Socio economic and demographic determinants of urban youth unemployment. The case of Hawassa City in Ethopia

TABLEOF CONTENTS

Contents Pages

ACKNOWLEDGMENTS

ABBREVIATION AND ACRONYMS

LIST OF FIGURES

LIST OF TABLES

ABSTRACTS

TABLE OF CONTENTS

CHAPTER ONE INTRODUCTION
1.1. Background of the study
1.2. Statement of the Problem
1.3. Objective of the Study
1.3.1. General objective
1.3.2. Specific objectives of the study
1.3.3. Research Question
1.4. Significance of the study
1.5. Scope of the Study
1.6. Limitations of the Study
1.7. Organization of Chapters
1.8. Ethical Consideration

CHAPTER TWO LITERATURE REVIEW
2.1. Theoretical Literature
2.1.1. Concepts and Definitions
2.1.2. Youth Employment Global Perspective
2.1.3. Employment trends in Sub-Saharan Africa
2.1.4. Youth Unemployment Ethiopia Perspective
2.1.5. Types of unemployment
2.1.6. Theories of Unemployment
2.2. Factors that influence Youth Unemployment
2.2.1. Demographic Factors of Youth Unemployment
2.2.2. Socio-Economic Factors of Youth Unemployment
2.3. Consequences of Youth Unemployment
2.4. Economic costs of unemployment
2.5. Empirical Evidences
2.6. Conceptual Framework

CHAPTER THREE RESERCH DESIGN AND METHODOLOGY
3.1. Description of the Study Area
3.1.1 Economically Active and Not active Population and Activity Rate of the study area
3.2. Research Design
3.3. Data Source and Type
3.5. Sample Design and Procedures
3.6. Sample Size Determination
3.7. Method of Data Collection
3.8. Method of Data Analysis
3.8.1. Descriptive Analysis
3.8.2.1 Model Specification
3.8.2.2 Diagnostic Test
3.9.4. Description of Variables

CHAPTER FOUR RESULTS AND DISCUSSION
4.1. Descriptive Analysis
4.1.1 Demographic Characteristics of Respondents
4.1.1.1. Sex of Respondents
4.1.1.2. Age of Respondents
4.1.1.3. Marital Status of Respondents
4.1.1.4. Migration Status of the Respondents
4.1.2. Socio-Economic Profile of Respondents
4.1.2.1. Educational level of Respondents
4.1.2.2. Job Preferences of Respondents
4.1.2.3.Social Network Density of Respondents
4.1.2.4. Work Experience of Respondents
4.1.2.5. Business Advisory Service
4.1.2.6. Mothers Education Status of the Respondent
4.1.2.7. Father's Educational Status of the Respondents
4.1.2.8. Income Status of Household
4.1.2.9. Employment Status of Respondents
4.1.3. Differentials and Determinants of Youth Unemployment
4.1.4. Bi-Variate Analyses (Differentials of Youth Unemployment)
4.2 Econometrics Analysis
4.2.1. Determinants of Youth Unemployment (Logistic Regression Analysis)

CHAPTER FIVE CONCLUSIONS AND RECOMENDATIONS
5.1. CONCLUSIONS
5.2. RECOMMENDATIONS

REFERENCES

APPENDIXES

AFRICA BEZA COLLEGE SCHOOL OF GRADUATE STUDIES ADVISORS APPROVAL SHEET

This is to certify that the thesis entitled “Socio Economic and Demographic Determinants of Urban Youth Unemployment: The Case of Hawassa City, Ethiopia” submitted in partial fulfillment of the requirements for MSc. In Economics, school of graduate studies Africa Beza College has been carried out by Fantu Bekele ID No ABCGS/137/11, under our supervision. Therefore we recommend that the student has fulfilled the requirements and hence here by can submit the thesis to the department.

Abbildung in dieser Leseprobe nicht enthalten

AFRICA BEZA COLLEGE SCHOOL OF GRADUATE STUDIES THESIS APPROVAL SHEET

We, the undersigned, members of the Board of Examiners of the final open defense by Fantu Bekele, have read and evaluated his/her thesis entitled “Socio Economic and Demographic Determinants Of Urban Youth Unemployment: The Case of Hawassa, Ethiopia”, and examined the candidate. This is, therefore, to certify that the thesis has been accepted in partial fulfillment of the requirements for the degree MSc in Economics.

Abbildung in dieser Leseprobe nicht enthalten

Final approval and acceptance of the thesis is contingent upon the submission of the final copy of the thesis to the School of Graduate Studies (SGS) through the School Graduate Committee

Stamp of SGS Date:

ACKNOWLEDGMENTS

First and foremost I would like to extend my sincere gratitude to my Lord and Savior, Jesus Christ for giving me the wisdom to complete this task. “I cried out, “I am slipping!” but your unfailing love, O Lord, supported me. When doubts filled my mind, your comfort gave me renewed hope and cheer.” Psalm 94:18-19. Indeed you are faithful.

I wish to thank the following people for their support during the course of this study:

- My mentor and supervisor, Dr. Abate Yisegat, for his expertise, guidance and assistance. May God bless you abundantly!
- Mr. Abrham A. for valuable comments.
- Mr. Desalew Demissie for assisting me with the professional editing of this dissertation. .
- My wife, Tigeset Negash, and my two beautiful daughters Ye'adonay and Robel for their love and support. Their love is always a source of my inspirations and joy wherever I am.
- Hayleyesus Berhanu and Besufekad Tadesse my friends and colleague for the caring words of encouragement. You have shown me that family is not only defined by blood.
- I also forward my thanks to my beloved brother Takele Bekele.
- To all the graduates who participated in this study, you are my heroes!

Dedicated to my mother, Tsehay Mengesha, who has always reminded me to stay humble, work hard and be kind.

ABBREVATIONS AND ACRONYMS

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List of Figures

Figure: Page

Figure 2.1: Conceptual framework of urban youth unemployment

Figure 3.2: Graphic arrangement of sampling procedure

List of Tables

Table: Page

Table 3.1: Populations of Hawassa Towns Aged Ten Years and Above by Activity Status and Activity Rate and Sex during the Last Seven Days (Current Status Approach)- January 2020 survey

Table 3.2: List of the names, descriptions and codes of the independent variable

Table 4.1:Distribution of Respondents by gender, Hawassa Town, November 2020

Table 4.2: Age of the respondents, Hawassa Town, November 2020

Table 4.3:Marital status of the respondents, Hawassa Town, November 2020

Table 4.4: Migration status of the respondents, Hawassa Town, November 2020

Table 4.5:Frequency distribution of Educational level of Respondents

Table 4.6:Frequency distribution of Job Preferences of Respondents of Respondents

Table 4.7:Social Network Density of Respondents

Table 4.8:Work experience of Respondents

Table 4.9: Business Advisory service of Respondents

Table 4.10:Frequency distribution of Mothers Education Status of the respondent

Table 4.11: Frequency distribution of Mothers Education Status of the respondent

Table 4.12:Frequency distribution of Income Status of household of the respondent

Table 4.13:Frequency distribution of employment Status of household of the respondent

Table 4.14:Chi-Square test result of the association between Youth Employment status and Demographic Variables, Hawassa 2020

Table 4.15:Socio-Economic Differentials of Youth Unemployment (ordered variables)

Table 4.16: Socio-Economic Differentials of Youth Unemployment (categorical variables)

Table 4.17: Mean Comparison of Continuous Variables

Table 4.19: Results of Binary Logistic Regression Model

ABSTRACT

Youth are important driving forces for social, political and economic development in any country. In Ethiopia urban, youth make up approximately 34.27% (23,081,127 total urban population and 7,909,465 urban youth)of the population but the problem of youth unemployment is a central issue of public discourse in Ethiopia. The main objective of this study is to identify the demographic and socio-economic determinants of urban youth unemployment in Hawassa city, Sidamma Regional state. To achieve the specified objective, both primary and secondary data sources were used. Multi-stage sampling designs were engaged in order to select respondents who live in the study area during the reference period. The primary data was collected from 437 sample respondents through structured questionnaire from six kebeles. Logit model was employed to analyze the collected data. While age of respondent affects employment negatively whereas educational level of respondent, work experience, business advisory service, job preference, social network, frequency of contact and family income of urban youths affects employment positively. The study recommends that the government should take a measure of action to support the very poor, and to bring about rapid economic growth at the national level. To this effect, adopting job-rich macro-policies by ensuring macroeconomic stability, optimizing the job­creation potential of public investment, improving the financial sector, and upgrading the institutional and statistical framework for job-rich macro-policies. And, the government needs to build a vibrant local private sector by revamping the current support to MSMEs, effectively supporting high-potential and high-growth MSMEs, and improving the quality of business development services.

Key Words: Urban Youth, Unemployment status and Binary Logit

CHAPTER ONE INTRODUCTION

1.1. Background of the study

Youth's transition into the labour market has long-term impacts on their lives as well as on the socio-economic development of their countries. It is thus essential and global interest to understand their pathways into the world of work and how they are engaging - or not, as the case may be - in employment.Many countries at different levels of development are trying to cope with this problem. International Labor Organization (2015) defines that unemployment is workers who, when asked, say they are willing and able to work more than they are currently working, according to a defined threshold of working time. People who voluntarily do not want to work, full time students, retired people and children are no included in unemployed category. In short, unemployment means the state when people who are willing and able to do a job but fail to get the desired job. Youth unemployment is a problem that affects most countries. The ability of youth to engage in productive activities has both social and economic consequences for an economy. While young people in all countries of the world face a higher risk of unemployment than adults, this phenomenon is especially marked in Africa. In 2019, the youth unemployment rate was 30.2 per cent in North Africa, compared with an aggregate unemployment rate of 12.1 per cent (i.e. for all workers aged 15 and older); and 8.7 per cent in sub-Saharan Africa, compared with 5.9 per cent on aggregate. As larger numbers of young workers enter the African labour market every year, the need to create employment opportunities becomes even more pressing. Already at present, the availability and quality of jobs in Africa indicate that young workers face deeply ingrained decent work deficitsand sustainable jobs (ILOWorld Employment and Social Outlook Trends, 2020).

Unemployment is a multidimensional concept that involves economic, social and politic dimensions. We can't provide a common definition for youth unemployment due to the issue depends on the social setting, cultural setting, economy setting and structure, and the education system of a given country. For Example, the United Nations (2005) defines ‘youth', as those persons between the ages of 15 and 24 years, without prejudice to other definitions by Member States; Ethiopia's national youth policy (2004) defines youth as those aged between 15-29. WHO defines 'Adolescents' as individuals in the 10-19 years age group and 'Youth' as the 15-24 year age group. While 'Young People' covers the age range 10-24 years.

According to ILO (2018b) figures, Labour markets in sub-Saharan Africa differ markedly from those in North Africa. Employment in sub-Saharan Africa is characterized by widespread low-productivity employment in smallholder agriculture. This is a major reason why 35.9 per cent of workers in the sub region were living in extreme poverty and an additional 25.4 per cent in moderate poverty in 2019. The total number of workers living in poverty was 240 million. Significantly, 140 million out of the 234 million workers living in extreme poverty across the world are in sub-Saharan Africa (i.e. 59.8 per cent). This share is projected to rise, since poverty reduction in the sub region is proceeding at a slower pace than elsewhere. Informal employment is essentially the norm, affecting 89.2 per cent of workers. Even when agricultural workers are excluded, the informality rate still stands at 76.8 per cent (ILO, 2018b).

The inadequate employment situation of youth has a number of socio-economic, political and moral consequences (Berhanu et.al, 2005; Toit, 2003). Some of the consequences of youth unemployment are as follows. Unemployment fosters drug addictions among youths: Unemployed young people are more likely to abuse illicit substances than are employed young people. According to UN (2003) report, unemployed youth are the main drug users in Sub Sahara Africa, which accounts 34 million young people representing 7.7 percent of the continent's youth population. The report also indicated that Cannabis sativa or marijuana is the main drugs consumed by youth in the region. Similarly, Curtain (2000) stated that in the continent, delinquency, crime and drug abuse are on the increase among unemployed youths.

A high level of unemployment and underemployment is one of the critical socio-economic problems facing Ethiopia. While the labour force grows with an increasing proportion of youth, employment growth is inadequate to absorb labour market entrants. As a result, youth are especially affected by unemployment. Moreover, young people are more likely to be employed in jobs of low quality, underemployed, working long hours for low wages, engaged in dangerous work or receive only short term and/or informal employment arrangements (Berhanu et al., 2005).

Unemployment in Ethiopia is more of a problem of urban youth than that of adult and older age. According to Ethiopian central statistical survey report, the rate of unemployment for youth 25.7 percent covers 1,249,878 youth unemployed population, which was higher than that of the total, adult and older age categories(CSA, 2020).

The Central Statistical Authority population estimation figure shows, the total population of Hawassa to be 256,591(120,724 male & 144,867 female) with unemployment rate of26.1 made the town one of the highest economically active unemployment in the region, 26.7 rate(CSA, 2020).

As a result, identifying the underlined factors influencing unemployment of urban youth dwellers should be the first step to come up with the alternative strategies to solve the problem. In this regard, even though few studies were conducted on the determinants of youth unemployment in Ethiopia (Getinet, 2003; Duguma et. al.,2019; Esay, 2020; Tsegaw, 2019; Asalfew, 2011; Ahemedteyib, 2020;Tegegne, 2011; Asmare et. al., 2014; Gebeyaw, 2011; Nganwa et al., 2015; Dejene et al., 2016 and Aynalem et.al,2016), the results of these studies are varied depending on the specific socio-economic situation andmethodological differences of the study area. Hence, this study is conducted to examinethe determinants of urban youth unemployment at Hawassa city of Sidamma Regional State, Ethiopia.

1.2. Statement of the Problem

The government of Ethiopia has placed job creation at the top of its policy agenda and consequently the idea of institutional financing was conceived as a way of addressing unemployment. The concept is based on the premise that micro, small, and medium enterprise development initiatives are likely to have the biggest impact on job creation. The government has so far implemented various interventions to address the challenge of youth employment through human capital development like the Youth Revolving Fund to provide youth with access to finance for self-employment activities. There is a National Job creation Council and Executive Committee of the National Job Creation Council headed by the vice prime minister of the Government over seeing implementation of plans, policies, and projects on Job Creation and also coordination of inter sectoral issues. And, the recently endorsed ten year development plan of Ethiopia has placed the unemployment rate in urban areas is expected to down to a single digit and stand at nine percent from close to 19 percent by the end of plan year, 2030; andthe National action plan (2020-2025) of Job creation Commission document have also acknowledged the problem of youth unemployment and prescribed policies and strategy to deal with it.

In spite of these efforts, unemployment and under-employment among the youth still remains a big problem. Many youths in Ethiopia still remain unemployed and vulnerable to crime and social unrest.In this regard, even though few studies were conducted on the determinants of youth unemployment in Ethiopia (Bizuneh et al., 2001, Getinet, 2003; Duguma et. al.,2019; Esay, 2020; Tsegaw, 2019; Asalfew, 2011; Ahemedteyib, 2020;Tegegne, 2011;Asmare et. al., 2014; Gebeyaw, 2011; Nganwa et al., 2015; Dejene et al., 2016 and Aynalem et.al,2016)the results of these studies are varied depending on the specific socio-economic situation andmethodological differences of the study area. In view of this, this paper seeks to fill that gap by identifying the determinants of youth unemployment by examining the extent to which those explanatory variables explain differences in youth unemployment status in urban Ethiopia by carrying out a study in Hawassa city.

1.3. Objective of the Study

1.3.1. General objective

The general objective of this study is to examine the characteristics and determinants that affect youth unemployment status in urban Ethiopia.

1.3.2. Specific objectives of the study

The specific objectives of this study are:

- To explore characteristics of urban youth unemployment in the study area.
- To identify the demographic and socio-economic factors of urban youth unemployment at the study area.
- To assess the magnitude of youth unemployment in the study area.

1.3.3. Research Question

The central research question that guides this study to achieve stated objective are:

- What are the majorcharacteristics of urban youth unemployment in the study area?
- What are main demographic and Socio-economic factors affecting youth unemployment in the study area?
- What is the magnitude of youth unemployment at the study area?

1.4. Significance of the study

Youth unemployment is the global issue in the world and of which in Ethiopia particularly in Hawassa city in partcular. The study on AhemedteyibKemal(2020), Esay Solomon, 2020; Tsegaw Kebede, 2019 try to concentrate on the degree and determinants of the definite factors that hinder youth employment. The one which makes this study different from the other will be the fact that it tries to address factors that correlates high youth unemployment rate in the study area.

Accordingly, the study is restricted to a single town, so it will be helpful to considerate the determinants of urban youth unemployment in Ethiopia in general and specifically of Hawassa city. It gives some clue on the characteristics and scope of the challenges related with high intensity of youth unemployment. And, the information on the determinants of youth unemployment is very important to the government and policy maker for filling the policy gaps relating to country employments and in addressing the issue of unemployment. The results of the study are also important to the employers and other labor market players, for understanding the source of problems resulting in unemployment of youth which account for a large share of the Ethiopian labor force. On the other hand, the study provides information to the youth themselves in Hawassa and in the country at large, to understand the determinants of unemployment and the possible ways to overcome it. Finally,the finding will be used as a bench mark in order to undergo further analysis on the subject.

1.5. Scope of the Study

Unemployment is the key problem of youth in Ethiopia. Likewise the number of unemployed youth is increase in Hawassa city from time to time. But this study has been focused mainly on the Socio-economic and Demographic Determinants of Urban Youth Unemployment in Hawassa city, specially focused on urban kebele‘s of the town because to cover the over all areas of the country remain a numbers of problems that constraints such as lack of enough time and skilled human power.

1.6. Limitations of the Study

The major assumed difficulty encountered during this study was omission of variable data. This caused the researcher not to capture relevant information on the variables. Similarly the study faces challenges of coverage of the total population, because such type of study might be requires the consideration of large sample size. Other additional limitation occurs due to unwillingness of respondents' cooperation or interviewer error, address changing, the frequency of interviewing may arise because of faulty responses due to vague questions, memory errors, deliberate distortion such as prestige bias, in appropriate informants, miss recording data of responses and interviewer effects. Beside the above limitations since the study is specified to a single town this may create some problem in generalizing the whole challenges of youth unemployment in the country level. Assessing the determinants of youth unemployment is difficult as it is the collective effect of different socio-economic and demographic factors.

1.7. Organization of Chapters

This research thesis is organized as follows. Chapter one covers background of the study, statement of the problem, objectives of the study, significance and limitations of the study. Chapter two covers literature of past researches done in relation to youth unemployment and broad unemployment. Chapter three discusses the methodologies used in this paper to reach the objectives set in the chapter one. Chapter four discusses the findings of the result; chapter five discusses conclusions and recommendation of the research.

1.8. Ethical Consideration

In conducting a research, observing to the principles of research ethics is crucial. First, a letter written from the college were submitted the town administrative body in order to get permission to conduct the study. The objectives of conducting the study were explained to the city administrative head. Secondly, respondents were informed about the objectives of the study and their response was kept confidential and not used for purpose other than the objectives of the study. Thirdly, they were also informed that they have the right not to answer to any of the questions. Finally, respondents were up-to-date about the rights they have to know the results of the study. Further the name of respondents was avoided from the questionnaires.

CHAPTER TWO LITERATURE REVIEW

2.1. Theoretical Literature

2.1.1. Concepts and Definitions

Economic activity: As defined by the Ethiopian central statistical agency(CSA), which involves the production of goods and/or services for sale or exchange and production and processing of primary products for own consumption. Usual and current status approaches were used for measuring the economically active population in relation to a short reference period, (seven days) and long reference period, (six calendar months), respectively (CSA, 2020).

Economically Active Labor Force: E conomically active population comprised all persons of either sex who furnish the supply of labour for the production of goods and services. In other words, it refers to persons who are engaged in work or available to engage in the economic or productive activities (CSA, 2020).

Underemployment: A situation in which the worker could increase utility by taking less leisure and more income; a level of work wherein the wage rate exceeds the marginal rate of substitution of leisure for income. This term may also refer to a situation in which the worker is employed in a position for which he or she is overqualified (McConnell, 1995).

Decent work: Productive work in which rights are protected, which generates an adequate income, with adequate social protection. Also means sufficient work, in the sense that all should have full access to income-earning opportunities (ILO, 2015).

Employed Labour Force: The employed population is defined as persons above specified age who perform some work for wage, salary, profit or family gain in cash or in kind during the reference period. More generally, employed persons are those people who are engaged in economic activity to produce goods and services (CSA, 2020).

Employed person:- persons who have been working for most of the weeks during the six month prior to the survey date were considered as being employed(CSA, 2020).

Economically Inactive Population: This refers to persons who are neither engaged nor available to provide their labour. Economically inactive population are not considered as unemployed person and they are excluded from unemployment analysis because of not fulfill the definition of unemployment (CSA,2020).

Unemployed population: Consists of persons without work but who are available and ready to work if any job was found. Those who are neither engage nor available to work are classified as economically not active (CSA,2020).

Unemployed Labour Force: According to CSA definition, those who work for less than 12 weeks or those who did not work at all but who were looking or available for work were counted as being unemployed labour force(CSA,2020).

Employment rate:- The fraction of the labor force that is employed, i.e. the number of employed divided by the total labor force(CSA, 2020).

Unemployment rate:- The percentage of the labor force that is unemployed. It is the ratio of total unemployment to the total labor force, where the latter is the sum of employment and unemployment(McConnell, 1995).

Youth:- The UN defines youth as the age group between 15 and 24 years old, but, the term =youth‘ follows the Ethiopian context definition of those persons between the ages of 15 and 29 years (FDRE, 2004).

Youth labor force:- Consists of people between 15 and 29 years old who are either working or actively looking for work, excluding youth who are economically inactive (MOY, 2004).

2.1.2. Youth Employment Global Perspective

Worldwide, there are approximately 1.3 billion young people between the ages of 15 and 24. Their transition into the labour market has long-term impacts on their lives as well as on the socio-economic development of their countries. It is thus essential to understand their pathways into the world of work and how they are engaging - or not, as the case may be - in employment. Around 497 million young people, or roughly 41 per cent of the global youth population, are in the labour force. Of these, 429 million are employed, while nearly 68 million are looking for, and are available for, work (these are defined as unemployed). More than half of young people - around 776 million - are outside the labour force, meaning that they are not in employment and are not looking and available for a job. A considerable proportion of youth are pursuing an education; alongside their studies they may be employed, searching for a (part-time) job and hence considered un- employed, or abstaining from participation in the labour market. A useful broad measure of youth labour underutilization is therefore the number of young people who are not in employment, education or training (NEET), which stands at 267 million - a high figure reflecting how many young people around the globe are currently not contributing to self-development and to national development by acquiring skills or engaging in work(ILO, 2020).

Even for young people who are engaged in employment, not all is well. Around 126 million, or 30 per cent of employed youth, remain in extreme or moderate poverty despite having a job. In addition, over three-quarters of young workers are engaged in informal employment. Globally, some 46 per cent of young workers are own-account workers or contrib- uting family workers, whereas nearly 54 per cent are wage and salaried workers, though often in non-standard arrangements. Jobs held by young people are frequently associated with low pay, limited legal and social security and poor working conditions. The challenges faced by young people have been commanding increasing attention on the global agenda, as reflected in the Sustainable Development Goals (SDGs). Effectively addressing the challenges requires strong labour market information systems. There is a direct link between decent employment and a dignified livelihood; as new entrants to the world of work, young people are particularly vulnerable (UNDESA, 2018). The kind of jobs that they are able to access - and the point in time at which they enter the labour market - influences not just their individual career and earning prospects but also the development trajectories of their countries (Global employment trends for youth, 2020).

The decline in labour force participation rates and employment-to-population ratios among young people can be partly attributed to the longer time spent in education. Currently, there are over half a billion young people engaged exclusively in education. The gross enrolment ratio in secondary education worldwide rose from 59 per cent in 1999 to 76 per cent in 2018; the corresponding ratio for tertiary education increased from 18 per cent to 38 per cent over the same period (UIS, 2019). This suggests that low labour force participation rates could result in a better-skilled adult labour force and possibly higher aggregate participation rates in the future (ILO, 2019).

Unemployment affects 67.6 million young women and men, or 13.6 per cent of the youth labour force. Youth unemployment is highest in Northern Africa and in the Arab States, at around 2.2 and 1.7 times the global rate, respectively. In these two sub regions, youth unemployment rates have been considerably higher than those in the rest of the world since at least 1991, suggesting that there are structural barriers preventing young people from engaging in the labour market (ILO, 2015; UNDESA, 2018). Despite having the lowest unemployment probability across all sub regions, young people in sub-Saharan Africa and Northern America faced an unemployment rate of almost 9 per cent in 2019. Hence, there is a general need to help young people enter employment. The global youth unemployment rate is projected to rise by 0.1 percentage point in 2020 and a further 0.1 percentage point in 2021(Global employment trends for youth, 2020).

2.1.3. Employment trends in Sub-Saharan Africa

Labour markets in sub-Saharan Africa differ markedly from those in North Africa. Employment in sub-Saharan Africa is characterized by widespread low-productivity employment in smallholder agriculture. This is a major reason why 35.9 per cent of workers in the sub region were living in extreme poverty and an additional 25.4 per cent in moderate poverty in 2019. The total number of workers living in poverty was 240 million. Significantly, 140 million out of the 234 million workers living in extreme poverty across the world are in sub-Saharan Africa (i.e. 59.8 per cent). This share is projected to rise, since poverty reduction in the sub region is proceeding at a slower pace than elsewhere. Informal employment is essentially the norm, affecting 89.2 per cent of workers. Even when agricultural workers are excluded, the informality rate still stands at 76.8 per cent (ILO, 2018b).

Very low household incomes and the widespread lack of social protection force people to take up any kind of economic activity in order to survive. This is the reason behind the relatively low rate of unemployment in many countries in sub-Saharan Africa (ILO, 2019d and 2019e).

Almost half of the countries in the sub region have estimated unemployment rates below 5 per cent (though in some them, notably South Africa, the unemployment rate exceeds 20 per cent). On aggregate, an estimated 5.9 per cent of the sub region's total labour force was unemployed in 2019; very little change in that rate is projected for 2020-21. Despite relatively low unemployment, the combined rate of labour underutilization in 2019 was much higher, at 21.5 per cent. Sub-Saharan Africa is in fact the sub region with the largest discrepancy between the unemployment rate and total labour underutilization, with the latter being more than three times as high as the former. Half of total labour underutilization is due to time-related underemployment, which shows that jobs in the sub region are often of poor quality. The high combined share of own-account and contributing family work (74 per cent in 2019) is also symptomatic of the sub region's decent work deficits. The general lack of decent work opportunities affects both men and women in sub-Saharan Africa, where gender gaps tend to be narrower than in North Africa. This, however, does not imply that women do not face disadvantages and discrimination, on the contrary. The sub-Saharan gender gap in informality amounts to 6 percentage points (92.1 per cent for women versus 86.4 per cent for men), and the combined rate of labour underutilization is lower for men (at 19.2 per cent) than for women (at 23.9 per cent). Almost a third of women (30.0 per cent) are contributing family workers, compared with only 13.6 per cent of men. This reflects the fact that in many countries in the subregion property rights are biased in favour of men, who are the main landholders (Doss et al., 2015).

The labour market challenges described above are expected to become even more pronounced in the near future because Africa's youth labour force is growing very strongly in absolute numbers (see ILO, 2017c and forthcoming b). In addition to strong population growth in the continent as a whole, young people aged 15-24 are expected to number 283 million by 2030 in sub-Saharan Africa alone. This means that, compared with 1990, the absolute population size of this age group will have tripled by 2030. Indeed, rapid population growth in sub-Saharan Africa is a main driver of the projected population growth worldwide. In North Africa, the youth population is also growing significantly in absolute terms. By 2030, the population aged 15-24 is expected to amount to 51 million, which is almost twice the absolute population size of that group in 1990. As larger numbers of young workers enter the African labour market every year, the need to create employment opportunities becomes even more pressing. Already at present, the availability and quality of jobs in Africa indicate that young workers face deeply ingrained decent work deficits. To begin with, informality is by far the most important type of employment for young workers in Africa, affecting 94.9 per cent of them. Despite some heterogeneity across the region, youth informality is high everywhere, ranging from 56.4 per cent in Southern Africa to 97.9 per cent in West Africa (ILO, 2018b). While young people in all countries of the world face a higher risk of unemployment than adults, this phenomenon is especially marked in Africa. In 2019, the youth unemployment rate was 30.2 per cent in North Africa, compared with an aggregate unemployment rate of 12.1 per cent (i.e. for all workers aged 15 and older); and 8.7 per cent in sub-Saharan Africa, compared with 5.9 per cent on aggregate. In addition, a substantial number of young people in Africa are not in employment, education or training (NEET). The proportion of young people with NEET status in Africa was 20.2 per cent in 2019; it was considerably higher in North Africa than in sub-Saharan Africa. One characteristic of the youth NEET problem in the region is stark gender disparities, with much higher NEET rates among young women. In North Africa in particular, 36.1 per cent of young women had NEET status in 2019, as against 18.1 per cent of young men. In sub Saharan Africa, the female NEET rate in the same year was 23.5 per cent, while the male rate was 14.5 per cent. The large number of young workers has implications on both the demand and supply sides of the labour market. On the demand side, additional jobs need to be created and these should, moreover, offer decent working conditions. This would require both stronger economic growth and a form of growth that fosters greater complexity of economic production. However, Africa has seen workers transition from agriculture into low-skill services rather than into high value added manufacturing. A structural transformation is therefore required that involves a reorientation from resource extraction and agriculture to sectors with higher value added, including manufacturing and knowledge­intensive services (AfDB, 2019).

In addition, a significant proportion of the growing youth population in Africa lives in rural areas, where labour productivity is relatively low and employment and entrepreneurial opportunities are limited (Sedik, 2018; IFAD, 2019). Thus, it is important to provide improved employment and entrepreneurial opportunities for young workers in rural areas; also because these workers represent the future of agri-food systems. On the supply side of the labour market, skills and education mismatches are among the most pressing policy concerns. In many industries there are high shares of young workers who, despite having formal qualifications, lack the actual skills demanded by employers. According to representative survey data from ten African countries, 17.5 per cent of young workers reported that they were over skilled for their current jobs, while 28.9 per cent said that their skills were below the required skill level. In addition, compared with job experts' assessment of the educational level required for specific occupational groups, 56.9 per cent had too low and 8.3 per cent too high a level of educational attainment (Morsy and Mukasa, 2019).

The prevalence of such mismatches suggests that skills development should become a central strand of national policy-making. More generally, the labour market challenges for young workers point to a need to improve public employment services and establish technical and vocational training systems that are tailored to the needs of young workers and their potential employers. Training programs to enhance the skills of young men and women and active labour market policies in general can likewise play a positive role as long as they are well designed(O'higgins, 2017; Kluve et al., 2019).

2.1.4. Youth Unemployment Ethiopia Perspective

The Ethiopian Central Statistical Authority, survey result estimated the total urban population of the country in January 2020 to be 23,081,127, of which 11,104,677 (48.1 percent) is males and 11,976,450 (51.9 percent) are females. Among the total urban population aged 10 years and above 81.8 percent were found to be literate.The proportion of literates among the males (89.2 percent) is higher than that of the females (75.2.perecent). On the other hand, substantially high proportion of the literate (49.9 percent) attained elementary education (Grade 1-8). Regarding training status, out of the total persons aged 10 years and above, about 21.1 percent were trained. Any sort of theoretical or practical training exercise provided in class or outside class rooms and that has awarded certificate or diploma is considered as training. The proportion of trained males 27.1 percent exceeds more than that of the females 15.7 percent (CSA, 2020).

Economically Active and Inactive Population of Urban area

The sizes of the economically active population of the urban parts of the country in current status approaches are estimated to be 10,780,552 persons with activity rates of 61.1 percent. The activity rate of males is found to be higher than their female counterparts. Regarding the relationship between age and activity rate, the data depicts a curvilinear association. That is, low and increasing participation of persons at a younger age and high and relatively stable for middle ages (between age group 25-54 years) and then a steady decline at older age groups. Higher proportion of females tends to exit the labour force earlier (age 40-44 years) than the males (age 55-59 years. Looking the activity rates of regions in the last seven days, the highest was reported for Addis Ababa City Administration 64.7percent and Amhara region 64.1percent, while the lowest in Somali region compared to other regions i.e., 46.7 percent. The number of economically inactive persons during the last seven days prior to January 2020 was estimated to be 6,865,362 persons. Being a student (65.6 percent) and homemaking (13.3 percent) and old age (6.5 percent) are the major reasons for inactivity status (CSA, 2020).

The economic dependency ratio (EDR), which relates the number of non-workers to the number of workers in a given economy, provides a better representation of the share of the dependent population. The 2020 UEUS result shows that EDR is 163 dependents at country level. This means for each 100 employed persons, there are almost 163 dependents to be supported in terms of food, clothing, health, education and so on. This means that there were 163 non-employed persons per one hundred employed persons. At Regional level, the highest EDR registered in Somali (280) and the lowest is recorded for Addis Ababa City Administration (134). Similarly, at country level, EDR is more intense on females than males (CSA, 2020).

Characteristics of the Employed Population

On the Ethiopian Central Statistical Authority 2020 survey, at country urban level, the size of employed population aged ten years and above was 8,762,362 persons in January 2020 of which the share for males 5,045,256 (57.6 percent) and females were 3,717,107 (42.4 percent). On the other hand, employment to population ratio provides information on the extent to which the population is engaged in economic activities. It is calculated as the percentage of total employed persons to the total population aged ten years and above. A high employment to population ratio implies large proportion of the population is employed, while low employment to population ratio reflects large share of the population is not involved in economic activities due to unemployment or out of the labour force.

According to January 2020 CSA survey, the employment to population ratio of urban population was 49.7 percent. This means about 50 percent of the total urban population of the country aged ten years and above are employed. The differential by sex also depict that the ratio of males 60.6 percent is significantly higher than females about 39.9 percent . As the survey result shows, nearly two-thirds of urban employed population is engaged in three occupations, namely: service, shop and market sales workers (28.1 percent); craft and related activities (6.1 percent) and elementary occupation (24.4 percent). Professional and Technical and associate professionals together constituted (18.2 percent) while those persons working as legislator and managers contributed the lowest proportion only about 1.8 percent (CSA, 2020).

Facts inspection relating to status in employment in urban areas of the country reveals, that the majority of employed populations are paid-employees (49.4 percent), of which those employees working in government civil and public services and government development agents together accounted about 23 percent, private organization employees contributed the next significant addition (19.9 percent). Self-employed in agriculture and private business together accounted to be 36.2 percent. The unpaid family workers in agriculture and business the sum was also substantial (10 percent), where as members of cooperatives, small scale enterprise, employers and apprentice contributing insignificant share among the total employed population. Relatively more males found in all cases than females counterparts. However, the proportion of females exceeds a little in government, trade/business activities, domestic employees and unpaid family workers compared to the corresponding figure among the males (CSA, 2020).

This survey has collected data on earnings from paid employment. The average mean amount of earnings for the total paid employees of the country is estimated to be 3,735 Birr per month. In this survey, earnings for employees are related to gross remuneration and include bonus, overtime, allowances and other benefits that are obtained only from the main job. Comparison of mean amount of earning among different sectors (industries) has shown that the highest average payments were made in activities of extraterritorial organizations and bodies 9,573 Birr: of which male were paid 9,134 Birr and females mean monthly remuneration amounted 10,175 Birr. Financial ads insurance activities was the second highest payment total average 6,420 Birr: males receiving average amount of 7,272 Birr per month, female in same sector receive 5,208 Birr. Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use, as a sector, appeared the least remunerating i.e., with 1,907 Birr and 1,105 Birr earnings of males and females per month, respectively (CSA, 2020).

Levels and Distribution of Unemployment

According to January 2020 CSA survey, there were 2,018,190 unemployed persons, out of which males are 703,420 and females are 1,314,770. This means that the rate of unemployment in the current status approach for urban areas of the country is 18.7 percent.

The corresponding rate for the males and females are 12.2 percent and 26.1 percent, respectively . According to the national context, youth comprises those persons age 15-29 years. The rate of unemployment for youth 25.7 percent covers 1,249,878 youth unemployed population, which was higher than that of the total, adult and older age categories. Female and male youth unemployment rates were 31.7 percent against 18.8 percent, respectively . The size of unemployed population for literate persons at country urban level showed 1,668,333persons with 18.7 percent of unemployment rate whereas the corresponding figures for illiterate categories were 349,857with 18.2 percent. The unemployment rates for females were more pronounced in the literate than the illiterate categories in January 2020. Throughout the survey periods apart from the 2016, the overall unemployment rate of literate persons is higher than illiterate persons. In general term, the unemployment rate for literates depicts a declining pattern, however, the pattern for illiterate shows ups and downs (CSA, 2020).

The higher percentage share of unemployment by educational attainment at country urban level shows the highest 35.1 percent contributed by elementary educational level. The lowest unemployment share was registered for persons who attained preschool and Alternative Basic Education (ABE). With regard to sex, females found to be more unemployed than in elementary education, and in the never attended categories . The percentage share of unemployed persons who had formal training receiving certificate were 22 percent with 444,582 persons, while 78 percent (1,573,608 persons) do not have formal training. Females were more dominantly appeared in the not trained category than the trained one compared male counterparts. Out of the total urban unemployed persons in the country, 51.9 percent had no work experience and 48.1 percent have had previous work experience prior to the survey date. Among those unemployed persons who had prior work experience, females are higher than males, as well as female unemployed are more dominant than male among those without previous work experience. Regarding the duration of unemployment, about 47.3 percent of the unemployed persons are jobless for 12 months and less. For those stayed jobless for 13-24 months were 31.9 percent and 25-95 months 15.1 percent. The unemployed persons who stay without jobs for 96 months and above accounted to be 5.7 percent in January 2020. The long term unemployment that a person has been continuously unemployed for more than 12 months accounted to be 52.7 percent is higher than short term unemployment, which signifies relatively poor labour market performance and employment creation(CSA, 2020).

As per the survey results, the majority of unemployed about 55.6 percent were available to take up any kind of job followed by persons who sought or intended to establish own business (29.5 percent), while those who were looking for paid job in government institution accounted to be about 9.7 percent followed by 4.5 percent in private sector and the remaining other covers only 0.6 percent. The same pattern was observed for the two sexes. From the perspective of seeking for self-employment, the great majority of the unemployed who would like to establish their own business (63.4 percent) faced financial constraints. The next important reason is lack of working place or land 15.5 percent. Those responded lack of finance in combination with working place and training were about 18.9 percent. Besides, the survey was asked questions to the unemployed about reasons for not found paid job and the problem they faced and means of living.

Accordingly, more than half of the unemployed (58.4 percent) reported that they could not found paid employment due to lack of job opportunity and skill mismatch. Personal reasons like lack of experience and training were together accounted for 22.5 percent, and labour market related reasons covers about 19.1 percent. Regarding the problem they faced, decline of household or personal income and unable to cover household expenses reported to be 70.3 percent. Psycho-social problems like getting stress, hopelessness, isolation, exposed to addiction and family dissolution together reported about 17.3 percent followed by wandering for jobs 8 percent and the reaming percentages take up by other reasons 4.4 percent(CSA, 2020).

2.1.5. Types of unemployment

Several types of unemployment may be experienced in an economy such as that of Ethiopia and they include: frictional, seasonal, cyclical and structural unemployment.

2.1.5.1 Structural Unemployment

This type of unemployment is more difficult to define, but generally refers to the overall inability of the economy, due to structural imbalances, to provide employment for the total labour force even at the peak of the business cycle. This type of unemployment is not sensitive to changes in aggregate demand. Hence, structural unemployment is the unemployment that exists when the economy is at full employment. Unemployment experienced in South Africa is largely structural rather than cyclical (Chadha,1994).

Even during periods of high economic growth, job opportunities do not increase fast enough to absorb those already unemployed and those newly entering the labour market. There are various reasons for this, for example the rapid growth of the labour force, the use of capital or skill intensive technology, or an inflexible labour market. Structural unemployment could also refer to a skill mismatch, i.e. between the skill that the employers require and those that employees offer, or a geographical mismatch, i.e. between the location of job vacancies and those of job-seekers. The major proportion of unemployment in South Africa is Structural. Structural unemployment is caused by changes in the composition of labour supply and demand. Structural unemployment is part of the nation's natural rate of unemployment. However, this unemployment shares many of the same features as frictional unemployment but is differentiated by being long-lived. It therefore can involve considerable costs to those unemployed and substantial loss of forgone output to society (McConnell, 1995).

Improvements in agricultural technology over the past 100 years caused job losses for many farm operators and labourers who did not have readily transferable job skills in expanding areas of employment and who were not geographically mobile. Unemployment resulting from job losses associated with the spate of merges in the United States over the last decade is another example of structural unemployment, as is unemployment resulting from the deregulation of trucking and airline industries (McConnell, R 1995).

2.1.5.2. Frictional Unemployment

In a world of scarce and costly information, there will be at any point of time an ever­changing pool of unemployed job seekers in search of better paid and more suitable jobs and employers in search of particular types of workers. Those engaged in search attempt to match the marginal costs and benefits of such activity. For the proper functioning of the market, search unemployment is both inevitable and useful. Search costs are generally accepted as one of the factors which make the natural unemployment greater than zero. Frictional unemployment arises as a result of normal labour turnover that occurs in any dynamic economy and the time lags involved in the employment of labour. Because there are people moving between jobs and new entrants in the labour market, at any given time there are both unemployed persons and vacancies which can be filled by them, and usually takes time for those seeking work to find and fill these positions. Frictional unemployment can exist in a situation where there is no skill or location mismatch. Frictional unemployment also refers to an economically rational process of job search where people voluntarily remain unemployed while they seek out and weigh up suitable job vacancies. Frictional unemployment is relatively of short duration, which can be reduced even further by improving labour market information and placement services, so that the employer and the work-seekers can find each other sooner and more effectively (Barker,1999: 165).

2.1.5.3. Cyclical Unemployment

Unemployment that is attributed to economic contraction is called cyclical unemployment. The economy has the capacity to create jobs which increases economic growth. Therefore, an expanding economy typically has lower levels of unemployment. On the other hand, according to cyclical unemployment an economy that is in a recession faces higher levels of unemployment.When this happens there are more unemployed workers than job openings due to the breakdown of the economy. This type of unemployment is heavily concentrated on the activity in the economy. To understand this better take a look at our Business Cycles section. For instance, advances in technology and changes in market conditions often turn many skills obsolete; this typically increases the unemployment rate. For example, laborers who worked on cotton fields found their jobs obsolete with Eli Whitney's patenting of the cotton gin. Similarly, with the rise of computers, many jobs in manual book keeping have been replaced by highly efficient (ILO, 2009).

2.1.5.4. Seasonal unemployment

In a Free market the weekly earnings and hours worked by an individual display two types of variation over the course of the year. Regular variations occur in markets where the demand for labor and/or its supply follow a regular pattern of shifts from season to season. This is referred to as seasonal unemployment. Irregular variations are present in markets where the demand for and/or the supply of labor fluctuate within any particular season. This is extra-seasonal employment. Industries in which regular shifts in the demand for labor are present are called seasonal; others are called non-seasonal. Farming, construction, and tour­ism are seasonal; the industrial sector is non-seasonal. Assuming a fixed and upward sloping supply of labor to an industry in any given year, individuals who supply labor experience regular fluctuations in their wage and hours of work if the industry is seasonal; such fluctuations are usually absent if the industry is non seasonal. A further distinction is between time worked in tourism or in other industries, market time; and in time spent in self-employment, like farming, fishing, house- hold production or gardening, nonmarket time. Major activities included in nonmarket time include household production and operation, family care, human capital accumulation as in schooling, subsistence farming and gardening, as well as recreation such as noncommercial fishing and gardening. Both market time and nonmarket time may be seasonal; college courses, for example, may not be available in the summer. Some individuals work in tourism in the summer, agriculture in the winter, others in either area only. All workers who regularly leave a market occupation during part of the year are denoted seasonal workers; others are non-seasonal workers. Depending upon the reasons for the shift, three types of seasonal workers can be distinguish Those who do so because their time is more valuable to them in nonmarket occupations, like students and housewives, are called intra-seasonal workers. Those who shift from one market occupation to another in the same region are designated non migrant workers and those who shift from one market occupation (say tourism) to another in a different region (say farming) are referred to here as migrant workers (Panos,1988)

2.1.6. Theories of Unemployment

The theoretical issues of unemployment attempts to explain the causes and effects of unemployment in many nations. Economic literature provides many explanations for the determinants of unemployment. Some causes blame the economic systems, and others blame other factors such as foreign debt and population growth. Still, other theories shift the problem to external sources and shocks, or unpredictable events, and other argue that technology and labour market institutions are the causes of unemployment. In addition, other theories assumed that deficiency in aggregate spending and innovations are the essential factors for explaining the problem of unemployment.

2.1.6.1 Human capital theory

According to this theory, education is considered as an important asset for economic development as well as securing decent and productive job. Schultz (1961) noted that education plays a great and significant role in the economy of a nation. It increases the productivity and efficiency of people by increasing the level of cognitive stock of economically productive human capability which is a product of innate abilities and investment in human beings. He further illustrated that education increases the chances of employment in the labour market, allows people to reap pecuniary and non-pecuniary returns and gives them opportunities for job mobility, and leads to greater output for society and enhanced earnings for the individual worker. He furthermore, stated that higher education provides the skills needed to perform complex jobs, making people more productive, thus sustaining economic growth. People with the most human capital are said to be the most productive, and thus secure the best jobs and the highest salaries. Thus, education plays an important role in determining the employment status of an individualSchultz (1961).

2.1.6.2. Social Capital Theory

The necessity of social capital begins in the work of the James Coleman, Francis Fukuyama, Robert Putnam, and Pierre Bourdieu; 2011 as cited by (Aslefew, 2011). Whereas these four scholars vary in disciplinary base and emphasis, they contribute to a focus on feature of social relations, namely, values, norms, and networks or social capital and the role they play in social cohesion.

Community is central to theories of social capital in that norm, values, and networks produce and reproduce communities, are they geographical, face-to-face neighborhood communities, informational communities and networks, or civic communities of social or political engagement. Social capital is concerned with specific types of social bonds that sustain a sense of connection among individuals. Popular anxieties about a loss of community have entered social scientific discourse through the concept of social capital. This theory advances on the necessities of the social relation which help as the means to find a job. Granovetter (1973) noted that a close relation or social networks within the people are regular, expressively concentrated association with the colleagues, and other members of workers as cited by (Aslefew, 2011). The latest news will be easily dispatched and shared among other members and creates conducive condition for the accessibilities of the job. Social capital is concerned with specific types of social bonds that sustain a sense of connection among individuals. Popular anxieties about a loss of community have entered social scientific discourse through the concept of social capital. While there may be broad agreement about the specific elements of the social that are collectively called social capital, there are very important differences among these key theorists. Those who have weak social relation with others are marginalized and damaged with lack of in accessibilities of information which makes them to missed job opportunities in some degree, in addition poor social relation leads to friendless and discriminate them from the community(Aslefew, 2011).

(Bourdieu's, 2011) notion of social capital does not fit into this continuum, which, broadly speaking comes from a consensual, functionalist model of society. Bourdieu operates within a conflict model of society, and his emphasis is on how networks recreate unequal social relations. The consensual perspective tends to regard social networks as equally available to all. In the conflict perspective, all social groups have networks, but not all networks provide equal access to resources. Socially bounded and stratified networks reproduce those unequal social relationships.

2.1.6.3. The Job-Matching Theory

The concept of Job-matching depends on the labor market and the idea contains different multidisciplinary professional skills with respective experience levels. As (Jovanovic, 1979) pointed out positions that need skilled person are occupied by most educated adults as cited by (Aslefew, 2011). A mismatch between the skill sets of the unemployed and the needs of employers is the main reason behind structural unemployment. The mismatch comes about because the unemployed are unwilling or unable to change skills or to move to a location where their skills are in demand. As a result, it becomes very costly to match workers with jobs and unemployment is often prolonged.

For example, businesses in a certain area may require young people with advanced information technology skills. A young person living in this area but without these skills will have difficulty finding a job his/her skills are not matched to the demand. Down a similar line of reasoning, a young person with the required skills set but living in an area where these are not in demand because employers are looking for agricultural workers, will have an equally difficult time finding work or may become underemployed. An important trend in labor markets in more developed economies, influenced to a large extent by globalization, has been a steady shift in demand away from the less skilled toward the mores killed. This is the case however skills are defined, whether in terms of education, experience or job classification. The result of the changing composition of labor demand has led, and is leading, to a reduction in the number entry-level, unskilled jobs, resulting in a mismatch for young people with low education and skills levels. Cyclical unemployment can also influence skills mismatches Skills mismatches are generally caused by two factors. Firstly, at a general level, the school curriculum may not provide the skills employers are looking for. In most education systems, there is still a clear lack of practical and experiential learning as well as of teamwork learning. Experiential learning is very rarely used, as an effective way of gaining knowledge and experience, yet it is probably the most powerful way of learning entrepreneurship. Moreover teachers and university professors often have only limited experience in, and understanding of, small businesses and self-employment. So they are not adequately trained or educated to teach entrepreneurial skills young people. Secondly, the absence, or inaccuracy, of Labour Market Information (LMI), i.e. information on what skills are in demand and where jobs are, will lead to many young people making a choice of career that is not based on the realities of the labour market(Aslefew, 2011).

2.1.6.4. The Theory of Job Search

Stephen and Jackman formulated the theory of job search. For Stephen and Jackman (1991), a typical unemployed person looking for work is expected to pass three stages. At stage one; he/she collects information about job vacancies. Vacancies come with different pre-assigned wage and conditions. In stage two, he/she decides to apply for the vacancies that he/she learns of. The decision to apply for it depends on the expected value of getting a job or not. Lastly, he/she accepts the offer of any job for which he/she applied in getting it. The success of individual's application depends on his/ her personal characteristics. Thus, they concluded that individual factors and the degree of competition from other job seekers could affect the chance of finding a productive job,Stephenet. al.(1991).

2.2. Factors that influence Youth Unemployment

Youth unemployment is the outcome of different socio-economic and demographic factors at macro and micro level. The micro level factors are directly associated to individuals' demographic and socioeconomic attributes while the macro level factors are related to the national issues (Toit, 2003). This study emphasizes on assessing individuals' demographic and socioeconomic attributes that influence youth employment. These are broadly classified as demographic and socio-economic factors. The detail is presented as follows.

2.2.1. Demographic Factors of Youth Unemployment

2.2.1.1 Rural Urban Migration

The movement of young people is one of the causes for the high levels of urban youth unemployment problem in most developing countries (Raphael, 2005). Since young people view migration as an avenue to improve their status and learn new skills, they move in to urban areas for various reasons (Harris, 2010). Similarly, ILO (2007) and MOY (2004) noted that migration of young people in their twenties is very high in Africa. Moreover, they also state that youth often move to institutions for education and training, but many migrants move for employment related reasons followed by their families. In line with this, Okojie (2003) depicted that migration of youth has resulted in a concentration of youth in cities and towns where there are few jobs available in modern sector establishments.

In addition to this, Todaro (1994) and Mlatsheni and Rospabe (2002) state that rural to urban migration of young and educated people is the very root cause for the high and ever rising levels of urban unemployment. A study conducted by Anh et al (2005) and Yisak (2006) showed that youth having migration experience are more likely to be unemployed than other migrants. Confirming this, Nwuke (2002) noted that young migrants are highly unemployed in urban areas. He further stated that in a context where social relations are as crucial as qualifications, young urban migrants searching for a job face an uphill struggle of surviving, with limited social networks. Sarr (2000) also reaffirmed that youth migrants are three times more unemployed than other migrants in Africa. It might be possible to deduce that young migrants are more vulnerable to unemployment in urban areas.

2.2.1.2.Sex

According to Halleriid and Westberg (2006), being one of the demographic variables, sex reveals substantial differences between female and male with respect employment opportunity. Females are vulnerable both in short term and long term unemployment than males. ILO (2004a) also conforms the activity rate of young males have been much higher than that of young females due to the different opportunities society provide to males and females, and domestic activities for personal or household use. Strengthening this point, Mlatsheni(2002) and Rospabe (2009) found that lack of employment is more severe for females than for males as 63 percent of economically active females are unemployed whereas 53 percent of males remain without jobs in South Africa. They further noted that one of the reasons behind females‘ unemployment is that girls spend much time in doing domestic work than boys. This leads them to poor academic performance and sometimes withdrawal from education. It could be concluded that girls therefore end up with less education and limited skills, and thereby resulting in high number of unemployed females. In the same manner, differences between male and female with respect to employment has also been prevalent in Ethiopia. With this regard, Guracello and Rosati (2007) state that female youth across all ages are more likely to be unemployed and are much more likely to be jobless than male youth. Another research conducted by Berhanu et.al (2005) noted that unemployment rate among young female (20.24) was 38.7 percent while it was only 23.2 percent for young male in the same age category during the same year. Besides, the CSA (2010a) unemployment report also shows that out of 1,168,591 unemployed persons 41.2 percent were female youth.

2.2.2. Socio-Economic Factors of Youth Unemployment

2.2.2.1. Education

Education is one the basic factors of youth employment. The achievement of lower educational level reduces the chances of getting decent and productive jobs in the world of work In line with this, Salvador and Killinger (2008), WB (2009), and Morris (2006) noted that unemployment rate of less educated youth tends to be higher than the unemployment rate of more educated youth in developing countries because their skills and competencies may not correspond to the demand of the labour market, In other words, the chance of getting employment for more educated youth is higher as compared to lower educated youth since they had the required knowledge and skills, Similarly, Mlatsheni and Rospabe (2002) found that young people with secondary level education (from grade 8 to grade 12) do not have a better chance to get a job than people with no education. ILO (2004a) also confirms that young people with some education are vulnerable to unemployment due to the lack of knowledge and skills required by the labour market.

Accordingly, unemployment is higher for youth had lower educational level in Africa. With this respect, Okojie (2003) stated that unemployment in Africa concentrated among youth who have received some education, He further added that youth who had limited education lack the industrial and other skills demanded in the labour market, thereby making them unattractive to employers who prefer skilled and experienced workers. Confirming this idea, Haji (2007) and Anh et al (2005) found that youth who attain limited education are more prone to unemployment in the continent. In addition to this, they noted that, training in Africa remains largely unrelated to the labour market needs, which foster the existence of a degree of mismatch between the demand for and supply of education. In the same fashion, less educated youth has also been faced the challenge of being unemployed in Ethiopia. In this regard, Guracello and Rosati (2007) found that among youths, the less educated youth face more difficulties in finding employment in urban areas of the country.

2.2.2.2. Work Experience

According to ILO (2004), the lack of work experience reduces the chances of getting employment in the modern sectors of the economy. On the other hand, it also added that young people having work experience, something very much desired by most employers, increases the possibilities of getting employment. Similarly, a study conducted by Foot (1986) found that because of limited work experience and other personal characteristics, youth unemployment tends to be high. Moreover, Osterman (1980) noted that employers with desirable job characteristics preferred to hire persons who already had some experience in the labour market. This invariably excluded young entrant from the labour force. Anh et al (2005) and Hassen (2005) also illustrated that besides to insufficient work experience, poor work habits, unreliability, and lack of dedication to the job lead to the segmentation of young workers. They further noted that employers are usually hesitant to hire young people who have little or no practical work experience since the costs to retrain and/or upgrade skills of young workers are often too high. As a result, youths are suffering from the lack the work experience, so that they spend considerable time in looking for a job.

2.2.2.3. Household income

Household income is one of the socioeconomic factors that contribute to the problem of youth unemployment. ILO (2004b) indicated that unemployment rates among young people tend to decline as household income increases. Youths who reside in a better off family had higher chance of getting employment since their family tends to invest more in the education of their sons or daughters. Likewise, a research conducted by Anh et al (2005) and Rees and Gray (1982) found that family income serves as an important factor in determining the employment experience of Vietnamese youth. A family in which a young person lives is the strongest predictor of his or her future in the job market. On the other side, they added that youth who reside in low income earning family are less employed in the labour market.

Correspondingly, Morris (2006) showed that the significant effects of family economic status, paternal occupation, education and parental divorce are notable in affecting the employment status of youth. He further noted that a better income earning household had a number of opportunities, i.e. higher income can enable youth to have greater access to education, information and connections. This could facilitate easy access to employment opportunities available in the market. Also ILO (2010) reveals that young people who reside in low income household have higher likelihood of being unemployed than adults of being among the working poor. It also indicates an estimated l52 million young people were living in poor households (with per-capita expenditure below US$1.25 a day) in 2008, were unemployed. Strengthening this point, a study conducted by Echebiri (2005) depicts that unemployment has affected youths from a broad spectrum of socioeconomic groups, both the well and less well educated, although it has particularly stricken a substantial fraction of youths from low income backgrounds.

2.2.2.4. Mothers and Fathers Education

Social network and status in family background have too much influence on youth unemployment. If parents are unemployed, low education, live in poverty, are likely to replicate similar style to the youth people, in the same way. Anita (2012) explained that family background in education has its own impact on the supplementation of youth to the labor market and also they stated that the higher the parents are educated, the less number of firms visited, and large proportion of youth who have got job. (UNESCO, 2012) indicated that as a measure of social status, family education's is an important factor in determining employment status of youth. So, youth who had well educated parents could face less difficulty in getting jobs compared with those youth whose parents were less educated or illiterate.

Similarly, Schiefelbein and Farrell (1982) stated that family background in particular father education has an impact on the insertion of youth to the labor market. They also indicated that the higher the education of the father, the smaller the number of firms visited, and the higher the proportion of individuals who have found employment. Furthermore, Morris (2006) noted that as a measure of social status, father education's is an important factor in determining employment status of youth. Therefore, youth who had well educated father could face less challenge in finding jobs compared with those youth whose father were less educated or illiterate.

2.2.2.5. Job Preference

Instead of perceiving for rewarding employment, self or otherwise, the youths waited for the government to find employment for them (ILO, 2010). The Ethiopian government has these days eyed on creating much more job opportunities for a number of citizens thereby reducing youth mobility caused by poverty, through innovative policies that will create jobs and businesses for young people in micro and small enterprises, urban agriculture, agricultural undertakings both in rural urban areas (Xinhua, 2019). A study conducted by Echcbiri (2005) in Nigeria found that most young job seekers preferred employment in the private sector. They would like to work in banks, oil companies, manufacturing companies, major marketing companies, and so on. While a large proportion of youth also preferred to work in the public sectors. With this regard, Berhanu et al (2005) indicates wrong kinds of attitudes and job expectations on the part of youth is prevalent, including the preference for white collar jobs as opposed to agricultural and manual work. Moreover, they state that one of the reasons for wrong kinds of attitudes towards jobs is the inadequacy and excessively academic orientation of the educational systems of the country, and the result is still visible in the current situation. Therefore, job preference could be seen as a factor for youth unemployment (Asalfew, 2011).

2.2.2.6. Social Networks

Social capital is key properties to search employment. Social networks are vital instrument to find a job in urban areas with less expense and difficulty Social capital (Adams, 2008). Found that youth who use social networks in finding employment are successful. On the other hand Coleman, (1990) and Granovetter (1983) they also showed that young workers not utilizing personal networks may miss job opportunities available through personal networks. Similarly, Fernandez and Kelley (1995) also confirmed that youths with limited or deficient personal networks may lack knowledge of employment opportunities available in the state or regions. Consistently, Holzer,(1996) also discussed that the lack of labour information can be harmful to young people labour market outcomes, which are influenced by an individual's access to employment information via social networks. Toti, (2003) also noted that lack of labour market information and access to the main information networks in the labour market decreases the chance of getting employment.

2.3. Consequences of Youth Unemployment

The inadequate employment situation of youth has a number of socio-economic, political and moral consequences (Berhanu et.al, 2005; Toit, 2003). Some of the consequences of youth unemployment are as follows. Unemployment fosters drug addictions among youths: Unemployed young people are more likely to abuse illicit substances than are employed young people. According to UN (2003) report, unemployed youth are the main drug users in Sub Sahara Africa, which accounts 34 million young people representing 7.7 percent of the continent's youth population. The report also indicated that Cannabis sativa or marijuana is the main drugs consumed by youth in the region. Similarly, Curtain (2000) stated that in the continent, delinquency, crime and drug abuse are on the increase among unemployed youths. Other scholars Chigunta (2002) and Haji (2007) also confirmed that some of the unemployed youth have become drunkards; others are on drugs such as marijuana and mandrax. Therefore, unemployment fosters drug addictions among youth. Youth unemployment contributes to crime and violence: Youth unemployment also contributes for the prevalence of crime and violence in societies where employment opportunities are limited. In line with this, Okojie (2003) and Haji (2007) found that many unemployed youth run criminal enterprises engaged in violence, armed robbery, car snatching, illegal fuel sales, and illegal importation of arms. Some of which have reached alarming levels in several African cities, having names such as "Area Boys" in Nigeria and "Manchicha" in Uganda. Echebiri (2005) also noted that urban society is becoming increasingly criminalized, especially with the proliferation of youth gangs. He added that crime and violence have been increasing in many parts of Sub-Saharan Africa as a result of youth unemployment. Further, Chigunta (2002) states unemployed and disaffected youth appears to play a significant role in African conflict.

Unemployment results in psycho-social problem on youth: Unemployment is a stressful life event that makes people unhappy. Increases in the unemployment rate lower the happiness of everyone, particularly the unemployed (Bell and Banchflower, 2010). Consistent to this, Toit (2003) also found that depression experience is the consequences of unemployment. Moreover, Berhanu et.al (2005) state unemployment results social exclusion and a sense of hopelessness on youth.

Commercial sex work is common among young unemployed girls: Youth unemployment also facilitates the development of street youths. Likewise, Echebiri (2005) noted that unemployment has driven many young women and girls into sex work in Africa. Struggling to Support their families and provide care members of the household, they are often restricted in their opportunities for education and training. The lack of job opportunities and their disadvantageous social role make them more likely to end up as sex workers. Okojie (2003) also explained that lack of employment opportunities has contributed to increasing feminization of poverty, and also encouraged prostitution as a means of survival in several African towns and cities. Further, ILO (2005) stated that, in Ethiopia, young unemployed women are unwittingly drawn into prostitution.

2.4. Economic costs of unemployment

Unemployment affects economic development: Youth unemployment is challenging not only for those affected, but also for the economy as a whole. Salvador and Killinger (2008) found that unemployment among young people implies unutilized labour potential and thus has a negative impact on potential growth of the economy. Similarly, Berhanu et.al (2005) state that unemployment is the failure to make use of an important factor of production for fostering economic growth. On the other hand, the increase in criminality in a country as a consequence of youth unemployment causes losses in foreign direct investment. For example, foreign investors have cited crime as the biggest deterrent for investment (UNODC, 2003).

2.5. Empirical Evidences

There are a numbers of studies have looked at different aspects of the urban labour market in Ethiopia (Serneels, 2001; Bizuneh et al., 2001, Getinet, 2003; Duguma et. al.,2019;Esay, 2020;Tsegaw, 2019; Asalfew, 2011; Ahemedteyib, 2020;Tegegne, 2011;Asmare, 2014; Gebeyaw, 2011; Nganwa et al., 2015; Dejene et al., 2016 and Aynalem et.al,2016).

Some studies from Ethiopia indicate that the potential causes of unemployment in urban Ethiopia include increasing number of youth labor force, the rising internal migration, literacy rate, poor to modest macroeconomic performance, low level of job creation and low level of aggregate demand in the economy (Getinet, 2003; WB, 2007). Youth unemployment is the outcome of different socio-economic and demographic factors at macro and micro level. The micro level factors are directly associated to individuals‘demographic and socioeconomic attributes while the macro level factors are related to the national issues (Toit, 2003).

Ahemedteyib (2020),conducted the binary logistic regression to assess the determinants of youth unemployment at Adama city, East Shoa Zone, Oromia National Regional State, Ethiopia.Their result showed that among the demographic variables, marital status, work experience, social network, family income, Job preference, fathers education, and mothers education affects unemployment negatively where as sex, education and migration status of urban youths affects unemployment positively.

Duguma et. al. (2019) the regression results from a binary logit model estimation show that sex, educational level, marital status, skill match and access to credit use of youth are found to be the significant determinants to urban youth unemployment while family prosperity and market information were statistically insignificant to urban youth unemployment in the town.

Esay (2020), conducted the probit regression to investigate the determinants of youth unemployment in Hawassa City, Sidamma Regional state. Their result of the study thus concludes that education level of the parent (father), education level of the youth, credit facilities made available for the youth through microfinance and other financial institutions and a positive attitude towards a given job are statistically significant and quantitatively large determinants of youth unemployment in the study area.

Tsegaw (2019), conducted the logistic econometrics model regression to investigate the determinants of youth unemployment in Wolayta Sodo Town, South Nation Nationalities Regional state. From the result revealed that confidence, education status, access to information human related factor, institutional factors, socioeconomic and demographic factors were significantly affect the youth unemployment status in the study area . The result also indicates that the higher months spent on searching, the higher the probability of getting job. This shows the cost of job search is positively related with employment status of the youth, as cost of search increases the higher the probability of being employed. The result also indicates that educational level of the youth's household head has a negative and statistically significant effect on employment status of the youth at 10 percent level of significance. This finding is inconsistent with the general fact and the hypotheses of this study.

Asalfew (2011), the multivariate analysis showed that sex, migration, education, social network, job preferences and access to business advisory services significantly determine youth unemployment in DebreBirhan town. However, household income, father education, and marital status were found insignificantly related to youth unemployment.

According to Tegegne (2011), examined the association between socio-demographic variables and unemployment in Addis Ababa, the econometric analysis has confirmed that sex and age are statistically significant and have negative relationship, signifying the inherent problem of unemployment among women and the youth. Regarding migration status, in spite of the type of job, a migrant is more likely to be employed than a non­migrant. This result can be an indication of the obvious fact that there is unmet demand for domestic and casual labor in the city, a pull factor for the rural poor and marginalized youth, particularly women. Thus, given the existing push and pull factors from rural areas and the unmet labor demand in urban centers; the migrants' supply of labor would be mutually beneficial to both the urban as well as the rural communities.

Dejene et al., (2016), conducted the binary logistic regression to assess the determinants of youth unemployment at Ambo, Ethiopia. Their result showed that among the demographic variables, age of the respondents and migration status were significantly related to youth unemployment whereas marital status of the respondents was not significant. From the human capital variables included in the model, education and health status of the respondents were significantly related to youth unemployment, whereas participation in employment related trainings was not statistically significant. Among the economic determinants, household income, access to credit and saving services and work experience were significant. Access to job information and psycho-social factors were the two social capital variables that were significantly related to youth unemployment. As youths are more vulnerable to unemployment, efforts should be made by the government to provide credit and training so as to facilitate their entry into business and entrepreneurship. Migrants are the victims of unemployment in town. Therefore, the pushing factors of migrants should be identified to arrest the continuous drift of youth towards urban areas as this may worsen the unemployment situation in urban areas.

Asmare (2014) stated the major factors supposed to be affecting urban youth unemployment, particularly graduates from higher institutions. These were: lack of good governance (nepotism, corruption, bias and discrimination), lack of social networks, divergence between skills and the labor market and low quality educational policy and system.

According to Nganwa et al (2015), between 2006 and 2011, the prevalence of urban youth unemployment was high as compared to the total unemployment rate in Ethiopia. The study showed that place of residence (regions), gender, age, and marital status significantly affect the urban youth unemployment.

Aynalemet. al. (2016) examined the factors which determine urban youth unemployment in East Gojjam zone of Amhara Region, Based on the result of the logit model, seven of the explanatory variables were found significant determinants of urban youth unemployment; of which age, work experience, skill match, social network, and family prosperity affects unemployment negatively whereas education and migration status of urban youths affects unemployment positively. The finding of the study indicated that urban youths who attend higher education are more unemployed compared to illiterate at 10% level of significance. However primary and secondary education did not affect unemployment significantly. In addition, it is found that migrant urban youths are more likely to be unemployed compared to non-migrants.

2.6. Conceptual Framework

There are different factors that are responsible for youth unemployment. Hence, unemployment is a complex and dynamic socio-economic phenomenon. The following conceptual framework gives a brief illustration about factors that determine youth unemployment in case of socioeconomic and demographic determinants was identified.

Figure 2.1:Conceptual framework of urban youth employment

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CHAPTER THREE RESERCH DESIGN AND METHODOLOGY

3.1. Description of the Study Area

Hawassa is located in the Sidama Region(a sub-division of SNNPR, since its separation on 18 June 2020 from the SNNPRS) of the shores of lake Hawassa in the greatest rift valley; 273 km South of AdissAbeba via DebreZeite and 1,125 KM of North Of Nirobi. The city lays on the trans-African high way-4 an international road that starched from Cairo (Egypt) to Cape Town (S.Africa). Geographically the city lays between 7°3'N 38°28'Elatitude and 7°3'N 38°28'Elongitude and an elevation of 1,708 meters (5,604 ft) above sea level. Its name comes from a Sidamic word meaning "wide body of water". Hawassa City is bounded by Lake Hawassa in the west, Oromia Region in the North, Wendogenetwereda in the east and ShebedinoWoreda in the south (DoFED, 2020). Hawassa served as the capital of SidamaRegion.The city administration has an area of 157.2 sq.kms, divided into 8 sub cities and 32 kebeles, these eight sub cities are Hayke dare, Menehariya, Tabore, Misrak, BahileAdarash, Addis ketema, Hawela-Tula and Mehal Sub city (DoFE, 2020).

The 2020 Central Statistical Authority population estimate of the area shows the total population to be 256,591(120,724 male & 144,867 female) with unemployment rate of26.1 made the town one of the highest economically active unemployment in the region (26.7 rate).While 61% are living in the city of Hawassa, the rest of the population of this city is living in surrounding rural kebeles. A total of 61,279 households were counted in this zone, which results in an average of 4.22 persons to a household, and 57,469 housing units. In 2016, a new Industrial Park was built in Hawassa to accommodate 60,000 jobs at a 1.3 km2(0.50 sq mi) site.The five largest ethnic groups reported in Hawassawere the Amhara (17.43%), the Sidama (48.67%), the Wolayta (13.9%), the Oromo (5.21%), and the Gurage (2.33%); all other ethnic groups made up 12.46% of the population. Amharic is spoken as a first language by 47.97% of the inhabitants, 21.01% speak Sidamo, 9.58% speak Wolayta, and 2.07% Oromiffa; the remaining 9.37% spoke all other primary languages reported(DoFE, 2020).

3.1.1 Economically Active and Not active Population and Activity Rate of the study area

As far as the economic activity status of major Hawassa town population is concerned, 59.0percent of the populations were economically active while the remaining 41.0 percent were found to be the population not economically active. In terms of sex, the activity rate of male (68.0 per cent) was higher by 17.5 per cent than female (50.5 per cent). The size of economically active and not active population has its own implication on the development of the local as well as the national economy of a region. The high activity rate of a population implies the prevalence of huge potential of the human resource or human labour that foster sustainable economic development. On the other hand, the low economic activity rate also implies the lack of adequate and trained human labour or the existence of large number of economically non-productive population which has no contribution for development at the time. Hence, the economic activity rate of the Hawassa can be viewed from these perspectives(CSA January Survey, 2020).

Table 3.1: Populations of Hawassa Towns Aged Ten Years and Above by Activity Status and Activity Rate and Sex during the Last Seven Days (Current Status Approach)- January 2020 survey

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Source: CSA, 2020 Survey

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3.2. Research Design

This study applied both qualitative and quantitative research approaches. Because mixed method enables crosschecked against the results of using a method associated with another strategy; can be offset by including a qualitative or a quantitative method that has its own strengths; and that the gaps left by one method (for example, a quantitative one) can be filled by another (for example, a qualitative one). Both qualitative and quantitative approaches and tackle disadvantages of both designsin order to strengthen salience of the conclusion and final recommendations.The quantitative approach involves the generation of data in quantitative form. The research design was cross sectional and the sample size selected simple random techniques. The target population is consist of youth age between (15-29) years at the time of survey is going to be studied (questionnaires, observations, semi- structurally interviewed and focused group discussion) to determine its characteristics.

3.3. Data Sourceand Type

The study used quantitative data collected through individual interviews and structured questionnaires. The questionnaires were designed and formulated to collect information about demographic and socio-economic correlates of youth unemployment from sampled youth. Qualitative data were also collected through interviews, focus group discussions (FGDs). The FGDs were administered with those youths who unemployed at the time of the survey and parents of youth who unemployed at the time of the survey. It was carried out to together information in order to validate the findings obtained through structured questionnaires. In addition, secondary data obtained from records of administrative offices, publications, journals, books and other sources relevant to this study were also used to enrich the investigation.

3.5. Sample Design and Procedures

Multi-stage sampling designs were engaged in order to select respondents who live in the study area during the reference period.

Stage 1: The primary sampling units were Sub-city. Three sub-city namely Adissketema, Mehal and Misrak out of the eighth sub-city in the town were selected using purposive sampling techniques due to greatest numbers of urban job seekers are stayed there.
Stage 2: The secondary sampling units were enumeration areas. Enumeration areas were selected using simple random sampling techniques. Two enumeration areas from each the three sub-citys, a total of six enumeration areas/kebele/ were selected for the study. The respondents from six kebele were selected randomly from the list of youth in the kebele(One stop service center/OSS - Employment service center) by using simple random sampling techniques
Stage 3: Using fresh list of youth in each enumeration areas as a sampling frame, samples were selected using systematic random sampling techniques for the study.

3.6. Sample Size Determination

The populations of the study were unemployed youth living in Hawassa. Since the focus of the study was on unemployed youth, the sample frame from which the actual samples were drawn from youth whose age ranges (15-29), whose level of education ranges from non- educated/illiterate to university graduate (undergraduate). To get the total number of unemployed youth, the researcher contacted the Hawassa Administration Finance and economic development department. The Department's officials provided me published data of total number of unemployed youth, which living in the city at 2019/20. According to the department there was 131,889 youths. Thus, the researcher selects 437 respondent based on Yamane Taro statistical formula.

The Yamane Taro (1967) formula is:

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Where n is the representative sample size, N is the total youth population of the town which is found to be the total 131,889 and e is the desired level of precision. For a 95% confidence level, the researchers have selected the representative sample of:

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Due to the unwillingness and not return the questioner, the calculated sample size was adjusted by design effect factor (DEF); which is the ratio of actual variance under the sampling method actually used to the variance computed under the (Ariawan, 2005) For the sake of this study, DEF preferred.

Therefore, n adjusted = 398 x DEFF = 398 x 2 = 796

The overall sample size of the survey was also increased by 5% for non-response which is 796 x 5% = 39.

The total sample size of the study would be, 398 + 39 = 437

Thus, 437 youth were selected by simple random sampling methods, representing variations in gender, levels of education (illiterate, primary education, general secondary school completion, TVET certificate holders, diploma holders, and university graduates), age composition and duration of unemployment. Unemployed youth from fresh job seeker to individuals who experienced longer year duration of unemployment was included. The overall Sampling procedure was schematically presented in the next Figure.

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Figure 3.2: Graphic arrangement of sampling procedure

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Note: EA- Enumeration Area, HH-Households, SS- Sample selected, EY- Eligible youth

3.7. Method of Data Collection

3.7.1. Primary Data Collection

Primary data were collected on first hand data sources from sample respondents. The primary data were both quantitative and qualitative data in nature. The researcher collected the primary data at the time of field survey. Questionnaires, interviews and FGDs were the most important methods used to collect the primary data.

One set of questionnaire, containing both open-ended and close- ended types were designed and administered to the selected respondent. Initially the questionnaires were prepared in English but it was translated in to Amharic due to the local language to make the questions simple, clear, and understandable to respondents. Checklists also developed and used to guide the interview and FGDs. Two FGDs have 5 participants from each group conducted and both groups are parents and youth unemployed from both sex attend.

3.7.2. Secondary Data Collection

The secondary data were gathered from official statistical resources like BOFED, CSA, publications and municipal documents. Additional secondary data and information were used from other journals, project reports, internet sources, research findings of various scholars on the topic under investigation, and other publications produced on youth unemployment in Ethiopia were consulted and referred.

3.8. Method of Data Analysis

3.8.1. DescriptiveAnalysis

Descriptive and econometric analysis has been employed to meet the main objective of the study. In the case of descriptive analysis graphs, tables, and chi-square tests have been employed.

3.8.2 Econometrics analysis

In the case of econometric analysis the binary logit model has been applied to identify the major determinants of urban youth unemployment. As many econometric and related literature recommended, specifying the association between a dichotomous dependent variable and a set of relevant explanatory variables, binary outcomes models are recommended to be appropriate (Wooldridge, 2001). In the analysis, diagnostic tests were used to be sure that the functional form of the model was appropriate.

3.8.2.1 Model Specification

Descriptive and econometric analysis was employed to meet the main objective of the study. In the case of descriptive analysis graphs, tables, t-tests and chi-square tests have been employed. While the econometric analysis was applied the binary logit model to identify the major determinants of urban youth unemployment.

Econometrics model Specification

Unemployment status of urban youths: dependent variable of the model that is dichotomies or dummy variable that take value 0 = if urban youth is employed and 1= if urban youth is unemployed. The appropriate econometric technique deal with such type of data is using binary logit and probit models and the most popular statistical techniques were used to analysis the probability of a dichotomous outcome with a set of explanatory variables. Binary logistic regression model were used to identify determinants of urban youth unemployment. It is a special type of logistic regression model which is used to describe the relationship between one or more independent variables and a binary outcome variable that has only two possible values. Logistic regression is used in a wide range of applications leading to categorical dependent data analysis (Agresti, 2002).

Gujarati (2004) the logistic model could be written in terms of the odds ratio and log of odds ratio, which enable one to understand the interpretation of the coefficients. In this study, the odds ratio is the ratio of the probability that the youth were unemployed (Pi) to the probability that he/she were employed (1-P i ).

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Since, Zi=(a+PiXt) the above formula can be rewrite as shown below for easily understanding.

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Therefore,

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Taking the natural logarithm of equation (4)

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Where,

Yi is our dependent variable either the youth is employed or not in the labor market. K: the number of explanatory variables included in the model

Xi: are vectors of all explanatory variables

Pi: the coefficient or the parameter to be estimated in the model

Pi: is the probability that the youth is employed in the labor market

1-Pi: is the probability of failure or the probability that the youth is unemployed

Ui: is the disturbance (error) term showing the effect of other variables (other than the included variables) on our dependent variable.

3.8.2.2 Diagnostic Test

Regression diagnostics are used to detect problems with the model and suggest improvements. Multi-collinearity, Test Goodness-of-Fit Test and Model Specification Tests were use in this study to detect problems.

3.8.2.2.1. Multicollinearity Tests

Multicollinearity is a question of degree and not of kind. The meaningful distinction is not between the presence and the absence of multicollinearity, but between its various degrees. Since multicollinearity refers to the condition of the explanatory variables that are assumed to be non-stochastic, it is a feature of the sample and not of the population. Multicollinearity is essentially a sample phenomenon, arising out of the largely non experimental datacollected in most social sciences; we do not have one unique method of detecting it or measuring its strength. We now consider some of thumb rules high R2 but few significant t ratios. As noted, this is "classic" symptom of multicollinearity. If R2 is high, say, in excess of 0.8, the F test in most cases will reject the hypothesis that the partial slope coefficients are simultaneously equal to zero. Multicollinearity in logistic regression is a result of strong inter-relation among the independent variables (Montgomery, Peck, Garson, 1992, 2009).To evaluate multicollinearity effect in the model, bi-variate correlation analysis, Variance Inflation Factor (VIF) and tolerance was used.

Kendall's tau bi-variate correlation is one of the statistical techniques used to detect inter­correlation between explanatory variables. Based on the values of r, the existence of multicollinearity is known. The result of bi-variate correlation analysis shows that there is no strong association between the explanatory variables.Besides, the effect of multicollnearity can also be tested by using Variance Inflation Factor (VIF) and Tolerance. Tolerance is 1-R2 (coefficient of determination) for the regression variable on all other independent variable, ignoring the dependent variable (Garson, 2009).

Some author uses VIF as indicator of multicollinearity. The higher the value of VIF, the more collinear the variable. As a rule of thumb, if the VIF of a variable exceeds 10, which will happen if R2 exceeds 0.9 that variable is said to be highly collinear. in addition another testing method of multicollinearity is Tolerance it uses as a measure of multicollinearity in view of its intimate connection with VIF, the closer is the tolerance, to zero the greater the degree of collinearity of that variable with the other regressor, on the other hand the closer tolerance, is to 1the greater the evidence is that one regressor is not collinear with other regressors.

The higher the inter correlation of predictor variables, the Tolerance estimate approach to 0 (zero); when the inter correlation gets lower, the estimate approach to 1 (one). VIF is the reciprocal of Tolerance (1/ 1- R2).

3.8.2.2.2. Goodness-of-Fit Test

Different possible ways of assessing goodness of fit the model to examine how likely the sample results are, given the parameter estimates. Check the overall fit of the model to the data. This is testing: Ho: the hypothesized model fits the data. HI: not Ho. The Test Statistic is based on -2LL.To test if the inclusion of independent variables significantly improves the model. This is testing : HO :P=O and HI :p:t:O. The Test Statistic is based on Deviance. D=- 2(LLo-LLc), which will chi-square distribution with df=k-I Wald Statistic can also be used. The techniques that we are used to investigate the goodness of fit of a model are Hosmer and Lemeshow test.

Concerning this technique of test it is used to accept or reject the alternative hypothesis that the model effectively defines the data. In this regard if the significance level of the test is less than 0.05, it implies that the alternative hypothesis is rejected and the null hypothesis which states the insufficiency of the model to define the data is accepted. Accordingly, the alternative hypothesis that describes the model will be tolerable to describe the data will accepted.

During the utilization of binary logistic regression in this study the dependent variable 'employment status' will be coded as 1 if the respondents were unemployed and otherwise a value of 0 if the respondents were employed. In the application of binary logistic regression enter method was used and sets of explanatory variables which are found significant in the bi-variate analysis: namely sex, migration status of the respondents, educational level, mothers' education, job preference, household income, social network density, father education and marital status were entered in to the model.

3.9.4. Description of Variables

Both the dependent and the independent variables were selected based on available similar studies. The independent variables were thought to be determining factors of the response variable, that is, unemployment.

3.9.4.1 The Dependent Variable

The response variable to this study is employment status of youth in Hawassa. For the purpose of this study, the response variable, “employment status” is dichotomized as “unemployed” and “employed”. Therefore, the outcome for the ith individual is represented by a random variable Yi with two possible values (unemployed and employed).

3.9.4.2 Independent Variables

Based on the theoretical background and empirical results of different studies on urban youth unemployment carried out in different countries including Ethiopia, the following variables are hypothesized to influence youth unemployment status of urban dwellers in the study area.

3.9.4.2.1 Sex

In the list of independent variables one of the variable which was taken in the model was sex of a respondent and it was grouped as (2) male (1) female and in the model male was taken as a reference category.

3.9.4.2.2 Migration status

The other factor which also projected to persuade the employment status of a respondent was migration status and was classified as (1) migrant (2) non-migrant. In this study non­migrant resident were taken as a reference category in regression model analysis.

3.9.4.2.3 Educational status

Educational status is the key factor that brings influence in the determination of youth employment status. As the curriculum of education system of the country reveals, in this study the respondents were classified in to four groups namely (0) illiterate, (1) primary education (1-8), (2) secondary education (9-12), (3) Certificate and Diploma and (4) higher education which includes university degree and above.For this study purpose university degree and above has taken as a reference category in the model.

3.9.4.2.4 Job Preference

This study focused on how the option of job accessibility in the labor market affects the employment status of the respondents in their life situation. During the analysis it was grouped as (1) self-employment, (2) paid private, (3) paid government, (4) any available jobs in the labor market and (5) others. For this study in the model selection of any available jobs in the labour market was considered as a reference category.

3.9.4.2.5 Household income

In human being life direct or indirect income is mandatory to survive on this challenge full world mainly at house level incomes were classified in either to in cash and in kind this income might begotten on regular or irregular basis, but for this study purpose the income that a house hold earned was on monthly basis and it includes governmental and non­governmental paid employment, allowance, self-employment, pension and rents from plant asset. In this regard house hold income is also taken as a variable that affects employment status of the respondents.

3.9.4.2.6 Social Network Density

In this digital age period the social network density is the key factor for the chasing of the wanted things, mainly for the sake of this study it helps for the sharing of information and idea exchange to search for the accessibility jobs in the labor market. So then having too tied social network helps to get information and share idea because he/she is densely populated among the people so social network density is having relation with people to communicate each other. During the analysis it was grouped as (1) self-employment, (2) paid private, (3) paid government, (4) any available jobs in the labor market and (5) others. For this study in the model selection of any available jobs in the labour market was considered as a reference category.

Social network density can also affect the employment status of the respondents; it was regarded in the model being classified as (0) no social networks, (1) has social network. For the analysis social network was taken in the model as reference category use.

3.9.4.2.7 Mather's Education

Educated families are the key determinants for their children's future life, mothers educational status is taken as the factor for the respondents during survey period mainly of educated mothers. The educational level of mother's was likely affects the employment status of the respondents, and categorized into (1) literate (0) uneducated (illiterate). In the regression model illiterate was taken as a reference category.

3.9.4.2.8 Father's Education

In most cases educated families are the designer of their children's future life in this regard the Contribution of educated father is very crucial. Highly educated father's refers to father's educational status in this study during the survey period and it is predicted to affect the employment status of a respondents where categorized in to (1) literate (0) uneducated (illiterate). In the regression model uneducated was taken as a reference category.

3.9.4.2.9 Marital Status

Marriage has its own social value in the formation of family if it is managed properly and the process undergoes in the right age and economic status. For the purpose of this study marital status taken as a variable that affects the employment status of respondents. It was grouped in to four namely (1) single, (2) Married, (3) Divorced and (4) Widowed: in the model single taken as a reference category.

3.9.4.2.10 Work Experience

During the survey period the respondents were asked if they are involved in different income generating activities earlier to identify whether they were experienced or not on thespot of data collection time. The variable was grouped as (0) no work experience and (1) had work experience. In the model no work Experience had taken as a reference category.

Table 3.2: List of the names, descriptions and codes of the independent variable

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CHAPTER FOUR RESULTS AND DISCUSSION

This chapter focuses on results obtained from the data collection and analysis carried out using SPSS. The results are displayed in the following order: percentage, univariate analysis, bivariate analysis and multivariate analyses.

4.1.Descriptive Analysis

During data collection period by following the rule and procedure of surveying system the necessary variables which were mentioned earlier in determining the youth employment status were captured and made for the analysis and interpretation purpose. When data collection were under gone the necessary issues on socio-economic and demographic characteristics were extensively accessed for the manipulation of findings to create clarity on the understanding of the outcome of the study on demographic and socio-economic determinants of youth unemployment.

The whole condition and characteristics which were comprised the demographic and socio economic summary of respondents those interviewed during the survey in the study area namely migration status, age, sex, marital status, education, work experience, social network density, mother education, household income, job preference and father education were deeply discussed below by using tables and figures.

4.1.1 Demographic Characteristics of Respondents

4.1.1.1. Sex of Respondents

One of the variables used to discuss the demographic characteristics of the respondents is gender. As presented in Table 4.1 the majority of the respondents are male in their sex. Of the total responses, 56.1 percent were male, whereas females comprise only 43.9%. There were more male respondent in the study than female because of most of the female respondent do not cooperate to accept the questioner at the time of data gathering. The male respondents were found more active in this study.

Table 4.1:Distribution of Respondents by gender, Hawassa Town, November 2020

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Source: Survey data, 2020

4.1.1.2. Age of Respondents

The age distribution of respondents included in the survey is presented in table 4.2. The highest proportion of respondents were found in the age group 20-24 years (52.4%), 25-29 years had response of (26.32%) whereas age group between 15-19 years had response of (21.28%). The age group from 20-24 years had the majority response because most of youths completed their education in between this age group and entering the labor market. So thus, as a job seeker they come to look vacancies and they were easily available during gathering of information.

Table 4.2: Age of the respondents, Hawassa Town, November 2020

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Source: Survey data, 2020

4.1.1.3. Marital Status of Respondents

Marital status is another factor that included in the analysis of the data. The result shows that 281(64.3 percent) were single and 156(35.7 percent) were marriedas shown in the fig.4.3below. The majority of the respondent found in this study was single. In addition to closed ended question the researcher asked an open ended question, the reason why they were not married. Most of the respondent indicated that being unemployed means being economically unwell. Thus with this situation it's too difficult to think about marriage.

Table 4.3: Marital status of the respondents, Hawassa Town, November 2020

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Source: Survey data, 2020

4.1.1.4. Migration Status of the Respondents

During the data collection respondents were asked regarding their migration Status. The response shows that 51.7 percent of the respondents were migrants and 48.3 percent were non-migrants. This data show us there is rural-urban and urban-urban migration of youth from different areas of the country for searching of better job. Beside migration most youths that are not resident but completed their education in this city most probably don't want to return to their place of residence after completion of their education rather they stayed and searching for an available jobs .

Table 4.4: Migration status of the respondents, Hawassa Town, November 2020

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Source: survey data Hawassa, 2020

4.1.2. Socio-Economic Profile of Respondents

4.1.2.1. Educational level of Respondents

In this study educational level was taken as independent variable to analyze and interpret the background of respondent's and socio-economic status. The data on the highest educational level of respondents illustrated that the higher proportion (30.9 percent) was secondary education and (21.5 percent) of the respondents higher education university degree and above, respectively. And 19.2 percent of the respondents attained diploma and a small proportion (13.7 percent) were no education (illiterate) (Table 4.5).

Table 4.5: Frequency distribution of Educational level of Respondents

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Source: Survey data Hawassa, 2020

4.1.2.2. Job Preferences of Respondents

Data was also collected about the type of job during the survey period. The respondents were asked the category of job that they are willing to involve in the labour market. The data regarding job preference of respondents shows that 19.5 percent preferred any available work in any organization, 67 percent preferred self-employment, 2.3 percent of the respondents preferred Paid private and 8.2 percent of the respondents preferred Paid government job and 3.0 percent preferred other unexpressed jobs (Table 4.6).

Table 4.6: Frequency distribution of Job Preferences of Respondents of Respondents

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Source: Survey data Hawassa 2020

4.1.2.3.Social Network Density of Respondents

Regarding the social link and network density of the respondents' the survey was undergone and data was gathered. As in Table 4.4, shows the evidence of the collected data express from the total interviewed respondent 53.5 percent of the defendant had no social network density, 46.5 percent had social network (Table 4.4).

Table 4.7: Social Network Density of Respondents

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Source: Survey data Hawassa2020

4.1.2.4. Work Experience of Respondents

The respondents were also asked whether they had been involved in any productive work or not prior to the survey date. The collected data shows that 60.6 percent of the respondents had work experience and 39.4 percent of the respondents had no work experience at the survey period (Table 4.5).

Table 4.8: Work experience of Respondents

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Source: Survey data Hawassa2020

4.1.2.5. Business Advisory Service

Business Advisory service status is another socio-economic characteristic of respondents. According to the collected data, 68.4 percent or the respondents did not get any kind of business advisory service, while 33.2 percent receives advisory services at least once and above previous to survey period(Table 4.9).

Table 4.9: Business Advisory service of Respondents

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4.1.2.6. Mothers Education Status of the Respondent

The respondents were asked about the educational level of their mothers at the time of the survey. According to the collected data, 73 percent of the respondents answered that their mothers can read and write, while 27 percent of the respondents were replied that their mothers were illiterate prior to survey period as the (Table 4.7).

Table 4.10: Frequency distribution of Mothers Education Status of the respondent

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Source: Survey data Hawassa,2020

4.1.2.7. Father's Educational Status of the Respondents

Father's educational status was regarded as a variable that determine socio-economic profile of the respondents. During the survey period the respondents were asked about their father's educational status, as a result 17.6 percent respondents' fathers were illiterate and 82.4 percent respondents' fathers were knowledgeable (Table 4.8).

Table 4.11: Frequency distribution of Mothers Education Status of the respondent

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Source: Survey data Hawassa,2020

4.1.2.8. Income Status of Household

The respondents were asked their household's income per month during the survey time, so the following information was gained by considering their household income (Table 4.9).

Table4.12: Frequency distribution of Income Status of household of the respondent

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Source: Survey data Hawassa,2020

4.1.2.9. Employment Status of Respondents

During the data collection respondents were specifically requested about their employment status earlier to the survey time. At the survey time the maximum number of the respondents was employed and from the total sample size of 437 interviewed respondents 39.4 percent were unemployed and 60.6 percent of the respondents were unemployed at the time of data collection period (Table 4.10).

Table 4.13: Frequency distribution of employment Status of household of the respondent

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4.1.3. Differentials and Determinants of Youth Unemployment

There are different approaches of assessing the association between two variables. Pearson Chisquare test is one way for investigative a bivariate relationship. It measures the degree of association between a given independent variable and the dependent variable keeping the effect of the other variable constant (Montgomery and Peck, 1992). For all demographic and socioeconomic predictor variables such as sex, migration status, marital status, education, accuses to social network density, work experience, household income, job preference, business advisory services, and father education taking one a time, a test of association was carried out using the chi square test.

The chi-square of independent was made to know whether or not there was a significance association between the independent variables challenges of youth unemployment and a set of explanatory variables with a significant level (p- value). When p- value is < 0.05 at, there is a significant association between each of independent variable with dependent variable.

4.1.4. Bi-Variate Analyses (Differentials of Youth Unemployment)

4.1.4.1. Demographic Factors Associated with Youth Unemployment

Youth unemployment by gender: The association between sex and youth employment status shows that among 192 females included in the sample, 56.25 percent were unemployed where as40.41 percent of males among 245 total male accounted were unemployed (Table 4.13). This confirms that female unemployment is more seeking a work than male unemployment. The Chi -square test specified a statistically significant relationship between sex and employment status (P <.0.001).

Youth unemployment by migration status: Migration status was taken as one variable and the respondents were requested about their migration status at the time of the data collection period. Depending on their response, the variation of youth employment status was evaluated. As (Table 4.13) below shows, non-migrant youths exhibited a higher percentage of unemployment in the town compared to migrant (56.4 percent Vs 38.94 percent). The difference was statistically significant (P< 0.000).

Youth unemployment by marital status: As far as the relationship between marital status and youth employment status is concerned, the percentage of unemployment was higher for married youths during the survey time (58.33 percent) than unmarried youth (41.3 percent) as (Table 4.13) shows. The statistical test of association was significant (P < 0. 01).

Table 4.14: Chi-Square test result of the association between Youth Employment status and

Demographic Variables, Hawassa 2020

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4.1.4.2.Socio-Economic Differentials of Youth Unemployment

Youth Unemployment by Educational Status: Education plays a vital role for employment. As shown in (Table 4.14) that the association between educational level of youth employment status that shows unemployment was greater for those respondents who were no education (96.67 percent), and those having primary educational grade 1-8 also (67.19 percent), secondary education Grade 9-10+3 (57.04 percent) and diploma (31.95percent), and so those who have Higher education university degree and above (3.19 percent). In general, as the educational level of youth increased, youth unemployment decreased. The Pearson chi-square test confirmed that the association was statistically significant (P< 000).

Youth Unemployment by Mothers' education: The association between youth employment status and their mothers' educational level was found to be statistically significant. As shown in (Table 4.14) that the association between mother educational level of youth employment status that shows unemployment was greater for those respondents who were no education (57.27 percent), and those having primary educational grade 1-8 also (47.62 percent), secondary education Grade 9-10+3 (50.65 percent) and diploma (37.5 percent), and so those who have Higher education university degree and above (34.62 percent). In general, as the mother educational level of youth increased, youth unemployment decreased. The Pearson chi-square test confirmed that the association was statistically significant (P< 028).

Youth Unemployment by Fathers' education: Insofar as the association between respondents youth employment status and their fathers educational status is concerned, the percentage of unemployment was greater for those respondents who were no education (68.18 percent), and those having primary educational grade 1-8 also (50.96 percent), secondary education Grade 9-10+3 (47.19 percent) and diploma (35.62 percent), and so those who have Higher education university degree and above (39.19 percent). In general, as the mother educational level of youth increased, youth unemployment decreased. The Pearson chi-square test confirmed that the association was statistically significant (P< 006).

Employment Status df

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Source: Own computation Hawassa, 2020

TVET training: Insofar as the association between respondents youth employment status and their TVET training is concerned, the percentage of unemployment was higher (52.68 percent) among those respondents those were no TVET training than those respondents whose trained TVET (41.78 percent). The test of association was significant (P <0.023).

Business Advisory Service: In so far as the association between respondents youth employment status and their Business advisory service is concerned, the percentage of unemployment was higher (54.11 percent) among those respondents those had no business advisory service taken than those had respondents whose took business advisory service (33.79 percent). The test of association was significant (P <0.000).

Youth Unemployment by Job Preferences: In another case Job preference has also another socio-economic determinant which was related to youth employment status to undergo study on it in this research. As enlightened in different literature review (Okojie, 2003; Haji, 2007), a significant amount of young people wish to work in the formal sectors. In this regard, as the table below explains (61.11 percent) of the unemployed respondents preferred to work in any available job, whereas(20.81 percent) of the unemployed respondents preferred to work in the formal sectors(government and private institutions). The chi-square test of association result indicated that the existence of a statistically significant association between job preference and youth employment status (P <0.000).

Youth Unemployment by Social Network Density: Another variable which was considered in this study was Social network density of a respondent and taken as one of the social capitals associated to youth employment status. The table given below explains briefly that, the percentage of unemployment was higher (57.69 percent) among those respondents those had no social networks when compared with those had strong social relation (35.47 percent) during the survey. The bivariate analysis publicized that there was the existence of relationship between social network density and youth employment status (P = 000).

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Source: Own computation Hawassa, 2020

Age of the Respondent: The average year of the total sample is 25.99 years, of which the unemployment average year were 26.82 and the standard deviation were 3.13. The t-test of association result indicated that the existence of a statistically significant association between job preference and age of respondent (P <0.000).

Size of the Household: The average size of the total household is 5.55 and the standard deviations were 2.94, of which the unemployment average household size were 5.94 and the standard deviation were 2.46. The unemployed average size were grater from employed average size(5.2) and the total average size(5.5). The t-test of association result indicated that the existence of a statistically significant association between job preference and household size of respondent (P <0.009).

Work Experience: The relationship between youth employment status and work experience was considered as one of the socio-economic determinants of youth employment status of the respondents during the survey period. As far as the relationship between respondents youth employment status and their work experience is concerned, the number of unemployment was higher (67.6 percent) among those respondents who had no work experience when compared with those had work experience (38.2 percent) during the survey. The chi-square test of association was significant (P < 000).

Household Income: When the income status of the household increases the tendency of being unemployed decreases because chance of attending further education and getting different training on other income generating activities would be increased and as a result unemployment declines. Concerning household income and youth employment status, the Chi-square analysis revealed that statistically significant relation was found between the two dependent and independent variables. The unemployed youth lived in a household earning an average 4,570.55. In addition as the chi-square test revealed significant association between household income and youth employment status at (P < 0.00).

Frequency of Contact for Job Information: Another variable which was considered in this study was frequency of contact for job information of a respondent and taken as one of the social capitals associated to youth employment status. As table 4.16 explains briefly that, the average frequency of contact is 7.19 and the standard deviations were 8.1, of which the unemployment average frequency of contact were 4.33 and the standard deviation were 5.35. The unemployed average frequency of contact was less from employed average size (9.77) and the total average size (7.19). The t-test of association result indicated that the existence of a statistically significant association between job preference and household size of respondent (P <0.000).

Table 4.17: Mean Comparison of Continuous Variables

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Source: Own computation Hawassa, 2020

4.2 Econometrics Analysis

4.2.1. Determinants of Youth Unemployment (Logistic Regression Analysis)

In this section attempts have been made in explaining the main demographic and socio­economic determinants of urban youth unemployment. As mentioned earlier, Logit model was selected to identify the determinants of Unemployment in the study area. The estimated logit model is presented below in table 4.19, in which the dependent variable being unemployed status regressed on different demographic and socioeconomic variables which are expected to affect unemployment in the study area.

Table 4.18: Results of Binary Logistic Regression Model

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*** and ** indicates at 1%, 5 % significance level respectively Source: Own computation Hawassa, 2020

A negative sign in column labeled “Coefficients” indicates an inverse relationship of explanatory variable with the log odds of the dependent variable. In contrast a positive coefficient column labeled “Coefficients” indicates a positive relationship to the log odds of the dependent variable.Based on the above table, age of respondent and household size has an inverse relationship with the dependent variable, employment status of youth.

According to Table 4.19,8 of the explanatory variables were found significant determinants of urban youth unemployment; of which, age of respondent affects unemployment negatively whereas educational level of respondent, work experience, business advisory service, job preference, social network, frequency of contact and family income of urban youths affects unemployment positively. Thus, the estimated model is given by:

Abbildung in dieser Leseprobe nicht enthalten

Where

X 1 ,...,X 8: were the predictor variablesage, education status of the respondents, work experience,Business development service, job preference, social network density, frequency of contact of the respondentsand income status of the household respectively.

P indicated: the likelihood of the event being unemployed coded with 1 and being employed coded with 0.The regression coefficient composed of with their sign shows the degree and direction of the consequence in the log-odds, being the category of status of response variable for a unit of increase in the predictor variable.

A more appealing way to interpret the regression coefficient in logistic model is using odds ratio. The odds ratio indicate the effect of each explanatory variable directly on the odds of being unemployed rather than on log (odds). Estimates of odds greater than 1.0 indicate that the risk of unemployment is greater than that for the reference category. Estimates less than 1.0 indicate that the risk of unemployment is less than that for the reference category of each variable. So, the final model presented in Table 4.19 is interpreted in terms of odds ratio as follows:

The association between youth employment status and age of the respondenton the analysis of logistic regression model was undergone shows that negatively and statistically significant effect on employment status of the youth at l % level of significance. With the coefficient of 0.276, keeping other factors remain constant, thus we would predict that the log odds for the youth being unemployed decreases by 0.276 for every one unit increment in the age of the respondent. This shows that the age of youth‘s increases, we expect the youth‘s likely hood of being unemployed increases. In other hand, these youths have high age are most likely unemployed as compared to youths have low age. However, the result is consistent to the result of Ahemedteyib(2020) and Nganwa et al., 2015.

Educational status of an individual could be a key factor that affects employment status of youth in the town. Those people having high educational level or highly educated were more productive and they have relatively highly opportunistic and they were highly salaried comparatively. Instead when the youth lacked essential skills and knowledge, the probability of being unemployed is greater (Schultz, 1961).The result of this study also proves the above mentioned statement; those having lower level of education increase the odds of unemployed. The probability of being unemployed was 1.287 times higher for those respondents who had no education when compared with those who had higher education, and the association was significant at 1% level of significance.This finding is contradicted with the finding of Aynalem et al (2016); it is found that youths who attend higher education were more likely to be unemployed. On the other hand, the result is consistent to the result of Tsegaw (2019), Ahemedteyib(2020),Asalfew (2011) and Duguma et. al.(2019),Esay Solomon (2020),.

In line with the priori expectation of the researchers work experience affects unemployment negatively at 1 percent significance level. The result indicates that the odds ratio of being unemployed decreases by 1.381 if the individual have work experience compared to those individual had no work experiencecontrolling the other variables in the model. It means that lack of work experience increases the chance of unemployment. This result is consistent to the results of ILO (2004), Ahemedteyib(2020),Aynalem et al (2016), and CSA January survey(2020).

Business development service (BDS) status of an individual could be a key factor that affects employment status of youth in the town. The result of this study also proves the above mentioned statement; those having BDS increase the odds of unemployed. The probability of being unemployed was 2.337 times higher for those respondents who had no BDS when compared with those who had BDS, and the association was significant at 5% level of significance. This finding is consistent with the finding of Asalfew, 2011.

The likelihood of being employed for those respondents who preferred any available work was 4.463 times more likely as compared to those who preferred paid gov't in the labourmarket, and the association was significant at 1% level of significance. This finding is consistent with the finding of CSA January survey, 2020;Esay Solomon, 2020; Asalfew, 2011; Ahemedteyib, 2020.

Social networks are keys to find a job in urban areas (Lange and Martin, 1993). Youth who do not utilize personal networks could miss job opportunities available through personal networks. The lack of social network could increases the risk of unemployment. The findings of this study also found that social network affects individuals' unemploymentstatus negatively and significantly. The odds ratio of being unemployed decreases by 2.997 (at 1% significant level), if individuals had social network compared to those who have no social network. This result is similar to Asalfew, 2011; Ahemedteyib, 2020; Asmare et. al. 2014 and Aynalem et.al,2016confirm the underline statement that lack of social network increases the odds of unemployment. According to some information gathered from key notes of interview also implies that majority of us do not have appropriate social networks to find employment because of economic, social and cultural barriers we have. Besides this lack of initiation and attitudes towards search of jobs is too weak; as a result some chances of job opportunities were pass able that came through individual networks.

The association between youth employment status and household income during the analysis of logistic regression model was undergone shows that negatively and statistically significant effect on employment status of the youth at l % level of significance. With the coefficient of 2.700, keeping other factors remain constant, thus we would predict that the log odds for the youth being unemployed decreases by 2.700 for every one birr increment in the monthly income his/her parent. This shows that as income and wealth related factors of the youth‘s family increases, we expect the youth‘s likely hood of being unemployed decreases. In other hand, these youths from relatively poorer families are most likely unemployed as compared to youths from richer households. This result might be the fact that youths from relatively higher income families may have better inputs for searching jobs or else they can easily get initial capital to start their own business. This finding is consistent with the finding of Ahemedteyib (2020) and Aynalem et al (2016).

CHAPTER FIVE CONCLUSIONS AND RECOMENDATIONS

5.1. CONCLUSIONS

Youth's transition into the labour market has long-term impacts on their lives as well as on the socio-economic development of their countries. It is thus essential and global interest to understand their pathways into the world of work and how they are engaging - or not, as the case may be - in employment.The challenges faced by young people have been commanding increasing attention on the global agenda, as reflected in the Sustainable Development Goals (SDGs). Effectively addressing the challenges requires strong labour market information systems. Likewise, Ethiopia's steady and fast economic growth has not created employment opportunities for the increasing number of youth. This studies had the objective to identify the most important socio-economic and demographic determinants of unemployment based on the 2020 own surveyed data of Hawassa. To achieve its objective, the study has employed Binary logit regression model. In the model unemployment status of urban youths were taken as dependent variable and 15 explanatory variables were included. Based on the result of the logit model eight of the explanatory variables were found significant determinants of urban youth unemployment; of which, education status of urban youths,work experience, business advisory service, Job preference, social network, frequency of contact and family incomeaffects employment positively where as age of respondent affects employment negatively. From all the analysis done in chapter four it is observable that the objectives set in this research paper have all been executed. As expected from literature it is evident from the results that demographic and socio- economic factors covered in this paper play a role in influencing unemployment within the Hawassa town youth.

The Characteristics of urban youth unemploymentcould include students in school, wives not seeking employment, the sick or injured at the time of the survey, those engaged in household activities, and the like. According to the study, the employment to population ratio of the sample was 52.4 percent. This means about 47.60 percent of the total surveyed youth were without work from the population of the sampled area, aged between 14 and 29 years. The differential by sex also depict that the ratio of males 42.18 percent is significantly higher than females about 47.82 percent.

As discussed in the conceptual framework of the study, this sub-section presents the major determinants of urban youth unemployment in Hawassa. These factors include: Age, education status,work experience, Business advisory service, Job preference, social network, frequency of contact andfamily income.

The age of youth‘s increases that the youth‘s likely hood of being unemployed increases: The association between youth employment status and age of the respondenton the analysis of logistic regression model was undergone shows that negatively and statistically significant effect on employment status of the youth at l % level of significance. With the coefficient of 0.276, keeping other factors remain constant, thus we would predict that the log odds for the youth being unemployed decreases by 0.276 for every one unit increment in the age of the respondent.

Lower educational level of youth related with higher risks of Unemployment: Educational level of the youth has a positively and statistically significant effect on employment status of the youth. It shows that years spent on education or investment on education acts as a better signaling of productivity of the youth, thereby it increases the probability of being employed

Work experience affects unemployment negatively: Work experience affects unemployment negatively at 1 percent significance level. The result indicates that the odds ratio of being unemployed decreases by 1.381 if the individual have work experience compared to those individual had no work experiencecontrolling the other variables in the model.

Business Development ServicePlays Positive Significant Role on Youth Employment: The result of this study also proves the above mentioned statement; those having BDS increase the odds of unemployed. The probability of being unemployed was 2.337 times higher for those respondents who had no BDS when compared with those who had BDS, and the association was significant at 5% level of significance.

Job Preference Increases the Probability of Youth Unemployment: The findings of this study also indicated that nearly 67.0 percent are self employed followed by any available work 19.5 percent. So, youth those preferred paid employment were unemployed when compared with those preferred to work any available work.

Weak Social Network Density Related with Higher Risks of Youth Unemployment: The result indicates youths who have more social networks for the purpose of job searching indicator of playing field (market), the higher the youth being employed. Access to market information is significantly associated with youths ‘employment status.

Poorer Families are Most likely Unemployed as Compared to Youths from Richer Households: The association between youth employment status and household income during the analysis of logistic regression model was undergone shows that negatively and statistically significant effect on employment status of the youth at l % level of significance. With the coefficient of 2.700, keeping other factors remain constant, thus we would predict that the log odds for the youth being unemployed decreases by 2.700 for every one birr increment in the monthly income his/her parent. This shows that as income and wealth related factors of the youth‘s family increases, we expect the youth‘s likely hood of being unemployed decreases.

5.2. RECOMMENDATIONS

From the findings the study several recommendation are made:­

- The government should take a measure of action to support the very poor, and to bring about rapid economic growth at the national level. To this effect, adopting job­rich macro-policies by ensuring macroeconomic stability, optimizing the job-creation potential of public investment, improving the financial sector, and upgrading the institutional and statistical framework for job-rich macro-policies;
- Building a vibrant local private sector by revamping the current support to MSMEs, effectively supporting high-potential and high-growth MSMEs, and improving the quality of business development services;
- The government should measure of action to capacitating the awareness of youth towards jobs. Preferring jobs only in the formal sectors particularly jobs in government offices increases the likelihood of being unemployed. Thus, to improve the awareness of youth advocating the importance of self-employment by using role models; enabling youth to bring attitudinal change through education by organizing awareness creation programs is necessary;
- To minimize social network the government should strengthening labor market intermediation and linkages by (i) building modern employment centers that provide effective employment services and (ii) developing a labor market information system to reduce the asymmetry of information and improve social and spatial mobility in the labor market;
- The city administration, Hawassa, needs to help the young unemployed to find better jobs. Active labor market policies, such as job search assistance, employability training, public support for apprenticeship and internship programs, and on-the-job training subsidies can be used to increase the employability of young workers. Governments can also provide tax incentives to firms to recruit and retain young workers.

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APPENDIXES

Appendix I: Contingency Coefficient for Categorical Variables

Abbildung in dieser Leseprobe nicht enthalten

Appendix II: VarianceInflation Factor for Continuous Variables ntingency Coefficient for Categorical Vari

Appendix III: Questionnaire

The Socio-economic and Demographic Determinants of Urban Youth Unemployment:

The Case of Hawassa city, Sidamma National Regional state

Set by: FantuBekele, MSc Student at Graduate School of Economics, Africa Beza College Questionnaire

Dear Respondent,

The present study is an endeavor to study is to analyze the determinants of youth unemployment in Hawassa city, SidammaNatinal regional state. The information provided by you will be used only for research (MSc) that can be useful for policy suggestions. Please spare a few minutes from your valuable schedule and share your true feelings. The questionnaire may take between 10-15 minutes to complete. I will ensure the confidentiality of the information provided.

Thank you for your cooperation

NB

- Please Do Not Relate Your Answer To CO VID-19
- Circle The Answer Of Your Choice For Close Ended Question
- Fill In The Space Provided For Open Ended Question

Section - II Demographic and Socio-Economic Information of Respondents ntingency Coefficient for Categorical Vari

Section- III Employments status of Respondents

Abbildung in dieser Leseprobe nicht enthalten

Section V: Household Income and other Characteristics of Respondents

Abbildung in dieser Leseprobe nicht enthalten

Section VI: Characteristics of unemployed Respondents

Abbildung in dieser Leseprobe nicht enthalten

Focus group discussion (FGD) Plans

1. What did you say about the common youth problems in the town?
2. How is higher the risk of youth unemployment in the town?
3. Are there any job opportunities available for youth in the town?
4. What are the issues that are responsible for the youth unemployment?
5. Linking to the other group of population, how much the degree of Unemployment affects Youth in the town?
6. Is there any measure taken to reduce the problem of youth unemployment in the town by the concerned body?
7. What are the problems that affect people who attempt to participate in the self­Employment struggle?
8. From the educated youth group, which level of educated youth highly Affected by the risk of unemployment? Why?
9. Why male youth are more employed than female?
10. What kind actions do you think successful to minimize the risk of youth Unemployment in Hawassa?

[...]