
Years unemployed--risk averters 1.3 1.6 1.5

Total income of all working household members.
Risk attitudes measured by choice between secure job and
uncertain job with same expected earnings.

Thus the most important group of unemployed are the young school-leavers

who had not worked before.

Both educated and uneducated unemployed migrants had on the average

been unemployed for about one year. This compares with nine years for the

average time period for an employed migrant to obtain a job. A few migrants,

however, reported being unemployed for up to five years.

The survey of unemployed migrants revealed that they were in general

quite active in searching for a job. Most reported undertaking job search

activities, such as inquiry, request through relatives, applications, etc.,

several times per week. In all, the costs of this activity in transport,

influence, etc., are not insignificant amounting to about one leone per week.

Very few unemployed migrants reported to be seeking or doing casual work.

Most felt that their chances of obtaining casual labor on a daily basis

were too small. Significantly, less than half of our sample--particularly

uneducated migrants--were currently registered with the employment exchange.

This suggests that the use of registered unemployed figures from the em-

ployment exchange to measure unemployment is quite unreliable. The corre-

spondence obtained by Levi [1973] between the number registered as unem-

ployed and the number of unemployed derived from surveys is possibly in

part due to employed persons seeking to change jobs through the exchange.

Finally there is a very pronounced difference between the educated

and uneducated with respect to the income of the households in which the

unemployed reside. Given that the average household income in Freetown

is about Le 50 per month [Central Statistics Office, 1967], the esti-

mates from our survey show that,the educated migrants reside in households

Average household income of Le 45 in 1967 adjusted for 11 percent
wage increases.

with above average incomes of Le 62 per month. The uneducated on the other

hand live in quite poor households earning an average of only Le 25 per

month. This difference is due in large part to the fact that the educated

unemployed are supported in households by other educated migrants working

at a relatively high pay.

Attitudes and Expectations of the Unemployed Migrants

The unemployed migrants were asked various questions about their ex-

pectations concerning a job. The expected wage of the job they were seek-

ing was slightly higher than the average wage of working migrants in Free-

town in a comparable age and education category (Table 27). However, all

migrants were willing to accept a job with an income below that average.

Thus, the unemployed would seem to be quite well informed about the urban

labor market. Educated migrants seemed more confident that they could ob-

tain a job with 85 percent reporting that they were certain or fairly

certain of obtaining the job they were seeking.

An experimental question was asked of all unemployed migrants to

measure their risk attitudes. The hypothetical question was posed where-

by a migrant had to choose between (a) a job paying his minimum accept-

able salary and (b) a job paying twice that salary but with a training

period after which he must take an exam with only half a chance of passing.

The expected wage in both cases is the same but the second job is risky

as opposed to the secure first job. On the basis of their response migrants

were classified as risk takers, risk averters and risk neutral. Educated

migrants were more likely to be risk takers possibly reflecting the fact

1Households in which the head is unemployed and which receive no
income are included in this average.

that they live in higher income households. The most interesting find-

ing is that risk takers had been unemployed less than six months while

risk averters had been unemployed for one and one-half years. It would

appear that migrants generally begin their job search with higher aspira-

tions holding out for a good job but as the period of unemployment length-

ens they are willing to revise these aspirations downward.


An analysis of the employment and earnings of migrants provides use-

ful insights into the urban labor market in which migrants participate.

Female labor force participation in our sample.is quite low (30 percent)

compared to rural areas. Moreover, females of both education levels tend

to participate in the small-scale sectors. Males on the other hand par-

ticularly those with education are employed in large-scale sectors where

the government is the dominant employer.

As expected education is one of the most important determinants of

urban earnings. We also found evidence of a dual urban labor market where

large-scale sectors--private and government--pay a wage considerably above

the wage in small-scale sectors. In fact, wage differences between urban

areas could largely be explained by the differences in composition of employ-

ment between urban areas.

Migrants who obtain a job, receive in the long run a wage substan-

tially above rural wages although this difference is not large if the

migrant is employed in small-scale sectors. In the short run, however,

given the probability of unemployment, the expected wage of an uneducated

migrant is very little higher than rural wages. This implies that for

uneducated labor, the rural and urban labor markets are quite competitive.

There is, however, still a substantial differential in rural and urban

wages for educated persons. This helps explain the back and forth mobil-

ity of uneducated migrants between rural and urban areas noted earlier.

Unemployment rates for migrants are particularly high averaging 33

percent for young, educated males. However rates of unemployment for

migrants are very comparable to unemployment rates among nonmigrant urban

residents. Numerically the most important group of unemployed are school-

leavers who have not previously worked and who are concentrated in Free-


Although unemployment and poverty are widely equated, our survey

indicates that this applies only for unemployed persons without education.

The educated unemployed are largely supported by relatives with well pay-

ing jobs and in fact reside in households with above average incomes.

The unemployed in our sample had been without work for an average

of one year. However, evidence was obtained that migrants, particularly

school-leavers, are initially risk takers willing to wait for a job con-

sistent with their above average expectations of earnings rather than

take the first job available. These results lead us to conclude that

urban unemployment is not a critical problem partly because many unem-

ployed are not suffering from poverty and partly because an element of

voluntary unemployment is present as migrants wait for the "right" job.

However there is a considerable cost of unemployment associated with the

loss of on-the-job skill acquisition.



From a policy perspective it is not cnly necessary to know who mi-

grates but to understand factors determining the rate of migration. The

elasticity of migration rates to such variables as rural and urban wage

rates is clearly an important consideration in formulating migration


Econometric analysis of migration rates is now a standard part of re-

search on migration. However, several problems are inherent in past ana-

lyses of this type in developing countries. First migration is often

estimated from birthplace information in census data (e.g., Beals, Levy

and Moses [1967], Sahota [1968], Adams [1969] and Greenwood [1969]).

The use of these data is questionable since migration which has occurred

over a long period of time is related to present economic variables which

in themselves are a function of past migration flows. Second, most ana-

lyses of migration have focused on interregional migration which includes

both rural-rural and rural-urban migration (e.g., Beals, Levy and Moses

[1967], Sahota [1968]). Although a few studies have delineated rural-

urban migration for separate analysis we are not aware of any analysis

which examines both rural-urban and rural-rural migration and examines

possible differences in structural and behavioral characteristics. Fur-

thermore we have noted that migration rates depend markedly on education.

Although this has been observed in other studies the education variable

has been very superficially included--usually by using average levels

of education for the origin and destination regions. For example, studies

in Egypt by Greenwood [1969, 1971], in Ghana by Beals, Levy and Moses


[1967], in Brazil by Sahota [1968] and in Columbia by Schultz [1971] reach

quite inconsistent conclusions regarding the effects on migration of edu-

cation in origin and destination areas. Two recent studies by Levy and

Wadycki [1974] and Barnum and Sabot [1975] have disaggregated the popu-

lation by education and found structural differences in migration rates

by educational level which cannot be explained by the effect of education

on earnings differentials. Finally measurement of rural incomes is a

universal difficulty of almost all analyses of migration. Often proxy

variables are included such as regional per capital income (e.g., Sabot

[19671 or even per capital food production [Levi, 1973].

In the following analysis some of these deficiencies in earlier

analyses are overcome through data collected specifically for the purpose

of analyzing migration rates. This survey data was used to compute

education specific rates of migration for the last five years as dis-

cussed earlier in this report. Migration rates were analyzed for both

rural-urban and rural-rural migration. Rural-urban migration rates are

analyzed by two educational subgroups using education specific urban wage

and unemployment rates. Finally rural wages are obtained from a sample

of 25,000 wage observations obtained in a farm management survey.

The Model

The objective of the analysis is to quantify the effects of several

variables on migration rates from specific rural destinations to specific

rural and urban destinations. The model builds upon our earlier theore-

tical framework in which costs and benefits of migration are the major

determining factors of migration. However, since the objective is to ex-

plain aggregate rates of migration and not individual decisions to migrate

variables employed in the model are those that are characteristic of

particular rural and urban locations and not variables such as age, sex,

urban social ties, etc., which are important in individual decisions

but which are not location specific. These latter variables are being

included in ongoing micro-analyses on the decision to migrate. Further-

more in analyzing aggregate migration rates scholars are specifically

excluded since other variables such as the location and quality of schools

are probably more important than variables such as wages used to explain

migration of the working population. Finally we include both males and

females in computing migration rates. Because the most important rea-

son for female migration is marriage usually to a male from the same rural

area, female migration is highly correlated to male migration. In fact,

in our sample the correlation coefficient between male and female migra-

tion from specific origins to specific definitions was 0.78 for unedu-

cated migrants and 0.87 for educated migrants. For these reasons our

model is formulated in terms of variables which are more relevant to

male migrants who are largely in the labor force. However since persons

in the labor force provide the economic base for other nonworking migrants,

particularly housewives from the same area as shown by the above corre-

lations, the model is used to explain total migration (excluding scholars).

The variables of the rural-urban migration model are given by:

mjk = f (Wi, Wjk Ujk. Pj, Dij, e)

where mj = the cohort specific gross rate of adult migration for
Sthe kth educational cohort from rural origin i to urban
destination j

Wi = average daily agricultural wage of adult males in rural
region i

Wjk, Ujk = average monthly income and percentage unemployed
respectively for the kth educational cohort of male
migrants in the jth urban center

P. = population size of the jth urban area

D.. = the road distance in miles between the main center
3 of rural region i to urban center j

e = random error

and i = 1, 2,...8, corresponding to the eight rural resource
regions of Figure 1

j = 1, 2,...5, corresponding to the five urban centers
above 20,000 population--Freetown, Kono, Bo, Kenema
and Makeni

k = 1, 2, representing two educational cohorts--less than
five years education and greater than five years edu-

Some comments on the specification of the variables and the hypothe-

sized relationships are in order. The measure of rural income used here

is wage rate rather than household income. This measure of rural income

was chosen because (a) it was shown that an active and competitive rural

labor market exists [Spencer and Byerlee, 1976] and (b) given this com-

petitive market and dominance of household rather than individual deci-

sion making this wage rate should be a close approximation of the supply

price of labor [Knight, 1972].1 Furthermore since females have a low

participation rate in the urban labor market, male wage rates were used.

However, the same rural wage rate was used for both educational cohorts

on the assumption that educated persons receive the same wage rate in

traditional farming activities as those without education.

In the case of individual decision making the relevant income is
the value of the average product if income is shared among household mem-

Urban wage rates were estimated from wage rates of all working ur-

ban migrants analyzed in the previous section. The urban wage is then

the weighted average of wage rates in the large-scale and small-scale

sectors for each urban destination area. The inclusion of urban unemploy-

ment as an explanatory variable, of course, follows the Todaro [1969]

model of migration where it is hypothesized that high unemployment rates

tend to reduce migration.

The size of the urban area is included to represent a number of fac-

tors such as a larger labor market with possibly more perceived oppor-

tunities and also urban amenities (i.e., "bright lights"). Distance is

also a proxy variable for a number of costs associated with moving includ-

ing (a) the economic cost of moving and (b) the social costs of leaving

home which become greater the longer the move and the more cultural or

ethnic differences between home and town. Also distance is likely to

be a factor in determining available information.

The model for rural-rural migration is essentially similar. However

since education is considerably less significant in rural-rural migration

we did not disaggregate by education. Also unemployment is not concep-

tually meaningful in rural areas and hence is not included in the analy-

sis. Finally an ethnic dummy variable was used to test the hypothesis that

rural-rural migrants will move to areas with the same ethnic group to

facilitate social adjustment and access to land.

Data and Estimation Procedures

All data with the exception of urban unemployment and urban size

were obtained from our survey information. Although urban unemployment

data are available from our sample, the sample was too small to estimate

education specific unemployment rates for the medium size towns of Bo,

Makeni and Kenema. Unemployment data were derived from the urban house-

hold survey of the Central Office of Statistics [1967-1971] which we have

previously shown to be highly consistent on a national basis with our own

unemployment data. Also our sample size prevented us from estimating

reliable wage rate data for the small towns (less than 20,000) and hence

they were excluded from the analysis.

Migration rates can be both gross and net as defined earlier. From

a policy perspective both flows are important. Net flows are an indica-

tor of overall rates of urbanization. However it has been previously es-

tablished that return migration is dominated by older persons and hence

gross flows are a better indicator of those entering the urban labor force--

particularly the young who constitute the bulk of the unemployed. A further

important factor is the extent to which variations in net migration are

the result of variations in gross out-migration or of variations in gross

in-migration. In fact in our data the correlation coefficient between net

migration and gross out-migration from rural areas is .891 while the corre-

lation between net migration and gross in-migration is only -.14. Hence

the bulk of variation in net migration from rural areas is due to varia-

tions in gross out-migration, a conclusion similar to Beale's [1969] obser-

1For subgroups of the migration streams the correlations are slightly
lower. The correlation between net and gross migration for uneducated
migrants is .68 and for educated migrants is .87.

vations on net and gross migration flows in areas of the United States

with a net out-migration rate. For these reasons and because net rates

are more unreliable since they include residual errors in estimating rural-

urban and urban-rural migration rates, we analyze gross out-migration


The estimation procedure employed was ordinary least squares regres-

sion. Both linear and log-log functions were tried but linear functions

consistently improved the estimation ability and hence are reported here.

To test if there is any significant difference between the behavior

of educated and uneducated migrants, data for both types of migrants were

pooled and the following linear relationship was fitted:

mijk = b + b + b Wi +b EWi +b Wijk + bEWjk + b Ujk

+ b7EUjk + bP + bEP + b0Di + b EDij + e,

where all variables except E are as defined previously. Following Barnum

and Sabot [1975], E is a dummy variable for education such that E = 0 for

an observation on uneducated migration and E = 1 for educated migration.

The coefficient on these interaction terms indicates whether migration

response differs significantly for educated and uneducated migration


Empirical Application of the Model

Table 28 contains the estimated relationships for rural-urban migra-

tion by educational subgroups. The first figure below each coefficient

is the "t" statistic while the second figure is the elasticity calculated

at the mean value of the variables. Up to three equations are reported

for each group. First is the standard linear form on all variables in

the model. In the case of educated migration, however, strong multicolli-

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