STATISTICAL ANALYSIS OF SOCIO-ECONOMIC DETERMINANTS ON CHILD LABOUR … · 2018-12-11 · iii...

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STATISTICAL ANALYSIS OF SOCIO-ECONOMIC

DETERMINANTS ON CHILD LABOUR AND SCHOOLING IN

GHANA.

BY

TWUM ERIC OHENEBA

(10084920)

THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON IN

PARTIAL FULFILMENT OF THE REQUIREMENT FOR THE AWARD OF MPHIL

STATISTICS DEGREE.

JUNE, 2015

University of Ghana http://ugspace.ug.edu.gh

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DECLARATION

Candidate’s Declaration

This is to certify that this thesis is the result of my own work and that no part of it has been

presented for another degree in this University or elsewhere.

SIGNATURE………………………….. DATE……………………………

TWUM ERIC OHENEBA

(10084920)

Supervisors’ Declaration

We hereby certify that this thesis was prepared from the candidate’s own work and supervised in

accordance with guidelines on supervision of thesis laid down by the University of Ghana.

SIGNATURE……………………………. DATE………………………..

DR. SAMUEL IDDI

(Principal Supervisor)

SIGNATURE………………………….. DATE…………………………..

DR. EZEKIEL N.N. NORTEY

(Co – Supervisor)


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ABSTRACT

The objective of this study was to find the socio-economic determinants of child labour and

schooling in Ghana. To this end, the 2003 Ghana Child Labour Survey data was analysed. The

main techniques used were the simple logistic regression and multilevel logistic regression

analysis. Results of the analysis showed that gender of head of household, marital status of

parents, father’s occupation, mother’s occupation, relationship to head of household, place of

residence, literacy of head of household, sex of the child and highest educational level attained

by parents are all significant determinants of child labour and schooling in Ghana. It was also

found out that if a parent is an unpaid apprentice, it raises the probability that, his/her child will

attend school and work. The children who are sons and daughters of the household head are not

as likely to find themselves in school and work as opposed to other relations living in the

household. In spite of the fact that 10-14 years of age is a typical school going age, in the case of

the groups that were studied, it came out that, majority of this age group were found working.

Children who combined school with work mainly come from parents who are single. These

children lived in urban areas where job opportunities are available.


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DEDICATION

This work is dedicated to my lovely mother, my sweet wife and my precious jewels; Kofi and

Maame.


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ACKNOWLEDGEMENT

I thank the Almighty God who has given me care, knowledge and the opportunity to pursue

education up to this level.

There are many people without whom this work could not have been possible. I render my

sincere gratitude’s to my supervisors; Dr. Samuel Iddi and Dr. E.N.N. Nortey for their countless

guidance, advice and constructive criticism throughout this work. I would also like to thank all

the lectures of Statistics Department for their pieces of advice and encouragements throughout

my years of study in this University.

I would also thank my mother, my wife and my only brother for their financial support. I again

thank all my friends and loved ones for their patience throughout my studies. I say the good Lord

continue to bless you.

Finally, to my good friend David Coffie Darko of Presec, Legon for his support and

encouragements and all my friends especially 2015 batch of MPhil Statistics students, I pray for

God’s mercies and favour for you all.


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TABLE OF CONTENTS

CONTENT PAGE

DECLARATION…………………………………………………………………. i

ABSTRACT……………………………………………………………………….. ii

DEDICATION…………………………………………………………………..... iv

ACKNOWLEDGEMENTS……………………………………………………… v

TABLE OF CONTENT…………………………………………………………... vi

LIST OF TABLES………………………………………………………………... x

LIST OF FIGURES………………………………………………………………. xii

CHAPTER ONE: INTRODUCTION…………………………………………... 1

1.1 Background ………………………………………………………….. 1

1.2 Child Labour and Schooling Situation in Ghana …………...………… 6

1.3 Statement of the Problem …………………………………..………… 7

1.4 Objective of the Study ……………………………………………….. 11

1.5 Research Question …………………………………………………… 11

1.6 Research Methodology……………………………………………...... 11

1.6.1 Source of Data………………………………………………... 11

1.6.2 Description of the Data………………………….…………… 12

1.7 Significance of the Study……………………………………………... 12

1.8 Method of Analysis…………………………………………………… 12

1.9 Outline of Dissertation……………………………………………....... 13

CHAPTER TWO: LITERATURE REVIEW………………………….………. 14

2.0 Literature Review…………………………………………………...... 14


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CHAPTER THREE: PRELIMINARY ANALYSIS ………………………….. 29

3.0 Introduction …………………………………………………………... 29

3.1 Data and Scope ………………………………………………….…… 29

3.2 Logistic Regression……………………………………………….…... 30

3.3 The Exponential Family of Distributions ………………………......... 31

3.3.1 Properties of Exponential Family of Distributions…………... 32

3.3.2 Maximum Likelihood Estimation of the

Exponential Family ………………………………………....... 34

3.4 The Generalized Linear Models (GLM) ……………………………... 35

3.5 The Basic Logistic Regression Model ……………………………...... 37

3.5.1 Assumptions Underlying Logistic Regression……………….. 38

3.5.2 Application of Logistic Regression …………………………. 39

3.6 The Odds ……………………………………………………………... 39

3.7 The Odds Ratio ………………………………………………………. 40

3.8 Estimation of Model Parameters ……………………………………... 41

3.8.1 Maximum Likelihood Estimator (MLE) …………………….. 41

3.9 Testing the Goodness-of-Fit ………………………………………… 43

3.9.1 Deviance and Likelihood Ratio Tests ……………….………. 43

3.9.2 Pearson’s Chi-square Test …………………………….…....... 45

3.9.2.1 Phi and Cramer V Test ………………….…………. 46

3.9.3 Pseudo R-Square ………………………………………..…… 47

3.10 Test of Individual Model Parameters ……………………...……....... 48

3.10.1 The Likelihood ratio Test …………………………………... 48


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3.10.2 Wald Statistic ………………………………………………. 49

3.11 Confidence Interval Estimation…………………………………....... 49

3.12 Multilevel Modelling Approach to Clustered Data ………………… 50

3.12.1 Cluster-Specific Models……………………….……………. 51

3.12.2 Generalized Linear Mixed Models (GLMM)….…………… 51

CHAPTER FOUR: FURTHER ANALYSIS ………………………………….. 53

4.1 Preliminary Analysis …………………………………………………. 53

4.1.0 Introduction ………………………………………………….. 53

4.1.1 Characteristic of Sample …………………………………….. 53

4.1.2 Occupation of Children’s Parents …………………………… 63

4.1.3 Education of Parents ………………………………………… 64

4.1.4 Marital Status of Parents …………………………………….. 65

4.1.5 Regional Distribution of Children …………………………... 65

4.1.6 Ethnicity……………………………………………………… 67

4.1.7 Measurement of Children’s Work …………………………... 71

4.1.8 Activity Status of Children ………………………………….. 73

4.2 Further Analysis ……………………………………………………... 75

4.2.0 Introduction ………………………………………………….. 75

4.2.1 Findings ……………………………………………………... 76

4.2.2 Model for Children’s Schooling and Working

Using Logistic Regression ………………………………........ 76

4.2.3 Characteristics of Children…………………………………... 80

4.2.4 Characteristics of Parents ………………………………......... 81


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4.2.5 Characteristics of Household ………………………………... 82

4.2.6 Multilevel Analysis ………………………………………….. 83

CHAPTER FIVE: SUMMARY, DISCUSSIONS, CONCLUSIONS

AND RECOMMENDATIONS ……………………………… 89

5.1 Summary……………………………………………………………. 89

5.1.1 Limitations of the Study………………………………............ 91

5.1.2 Study Strengths ……………………………………………… 92

5.2 Discussions ………………………………………………....……….. 92

5.3 Conclusions ………………………………………………………… 94

5.4 Recommendations …………………………………………………. 95

REFERENCES………………………………………………………………...…. 97

APPENDICES……………………………………………………………..……… 106


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LIST OF TABLES

Table Page

Table 4.1: Distribution of Children Aged 5-17 Years by Region …………………... 54

Table 4.2: Sex Distribution of Children Combining Schooling

and Economic Activity by Age and Locality of Residence ……………... 55

Table 4.3: Average Age of Children by Region…………………………………….. 56

Table 4.4: Average Household Size by Region…………………………………....... 57

Table 4.5: Average Enrollment Age by Level of Schooling and

Grade Completed……………………………………………………....... 58

Table 4.6: Percentage Distribution of Non-Working and Working

Children by School Attendance………………………………………….. 58

Table 4.7: Percentage Distribution of Non-Working Children by Sex……………… 59


Children by Relationship to Head of Household………………………… 60


Children by Age Group………………………………………………….. 60


Children by Size of Household…………………………………………... 61

Table 4.11: Percentage Distribution of Non-Working Children by

Age of Household Head…………………………………………………. 62

Table 4.12: Percentage Distribution of Working and Non-Working

Children by Literacy of Household Head……………………………….. 63


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Children by Major Occupation of Parent……………………………….. 63


Children by Level of Education of Parent………………………………. 64


Children by Parent Marital Status………………………………………. 65


Children by Region…………………………………………………....... 66


Children by Ethnicity…………………………………………………… 67


Children by Locality of Residence……………………………………… 68


Children by Sex of Head of Household…………………………………. 68

Table 4.20: Reason for Leaving School…………………………………………....... 71

Table 4.21: Activity Status of Children by Sex and Age…………………..………... 73

Table 4.22: Omnibus Test of Model Coefficients…………………………………… 77

Table 4.23: Model Summary………………………………………………………… 77

Table 4.24: Classification Table for Binary Logistic Regression……………………. 78

Table 4.25: Model 1: Parameter Estimate for Schooling Status of

Children Using Binary Logistic Regression…………………………….. 79

Table 4.26: Classification Table for the Intercept only Model………………………. 84

Table 4.27: Classification Table for the Null Model………………………………… 84


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Table 4.28: Model 2: Parameter Estimates for Schooling Status

Of Children Using Multivariate Logistic Regression…………………… 86

Table 4.29: Covariance Parameters for the Null Model……………………………... 88

Table 4.30: Covariance Parameters for the Full Model (Model 2)…………….......... 88


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LIST OF FIGURES

Figure Page

Figure1: Children Not Enrolled in School by Age…………………………........ 67

Figure 2: Children Not Enrolled in School by Age and Sex……………………. 70

Figure 3: Distribution of Children by Activity Status………………………....... 72


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CHAPTER ONE

1.0 INTRODUCTION

1.1 Background

Children„s development has become a very important issue to all countries in the world. It is

realized that the future of any country depends on how the country takes care of her children.

Child labour levels are high in many developing countries. Any activity, economic or non-

economic, performed by a child, that has the potential to negatively affect his/her health,

schooling and normal developments constitute child labour.

According to recent International Labour Organization‟s (ILO) estimates, about 211 million

children aged 5-17 years were economically active globally (ILO-IPEC, 2002). About 73

million of these working children were below 10 years. The highest number of child workers in

the age group 5-17 years is found in Asia-Pacific (127.3 million) followed by sub-Saharan Africa

(48 million), Latin America and the Caribbean (17.4 million) and Middle East and North Africa

(13.4 million). While Asia has the highest number of child workers, sub-Saharan Africa has the

highest proportion of working children. In Ghana some children stop school or do not attend

school in order to work.

In the Ghanaian context, the incidence of child labour is considered very high. According to the

2003 Child Labour Survey of Ghana, 38.9 percent of the 6,361,178 children in the age group

5-17 years were, found to be economically active (GSS, 2003). This puts the child labour force at

about 12 percent of the total labour force of Ghana.


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The highest proportion of child labourers in Ghana is found in agriculture/fishing/forestry

(57.0%), followed by sales (20.7%), production (9.5%). The rest were engaged throughout the

text as truck-pushers, porters, labourers, driver-mates (11.0%). The major occupation for both

males (69.0) and females (44.0) is Agriculture, Fishing and Forestry. Another major occupation,

for females, is sales (30.4%) (GCLS, 2003).

Although in Ghana, child labour attracts most international attention, child labour is much more

common in the rural informal sector. Statistics on child labour in Ghana and other developing

countries also reveal that a vast majority of working children are employed in domestic service

where children perform household chores such as fetching water, collecting firewood, cooking

and taking care of younger siblings. Although many of these children are working under family

supervision, full-time work can deter them from attending school, and many home-based

activities can be as harmful as work performed outside the home.

In our Ghanaian society, it is a tradition that children engage in all kinds of work, which forms

part of their training. Unfortunately, the rate at which these children flock the streets of Ghana

for economic ventures these days in a bid to assist parents has assumed increasing dimensions

and therefore questionable. It is these observations that have aroused the interest to research into

these areas to look into the reasons why children engage themselves actively in economic

activity while schooling and the effects this practice has on their development.

According to Safo (1990), Ghana was the first country to ratify the convention on the rights of

the child, “Ghana demonstrated to the world her preparedness to promote the cause of children to

new and higher levels and to abide by the provisions of the convention”. The year 1990


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witnessed the arduous drafting process within the U.N. Commission on human rights, which

culminated in the 54th article convention outlining the rights of children. The law was in favour

of states which could not meet the rights of their children due to lack of resources. The law made

available three areas of assistance to states in need: provision, protection and participation.

Firstly, children were provided with the right to life, to name and to freedom of taught, conscious

and religion. Secondly, they were protected from physical and mental violence with special

attention paid to the protection of children who were disabled or belonged to minorities or who

were refugees. Lastly, the convention also provides for the child the right to be heard on

decisions affecting his or her life. Its continuation, however, was a step towards the provision of

minimum standard of treatment.

In a similar vein, the Education Acts 1961 (Act 87) provides for compulsory education for every

child in Ghana who had attained the school going age. Another declaration of the rights of the

child is the U.N. Declaration of the Rights of Children, Article 9 which states in part that “The

child shall be protected against all forms of neglect cruelty and exploitation. He shall not be the

subject of traffic in any form”.

According to the labour amendment Decree 1973 (NRCD 150) section 44 of NCD 157 of Ghana

among other things provided that “No person shall employ a child except where the employer is

with the child‟s own family and involves in a light work of agricultural or domestic character

only”. Sanction for non-observation of this provision is the imposition of a fine or summary

convention of the culprit. Section 47 of this law defines a child as a person under 15 years old.

This particular law is debatable with the reason that farm work for one is known to be very


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tedious and cannot be termed high work besides; it is this work that almost children in the rural

areas are introduced to.

In a keynote address delivered by Attorney General and Provisional National Defence Council

Secretary for Justice at a National Workshop on Child Labour (10th-14th August,1988), noted that

legislation was not enough to curb child labour. He pointed out that children are prevented from

going to school because of the activities they find themselves in. These activities include fishing

and hawking. It was unlawful to prevent a child from going to school. However, the Education

Act 1961(Act 87), provides for compulsory education of every child in Ghana who has attained

the school-going age. Under section 2(2), any parent who fails to comply with provisions of the

preceding subsection commits an offence and shall be liable on summary conviction to a fine.

But a legislation that is not enforceable is meaningless. The fact is that the government cannot

afford a fee-free education policy any more, what then is the justification for prosecuting a parent

for non-conformity to the Education Act? For most parents, a child must first of all eat before he

or she can go to school. Besides, sending a child to school does not mean paying the school fees,

which is only manifest function, but also supplying latent demands like school uniforms and in

certain instances provide table and chairs and stationery. One setback of the Education Act,

therefore is that, it assumes that educational facilities are easily available and affordable. In

1988, out of 2.2 million pupils of school-going age, only 1.3 million were in school. Here the

intake facilities were not just there. The Attorney- General also quoted the Labour Decree 1976

(NLCD 157) section 44 (1), which states that: “No person shall employ a child except where the

employment is with the child‟s own family and involves light work of an agricultural or


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domestic character only”. It is however, not clear in his decree the limits and boundaries of what

qualifies to be light work of an agricultural or domestic character.

Another important law which protects the interest of the child is section 79 of Criminal Code

(Act 29) states that: “A man is under duty to supply the necessaries of health and life to his wife

being actually under his control and to his legitimate and illegitimate son or daughter, being

actually under his control and not being of such age and capacity as to be able to obtain such

necessaries. A guardian is under the like duty with respect to his ward being actually under his

control”. Also, the law has not been able by its mere existence to ensure the provision of

“essential necessaries” of health and life to children in Ghana.

A representative of the National Council on Women and Development‟s (NCWD), drew

attention to female domestic servants, some of whom find themselves in this role because the

traditional notions about females was that, formal education is not important to them. This was

because they ended always in the kitchen. She also made reference to children who got married

at very tender ages as low as twelve years old. At this tender age they become vulnerable to

exploitation by members of their husband‟s family.

The NCWD is sympathetic towards children in work roles. They did not advocate for its total

abolition but rather were interested in securing a minimum age for child work and the provision

of adequate remuneration for it. Apart from their concern on a minimum age of employment,

they also suggested that the law on compulsory education must be enforced. Parent must be

penalized for trading their children off instead of sending them to school. Whether this

enforcement is possible is another question.


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The views of the Ghana Education Service were on opinion that, child labour has been generally

accepted as a means of solving chores. It is the result of polygamy and it adds cumulatively to

the illiteracy rate.

According to Dr. Abdullah (1978) (then Secretary for Education in Bangladesh), “the most

disturbing aspect of this problem of school is that 40 percent of the children of school-going age

are not in school. Moreover, enrolment ratio in the southern half of the country is double those of

the northern half”. The problems were not only a question of enrolment, but also a problem of

drop out or sustenance of those who are already in school. In 1984, the drop-out rate was 36.58

percent. In addition, the quality of teachers was also a problem. Dr. Abdullah, (1985) said

“untrained teachers of the primary school form 48 percent while at the Middle School; they

formed 38 percent of all the teaching staff”. It is difficult to see how the interest of the children

could be sustained at school given these relatively low skilled personnel.

On the contrary, the Ghana Education Service had not only recognized that child labour was a

problem but had also made attempts as to how they could attack this problem. This review

therefore offers an opportunity for all especially policy planners and parents to take a new look at

children engagement in economic activities and educational aspirations.

1.2 Child Labour and Schooling Situation in Ghana

The official age of entry into primary school is 6 years (according to the Primary Education Act,

1992), although many children attend primary school at the age of 4 or 5 years. Late entry into

primary school is also very common in rural areas.


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In Ghana, primary education is compulsory for all children. The Government has introduced the

Free Compulsory Universal Basic Education (FCUBE) to get all children of school going age to

school and to prevent children from early labour. According to the Ghana Primary School Act

(1992), a child of 6 years old must go to school. To make the school attendance easier especially

for children from poor parents, textbooks are supplied free of charge to all children up to Junior

Secondary School (JSS). An alternative subsidy program (the School Feeding Programme) or

(Food-For Education), has been implemented to help children and their parents. In-spite of all of

these measures, a large proportion of children are not enrolled in school.

This study therefore examines the socio-economic factors which account for school children‟s

engagement in economic activities in Ghana.

1.3 Statement of the Problem

The high incidence of child labour in many developing countries, including Ghana, may be

attributed to socio-cultural norms in these settings. In the olden days children were considered as

factor of production; labour force for the family. This was not a problem because, in the absence

of modern technology majority of the parents had to depend on their children as sources of

labour especially in the cocoa growing areas, fishing communities and others where manual

labour was most certainly required.

Traditionally, children predominantly feature in the family upkeep in many ways. However, it is

this mode of contribution today due to social change and money-using economics that raises an

enquiry and understanding of the phenomenon.


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Most often the labour force of a nation consists of men, women and children. The employment of

children is socially and legally determined as illegal because in most countries, children with

minimum age (usually 15 years) are not legal to be used for full time employment. However, it is

their mode of contribution where they are employed mostly in commercial activities that have

been the major concern for the working child, families and the society as a whole. This high

incidence of child labour and the subsequent low school attendance rates in Africa has attracted

the attention of most governments, past and present. This is evidenced by a constant search for

adequate measures to arrest the situation. The issue of child labour is a major concern of the

Government of Ghana, as it is for many other countries. The problem has long been recognized

and the Government has enacted laws to prohibit child labour, increase school enrolment rates

and develop other national programmes to meet the urgent needs of children in the country.

In spite of these efforts, millions of children continue to work as forced labourers in a wide range

of sectors, industries or occupations either to pay for the debts of their parents or to help earn

income for their care givers. In some instances, these children are drawn into certain economic

activities under false pretexts from which they are not allowed to leave.

Many countries have undertaken labour force surveys that define participation in the labour force

as being engaged in „economic activity‟. Such surveys do not adequately capture participation in

economic, but illegal activity, work that is unpaid for and undertaken in household enterprises

whose product is mainly for household consumption. Also, some activities, while certainly

involving work, were not deemed „economic‟ and are therefore excluded from such surveys.

While many of these problems arise in canvassing information about adults as well as children,

they are particularly severe in canvassing information about the work of children.


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Another serious problem involves children who are neither classified as economically active nor

as students enrolled in or attending schools. In parts of Africa where child fostering is prevalent,

it is often difficult to determine whether this is in fact disguised child labour. As such, economic

activity as a proxy for child labour could understate the number of working children.

Children perform different types of work under a variety of conditions and for a variety of

reasons. Therefore, in assessing child labour, the types and conditions of work, the age of the

children who perform the work, and the developmental level of the country must all be taken into

account. This is because not all child work is considered to be detrimental to the growth and

well-being of children.

The ILO defines light work as work that is not likely to be harmful to children‟s health or

development and not likely to be detrimental to their attendance at school or vocational training.

In determining whether work is likely to be harmful, the ILO takes into consideration the

duration of work, the condition under which the work is done, and the effect on school

attendance, among other factors. However, the ILO does not provide any operational guidance

for assessing these factors and determining whether any given form would qualify as light work.

Hazardous work includes „work which by its very nature or the circumstances in which it is

carried out is likely to jeopardize the health, safety or moral of young persons‟ (International

Labour Organization, 1973). It is left to individual governments to determine which types of

work fall under the rubric of light or hazardous.


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Most children in Ghana are not able to pursue their education these days as is expected of them.

This problem is attributed to the fact that children are often actively engaged in economic

activities while attending school, they are seen hawking, cracking stone, farming, weaving kente

cloth for money. As a result, most of them are not able to complete their elementary education.

Besides, what they hope to become in future is either hindered or not achieved. Another

unfortunate aspect of the whole issue is that, these children tend to pick up some kind of

behavior like disrespect and the practice of deliberately staying away from school without

permission. Furthermore, some of these children later „drop-out‟ of school because they are

either not able to perform well in class or have more interest in working for money than

schooling.

Schooling and other forms of education can help to lower the incidence of child labour. However

the pace of reducing child labour and improvement in school participation rates is somewhat

slow in Ghana. In a number of developing countries, targeted enrollment subsidies have been

used as an effective way to break the cycle of poverty and illiteracy and address both the income

loss to parents and education for children. This problem is all over the country and need to be

redressed. In order to implement effective child labour policies and schooling programmes, there

is the need to isolate the factors that contribute to child labour which in turn affect school

enrolment rates. In 2001, the Ghana Statistical Service organized a child labour survey, within

the framework of the IPEC, to facilitate the assessment of the impact of policies and programmes

that had been implemented in Ghana to reduce child labour. This study therefore examines the

socio-economic factors which account for school children‟s engagement in economic activities

in Ghana.


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1.4 Objectives of the study

The general objective of this study is to determine the socio-economic factors that influence

child labour and schooling in Ghana.

The specific objectives are:

(i) To determine socio-economic factors contributing to child labour and schooling.

(ii) To determine socio-economic factors causing school children‟s engagement in

economic activity.

(iii) To establish the relationship between child labour and schooling,

(iv) To make recommendations, based on the findings, for appropriate intervention

measures to reduce child labour.

1.5 Research Questions

In line with the above objectives, the study poses the following research questions:

(i) What are the major socio-economic factors that contribute child labour and schooling?

(ii) What are the socio-economic factors causing school children‟s engagement in economic

activity?

(iii)What is the relationship between child labour and schooling?

1.6 Research Methodology

1.6.1 Source of Data

The main source of data for this study was the Ghana Child Labour Survey (GCLS,2003), the

Ghana Living Standard Survey Round Six (GLSS6) : Labour Force Module, administered by

Ghana Statistical Service (GSS, 2013), and other secondary sources were also utilized.


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1.6.2 Description of the Data

The sample design and sampling procedure for used for the Ghana Child Labour Survey (GCLS)

comprised both a nationwide probability sample survey of all households in Ghana and

supplementary non-probability survey of street children.

The questionnaire collected information on housing/household characteristics, socio-

demographic characteristics of all household members, information on economic activity, and

other conditions of children.

1.7 Significance of the Study

(i) The study is geared towards providing the necessary statistical justification regarding

socio-economic determinants of child labour.

(ii) The study will furnish decision makers and other stakeholders with information regarding

the major socio-economic determinants of child labour and schooling in Ghana.

1.8 Method of Analysis

In this study, bivariate distributions of child labour are carried out to examine the relationship

between child labour and their covariates. In order to make full use of available information,

these bivariate investigations were limited to children aged 5-17 years that are working and not

working.

The study also examined the data structure to find out if multilevel models can be applied to

these data to determine the extent of familial and clustering effects. Multivariate analyses are

then conducted to identify the factors which have influenced recent child labour levels. The


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multivariate analytical models applied are standard logistic regression models and multilevel

logistic regression models. The results of this research are compared to those obtained by others

to identify the factors which contributed to child labour and schooling in Ghana.

1.9 Outline of Dissertation

The study comprises five chapters. Chapter one presents the background of the study, problem

statement, objective, research questions, methodology, significance and outline study. Chapter

two reviews some of the relevant literature related to the work. This is followed by chapter three,

which presents an in-depth discussion of the methodology employed. Chapter four presents

detailed analysis and discussion of results. Summary, conclusions and recommendations are

captured in the fifth chapter.


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CHAPTER TWO

LITERATURE REVIEW

Significant differences in child labour and schooling exist among populations in nearly all

countries. Studies have identified specific significant determinants to include educational

attainment, occupational status, marital status and place of residence.

Zelizer in 1994 addressing a conference on child labor in America, stated that, “The term „„child

labour‟‟ is a paradox, for when labour begin… the child ceases to be” (Wise, 1910). The

International Labour Organization‟s (ILO) new convention on child labour was both an advance

and a strategic retreat from the minimum age standards set by its 1973 Convention No. 138

which states that, all children under 18 years of age, should not be engaged in economic

activities (ILO, 1999). But Convention No. 182 also signals a retreat from the ILO‟s offensive

against child labor (Comparative Education Review). Comparativists should observe that, in

Conversion No. 182, work preventing school access or success is not, in itself, viewed as

inherently intolerable, nor is it to be prioritized for immediate eradication. This geopolitical

novelty can be seen in the context of ongoing debate over the educational implications of the

emerging global market for skills, products, and services.

The ILO‟s new approach highlights the centrality of child welfare for researchers and advocates

of education. A case in point: What do we know about the effects of „„globalization‟‟ for

children‟s work and schooling? In many ways, international markets and the world

institutionalization of children‟s rights through the Convention on the Rights of the Child (CRC)

carry contradictory, even paradoxical implications. On one hand, there are more opportunities for


15

cash-compensated work; there is also greater inequality, along with new „„needs‟‟ of children for

products advertised by global markets. On the other hand, there is a growing sense that children

should be students, not workers (Nieuwenhuys, 1996). Out of this contradiction, and from

distinct professional orientations, a rich but still very disparate literature is emerging (including

children‟s literature).

Rosenberg (1993) provided a critical counterpoint to advocate for street children who exaggerate

the numbers in order to draw attention to the neoliberal orientations they believe have worsened

their plight. In the first chapter of her book, gave an invaluable history of the discourse over

street children, tracing the worldwide dissemination of this concept to a UNICEF advisor who

suggested in 1981 that there were 100 million street children in the world, half of whom were in

Latin America (Black, 1986). In later estimates, UNICEF continued to revise downward the

number of street children. Meanwhile, by the mid-1980s, Brazilian UNICEF offices estimated

that there were 1.9 million abandoned children and 13.5 million needy children in that country.

But only since the mid-1980s have there been systematic investigations of the status of Brazil‟s

children.

In the early 19"' century, there was an extensive use of child labour over the entire world.

Children often started to work within their family activities (Shah, 1985). This kind of labour has

been seen as part of the integral process of the socialization and training of children for adult

responsibilities (Estrella, 1994). Today, participation of children in the workforce is on the

increase in many parts of the world particularly in the developing countries (Pinto, 1989).


16

In Ashanti region of Ghana, Rettray (1927) mentioned among other things that, male children

followed their fathers to farm, tended animals like cattle and goats, which form part of their

socialization process. They also learnt crafts like kente weaving and goldsmithing. Oppong

(1973), in his research also observed that, male children follow their fathers or learn to become

butchers, barbers, blacksmiths, farmers and drummers while the girls help in the home routine

tasks like sweeping, cooking and also carry farm produce home, pick legumes and fruits.

Among the Anlos in the Volta region, Nukunya (1969), in his research into child training

mentioned that children enjoy freedom until about 10 years when they are introduced to various

occupations like farming and fishing which is their major work. Boys are taught to pursue

economic activities, which give them substantial income to enable them alleviate their

dependence on their parents. The girls on the other hand, help their mothers in female

occupations like petty trading, baking and fish smoking.

Busia‟s social survey (1950), of Sekondi Takoradi also revealed that boys and girls engage in

several activities that deal with money as a predominant way of life. In the same vein, Acquah in

a survey work in Accra revealed that 30% of the total number of school children in selected

districts in Accra were gainfully employed in activities like selling at the markets, carrying fish

from the beach to the market, domestic servants and newspaper vendors (Acquah, 1972).

Children‟s engagement in work roles in our Ghanaian traditional society is not a new practice.

According to Mends et al. (1988) research work on the native and problems of child labour

stated that children‟s involvement in work roles form part of their training into adulthood.


17

The rapid growth of the manufacturing sectors (Banerjee. 1995) and poverty (Damodaran, 1997)

may form the common reasons of child labour. On the one hand, the child is a cheap labourer,

obedient and less likely to strike (Trattner, 1970). On the other hand, working children were an

important resource for significant and early contributions to the household income (Cain, 1977).

However, the view those children make a significant contribution to the household income

encourages many families to have more children (Mamdani, 1972).

Accurate statistics on the prevalence of child labour are not available. According to estimates of

ILO, 100-200 million children less than 15 years were working (Habenich, 1994), (Pollack et al.,

1990). This is thought to be an under-estimated value (Ashagrie, 1997) since in some countries,

many young workers below the age of 15 are not included in the labour force statistics because

of the large variety of terms used to describe the notion of childhood and labour (Shah, 1985).

Children who both work and attend school are usually considered as pupils rather than workers.

Moreover, in most countries child labour is clandestine and hidden (Habenich, 1994).

According to Rosemberg (1999), all researchers soon agreed that small numbers of children and

adolescents in metropolitan areas lived apart from either parents. Given that any number of

children living on the street is unacceptable and that little is to be gained by erroneous

estimations, Rosemberg argues that some of the previous characterizations of street children by

UNICEF are at best inflated and at worst self-serving.

Children appear to be involved in a wide range of economic activities. They are engaged in

waged labour in agriculture, services, factories, self-employment in street trades and domestic

services. Some receive part of their wage in kind and many others are unpaid and work for their


18

families, relatives or friends in the home or on the land. Some others are engaged in marginal

economic activities on the streets and are exposed to drugs, violence, criminal activities and

abuse that damage their health, morals, and emotional development (Heward, 1993).

Large numbers of child labourers start work before 8 years of age. For example, in the carpet

industry, they are preferred to adults because of their docility, fast fingers, low cost and they are

less demanding, (Rodger and Standing, 1981). Under the supervision of ILO, four surveys on

child labour were carried out in urban and rural areas of Ghana, India, Indonesia, and Senegal

during the period of 1992-93. (ILO, 1996). The surveys intended to collect relevant statistics on

the child labour phenomenon. 4000 to 5000 households, both urban and rural, in each of the four

countries were selected as the study sample.

The results of the survey showed that slightly more than 10% of children between the age of 5

and 15 years were found to be economically active during the twelve months prior to the survey.

In Ghana, 80% of working children were engaged in trading activities, about 40% of whom were

working for more than 8 hours per day. More than two thirds were unpaid family workers, while

the average monthly income of 75% of paid workers was far lower than the national minimum

wage.

In the city of Enugu, a study conducted by Asogwa on 400 street hawkers under the age of 15

years and 200 control non-working children with the aim of studying the sociomedical aspects of

child labour in Nigeria, showed that the average age of working children was about 12 years, and

33% of them worked more than 7 hours per day (Asogwa, 1986).


19

Children work for a variety of reasons. Many authors state that child labour is rooted where

households are suffering of low income, poor living conditions, high unemployment rate and

insufficient opportunities for education (Blanchard, 1983). Poverty emerges as the most

compelling reason why children work. Poor households need money to ensure survival and

children are the only means within their choice and capacity to do that. Children work even

though they are not well paid because they still serve as major contributors to family income in

many parts of the world.

In developing countries, the decision to send children to school or to work is likely to be based

on the relative rate of each return. The costs of education are relatively very high for poor

households sometimes preventing them from sending their children to school and consequently

leading to high rates of child labour (Addison et al., 1997).

Illiterate or low educated parents may not be aware of the importance of educating their children.

Moreover, these parents are more interested to send their children to work rather than to school

because their contribution to household income is highly needed, since the illiterate parents are

usually unskilled people with low income. Thus, with illiteracy among adults, child labour tends

to increase.

Patrinos and Psacharopoulos (1995) used multiple regression to show that factors predicting an

increase in child labour also predict reduced school attendance and an increased chance of grade

repetition. The authors estimated this relationship directly and show that child work is significant

predictor of age-grade distortion (Patrinos and Psacharopoulos, 1997). Akabayashi and

Psacharopoulos (1999) showed that, in addition to school attainment children‟s reading


20

competence decreases with child labour hours. In addition, Heady (2003) used direct measures of

reading and mathematics ability and finds a negative relationship between child labour and

educational attainment in Ghana.

All of these papers examine the correlation, rather than the causal relationship between child

labour and schooling outcomes. However, Cavalieri (2002) used propensity score matching and

finds a significant, negative effect of child labour on educational performance. Ray and

Lancaster (2003) instrument child labour with household measures of income, assets and

infrastructure, to analyze its effect on several school outcome variables in seven countries. But

their instrumenting framework is questionable, as they make the strong assumption that

household income, assets, and infrastructure are exogenous to the schooling equations.

In order to test whether child labour is efficient or not, Baland and Robinson, (2000) assumed

that there is a trade-off between child labour and the accumulation of human capital.

Child labour is perceived to be a serious problem as it is believed to be destructive to children‟s

intellectual and physical development, especially that of young children. The danger is

exacerbated for those children who work in hazardous industries. This is the theory behind the

„child labour trap‟ – if a child is employed all through the day, the child remains uneducated and

subsequently has low productivity as an adult so child labour can directly contribute to adult

unemployment in developing countries. A major caveat of the literature to date is that there is

very little treatment of such long-term dynamic consequences of child labour.


21

The accessibility and quality of education and its relevance to the labour market is one factor in

parents‟ decision to send their children to school. Although, as many boys and girls combine

school attendance and work increased enrolment rates can have an effect on working hours and

on the kind of work done (Sudharshan/Coulombe (1997), Odonkor (2007), Ghana Statistical

Service (2003).

However, as Odonkor (2007) claims, “rural parents should rather be seen as dissatisfied clients

of the educational system than as illiterates, ignorant of the value of education”. It is striking that

although about 90 percent of the children in cocoa growing areas are enrolled in schools, 54

percent cannot read or write (MMYE/NPECLC 2008). Because of the poor quality of schools,

the difficulties of access and the uncertainties about finding an adequate job after graduation,

parents have developed a strategy to spread the risks, which involves sending some of their

children to school while others help with fishing, farming or other economic activities.

The findings of this case study are in line with the research done by Kufuogbe (2005) on children

in fishing in the Central Region, both with regard to the kind of work they do and to the

remuneration they receive. Based on a quite large sample of 356 children, from randomly

selected households, several schools and landing sites using multiple regression. He estimates

that 10 to 20 percent of the children living in coastal areas are involved in fishing, especially in

the main season. The author concluded that being at the beach has become a “way of life” for

them, allowing them both to earn an income and to play and swim for leisure, (Kufuogbe, 2005).


22

The daily routines for cattle boys in North and South Tongu are similar. They also work for ten

to twelve hours a day, mostly from 6-8 am in the morning to 6 pm in the evening, seven days a

week. Their main tasks are to herd cattle to the field at an agreed time to graze and drink, to

ensure that the animals do not destroy people‟s farms and that they do not get lost or stolen. In

addition, the boys help with other husbandry activities such as spraying and bathing the cattle.

Some also have to collect firewood for the cattle owner‟s wife before they can eat and leave for

the field. The boys operate in teams of up to three. Most of them eat twice a day, mainly

breakfast and supper. For lunch, they hunt for rodents and gather fruit or catch fish from nearby

waters, (Afenyadu, 2008).

Nearly every study on the relationship between child labour and education compares the

educational outcomes of children who don‟t work, or who work less, and those who do work, or

work more. The first hurdle that needs to be surmounted, then, is accurate measurement of both

these variables. “Education” is difficult to define and measure because it is multi-faceted. It can

take the form of school attendance, school performance or skill acquisition, and each of these can

be approached in more than one way. But child labour is also far from simple to measure.

Most children who work are engaged in household enterprise activities, whether it is a farm, a

home-based manufacturing operation, or a retail enterprise. The productive assets would have

mixed impacts on child labour. On the one hand, they may raise a child‟s opportunity cost of

time in school because the child is productive in labour activities. On the other hand, adults in

the household are also more productive, so the household can better afford allocating child time

to schooling activities. Cockburn (2000) used the productivity model to explains why some


23

agricultural households used measures of the farm capital stock to lower child labour, while

others find the opposite (Rosenzweig and Evenson, 1977).

According to Cockburn & Dostie (2007), it is not always the poorest households that engage in

child labour. While household income draws children out of school, the productivity effect of

underlying greater asset holdings does the contrary. Beegle, Dehejia and Gatti (2006) found out

that there is a positive and significant relationship between the level of household assets and the

use of child labour. This is initially surprising (since child labour is normally portrayed as being

negatively associated with household wealth), but in agricultural settings a positive association

can be rationalised. Rural households with larger farms are more likely to demand higher levels

of child labour from their children.

Graitcer and Lerer (1998) provided a comprehensive international review of the state of

knowledge of the impact of child labour on health. Data on the extent of child labour itself is

subject to considerable error, but data on the incidence of child injuries on the job are even more

problematic. Sources of information come from government surveillance, sometimes

supplemented by data from worker‟s compensation or occupational health and safety incidence

reports. These latter sources are less likely to be present in the informal labour markets in which

child labour is most common, and government surveillance is often weak. Consequently,

Graitcer and Lerer conclude that published epidemiological studies of the health consequences of

child labour almost certainly underestimate the incidence of injuries.

Dunn et al. (1998) presented evidence that children in poorer families have significantly worse

health than children in richer families. On the other hand, children from the poorest households


24

are the most likely to work, and growing up in poverty may be correlated with adverse health

outcomes. Thus, the early incidence of child labour may be correlated with unobservable positive

or negative health endowments that could affect adult health in addition to any direct impact of

child labour on health. These unobserved health endowments cloud the interpretation of simple

correlations between child labor and adult health outcomes.

Another confounding factor is that child labour may affect a child‟s years of schooling

completed, and education has been shown to positively affect adult health. Studies have

consistently found a large positive correlation between education and health. (O‟ Donnel et al.,

2002).

Most of the studies that evaluate the impact of child labour on time in school concentrate on

whether or not the child is enrolled. In many countries, enrolment rates for working children do

not differ dramatically from those children who are not working, particularly at younger ages.

Some have pointed to this evidence as suggesting that child labour and schooling are not

mutually exclusive.

According to Ravallion and Wodon (2000), less is known about the relationship between child

labour and school attendance because it is more difficult to elicit information on school

attendance from household surveys. Parents‟ impressions of their child‟s attendance record are

likely fraught with error. It is possible to integrate official attendance records from the school

with household survey data, but this has not been done frequently in practice, (Dustmann et al.,

1996). In the end, time spent in school, which is an input into the educational production process

is no more a measure of schooling outcomes than is child labour. If child labour and time in


25

school are both measured in hours, the time budget imposes an almost certain negative

relationship between the two, even if child labour does not harm learning. Consequently, the

impact of child labour on learning is unlikely to be well-measured by the impact of child labour

on time in school.

Evidence of the impact of child labour on schooling attainment is mixed with some studies

finding negative effects (Psacharopoplous, 1997) while others (Patrinos and Psacharopoulos,

1997 and Ravallion and Wodon, 2000) finding that schooling and work are compatible. There is

stronger evidence that child labour lowers test scores, presumably because it makes time in

school less efficient (Orazem et al., 2004). On the other hand, child labour may retard child

cognitive attainment per year of schooling, and it may also lead to earlier exit from school into

full time work.

Longer school days may influence the amount of knowledge a child can gain. However, longer

school days may also influence child labour. The longer the school session, the less time a child

has to work. Khanam (2004) found that the imposition of an after school programme in rural

Brazil resulted in a large reduction in the probability of child labour. Length of term can also

affect the amount a child learns in a school year. Differences in the length of school term

between black and white schools in the United States in the segregated era have been shown to

explain differences in school achievement (Orazem, 2004) and earnings (Basu, 1999) between

blacks and whites.

Most of the studies up to this point have focused on the relationship between child labour and

school enrolment. It has been commonly observed that in many countries, the majority of


26

working children are enrolled in school. For example, Ravallion and Wodon (2000) found that

increases in enrolment in a sample of girls in Bangladesh were not associated with appreciable

decreases in child labour. The authors concluded that the adverse consequences of child labour

on human capital development are likely to be small. However, it is possible that working

children remain enrolled in school but do not attend as regularly. Several recent studies have

examined that possibility.

Boozer and Suri (2001) studied children aged 7-18 in Ghana in the late 1980s. The authors

concluded that an hour of child labour reduced school attendance by approximately 0.38 hours.

Another study by Edmonds and Pavcnik (2002), using a panel of Vietnamese households, found

that increases in the real price of rice, a major export, lowered child labour. The reductions in

child work were largest for girls of secondary school age who also experienced the largest

increase in school attendance.

Edmonds (2002) again examined how child labour and education in a sample of poor black

households in South Africa responded to a fully anticipated increase in government transfer

income. Households that were eligible for a social pension programme experienced a sizeable

decrease in child labour and an increase in schooling attendance.

There is indirect evidence that child labour limits a child‟s human capital development. Child

labour has been linked to greater grade retardation, lower years of attained schooling

(Psacharopoulos, 1997), and lower returns to schooling and a greater incidence of poverty as an

adult (Ilahi et al., 2003). On the other hand, some studies have found that child labour and

schooling may be complementary activities (Patrinos and Psacharopoulos, 1997).


27

According to Mundy (1998), legal reform was possible when NGOs turned from providing only

social services to becoming advocates for legal reform. An emerging literature shows how values

about children have become legitimated by supranational levels of authority as the result of

alliances between national and international NGOs and associations. For example, Brazil was the

first Latin American country to enter ILO‟s program to eradicate child labor, a move that added

to the legitimacy of Brazilian children‟s movements.

According to Post (2001), if the decision was made to leave school for work, then there is a

decision to be made about the child‟s type of work, domestic work or work outside of the home.

Hypotheses about what leads children to each outcome at each sequence of this decision tree can

be tested using probit regression analysis (but curiously, the authors chosen to report the

statistical significance of each test only at the 90 percent confidence level, which is an

unconventional approach when dealing with large survey data sets). David Post also presented

results using a completely different approach to child labour decisions; one based on a

multinomial logistical regression model that assumes choices are made simultaneously between

all available options, rather than sequentially.

Many countries have undertaken labour force surveys that define participation in the labour force

as being engaged in „economic activity‟. Such surveys do not adequately capture the

participation in economic, but illegal activity and work that was unpaid and undertaken in

household enterprises and whose product was mainly for household consumption. Also, some

activities, while certainly involving work, are not deemed „economic‟ and are therefore excluded

from the survey. While many of these problems arise in canvassing information about adults as


28

well as children, they are particularly severe in canvassing information about the work of

children.

The new ILO strategy needs the emergent, disparate literature on the child labour paradox. The

titles reviewed here are representative of a burgeoning literature that is now appearing and that

includes, in its scope, issues of homelessness, poverty, exploitation, and the implications of these

issues for schooling. In the field of comparative education, sad to say, this area has taken a

backseat behind other concerns over social development, economic growth, student achievement,

governance, planning, international relations, and curriculum.

In summary there has been a lot of research into issues of child labour and schooling. Most of

these researches use regression analysis, multivariate analysis of variance and others in their

analysis. The multivariate methods involving binomial and multilevel logistic regression analysis

is used to identify the latent variables that promote children to school and work demonstrate the

uniqueness of this work. This work looks to add to the body of evidence and provide literature in

Ghanaian context.


29

CHAPTER THREE

REVIEW OF BASIC THEORY AND METHODS

3.0 Introduction

This chapter presents the methodology used in the research. It explains the steps in the modeling

process, which would include the data processing and models to be used in order to achieve the

research objectives.

3.1 Data and scope

This study investigates child labour and schooling of children in households. The study will use

the data from 2003 Ghana Child Labour Survey (GCLS), since this is the only standalone survey

on child labour conducted by the Ghana Statistical Service (GSS, GCLS, 2003).

The survey collected extensive information on 6,316,180 children aged 5 – 17 years and were

made up of 3,313,495 males and 3,047,685 females. A sample of 10,000 households was

selected out of which 9,889 were successfully interviewed, indicating a household response rate

of 98.9 percent. A similar response rate was achieved in urban/rural areas and all regions in

Ghana.

The economic activities (working or not working) of the children which is considered a measure

of child labour was used as dependent variable in the bivariate analysis. Further, schooling status

of the children was used as the dependent variable. Under this, when a child attends school and

work, the dependent variable takes the value 1 and 0 if the child reported schooling only. Some

of the characteristics of interest which will be considered as covariates in this study include sex

of the child, relationship to head of household, age group of children, size of household, place of


30

residence, literacy of parent, higher level of education of parent, major occupation of parent,

marital status of parent, sex and age of head of household, employment status of both father and

mother, religious affiliation and ethnicity.

The dependent variable used in this study is dichotomous and the independent variables are

either continuous or categorical. Hence, a logistic regression model and multilevel logistic model

would be used to predict the probability of a child attending school and working or schooling

only.

3.2 Logistic regression

Logistic regression is an extension of linear regression that allows us to predict categorical

outcomes based on one or more predictor variables. It measures the relationship between the

categorical dependent variable and one or more independent variables, by estimating

probabilities. The probabilities describing the possible outcomes of a single trial are modeled, as

a function of the explanatory (predictor) variables, using a logistic function.

Logistic regression can be binomial or multinomial. Binomial or binary logistic regression deals

with situations in which the observed outcome for a dependent variable can have only two

possible types (for example, "yes" or "no"). Multinomial logistic regression deals with situations

where the outcome can have three or more possible types.

Logistic regression is an extension of the Linear Models which also forms part of the

Generalized Linear Models (GLM‟s). The GLM‟s also comes from a family of distributions

called the exponential family.


http://en.wikipedia.org/wiki/Multinomial_logit

31

3.3 The Exponential Family Distributions

A single-parameter exponential family is a set of probability distributions whose probability

density function (or probability mass function) can be expressed in the form;

( | ) ( )exp ( ). ( ) ( )xf x h x T x A (3.1)

where T(x), h(x), η(θ), and A(θ) are known functions.

Alternatively, equivalent form is often given as;

( | ) ( ) ( )exp ( ) ( )xf x h x g T x (3.2)

The value θ is called the parameter of the family.

T(x) is a sufficient statistic of the distribution.

is called the natural parameter. The set of values of η for which the function ( ; )xf x is

finite is called the natural parameter space. If η(θ) = θ, then the exponential family is said to be

in canonical form. By defining a transformed parameter η = η(θ), it is always possible to convert

an exponential family to canonical form. Thus, becomes the link function in GLM‟s.

Distributions such as normal, Multinomial, Bernoulli, Binomial, Gamma, Poisson, Exponential,

among others are all members of this family. Members of this family exhibit some common

properties.


32

3.3.1 Properties of the Exponential Family of Distributions

Some of the properties of the exponential family include;

(i) In one-parameter exponential family, the random variable (X) is sufficient for θ.

(ii) The probability density function of T(x) belongs to one-parameter exponential family.

(iii)If Xi is independent identically distributed random variables from one-parameter exponential

family, then the joint probability density function for X = (x1, …, xn) also belong to the one-

parameter exponential family with the sufficient statistic 1

( ) ( )n

ii

T X T x

.

(iv) In addition to this, the expected value and the variance of T(X) can be found from the

probability density function.

( )( ) ( ) ( ) T xp x g h x e (3.3)

Equation (3.3) must be normalized, so that;

( ) ( )1 ( ) ( ) ( ) ( ) ( ) .T x T x

x x xp x dx g h x e dx g h x e dx (3.4)

In finding the mean of a single parameter exponential family, we take the derivative of both sides

of equation (3.4 ) with respect to η.

( ) ( )0 ( ) ( ) ( ) ( )T x T x

x x

dg h x e dx g h x e dxd

(3.5)

Interchanging the order in integration and differentiation, the above equation (3.5), becomes


33

( ) ( )

( ) ( )

( ) ( )

0 ( ) ( ) ( )

( ) ( ) ( ) ( ) ( )

( ) ( ) ( ) ( ) ( ) ( )( )

( ) ( ) ( ) ( )( )

( ) ( )( )

(

T x T x

x x

T x T x

x x

T x T x

x x

x x

dg h x e dx g h x e dxd

g h x e T x dx g h x e

gT x g h x e dx g h x e dxg

gT x p x dx p x dxg

gE T xg

E T x

) In ( )d gd

(3.6)

Therefore,

( ) In g( ) ( )d dE T x Ad d

(3.7)

where,

In ( ) ( )g A

A similar proceedure therefore, can be used to find the variance of the sufficient statistic ( )T x ,

of the exponential family of distributions. This can be achieved by finding the second derivative

of A .


34

Thus, 2

222

( )var ( ) ( ) ( )id AT x E T x E T x

d

. (3.8 )

A(η) is called the log-partition function because it is the logarithm of a normalization factor,

without which ( ; )xf x would not be a probability distribution.

( ) ( )exp ( ) ( )x

A In h x T x dx (3.9)

The function A is important because the mean and variance of the sufficient statistic T(x) can also

be derived simply by differentiating A(η).

3.3.2 Maximum likelihood estimation in the Exponential Family

Let x1, x2, … , xn be an independent identically distributed random sample from the exponential

family ( | )p x . Then,

1 2( , ,..., | ) ( | )

( ) exp ( ) ( )

n ii

Ti i

ii

p x x x p x

h x T x nA

(3.10)

This shows that sufficiency vector does not grow as the number of samples and the density

function remains in the exponential family.

The likelihood is given by;

1 1

1

( ; ... ) log ( ... | )

log ( ... ) ( ) ( )

n n

Tn i

i

l x x p x x

h x x T x nA

(3.11)


35

Differentiating the likelihood function with respect to and setting it to zero, we get the

maximum likelihood. Thus,

1( ; ... ) ( ) ( )n ii

l x x T x n A

(3.12a)

This implies,

( ) ( ) 0ii

T x n A

( )

( ) iiT x

An

(3.12b)

This is a general solution to the maximum likelihood parameter estimation problem across all

members of the exponential family.

3.4 The Generalized Linear Models (GLM)

Generalized linear models were formulated by John Nelder and Robert Wedderburn as a way of

unifying various other statistical models, including linear regression, logistic regression and

Poisson regression (Nelder and Wedderburn, 1972). In a generalized linear model (GLM),

outcome of the dependent variables, Y, is assumed to be generated from a particular distribution

in the exponential family. The mean, μ, of the distribution depends on the independent variables,

X, through:

1( ) ( )E Y g X (3.13)

where E(Y) is the expected value of Y,

Xβ is the linear predictor, a linear combination of unknown parameters β and g is the link

function.


36

The GLM generalizes linear regression by allowing the linear model to be related to the response

variable through a link function and by allowing the magnitude of the variance of each

measurement to be a function of its predicted value. Hence, the variance is typically a function,

V, of the mean.

1( ) ( ) ( )Var Y V V g X (3.14)

Hence, the 'iY s under the generalized linear model has three major components and as a

member of the GLMs, the logistic regression is also associated with these components, namely;

(a) The random component. In this component, the dependent variables 1 2, , , nY Y Y are

assumed to share the same distribution from the exponential family, thus specifies the

distribution of the response variable.

(b) Systematic component is the linear combination of the predictor variables and the regression

coefficients in the form, .X The explanatory variables may be continuous, discrete or

both.

(c) Link function is a smooth and invertible linearizing function g that transforms the

expectation (μ) of the ith response variable, i iE Y to the linear predictors. It can also be

written as;

, where i i i i ig X E Y


37

3.5 The Basic Logistic Regression Model

If the data consists of k independent observations y1, y2, … , yk, and that the ith observation can be

treated as a realization of a random variable Yi. We assume Yi has a Bernoulli distribution with

parameter , where ( 1).P x

The probability density function (p.d.f) of the Bernoulli distribution is given by

11 , 0 or 1. 0 1|0, otherwise

xx xp x

(3.15)

Expressing the pdf in the general exponential form, we write,

1| exp log 1

exp log (1 ) log(1 )

exp log log(1 )1

xxp x

x x

x

(3.16)

Comparing to the general single parameter exponential family of distributions of the form;

( | ) ( )exp( ( ) ( ))Xf x h x T x A (3.17)

where,

log , ( ) ,1

T x x

( ) log(1 ) and ( ) 1.A h x

Rearranging the natural parameter, we have,

1log log1

1 log 1

1 1e


38

1

1 e

( )1

1 iXe

(3.18)

Equation (3.26) is called the logistic regression model, where estimated predicts the

probability that an individual iX assuming that the ' s are known. However, before the logistic

regression model can be used to fit a data, certain assumptions on the data must be met in order

to ensure its suitability for logistic regression analysis.

3.5.1 Assumptions underlying Logistic Regression

The following assumptions are essential in the use of the logistic regression model.

(a) The response variable, Y1, Y2, ..., Yn are independently distributed.

(b) Distribution of Yi is Bernoulli( ).i The dependent variable, Y does not need to be

normally distributed, but it typically assumes a distribution from an exponential family.

(c) Does not assume a linear relationship between the dependent variable and the

independent variables, but it does assume linear relationship between the logit of the

response and the explanatory variables.

(d) The homogeneity of variance does not need to be satisfied.

(e) Errors need to be independent but not normally distributed.

(f) It uses Maximum Likelihood Estimation (MLE) rather than Ordinary Least Squares

(OLS) to estimate the parameters, and thus relies on large-sample approximations.

(g) Goodness-of-fit measures rely on sufficiently large samples.


39

Apart from the above assumptions, logistic regression can be applied in so many situations.

Some of which are listed below.

3.5.2 Application of logistic regression

Logistic regression is applicable, for example, if:

We want to model the probabilities of a response variable as a function of some

explanatory variables.

We want to perform descriptive discriminate analyses such as describing the differences

between individuals in separate groups as a function of explanatory variables.

We want to predict probabilities that individuals fall into two categories of the binary

response as a function of some explanatory variables.

We want to classify individuals into two categories based on explanatory variables.

3.6 The odds

In logistic regression analysis, the odds of the dependent variable is the ratio of the probability of

an event occurring to the probability of its compliment (event not occurring). It is said to be

equivalent to the exponential function of the linear regression expression. This illustrates how the

logit serves as a link function between the probability and the linear regression expression. So we

define odds of the dependent variable equaling a case (given some linear combination X of the

predictors) as;

P(event occuring)OddsP(event not occuring)

(3.19)


http://en.wikipedia.org/wiki/Logit

40

In binary logistic regression, if the probability of a case happening, ( 1)P Y and the

probability of that case not happening,

( 0) 1P Y then the odds is given by;

(3.20)

3.7 The odds ratio

The odds ratio is defined as ratio of two odds. This is given by

Odds Ratio (OR) = Odds of an event occuringOdds of the event not occuring (3.21)

If the odds of an event occurring is 1 and the odds of the event not occurring is 0 , then the

odds ratio is given by

Odds ratio =

1

1

0

0

1

1

(3.22)

The odds ratio for a unit increase in X for a simple logistic regression model with only one

explanatory variable is given by

0 1

10 1

odds( 1)ORodds( )

11 1 exp ( 1)

expexp

1

xx

xx x

xxx

(3.23)

Odds exp( )1 iX


41

This is an estimated odds ratio. This exponential relationship provides an interpretation for 1 ,

thus for every unit increase in X, the odds that the characteristic is present is multiplied by

exp(β1). In general, the logistic model stipulates that the effect of a covariate on the chance of

"success" is linear on the log-odds scale, or multiplicative on the odds scale. The odds ratio of

one (1) means that the odds do not change with X. Thus,

If βi > 0, then exp(βi) > 1, and the odds increase.

If βi < 0,then exp(βi) < 1, and the odds decrease.

3.8 Estimation of the model parameters

This involved the estimation of the parameter(s) of the logistic regression model using the child

survey data. In this research, method used in the estimation is the maximum likelihood

estimation.

3.8.1 Maximum Likelihood Estimator (MLE)

The maximum likelihood estimators of a distribution type are the values of its parameters that

produce the maximum joint density for the observed data x. In the discrete distribution, MLEs

maximize the actual probability of the distribution type being able to generate the observe data

(Vose, 2010).

The maximum likelihood estimates were used because they have several desirable properties

which include: consistency, efficiency, asymptotic normality and invariance. The advantage of

using maximum likelihood estimators is that it fully uses all the information about the parameters

contained in the data and it is highly flexible than others (Denuit et al., 2007).


42

Consider a probability distribution type defined by a single parameter . The likelihood function

( )L that a set of n independent data points (Xi) could be generated from the distribution with

probability density f(x), then the likelihood of the whole sample is the product of the individual

likelihoods over the observations. Assuming the survey data is a vector 1,..., nX X X with

parameter vector 1,..., n defined on a multi-dimensional parameter space from an

unknown population with pdf 1, ,..., .nf X For each model, the likelihood is given by

1 1 11

,..., | ,..., ( | ) ( , ,..., )n

n n i nL X X L X f X (3.24)

Thus, 1 1 2 2 1 1 1( | ) ( , ,..., ) ( , ,..., ) ... ( , ,..., ) ( , ,..., )n n n n n nL X f X f X f X f X

Because the log function is monotone, maximizing the likelihood is the same as maximizing the

log likelihood and for many reasons it is more convenient to use log likelihood rather than the

likelihood (Geyer, 2003). The log likelihood is the given by

1

In ( | ) In ( , ) In ( , )n n

i iii

L X f X f X

(3.25)

Therefore, the MLE of ̂ is then the value of that maximizes ( )L . ̂ is determined by finding

the derivatives of In ( | )L X with respect to and setting it to zero. That is;

ˆ

In0

L

(3.26)

is made the subject to obtain the value of the parameter. Thus, MLE is by far the most popular

method of parameter estimation and is an indispensable tool for many statistical modeling

techniques, particularly in non-linear modeling with non-normal data (Myung, 2003).


43

3.9 Testing the Goodness – of – Fit

The goodness – of – fit test measures the compatibility of a random sample with a theoretical

probability distribution function. In other words, a test for goodness of fit usually involves

examining a random sample from some unknown distribution in order to test the null hypothesis

that the unknown distribution function is in fact a known specified distribution. Thus, the

hypothesis is stated as;

: The current model fits well.

: The current model does not fit well.

The general procedure consists of defining a test statistic which is some function of the data

measuring the distance between the hypothesized distribution and the data, and then calculating

the probability of obtaining data which have a larger value of this test statistic than the value

observed. Assuming the hypothesis is true, this probability is called the confidence level. Some

of the techniques used in accessing the model fit are as follows.

3.9.1 Deviance and likelihood ratio tests

Deviance is a measure of the lack of fit to the data in a logistic regression model. When a

"saturated" model is available (a model with a theoretically perfect fit), deviance is calculated by

comparing a given model with the saturated model. This computation gives the likelihood-ratio

test.

likelihood of the fitted model2lnlikelihood of the saturated model

D (3.27)

where D represents the deviance. The log of the likelihood ratio (the ratio of the fitted model to

the saturated model) will produce a negative value, so the product is multiplied by negative two


44

times its natural logarithm to produce a value with an approximate chi-squared distribution.

Smaller values indicate better fit as the fitted model deviates less from the saturated model.

When assessed upon a chi-square distribution, non-significant chi-square values indicate very

little unexplained variance and thus, good model fit. Conversely, a significant chi-square value

indicates that a significant amount of the variance is unexplained.

Two measures of deviance are particularly important in logistic regression, null deviance and

model deviance. The null deviance represents the difference between a model with only the

intercept (no predictors) and the saturated model. The model deviance represents the difference

between a model with at least one predictor and the saturated model. In this respect, the null

model provides a baseline upon which to compare predictor models.

Thus, to assess the contribution of a predictor or set of predictors, we subtract the model

deviance from the null deviance and assess the difference on a 2s p chi-square distribution with

degrees of freedom equal to the difference in the number of parameters estimated.

Let

nulllikelihood of the null model2ln

likelihood of the saturated modelD

fittedlikelihood of the fitted model2ln

likelihood of the saturated modelD

Then


45

null fittedlikelihood of null model likelihood of fitted model2ln 2ln

likelihood of the saturated model likelihood of the saturated modelD D

likelihood of null model likelihood of fitted model2 ln lnlikelihood of the saturated model likelihood of the saturated model

likelihood of null modellikelihood of the saturated model2ln

likelihood of fitted modellikelihood of the saturated model

likelihood of null model2ln

likelihood of fitted model

(3.28)

If the model deviance is significantly smaller than the null deviance then one can conclude that

the predictor or set of predictors significantly improved model fit.

3.9.2 Pearson's chi-squared test

Pearson's chi-squared test statistic is calculated by finding the difference between each observed

and theoretical frequency for each possible outcome, squaring them, dividing each by the

theoretical frequency, and taking the sum of the results.

The test-statistic is

,)(

1

22

n

i i

ii

EEO

(3.29)

where

2 is the test statistic that asymptotically approaches a chi-square distribution.

iO is an observed frequency;


46

iE is an expected (theoretical) frequency, asserted by the null hypothesis;

n is the number of possible outcomes of each events.

It is used to assess two types of comparison. That is, tests of goodness of fit and tests of

independence. A test of goodness of fit establishes whether or not an observed frequency

distribution differs from a theoretical distribution. A test of independence assesses whether

paired observations on two variables, expressed in a contingency table, are independent of each

other.

We reject or fail to reject the null hypothesis that the observed frequency distribution is different

from the theoretical distribution based on whether the test statistic exceeds the critical value of

2 .

3.9.2.1 Phi and Cramér V Test

This is a measure of association between two nominal variables, giving a value between 0 and 1

inclusive. It is based on Pearson‟s Chi-square statistic. Cramér V is a way of calculating

correlation in tables which have more than 2 × 2 rows and columns. It is used as post-test to

determine strengths of association after Chi-square has determined significance.

Cramér V statistic is given by, 2

( 1)V

n k

where, n is the sample size and k is the smaller

value of the number of rows and columns.

The Phi statistic is used when both of the nominal variables under consideration have exactly

two possible values. It calculates correlation in tables which has a 2 × 2 rows and columns.

Phi statistic is given by, 2

n

where, n is the sample size.


47

3.9.3 Pseudo R-Square

Another way of evaluating the effectiveness of a regression model is to calculate how strong the

relationship between the explanatory variable(s) and the outcome is. This was represented by the

R2 statistic in linear regression analysis. R2, or rather a form of it, can also be calculated for

logistic regression. However, there are more than one version. This is because the different

versions are pseudo-R2 statistics that approximate the amount of variance explained rather than

calculate it precisely. Although it gives an approximated value, it can still sometimes be useful as

a way of ascertaining the substantive value of the model. The two versions most commonly used

are Hosmer & Lemeshow‟s R2 and Nagelkerke‟s R2. Both describe the proportion of variance in

the outcome that the model successfully explains. Others forms include McFadden's R2 and Cox

and Snell R2. Like R2 in multiple regression, these values range between „0‟ and „1‟ with a value

of „1‟ suggesting that the model accounts for 100% of variance in the outcome and „0‟ that it

accounts for none of the variance. The test statistic given by McFadden's R squared measure is

defined as

( )2

( )

1 ,full

null

LLPseudo R

LL (3.30)

where

( )fullLL : Log likelihood of the full model.

( )nullLL : Log likelihood of the null model.


48

3.10 Test of individual model parameters

After fitting the model, the contribution of individual predictors must be examined. To do so, we

examine the regression coefficients. In logistic regression, the regression coefficients represent

the change in the logit for each unit change in the predictor. Given that the logit is not intuitive,

we focus on a predictor's effect on the exponential function of the regression coefficient – the

odds ratio. There are several different tests designed to assess the significance of an individual

predictor, most notably the likelihood ratio test and the Wald statistic.

3.10.1 The Likelihood Ratio Test

The likelihood test is based on deviance test. The likelihood ratio test is a test of the significance

of the difference between the likelihood ratio for the fitted model and the likelihood ratio for a

null or reduced model. This difference is called "model chi-square". The likelihood ratio is used

to test the null hypothesis that;

0 1 2: ... 0pH

The likelihood-ratio test is the ratio of the maximized value of the likelihood function for the null

model (L0) to the maximized value of the likelihood function for the fitted (full) model (L1). The

likelihood-ratio test statistic is then given by

)(2)log()log(2log2 1010

1

0 LLLLLL

, (3.31)

This log transformation of the likelihood functions yields a chi-square statistic. This is the

recommended test statistic to use when building a model through backward stepwise elimination.


49

The likelihood ratio test is generally preferred over its alternative, the Wald test, discussed

below.

3.10.2 Wald statistic

When assessing the contribution of individual predictors in a given model, the Wald statistic may

be used. It is used to assess the significance of individual coefficient in the model by testing the

null hypothesis; 0 : 0iH . 1,2,...,i n .

Wald statistic is the ratio of the square of the regression coefficient )( i to the square of the

standard error of the coefficient and is asymptotically distributed as a chi-square distribution,

(Menard and Scott, 2002). The Wald statistic ( iW ) is given by;

2

2 ( )i

ii

WSE

(3.32)

Although, Wald statistic is used to assess the contribution of individual predictors, it has

limitations. When the regression coefficient is large, the standard error of the regression

coefficient also tends to be large increasing the probability of Type-II error. Manard (1995)

warns that for a large coefficient, the standard error is inflated, lowering the Wald statistic (chi-

square) value. The Wald statistic also tends to be biased when data are sparse. Agresti, (1996)

states that the likelihood-ratio test is more reliable for small sample sizes than the Wald test.

3.11 Confidence Interval Estimation

The basis for construction of the interval estimators is the same statistical theory used to

formulate the tests for significance of the model. The confidence interval estimators for the slope


50

and intercept are based on their respective Wald tests. The endpoints of a 100(1- α) % confidence

interval for the slope coefficient are given as:

),ˆ( 12/11 SEZ (3.33)

where 2/1 Z is the upper 100 1 / 2 % point from the standard normal distribution and

)ˆ( 1SE denotes a model-based estimate of the standard error of the respective parameter

estimator.

3.12 Multilevel modeling approach to clustered data

Most surveys aim to be representative for a certain population. To obtain a representative sample

of a population, one often resorts to strata to ensure that not only overall, but also within certain

subgroups, the number of respondents is under control. Typical stratification variables are age,

sex, and geographical location. Further, in order to reach respondents (target units), one often

resorts to a multi-stage sampling scheme. For example, one first selects towns (primary sampling

units), then a number of households within towns (secondary sampling units), and finally a

number of household members within a household (target or tertiary sampling units).

A consequence of such a sampling scheme is that a number of respondents stem from the same

household and the same town. One then cannot ignore the possibility of individuals within

families being more alike than between families, with the same to a lesser extent holding for

towns. In the way described above, clustering arises as a by-product of the chosen multi-stage

sampling design. These notwithstanding, an important class of models, known under the generic

name of multilevel models has been developed to represent such structures.


51

Two main approaches to the analysis of clustered binary data are the Cluster-Specific (CS)

approach and the Population-Averaged (PA) approach (grouping the conditional and marginal

approaches. Examples of CS models are mixed-effect logistic regression, with either parametric

or nonparametric mixing distributions for the cluster effects, and conditional logistic regression.

In contrast, population-averaged models do not include cluster effects, and thus are most useful

for assessing the effects of cluster-level covariates. Cluster-level covariates take on the same

values for every unit in the cluster. The effects of individual-level covariates can also be

estimated from population-averaged models, but their interpretations are based on the overall

population, without adjusting for cluster effects. Quasi-likelihood models and models based on

generalized estimating equations fall under the heading of PA models. Population-Average (PA)

approaches model the average response to changes in the covariates, and are thus best-suited for

evaluating between-cluster effects. There are several examples of Population-Averaged (PA)

models, including the beta binomial, quadratic exponential, quasi-likelihood, and Generalized

Estimating Equation (GEE) approaches. Population average models are however not considered

in this thesis.

3.12.1 Cluster-Specific Models

Cluster specific approaches are used for within-cluster comparisons. Cluster-specific approaches

can further be subdivided into conditional and marginal models. The generalized linear mixed

model is a commonly used conditional model.

3.12.2 Generalized linear mixed models (GLMMs)

A Generalized Linear Mixed Model (GLMM) is a GLM with fixed and random effects in the

linear predictor. The term „mixed‟ in GLMM comes from the fact that both fixed effects and


52

random effects are included in the model. The fixed effects are viewed as constant in the

population, whereas random effects are considered stochastic. The fixed effects convey

systematic and structural differences in responses. The random effects convey stochastic

differences between groups or clusters. The addition of random effects permits generalizations to

the population from which clusters have been randomly sampled, account for differences

between clusters and within clusters.

The conditional distribution of y given . The response variable, y is typically but not necessary

assumed to consist of conditionally independent elements each with a distribution from the

exponential family. Let Yij be the response of observation i in cluster j. Then, we have

( | )ij i iE Y b (3.34)

with ' '( ) ,i ij ij ig X Z b ~ (0, )ib N R

is cluster specific random effect and R is the covariance matrix.

But 1 1 ' '( ) ( )i ij ij ig g X Z b (3.35)

Equation (3.20) can be written as 1 ' '( | ) ( )ij i ij ij iE Y g X Z b (3.36)


53

CHAPTER FOUR

4.1 PRELIMINARY ANALYSIS

4.1.0 Introduction

This chapter implements the theoretical aspects discussed in chapter three to build a model based

on the data set. It will look at the information that can be derived from the data collected and

make inferences based on this information to be able to come out with solid conclusions and

recommendations. The analysis is conducted in three stages; namely, preliminary analysis, which

tries to establish some relationships between the working status of the children and the various

predictors of interest. The rest are logistic regression analysis and multilevel logistic regression

analysis of the survey data. Under this, the schooling and working status of the children as a

response variable is examined with the various predictors of interest. These two models are then

compared for optimal use in further analysis.

4.1.1 Characteristics of Sample

The International Labour Organization (ILO) defines a child as a person less than 15 years of age

and working children in the age group of 5-14 years should be considered as child labour. This is

due to the fact that, the Children‟s Act, 1998 (Act 560), defines exploitative labour as “work that

deprives the child of his/her health, education or development”. It set the minimum age for

admission into employment at 15 years for general employment, 13 years for light work and 18

years for hazardous work. The Act defines hazardous work as “work posing a danger to the

health, safety or morals of a person”, and provides an inexhaustible list, including fishing,

mining and quarrying, porterage and carrying of heavy load, work involving the production or


54

use of chemicals, and work in places where there is a risk of exposure to immoral behaviour.

Thus, any child aged below 18 years should not be subjected to the above mentioned hazards.

The inclusion of children of 15-17 years allows for the consideration of late entry and grade

repetition. This study also uses a minimum age of 5 years, which is the cut-off age between

infancy and childhood. However, the age range of 5-17 years is selected for this analysis.

Table 4.1 shows the regional distribution of children aged 5-17 years. Ashanti region recoded the

highest number of children (988,779) in 1,740 households followed by Greater Accra with

747,007 children in 1,560 households. Upper East recorded the lowest number of children

(286,106) in 300 households.

Table 4.1: Distribution of children Aged 5-17 Years by Region

Regions Households Children (5-17) Male Female

Western 1,000 639,439 308,645 330,794

Central 860 516,694 270,191 246,503

Greater Accra 1,560 747,164 355,553 391,611

Volta 1,140 519,007 267,691 251,316

Eastern 880 733,780 390,289 343,491

Ashanti 1,740 988,779 505,483 483,296

Brong Ahafo 1,000 657,128 337,919 319,209

Northern 1,020 891,096 490,702 400,394

Upper West 500 381,987 214,914 167,073

Upper East 300 286,106 172,108 113,998


55

All Regions 10,000 6,361,180 3,313,495 3,047,685

Source: Ghana Child Labour Survey

The proportion of male children (64.8%) that combine school with work (Table 4.2) is higher

than their female counterparts (63.6 %). The 10-14 year age group has the highest proportion

among children who combine school with work (71.0 %). Estimates from Table 4.2 indicate that

1,590,747 children were indeed attending school while working. The urban/rural comparison

shows that a higher proportion (71.5%) of children in the urban areas compared to 62.4 percent

of children in the rural areas combined work with schooling.

Table 4.2: Sex Distribution of Children Combining Schooling and Economic Activity by Age and Locality of Residence

Selected characteristics

All

Combine school with work

Male

combine school with work

Female

combine school with work

Estimated Number

Combine school with work

Total

Age Group

5-9 63.7 61.4 66.3 374,861 558,081

10-14 71.0 71.4 70.1 851,049 1,199,089

15-17 53.0 55.8 49.9 364,838 687,375

Locality

Urban 71.5 75.9 68.3 354,566 496,266

Rural 62.4 62.7 62.1 1,236,181 1,978,279

Total 64.3 64.8 63.6 1,590,747 2,474,545

Source: Computed from GCLS (GSS) Data file


56

Table 4.3 represents average age of children by region. Greater Accra has the highest mean of

11.1 whilst Northern region has the least mean of age of 10.2. The average age of children in the

sample was 10.6 years old.

Table 4.3: Average Age of Children by Region

Regions Mean Standard

Deviation

Western 10.6 3.6

Central 10.7 3.6

Greater Accra 11.1 3.6

Volta 10.6 3.5

Eastern 10.5 3.6

Ashanti 10.8 3.6

Brong Ahafo 10.7 3.6

Northern 10.2 3.6

Upper West 10.5 3.6

Upper East 10.2 3.6

All 10.6 3.6


Average household size by region is shown in Table 4.4. It is observed that, in Northern and

Upper East regions, both have the highest average household size of 4.4. The region with the

lowest average household size is Central (3.4). In general, 3.7 is the average household size.


57

Table 4.4: Average Household Size by Region

Regions Mean household size Standard

Deviation

Western 3.6 1.2

Central 3.4 1.2

Greater Accra 3.4 1.1

Volta 3.7 1.2

Eastern 3.6 1.2

Ashanti 3.5 1.2

Brong Ahafo 3.5 1.2

Northern 4.4 1.2

Upper West 3.9 1.3

Upper East 4.4 1.1

All 3.7 1.2


The average enrollment age by level of schooling is as shown in Table 4.5. The average age of

children with no education was 10 years. Children with Pre-School education had the least

average enrollment age of 5.7 years whilst, the highest average enrollment age of 17 years was

among children with Agricultural, Nursing and Teacher training education. The average

enrollment age was 10.6.


58

Table 4.5: Average Enrollment Age by Level of Schooling and Grade Completed

Level of Schooling Mean Standard Deviation

No Education 10.0 3.8

Pre-school 5.7 0.8

Primary 10.1 2.8

Middle/JSS 14.7 1.6

Secondary/SSS 16.3 0.8

Voc/Tech/Commercial Post 16.3 0.9

Sec (Agric/Nursing/Teacher Training 17.0 0.0

All 10.6 3.6


Table 4.6 gives a brief description of the percentage distribution of non-working and working

children by school attendance. Out of the proportion of children who are not working, 85.6

percent of them are still attending school. Working children who still attend school constitute

62.7 percent whilst 37.3 percent have never attended school.

Table 4.6: Percentage Distribution of Non-Working and Working Children by

School Attendance

School Attendance Non-working Children (%)

Working Children (%)

Total No. of Children

Never attended school 14.6 37.7 1,497,661

Still attending school 85.4 62.3 4,863,517

Total 100 .0 100.0 6,361,178

Source: Computed from GCLS (GSS) Data file A Chi-square test performed on Table 4.6 gave, 2 (1, N = 6,361,178) = 444,320.143, p < 0.001

(Appendix 1). Since the significance probability quoted is less that critical value of 0.05, there is

a significant association. This is evidence that there is an association between working status of


59

children and their school attendance in the population in which the sample has been drawn.

Although, there is an association between the two variables, the strength of association is

moderate (Phi and Cramer‟s value V of 0.264) with .001p .

The sex distribution of working and non-working children is represented in Table 4.7. Male

working children are more (53.1%) than their female counterpart (46.9%). There is a similar

proportion of male (53.1%) and female (46.9%) working children.

Table 4.7: Percentage Distribution of Non-Working and Working Children by Sex

Sex Non-working

Children (%) Working

Children (%) Total No. of

Children Male 51.4 53.1 3,313,494

Female 48.6 46.9 3,047,684

Total 100.0 100.0 6,361,178


Table 4.7 gave a Pearson Chi-square statistic 2 1680.367 and P < 0.001 (Appendix 1). Hence

there is a very small probability of the observed data under the null hypothesis of no relationship.

The null hypothesis is rejected, since p < 0.05. The sex of the children seems to be related to the

work status. The strength of this association between working and non-working children is

statistically significant but very weak with Phi and Cramer‟s V value of 0.016 at P < 0.001.

The relationship of children to head of household of the working children and non-working is

shown in Table 4.8. The proportion of the son/daughter of head of the household for working

children is (77.4%), whilst “other relative” constitutes the lowest (22.6%). This shows that heads


60

of households will wish that their children will not work (76.8%) but the prevailing conditions

will force them to do otherwise.

Table 4.8: Percentage Distribution of Non-Working and Working Children by Relationship to Head of Household

Relationship to head of household

Non-working Children (%)



Son/daughter 76.8 77.4 4,922,720

Other relations 23.2 22.6 1,438,458

Total 100.0 100.0 6,361,178

Source: Computed from GCLS (GSS) Data file.

The percentage distribution of children by age group is represented in Table 4.9. Majority of the

children who are working (48.1%) are in the age group 10-14 years. On the other hand, 53

percent of the non-working children are in the age group 5-9 years. At this age, the children are

very young to engage in any economic activity.

Table 4.9: Percentage Distribution of Non-Working and Working Children by Age Group

Age group of Children age 5-17




5-9 53.2 23.7 2,657,285

10-14 34.1 48.1 2,515,490

15-17 12.7 28.2 1,188,403

Total 100.0 100.0 6,361,178


A Chi-square test performed on Table 4.9 rejected the null hypothesis of no association between

the working status of the children and the relationship to head of household


61

[ 2 (2, N= 6,361,178) = 606298.09, p < .000], though there is a moderate relationship between

them, (Phi and Cramer V value = 0.309).

Table 4.10: Percentage Distribution of non-working and working

Children by Size of Household Household Size




Less than 3 1.8 1.4 105,418

3-4 16.9 11.4 939,200

5-6 34.0 28.2 2,020,048

7-8 24.5 26.4 1,605,208

9-10 14.9 19.5 1,062,153

Over 10 7.9 13.1 629,152

Total 100.0 100.0 6,361,179


Table 4.10 shows the household size for non-working and working children. It is observed that,

the proportion of non-working and working children increases with increasing household size up

to 5-6 and decreases with increasing household sizes. A larger proportion of the working

children (19.2 %) are found in households whose heads are 65 years and over (Table 4.11). A

Chi-square test on the working status of the children and the household size shows a significant

association between them, 2 (5, N= 6,361,179) = 86208.049, p < .000 with moderately weak

strength of association, (Phi and Cramer V value = 0.116).


62

Table 4.11: Percentage Distribution of Non-Working and

Working Children by Age of Household Head Age of Head of

Household

Non-working

Children (%)

Working

Children (%)

Total No. of

Children

15-34 12.6 7.8 682,105

35-39 15.1 11.1 859,832

40-44 15.9 14.5 978,767

45-49 16.0 15.5 1,007,309

50-54 11.6 14.6 814,128

55-59 7.5 8.3 495,638

60-64 6.9 9.0 491,380

65+ 14.4 19.2 1,032,020

Total 100.0 100.0 6,361,179


With regard to literacy (Table 4.12), 66.1 percent of working children were found in households

whose heads are not literate whereas a little above half (51.3%) of the non-working children

came from households whose heads are literate. A further test on the data shown in Table 4.12

gave a Pearson Chi-square statistic, 2 188526.223 at 1df and P < 0.001. This shows a

significant association between the work status of the children and the literacy of their household

heads. Phi and Cramer‟s V value of 0.172 at P < 0.001 shows a moderately weak association

between the two variables. Thus, literate parents tend to prevent their children from working than

those who are not literate.


63

Table 4.12: Percentage Distribution of Working and Non-Working Children by Literacy of Household Head

Literacy of Household Head Non-working Children (%)



Not literate 48.7 66.1 3,539,077

literate 51.3 33.9 2,822,101

Total 100.0 100.0 6,361,178


4.1.2 Occupation of Children’s Parents

Table 4.13 shows that, majority of the working children (44.2%) have parents, whose major

occupation was farming, 5.5 percent of the working children have parents who were traders.

Among the non-working children only a few have parents who were farmers (1.1%), service

(0.3%), traders (0.7%) and Day/wage labourers (0.1%).

Table 4.13: Percentage Distribution of Working and Non-Working Children by Major Occupation of Parent

Occupation of Parent Non-working Children (%)



Farming 1.1 44.2 1,131,785

Service 0.3 9.6 249,813

Trade 0.7 15.5 410,596

Day/wage labour 0.1 7.4 188,213

Other occupation 97.8 23.3 4,380,770

Total 100.0 100.0 6,361,177



64

4.1.3 Education of Parents

The survey solicited information on the highest level of school attended and the highest grade

completed at that level for parents of children age 5-17 years. Education is categorized into: No

Education, Primary, Junior Secondary and Senior Secondary School or higher. The percentage

distribution of non-working and working children by highest level of parents‟ education is shown

in Table 4.14. Twenty eight percent of parents of working children had no education. Of those

children, parents with primary education are very high (52.7%) whilst a small percentage (1.1%)

had a higher level of education.


Children by Level of Education of Parent Level of Education of Parent




No education 11.5 27.6 1,141,040

Primary 71.7 52.7 4,077,353

Junior secondary 14.7 18.6 1,036,681

SSS or higher 2.1 1.1 106,105

Total 100.0 100.0 6,361,179


A Chi-square test performed on Table 4.14 gave, 2 (3, N = 6,361,179) = 329128.933, p < 0.000

(Appendix 1). Since the significance probability quoted is less that critical value of 0.05, we

reject the null hypothesis of no association. This is evidence that there is an association between

working status of children and the educational level of their parents in the population in which

the sample was drawn. Although, there is an association between the two variables, the strength

of association is moderate (Phi and Cramer‟s V value of 0.227) with .001p .


65

4.1.4 Marital status of Parents

For the purpose of this study, marital status has been categorized into married and not married.

The married category comprises married and living together and not married is made up of those

separated, widowed, divorced and single. The distribution of non-working and working children

by marital status of parent is shown in Table 4.15. It can be observed from the results that, when

parents are together, a greater proportion (69.4 percent) of their children do not work. However,

a little more than half (56.5%) of single parents tend to involve their children in economic

activity in other to support the family.

Table 4.15: Percentage Distribution of Working and Non-Working Children by Parent Marital Status

Marital Status of Parent




Married 69.4 43.5 3,757,093

Not Married 30.6 56.5 2,604,085

Total 100.0 100.0 6,361,178


A Chi-square test performed on Table 4.15 gave, 2 (1, N = 6,361,178) = 424437.429, p < 0.001

(Appendix 1). Hence, the null hypothesis of no association is rejected to p < .000. Although,

there is an association between the two variables, the strength of association is moderate (Phi and

Cramer‟s V value of 0.257) with .001p .

4.1.5 Regional Distribution of Children

Knowledge of the regional distribution of respondents is essential to understanding disparities in

child labour. This is because there is differential resource endowment and behavioral practices in


66

the regions under consideration. Residents in certain regions are exposed to relatively poor living

conditions and consequently child labour experiences. The percentage distribution of

respondents by region is shown in Table 4.16. Five regions namely Western, Volta, Eastern,

Ashanti and Northern each have over 10 percent of the total working children.

Upper East has the lowest percentage (5%) of working children. Ashanti Region has the highest

percentage (19.0%) of non-working children. This is followed by Greater Accra region (15%),

with Upper West having the lowest percentage (3.7%) of non-working children.


Children by Region

Region Non-working

Children (%)

Working

Children (%)

Total No. of

Children

Western 8.6 12.4 639,438

Central 8.7 7.3 516,693

Greater Accra 15.2 6.2 747,164

Volta 6.7 10.5 519,006

Eastern 9.8 14.2 733,781

Ashanti 18.7 10.6 988,778

Brong Ahafo 13.4 5.5 657,128

Northern 10.9 18.8 891,096

Upper West 3.7 9.6 381,987

Upper East 4.3 4.8 286,106

All 100.0 100.0 6,361,177



67

4.1.6 Ethnicity Akan (Asante, Fante, and other Akan) are by far the most dominant ethnic group recorded

showing respectively 49.4 percent and 36.3 percent of non-working children and working

children.

Table 4.17: Percentage Distribution of Working and Non-Working Children by Ethnicity Ethnicity Non-working

Children (%)

Working

Children (%)

Total No. of

Children

Akan 49.4 36.3 2,755,586

Ga-Adangbe 7.3 7.8 465,084

Ewe 11.1 12.8 731,690

Guan 4.2 3.2 235,823

Gruma 5.0 10.6 446,851

Mole-Dagbani 16.9 21.4 1,157,985

Grussi 3.0 4.4 219,006

Mande 1.3 2.8 119,814

Other 1.8 0.8 88,717

Total 100.0 100.0 6,220,556


The rural-urban distribution of respondents is as shown in Table 4.18. The table reveals that,

79.9 percent of the respondents who are working reside in rural areas whilst 20.1 percent are in

the urban. A little over half (50.6%) of the non-working children were in the rural areas.

A Chi-square test performed this two variables gave, 2 (1, N = 6,361,179) = 559854.28,

p < 0.000 (Appendix 1). This shows significant evidence that there is an association between

working status of children and their locality of residence in the population in which the sample


68

was drawn. The strength of this association is moderate (Phi and Cramer‟s V value of 0.0.300)

with .001p .

Table 4.18: Percentage Distribution of Working and Non-Working Children by Locality of Residence Locality of Residence




Urban 49.4 20.1 2,398,096

Rural 50.6 79.9 3,963,083

Total 100.0 100.0 6,361,179


The Percentage distribution of non-working and working children by sex of household head is

presented in Table 4.19. In a male-headed households, about three-quarters (78.4%) of the

children work. On the contrary, majority of the male household heads (72.5%) do not want their

children to work. A Chi-square test performed on the working status of the children and the sex

of head of head of household gave, 2 (1, N = 6,361,177) = 28121.007, p < 0.000 (Appendix 1).

The null hypothesis of no relationship is therefore rejected. Although, there is an association

between the two variables, the strength of association is very weak (Phi and Cramer‟s value of

0.066) with .001p .


Children by Sex of Head of Household Sex of Head of Household




Male 72.5 78.4 4,764,320

Female 27.5 21.6 1,596,857

Total 100.0 100.0 6,361,177



69

Figure 4.1 shows the proportion of children not enrolled in school by age 5 to 17. Generally, the

non-enrolment figure increases from age 6 to age 17 whilst at ages 11 and 16, the non-enrollment

figures decreased.

Source: GCLS, (GSS) Data file Figure 4.1: Children Not Enrolled in School by Age

Figure 4.2 depicts how non-enrolment rates vary by sex of child. This figure shows an opposite

picture of the conventional belief that boys are more likely to be enrolled in school than girls. In

this study, boys‟ non-enrolment rate is higher than that of girls. There is however a drop at age

11 and 16 for both sexes. At age 17, while the non-enrollment rate for girls is almost at the same

level as at age 16, that of boys non-enrollment rate is almost as high as 30 percent. This possibly

reflects the fact that girls may have married or may have withdrawn from school. At the age of

17 boys‟ non enrolment rate is much higher than that of girls.

Pe

rce

nta

ge o

f n

on

-en

rollm

en

t

Age

5 6 7


70

Source: GCLS, (GSS) Data file

Figure 4.2: Children Not Enrolled in School by Age and Sex

Children who dropped out of school were asked the reason for dropping out from school. The

main reasons are presented in Table 4.20. Majority (44.2%) were of the view that their parents

cannot afford schooling. This shows that poverty is one of the major contributors of children not

attending school therefore pushing the children to work. Another worrying situation is that, the

schools are sited too far from them (18.4%), which has made them loose interest in schooling

(17.1%).

Perc

en

tag

e o

f n

on

-en

rollm

en

t

Age

Male Female


71

Table 4.20: Reason for Leaving School

Reason Percent

Parents cannot afford schooling 44.2

School too far away 18.4

Not interested in school 17.1

Family does not allow schooling 5.0

Illness/disabled 2.1

Both parents not alive 0.3

Father not alive 1.3

Mother not alive 1.0

Other reason 10.7

Total 100.0

Source: GCLS (GSS) Data file

4.1.7 Measurement of Children’s Work

The survey asked questions about the occupation of all household members. To classify

children‟s activities, however, we focused on the occupation of children reported by household

heads. Work was defined broadly to include non-wage work and housework.

Two occupations (primary and secondary) were considered as the key indicators of child work.

Work and study are not mutually exclusive as the data suggests, some children reported

attending school, while at the same time performing some form of paid or unpaid work. Thus,

four mutually exclusive categories are created to define child‟s activity. These categories are;

study only, work only, work and study, neither work nor study. Children are classified into


72

“study only category”, if their primary and secondary occupation is student or they do not have a

secondary occupation. Similarly, “work only” category includes those children whose primary

and secondary occupation is work or they do not have any secondary occupation. Children who

work and attend school as well are included in “work and study” category. Neither “work nor

study” category considers those who are reported as children but not in a position to work or go

to school although they are of school going age.

Figure 4.3 shows that 5.5 percent of children attended school (study) as their only activity. A

smaller percent of the children were engaged in work only (0.5%) whilst 89.7 percent combines

study with work.

Source: Computed from GCLS Data file

Figure 4.3: Distribution of Children by Activity Status

0.5 5.5

89.7

4.3

0

10

20

30

40

50

60

70

80

90

100

work only attending schoolonly

working andattending school

neither workingnor attending

school


73

4.1.8 Activity Status of Children Table 4.21 represents activity status of children by sex and age. The data shows that there are

some children who begin to work from 5 years of age. The proportion of children in the “work

only” category increases with age, particularly, from 12 years to 17 years. Generally, an

overwhelming majority (91.9% boys and 87.3% girls) of children study and work at the same

time.

Table 4.21: Activity Status of Children by Sex and Age

Activity Status

Study only Work and Study Work only

Neither

Total

Number of Children

Sex

Boys

4.6

91.9

0.3

3.2

100.0

3,313,494

Girls

6.5

87.3

0.7

5.5

100.0

3,047,685

All

5.5

89.7

0.5

4.3

100.0

6,361,179

Age

5

1.5

94.7

0.0

3.8

100.0

481,517

6

2.4

94.2

0.1

3.3

100.0

553,646

7

2.5

93.7

0.1

3.7

100.0

573,875

8

3.8

92.7

0.1

3.5

100.0

541,495

9

3.5

91.6

0.4

4.5

100.0

506,752

10

5.4

89.7

0.2

4.7

100.0

596,769

11

7.7

86.6

0.1

5.6

100.0

429,827

12

6.1

88.0

0.6

5.2

100.0

577,183


74

13

7.4

88.1

0.6

3.9

100.0

465,577

14

7.4

87.4

0.8

4.4

100.0

446,138

15

7.9

86.6

1.2

4.3

100.0

513,131

16

11.7

82.8

1.0

4.5

100.0

371,608

17

8.8

84.7

2.0

4.6

100.0

303,664

All

5.5

89.7

0.5

4.3

100.0

6,361,179



75

4.2 FURTHER ANALYSIS

4.2.0 Introduction

Bivariate logistic regression and multilevel techniques are used to assess differentials in child

labour and schooling. The aim here is to determine whether the observed pattern in child labour

and schooling varies significantly among categories of a given variable.

Logistic regression is one of the analytical methods employed in this study. A major component

of this study is the analysis of the various differentials in child labour and schooling. To identify

the important factors affecting child labour, a logistic regression model, which allows the

assessment of the relative influence of each variable, is used. The odds of a child labour can be

evaluated using the standard logit model.

In the multilevel analysis, the children in the various households are examined to see the

influence their respective households can have to let a child school and work at the same time or

school without working while correcting for association within households. The children in the

households are regarded as level one unit and the households as level two units. The various

predictors that may cause a child to engage in economic activity were examined within the

various households.

The following are used as predictors in this analysis; age of head of household, gender of head of

household, marital status of parents, father‟s occupation, mother‟s occupation, relationship to

head of household, residing in rural/urban area, literacy of head of household, sex of the child

and highest educational level attained with the schooling and working status of the child being

the response variable.


76

The statistical software used in the analysis of the multilevel differentials and to estimate the

'i s in the regression model is the Statistical Package for the Social Science, SPSS (SPSS Inc.,

1988).

In SPSS, the parameters of the logistic regression model are estimated using the maximum-

likelihood method. SPSS employs two main approaches to assess the goodness of fit. These are

the Wald test and the Likelihood Ratio Test for two nested models. The unit of analysis in this

chapter is the children aged 5-17 years. The variables included in the logistic regression as well

as their estimated coefficients, standard errors, significant values, odds ratio and confidence

intervals are reported in Table 4.26.

4.2.1 Findings

In the main analysis (Table 4.26), the work and school status (a child working and schooling at

the same time or schooling only) was used as dependent variable and the variable taking value 1

if the child is reported schooling and working or 0 if the child reported schooling only.

.

4.2.2 Model for children’s schooling and working using logistic regression

Logistic regression compares the null model (model without the coefficients) with a model

including all the predictors to determine whether the latter model is more appropriate. The

classification table for the null model suggests that if we knew nothing about our variables and

guessed that a child would attend school and work, we would be 86.1 percent correct of the time.

This table describes the baseline model (Appendix 1),


77

The Omnibus Tests of Model Coefficients (Table 4.22) is used to check that the new model (with

explanatory variables included) is an improvement over the baseline model. It uses chi-square

tests to see if there is a significant difference between the Log-likelihoods (-2LLs) of the baseline

model and the new model. If the new model has a significantly reduced -2LL compared to the

baseline then it suggests that the new model is explaining more of the variance in the outcome

and is an improvement. The chi-square is highly significant (chi-square =1900671.06, df = 23,

p<.001) so the new model is significantly better.

Source: Computed from GCLS Data File. The Model Summary (Table 4.23) provides the -2LL and pseudo-R2 values for the full model.

The -2LL value for this model (3226822.9) is what was compared to the -2LL for the previous

null model in the “omnibus test of model coefficients” which shows that there was a significant

decrease in the -2LL. That is, the new model (with explanatory variables) is significantly better

fit than the null model. The R2 values gives approximately how much variation in the outcome is

explained by the model. The Nagelkerke‟s R2 suggests that the model explains roughly 46.7% of

the variation in the outcome. Both Nagelkerke‟s and Cox & Snell R square give different values

which are all approximations but Nagelkerke‟s R2 is more robust.

Table 4.23 Model Summary Step -2 Log

likelihood Cox & Snell R

Square Nagelkerke R Square

1 3226822.932 .258 .467 Source: Computed from GCLS Data File.

Table 4.22 Omnibus Tests of Model Coefficients Chi-square df Sig.

Step 1 Step 1900671.059 23 .000 Block 1900671.059 23 .000 Model 1900671.059 23 .000


78

Table 4.24 shows that the full model correctly classifies 90.0% of the model as compared to

86.1% in the null model. This means that, by adding the variables, we can now predict with

90.0% accuracy. Thus the model correctly fits the data.

Table 4.24 Classification Table for Binary Logistic Regression

Observed

Predicted Attending school and working Percentage

Correct Attending school and

work

Schooling only

Attending school and working

Attending school and work 5335748 141623 97.4 Schooling only 491960 391847 44.3

Overall Percentage 90.0 Source: Computed from GCLS Data File. Table 4.25 provides the regression coefficient ( ), the Wald statistic, to test the statistical

significance of the model and if the significance level is less than 0.05, we reject the null

hypothesis and accept the alternative hypothesis. Where the null and the alternate hypothesis

states that;

: , Xi makes no significant contribution in the model

: Xi makes a significant contribution in the model.

Estimates from Table 4.25 shows that all the predictors of interest (Xi‟s) are all significant at

p < .000. Hence we reject the null hypothesis and conclude that the predictors make significant

contribution in the model.


79

Table 4.25: Model 1: Parameter Estimates for Schooling status of children using Binary Logistic Regression

Variable Names Coeff. ( )

S.E. Wald df Sig. Exp( ) 95% C.I.for EXP( ) Lower Upper

Constant -1.348 .012 12214.684 1 .000 .260

SEX Male(Ref) - - - - - 1.000 - -

Female .010 .003 11.913 1 .001 1.010 1.004 1.016 LITERACY OF HH Not literate (Ref) - - - - - 1.000 - - Literate -.579 .004 23367.865 1 .000 .561 .557 .565 PLACE OF RESIDENCE

Urban (Ref) - - - - - 1.000 - - Rural .624 .004 24751.547 1 .000 1.866 1.852 1.881 MARITAL STATUS OF PARENTS

Married (Ref) - - - - - 1.000 - - Not married 1.660 .004 218246.341 1 .000 5.260 5.224 5.297 EMPLOYMENT STATUS OF MOTHER

Employed full time(Ref)

- - 2979.816 3 .000

- -

Own account worker .111 .010 118.939 1 .000 1.117 1.095 1.140

Unpaid family worker .298 .011 772.957 1 .000 1.347 1.319 1.375 Unpaid Apprentice .049 .011 20.089 1 .000 1.051 1.028 1.074 EMPLOYMENT STATUS OF FATHER

Employed full time (Ref)

- - 5397.716 3 .000

- - -

Own account worker .240 .006 1817.416 1 .000 1.271 1.257 1.285 Unpaid family worker -.831 .030 750.884 1 .000 .436 .411 .462 Unpaid Apprentice .408 .006 4036.948 1 .000 1.504 1.485 1.523 RELATIONSHIP TO HEAD OF HOUSEHOLD

Son/Daughter (Ref) - - 14341.723 2 .000 - - -

Table 4.25 continued


80

Variable Names Coeff. ( )

S.E. Wald df Sig. Exp( ) 95% C.I.for EXP( ) Lower Upper

Other Children .391 .005 6139.251 1 .000 1.479 1.465 1.494 AGE OF HEAD OF HOUSEHOLD

15 – 34 (Ref) - - 9002.021 7 .000 - - -

35 – 39 -.026 .007 15.502 1 .000 .974 .962 .987 40 – 44 .047 .006 53.802 1 .000 1.048 1.035 1.062 45 – 49 .296 .006 2244.210 1 .000 1.344 1.328 1.361 50 – 54 .299 .006 2215.788 1 .000 1.349 1.332 1.366 55 – 59 .225 .007 988.610 1 .000 1.252 1.235 1.270 60 – 64 .343 .007 2387.346 1 .000 1.409 1.390 1.429 65+ .043 .006 47.078 1 .000 1.044 1.031 1.057 GENDER OF HOUSEHOLD HEAD

Male (Ref) - - - - - 1.000 - - Female -.119 .004 839.366 1 .000 .888 .880 .895 EDUCATIONAL LEVEL OF PARENTS

No Education (Ref) - - 853540.037 3 .000 - - -

At most Primary -3.298 .004 804850.888 1 .000 .037 .037 .037 Junior Secondary -3.072 .005 382365.947 1 .000 .046 .046 .047 SSS or Higher -2.839 .012 57692.449 1 .000 .059 .057 .060

(The reference category is Attending School & working) Source: Computed from GCLS Data File.

4.2.3 Characteristics of Children

Child characteristics, such as sex and relationship to household head appear to be important

determinants of whether or not children attend school and work. The sex coefficient has a

positive effect on the probability of attending school and working. Female children are more

likely to attend school and work as compared to male children and the odds of attending school

and working is 1.010. This suggests that female children are 1.010 times as likely to be classified


81

as attending school and working as male children holding constant the other independent

variables.

Table 4.25 indicates that if a child is not the son or daughter of the head of household, then the

probability of attending school and working is moderately high. The odds of attending school

and working of other relations are 1.465 (or 46.5%) times as likely as that of a child of the head

of household. This coefficient shows significant positive effect on the probability of attending

school and working. This implies that household heads favour their own children by preventing

them from attending school and working at the same time.

4.2.4 Characteristics of Parents

All the parental characteristics namely, highest education level of parents, marital status of

parent, literacy of parent and employment status of both father and mother are all statistically

significant. The literacy of parents has negative effect on the probability of attending school and

work. For example, the odds ratio for a literate parent is 0.561. This suggests that children of

literate parents‟ are 0.561 times as likely to attend school and work as children of illiterate parent

holding constant the other independent variables. The marital status of the parents has a great

impact on whether or not a child attends school and work. The odds ratio of a single parent (not

married) is 5.26. This suggests that children from single parents are 5.260 times as likely to

attend school and work as children whose parents are still married holding all other variables

constant.

Both father‟s and mother‟s employment status have significant impact on the probability of a

child “attending school and working”. The odds ratio of unpaid family worker of the


82

employment status of mother is 1.347. This shows that if a child‟s mother is an unpaid family

worker, that child is 1.347 times (or about 35%) more likely to attend school and work than those

whose parents are in full time employment.

On the other hand, own account worker of the employment status of father indicate that, their

children are as likely to attend school and work. The odds ratio is 1.257. Again, children whose

father is an unpaid apprentice are 50.4% (OR = 1.504) more likely to attend school and work

than those whose father is in full time employment.

Highest educational level of the parents significantly contributes to a child schooling or working.

A parent who has attained either SSS or higher education is less likely than a parent who has no

education to let his/her child attend school and work (OR = 0.056). A parent whose highest

education is Junior Secondary has odds ratio of 0.046 while a parent with at most primary

education has odds ratio of 0.037. This shows that increasing the educational level of parents

tends to decrease the likelihood of his/her child schooling and working.

4.2.5 Characteristics of Household

Among the household characteristics, age of household head and gender of household head are

all statistical significant. The age of head of household plays an important role as to either a child

should attend school and work or not. It is shown from Table 4.25 that, the odds ratio of children

living with age group of household head, 45 - 49 and 50 - 59 are respectively about 34%

(OR = 1.344) and 35% (OR = 1.349) more likely to let their children attend school and work as

those aged 15 – 34 years. A child is more likely to attend school and work (OR = 1.409) when

the head of his/her household reaches the pension age (60 – 65 years). This notwithstanding,


83

younger household heads (35 – 39 years) are less likely (OR = 0.974). Children of female headed

household children are less likely to attend school and work as children to a male headed

household (OR = 0.888).

Another socio-economic variable, which is significant, as an independent determinant of children

attending school and working, is the type of place of residence. The analysis shows that children

living in the rural areas are 1.822 (82.2%) times as likely to attend school and work as those in

the urban areas.

4.2.6 Multilevel analysis

The reason for this analysis is that, information from children coming from the same household

are not independent. Using the logistic regression may under-estimate the precision of the

estimates. Hence, a cluster-specific model may be appropriate.

The children in the various households are examined to see the effect these households can have

on a child schooling and working at the same time using multilevel logistic regression. The

children in the households are regarded as level one unit and the households as level two units.

The various predictors that may cause a child to school and work were examined within the

various households. All the predictors used in the binary Logistic are also used in this analysis.

Table 4.28 shows that the households significantly affect the children to attend school and work

since all are statistically significant except the household head whose age is between 35 and 39

years.


84

Table 4.26 gives the observed and predicted values for the null model using multilevel logistic

regression. This shows that if we knew nothing about the model and decide to guess, we will be

62.1% correct of predicting children who are only attending school and 98.1% of those who

combines school and work with overall correct prediction of the model as 93.1%.

Table 4.26 Classification table for the Intercept only model

Observed Predicted

Schooling Attending school and work

Schooling only Percent

62.1%

37.9%

Attending school and work

Percent

1.9%

98.1%

Overall Percent correct 93.1%

Source: Computed from GCLS Data File

When all the predictors are added, the model (Table 4.27) was able to predict correctly 87.2% of

children who are only attending school and 98.5% of those who are combining school and work.

The model gave the overall correct percentage as 96.9%. The overall performance of the model

using logistic regression was predicted as 90.0%. These predictions depict that the average

performance of the multilevel logistic model fits the data better than the binary logistic model.

Table 4.27 Classification for the full model

Observed Predicted

Schooling Attending school and work

Schooling only Percent count

87.2% 770,051

12.8% 113,491

Attending school and work

Percent count

1.5% 84,251

98.5% 5,391,706

Overall Percent correct 96.9%



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Table 4.28 presents the coefficients of the predictor variables and their odds ratios. From the

results, the effect of sex is significant and negative, indicating that female children are less likely

to attend school and work than males. The children who are not sons/daughters (other children)

of the head of a household are three (OR = 3.134) times more likely to attend school and work

than the sons/daughters of the head of the households. This ratio is higher as compared to the

case of the logistic regression.

The parental characteristics such as marital status, literacy of parents, employment status of

mother and father and highest educational level of parents are all statistically significant

(p < 0.000). The model estimated that children from single parents are forty six (OR = 46.288)

times more likely to attend school and work than those whose parents are married and staying

together. The change is significantly high as compared to the logistic regression model (OR

change 5.260 to 46.288). This large value may be due to the clustering effect inherent in the data.

Again, estimates from model 2 shows that literate parents are more likely to let their children

school and work at the same time (OR = 6.405). This is in contrast to the estimate in model 1.

However, the odds ratios of employment status of the mother have not changed much and are all

significantly more likely to let their children attend school and work than full time employee

children except for unpaid family worker whose children is significantly less likely (OR = 0.028)

to attend school and work. Concerning the highest level of education of parents, the household

have little influence on whether a child will attend school and work. The odds ratio of a parent

who is a Junior Secondary leaver is 0.006 time likely for his/her children to attend school and

work as those with no education.


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The ages of head of households are all significant at p < 0.001 except age group of 35 – 39 years.

Age group of head of household between 55 – 59 years are seventy one (OR = 71.194) times

more likely for their children to school and work at the same time. The same can be said for

those between 45 – 49 years. Their children are 18.783 times more likely to attend school and

work as those between 15 – 34 years controlling all other predictors. The odds of a female of a

female headed household has not changed much (OR change 0.888 to 0.716) and they are all less

likely to attend school and work as their male counterpart. The differences in odds ratio

compared may be explained by the random effect imbedded in the multilevel logistic model.

Table 4.28 Model 2: Parameter Estimates for Schooling status of children using Multilevel Logistic Regression

Variable Names Coeff.(B) Sig. Exp(B)

Constant -9.995 .000 0.000

SEX Male(Ref) - - 1.000

Female -0.110 .000 0.896 LITERACY OF HH Not literate (Ref) - - 1.000 Literate 1.858 .000 6.405 PLACE OF RESIDENCE Urban (Ref) - - 1.000 Rural 3.128 .000 22.834 MARITAL STATUS OF PARENTS

Married (Ref) - - 1.000 Not married 3.835 .000 46.288 EMPLOYMENT STATUS OF MOTHER

Employed full time(Ref) - .000 1.000

Own account worker 0.427 .000 1.532 Unpaid family worker 0.323 .000 1.382 Unpaid Apprentice 0.813 .000 2.255 EMPLOYMENT STATUS OF FATHER

Employed full time (Ref) - .000 1.000


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Table 4.28 continued Variable Names Coeff.(B) Sig. Exp(B)

Own account worker 0.385 .000 1.470 Unpaid family worker -0.385 .000 0.028 Unpaid Apprentice 0.205 .000 1.679 RELATIONSHIP TO HEAD OF HOUSEHOLD

Son/Daughter (Ref) - .000 1.000

Other Children 1.143 .000 3.134 AGE OF HEAD OF HOUSEHOLD

15 – 34 (Ref) - .000 1.000

35 – 39 -0.119 .124 0.888 40 – 44 -0.538 .000 0.584 45 – 49 2.933 .000 18.783 50 – 54 0.959 .000 2.610 55 – 59 4.265 .000 71.194 60 – 64 1.404 .000 4.070 65+ 1.3.3 .000 3.680 GENDER OF HOUSEHOLD HEAD

Male (Ref) - - 1.000 Female -0.334 .000 0.716 EDUCATIONAL LEVEL OF PARENTS

No Education (Ref) - .000 -

At most Primary -5.601 .000 0.004 Junior Secondary -5.136 .000 0.006 SSS or Higher -3.476 .000 0.031

(The reference category is Attending School & working) Source: Computed from GCLS Data File. The covariance of the random effects between the children and the households for the baseline

model is displayed in Table 4.29. When no predictor has been entered at level one, there is still a

significant variability between the children and the households. (Wald Z = 460626, p < .000).


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Table 4.29 Covariance Parameters for the null model 95% Confidence

Interval

Random Effect Block

Estimate(B) Std. Error

Z Sig. Lower Upper

Var(intercept) 26.556 0.570 460626 .000 26.463 27.696

Source: Computed from GCLS Data File. Table 4.30 again shows the estimates of the random effects that exist between the children level

and the household level. The inclusion of the predictors of interest to the model significantly

increased the variability between these levels.

Table 4.30 Covariance Parameters for the full model (model 2) 95% Confidence

Interval

Random Effect Block

Estimate(B) Std. Error

Z Sig. Lower Upper

Var(intercept) 33.900 0.809 41.887 .000 32.350 35.524



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CHAPTER FIVE

SUMMARY, DISCUSSIONS, CONCLUSIONS AND RECOMMENDATIONS

This chapter presents summary of the findings from the study, and recommends rational measures

for government, stockholders and those in child care. It again recommends

5.1 Summary

The objectives of the study were to establish the relationship of child labour and schooling, to

determine socio-economic factors affecting child labour and schooling and make

recommendations, based on the findings, for appropriate intervention measures to further reduce

this menace.

The 2003 GCLS data was the main source of data used for the analysis. The target population

was children aged 5-17. This study has examined the relationship between selected socio-

economic variables influencing child labour and schooling in Ghana. These variables are

parents‟ educational level, major occupation of parent, type of place of residence, region and

place of residence, marital status, employment status of mother and father, relationship to the

head of household, age of the head of household, sex of the head of household, literacy of parent,

religious affiliation and ethnic group.

The examination of the determinants of child labour in Ghana was done using bivariate and

multivariable techniques. In the bivariate analysis, children working and not working were

applied to all the variables already stated. In multivariable analysis, logistic regression and


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multilevel logistic regression were applied to children aged 5-17 years old who were attending

school. Further, the multilevel analysis was used to correct for within-household association.

Empirical results show that if the parent was employed in a vulnerable occupation, for example,

day-labour or wage-labour, it raises the probability of the child attending school and work.

Most of the children in the study are engaged in household work that allows them to attend

school and work because household work is more flexible than formal or informal wage earning

jobs. Another interesting finding of this study is that boys‟ non-enrollment rate is higher than

girls‟ in some ages.

It was also found out that children who are the sons and daughters of the head of household are

less likely to attend school and work as the other relations. This may reflect the fact that if the

household head is resource constrained then it is likely for him to choose first his own child for

schooling. This finding further threw light on the relationship of child labour and poverty.

The expected results indicate that children whose parents are traders and unpaid family workers

are likely to attend school and work; the reason being that parents‟ income level cannot afford to

carter for the entire household without their children supplementing it. Again, the likelihood of a

child from a single parent to attend school and work is very high.

It was again found out that, households influence their respective children to attend school and

work at the same time. If a child will engage in any economic activity, it will depend largely on

the household in which he/she lives. Engaging in economic activity also affects the education of

the child involved.


91

Even though the study attempted to control the effects of socio-economic variables in order to

assess the proper effect of work on their health and psychosocial status, the attempt might not

have succeeded fully because of the differences between the socio-economic status of children

may be variable within the same group. Also socio-economic variables may have a profound

effect on both the health outcomes and on the work status of children at the same time making it

difficult to disentangle their separate effects.

5.1.1 Limitations of the study

Data is a major obstacle in carrying out child labour research. Most studies suffer from difficulty

in obtaining reliable child labour data. Reasons for this are, firstly, there is difficulty in reaching

households with child labour, and even if they are questioned, stigma and illegality of child

labour prompts them to deny its existence.

Secondly, the survey relies on the recall memory of parents, so it is possible that there has been

systematic under-reporting of some work-related and family socio-economic characteristics. It is

impossible to verify such details in a survey of this nature and some caution should be exercised

in interpreting the results.

Another difficulty when collecting information in rain fed regions is migration. In dry seasons,

especially in villages with no functional irrigation facilities, many households migrate to more

water rich regions as agricultural labourers, or to cities for other jobs such as construction works.

Depending on duration, migrant families sometimes take their children with them.


92

Although the survey attempted to identify a representative sample of working children, it is

likely that the sample does not fully represent the extent of child labour since many working

children are engaged in domestic and invisible activities. This may form an obstacle to the

generalization of results to the whole of country.

5.1.2 Study strengths

The study has a number of strengths, which lend support to the plausibility of its findings. Strong

associations were observed in relation to the main objectives of the study. The results reported in

this study were also consistent with those previously published by a number of different

researchers using different methodologies

A clear definition of child labour was employed which made the sampling procedure easier and

more accurate. The study area chosen has a wide range of different types of community, both

rural and urban with a fair geographical spread, and a wide variety of economic activities. Thus,

it might provide a realistic indicator of the national status of child labour allowing generalization

of the results to the country as a whole.

5.2 Discussions

One of the aims of this study was to find out the various factors which influence children aged

5-17 years to engage in various kinds of economic activities while schooling. Following from the

data collected, it emerged that one of the main reasons why children engage in economic activity

while schooling was to help supplement their parents‟ income. Thus, when parents are

financially sound the child‟s needs could easily be met.


93

The findings of the study showed that both working and non-working children came from large

families with household size between 3- 4 and 5 - 6. It then decreases from household size of

7 - 8, 9 - 10 and higher than 10 (Table 4.10). This confirms the finding of Becker who said that a

household with many potential workers, the probability that a child will attend school and work

is somewhat lower (Becker, 1993).

According to the study, a son or a daughter of head of household was being favoured in terms of

attending school and work. So it is not surprising that the other relations have the higher

probability of attending school and work than that of the sons or daughters of the head of

household.

Although this study could not provide any specific direction on the dependency of child labour

and household welfare, it tries, however, to indicate that child labour is negatively related with

household income and welfare that is proxied by both the occupation and status of employment

of parents.

The study reveals that children who combine school with work could be found in all the year

groups 5-9, 10-14 and 15-17, even though it is higher in the age group 5-9 (63.7%) and age

group 10-14 (71.0%). A number of children combining schooling with work in the age group 15-

17 constitute (53.0%). A significant number of children (90%) attend school and work at the

same time. It follows that many children are combining schooling with various income earning

activities, perhaps because the trade is very lucrative or simply to supplement effort of guardian

in catering for their schooling.


94

5.3 Conclusions

In assessing the socio-economic determinants of child labour and schooling in Ghana, logistic

regression was applied to the variables in order to capture the predictor of child labour and

schooling. These analyses have assisted in identifying the significant variables; sex, relationship

to head of household, age group, major occupation of parent, literacy of parent, employment

status of father, residency and religion associated with the child working and attending school.

The most significant findings from this study are that, male children are as likely to attend school

and work as female. In spite of the fact that 15-17 years of age is a typical school going age, in

the case of the group that was studied, it came out that, the 15-17 year group combined school

with work.

The result indicates that, children combined school with work as a result of their relationship to

the head of household. This time the emphasis is on the nuclear family and not the extended

family.

Children who combined school with work tend to be in a household with the major occupation of

parent being “other” occupation, unpaid apprentice and also lived in the rural areas because

many of the rural dwellers are not financially sound and they will need their children to work to

supplement the household. These children are likely to come from parents who are not literate.


95

5.4 Recommendations

The results of the study and the literature show that the problem of child labour is extensive and

there is an urgent need for action. Ghana, in common with most developing countries, is not

financially and institutionally prepared to put an end to the phenomenon at once as child labour

and poverty are intertwined and the earnings of working children are essential for their families'

survival. Therefore, the most logical and acceptable strategy in the country could be to focus on

the eradication of the most intolerable forms of child labour and the protection from hazardous

occupations, exploitation and abuse of those, especially the very young, who continue to be

economically active.

The problem related to the size and nature of child labour should be addressed. Working children

are usually engaged in invisible hazardous work, which is likely to affect their education and

endanger their health. To achieve visibility, a national survey should be carried out as soon as

possible forming an essential point of departure for any action for elimination or at least

decreasing the incidence of hazardous work among children.

Theoretically education is compulsory in the country up to Junior High School level but

unfortunately the situation in practice is different. Drop out from school by working children is

quite common and usually no action is taken. This is because some parents may lack faith in

education and consequently they encourage their children to leave school and engage in work

while these children can learn skills and gain experience for the future. Drop out from school

may be due to the difficult economic status of parents who cannot afford the expense of their

children's education. More attention should be paid to children of less literate and poor parents

(estimated by occupation) as they cannot afford schooling


96

In Ghana, the enforcement of law and the improvement of education alone are unlikely to

eradicate child labour. Simply making schools available and improving their quality will not be

sufficient to overcome all the problems faced by the poorest families in the quest to send their

children to school. The hard economic condition of the families and the high rate of

unemployment among the people force them to send their children to work. Child labour cannot

be eliminated without action to address poverty.

One of the important conclusions that can be drawn from this study is that, if there is no

conscious effort of the Ghana Education Service to encourage girls to attend school, then those

girls who are combining school and work would move into working instead of schooling.

Moreover, appropriate policy can shift children who are both attending school and working

toward schooling as their only activity. Hence, the government of Ghana should continue with a

program that will encourage children all over the country to attend school while more focus

should be given to its proper and fruitful implementation.


97

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106

APPENDICES

APPENDIX 1

Percentage Distribution of Non-Working and Working Children by School Attendance

Chi-Square Test Value Df Asymp. Sig.

(2-sided) Exact Sig. (2-sided)

Exact Sig. (1-sided)

Pearson Chi-Square 444230.143a 1 .000 Continuity Correction 444228.872 1 .000 Likelihood Ratio 437768.633 1 .000 Fisher's Exact Test .000 .000 Linear-by-Linear Association 444230.073 1 .000

N of Valid Cases 6361178

Symmetric Measures

Value Approx. Sig.

Nominal by Nominal Phi .264 .000 Cramer's V .264 .000

N of Valid Cases 6361178 Source: Computed from GCLS Data File

Percentage Distribution of Non-Working and Working Children by Sex Chi-Square Tests Value df Asymp. Sig.



Pearson Chi-Square 1680.367a 1 .000 Continuity Correction 1680.301 1 .000 Likelihood Ratio 1680.768 1 .000 Fisher's Exact Test .000 .000 Linear-by-Linear Association 1680.367 1 .000



107

Symmetric Measures Value Approx. Sig.


N of Valid Cases 6361178 Source: Computed from GCLS Data File Percentage Distribution of Non-Working and Working

Children by Age Group Chi-Square Tests Value df Asymp. Sig.

(2-sided) Pearson Chi-Square 606298.090 2 .000 Likelihood Ratio 625068.679 2 .000 Linear-by-Linear Association

574527.132 1 .000

N of Valid Cases 6361178 Symmetric Measures Value Approx. Sig.

Nominal by Nominal

Phi .309 .000 Cramer's V .309 .000



108

Percentage Distribution of non-working and working Children by Size of Household

Chi-Square Tests Value df Asymp. Sig.


82713.642 1 .000


Nominal by Nominal

Phi .116 .000 Cramer's V

.116 .000


Percentage Distribution of Non-Working and Working

Children by Age of Household Head


(2-sided) Pearson Chi-Square 94124.148 7 .000 Likelihood Ratio 95149.060 7 .000 Linear-by-Linear Association 83126.557 1 .000



109


Nominal by Nominal

Phi .122 .000 Cramer's V

.122 .000

N of Valid Cases 6361179 Source: Computed from GCLS Data File Percentage Distribution of Working and Non-Working Children by Literacy of Household Head Chi-Square Tests Value Df Asymp. Sig.



Pearson Chi-Square 188526.223 1 .000 Continuity Correction 188525.516 1 .000 Likelihood Ratio 190742.736 1 .000 Fisher's Exact Test .000 .000 Linear-by-Linear Association 188526.194 1 .000

N of Valid Cases 6361178 .





110

Percentage Distribution of Working and Non-Working

Children by Level of Education of Parent



73490.731 1 .000


Nominal by Nominal

Phi .227 .000 Cramer's V .227 .000


Percentage Distribution of Working and Non-Working Children by Parent Marital Status Chi-Square Tests Value df Asymp. Sig.






111


Nominal by Nominal

Phi -.257 .000 Cramer's V

.257 .000

N of Valid Cases 6361178 Source: Computed from GCLS Data File Percentage Distribution of Working and Non-Working Children by Locality of Residence Chi-Square Tests Value df Asymp. Sig.





Nominal by Nominal

Phi -.297 .000 Cramer's V .297 .000



112

Percentage Distribution of Working and Non-Working

Children by Sex of Head of Household




Pearson Chi-Square 28128.007 1 .000 Continuity Correction 28127.694 1 .000 Likelihood Ratio 28482.040 1 .000 Fisher's Exact Test .000 .000 Linear-by-Linear Association

28128.003 1 .000



N of Valid Cases 6361178 Source: Computed from GCLS Data File Classification Table for Binary Logistic Regression (Baseline model)

Observed Predicted

attending school and

working Percentage Correct

attending school and work

schooling only

attending school and working

attending school and work 5477371 0 100.0

schooling only 883807 0 .0 Overall Percentage 86.1 Source: Computed from GCLS Data File


113

APPENDIX 2

Distribution of children age 5-17 years by socio-economic Variable, 2003 GCLS Socio-economic Variable Frequency Percent

Sex

Male(Ref.) 3,313,494 52.1

Female 3,047,684 47.9

Relationship to head of Household

Son/daughter (Ref.) 4,922,720 77.4

Other relations 1,438,458 22.6

Age group of children

5 - 9 (Ref.) 2,657,286 41.8

10 - 14 2,515,490 39.5

15 - 17 1,188,403 18.7

Size of Household

Less than 3 (Ref.) 105,418 1.7

3 - 4 939,200 14.8

5 - 6 2,020,048 31.8

7 - 8 1,605,209 25.2

9 - 10 1,062,152 16.7

Over 10 629,152 9.9

Residence

Urban(Ref.) 3,963,096 37.7

Rural 2,398,082 62.3


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(cont‟d.)

Socio-economic Variable Frequency Percent

Age of Household Head

15 - 34 (Ref.) 682,106 10.7

35 - 39 859,832 13.5

40 - 44 978,767 15.4

45 - 49 1,007,308 15.8

50 - 54 814,128 12.8

55 - 59 495,638 7.8

60 - 64 491,379 7.7

65+ 1,032,020 16.2

Major Occupation of Parent

Farming (Ref.) 1,131,785 17.8

Service 249,813 3.9

Trade 410,596 6.5

Day/Wage Labour 188,213 3.0

Other occupation 4,380,771 68.9

Highest level of Education of Parent

No education (Ref.) 1,141,039 17.9

At most primary 4,077,352 64.1

Junior secondary 1,036,681 16.3

SSS or Higher 106,105 1.7


115

(cont‟d.)

Socio-economic Variable Frequency Percent

Marital status of parent

Married (Ref.) 3,757,093 59.1

Not Married 2,604,085 40.9

Region

Greater Accra (Ref.) 747,164 11.7

Western 639,438 10.1

Central 516,693 8.1

Volta 519,006 8.2

Eastern 733,780 11.5

Ashanti 988,779 15.5

Brong Ahafo 657,128 10.3

Northern 891,096 14.0

Upper East 381,988 6.0

Upper West 286,106 4.0

Literacy of household head

Not literate (Ref.) 5,610,394 88.2

Literate 750,784 11.8



116

APPENDIX 3

LOGISTIC REGRESSION VARIABLES schworking

/METHOD=ENTER sex lithead urbrur marital modawk fadawk rel1 agehead2 hhead edu

/CONTRAST (sex)=Indicator(1)

/CONTRAST (lithead)=Indicator(1)

/CONTRAST (urbrur)=Indicator(1)

/CONTRAST (marital)=Indicator(1)

/CONTRAST (modawk)=Indicator(1)

/CONTRAST (rel1)=Indicator(1)

/CONTRAST (agehead2)=Indicator(1)

/CONTRAST (hhead)=Indicator(1)

/CONTRAST (fadawk)=Indicator(1)

/CONTRAST (edu)=Indicator(1)

/PRINT=GOODFIT CI(95)

/CRITERIA=PIN(0.05) POUT(0.10) ITERATE(20) CUT(0.5).

*Generalized Linear Mixed Models.

GENLINMIXED

/DATA_STRUCTURE SUBJECTS=distid*BaseID*clustID*hnum

/FIELDS TARGET=schworking TRIALS=NONE OFFSET=NONE

/TARGET_OPTIONS REFERENCE=1 DISTRIBUTION=BINOMIAL LINK=LOGIT

/FIXED USE_INTERCEPT=TRUE

/RANDOM USE_INTERCEPT=TRUE SUBJECTS=distid*BaseID*clustID COVARIANCE_TYPE=VARIANCE_COMPONENTS

/RANDOM USE_INTERCEPT=TRUE SUBJECTS=distid*BaseID*clustID*hnum COVARIANCE_TYPE=VARIANCE_COMPONENTS


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/BUILD_OPTIONS TARGET_CATEGORY_ORDER=ASCENDING INPUTS_CATEGORY_ORDER=ASCENDING MAX_ITERATIONS=100 CONFIDENCE_LEVEL=95 DF_METHOD=RESIDUAL COVB=MODEL

/EMMEANS_OPTIONS SCALE=ORIGINAL PADJUST=SEQBONFERRONI.

*Generalized Linear Mixed Models.

GENLINMIXED

/DATA_STRUCTURE SUBJECTS=distid*BaseID*clustID*hnum

/FIELDS TARGET=schworking TRIALS=NONE OFFSET=NONE

/TARGET_OPTIONS REFERENCE=1 DISTRIBUTION=BINOMIAL LINK=LOGIT

/FIXED EFFECTS=sex agehead2 hhead marital modawk fadawk rel1 urbrur edu lithead USE_INTERCEPT=TRUE

/RANDOM USE_INTERCEPT=TRUE SUBJECTS=distid*BaseID*clustID COVARIANCE_TYPE=VARIANCE_COMPONENTS

/RANDOM USE_INTERCEPT=TRUE SUBJECTS=distid*BaseID*clustID*hnum COVARIANCE_TYPE=VARIANCE_COMPONENTS

/BUILD_OPTIONS TARGET_CATEGORY_ORDER=DESCENDING INPUTS_CATEGORY_ORDER=DESCENDING MAX_ITERATIONS=100 CONFIDENCE_LEVEL=95 DF_METHOD=RESIDUAL COVB=MODEL

/EMMEANS TABLES=sex CONTRAST=NONE

/EMMEANS TABLES=agehead2 CONTRAST=NONE

/EMMEANS TABLES=marital CONTRAST=NONE

/EMMEANS TABLES=modawk CONTRAST=NONE

/EMMEANS TABLES=fadawk CONTRAST=NONE

/EMMEANS TABLES=rel1 CONTRAST=NONE

/EMMEANS TABLES=urbrur CONTRAST=NONE

/EMMEANS TABLES=edu CONTRAST=NONE

/EMMEANS TABLES=lithead CONTRAST=NONE


118

/EMMEANS TABLES=hhead CONTRAST=NONE

/EMMEANS_OPTIONS SCALE=ORIGINAL PADJUST=SEQBONFERRONI.


STATISTICAL ANALYSIS OF SOCIO-ECONOMIC DETERMINANTS ON CHILD LABOUR … · 2018-12-11 · iii...

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Transcript of STATISTICAL ANALYSIS OF SOCIO-ECONOMIC DETERMINANTS ON CHILD LABOUR … · 2018-12-11 · iii...