Explaining Causes and Responses to Urban Unemployment in Ethiopia

Explaining Socio-economic Causes of Urban Unemployment
and Policy Responses in Ethiopia

By
Tesfaye Chofana and Tegegn Gebeyaw
tesfayechofana@yahoo.com and tegegnw@gmail.com

2013
Addis Ababa

1

Acknowledgment
We are very grateful to the Organization for Social Science Research in Eastern and Southern
Africa (OSSREA) for funding the research project and providing training to facilitate the task
and supervising. We would like to express our appreciation to the Central Statistical Agency
(CSA) for providing the secondary data required for the research. We would also extend our
thanks to the respective woreda and kebele offices of Addis Ababa, Bahir Dar and Hawassa
cities for significant supports they provided during primary data collection.

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Abstract
The study explores the socioeconomic causes of urban unemployment and effects of policy
interventions. It made use of primary cross-sectional data collected from three major cities and
secondary data primarily from the CSA of Ethiopia. Mainly a quantitative approach is
followed using both descriptive and inferential methods of analysis. Despite the sound
economic growth and the deliberate effort of the government to address the problem, the urban
labor market is characterized by high and persistent unemployment. Although the rate declined
from 26 percent in 2003 to 18 percent in 2011, it is still a cause for concern. The downward
inflexible unemployment rate may signify that the rapidly growing economy for almost a
decade does not result in equivalent employment opportunity. Rapidly growing urban
population and lack of vibrant non-agricultural sector are among the contributing factors of
urban unemployment while the effect of FDI inflow on unemployment is mixed. Furthermore,
the skill-mismatch and the tendency of queuing for public or formal private sector jobs are
found to be possible causes of unemployment.

The likelihood of unemployment is associated with demographic, location and education
variables. A desirable employment effect of education at individual level is found to be more
pronounced at tertiary level of education. Relative to lower primary education, all other
categories of educational qualifications below tertiary level are associated with higher rate of
unemployment. Training has a relatively desirable effect on the labor market outcomes of some
groups of the labor force; however, it makes no difference in reducing gender and age
disparity of unemployment and in encouraging self-employment. Above all, what seems
paradoxical and that requires immediate measure is TVET is likely to increase unemployment
and to decrease self-employment after eight years of implementation practices. TVET program
is also criticized for being less relevant, less responsive, non participatory, less efficient and
effective, and is less flexible. On the other hand, the employment effect of grade ten graduates
is consistently improving. The employment effect of MSEs is found to be insignificant and only
one third of them registered positive employment growth since startup. Moreover, employment
growth effects of human capital endowments of new firms, social capitals and access to credit
is nil.

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Indeed, as the recent years experience of the country witnesses, despite the ongoing education
policy reform and MSE development and promotion efforts of the government, further
considerations are critical to achieve the desired results from policy interventions. It is
therefore important to evaluate the existing system of education and training and taking timely
measure to improve its relevance and quality. Particularly, the unsatisfactory performance of
the TVET program reminds the need to reconsider the limitations and take timely measure so
as to link the program with the labor market demand. Another important policy implication of
the finding is the need to provide support to MSEs in terms of market for their products, easy
access to supply of raw materials, and work place.

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Table of Contents
ACKNOWLEDGMENT .............................................................................................................. 2
ABSTRACT ................................................................................................................................. 3
LIST OF TABLES........................................................................................................................ 6
LIST OF FIGURES ...................................................................................................................... 7
1. INTRODUCTION .................................................................................................................... 8
1.1. Background of the Study .................................................................................................................................... 8
1.2. Objective of the Study ...................................................................................................................................... 15
1.3. Data Sources and Methodology ........................................................................................................................ 15
1.4. Significance and Scope of the Study ................................................................................................................ 16
1.5. Limitation of the Study ..................................................................................................................................... 16
1.6. Organization of the Paper ................................................................................................................................. 17

2. LITERATURE REVIEW ...................................................................................................... 18
2.1 Definition and Concepts of Unemployment ...................................................................................................... 18
2.2. Type of Unemployment .................................................................................................................................... 21
2.3. Theories of Unemployment .............................................................................................................................. 23
2.4. Causes of Unemployment................................................................................................................................. 27
2.4.1. Supply Side Factors .................................................................................................................................. 28
2.4.2. Demand Side Factors ................................................................................................................................ 33
2.5. Active Labor Market Policies to Address Unemployment ............................................................................... 38
2.6. An Overview of Empirical Evidences on Unemployment in Ethiopia ............................................................. 41
2.7. Policy Responses to Address Unemployment in Ethiopia ................................................................................ 43
2.7.1. Expansion of Technical and Vocational Education and Training Programs ............................................. 43
2.7.2. Micro and Small Scale Enterprises (MSEs) Development ....................................................................... 45

3. METHODOLOGY ................................................................................................................. 49
This section presents a discussion of the specific steps used in conducting the research. It provides information on
research methodology, data sources, sampling techniques, data collection instruments, methods of data analysis
and specification of econometric models. ............................................................................................................... 49
3.1. Research Method .............................................................................................................................................. 49
3.2. Data Sources ..................................................................................................................................................... 49
3.3. Sampling Techniques and Procedures .............................................................................................................. 50
3.4. Data Collection Instruments ............................................................................................................................. 51
3.2. Data Analysis ................................................................................................................................................... 52
3.2.1. Pooled Cross-sectional Data Analysis ........................................................................................................... 52
3.2.2 Specification of Study Variables ............................................................................................................... 57

4.

RESULTS AND DISCUSSION......................................................................................... 57

4.1. Demographic Characteristics of Respondents .................................................................................................. 57
4.2. The Urban Labor Force Participation Trends ................................................................................................... 58
4.3. Urban Versus Rural Unemployment ................................................................................................................ 59
4.4. Urban Employment Trends .............................................................................................................................. 60
4.5. Urban Employment-to-Population Ratio .......................................................................................................... 61
4.6. Urban Unemployment Trends .......................................................................................................................... 62

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4.7. Regional Unemployment Trends ...................................................................................................................... 65
4.8. Urban Unemployment and Education .............................................................................................................. 66
4.9. Unemployment Duration .................................................................................................................................. 69
4.10. Urban Unemployment and Training ............................................................................................................... 71
4.11. Training and Self-employment ....................................................................................................................... 73
4.12. School to Work Transition ............................................................................................................................. 74
4.13. Socioeconomic Causes of Urban Unemployment .......................................................................................... 75
4.14. Theories of Unemployment ............................................................................................................................ 79
4.15. Effect of Education and Training Polices on Labor Market Outcomes .......................................................... 83
4.15.1. Effect of Education Polices on Urban Unemployment ........................................................................... 84
4.15.2. The Effect of Training Polices on Urban Unemployment ...................................................................... 89
4.15.3. Effect of Education and Training Polices on Self-employment and School-to-Work Transition ........... 90
4.16. An Assessment of Strategies to Promote Employment in Ethiopia ................................................................ 91
4.16.1. Strategies to Increase Employment through TVET ................................................................................ 91
4.16.2. Employment Growth within Micro and Small Scale Enterprises ......................................................... 100
4.16.2.1. Characteristics of Micro and Small Scale Enterprises .................................................................. 101
4.16.2.2. Employment Contribution of MSEs.............................................................................................. 103
4.16.2.3. Startup Motives of MSEs .............................................................................................................. 105
4.16.2.4. Constraints of Micro and Small Scale Enterprises ........................................................................ 105
4.16.2.5. Market and Other Constraints to Expand Business ................................................................... 105
4.16.2.6. Source of Startup Capital and Capital Growth .............................................................................. 107
4.16.2.7. Cause of Job Interruption .............................................................................................................. 108
4.16.2.8. Assistance Needed from Government ........................................................................................... 109
4.16.2.9. Determinants Urban Employment Growth within MSEs .............................................................. 110

5.

CONCLUSIONS AND RECOMMENDATIONS ........................................................... 112

5.1. Conclusions .................................................................................................................................................... 112
5.2.
Recommendation..................................................................................................................................... 120

REFERENCES ......................................................................................................................... 123
ANNEX .................................................................................................................................... 127

List of Tables
Table: 2.1 Number of establishments and jobs created and amount of loan............. Error! Bookmark not defined.
Table 4.1: Regional unemployment distribution (%) .............................................................................................. 66
Table 4.2: Unemployment rate by education ........................................................................................................... 68
Table 4.3: Distribution of respondents .................................................................................................................... 92
Table 4.4: Evaluation of the innovativeness of the program ................................................................................... 93

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Table 4.5: Evaluation of the feasibility of the program ........................................................................................... 94
Table 4.5: Evaluation of the TVET program responsiveness ................................................................................. 95
Table 4.6: Evaluation of the relevance of the TVET program................................................................................. 97
Table 4.7: Evaluation of the relevance of the TVET program................................................................................. 98
Table 4.8: Evaluation of the efficiency and effectiveness of the program............................................................. 98
Table 4.9: Up Scalability of the Program ................................................................................................................ 99
Table 4.10: Coordination of the TVET program ................................................................................................... 100
Table 4.12: causes of job interruption ................................................................................................................... 108
Table 4.13: Assistance needed from government .................................................................................................. 109

List of Figures
Figure 2.1: The ILO’s Labor Force Framework ..................................................................................................... 20
Figure 4.1: urban labor force participation rate (%) ............................................................................................... 58
Figure 4.2: The trend of labor supply by years of schooling (%) ........................................................................... 59
Figure 4.3: Urban employment trends (%) .............................................................................................................. 60
Table 4.4: Urban employment-to-population ratio (%) ........................................................................................... 62
Figure 4.5: urban unemployment rate (%) ............................................................................................................... 63
Figure 4.6: Mean spell of unemployed (in year) ..................................................................................................... 69
Figure 4.7: The comparison of unemployment rate by training (%) ....................................................................... 71
Figure 4.8: Unemployment differential between female, youth and adult male with training ................................ 72
Source: UEUS 2003-11 ........................................................................................................................................... 72
Table 4.9: Unemployment differential between TVET and secondary school graduates ........................................ 73
Table 4.10: Self-employment by training ................................................................................................................ 74
Figure 4.11: Average time from school to work transition by education ............................................................... 75
Figure 4.12: Relationship between unemployment rate and GDP ........................................................................... 76
Figure 4.13: relationship between participation and employment ratio................................................................... 77
Figure 4.14: Employment contribution of MSEs (%)............................................................................................ 103
Figure 4.15: Employment by type of MSEs (%) ................................................................................................... 104

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1. INTRODUCTION
1.1. Background of the Study
The developing economies of the world are characterized by a rapidly growing urban
population and urban work force combined with a much slower increase in employment
opportunities and, as a result, high urban unemployment and under-employment. Indeed, a
rising level of urban unemployment could be a great social evil as it is one of the prime sources
of urban poverty and political instability. Moreover, the presence of large numbers of poor and
jobless people in urban areas has depressing impact on tax revenues while putting a great deal
of pressure on government’s current expenditures to meet rising demands for basic urban
services and to create jobs for the unemployed. This will inevitably have a crowding effect on
resource allocation for growth enhancing sectors of the economy. For these and other reasons,
the general consensus among social scientists and policy makers is that the issue of urban
unemployment has to be wisely managed, particularly in developing countries where social
security services are nonexistent. Therefore, the study of unemployment is an area of
considerable importance which is of both theoretical and empirical interest.
Unemployment and underemployment are among the greatest challenges to the development of
African continent. Africa’s labor force, with over 368 million women and men predominantly
engaged in agriculture and rural non-farm activities, accounts for 11.9 per cent of the total
world labor force. The overall unemployment rate in sub-Saharan Africa was estimated at 9.8
per cent in 2006 (ILO, 2007) and stood at an estimated 7.9 per cent in 2008 (ILO, 2009a).
Although the official unemployment rates seem declining and relatively lower, when the
number of working poor reflected mainly in underemployment and vulnerable employment is
included, the employment situation looks even more desperate. As stated in ILO (2007)
concerning the decent work agenda in Africa, the total number of people worldwide living on
less than $1 a day declined from 1.45 billion in 1981 to 1.1 billion in 2001. In contrast, the
number in sub-Saharan Africa increased from 164 million to 314 million during the same
period, of which roughly 50 per cent are women and men of working age. Consequently,
Africa has the largest number of working poor in total employment of any region.

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The fact that most African countries lack formal social insurance schemes make most poor
people to have no option other than being employed, underemployed or dependent on
employed people through informal social networks for their livelihood. Thus, even people
outside the labor market tend to be dependent on individuals in the labor market. In effect,
labor markets are central to the livelihoods of poor people in Africa both in and outside of the
labor force (ECA, 2005). Africa, like its higher rate of poverty, is also known for its higher
unemployment. The failure to create more and better paid jobs to meet the needs of the
growing labor force and reduce poverty remains a fundamental issue in many African
countries. A spatial perspective of Africa’s labor market outcome witnessed higher rates of
unemployment in urban areas than in rural ones. It is about 3 times higher in urban areas than
in rural areas (ADB, 2010).
According to international labor organization, despite the constraints of reliable and
comprehensive data, it is estimated that around three-quarters of activities in the urban
economies of Africa are informal in nature. This is why improving productivity and market
access for workers and producers in the informal economy should be at the heart of many
poverty reduction efforts in Africa. In the face of considerable improvement in macroeconomic
performance in recent years across the region, the resulting job opportunities are not sufficient
(ILO, 2007). The implication is that if the MDG of halving extreme poverty by 2015 is to be
realized in the region, an employment-centered growth strategy coupled with active population
policy is required.
Similar to other sub-Saharan Africa countries, employment in Ethiopia is characterized by a
heavily segmented labor market situation. It can be divided among different segments, with
significant distinction between formal and informal employment, private and public
employment, wage and self-employment, and urban and rural employment (EEA, 2007). From
a rural-urban perspective, the Ethiopian labor market exhibits a significant disparity. Generally,
the rural labor market is known by a pervasive problem of underemployment while the urban
one is characterized by a severe open (or official) unemployment.

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As noted in Guarcello, Lyon and Rosati (2008), in rural areas, unemployment is lower but
with extremely low level of human capital, high underemployment or disguised
unemployment, and few chances to be employed in the formal sector. In urban areas, on the
other hand, although the labor force may face relatively better prospects in terms of income and
employment quality, finding a job is difficult and hence unemployment, especially youth
unemployment, is higher. Similarly, labor force surveys (LFS) by the Central Statistics Agency
(CSA) of Ethiopia indicate that the average unemployment rates for urban areas were 26.4
percent and 20.6 percent in 1999 and 2005, respectively while they were 5.1 and 2.6 percent
for rural areas in the same periods. The situation is rather worrisome in relatively bigger cities.
For instance, in Tegegn (2011), the overall unemployment rate in Addis Ababa was as high as
38.5 percent in 1999 and decreased to 31.7 percent in 2005, but elevated above very unpleasant
urban average rate (Tegegn, 2011).
The current government of Ethiopia has been implementing poverty and unemployment
reduction polices since the reform period 1991. Particularly, promoting micro and small scale
enterprises, expanding microfinance services, reforming the education and training system and
increasing its accessibility at all levels, encouraging inflow of FDI and promoting laborintensive technologies are among those worth mentioning. Yet, it is apparent that poverty
reduction and development policies and strategies of Ethiopia cannot bring the desired result
without creating gainful employment for the unemployed and underemployed population.
Despite the impressive economic growth in the past eight or so years and the various
development policy efforts, the incidence of urban unemployment is still higher and persisting.
According to the urban employment-unemployment surveys of CSA, the average urban
unemployment rates of Ethiopia for people aged between 10 and 64 years was 26.3 percent in
2003 and it stood at 18 percent in 2011. This means that the rates decreased only by 8
percentage points in the 8 year periods, implying a merely 1 percent average annual reduction.
Given the existing efforts, the annual reduction rate is slower and disappointing. Such
persistent and higher incidence of unemployment suggests the urgency of a deep and rigorous
examination of the root causes of the problem, which might be the key step towards the
solution.

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There have been a number of empirical studies conducted on urban unemployment in Ethiopia.
For instance, Tegegn 2011; Guracello, Lyon and Rosati 2008; WB 2007; Seife 2006; Serneels
2008; Serneels 2007; Birhanu, Abraham, and van der Deijl 2005; Getinet 2003; Mulat et al.
2003; Krishnan, Gebreselassie and Dercon 1998 can be mentioned. Most of them did focus on
discussing either the demographic determinants of unemployment or duration of
unemployment using relatively older and single period cross-sectional data.
Tegegn (2011) assessed the socio-demographic determinants of urban unemployment in
Addis Ababa using data from 1999 and 2005 labor force surveys (LFS) of CSA. The
estimation results of the Logit model imply that a person’s sex, age, migration status, level of
education and training status are statistically significant and most important factors that
determine the unemployment probability of an urban worker. However, the scope of the study
is limited only to Addis Ababa and also didn’t explicitly discussed policy issues. Although he
used a relatively recent data, he estimated the two cross-section data sets separately and
didn’t link them and show the trend of unemployment in the model.
Guracello, Lyon and Rosati (2008a) also studied the challenges of child labor and youth
employment in Ethiopia using a 2001 LFS data. The estimation results of the Probit model
imply the employment chance of a young worker does significantly vary by sex, household
income and education. However, they used a single cross-section data. They did not clearly
indicate the reference education dummy in their discussion and also didn’t consider urban
location.
Seife (2006) examined the determinants of unemployment duration in urban Ethiopia using the
2000 Ethiopian Urban Socio-Economic Survey data and employed parametric and semiparametric models. The results of the regression analysis imply that age, marital status, level of
education, location of residence and support mechanism significantly affect the duration of
unemployment while ethnicity and gender do not. However, this duration study used only a
single cross-section data and also didn’t explicitly discuss the effect of policies meant for
addressing unemployment.

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Serneels (2007) assessed the incidence and duration of unemployment among young men
(aged 15-30) in urban Ethiopia, He used the 1994 first round household data from the
Ethiopian Urban Socio-Economic Survey (EUSES) and analyzed by a probit and proportional
hazard duration models. He argues that male unemployment in urban Ethiopia does fit with
queuing model of unemployment. However, the study used a single cross-sectional data and
also its scope is too narrow and limited only to young males. Therefore, it is not
representative of the labor force and the current situation. Besides, the reference line of
education is not clearly indicated in the discussion.
Getinet (2003) studied the effect of individual characteristics on the incidence of youth
unemployment in urban Ethiopia using the first (1994) and fourth (2000) waves Urban SocioEconomic Survey (EUSES) data. The findings of the multinomial logit analysis indicate that
young people who completed secondary education are more likely to be both unemployed
and active. On the other hand, those with at most elementary level education are more likely
to be in self-employment and casual/domestic types of activities as compared to those with
tertiary level education. Although he used two different cross-section data, he estimated the
two cross-section data sets separately and didn’t link them and show the trend in the model.
What remains to be explored, however, is how unemployment responding to education level
attained and training received and how it is changing overtime and variation in urban
location.
Promoting micro and small- scale enterprises (MSEs) was one of the strategies explicitly stated
in PASDEP (Plan for Accelerated and Sustained Development to End Poverty) to create
employment and generate income, primarily to reduce urban unemployment. Still the latest
five-year plan, the Growth and Transformation Plan 2010/10 – 2014/15 (FDRE, 2010), has
given particular attention to the expansion and development of micro and small-scale
enterprises. The sector is believed to be the major source of employment and income
generation for a wider group of the society. In this regard, identifying factors that affect
employment creating capacity of MSEs has policy relevance to take action in a way to enhance
employment potential of these enterprises in which many get employed and still a potential
source of employment for the unemployed.

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Unfortunately, it is difficult to find empirical evidences on the employment effect of MSEs in
Ethiopia. Birhanu, Abraham, and van der Deijl (2005) did attempt to discuss the support given
to MSEs and the employment created before some 8 years relying mainly on the report of
FeMSEDA. Nevertheless, in recent years the government has given more emphasis to the
sector and significant changes would have been occurred. Recently, Rahel and Paul (2010)
assessed the growth determinants of women operated MSEs in four kebeles of Addis Ababa
city. However, firstly, the scope of the study is too limited and lacked strong and objective
analysis. Secondly, they didn’t adequately discuss the determinants of employment growth in
the MSEs. Nevertheless, there are enormous studies emphasized on causes of firm growth in
US, Canada and Europe and a few studies on causes of new firm growth in Latin American
countries (Capelleras and Rabetino, 2008). Even these studies already we have focused on
growth of new firms in general and but this work focus on exploring the factors that determine
average annual employment growth in MSEs.
Considering the drawbacks of the previous education system, a new education and training
policy has been designed and implemented since 1994. The new policy has given emphasis to
education and training that offer specific learning skills related to the market needs, i.e.
gainfully tradable skills based on demand driven and in response to the country’s development
approach. Consequently, considering the strategic importance of training, the first National
TVET strategy has been in effect since 2002. Furthermore, acknowledging the limitations of
former graduates of TVET in meeting the expectations and demand of the labor market, a
comprehensive development vision for the TVET sector has been outlined in the Education
Sector Development Program (ESDP) III (MoE, 2008). All these efforts are supposed to
improve the skill and employability of the trainees and thereby address the problem of urban
unemployment. Equally important, assessing whether these policy efforts are effective in
achieving the desired goals they are supposed to or not is necessary in order to take corrective
measures timely and to minimize the wastage of scarce resources. However, objective
assessments on the effectiveness of policies are uncommon in Africa in general and in Ethiopia
in particular. None of the so far empirical studies in Ethiopia did clearly and objectively
analyze the effect of the TVET program on unemployment, spell of unemployment and schoolto-work transition and self-employment by setting relevant referent group. Although Birhanu,
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Abraham, and van der Deijl (2005) and Guracello, Lyon and Rosati (2008a) discussed the
existing education and training policies, they didn’t empirically examine their effects on
unemployment. For this reason, this study sheds light on the existing research gap by
attempting to explicitly examine the effect of the TVET program on labor market outcomes,
particularly on urban unemployment.
Evidently, the preceding discussions indicate that although there have been previous studies on
the issue of urban unemployment in Ethiopia, most of them focused mainly either on the
demographic determinants of unemployment or duration of unemployment. They used not only
older data but also a single cross-sectional data, except that two studies used two cross-section
data sets. Therefore, they didn’t empirically explain how unemployment changed overtime. In
addition, they didn’t adequately and explicitly analyzed the effect of policy responses meant
for reducing unemployment such as the TVET and MSEs sectors. Some of them are limited in
scope; and most of them, but two studies, didn’t consider urban location as important factor in
explaining urban unemployment.

Therefore, we argue that, relative to the persistent and severe unemployment problem in urban
Ethiopia, empirical studies conducted so far on the causes of urban unemployment are limited
in number and are not recent enough to explain the current situation. We also argue that effect
of policy interventions aimed at addressing the problem of urban unemployment is yet under
researched issue in Ethiopia. Previous studies didn’t duly consider the effects of policy
interventions, such as expansion of TVET and promotion of MSEs, on unemployment.
Accordingly, this study is timely and to some extent attempted to fill the research gaps
identified above. Unlike the other studies, we used five cross-sectional data sets ranging from
2003 to 2011 and combined to create pooled data that can better estimate population
parameters relative to a simple cross-section data. This helped us to better explain the trend and
the recent situation of urban unemployment. In doing so, we identified the following specific
research questions and tried to address them correspondingly.
1. What are the characteristics of urban unemployment in Ethiopia?
2. What are the socio-economic causes of unemployment in urban Ethiopia?
3. What are the effects of TVET program on unemployment?
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4. What are the factors that determine the employment growth within MSEs?

1.2. Objective of the Study
The general objective of the study is to examine the major socioeconomic causes of urban
unemployment and the effect of policy interventions, through expansion of TVET and
promotion of MSEs, on urban unemployment in Ethiopia. The specific objectives of the study
are to:
1. Describe the characteristics of urban unemployment in Ethiopia.
2. Investigate the socio-economic causes of urban unemployment.
3. Examine the effect of TVET program in reducing unemployment in urban Ethiopia.
4. Identify the factors that determine average employment growth within MSEs in urban
Ethiopia.
5. Suggest some policy implications

1.3. Data Sources and Methodology
In order to address the aforementioned objectives, we made use of both primary and secondary
data sources. The primary data were collected from three cities, namely Addis Ababa, Bahir
Dar and Hawassa. The secondary data were obtained from the labor force surveys (1999 and
2005) and urban employment unemployment surveys (2003, 2004, 2006, 2010 and 2011) of the
Central Statistical Agency of Ethiopia. In addition, data on some macroeconomic variables
were taken from the World Bank database.
The available data were analyzed by both descriptive and regression methods of analysis. The
descriptive analysis is used to describing the characteristics of urban unemployment. The
regression analysis involves econometric models to examine the effects of policy interventions.
We employed probit and duration (proportional hazard) models for analyzing the pooled crosssectional data and cross-sectional data.

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1.4. Significance and Scope of the Study
The study is expected to provide some empirical overview on the socio-economic causes of
urban unemployment and on the role of TVET and MSEs in reducing urban unemployment.
First, understanding the relationships among education and training in general and TVET in
particular and unemployment can help to reveal underlying effects of improving human capital
on unemployment and can help concerned bodies to evaluate strategies. Hence, the research
report can be an input for concerned bodies at different levels who are interested in the issue.
Second, determining factors that increase employment size of MSEs are fundamental to
appropriate intervention to curb high urban unemployment. Therefore the study will be used to
reassess the development and implementation of employment policies and programs in
Ethiopia. Third, this work can supplement the existing empirical studies on urban
unemployment and serve as a reference material for teaching as well as for others who will
conduct related studies. Fourth, it may encourage interested researchers to undertake impact
evaluation to examine the effect of education and training on unemployment to fill the existing
gap in depth.
The scope of the study is limited in that its focuses only on the causes of unemployment
attributable to socio-economic factors (i.e. due to serious time series data shortage on
unemployment rate) and effects of public interventions on urban unemployment. Its spatial
coverage, as the title implies, is confined to urban Ethiopia. Nevertheless, the implications of
the findings are expected to be useful and applicable for rural parts of the country and for urban
areas of other sub-Saharan African countries as well.

1.5. Limitation of the Study
The major limitations of this study emanate from the obvious constraints of the availability of
employment-unemployment data in Ethiopia. The most important data this study made use of
are the labor force surveys and the urban employment unemployment surveys obtained from
the Central Statistical Agency of Ethiopia. Although the data set are comprehensive and cover
all regions of the country, they lack some important information supposed to be crucial for the
purpose of this study. For this reason, we had to collect primary data to supplement the
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available secondary data. The primary data has also its own limitation pertaining to time and
other resource constraints. Hence, it covered only limited sample individuals and enterprises
located in three cities. However, an attempt was made to make the sample as representative as
possible so that the findings are believed to explain the same issue in other areas too.

1.6. Organization of the Paper
This paper is arranged in five sections. The next section reviews theoretical and empirical
literature while the third one describes the source and nature of the data and the method of
analysis. Section four presents the descriptive statistics and discusses the findings of the
regression analysis. Lastly, the fifth section concludes and put forth policy implications.

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2. LITERATURE REVIEW
2.1 Definition and Concepts of Unemployment
Unemployment is usually viewed and defined from the human element point of view.
Although any factor of production can be unemployed, economists have put particular
emphasis on the human element –the unemployment of labor. According to Sapsford and
Tzannatos (1993), this is mainly due to the mental and sometimes physical sufferings and
hardships that the unemployed and their dependents experience. Thus unemployment
generally refers to a status in which individuals are without job and are seeking a job. For the
purpose of this paper, we make use of the Key Indicators of the Labor Market (KILM)
standard definitions of ILO, as adopted by the 13th International Conference of Labor
Statisticians (ICLS) in 1982 and 1998. Accordingly, the ensuing section presents the standard
definitions of the key indicators of a labor market such as activity rate, employment,
underemployment, unemployment and not currently active and then followed by an overview
of the labor force conceptual framework.
Activity rate or labor force participation rate refers to the share of the population aged
between 15-64 years and either engaged in, or available to undertake, productive activities.
Hence it captures the idea of labor supply for all productive activities according to the 1993
UN system of National Accounts. Employment is defined in terms of paid employment and
self employment. Paid employment covers persons who during the reference period
performed some work for wage or salary, in cash or in kind, as well as persons with a formal
attachment to their job but temporarily not at work. Self employment covers persons who
during the reference period performed some work for profit or family gain, in cash or in kind,
and persons with an enterprise but temporarily not at work. Hence employment rate is the
share of the employed over the labor force population aged from 15 years to 64, rather than
between 10 and 64 years as adopted by the CSA.
Underemployment is a concept that has been introduced for identifying the situations of
partial lack of work. According to the ILO, the “underemployed” comprise all persons in paid
or self-employment, involuntarily working less than the normal duration of work determined
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for the economic activity, who were seeking or available for additional work during the
reference period. Thus the “underemployed” can be considered as a subgroup of the
“employed”.
On the other hand, the international standard definition of unemployment is based on three
criteria, which have to be met simultaneously. According to the definition, the unemployed
comprise all persons above the age specified for measuring the economically active
population who during the reference period were: (a) "without work", i.e. were not in paid
employment or self-employment as defined by the international definition of employment; (b)
"currently available for work", i.e. were available for paid employment or self-employment
during the reference period; and (c) "seeking work", i.e. had taken specific steps in a specified
recent period to seek paid employment or self-employment.
The aforementioned three criteria to define unemployment imply that merely joblessness per
se cannot qualify a person to be counted officially as an unemployed. A person without a job
is said to be involuntarily unemployed as long as he/she is available and willing to be
employed at the going wage rate; otherwise he/she is considered as voluntarily unemployed
and does not appear in the official statistics as he/she has dissociated himself from the labor
force. The unemployment rate is therefore, the share of the unemployed over the labor force
population aged between15 and 64 years. However, this standard definition is different from
Ethiopia’s official definition of unemployment by the CSA. The CSA definition, therefore,
relaxes the criterion of "seeking work" and adopts a relaxed definition which leads to higher
unemployment rates. The main rationale for relaxing the definition in Ethiopia is attributable
to the unorganized nature of the country’s labor market, in which job search media are not
well developed or quite limited and not accessible to majority of the job seekers.

The population not currently active (economically inactive populations) refers to the residual
category comprising those without work but were neither seeking nor available for work, such
as students, home keepers and the retired, as well as those below the minimum age specified
for measuring the economically active population.

19

In what follows, just to have a clear understanding of the statistical definitions and the
conceptual relations among them, the ILO's conceptual labor force framework is briefly
presented as follows. The labor force framework was developed according to the ILO
Resolution concerning statistics of the economically active population, employment,
unemployment and underemployment, adopted by the Thirteenth International Conference of
Labor Statisticians (October 1982). The employed and unemployed categories together make
up the labor force (or the currently active population), which gives a measure of the number of
persons furnishing the supply of labor at a given moment in time. The third category (not in the
labor force), to which persons neither seeking nor available for work plus those below the age
specified for measuring the economically active population are included, represents the
population not currently active. In short, these relationships may be expressed as:

Population = Labor Force + Inactive Population
Labor Force = Employed + Unemployed

Figure 2.1: The ILO’s Labor Force Framework

Total Population

Population above Specified Age

Population below
specified Age

Currently active population
Source: Prakash (2001)
(the labor force)

Employed

Population Not Current
ly Active (NILF)

Because of:
school attendance,
household duties,
retirement (old
age), or other
reasons

Unemployed

Source: ILO

20

2.2. Type of Unemployment
The theoretical literature identifies various types of unemployment categories on the basis of
their sources. Although there are more, the most frequently stated classifications are Demand
Deficient or Cyclical, Frictional, Structural, and Seasonal unemployment. However, it is worth
noting that the real-world unemployment may combine different types simultaneously, and
thus distinguishing clearly one from the other and measuring the magnitude of each of them is
difficult, partly because they overlap (EEA, 2007, Henderson, 1991).
Cyclical unemployment is involuntary unemployment arising from the business cycle effect

as a result of insufficient effective aggregate demand for goods and services. When there is a
recession or a severe slowdown in economic growth, economies face with a rising
unemployment because of plant closures, business failures and an increase in worker lay-offs
and redundancies. This is due to a fall in demand leading to a contraction in output across
many industries. According to Sapsford and Tzannatos (1993), this type of unemployment
coincides with unused industrial capacity; and as traditional Keynesian economics suggests, its
cure lies in policies that succeed in increasing the level of aggregate demand.

For Keynesian economists, unemployment is a situation in which the number of people who
are able and willing to work at prevailing wage exceeds the number of jobs available. When
the number of unemployed is significant, the demand in the product market will be negatively
affected, as a result, firms are unable to sell all the goods they would like. Businesses respond
to a declining demand for goods and services by cutting employment in order to control costs
and restore some of their lost profitability. Consequently, the higher unemployment will tend to
impede the growth of gross output, implying a vicious circle.
Frictional or Search unemployment is transitional and temporary unemployment that arises

because a person may take time to find a new job after losing or quitting a job, or after entering
or reentering the labor force following schooling, illness, or some other reason for being out of
the labor force. It usually occurs due to imperfect information in the labor market (Henderson,
1991, Mankiw, 2001). It is a consequence of the short run changes in the labor market that
constantly occur in a dynamic economy in response to changes in the product market. It arises
21

because the process of matching unfilled vacancies and unemployed workers is not
instantaneous (Sapsford, 1993).
In the context of developed economies, incentives such as unemployment benefits can also
cause some frictional unemployment as some people actively looking for a new job may opt
not to accept paid employment if they believe the tax and benefit system will reduce the net
increase in income from taking work. When this happens there are disincentives for the
unemployed to accept work. Normally, frictional unemployment may not pose much threat to
individual’s welfare as long as it is temporary and does not last long. There may be little that
can be done to reduce this type of unemployment, other than provide better information to
reduce the search time. This suggests that full employment is impossible at any one time
because some workers will always be in the process of changing jobs.
Structural unemployment refers to a mismatch of job vacancies with the supply of labor

available. It is caused by long-run changes in the structure of the economy, which give rise to
changes in the demand for labor in particular regions, industries or occupations. For instance,
technological progress may make an industry capital intensive from a purely labor intensive
one. The release in labor from such an industry gives rise to the problem of unemployment.
Although workers are available for employment, they may lack the skills that the available
vacancies required or they may be in the wrong location to take the available jobs (EEA, 2007,
Sapsford,

1993,

Henderson,

1991).

Increasing

international

competition

due

to

globalization leads to changes in the patterns of trade between countries over time; and hence
it could be one of the reasons for structural unemployment. Because structural unemployment
lasts longer, demand management instruments alone may not be effective remedies to the
problem. Besides, other instruments such as facilitating training programs and subsidizing
mobility of workers are required along with demand management policies so as to significantly
reduce its incidence (EEA, 2007).
Structural unemployment can also arise from the immobility of labor. In an economy,
industries that are growing and need labor are not necessarily able to employ the same workers
who have been displaced in the declining industries. This situation can be attributable to the
problem of labor immobility. Labor immobility includes geographical immobility, industrial
22

immobility, and occupational immobility. Geographical immobility occurs when workers are
not willing or able to move from region to region, or town to town. Industrial immobility
occurs when workers do not move between industries. Occupational immobility arises when
workers find it difficult to change jobs within an industry. Industrial and occupation immobility
are most likely to happen when skills are not transferable between industry and job.
Information failure also contributes to labor immobility because workers may be immobile
because they do not know where all the suitable jobs for them are. A resulting problem with
labor market immobility is that it can create regional unemployment, which is a type of
structural unemployment. This means that a change in the structure of industry leaves some
people unable to respond by changing location, industry, or job and as a result, they remain
temporarily or permanently unemployed.

Seasonal unemployment occurs as a result of normal and expected changes in the economic

activities over the season of a year. Seasonal unemployment exists because certain industries
only produce or distribute their products at certain times of the year. As noted in Sapsford &
Tzannatos (1993), workers in the agriculture and construction sectors as well as in the tourism
industry, who are often out of work during the winter months are typical examples of
seasonally unemployed people. Indeed, such phenomena are common in most Sub Saharan
African economies where seasonal unemployment following the end of harvesting season is
inherent in the agricultural sector.

2.3. Theories of Unemployment
In Classical economic theory, unemployment is seen as a sign that smooth labor market
functioning is being obstructed in some way. In a smoothly functioning market the equilibrium
wage and quantity of labor would be set by market forces. The Classical approach assumes that
markets behave as described by the idealized supply and demand model. The labor market is
seen as though it were a single, static market, characterized by perfect competition, in which it
is assumed that every unit of labor services is the same, and every worker in this market will
get exactly the same wage. Because such a Classical (idealized) market for labor is free to
adjust, there is no involuntary unemployment; everyone who wants a job at the going wage
23

gets one. Thus, the only thing that can cause true unemployment is something that interferes
with the adjustments of free markets, such as a legal minimum wage and other regulations.
Nevertheless, this seems far from the reality. As Solow (1980) puts, the labor market is
segmented in that not everyone in it is in competition with everyone else, among others, due to
the obvious differences in abilities, experience and skills.
The presence of a legal minimum wage is commonly considered as one such factor that can
distort the smooth functioning of the labor market.

If employers are required to pay a

minimum wage that is above the equilibrium wage, this model predicts that they will hire fewer
workers and hence a fall in demand for labor. The market is, in this case, prevented from
adjusting to equilibrium by legal restrictions on employers. Now there are people who want a
job at the going wage, but can’t find one. That is, they are added to the unemployment pool,
letting the unemployment rate to rise. The empirical evidence, however, may not always
support the classical idea that minimum wages cause substantial unemployment. For example,
Card and Krueger (1993) found that a moderate increase in the minimum wage in New Jersey
did not cause low-wage employment to decline, and may even have increased it.
There are also other reasons that the economy might provide less than the optimal number of
jobs for the labor force. For instance, regulations on businesses often negatively affect their
demand for labor. Strong job protection, through employment protection legislation as well as
unionization, raises the cost of firing workers, which in turn causes firms to lower their demand
for labor (Pierluigi, 2008). Labor union activities and labor-related regulations such as safety
regulations, mandated benefits, or restrictions on layoffs and firings increase the cost of labor
to businesses. As a result, businesses tend to opt towards labor-saving technologies and thus
reducing job growth. Classical economists also argue against public safety net policies such as
disability insurance and unemployment insurance; they believe that such policies reduce
employment by causing people to become less willing to seek work. From a classical point of
view, labor-market recommendations tend to focus on getting rid of regulations and social
programs that are seen as obstructing proper market behavior. Like other classical proposals,
such labor market proposals assume that the economy works best under the principle of laissezfaire.
24

The classical theory of labor markets depends on quick market adjustment, in particular, the
elimination of any labor surplus through falling wages and a resulting full-employment
equilibrium at a lower wage rate. But ‘to what extent is this realistic?’ is a natural question that
comes to everyone’s mind. According to the well known explanation of Keynes, based on the
experiences of the Great Depression, certain aspects of real world human psychology and
institutions make it unlikely that wages will fall quickly in response to a labor surplus. Thus,
Keynesian-oriented economists developed ‘sticky wage’ theories, which hypothesize that
wages may stay at a level above equilibrium for some time. Wages may eventually adjust in
the way shown in the Classical model, but too slowly to keep the labor market always in
equilibrium. In addition to psychological resistance to wage cuts, a minimum wage might also
make wages sticky. Wages may also become set at particular levels by long-term contracts,
such as many large employers negotiate with labor unions.
Relatively in recent years, economists have also come up with two other theories: the insideroutsider theory and the efficiency wage theory. The insider-outsider theory hypothesizes that
the efforts of insiders may contribute to keeping wages high. ‘Insiders’ are people who already
have jobs within an organization while ‘outsiders’ are workers who are not in the organization
but who are potentially competitors of the insiders and can be hired in the future by the
organization. Insiders may be able to keep their wages high by setting up various barriers that
prevent their employer from dismissing them and hiring lower-priced outsiders. Insiders may
have contracts that specify a high wage and that make them difficult to fire. Or they may refuse
to cooperate with new workers or harass them, reducing new workers’ productivity. In the
insider-outsider theory, employed workers use the power they derive from such labor turnover
costs to keep their wages artificially high.
According to the efficiency-wage theory, employers may find it to their advantage to pay
employees wages that are somewhat higher than would be strictly necessary to get them to
work. Employers must attract, train, and motivate workers if their enterprise is to be
productive. Efficiency wage theory suggests that paying higher-than-necessary wages may
improve employee productivity. Workers may be healthier and better nourished, and therefore
more able to do quality work, when they are better paid. Also, workers may quit less often if
25

they know they are getting ‘a really good deal’. A lower likelihood of quitting makes
employees more valuable to an employer because the employer saves on the costs of training
new workers. Workers may also work more efficiently if being caught shirking means
potentially losing their “really good deal.” If the higher-than-necessary efficiency wages
creates a pool of unemployed people, this only further reinforces employees’ incentives to
work hard because then they will be even more afraid of losing their good jobs.
In sum, in the Classical-Keynesian synthesis, legally or contractually-set wages, fear of worker
unrest, the power of insiders, and efficiency wages are thought sometimes to cause wages to be
"sticky." By making real world labor markets work differently than the market pictured in the
classical model, these phenomena mean that it is unrealistic to expect that labor markets can
adjust rapidly to maintain full employment.
The supply-demand analysis, whereby the classical model of the labor market is described, is
simply a way of thinking about a single and spot market in which a single, completely
standardized good is being traded. However, the economy as a whole is not just one smoothlyfunctioning market in which prices move to equate quantity supplied and quantity demanded.
The economy is made up of several heterogeneous markets as well as a number of nonmarket
institutions and transfers of all sorts, which make it complex and difficult to explain by a
simple demand-supply analysis. For Keynesians, the classical theory, which assumes only an
idealized, abstract, and institutionless labor market, is fundamentally misleading and
unrealistic.
In the Keynesian model, aggregate employment depends on the level of aggregate demand in
the economy as a whole. If total spending is low and businesses cannot sell their goods, they
will tend to cut back on their investments and on the number of workers they employ. Prices as
well as wages may fall (as was observed during the Great Depression), keeping real wages
constant and thus giving employers no incentive to hire more workers. Low aggregate demand
for goods and services could lead to a vicious cycle of unemployment, low incomes, and low
spending in the economy as a whole. The Keynesians recommendation for fixing the problem
of unemployment in a recession or depression is stimulating aggregate demand in the economy,
and not just making labor markets work more smoothly.
26

According to Gunatilaka and Vodopivec (2010), the level of unemployment can also be
described by three hypotheses: the skill mismatch, the queuing, and the slow job creation
hypotheses. The skills mismatch hypothesis maintains that a mismatch between what the
education system teaches and what the labor market requires produces educated youth who
have few marketable job skills but who nonetheless aspire to ‘good’ jobs (jobs that are secure,
well-paid, and offer higher social status) and who spend a fair amount of time looking for such
jobs. The queuing hypothesis argues that the unemployed wait for an opportunity to take up
good jobs in the public sector and in the formal private sector. The public sector is often
characterized by job security, generous fringe benefits, low work effort, and high social status.
It is thus blamed for creating unemployment by encouraging job aspirants to queue for these
jobs.
The slow job creation hypothesis, also called the institutional hypothesis, argues that labor
market institutions raise the costs of formal job creation. In particular, highly restrictive
employment protection legislation and high wages resulting from strong bargaining power of
workers under conditions of virtually complete job security raise labor costs and impede job
creation. As a result, the job creation rate of the formal private sector is depressed and the
majority of workers are forced to opt to the unprotected informal employment (Gunatilaka,
2010).

2.4. Causes of Unemployment
As stated in the preceding theoretical discussion on types and theories of unemployment, the
classification of unemployment is based on factors that result in unemployment. There are a
number of factors that may affect the level of unemployment; and hence identifying the major
causes is the leading step so as to treat it effectively. As Mankiw (2001) states, the main
rationale for studying unemployment is to identify its causes and thereby to help improve the
public policies in favor of the unemployed.
The issue of unemployment has always been a matter of great debate among the traditional as
well as contemporary economists. For the Keynesian economists, unemployment is generally
caused by insufficient aggregate demand in the economy, as a result of which individuals lose
27

their jobs and added to the unemployment pool. The views of the classical economists differ
from their Keynesian counterparts. Unemployment, termed as classical unemployment or real
wage unemployment, is caused when wages are too high. This explanation of unemployment
was the dominant theory, particularly before the great depression of the 1930s, when workers
themselves were blamed for not accepting lower wages, or for asking for too high wages. Yet,
advocates of classical economics strongly argue that the rigidities in the labor market, which
are mainly explained by taxes, minimum wage laws and the power of labor unions, are the
main reasons behind unemployment. Unemployment incidence from the classical perspective
is, however, less likely to be situated in most sub-Saharan African economies where a large
proportion of the labor force is working in unprotected and low paid jobs in the informal
sectors. Thus the major problem in these countries is more likely the inadequate capacity of the
economy to sustain the constant labor supply growth rather than the rigidity of wages and
prices.
The unemployment literature suggests that both supply and demand factors are to blame for
impacting unemployment, and hence, its magnitude is determined by the balance between the
demand for and the supply of labor. Whenever the supply of labor exceeds the demand for it at
the prevailing wage rate, unemployment arises. Hence, the causes of unemployment are
primarily explained by either factors that can increase the supply of labor and/ or factors that
can negatively affect the demand for labor. The factors that increase the supply of labor are
associated with the increase in population and labor market conditions that can either positively
or negatively affect the labor market participation decisions of working age population. The
demand for labor is a derived demand as it is demanded to meet the demand for goods and
services. The demand for labor is determined by the performance of an economy and the
choice of production techniques, which in turn are shaped by the existing economic policies
((Bakare, 2011);(ECA, 2010);(EEA, 2007); (Adebayo, 1999)).

2.4.1. Supply Side Factors
In the African context, among the important supply factors that can affect urban unemployment
are high population growth, rapid rural-urban migration, poor quality education and training,
and other demographic variables (ECA 2010; EEA 2007; (Okojie, 2003). Population growth
28

can be an opportunity for an economy because it is a source of potential labor and
entrepreneurs. On the other hand, it can also burden economies with saturated labor market that
is unable to provide decent employment opportunities for already employed labor and for new
entrants.
According to the classical economics view, an increase in labor supply will tend to raise
employment although it dampens productivity increases. The higher labor supply will lead to
lower average wages and consequently to an increase in demand for labor (Kapsos, 2005
(Walterskirchen, 1999). Empirical evidences also confirm the positive and significant
association between the labor supply and employment elasticity. A 1-percentage point increase
in the average annual growth rate of the working-age population is associated with an increase
in the employment elasticity by 0.24 (Kapsos, 2005).
But the situation is different in Africa, where the demographic transition is lowest and the
population growth rate is still around 2.4 per cent. Over the past 20 years, the economically
active population of Africa has grown at an average rate of 3 per cent, rising from 231 million
in 1990 to 403 million in 2009. This represents a 43 per cent increase just in two decades, one
of the highest increases among all regions of the world (ECA, 2010). Therefore, high
population growth and growing labor participation has rather resulted in excessive supply of
labor, which has continued to outstrip the demand for labor. In this regard, it is worth
mentioning the situation in Ethiopia as it can be a good instance for this fact. Between 1994
and 2005, in a decade, the Ethiopian labor force increased by 21.3 percent while the
employment creation increased by 18.7 percent (EEA, 2007). It implies that, despite lack of
evidence on the quality of employment generated, nearly 3 percent new jobless individuals are
added to the unemployed population during the period.
A key supply factor in urban labor market leading to urban unemployment, often cited in the
literature, is the high degree of geographical mobility of people, especially the youth, in the
form of rapid rural-urban migration. The well known classical analysis of rural-urban
migration and urban unemployment is attributable to the works of Harris-Todaro and Todaro
(1969). According to Todaro (1969), as long as rural-urban wage differential attracts rural
29

people, urban unemployment cannot be reduced regardless of creating more jobs through labor
intensive methods of production. The implication is that apart from creating employment in
urban areas, making rural areas more attractive is also equally important. Similarly, referring to
Harris-Todaro’s model, Bencivenga and Smith (1995) document that labor migrates to
wherever its expected income is highest; and hence in equilibrium expected incomes must be
equated between rural and urban employment. Since urban wages are invariably much higher
than rural wage rates, the equilibration occurs through the existence of unemployed or
underemployed urban labor. This is due to an institutionally fixed urban real wages mainly
attributable to minimum wage legislation and /or the power of labor unions.
Rural-urban migration, which is the important factor for the rapidly growing urban labor force,
can be explained in terms of push-pull factors. Even though there is high recorded employment
in rural areas of most African countries, this employment generates insufficient incomes for
rural workers mainly due to lower agricultural labor productivity. Rural to urban migration
occurs, to a large extent, because rural Africans are so desperate that they are willing to try
their chances in the unpromising urban labor market. This has resulted in a concentration of
youth in African cities where there are few jobs available in the formal sector (Leibbrandt,
2004). Among the push factors of rural-urban migration are the pressure resulting from the
diminishing land-man ratio in the rural areas and the existence of serious underemployment
arising from seasonal nature of most SSA rural economies (Adebayo, 1999).
Proponents of migration argue that rural to urban migration occurs because it is part of the
optimization strategy of rural households, where differences in returns in different markets
determine the allocation of labor (Leibbrandt, 2004). Indeed, in earlier economic development
literature, rural–urban migration was viewed favorably as a natural process in which surplus
labor gradually withdraws from the rural sector to provide needed manpower for the expanding
urban industrial sector. However, there are also arguments against this proposition as witnessed
by the insufficient absorptive capacity of the urban sector relative to the massive rural-urban
migration. As noted in Todaro (1997), the past three decades of African experience has made
clear that rates of rural–urban migration have greatly exceeded rates of urban job creation. One
of the major consequences of the rapid urbanization process has been the burgeoning supply of
30

job seekers in both the modern (formal) and traditional (informal) sectors of the urban
economy. In most African countries, the supply of workers far exceeds the demand, the result
being extremely high rates of unemployment and underemployment in urban areas. Thus he
argues that migration can no longer be casually viewed by economists as a beneficent process
necessary to solve problems of growing urban labor demand. On the contrary, migration today
remains a major factor contributing to the phenomenon of urban surplus labor; a force that
continues to exacerbate already serious urban unemployment problems caused by the growing
economic and structural imbalances between African urban and rural areas (Todaro, 1997).
Although labor market outcomes depend on several factors, education and relevant skills
remain the main determinants of good labor market outcomes for individuals. Education plays
a central role in preparing individuals to enter the labor force and in equipping them with the
skills needed to engage in lifelong learning experiences. The primacy of education stems not
only from its fundamental role in increasing individual earnings, but also from its noneconomic
benefits such as lower infant mortality, better participation in democracy, reduced crime, and
even the simple the joy of learning that enhance and enrich the quality of life and sustain
development (Fasih, 2008).
Evidences from a range of countries shows that education enhances opportunities in the labor
market, as those with the best qualifications enjoy superior job prospects. In the developed
countries, the differential chances of unemployment for qualified and unqualified young people
have been increasing. In a number of developing countries, however, many highly educated
young people remain unemployed. This problem arises from two key factors: an inappropriate
matching of university degrees with demand occupations and the insufficient demand for
skilled higher-wage labor in the formal economy. As most new job growth is in the informal
sector of the economy, there remain few opportunities for young graduates to find work that
corresponds to their level of educational attainment (UN, 2003).

African youth have obtained more formal education over the years. However, educational
systems in Africa have witnessed declines in quality and infrastructure at all levels since the
last decades. They are geared toward providing basic literacy and numeracy and not industrial
31

skills, and are yet to adjust to the changing demands for knowledge, skills and aptitudes
required in the labor market. Youth unemployment in Africa is concentrated among those who
have received some education, but who lack the industrial and other skills required in the labor
market, making them unattractive to employers of labor who prefer skilled and experienced
workers. Furthermore, educated youth prefer wage jobs in the formal sector and would prefer
to remain unemployed until they get the type of job they prefer, that is, they have high
reservation wages (Chigunta 2002; cited in (Okojie, 2003).
The conventional theoretical argument for education suggests that higher educational
attainment leads to better employment outcomes, such as higher wages and lower
unemployment. Empirical evidences indicate that the desirable effect of education on
unemployment is not always evident, particularly for youth. For instance, Guarcello et al
(2008b) analyzed the effect of education on school-to-work transitions for 13 Sub-Saharan
Africa countries based on World Bank Priority survey data. Their findings indicate that higher
educational attainment has not led to a decrease in the unemployment rate for youth in these
countries. Youth with secondary and tertiary education, particularly in Burundi, Cameroon,
Ivory Coast, Kenya, and Madagascar, have higher rates of unemployment than youth with
lower educational attainment.
Labor market outcomes also vary among individuals pertaining to demographic factors both in
rural and urban areas. There are significant differences in participation and unemployment
rates between older and younger cohorts as well as between males and females. Almost in all
countries, both in developed and underdeveloped, the probability of unemployment is strongly
dependent on age cohort of the labor force. Typically, low rates of unemployment for primeage workers coexist with high rates for young cohorts.
Gender is another important demographic factor that determines individuals’ position in the
labor market. In many economies, notably in the developing world, females tend to be far more
vulnerable than males. A review of youth unemployment in 97 countries confirms that more
young women than young men were unemployed in two-thirds of the countries. In a quarter of
these countries, female unemployment was more than 20 per cent higher than male
32

unemployment, In around half of the countries in Latin America and the Caribbean,
unemployment rates for female youth exceeded those for young males by more than 50 per
cent (UN, 2003). The situation is similar in Ethiopia too. In 2005, average unemployment rate
among urban females was about 27.2 percent compared to 13.7 percent among urban males;
and similarly, in rural areas, the rate was about 4.6 percent for females while it is only 0.9
percent for males (MoLSA, 2009). In Addis Ababa, it was 48.6 percent in 1999 and 40.4
percent in 2005 for women while it was 28.3 percent in 1999 and 22.7 percent in 2005 for men
(Tegegn, 2011).

2.4.2. Demand Side Factors
The demand side factors that are supposed to impact unemployment include economic
performance, production technology, and economic policies and regulations that can affect the
labor market demand. Slower economic growth arises from low economic activity and low
investment rates, which are unable to generate enough additional job opportunities. In
theoretical terms, as stated in Bakare (Bakare, 2011), when foreign direct investment and
domestic investment increase, unemployment will be minimized. Gross capital formation
including private domestic investment is expected to have a desirable impact on
unemployment. The greater the gross capital formation and private domestic investment, the
smaller is the level of unemployment. Capacity utilization and gross capital formation are
highly significant and negatively related to unemployment rates both in the short and long run
(Bakare, 2011).
Technological changes and inappropriate policies can explain the slow growth of employment
in Africa. If inappropriate technologies are employed, the employment-creating effects of a rise
in national income can be offset by the employment-saving effects of modern technology. In
his earlier article on urban unemployment in east Africa, Elkan (1970) argues that
inappropriate techniques of production are the result of not only technological factors but also
inappropriate policies. For instance, policies that encourage capital intensive techniques, failure
to give adequate inducements for training of skilled labor, and failure to manage rapid
increases in wages may lead to poor labor absorptive capacity of an economy.

33

In Ethiopia, the post 1991 period is characterized by a move to a market led system that
included the adoption of structural adjustment program and a range of other policy reforms. In
relation to these events, some evidences show that economic growth in Ethiopia following the
structural adjustment (after 1991) was less employment generating than that in the pre reform
period. According to Mulat et al. (2003), the post reform period arc elasticity employment was
-0.23 while it was 1.9 in the pre reform period. This means that as the economy was growing at
a rate of 1percent, employment rate was declining by 0.23 percent in the reform period until
1999. The implication is that the massive improvement in growth performance that the
Ethiopian economy experienced since 1991 had little effect in reducing urban unemployment.
There are some possible explanations that are suggested in relation to this fact. Among the
possible reasons, as stated in EEA (2007), are firstly, there might had been “overstaffing” in
the pre reform period and cutbacks for more efficient use of resources in the post reform
period. Secondly, the incentive structure of the reform period might encourage employers to
choose labor saving technology (EEA, 2007). Two other explanations are also forwarded. The
first one is that the private sector, including self-employment, has not yet overcome the effect
of the repression it had experienced in the pre-1991 period (Krishnan, 2001). The other
explanation is attributable to the fact that the post-1991 growth came dominantly from the
agricultural sector which is weakly linked to the urban sector (Alemayehu, 2005).
In recent years, Africa’s economy has witnessed relatively better performance and rapid
growth with most countries experiencing economic growth above their population growth
rates, thus leading to rises in per capita income. This rapid growth episode had, however,
insignificant impact on employment. For most African countries, unemployment rates
remained almost unchanged even during the recent growth upturn that ended in the second half
of 2008. The rates were estimated to have risen from 7.4 percent to 8.2 percent between 1998
and 2009 in Sub-Saharan Africa and from 12.8 percent to over 13 percent in North Africa in
the same period. Narrow-based economic growth combined with rapid population growth and
labor market imperfections mean that Africa’s growth rates consistently fall behind the growth
rate needed to create adequate employment and reduce poverty (ECA, 2010).

34

Indeed, growth with no employment is not an exception for Africa. The history of fast-growing
countries and their continued inability to cope with the problem of unemployment indicate that
something else besides rapid growth is required for a solution. Africa’s growth has relied
mainly on capital-intensive sectors rather than labor-intensive ones. The nature of growth is as
important as its quantity if Africa is to meet its employment and poverty reduction objectives
In labor abundant economies, as the factor endowment theory suggests, growth must occur by
investing in relatively labor-intensive activities rather than those which are capital-intensive.
The rationale is that not only will this result in more rapid growth because of the low
opportunity cost of labor relative to capital, but will increase the rate of growth of employment
for any given level of investment (Elhiraika, 2011).
Employment growth is a function of the sectoral composition of employment, sectoral growth
rates and the output elasticities of employment in the various sectors. This implies that
employment growth depends on the aggregate growth rate as well as the sectoral composition
of aggregate growth. This is the line of reasoning that Elhiraika’s (2011) explanation for the
poor labor absorptive capacity of Africa’s growth is based on. He contends that the major
source of the recent economic growth in several African economies has been the growth of
natural resource extraction sectors, which by their nature are capital intensive and, with a few
exceptions, have limited linkages to the domestic African economies. Value added in the
mining sector, which employs less than 10 percent of the labor force, grew at over 10 percent
per year, while agriculture, manufacturing and services with combined employment of over 80
percent of the labor force grew at less than 2.5 percent per year in the last two decades. The
combination of small size and low employment elasticities implies that growth based on rapid
expansion of the mining sector will not generate high-employment growth. In turn, this
suggests that a broad based employment strategy will not only have to rely on higher aggregate
growth but must also pay attention to sectoral composition.
In a well-functioning labor market, the demand of labor is inversely related to its price. The
higher the price of labor, the lower is its demand. The price of labor relative to that of other
inputs such as capital can also change the demand for labor by inspiring the more concentrated
use of the relatively cheapest input. In other words, relatively cheap capital will prompt firms
35

to be more capital-intensive, while relatively cheap labor will necessitate more labor-intensity
(Onwioduokit, 2009). In the same way, as Bakare (2011) argues, the level of minimum wage
and wage increases contribute to rising unemployment rates. When the wage rate increases,
there is tendency to substitute machine for labor. When this occurs, it will increase the
unemployment rate implying a positive relationship between wages and unemployment rates.
Labor market institutions that keep an appropriate balance between labor market flexibility and
worker protection can contribute positively to job creation and efficient labor allocation while
simultaneously protecting fundamental rights of workers. But if these institutions are
unbalanced and provide undue protection to certain groups, they may adversely affect labor
market outcomes (Gunatilaka, 2010). The increased labor market inflexibility raises the
indirect cost of labor for firms, since more time and money have to be spent negotiating with
unions, and an increasing amount of time and money is lost due to strikes. High indirect costs
may warrant a substitution of labor with capital, which means that demand for labor will grow
slower than output (Pierluigi, 2008).
In the context of Ethiopia, minimum wage is limited to public sector employment and to some
extent formal private sector employment. The higher wages for public employment leads to
queuing for it. Lack of employment services increase frictional unemployment and results in
long unemployment duration (EEA, 2007). Ethiopia’s labor law framework, outlined for the
private sector by Proclamation No. 377/2003 does provide a series of protections for workers.
However, as argued in WB (2007), labor regulations and labor relations in Ethiopia are not
seen by firms as significant impediments to doing business. This might be largely because
these provisions are not generally enforced outside of the public sector.
Regulations that promote competition in the product market have positive effect on
employment. Lower barrier to entry encourage new firms to enter in to the market and curbs
market power and monopoly profits. As a result, the expansion of economic activities tends to
increase labor demand. Particularly, lower monopoly profits reduce the scope for existing
workers to share in the rents generated by excessive prices. Reduced rent sharing between
employers and employees would then tend to shorten the length of unemployment spells as it
36

would become less attractive for the unemployed to limit their search for job opportunities in
high-wage sectors only (Pierluigi, 2008).

Inflation is among the macroeconomic variables that affect the level of employment through
its impact on economic performance. A reasonable inflation rate stimulates investment and
consequently raises the labor demand. The well known theoretical explanation on the
relationship between unemployment and inflation is attributable to the Phillips curve. There
are two possible explanations on the relationship between unemployment and inflation
depending on the time frame: one in the short term and another in the long term. In the short
term, there is an inverse correlation between unemployment and inflation explained by a
downward sloping curve. Put differently, the short term relation states that when the
unemployment rate is high, inflation is lower and the inverse is true as well, implying a
tradeoff between the two. The Phillips curve in the long term is different from the one in the
short term. As per the classical economics explanation, the long term Phillips curve is
basically vertical as inflation is not meant to have any relationship with unemployment in the
long term. It is therefore assumed that unemployment would stay at a fixed point, commonly
known as the natural rate of unemployment, irrespective of the status of inflation.
However, the empirical evidence on effect of inflation on unemployment seems ambiguous and
inconclusive. For instance, Bakare (2011) finds a negative relationship between inflation and
unemployment in both short and long run periods and is significant at 1% level, which is in
agreement with the Philip’s curve explanation. Similarly, the empirical study by Palley (2005)
in which he compares the European labor market with that of the United states confirm that
permanently lowering the inflation rate by 1 percent point increases unemployment by 0.4
percentage points. In contrast, the findings of Kapsos (2005) indicate that the average annual
rate of inflation is negatively associated with employment elasticity, implying a positive
relationship between inflation and unemployment.

37

2.5. Active Labor Market Policies to Address Unemployment
A common way to look at the value of education and training for individuals is, as Becker’s
Human Capital Theory says, in terms of increased human capital based on the assumption that
the greater one’s human capital, the better are one’s labor market chances. Thus, human capital
accumulation from Active Labor Market Policies (ALMP)-training investments is expected to
increase the employability and labor market outcomes of the unemployed (Nordlund, 2010).
The faith in human capital has reshaped the way governments approach the problem of
stimulating growth and productivity, as has been shown by the emphasis on human capital in
both developed and developing countries.
Active labor market policies (ALMPs) are measures intended to improve the functioning of the
labor market that are directed towards the unemployed. The common active labor market
policies, through which governments intervene to deal with the problem of unemployment, can
be categorized in to three: i) labor market training in order to upgrade and adapt the skills of
job applicants;

ii) direct job creation, which may take the form of either public-sector

employment or subsidization of private-sector work; and iii) employment services (or job
broking) with the purpose of making the matching process between vacancies and job seekers
more efficient (Boone, 2004, Calmfors, 1994) . The desired effect of ALMPs is a change in the
allocation of the labor force among sectors, skills, and regions. For instance, if there is full
employment among skilled workers, or in certain regions, or sectors, and if wages are flexible,
such programs intended to increase the employability of unskilled workers or workers
employed in regions with high unemployment and wage rigidity have a positive effect on
output and employment (Altavilla, 2006).
Training programs are on the supply side of the labor market aimed at providing job seekers
with marketable skills that potentially increase their employability as well as their earning
capacity. Training involves some form of public support such as direct provision of training,
financial support for trainees, or providing infrastructure services (Sanchez Puerta, 2010).
From the human capital theory point of view, such training programs primarily serve to
enhance the human capital of the participants, which, as a result, will have two desirable effects
38

on participants' labor market outcomes. The first is increased probability of employment, either
by enhancing the attractiveness of participants to potential employers or by enabling them
acquire the necessary skills to establish their own business. The second one is increased
employment earnings of participants resulted from improved productivity.
The role of TVET in furnishing skills required to improve productivity, raise income levels and
improve access to employment opportunities has been widely recognized (Bennell, 1999).
Developments in the last three decades have made the role of TVET more decisive; the
globalization process, technological change, and increased competition due to trade
liberalization necessitates requirements of higher skills and productivity among workers in
both modern sector firms and Micro and Small Enterprises (MSE). Skills development
encompasses a broad range of core skills (entrepreneurial, communication, financial and
leadership) so that individuals are equipped for productive activities and employment
opportunities (wage employment, self-employment and income generation activities). The
Bonn Declaration of October 2004 noted that TVET is the “Master Key” for alleviation of
poverty, promotion of peace, and conservation of the environment, in order to improve the
quality of human life and promote sustainable development (UNESCO, 2004).
In reviewing some empirical works on impact evaluation of training programs, Sanchez
Pauerta argues that although the impacts are not homogeneous and vary across age, gender and
region, the net impacts in Latin America and the Caribbean proved that the employment and
earnings prospects of participants have been improved; particularly the employment impacts
are more significant for women and the youngest (Sanchez Puerta, 2010). Similarly,
Betcherman et al (2007) assessed 49 evaluations of training programs primarily aimed at the
unemployed, of which 10 are from transition countries and 4 are from developing countries. In
the case of transition countries, almost all programs had positive employment impacts. On the
other hand, of the four developing countries evaluations, only one showed any gains in terms
of employment or earnings.
The impact of education, in particular technical and vocational training, on individuals' career
employment prospects is a crucial aspect of the current debate. As Psacharopoulos (1997) put,
“Vocational education and training has been in the past, is today, and will remain in the future
39

one of the hottest debated subjects in all countries of the world”. The persistently high level of
unemployment and the increasing amount of money spent on labor market programs have
brought issues regarding the effects and efficiency of labor market policies into the public
debate (Torp, 1994).
Critics of training for employment creation programs base their assertions on a series of
reasonable arguments. The first is the so-called substitution effect. Under this line of argument,
training may very well increase the chances of an individual to obtain a job; yet the number of
jobs at any moment is a given, determined by other variables, mostly at the macro level. The
implication is that training substitutes one job candidate for another, and often does so at high
costs to the public. Even if the employment rates of trainees increase, as compared to the
comparison groups, the substitution effect remains. In this regard, convincing evidence need to
be produced to differentiate between two independent issues. The first issue concerns
increasing the employability of trainees, i.e., graduates of training programs get more jobs than
they would in the absence of the programme. The second issue deals with the aggregate impact
on employment levels of such programs, i.e., the jobs created add to the total number of jobs,
rather than merely changing the distribution of jobs in favor of those who received training
(Castro, 2000).
Despite the empirical difficulties of substantiating their impact, the arguments for training
programs still make sense. When firms have vacancies or potential vacancies that remain
unfilled due to lack of skills on the part of candidates, training can make a significant
difference. In this case, there is no substitution effect but a net increase in employment. Indeed,
there might be ample evidences of job openings that remain unfilled due to lack of qualified
and suitable candidates, even in the presence of high unemployment. Nevertheless, there is
another question behind such seemingly surplus vacancies. As the conventional
microeconomics suggests, demand is a function of prices. There may be vacancies that remain
unfilled, but at what wage levels? The issue of reservation wage is another issue of concern to
be raised at this point. If sufficiently higher wages are offered, someone will appear with the
required qualifications (Castro, 2000). On the other hand, training can still be justified from
equity perspective. Even if substitution exists, as long as the beneficiaries are the most
40

vulnerable and disadvantaged groups, it may be regarded desirable as it will increase the social
equity of the system.
The most robust argument in favor of skills training is its strong impact on productivity and the
consequent benefits of increased productivity on growth and employment creation. The logic is
straight forward that a well skilled and trained labor force is effective and efficient and
produces more output. Thus, even if training does not increase employment immediately for
the graduates, it remains more than justified in the long run (Castro, 2000). Indeed, the long run
impact argument may provide strong justification for developing countries to invest in
education and training regardless of its controversial immediate and desirable outcomes on the
labor market.

2.6. An Overview of Empirical Evidences on Unemployment in Ethiopia
Despite some improvements in recent years, unemployment and underemployment in Ethiopia
continue to be serious social problems, especially in urban areas and among the youth.
According to the 2005 National Labor Force Survey, the national unemployment rate, based on
the population aged 10 years and above, is estimated at 5 percent of the total labor force. In the
same period, the unemployment rate in urban Ethiopia is estimated at 20.6 percent which is
about eight times higher than the 2.6 percent rates in rural areas ((MoLSA, 2009). Using the
international definition, based on the population aged 15 and above, measured urban
unemployment is still high at 14 percent with distinctive patterns by age cohort, gender and
education. Adult male unemployment fell by one percentage point (from 9.1 to 8.1 percent)
from 1999 to 2005, and stagnated around 13 percent for adult women. The median duration of
unemployment fell considerably, from 24 months in 1999 to 10 months in 2005, providing very
encouraging evidence of dynamism. Despite decreasing duration, the persistence of high urban
unemployment remains a major policy challenge (WB, 2007).

Both supply and demand side factors are responsible for the problem. The pressure on the labor
market primarily comes from the supply of labor, which is induced by the rapidly growing
population. On top of the high growth rate of the labor force, low productivity and low skills of
41

the working poor contribute to the high incidence of both poverty and unemployment. On the
other hand, the insufficient employment generation capacity of the modern industrial sector of
the economy is among the demand side factors for the persistent urban unemployment
(MoLSA, 2009).
Among the demographic factors, the rapidly increasing labor supply, which is incompatible
with the economic performance of the urban sector, is the most important reason behind the
persistent unemployment in urban Ethiopia. Although it is not the most important factor, ruralurban migration does have a role in the excessively high level of youth unemployment in urban
areas (Getinet, 2003). The coefficients of migration status are statistically significant and
negatively related to the probability of unemployment in both the 1999 and 2005 data sets,
implying that a migrant is less likely to be unemployed than a non-migrant (Tegegn, 2011).
Age is also an important factor that is negatively related to the probability of unemployment.
Many empirical evidences also confirm the same. Age is statistically significant and negatively
related to the probability of unemployment (Tegegn, 2011); and for each 1-year increase in
age, there is about a 5.5 percent decrease in unemployment duration (Seife, 2006). In contrast,
Serneels (2007) found that age has strong positive effect on duration of unemployment among
young men aged 15 – 30. In terms of gender, females disproportionately suffer from
unemployment. As indicated in Guracello, Lyon and Rosati (2008a), the probability of a girl
being in employment is about 14 to 22 percent lower than that of a boy. Also in (Tegegn,
2011), a male worker is about 21.4 percent and 17.7 percent less likely to be unemployed than
a female in 1999 and 2005, respectively. However, Seife (2006) finds no variation in the
duration of unemployment by gender.
Previous studies also show that unemployment in urban Ethiopia does vary by level of
education and training status. According to (Tegegn, 2011), all levels of education, except for
first degree and above, are positively related to the probability of unemployment in the 1999
data set. A person with only primary education is 10.5 percent and with secondary education is
20.6 percent more likely to be unemployed than an illiterate person in 1999. However, in the
2005 data set all coefficients of the education dummies show negative signs and statistically
significant, except secondary level education. Training has desirable effect on unemployment
42

Explaining Causes and Responses to Urban Unemployment in Ethiopia

Explaining Causes and Responses to Urban Unemployment in Ethiopia

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